Reviewerly/exHarmony huggingface dataset:
SCRUM and Extreme Programming Agile Model Approach for Virtual Training Environment Design

Personnel training is a continuous process that must be focused according to the needs of each industry. The denominated “learning tools” arise, which due to their self-explanatory and easy to use, allow personnel to acquire new skills and to improve their technical performance. Within these new tools of industrial learning, is the virtual reality (VR), whose functionality allows us to train personnel in order to solve emerging problems during the work process and overcome obstacles on the way to the next stage of the process. The development of virtual reality systems must adopt characteristics of software development methodologies. In this paper the application of an Agile Development Process for virtual reality training systems focused on the calibration, connection and assembly of HART transmitters in Oil and Gas Facilities is described.
Facial Emotion Recognition in Videos using HOG and LBP

Emotions are found using verbal and non-verbal cues by analyzing voices and facial expressions. Monitoring emotional patterns of human is gaining importance in predicting the mood of a person. Facial emotion recognition is the process of detecting and recognizing different types of emotions in humans using facial expressions. The various steps include detection of the face and its landmarks, feature extraction of facial landmarks, and emotional state classification. The Haar cascading approach is used to detect different facial components such as eyes, mouth, and nose in an image. Facial features are analyzed using Histogram of Gradients (HOG) and Local Binary Pattern (LBP). The resultant feature vector is formed from the feature points. The three emotional states namely happy, sad and angry are classified using neural network classifier. The new feature points of test data are compared against trained data and their corresponding label values are displayed as the output for emotion recognition with the accuracy of 87% and 64 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup> is being achieved using HOG and LBP techniques.
Multilayer Security in Protecting and Hiding Multimedia Data using Cryptography and Steganography Techniques

Secure data hiding using Cryptography and Steganography. Simply encrypting the data and transmitting it will cause suspension among the viewers so in this project to avoid such suspension, the data is embedded in to a picture and this innocent looking picture is transmitted. If the data is transmitted via image, it can be in BMP format, and if the data is sent through video, it can be in MPEG format, last but not the least, if the data is sent in audio, it can be in WAV format. To achieve this, we are using least significant bit technique and Data Encryption Standard (DES) algorithm. We provide two levels of security for any kind of secret or confidential messages. First level is, using cryptographic techniques and in the second level, using steganographic techniques. Multi-level security is provided to ensure the protection and security of data.
Stereo matching for infrared images using guided filtering weighted by exponential moving average

Infrared imaging is less susceptible to illumination conditions and haze than visible light imaging. The advantage makes infrared sensing suitable for providing remote visibility with reduced distortion. However, infrared images tend to have low resolution and lack rich textures that facilitate stereo matching. To enhance the applicability of infrared stereo imaging, the authors re-examine the guided-image techniques to include advanced edge-aware filters for aggregation and propose a novel guided-image filtering scheme here. Based on the exponential moving average, the weights are recursively calculated such that all pixels on the infrared image pair can contribute to a discrepancy cost. The arrangement allows additional pixels to be involved in the cost aggregation to reduce the demand for rich texture. Experimental results using the colour and thermal stereo (CATS) benchmark testbed demonstrate that the proposed approach outperforms several state-of-art approaches in generating accurate disparity maps.
Design and Implement a Proposed Multi-Sources to Multi-Destinations Broadcasting Video-Signals

This paper presents design and implementation of a proposed system for Broadcasting Video-Signals from Multi-Sources to Multi-Destinations using fundamentals of Clients/Server and clustering. The system makes the improved application software to be used friendly with as maximum as possible of flexibility and full optional controlling to get complete features of the video broadcasting systems. The techniques of this software application are designed and implemented by Visual C#.Net language with using Microsoft Framework.Net. This system overcome the problems of delay, overlapping, attenuation, bottleneck of the bandwidth by limit number of resources and destinations, through controlling the permissions and broadcasting, and monitoring the configuration of the system during broadcasting. One of the most important enhancements achieved is via overcoming the problem of synchronization between two sides of the network (server and clients) due to the incoming signals from the cameras to the server and broadcasting these signals to the clients-side. Because of the high-flexibility of the designed software application, this system enables the administrator to monitor the dataflow from server-side to clients-side and apply compressing with many compression techniques on the incoming and broadcasted signals.
Inferring Super-Resolution Depth from a Moving Light-Source Enhanced RGB-D Sensor: A Variational Approach

A novel approach towards depth map super-resolution using multi-view uncalibrated photometric stereo is presented. Practically, an LED light source is attached to a commodity RGB-D sensor and is used to capture objects from multiple viewpoints with unknown motion. This non-static camera-to-object setup is described with a nonconvex variational approach such that no calibration on lighting or camera motion is required due to the formulation of an end-to-end joint optimization problem. Solving the proposed variational model results in high resolution depth, reflectance and camera pose estimates, as we show on challenging synthetic and real-world datasets.
Bandwidth Prediction Schemes for Defining Bitrate Levels in SDN-enabled Adaptive Streaming

The majority of Internet video traffic today is delivered via HTTP Adaptive Streaming (HAS). Recent studies concluded that pure client-driven HAS adaptation is likely to be sub-optimal, given clients adjust quality based on local feedback. In [1], we introduced a network-assisted streaming architecture (BBGDASH) that provides bounded bitrate guidance for a video client while preserving quality control and adaptation at the client. Although BBGDASH is an efficient approach for video delivery, deploying it in a wireless network environment could result in sub-optimal decisions due to the high fluctuations. To this end, we propose in this paper an intelligent streaming architecture (denoted BBGDASH <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">+</sup> ), which leverages the power of time series forecasting to allow for an accurate and scalable networkbased guidance. Further, we conduct an initial investigation of parameter settings for the forecasting algorithms in a wireless testbed. Overall, the experimental results indicate the potential of the proposed approach to improve video delivery in wireless network conditions.
A Cartoon+Texture Image Decomposition Variational Model Based on Preserving the Local Geometric Characteristics

This paper is devoted to decomposing one given image into the cartoon and texture.The cartoon parts correspond to the main large objects in the image, and the textural parts contain fine scale details.Mathematically, these two components should belong to the different function spaces corresponding to different norms.In most variational models of image decomposition, the cartoon parts are described by the total variation norm and the texture parts are characterized by its dual norm.Using these methods, the cartoon and texture can be separated from the original image.Some structures, such as edges, are well preserved in the cartoon image.However, as far as we know, none of these methods considers the geometric characteristics information of the texture position in the separated cartoon images.That is, the geometrical information in those separated cartoon images may have been destroyed, which can be seen from the experiments in this paper.To maintain these geometrical characteristics, we introduce one smoothed vector field and let it approximate to the negative gradient direction of the cartoon image, and then for the smoothed vector field, we let it belong to the Lebesgue space, thus a new variational decomposition model is proposed.The corresponding alternating direction method is discussed in detail.Experimental results are reported to show the visual qualities compared with other methods.
Secure the Internet of Things Software Updates with Ethereum Blockchain

Internet of Things (IoT) is a growing technology that is projected to have over 41.6 billion connected devices by 2025. The IoT idea is about connecting the unconnected things (sensors, clothes, refrigerators and others) to the existing network infrastructure. Across industries ranging from agriculture to manufacturing, organizations are rapidly growing business value with the IoT. However, many organizations are hesitating to further invest in IoT initiatives due to the fear of inadequate security. Security is a very critical consideration where everything becomes connected to the Internet. However, security in IoT is still a very open area that is yet to receive much attention. One challenge in IoT is ensuring the security of firmware updates on deployed IoT devices. Hackers can perform man-in-the-middle attacks to compromise an update procedure and take control of deployed devices if they do not use proper security mechanisms. This means that it is imperative to architect secure by design mechanisms to ensure security in IoT. Blockchain is an emerging technology that is touted as a potential cybersecurity transformational force. This work proposes the use of blockchain to provide a mechanism for nodes to verify the authenticity and integrity of firmware before downloading and ensures high availability of the firmware. A proof of concept system was implemented. The prototype illustrates how secure IoT firmware update can be realized through the use of smart contracts. Furthermore, we show that it is possible to check for authentic software by verifying it through blockchain smart contracts.
Application of the InterCriteria Analysis Method to a Dataset of Malignant Neoplasms of the Digestive Organs for the Burgas Region for 2014–2018

The aim of this paper is to analyze a statistical data for the registered patients with malignant neoplasms of the digestive organs in Burgas region for the period 2014-2018. The InterCriteria Analysis method is applied. The results are commented from different points of view: relations between age of the patients with malignant neoplasms of the digestive organs, relations between marital status and year of registration of patient and type of Malignant neoplasms of the digestive organs. The obtained results by InterCriteria Analysis method are compared by statistical analysis according to Pearson, Kendall and Spearman.
Understanding the effects of control and transparency in searching as learning

In this paper, we analyze the benefits of adopting user interfaces that offer control and transparency for searching in contexts of learning activities. Concretely, we conducted a user study with pharmacy students performing a problem-solving task in the course of a university lecture. The task involved finding scientific papers containing relevant information to solve a clinical case. Students were split into two independent groups and assigned one search tool to perform the task. The baseline group worked with PubMed, a popular search engine in the life sciences domain, whereas the second half of the class was assigned an exploratory search system (ESS) designed for control and transparency. In the analysis, we cover the objective and subjective dimensions of the task outcomes. Firstly, the objective analysis addresses the inherent difficulty of the search task in a learning scenario and identifies certain improvements in performance for those students using the ESS, most notably when searching for primary-source content. The subjective analysis investigates the human factors side, providing evidence that the ESS effectively increases the perception of control and transparency and is able to produce a better user experience. Lastly, we report on perceived learning as a subjective dimension measured separately from user experience.
Efficient Learning on High-dimensional Operational Data

In networked systems engineering, operational data gathered from sensors or logs can be used to build data-driven functions for performance prediction, anomaly detection, and other operational tasks. The number of data sources used for this purpose determines the dimensionality of the feature space for learning and can reach millions for medium-sized systems. Learning on a space with high dimensionality generally incurs high communication and computational costs for the learning process. In this work, we apply and compare a range of methods, including, feature selection, Principle Component Analysis (PCA), and autoencoders with the objective to reduce the dimensionality of the feature space while maintaining the prediction accuracy when compared with learning on the full space. We conduct the study using traces gathered from a testbed at KTH that runs a video-on-demand service and a key-value store under dynamic load. Our results suggest the feasibility of reducing the dimensionality of the feature space of operational data significantly, by one to two orders of magnitude in our scenarios, while maintaining prediction accuracy. The findings confirm the Manifold Hypothesis in machine learning, which states that real-world data sets tend to occupy a small subspace of the full feature space. In addition, we investigate the tradeoff between prediction accuracy and prediction overhead, which is crucial for applying the results to operational systems.
A Bengali Text Generation Approach in Context of Abstractive Text Summarization Using RNN

Automatic text summarization is one of the mentionable research areas of natural language processing. The amount of data is increasing rapidly, and the necessity of understanding the gist of any text is just a mandatory tool, nowadays. The area of text summarization has been developing since many years. Mentionable research has been already done through extractive summarization approach; in other side, abstractive summarization approach is the way to summarize any text as like human. Machine will be able to provide a new type of summarization, where the understanding of given summary may found as like as human-generated summary. Several research developments have already been done for abstractive summarization in English language. This paper shows a necessary method—"text generation" in context of Bengali abstractive text summarization development. Text generation helps the machine to understand the pattern of human-written text and then produce the output as is human-written text. A basis recurrent neural network (RNN) has been applied for this text generation approach. The most applicable and successful RNN—long short-term memory (LSTM)—has been applied. Contextual tokens have been used for the better sequence prediction. The proposed method has been developed in the context of making it useable for further development of abstractive text summarization.
GLoG: Laplacian of Gaussian for Spatial Pattern Detection in Spatio-Temporal Data

Boundary detection has long been a fundamental tool for image processing and computer vision, supporting the analysis of static and time-varying data. In this work, we built upon the theory of Graph Signal Processing to propose a novel boundary detection filter in the context of graphs, having as main application scenario the visual analysis of spatio-temporal data. More specifically, we propose the equivalent for graphs of the so-called Laplacian of Gaussian edge detection filter, which is widely used in image processing. The proposed filter is able to reveal interesting spatial patterns while still enabling the definition of entropy of time slices. The entropy reveals the degree of randomness of a time slice, helping users to identify expected and unexpected phenomena over time. The effectiveness of our approach appears in applications involving synthetic and real data sets, which show that the proposed methodology is able to uncover interesting spatial and temporal phenomena. The provided examples and case studies make clear the usefulness of our approach as a mechanism to support visual analytic tasks involving spatio-temporal data.
Ethiopia’s Hate Speech Predicament: Seeking Antidotes Beyond a Legislative Response

After waves of public protests that denounced long-time ruling party the Ethiopian People’s Revolutionary Democratic Front, Ethiopia has experienced a political transformation that, among other things, lifted restrictions on political speech and freed prisoners of conscience. While the transformative potential of social networking sites across the nation in keeping the momentum of the protests was evident, the rise of displacements, killings, and violence amplified by ethno-nationalist discriminatory discourses on platforms such as Facebook created a fervent debate on the role of the state in regulating hate speech online. By analyzing data primarily generated from interviews with government officials, members of civil society organizations, journalists, human rights advocates, and freedom of speech activists, this study charts the promises, parameters, and challenges of Ethiopia’s hate speech and disinformation bill. Findings indicate the Ethiopian government’s alarm bell on the rise of hate speech in social networking sites and the need for an intervention to counter this threat is warranted. However, informants expressed serious concerns about the government’s choice of intervention in the form of a legislative response by highlighting the bill’s definitional/procedural gaps as well as its potential to undermine freedom of speech, the government’s track record of abusing similar bills in the past to crackdown on legitimate expressions of dissent, and lack of implementation capacity. The study concludes by offering a comprehensive, multi-stakeholder, long-term approach in addition to a legislative response to minimize occurrences as well as impacts of hate speech.
Potential Adoption of Blockchain Technology to Enhance Transparency and Accountability in the Public Healthcare System in South Africa

The South African healthcare system is hampered by many challenges. Most of the challenges are systematic in nature and hence require strengthening processes and information flow throughout the entire healthcare. This entails putting in place mechanisms that simplify the workflow but also enhance transparency and accountability. Blockchain has been hailed for allowing a transparent and decentralised workflow. Immutability, traceability and decentralised ledger are some of the features that make blockchain appealing to many organisations. This paper presents a case for the use of blockchain within the public healthcare in South Africa. It further portrays how blockchain could be used to foster transparency and accountability in patient-centered care in South African public healthcare institutions.
Learning Multimodal Representations for Unseen Activities

We present a method to learn a joint multimodal representation space that enables recognition of unseen activities in videos. We first compare the effect of placing various constraints on the embedding space using paired text and video data. We also propose a method to improve the joint embedding space using an adversarial formulation, allowing it to benefit from unpaired text and video data. By using unpaired text data, we show the ability to learn a representation that better captures unseen activities. In addition to testing on publicly available datasets, we introduce a new, large-scale text/video dataset. We experimentally confirm that using paired and unpaired data to learn a shared embedding space benefits three difficult tasks (i) zero-shot activity classification, (ii) unsupervised activity discovery, and (iii) unseen activity captioning, outperforming the state-of-the-arts.
Stark: Fast and Scalable Strassen’s Matrix Multiplication Using Apache Spark

This article presents a new fast, highly scalable distributed matrix multiplication algorithm on Apache Spark, called <i>Stark</i> , based on Strassen’s matrix multiplication algorithm. Stark preserves Strassen’s seven multiplications scheme in a distributed environment and thus achieves asymptotically faster execution time. It creates a distributed recursion tree of computation where each level of the tree corresponds to division and combination of distributed matrix blocks stored in the form of Resilient Distributed Datasets (RDDs). It processes each divide and combine step in parallel and memorises the sub-matrices by intelligently tagging matrix blocks in it. To the best of our knowledge, Stark is the first implementation of a distribute Strassen’s algorithm on Spark platform. We also report a detailed complexity analysis for the proposed algorithm, taking into account computation and communication costs. Experimental results suggest that Stark outperforms existing distributed matrix multiplication implementations on Spark – <i>Marlin</i> and <i>MLLib</i> , for high matrix sizes ( <inline-formula><tex-math notation="LaTeX">$\geq 16384\times 16384$</tex-math></inline-formula> ). Our experiments reveal optimal block sizes for each matrix size, which is also shown from theoretical analysis. We also show that the experimental and theoretical running times for Stark match closely. It has also been shown experimentally that Stark exhibits strong scalability with increasing number of executors.
Subgroup analysis based on structured mixed-effects models for longitudinal data

Juan Shena & Annie Qub* a Department of Statistics, Fudan University, Shanghai, Chinab Department of Statistics, University of California at Irvine, Irvine, California, USA
A Robot Architecture for Outdoor Competitions

Autonomous navigation in unstructured environments is a common topic of research, being motivated by robotic competitions and involving several sets of skills. We present a modular architecture to integrate different components for path planning and navigation of an autonomous mobile robot. This architecture was developed in order to participate in the RoboMagellan competition hosted by RoboGames. It is divided in the organizational, functional and executive levels in order to secure that the developed system has programmability, autonomy, adaptability and extensibility. Global and local localization strategies use unscented and extended Kalman filters (UKF and EKF) to fuse data from a Global Positioning System (GPS) receiver, inertial measurement unit (IMU), odometry and camera. Movement is controlled by a model reference adaptive controller (MRAC) and a proportional controller. To avoid obstacles a deformable virtual zone (DVZ) approach is used. The architecture was tested in simulated environments and with a real robot, providing a very flexible approach to testing different configurations.