Reviewerly/exHarmony huggingface dataset:
Systematic literature review of Semantic Web for distance learning

The study intends to identify the emerging themes of e-learning through the Semantic Web. To highlight the key challenges and explore the core benefits of this mesmerizing technology was another goal of the study. To get the set goals researchers systematically reviewed the literature in five leading databases, out of which one specialized database of education; ERIC and four general databases i.e Scopus, J-store, Science Direct & Google Scholar. The preferred reporting items were used for systematic review and meta-analysis (PRISMA) guidelines. The selected 22 research studies identified eight emergent themes within the body of research. In answer to the second part of the research study, the review shows that Semantic Web technology has opened new horizons for e-learning. On the other hand, data analysis of selected studies reflects the technological, socio-cultural and other challenges which may cause the hurdle for the realization of these goals in true sense. The overall result shows, interoperability, data security & privacy, ontology’s & crowd sourcing technology or automatic annotation as future research trends indicated by the identified studies to meet up these challenges. This SR may be beneficial for stakeholders to understand the e-learning scenario through Semantic Web technology.
SpiderMon: Towards Using Cell Towers as Illuminating Sources for Keystroke Monitoring

Cellular network operators deploy base stations with a high density to ensure radio signal coverage for 4G/5G networks. While users enjoy the high-speed connection provided by cellular networks, an adversary could exploit the dense cellular deployment to detect nearby human movements and even recognize keystroke movements of a victim by passively listening to the CRS broadcast from base stations. To demonstrate this, we develop SpiderMon, the first attempt to perform passive continuous keystroke monitoring using the signal transmitted by commercial cellular base stations. Our experimental results show that SpiderMon can detect keystroke movements at a distance of 15 meters and can recover a 6-digits PIN input with a success rate of more than 51% within ten trials when the victim is behind the wall.
Emotion Analysis Using Deep Learning

Recognition of facial expression has many potential applications that have attracted the researcher's attention during the last decade. Taking out of features, is an important step in the analysis of expression that contributes to a quick and accurate recognition of expression, i.e., happiness, surprise and disgust, sadness, anger and fear are expressions of the faces. Facial expressions are most frequently used to interpret human emotions. Two categories contain a range of different emotions: positive emotions and non-positive emotions. The Face Detection, Extraction, Classification and Recognition are major steps used in the proposed system. The proposed segmentation techniques are applied and compared to determining which method is appropriate for splitting the mouth region, and then the mouth region can be extracted using techniques for stretching contrasts and segmenting the image. After the extraction of the mouth area, the facial emotions are graded in the face picture region of the extracted mouth based on white pixel values. The Supervisory Learning Approach is widely used for face identification algorithms and it takes more computation time and effort. It may also give incorrect class labels in the classification process. For this reason, supervised learning and reinforcement learning is being used. In general, it will be like a trial and error method that is, in the training process it tries to learn and produce expected results. It was specified accordingly. Reinforcement learning always tries to enhance the results. In this case, trained dataset act to give outcome as facial emotion.
CONVINCE: Collaborative Cross-Camera Video Analytics at the Edge

Today, video cameras are deployed in dense for monitoring physical places e.g., city, industrial, or agricultural sites. In the current systems, each camera node sends its feed to a cloud server individually. However, this approach suffers from several hurdles including higher computation cost, large bandwidth requirement for analyzing the enormous data, and privacy concerns. In dense deployment, video nodes typically demonstrate a significant spatio-temporal correlation. To overcome these obstacles in current approaches, this paper introduces CONVINCE, a new approach to look at the network cameras as a collective entity that enables collaborative video analytics pipeline among cameras. CONVINCE aims at 1) reducing the computation cost and bandwidth requirements by leveraging spatiotemporal correlations among cameras in eliminating redundant frames intelligently, and ii) improving vision algorithms' accuracy by enabling collaborative knowledge sharing among relevant cameras. Our results demonstrate that CONVINCE achieves an object identification accuracy of <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">~</sub> 91%, by transmitting only about <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">~</sub> 25% of all the recorded frames.
Artificial switched chaotic system used as transmitter in chaos-based secure communication

In this paper, a new idea of chaos synchronization and chaos-based secure communication is developed. First, instead of considering one single chaotic system, several uncertain chaotic systems are combined to form an uncertain switched chaotic system artificially. And then the switched chaotic system is used as transmitter in chaos-based secure communication consideration. Second, a drive signal is constructed and the information message is then embedded into the drive signal to form a transmitted signal for secure communication purpose. Third, an augmented switched chaotic system is constructed and a robust sliding model observer is proposed to sever as response system. Since the response system can not only estimate the transmitter's states but also recover the embedded information message, it can be regarded as a receiver for chaos-based secure communication. Besides, we discuss the stability problem in detail based on the stability theory of the switched system. Finally, two simulation examples are introduced to illustrate the effectiveness and the advantages of our methods.
Robotic Exploration of Unknown 2D Environment Using a Frontier-based Automatic-Differentiable Information Gain Measure

At the heart of path-planning methods for autonomous robotic exploration is a heuristic which encourages exploring unknown regions of the environment. Such heuristics are typically computed using frontier-based or information-theoretic methods. Frontier-based methods define the information gain of an exploration path as the number of boundary cells, or frontiers, which are visible from the path. However, the discrete and non-differentiable nature of this measure of information gain makes it difficult to optimize using gradient-based methods. In contrast, information-theoretic methods define information gain as the mutual information between the sensor's measurements and the explored map. However, computation of the gradient of mutual information involves finite differencing and is thus computationally expensive. In this work, we propose an exploration planning framework which combines sampling-based path planning and gradient-based path optimization. The main contribution of this framework is a novel reformulation of information gain as a differentiable function. This allows us to simultaneously optimize information gain with other differentiable quality measures, such as smoothness. The effectiveness of the proposed planning framework is verified both in simulation and in hardware experiments using a Turtlebot3 Burger robot.
NetCapVis: Web-based Progressive Visual Analytics for Network Packet Captures

Network traffic log data is a key data source for forensic analysis of cybersecurity incidents. Packet Captures (PCAPs) are the raw information directly gathered from the network device. As the bandwidth and connections to other hosts rise, this data becomes very large quickly. Malware analysts and administrators are using this data frequently for their analysis. However, the currently most used tool Wireshark is displaying the data as a table, making it difficult to get an overview and focus on the significant parts. Also, the process of loading large files into Wireshark takes time and has to be repeated each time the file is closed. We believe that this problem poses an optimal setting for a client-server infrastructure with a progressive visual analytics approach. The processing can be outsourced to the server while the client is progressively updated. In this paper we present NetCapVis, an web-based progressive visual analytics system where the user can upload PCAP files, set initial filters to reduce the data before uploading and then instantly interact with the data while the rest is progressively loaded into the visualizations.
Development of a Deep Learning–Based Model for Diagnosing Breast Nodules With Ultrasound

Objectives Artificial intelligence (AI) has been an important addition to medicine. We aimed to explore the use of deep learning (DL) to distinguish benign from malignant lesions with breast ultrasound (BUS). Methods The DL model was trained with BUS nodule data using a standard protocol (1271 malignant nodules, 1053 benign nodules, and 2144 images of the contralateral normal breast). The model was tested with 692 images of 256 breast nodules. We used the accuracy, precision, recall, harmonic mean of recall and precision, and mean average precision as the indices to assess the DL model. We used 100 BUS images to evaluate differences in diagnostic accuracy among the AI system, experts (&gt;25 years of experience), and physicians with varying levels of experience. A receiver operating characteristic curve was generated to evaluate the accuracy for distinguishing between benign and malignant breast nodules. Results The DL model showed 73.3% sensitivity and 94.9% specificity for the diagnosis of benign versus malignant breast nodules (area under the curve, 0.943). No significant difference in diagnostic ability was found between the AI system and the expert group ( P = .951), although the physicians with lower levels of experience showed significant differences from the AI and expert groups ( P = .01 and .03, respectively). Conclusions Deep learning could distinguish between benign and malignant breast nodules with BUS. On BUS images, DL achieved diagnostic accuracy equivalent to that of expert physicians.
YOLOv3 Algorithm with additional convolutional neural network trained for traffic sign recognition

The ability of perception and understanding all static and dynamic objects around vehicle in various driving and environmental conditions represent one of the main requirements for autonomous vehicles and most of Advanced Driving Assistance Systems (ADAS). Current promise to deliver safe ADAS in modern cars could be achieved by convolutional neural network (CNN). In this paper we present a software based on YOLO that is extended with a CNN for traffic sign recognition. Since real time detection is required for safe driving, YOLO network used in this paper is pre trained for detection and classification of only five objects which are separated in categories such as cars, trucks, pedestrians, traffic signs, and traffic lights. Detected traffic signs are further passed to CNN which can classify them in one of 75 categories. We demonstrate the high level of classification confidence by accurately recognition more than 99.2% of examined signs in quite diverse conditions.
Performance Comparison of Anti-Spam Technology Using Confusion Matrix Classification

Abstract This research aims to test the ability to capture malicious e-mail objects, spam e-mails, phishing e-mails, or malware e-mails on Proofpoint Email Protection and Fortimail antispam devices to prevent security threats to PT. XYZ. Email service was one of the primary services for an agency or company in providing electronic mail that could be used to exchange official information. The study was conducted by comparing two antispam devices of Proofpoint and Fortimail. The test was conducted sequentially within three months period on each device. Those devices were configured using the infrastructure of PT. XYZ. Besides comparing the functions or features of each device, device testing was also done on overcoming the problem of email security threats such as spoofing, impostor, and bulk email that often threaten email infrastructure. To measure the accuracy of the results of the two devices, we use the Confusion Matrix method to measure the performance of the classification system. As the result, the slights differences inaccuracy were the indication that the ability of each antispam device was not too far. A large number of spam emails which caught through antispam devices were illustrated that PT. XYZ had become the target of spam, phishing, and malware attack. For this reason, anti-spam device technology was needed to maintain the quality of PT. XYZ email service.
Vehicle Detection in the Autonomous Vehicle Environment for Potential Collision Warning

In this paper, a method for the vehicles detection in the surroundings of an autonomous vehicle and warnings of potential collision with them is presented. The method, which consists of two parts, is implemented in robot operating system (ROS). The first part is used to detect vehicles in an autonomous vehicle environment, in which, YOLO v2 algorithm, trained on a newly created set of images, is used. The YOLO v2 algorithm is configured to detect four classes of objects: a car, a van, a truck, and a bus. The second part of the proposed method is the ROS node for distance assessment. In particular, two ROS nodes for distance assessment are created; one ROS node used for distance assessment in the Carla simulator, while the other ROS node is used for real-world distance assessment. The testing results of the proposed method show promising results.
Design and Evaluation of a Real-time Pedestrian Detection System for Autonomous Vehicles

Design and development of autonomous vehicles capable of moving safely on roads by sensing the environment has motivated researchers to focus on design of pedestrian detection systems. Similarly, Convolution Neural Networks (CNN) is considered as one of the preferred image classification algorithms. Most of the papers reported in literature employ standard object detector modules available online for pedestrian detection. Design of a real-time pedestrian detection system using CNN for autonomous vehicles is proposed and the system is designed from scratch without using any standard module available. The performance evaluation of proposed system is carried out using INRIA dataset, PETA-CUHK dataset and realtime video input. The CNN parameters were also tuned to achieve best possible recognition accuracy. Recognition accuracies ranging between 96.73 - 100% is obtained on using the system, based on dataset employed. The results obtained are also compared with the results reported in literature and the system designed can be considered on par with those reported in literature. The proposed real-time pedestrian detection system can also be employed as driver assistance system for non-autonomous vehicles.
Sensor-based activity recognition of solitary elderly via stigmergy and two-layer framework

With the acceleration of aging process of population structure, the single resident lifestyle is increasing on account of the high cost of care services and the privacy invasion concern. It is essential to monitor the activities of solitary elderly to find the emergency and lifestyle deviation, as independent life cannot be maintained due to physical or mental problems. The unobtrusive systems are the most preferred choice for the real-life long-term monitoring, while the camera and wearable devices based systems are not suitable due to the privacy and uncomfortableness, respectively. We propose a novel sensor-based activity recognition model based on the two-layer multi-granularity framework and the emergent paradigm with marker-based stigmergy. The stigmergy based marking subsystem builds features by aggregating the context-aware information and generating the two-dimensional activity pheromone trail. The two-layer framework consists of coarse-grained and fine-grained classification subsystems. The coarse-grained subsystem identifies whether the input completed activity segmented by the traditional method is easily-confused, and utilizes our generalized segmentation method to increase the inter-cluster distance. The fine-grained subsystem employs machine learning or deep learning classifiers to realize the activity recognition task. The proposed model is a data-driven model based on the information self-organization. It does not need sophisticated domain knowledge, and can fully mine the hidden feature structure containing semantically related information and spatio-temporal characteristics. The experimental results demonstrate the effectiveness of the proposed method.
‘Not the Real Me’: Social Imaginaries of Personal Data Profiling

In this article, I present findings from my Data Personas study, in which I invited Australian adults to respond to the stimulus of the ‘data persona’ to help them consider personal data profiling and related algorithmic processing of personal digitised information. The literature on social imaginaries is brought together with vital materialism theory, with a focus on identifying the affective forces, relational connections and agential capacities in participants’ imaginaries and experiences concerning data profiling and related practices now and into the future. The participants were aware of how their personal data were generated from their online engagements, and that commercial and government agencies used these data. However, most people suggested that data profiling was only ever partial, configuring a superficial and static version of themselves. They noted that as people move through their life-course, their identities and bodies are subject to change: dynamic and emergent. While the digital data that are generated about humans are also lively, these data can never fully capture the full vibrancy, fluidity and spontaneity of human experience and behaviour. In these imaginaries, therefore, data personas are figured as simultaneously less-than-human and more-than-human. The implications for understanding and theorising human-personal data relations are discussed.
Quantitative wave-particle duality as quantum state discrimination

We consider the problem of quantitative wave-particle duality from the perspective of quantum state discrimination. Specifically, we obtain quantifiers of both the particle aspect and the wave aspect from state discrimination. We show that the familiar one-bet duality relation coincides with the relation obtained from considerations of ambiguous discrimination, and the quantifiers suggested by unambiguous discrimination provide us with additional duality relations. In contrast to earlier similar works, the path asymmetry of the interferometer provides the ensemble to be discriminated, and therefore there is no need to introduce which-path detectors.
A Perception-Based Inverse Tone Mapping Operator for High Dynamic Range Video Applications

The drastic visual improvements introduced by High Dynamic Range (HDR) technologies open new markets for a wide range of industries. Among them, a significant opportunity is offered to owners of legacy Standard Dynamic Range (SDR) content that can be converted to the new standard to take advantage of the enhanced capabilities of the HDR displays. Similarly, since SDR broadcasting infrastructure will continue to be around for the time being, such conversion process is becoming an obvious necessity. To this end, different approaches have tried to efficiently convert SDR images and videos to HDR format, a procedure well known as inverse Tone Mapping. In this paper, we propose a novel high visual quality video inverse Tone Mapping Operator (iTMO) that addresses the inadequacies of the state-of-the-art methods, resulting in high visual quality HDR videos that match the capabilities of the HDR technology. Our approach is based on human visual perception and employs a segmentation method according to the Human Visual System (HVS) sensitivity to brightness changes in different regions of the frame and constructs the mapping curve using the brightness distribution information of these regions. Our iTMO uses a hybrid approach to achieve an optimal balance between the overall contrast and brightness of the output HDR frame by maximizing a weighted sum of contrast and brightness difference between input SDR and generated HDR frame. The proposed iTMO works equally well for all levels of brightness, eliminating any visual artifacts by dynamically maintaining changes at non-perceivable levels. Subjective and objective evaluations validated the superior visual performance of our proposed iTMO over state-of-the-art methods.
Redefining fuzzy entropy with a general framework

A general entropy framework is proposed, which could lead to intuitive, interpretable and comparable entropy functions in both probabilistic or fuzzy domain, alike. Based on the proposed general entropy framework, the existing fuzzy entropy functions are redefined. The proposed entropy functions are also extended to the probabilistic-fuzzy domain. The usefulness of the work is shown in a real world case-study.
MAPPCN: Multi-hop Anonymous and Privacy-Preserving Payment Channel Network

Payment channel network (PCN) has become an indispensable mechanism to resolve scalability issues in blockchain-based cryptocurrencies. On the other hand, PCNs do not provide adequate security and privacy guarantee. Most of the existing payment schemes leak information about payer or payee to the nodes in the payment path. To address this issue, we propose a simple but effective, multi-hop, anonymous, and privacy-preserving PCN (MAPPCN). MAPPCN construction is based on Elliptic curve cryptography (ECC) and is proved to be secure while achieving payment path privacy, sender, and receiver anonymity. MAPPCN can be performed in $$(3 \cdot n + 5)$$ Elliptic curve scalar multiplication (ECSM) operations for an off-chain payment operation.
A Survey on Heterogeneous One-class Collaborative Filtering

Recommender systems play an important role in providing personalized services for users in the context of information overload. Generally, users’ feedback toward items often contain the most significant information reflecting their preferences, which enables accurate personalized recommendation. In real applications, users’ feedback are usually heterogeneous (rather than homogeneous) such as purchases and examinations in e-commerce, which reflects users’ preferences in different degrees. Effective modeling of such heterogeneous one-class feedback is challenging compared with that of homogeneous feedback of ratings. As a response, heterogeneous one-class collaborative filtering (HOCCF) is proposed, which often converts the heterogeneous feedback into two parts (i.e., target feedback and auxiliary feedback), aiming to care more about the target feedback (e.g., purchases ) with the assistance of the auxiliary feedback (e.g., examinations ). In this survey, we provide an overview of the representative HOCCF methods from the perspective of factorization-based methods, transfer learning-based methods, and deep learning-based methods. First, we review the factorization-based methods according to different strategies. Second, we describe the transfer learning-based methods with different knowledge sharing manners. Third, we discuss the deep learning-based methods according to the neural architectures. Moreover, we include some important example applications, describe the empirical studies, and discuss some promising future directions.