Technologies, Volume 9, Issue 3 (September 2021) – 25 articles

The easy access to electronic devices for users has resulted in the development of a vast range of programs and applications for visual evaluation and diagnosis that can be downloaded to any device. Some of them are based on tasks and stimuli that depend on luminance. The aim of the present study was to evaluate differences in luminance reproduction between electronic devices and their implications for contrast reproduction. A total of 20 Galaxy Tab A devices with 8-bit graphics processing units were evaluated. Characterization of every screen was performed obtaining the response curve for the achromatic stimulus. Mean, maximum and minimum luminance, standard deviation and coefficient of variation were obtained to assess differences between devices. Variation of luminance with increasing digital level was observed in all devices following a gamma distribution. Comparison between devices for mean results showed that some of them differed by as much as 45 cd/m². The coefficient of variation varied from ~5 to 9%. Mean percentage of differences in luminance between devices reached 30%. In conclusion, differences in luminance reproduction between devices were present, even considering devices from the same manufacturing batch. It cannot be assumed that the characterization of one device can be extrapolated to other devices. Every device used for research purposes should be individually characterized to ensure the correct reproduction. For clinical purposes, limitations should be considered by visual specialists. Full article

Three-dimensional (3D) printing in soil science is relatively rare but offers promising directions for research. Having 3D-printed soil samples will help academics and researchers conduct experiments in a reproducible and participatory research network and gain a better understanding of the studied soil parameters. One of the most important challenges in utilizing 3D printing techniques for soil modeling is the manufacturing of a soil structure. Until now, the most widespread method for printing porous soil structures is based on scanning a real sample via X-ray tomography. The aim of this paper is to design a porous soil structure based on mathematical models rather than on samples themselves. This can allow soil scientists to design and parameterize their samples according to their desired experiments. An open-source toolchain is developed using a Lua script, in the IceSL slicer, with graphical user interface to enable researchers to create and configure their digital soil models, called monoliths, without using meshing algorithms or STL files which reduce the resolution of the model. Examples of monoliths are 3D-printed in polylactic acid using fused filament fabrication technology with a layer thickness of 0.20, 0.12, and 0.08 mm. The images generated from the digital model slicing are analyzed using open-source ImageJ software to obtain information about internal geometrical shape, porosity, tortuosity, grain size distribution, and hydraulic conductivities. The results show that the developed script enables designing reproducible numerical models that imitate soil structures with defined pore and grain sizes in a range between coarse sand (from 1 mm diameter) to fine gravel (up to 12 mm diameter). Full article

Lack of sustainable strategic approaches has led to non-functional, unsafe, inaccessible, and fragmented urban green infrastructure within cities. In sub-Saharan African cities, the development and management of urban green infrastructure are not realized in many instances due to a lack of priorities and resources. The objective of the study is to develop strategic approaches that help to overcome the challenges of urban green infrastructure and promote a sustainable development and management system in Ethiopia with special references to the emerging towns of Oromia special zone that surrounds Finfinne. To design sustainable strategic approaches for an urban green infrastructure development and management system, the study collected data using key informant interviews, focus group discussion and document reviews. Findings identify seven potential strategic approaches that are needed to create a sustainable urban green infrastructure development and management system. Hence, improving the quantitative, qualitative, and accessibility standards on the provision of urban green infrastructure is needed for sustained development. Moreover, advanced development in budget allocation, capacity building, legal and institutional framework, awareness creation, and stakeholder’s involvement are also needed to promote a sustainable development and management system of urban green infrastructure in the urban centers of Ethiopia in general and emerging towns in particular. Full article

Implant periapical lesion (IPL) is an infectious-inflammatory alteration surrounding an implant apex. It is a multifactorial disease that may ultimately cause implant failure. The diagnosis of IPL is based on examination of clinical manifestations and apical radiolucency. Many etiologies have been attributed to IPL, including preexisting microbial pathology and surgical trauma. Moreover, many systems have been used to classify IPL based on different parameters. To date, non-surgical and surgical treatment, as well as removal of failed implants, have been considered to successfully manage IPL. However, prevention of IPL surpasses all modes of treatment. An increased number of IPL cases are expected as implants have become standard for tooth replacement in dentate arches. Therefore, it is necessary to understand IPL more comprehensively. Herein, an introduction to IPL, including its etiology, diagnosis, classification, treatment, and prevention, has been undertaken. Full article

Audio event detection (AED) systems can leverage the power of specialized algorithms for detecting the presence of a specific sound of interest within audio captured from the environment. More recent approaches rely on deep learning algorithms, such as convolutional neural networks and convolutional recurrent neural networks. Given these conditions, it is important to assess how vulnerable these systems can be to attacks. As such, we develop AED-suited convolutional neural networks and convolutional recurrent neural networks, and attack them next with white noise disturbances, conceived to be simple and straightforward to be implemented and employed, even by non-tech savvy attackers. We develop this work under a safety-oriented scenario (AED systems for safety-related sounds, such as gunshots), and we show that an attacker can use such disturbances to avoid detection by up to 100 percent success. Prior work has shown that attackers can mislead image classification tasks; however, this work focuses on attacks against AED systems by tampering with their audio rather than image components. This work brings awareness to the designers and manufacturers of AED systems, as these solutions are vulnerable, yet may be trusted by individuals and families. Full article

Myoelectric signals can be used to control prostheses or exoskeletons as well as robots, i.e., devices assisting the user or replacing a missing part of the body. A typical application of myoelectric prostheses is the human hand. Here, the development of a low-cost myoelectric thumb is described, which can either be used as an additional finger or as prosthesis. Combining 3D printing with inexpensive sensors, electrodes, and electronics, the recent project offers the possibility to produce an individualized myoelectric thumb at significantly lower costs than commercial myoelectric prostheses. Alternatively, a second thumb may be supportive for people with special manual tasks. These possibilities are discussed together with disadvantages of a second thumb and drawbacks of the low-cost solution in terms of mechanical properties and wearing comfort. The study shows that a low-cost customized myoelectric thumb can be produced in this way, but further research on controlling the thumb as well as improving motorization are necessarily to make it fully usable for daily tasks. Full article

The pressure pain threshold (PPT) is a useful tool for evaluating mechanical sensitivity in individuals suffering from various musculoskeletal disorders. The aim of this study is to investigate PPT at the heel area in order to assist in the design of orthotic shoes for sufferers of heel pain due to a calcaneal spur. The size and location of the calcaneal spur was determined by x-ray images, with PPT data measured around the spur at five points by using algometer FDIX 25. The pain test experiment was conducted by pressing each point to obtain the pain minimum compressive pressure (PMCP) and its location. The information of shoe size, spur location and dimensions, and the PMCP location for each individual is used to obtain the exact point location for applying a softer material to the shoe in-sole, in order to reduce heel pain. The results are significant as it can be used by designers to design appropriate shoe in-soles for individuals suffering from heel pain. Full article

Additive manufacturing, commonly known as 3D printing, is an advancement over traditional formative manufacturing methods. It can increase efficiency in manufacturing operations highlighting advantages such as rapid prototyping, reduction of waste, reduction of manufacturing time and cost, and increased flexibility in a production setting. The additive manufacturing (AM) process consists of five steps: (1) preparation of 3D models for printing (designing the part/object), (2) conversion to STL file, (3) slicing and setting of 3D printing parameters, (4) actual printing, and (5) finishing/post-processing methods. Very often, the 3D printed part is sufficient by itself without further post-printing processing. However, many applications still require some forms of post-processing, especially those for industrial applications. This review focuses on the importance of different finishing/post-processing methods for 3D-printed polymers. Different 3D printing technologies and materials are considered in presenting the authors’ perspective. The advantages and disadvantages of using these methods are also discussed together with the cost and time in doing the post-processing activities. Lastly, this review also includes discussions on the enhancement of properties such as electrical, mechanical, and chemical, and other characteristics such as geometrical precision, durability, surface properties, and aesthetic value with post-printing processing. Future perspectives is also provided towards the end of this review. Full article

Despite best efforts, the loss of biodiversity has continued at a pace that constitutes a major threat to the efficient functioning of ecosystems. Curbing the loss of biodiversity and assessing its local and global trends requires a vast amount of datasets from a variety of sources. Although the means for generating, aggregating and analyzing big datasets to inform policies are now within the reach of the scientific community, the data-driven nature of a complex multidisciplinary field such as biodiversity science necessitates an overarching framework for engagement. In this review, we propose such a schematic based on the life cycle of data to interrogate the science. The framework considers data generation and collection, storage and curation, access and analysis and, finally, communication as distinct yet interdependent themes for engaging biodiversity science for the purpose of making evidenced-based decisions. We summarize historical developments in each theme, including the challenges and prospects, and offer some recommendations based on best practices. Full article

A low-cost 1 kHz–400 kHz operating frequency fully-active electrode bioimpedance measurement module, based on Howland current source, is presented in this paper. It includes a buffered positive feedback Howland current source, implemented with operational amplifiers, as well as an AD8421 instrumentation amplifier, for the differential voltage measurements. Each active electrode module can be connected to others, assembling a wearable active electrode module array. From this array, 2 electrodes can be selected to be driven from a THS413 fully differential amplifier, activating a mirrored Howland current source. This work performs a complete circuit analysis, verified with MATLAB and SPICE simulations of the current source’s transconductance and output impedance over the frequency range between 1 kHz and 1 MHz. Resistors’ tolerances, possible mismatches, and the operational amplifiers’ non-idealities are considered in both the analysis and simulations. A comparison study between four selected operational amplifiers (ADA4622, OPA2210, AD8034, and AD8672) is additionally performed. The module is also hardware-implemented and tested in the lab for all four operational amplifiers and the transconductance is measured for load resistors of 150 Ω, 660 Ω, and 1200 Ω. Measurements showed that, using the AD8034 operational amplifier, the current source’s transconductance remains constant for frequencies up to 400 KHz for a 150 Ω load and 250 kHz for a 1200 Ω load, while lower performance is achieved with the other 3 operational amplifiers. Finally, transient simulations and measurements are performed at the AD8421 output for bipolar measurements on the 3 aforementioned load resistor values. Full article

Electrical impedance tomography is a low-cost, safe, and high temporal resolution medical imaging modality which finds extensive application in real-time thoracic impedance imaging. Thoracic impedance changes can reveal important information about the physiological condition of patients’ lungs. In this way, electrical impedance tomography can be a valuable tool for monitoring patients. However, this technique is very sensitive to measurement noise or possible minor signal errors, coming from either the hardware, the electrodes, or even particular biological signals. Thus, the design of a good performance electrical impedance tomography hardware setup which properly interacts with the tissue examined is both an essential and a challenging concept. In this paper, we adopt an extensive simulation approach, which combines the system’s analogue and digital hardware, along with equivalent circuits of 3D finite element models that represent thoracic cavities. Each thoracic finite element model is created in MATLAB based on existing CT images, while the tissues’ conductivity and permittivity values for a selected frequency are acquired from a database using Python. The model is transferred to a multiport RLC network, embedded in the system’s hardware which is simulated at LT SPICE. The voltage output data are transferred to MATLAB where the electrical impedance tomography signal sampling and digital processing is also simulated. Finally, image reconstructions are performed in MATLAB, using the EIDORS library tool and considering the signal noise levels and different electrode and signal sampling configurations (ADC bits, sampling frequency, number of taps). Full article

The unique art that was developed in Byzantine times is widely accepted as a precursor to the Renaissance and is still evident in monuments either from or influenced by the Byzantium. Visiting such a site is a unique experience due to the lavishly painted interior for both tourists and scholars. Taking advantage of the emerging 5G technologies, cloud/fog computing, and Augmented/Mixed Reality mechanisms, a common smart device (e.g., smartphone, tablet) could be employed to give a better experience to the end-users. The proposed framework is intended to provide visitors with interpretative information regarding the visited monuments and their paintings. Under the framework introduced in this paper, camera input is uploaded to a cloud/fog computing infrastructure where appropriate algorithms and services provide monument and painting recognition and the mobile application retrieves and projects the related information. In addition, the designed immersive user interfaces assist visitors in contributing in cases of monuments and paintings for which no available information exists. This paper presents the state of the art in approaches for immersive experiences in Digital Culture, reviews the current image recognition approaches and their suitability for Byzantine paintings, proposes interaction techniques appropriately designed for observation and interaction of paintings, and discusses the overall framework architecture that is needed to support the described functionality while stressing the challenging issues of the above aspects thus paving the road for future work and providing guidelines for a test-bed implementation. Full article

Congenital heart disease, the most frequent malformation at birth, is usually not fatal but leads to multiple hospitalisations and outpatient visits, with negative impact on the quality of life and psychological profile not only of children but also of their families. In this paper, we describe the entire architecture of a system for remotely monitoring paediatric/neonatal patients with congenital heart disease, with the final aim of improving quality of life of the whole family and reducing hospital admissions. The interesting vital parameters for the disease are ECG, heart rate, oxygen saturation, body temperature and body weight. They are collected at home using some biomedical sensors specifically selected and calibrated for the paediatric field. These data are then sent to the smart hub, which proceeds with the synchronisation to the remote e-Health care center. Here, the doctors can log and evaluate the patient’s parameters. Preliminary results underline the sensor suitability for children and infants and good usability and data management of the smart-hub technology (E@syCare). In the clinical trial, some patients from the U.O.C. Paediatric and Adult Congenital Cardiology- Monasterio Foundation are enrolled. They receive a home monitoring kit according to the group they belong to. The trial aims to evaluate the effects of the system on quality of life. Psychological data are collected through questionnaires filled in by parents/caregivers in self-administration via the gateway at the beginning and at the end of the study. Results highlight an overall improvement in well-being and sleep quality, with a consequent reduction in anxious and stressful situations during daily life thanks to telemonitoring. At the same time, users reported a good level of usability, ease of data transmission and management of the devices. Full article

The growing popularity of physical activity (PA) applications (apps) in recent years and the vast amounts of data that they generate present attractive possibilities for surveillance. However, measurement accuracy is indispensable when tracking PA variables to provide meaningful measures of PA. The purpose of this study was to examine the steps and distance criterion validity of freeware accelerometer-based PA smartphone apps, during incremental-intensity treadmill walking and jogging. Thirty healthy adults (25.9 ± 5.7 years) participated in this cross-sectional study. They were fitted with two smartphones (one with Android and one with iOS operating systems), each one simultaneously running four different apps (i.e., Runtastic Pedometer, Accupedo, Pacer, and Argus). They walked and jogged for 5 min at each of the predefined speeds of 4.8, 6.0, and 8.4 km/h on a treadmill, and two researchers counted every step taken during trials with a digital tally counter. Validity was evaluated by comparing each app with the criterion measure using repeated-measures analysis of variance (ANOVA), mean absolute percentage errors (MAPEs), and Bland–Altman plots. For step count, Android apps performed slightly more accurately that iOS apps; nevertheless, MAPEs were generally low for all apps (<5%) and accuracy increased at higher speeds. On the other hand, errors were significantly higher for distance estimation (>10%). The findings suggest that accelerometer-based apps are accurate tools for counting steps during treadmill walking and jogging and could be considered suitable for use as an outcome measure within a clinical trial. However, none of the examined apps was suitable for measuring distance. Full article

The formation phenomena of silver carbonate (Ag₂CO₃)–silver iodide (AgI) solid solutions were investigated by X-ray diffraction, thermogravimetry-differential thermal analysis, and electrical conductivity measurement. Results revealed that AgI and Ag₂CO₃ reacted with each other when mixed at room temperature. The reaction products were classified into three types: (1) AgI-based solid solutions in the AgI-rich region for x = 10% or less in x Ag₂CO₃–(1 − x) AgI; (2) Ag₂CO₃-based solid solutions in the Ag₂CO₃-rich region for x = 60% or more; and (3) silver carbonate iodides in the intermediate range for x between 10% and 60%. For the AgI-based solid solutions, the incorporation of Ag₂CO₃ into the AgI lattice expanded the unit cell and enhanced electrical conductivity. The solubility limit of Ag₂CO₃ into the AgI lattice estimated from the differential thermal analysis was x ≈ 5%. Full article

The visualization of objects of an abstract nature has always been a challenge for chemistry learners. Thus, augmented reality (AR) and virtual reality (VR) have been heavily invested in as immersive learning methods for these concepts. This study targets the segment of the chemistry curriculum involving the chemical elements of the periodic table. For this purpose, we developed the AR educational tool called MicroWorld. This Arabic educational AR app was developed in unity with Vuforia SDK. Using MicroWorld, students can visualize chemical elements microstructures in 3D, see 3D models of the elements in their substantial forms, and combine two chemical elements to see how certain chemical compounds can be formed. In this work, MicroWorld’s usability was evaluated by junior high school students and chemistry teachers using the Arabic system usability scale (A-SUS). The A-SUS average score was 71.5 for junior high school students, while the scale for teachers reached 76. This research aims to design, develop, and evaluate the AR app, MicroWorld. This app was built and evaluated through the lens of the design science research paradigm. Full article

Heart disease, one of the main reasons behind the high mortality rate around the world, requires a sophisticated and expensive diagnosis process. In the recent past, much literature has demonstrated machine learning approaches as an opportunity to efficiently diagnose heart disease patients. However, challenges associated with datasets such as missing data, inconsistent data, and mixed data (containing inconsistent missing data both as numerical and categorical) are often obstacles in medical diagnosis. This inconsistency led to a higher probability of misprediction and a misled result. Data preprocessing steps like feature reduction, data conversion, and data scaling are employed to form a standard dataset—such measures play a crucial role in reducing inaccuracy in final prediction. This paper aims to evaluate eleven machine learning (ML) algorithms—Logistic Regression (LR), Linear Discriminant Analysis (LDA), K-Nearest Neighbors (KNN), Classification and Regression Trees (CART), Naive Bayes (NB), Support Vector Machine (SVM), XGBoost (XGB), Random Forest Classifier (RF), Gradient Boost (GB), AdaBoost (AB), Extra Tree Classifier (ET)—and six different data scaling methods—Normalization (NR), Standscale (SS), MinMax (MM), MaxAbs (MA), Robust Scaler (RS), and Quantile Transformer (QT) on a dataset comprising of information of patients with heart disease. The result shows that CART, along with RS or QT, outperforms all other ML algorithms with 100% accuracy, 100% precision, 99% recall, and 100% F1 score. The study outcomes demonstrate that the model’s performance varies depending on the data scaling method. Full article

Speech perception in an adverse background/noisy environment is a complex and challenging human process, which is made even more complicated in foreign-accented language for bilingual and monolingual individuals. Listeners who have difficulties in hearing are affected most by such a situation. Despite considerable efforts, the increase in speech intelligibility in noise remains elusive. Considering this opportunity, this study investigates Bengali–English bilinguals and native American English monolinguals’ behavioral patterns on foreign-accented English language considering bubble noise, gaussian or white noise, and quiet sound level. Twelve regular hearing participants (Six Bengali–English bilinguals and Six Native American English monolinguals) joined in this study. Statistical computation shows that speech with different noise has a significant effect (p = 0.009) on listening for both bilingual and monolingual under different sound levels (e.g., 55 dB, 65 dB, and 75 dB). Here, six different machine learning approaches (Logistic Regression (LR), Linear Discriminant Analysis (LDA), K-nearest neighbors (KNN), Naïve Bayes (NB), Classification and regression trees (CART), and Support vector machine (SVM)) are tested and evaluated to differentiate between bilingual and monolingual individuals from their behavioral patterns in both noisy and quiet environments. Results show that most optimal performances were observed using LDA by successfully differentiating between bilingual and monolingual 60% of the time. A deep neural network-based model is proposed to improve this measure further and achieved an accuracy of nearly 100% in successfully differentiating between bilingual and monolingual individuals. Full article

Effectiveness, efficiency, scalability, autonomy, engagement, flexibility, adaptiveness, personalization, conversationality, reflectiveness, innovation, and self-organization are some of the fundamental features of smart environments. Smart environments are considered a good learning practice for formal and informal education; however, it is important to point out the pedagogical approaches on which they are based. Smart learning environments (SLEs) underline the flexibility of eclectic pedagogy that places students at the center of any educational process and takes into account the diversity in classrooms. Thus, SLEs incorporate pedagogical principles derived from (1) traditional learning theories, e.g., behaviorism and constructivism, (2) contemporary pedagogical philosophy, e.g., differentiated teaching and universal design for learning, (3) theories that provide specific instructions for educational design, e.g., cognitive theory of multimedia learning and gamification of learning. The innovative concept of transmedia learning is an eclectic pedagogical approach, which in addition to learning principles, blends all available media so far. WUIM is a transmedia program for training independent living skills aimed primarily at children with learning disabilities, which emerged from the composition of pedagogical theories, traditional educational materials and cutting-edge technologies such as augmented and virtual reality, and art-based production methodologies. This paper outlines the development of WUIM, from the prototyping presented at the 4th International Conference in Creative Writing (2019) to the Alpha and Beta stages, including user and expert evaluations. Full article

Fused filament fabrication allows the direct manufacturing of customized and complex products although the layer-by-layer appearance of this process strongly affects the surface quality of the final parts. In recent years, an increasing number of post-processing treatments has been developed for the most used materials. Contrarily to other additive manufacturing technologies, metallization is not a common surface treatment for this process despite the increasing range of high-performing 3D printable materials. The objective of this work is to explore the use of physical vapor deposition sputtering for the chromium metallization of thermoplastic polymers and composites obtained by fused filament fabrication. The thermal and mechanical properties of five materials were firstly evaluated by means of differential scanning calorimetry and tensile tests. Meanwhile, a specific finishing torture test sample was designed and 3D printed to perform the metallization process and evaluate the finishing on different geometrical features. Furthermore, the roughness of the samples was measured before and after the metallization, and a cost analysis was performed to assess the cost-efficiency. To sum up, the metallization of five samples made with different materials was successfully achieved. Although some 3D printing defects worsened after the post-processing treatment, good homogeneity on the finest details was reached. These promising results may encourage further experimentations as well as the development of new applications, i.e., for the automotive and furniture fields. Full article

Solid waste is one of the social and environmental challenges that urban areas are facing. The study assesses the state of solid waste in Addis Ababa during 2016–2020 to provide implications for achieving green architecture concepts through better management of solid waste and its economic contribution. The study uses secondary and primary data. Quantitative and qualitative data are analyzed through descriptive statistics and context analysis, respectively. The result reveals that most solid waste is generated from households, followed by commercial centers, street sweeping, industries/factories, hotels, and hospitals, respectively. From 2016 to 2020, an average of 80.28% of solid waste is collected, whereas 19.72% of the waste is not collected. There are little or no efforts made to segregate solid waste at the source. The generated waste is disposed of in the Reppi open landfill. Together with Ethiopian electric power (EEP) and the City Government of Addis Ababa, waste has been converted to energy since 2019. The study suggests minimizing waste from its source by reducing generation, composting, reusing, recycling, waste-to-energy strategy, and well-designed buildings to achieve the concept of green architecture in Addis Ababa through better solid waste management. Full article

Diseases have adverse effects on crop production and yield loss. Various diseases such as leaf rust, stem rust, and strip rust can affect yield quality and quantity for a studied area. In addition, manual wheat disease identification and interpretation is time-consuming and cumbersome. Currently, decisions related to plants mainly rely on the level of expertise in the domain. To resolve these challenges and to identify wheat disease as early as possible, we implemented different deep learning models such as Inceptionv3, Resnet50, and VGG16/19. This research was conducted in collaboration with Bishoftu Agricultural Research Institute, Ethiopia. Our main objective was to automate plant-disease identification using advanced deep learning approaches and image data. For the experiment, RGB image data were collected from the Bishoftu area. From the experimental results, the VGG19 model classified wheat disease with 99.38% accuracy. Full article

It is difficult for developers, researchers, and consumers to compare results among emerging wearable technology without using a uniform set of standards. This study evaluated the accuracy of commercially available wearable technology heart rate (HR) monitors using the Consumer Technology Association (CTA) standards. Participants (N = 23) simultaneously wore a Polar chest strap (criterion measure), Jabra Elite earbuds, Scosche Rhythm 24 armband, Apple Watch 4, and Garmin Forerunner 735 XT during sitting, activities of daily living, walking, jogging, running, and cycling, totaling 57 min of monitored activity. The Apple Watch mean bias was within ±1 bpm, and mean absolute percent error (MAPE) was <3% in all six conditions. Garmin underestimated HR in all conditions, except cycling and MAPE was >10% during sedentary, lifestyle, walk-jog, and running. The Jabra mean bias was within ±5 bpm for each condition, and MAPE exceeded 10% for walk-jog. The Scosche mean bias was within ±1 bpm and MAPE was <5% for all conditions. In conclusion, only the Apple Watch Series 4 and the Scosche Rhythm 24 displayed acceptable agreement across all conditions. By employing CTA standards, future developers, researchers, and consumers will be able to make true comparisons of accuracy among wearable devices. Full article

Digital cameras and mobile phones have given people around the world the ability to take a large number of photos and store them on their computers. As these images serve the purpose of storing memories and bringing them to mind in the potentially far future, it is important to also store the impressions a user may have from them. Annotating these images can be a laborious process and the work here presents an application design and functioning implementation, which is openly available now, to ease the effort of this task. It also draws inspiration from interface developments of previous applications such as the Nokia Lifeblog and the Facebook user interface. A different mode of sentiment entry is provided where users interact with slider widgets rather than select a emoticon from a set to offer a more fine grained value. Special attention is made to avoid cognitive strain by avoiding nested tool selections. Full article

The fast emergence of IoT devices and its accompanying big and complex data has necessitated a shift from the traditional networking architecture to software-defined networks (SDNs) in recent times. Routing optimization and DDoS protection in the network has become a necessity for mobile network operators in maintaining a good QoS and QoE for customers. Inspired by the recent advancement in Machine Learning and Deep Reinforcement Learning (DRL), we propose a novel MADDPG integrated Multiagent framework in SDN for efficient multipath routing optimization and malicious DDoS traffic detection and prevention in the network. The two MARL agents cooperate within the same environment to accomplish network optimization task within a shorter time. The state, action, and reward of the proposed framework were further modelled mathematically using the Markov Decision Process (MDP) and later integrated into the MADDPG algorithm. We compared the proposed MADDPG-based framework to DDPG for network metrics: delay, jitter, packet loss rate, bandwidth usage, and intrusion detection. The results show a significant improvement in network metrics with the two agents. Full article