Reviewerly/exHarmony huggingface dataset:
Detection of online review spam

Online reviews have become an important resource for customers. It has become a habit for customers to first read a review before deciding to make a purchase. But it can be used by fraudsters to make review spam. This activity can result in the wrong customer purchase decision. Automatic opinion mining methods can also provide inaccurate conclusions due to this activity. This paper aims to provide a literature review on the online review spam detection topic. We identify papers relevant to related topics since 2015, understanding each paper to extract findings, similarities, and research gaps. We find that studies on this topic can be categorized into three focus groups. Focus on review spam detection methods, studies on individuals who write review spam, and studies that examine the spammer groups. Each focus of research has its strengths and weaknesses method which provide benefits in the field of review spam detection.
Counting Richelot isogenies between superspecial abelian surfaces

Castryck, Decru, and Smith used superspecial genus-2 curves and their Richelot isogeny graph for basing genus-2 isogeny cryptography, and recently, Costello and Smith devised an improved isogeny path-finding algorithm in the genus-2 setting. In order to establish a firm ground for the cryptographic construction and analysis, we give a new characterization of {\em decomposed Richelot isogenies} in terms of {\em involutive reduced automorphisms} of genus-2 curves over a finite field, and explicitly count such decomposed (and non-decomposed) Richelot isogenies between {\em superspecial} principally polarized abelian surfaces. As a corollary, we give another algebraic geometric proof of Theorem 2 in the paper of Castryck et al.
Reliability of the system with loops: Factor graph based approach

Evaluating systems’ vulnerability is important in safety critical systems. For this reason, probabilistic safety assessments (PSAs) are widely conducted in the nuclear industry. To ensure efficient, and accurate reliability analyses, several reliability evaluation methods have been developed. The most familiar method is fault tree analysis. However, as the system becomes more complex, the fault tree structure becomes even more complicated. As an alternative, the graph-based reliability analysis method has been developed. However, conventional graph-based analyses have difficulty with considering a logical loop structure. Here, we suggest a factor graph and belief propagation algorithm based logical loop analysis method for intuitive logical loop consideration. To solve the problem efficiently we modified the belief propagation rules. The proposed methodology was successfully applied to 3-system-in-loop and 4-system- in-loop.
Visualizing Global Socio-Technogenic Human Transformation: Digital Challenges of Living Earth

In the XXI century, the world of man and nature develops mainly in socio-technogenic living conditions. Global technospherization of the planet Earth is the most important cause of continuous socio-cultural, techno-medical and biological changes in man. In technical, natural and human studies a human is usually considered from the point of view of only one discipline. This narrow approach leaves its incorrect mark when recreating the new image of a human in the virtual environment of the digital image of the world. Digital Earth technologies establish a link between the spheres: social, biospheric, natural-inanimate and artificial, created by society using a number of important sciences. It is necessary to approach systematically the representation of the evolving human being in the constantly updated digital space of the planet, to supplement the existing developments of scientists with a scientific and philosophical understanding of the interdisciplinary processes of socio-technogenic development of the biosphere life. Scientific visualization of interrelated evolutionary changes in man is of great interest for the Digital Earth project both from the point of view of studying anthropogenesis, and from the point of view of developing promising programs for preserving his biosphere body and natural health in a developing socio-technogenic world.
Calculation of a binary diffractive optical element to increase the imaging system depth of field in the task of classifying images by a neural network

Abstract Using an example of a real-world data set, it is shown that the accuracy of the image classifier based on a convolutional neural network does not deteriorate when using only one color channel. The binary diffractive optical element was calculated, which allows to increase the imaging system depth of field by several times. This is achieved by using the different color channels for various defocus values. A comparison of the MTF curves of the original and apodized imaging systems for a given minimum acceptable value of image contrast is presented.
Making Sense of Occluded Scenes using Light Field Pre-processing and Deep-learning

A combined approach of low-complexity light field depth filtering and deep learning is proposed for object classification in the presence of partial occlusions. The proposed approach exploits depth information embedded in multi-perspective four-dimensional (4-D) light fields via low-complexity 4-D sparse depth filtering and deep-learning. The proposed 4-D depth filter, designed using numerical optimization techniques by formulating as an ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> - ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> minimization problem, is shown to outperform typical light field refocusing based on 4-D shift-sum averaging filters. Experiments conducted using a light field dataset acquired by a Lytro camera verify 45% and 27% better performance in terms of object classification accuracy compared to the cases when no depth filtering is employed and standard shift-sum refocusing is employed, respectively.
Backbone Neural Network Design of Single Shot Detector from RGB-D Images for Object Detection

Recognition technology has gained state of art performance with the dawn of deep convolutional neural network and with these achievements in the field of computer vision, machine learning and 3D sensor, industries are near to start new era of the automation. However, object detection for robotic grasping in varying environment, low illumination, occlusion and partial images gives poor accuracy and speed to detect object. In this research, a multimodal architecture is designed to be used as a base network/ backbone network of Single Shot Detector (SSD). This architecture uses RGB and Depth images as an input and gives single output. Most of the researchers used VGG16/19, ResNet and MobileNet for detection purposes. In this paper, a new architecture is designed to perform a specific task of grasping. For classification using RGB-D architecture, it achieved an average accuracy of 95% with the learning rate of 0.0001 and outperforms the other architectures in accuracy for limited objects.
Are gold hoop earrings and a dab of red lipstick enough to get even Democrats on the offensive?

Abstract According to news media outlets, Alexandria Ocasio-Cortez (AOC), the newly-elected Democratic representative from New York, has stirred up conflicting feelings among Republicans and Democrats on Capitol Hill, attracting criticism, mockery and disapproval from both parties. The overarching aim of this study is to explore the extent to which these attitudes are acts of verbal aggression often dissimulated as patronizing warnings or manifestations of an opinion ( Ramirez and Andreu 2003 ; Wodak 2015 ). In particular, by analyzing a corpus of headlines regarding AOC’s political persona and activism collected from six major conservative and liberal newspapers circulating in the US, the study aims to detect linguistic markers associated with aggression and verify their level of toxicity ( Musolff 2012 ). To this purpose, both quantitative and qualitative methodological approaches are used with particular reference to Corpus Linguistics ( Baker et al. 2008 ; Kilgariff et al. 2014 ) and the Discourse-Historical Approach ( Reisigl and Wodak 2001 , 2016 ). Findings show that biased mediatized political discourse can influence attitudes toward aggressive speech behavior, and, therefore, intensify the devious nature of aggressive acts.
Motivation Dynamics for Autonomous Composition of Navigation tasks

In this article, we physically demonstrate a reactive sensorimotor architecture for mobile robots whose behaviors are generated by motivation dynamics. Motivation dynamics uses a continuous dynamical system to reactively compose low-level control vector fields using valuation functions that capture the potentially competing influences of external stimuli relative to the system's own internal state. We show that motivation dynamics naturally accommodates external stimuli through standard signal processing tools, and can effectively encode a repetitive higher level task by composing several low-level controllers to achieve a limit cycle in which the robot repeatedly navigates toward two alternatively valuable goal locations in a commensurately alternating order. We show that these behaviors are robust to perturbations, including imperfect models of robot kinematics, sensor noise, and disturbances, resulting from the need to traverse difficult terrain. We argue that motivation dynamics can provide a useful alternative to controllers based on hybrid automata in situations where the control operates at a low level close to the physical hardware.
Analyzing the socioenactive dimensions of creative learning environments with preschool children

Several published papers discuss how to encourage creativity in the classroom. An emerging concept is creative learning, which attempts to understand, experiment, and define methodologies and learning environments designed to promote creativity. Enaction theorists, a new thread of cognitive science, understand that cognition and creativity always emerge through real-time interaction with the environment and other agents in that environment. Socioenactive systems are extensions to the enactive system concept, bringing attention to socio-cultural values in the dynamic coupling of the human (through the body senses) and technology. This work aims to explore and understand the creative process of preschool children during their interaction with a tangible programming environment (TaPrEC+mBot) that allows programming of a robot car by arranging wooden blocks. The analysis draws on the physical, digital, and social dimensions of socioenactive systems. The results show that the children felt especially involved through their bodies and engaged in the activity of creating their own programming scenario. They also suggest that the children's reasoning and behavior seem to be connected or influenced by their body movements and their physical, social and digital experience and interaction with the tangible programming environment.
Uncovering Practical Security and Privacy Threats for Connected Glasses with Embedded Video Cameras

We address in this work security and privacy threats for connected camera glasses, for which very few investigations have been conducted so far, despite the considerable attention to understanding security concerns for other wearables, such as smartwatches and fitness trackers. To raise awareness about such threats, we present the results of a case study involving a low-cost spy camera glasses device, readily available on the market, that can be used to record and stream live video, for which we demonstrate infringement of several privacy requirements (regarding the camera glasses device itself, the data collected by the embedded video camera, the wearer of the device, and for bystanders as well) that lead to corresponding security threats (e.g., data confidentiality, integrity, availability, and access control). To foster replicability and reproducibility of our empirical results, investigation method, and implementation of attacks, we describe our case study in the form of a detailed activity log and release full C++ code implementing our approach. Furthermore, we present our findings to three IT security experts and summarize their recommendations for designing more secure connected camera glasses.
Learning History with Location-Based Applications: An Architecture for Points of Interest in Multiple Layers

Location-based applications (LBAs) capture the user’s physical location via satellite navigation sensors and integrate it as part of the digital application. Because of this connection, the real-world environment needs to be accounted for in LBA design. In this work, we focused on creating a database of geographically distributed points of interest (PoIs) that is optimal for learning local history. First, we conducted a requirements elicitation study at three outdoor archaeological sites and identified issues in existing solutions. Second, we designed a multi-layered prototype solution. Third, we evaluated the solution with nine experts who had prior experience with LBAs or similar systems. We incorporated their feedback to our design to iteratively improve it. As a whole, our work contributes to the LBA design literature by proposing a solution that is optimized for the learning of local history.
Automated Emotional Valence Prediction in Mental Health Text via Deep Transfer Learning

Sentiment analysis is a well-researched field of machine learning and natural language processing generally concerned with determining the degree of positive or negative polarity in free text. Traditionally, such methods have focused on analyzing user opinions directed towards external entities such as products, news, or movies. However, less attention has been paid towards understanding the sentiment of human emotion in the form of internalized thoughts and expressions of self-reflection. Given the rise of public social media platforms and private online therapy services, the opportunity for designing accurate tools to quantify emotional states in is at an all-time high. Based upon psychological research, in this work we propose a new type of sentiment analysis task using a two-dimensional valence scheme with four sentiment categories: positive, negative, both positive and negative, and neither positive nor negative. This work details the collection of a novel annotated dataset of real-world mental health therapy logs and compares several machine learning methodologies for the accurate classification of emotional valence. We found superior performance using deep transfer learning approaches, in particular using the recent breakthrough method of BERT. We argue that representing emotional sentiment on decoupled valence axes is an appropriate modification of traditional sentiment analysis for mental health tasks and that modern transfer learning approaches should become an essential component of automated mental health frameworks, where labeled data is often scarce.
Journal entry anomaly detection model

Summary Although numerous scientific papers have been written on deep learning, very few have been written on the exploitation of such technology in the field of accounting or bookkeeping. Our scientific study is oriented exactly toward this specific field. As accountants, we know the problems faced in modern accounting. Although accountants may have a plethora of information regarding technology support, looking for errors or fraud is a demanding and time‐consuming task that depends on manual skills and professional knowledge. Our efforts are oriented toward resolving the problem of error‐detection automation that is currently possible through new technologies, and we are trying to develop a web application that will alleviate the problems of journal entry anomaly detection. Our developed application accepts data from one specific enterprise resource planning system while also representing a general software framework for other enterprise resource planning developers. Our web application is a prototype that uses two of the most popular deep‐learning architectures; namely, a variational autoencoder and long short‐term memory. The application was tested on two different journals: data set D, learned on accounting journals from 2007 to 2018 and then tested during the year 2019, and data set H, learned on journals from 2014 to 2016 and then tested during the year 2017. Both accounting journals were generated by micro entrepreneurs.
A Method for the Calculation of Parallel Passages for Buddhist Chinese Sources Based on Million-scale Nearest Neighbor Search

For research on Buddhist textual material, citations and similar passages are of major importance. This paper explores the application of continuous word representations and nearest neighbor search in order to efficiently compute a network of parallel passages for the Chinese Buddhist canon. It also discusses methods of evaluating the quality of the detected parallels and demonstrates a potential use case for the resulting data in the form of a web application for philological research.
Multi-Point Gesture Recognition Leveraging Acoustic Signals and CNN

With the emergence of smart home appliances and AR/VR content, the demand for a better user interface is constantly increasing. Traditional user interface, touch-based interface is no longer applied to AR/VR applications and vision or RF-based gesture recognition requires camera and sensors, resulting in additional cost. The accuracy of above-mentioned methods highly depends on brightness and surrounding environment, therefore they fail to guarantee robustness. There are several researches on acoustic-based gesture recognition, but are limited to single point movement such as straight line, circle and triangle. In this paper, we design and implement multi-point gesture recognition system utilizing both the acoustic signals and machine learning technique. We use channel impulse response (CIR) containing multi-path information to recognize multi-point gestures, and construct a CNN model to learn its features. In addition, we present a method of constructing a CNN model suitable for gesture recognition. Evaluation results show that our system successfully recognizes multi-point gestures and demonstrate its efficacy.
Study of hybrid median filter for the removal of various noises in digital image

Abstract The aim of this study is to construct a hybrid median filter using MATLAB and use that filter to remove impulse noise from an image, in order to produce an enhanced image. This is done in order to show how a hybrid median filter can be a more optimal option than a conventional median filter for removing impulse noise.
GBTLX: A First Look

We provide a first look at GBTLX, a code generator that translates graph processing programs written using the GraphBLAS Template Library (GBTL) into high-performance C programs that match the performance of hand-tuned implementations. GBTLX refactors code written using GBTL into problems that capture the signature of algorithms and solvers that capture the semantics (input/output behavior of algorithms. Users provide classes that implement these two aspects using standard GBTL functions and encapsulate the targeted algorithm. GBTLX then performs a sequence of inspection, code generation, and high-performance execution. First, the user code is traced while running with the original GBTL. Then, the trace is used to define the semantics and signature of the algorithm to be produced in code generation. The SPIRAL system is used to generate high-performance C code that implements the user-specified algorithm, specializing the code for algorithm and hardware-dependent optimizations. Finally, the user-provided GBTL-based implementation is replaced by the SPIRAL generated C code. For triangle counting and k-truss enumeration, the resulting executables provide performance equivalent to hand-tuned implementations, while the source code is maintainable as it only uses the C++ GBTL library.
Novel Stencils Library for Designing Smart City based Government Websites

Most nations are moving towards establishing more and more smart cities and in due course faces the challenge of providing up-to-date information to its citizens and Considering the COVID-19 pandemic situation, citizens get effected by the false or incorrect information from a different source. Inconsistent designs, non-user-friendly layouts, non-performant pages, and inefficient designing strategies often lead to information not being passed on to the citizens. While the same accounts for usability challenges, the repercussions are huge as not just the citizens, but outsiders draw potentially relevant information about the state through these websites as well. Wrong impressions forwarded through the website can affect major Gross Domestic Product (GDP) growth using travel & tourism, attracting external talent, and also foreign investments. Concerning the research conducted, the observation states that while certain Government websites attract visitors, others simply confuse potential information seeker. The congested nature of certain websites often misleads the site visitors despite holding relevant information that the end-user was looking for. The challenge is to provide consistent designs and still be able to provide uniqueness which gets hampered by the usage of a strict template. Every nation wants to attract visitors and share information in their uniqueness which continues to be a big focus for the proposed disclosure.
Finding a Rational Set of Features for Handwritten Signature Recognition

In this paper we proposed the approach for dynamic handwritten signatures recognition. We proposed a formal model of the handwritten signature, containing fuzzy features of curvature of discrete handwritten signature functions. We proposed handwritten signature reference template creation algorithm, characterized by the use of the potential method for constructing membership functions of fuzzy features. The choice of a rational set of features has been implemented, which allows to minimize the false accept rate (up to 0.05%), as well as a rational set that minimizes the equal error rate (up to 0.36%), which significantly exceeds the efficiency of existing handwritten signature recognition algorithms.
An Aeromagnetic Compensation Algorithm Based on a Deep Autoencoder

Magnetic compensation is a necessary step in the aeromagnetic data processing. While the aeromagnetic compensation model is a linear regression model, the multicollinearity of the variables in the model reduces the accuracy of the compensation model. To solve this problem, we propose a deep autoencoder (DAE) aeromagnetic compensation algorithm. The DAE searches the direction of maximum change in the data by using the gradient descent backpropagation algorithm. The special structure of the encoder can compress the representation of the coefficient matrix and extract data features, thereby weakening the correlation between the coefficient matrix variables. The features obtained after dimension reduction are used in the compensation calculation. We validate the DAE algorithm by applying it to data collected by an unmanned aerial vehicle and demonstrate that the DAE magnetic compensation results are better than those of the principal component analysis algorithm.