Reviewerly/exHarmony huggingface dataset:
Prime and Punishment: Can Enforcements Stop Illicit Sellers on E-Commerce Platforms?

This paper provides the first exploration of how illicit sellers operate on e-commerce platforms and how they respond to enforcements. I use a novel data set of 71 million illicit — i.e., fraudulent, counterfeit, or replica — items that were removed from online marketplaces. By using natural language processing and computer vision techniques on these products and by quietly tracking business activities of the illicit sellers, I identify a large number of similar but previously unnoticed illicit products (UIPs) that are currently sold online. For each illicit product that was previously removed, I detect 16.91 UIPs. Of these, 84% remained on the market during the one-year period after the removal of the initial illicit product. Nonetheless, the total market value of these products decreased by up to 80% after enforcements. My findings suggest that enforcements against illicit products on e-commerce platforms encourage separating equilibria, in which illicit sellers have weaker incentives to pool with authentic producers than to be revealed as low-quality producers.
Feedforward Approach to Sequential Morphological Analysis in the Tagalog Language

Morphological analysis is a preprocessing task used to normalize and get the grammatical information of a word which may also be referred to as lemmatization. Several NLP tasks use this preprocessing technique to improve their implementations in different languages, however languages like Tagalog, are considered to be morphosyntactically rich for having a diverse number of morphological phenomena. This paper presents a Tagalog morphological analyzer implemented using a Feed Forward Neural Networks model (FFNN). We transformed our data to its character-level representation and used the model to identify its capability of learning the language's inflections. We compared our MA to previous rule-based Tagalog MA works and saw an improved accuracy of 0.93.
WaiterBot: Comparison of Fuzzy Logic Approaches for Obstacle Avoidance in Dynamic Unmapped Environments Using a Laser Scanning System (LiDAR)

Fuzzy logic is a control technique commonly applied in mobile robotics. This paper contains the analysis of three implementations of control systems for obstacle avoidance of a mobile robot. The simulation environment simulate situations with both fixed and dynamic obstacles - like people walking - aiming to compare the fuzzy control approach most suitable to avoid different kinds of obstacles. The analyzed approaches differ by segmenting the sensor data (provided by a LiDAR sensor) into three regions, five regions or by considering only the position of the nearest obstacle. The three approaches were tested in scenarios containing different obstacles and in a simulation where the robot delivers to tables in a dynamic environment, simulating a social event as an application example of a waiter robot.
Designing Intradiegetic and Extradiegetic Spaces for Virtual Reality

Fictions for virtual reality are mostly conceived either in an entirely virtual environment or in a real environment with very little transformation.It is infinitely rare to find examples of sets designed and built specifically for a 360°e xperience.Building, rather than remaining purely virtual, raises complex questions about the organization of a ``space to play'' and therefore how to produce a narrative for immersion and create a 360°film grammar.We thus have created a first 360°set for the famous director Marc Caro, for whom the question of off-screen raised a very complex technical problem regarding lighting, visual effects, staging, blocking and acting.In this contribution we will show how this was solved and why the definition of a 360°narrative grammar is crucial.These are important intricacies that place the user-spectator in the optimal conditions to appreciate the experience.
Unsupervised 3D Primitive Shape Detection using Mathematical Models

This study aims to propose the first unsupervised deep learning based framework to detect primitive shapes in unorganized point clouds. The benefits of the proposed model are as follows: first, modeling primitive shapes mathematically and making the annotation and generation of training data automatically can assist in training a network in an unsupervised manner without using any pre-annotated datasets. The proposed approach is able to expand to other 3D point clouds related tasks when the datasets are not big enough. Experiments and results demonstrate that the proposed unsupervised methods can achieve an accuracy on par with supervised approach for primitive shape detection in 3d point clouds.
Softly Constrained Particle Filter for Digital Terrain Elevation Data Aided Ground Moving Target Tracking

This paper presents Softly Terrain Constrained Particle Filter (STC-PF). STC-PF is designed to improve the performance of ground target tracking by applying the constraint that ground targets are constrained to the terrain surface. STC-PF is based on SIR PF, but the major difference is that STC-PF uses the elevation model. Due to the elevation model, knowledge of the horizontal position of a target enables us to infer the elevation of the target more precisely. Because STC-PF can reflect the uncertainty of DTED, STC-PF shows a smoother and less biased trajectory than SCKF in the numerical experiment. Moreover, MC simulation result shows that STC-PF is capable of keeping the track stable due to its large variance.
SuPEr - SAM: Using the Supervision Signal from a Pose Estimator to Train a Spatial Attention Module for Personal Protective Equipment Recognition

We propose a deep learning method to automatically detect personal protective equipment (PPE), such as helmets, surgical masks, reflective vests, boots and so on, in images of people. Typical approaches for PPE detection based on deep learning are (i) to train an object detector for items such as those listed above or (ii) to train a person detector and a classifier that takes the bounding boxes predicted by the detector and discriminates between people wearing and people not wearing the corresponding PPE items. We propose a novel and accurate approach that uses three components: a person detector, a body pose estimator and a classifier. Our novelty consists in using the pose estimator only at training time, to improve the prediction performance of the classifier. We modify the neural architecture of the classifier by adding a spatial attention mechanism, which is trained using supervision signal from the pose estimator. In this way, the classifier learns to focus on PPE items, using knowledge from the pose estimator with almost no computational overhead during inference.
Efficient Modification of the Upper Triangular Square Root Matrix on Variable Reordering

In probabilistic state inference, we seek to estimate the state of an (autonomous) agent from noisy observations. It can be shown that, under certain assumptions, finding the estimate is equivalent to solving a linear least squares problem. Solving such a problem is done by calculating the upper triangular matrix R from the coefficient matrix A, using the QR or Cholesky factorizations; this matrix is commonly referred to as the “square root matrix”. In sequential estimation problems, we are often interested in periodic optimization of the state variable order, e.g., to reduce fill-in, or to apply a predictive variable ordering tactic; however, changing the variable order implies expensive re-factorization of the system. Thus, we address the problem of modifying an existing square root matrix R, to convey reordering of the variables. To this end, we identify several conclusions regarding the effect of column permutation on the factorization, to allow efficient modification of R, without accessing A at all, or with minimal re-factorization. The proposed parallelizable algorithm achieves a significant improvement in performance over the state-of-the-art incremental Smoothing And Mapping (iSAM2) algorithm, which utilizes incremental factorization to update R.
Rethinking Blockchain Integration with the Industrial Internet of Things

Blockchain is a decentralized technology that protects sensitive data from being controlled by a central authority and can play a vital security role in developing the Industrial Internet of Things (IIoT). Most IIoT-blockchain systems focus on optimizing consensus mechanisms, which cannot provide satisfactory scalability until addressing the inherited problems of the networking infrastructure. Excessive messaging overheads and low networking performance are two critical issues of the current blockchain configurations. Moreover, the well-known architectural models, cloud-centric and fog-centric models, cannot provide acceptable performance in IIoT due to data aggregation and synchronization timing overheads, privacy, and trust issues. As such, we propose two novel blockchain solutions that can fulfill IIoT's strict challenges and requirements. First, the context-aware data-driven solution decreases irrelevant and useless data traffic. Second, the hybrid communications with sec-ond-layer chain solutions partition the data traffic through different communication media and provide better scalability and higher resistance against security issues. Finally, we present a future vision of the next blockchain generation that can fulfill the IIoT predictions.
Facial Expressions Recognition System Using FPGA-Based Convolutional Neural Network

Emotion detection has become one of the most significant aspects to consider in any project related to Affective Computing. There are several researches in emotion recognition,where Convolutional Neural Network is considered as an efficient algorithm that achieves the state-of-the-art performance in image recognition. There have been plenty of methods applying CNN model and conducting in software. However, traditional software based computation is not fast enough to meet the demand of real-time image processing. Therefore, we propose an efficient method for expressions recognition using FPGA-based. This model is used for classifying seven elementary types of human emotions: angry, fear, disgust, happy, sad and neutral by extracting features through those layers respectively. We also tested the functions of this model on FPGA simulation and achieved over 65% of accuracy using FER2013 dataset.
A Deep Network Model for Paraphrase Detection in Punjabi

Paraphrase refers to the text which tells the same meanings but with different expressions. It is important in NLP as it deals with many applications such as information retrieval, information extraction, machine translation, query expansion, question answering, summarization and plagiarism. Paraphrase detection is to find that given two texts are semantically similar or not similar. Though paraphrase detection has wide literature, there is no proper algorithm for paraphrase detection in Punjabi language. A new paraphrase detection model for Punjabi language is developed in this paper. We use two deep learning methods to map sentences as vectors, and these vectors are further used to detect paraphrases. Despite other implementations of paraphrase detection, our model is simple and efficient to detect paraphrases. Qualitative and quantitative evaluations prove the efficiency of the model and can be applied to various NLP applications. The proposed model is trained on Quora’s question pair dataset which makes new directions for paraphrasing in Indian languages.
Assessment of Conflict Resolution Training at University

The growth of conflicts in a changing world makes conflict resolution competence an important component of entrepreneurial competencies. Higher education should empower students to effectively prevent and resolve conflicts. The problem of assessing the educational process in the field of conflict resolution training and the result of this training is discussed in this article. The authors cite the results of an empirical study, the purpose of which was to determine the needs of students of the State University (Chelyabinsk, Russia) with regards to conflict management preparation. The survey relied on an analysis of the self-assessment of students´ own skills and commitment to the principles of tolerance, an assessment of the educational process, and the need to increase their own conflict management competence. 152 students of the State University took part in the survey, of which 63 were male and 89 female. The Likert scale was used. The results of the study showed that students assess the level of their own conflict resolution competence as high, as do they the educational process at the university. The limitations of the study are discussed at the end of the article.
Adversarial Black-Box Attacks with Timing Side-Channel Leakage

Artificial intelligence (AI), especially deep learning (DL), has been remarkable and applied to various industries. However, adversarial examples (AE), which add small perturbations to input data of deep neural networks (DNNs) for misclassification, are attracting attention. In this paper, we propose a novel black-box attack to craft AE using only processing time which is side-channel information of DNNs, without using training data, model architecture and parameters, substitute models or output probability. While, several existing black-box attacks use output probability, our attack exploits a relationship between the number of activated nodes and the processing time of DNNs. The perturbations for AE are decided by the differential processing time according to input data in our attack. We show experimental results in which our attack's AE increase the number of activated nodes and cause misclassification to one of the incorrect labels effectively. In addition, the experimental results highlight that our attack can evade gradient masking countermeasures which mask output probability to prevent crafting AE against several black-box attacks.
A multi agent broker framework and decision support for enhanced cloud computing discovery

It is remarkable that the tremendous expansion in the world of cloud computing in recent times, and in light of the Covid 19 pandemic that hit the entire world, the challenge has become more difficult and the intense focus on moving to the world of cloud computing is a necessity not a luxury. Hence, the biggest and most difficult challenge from both sides emerged. Therefore, this study attempts to provide an intelligent search scope to meet the clients' needs in finding the best service provider for their financial capabilities at the right time. It also gives a greater opportunity for service providers to display their services more clearly, by introduce multi-agent framework with decision support approach and recommendation concept, also a proposed cloud service discovery testbed introduced with management tools and an efficient <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$GUI$</tex> dashboard.
What does the software requirement specification for local E-Government of citizen database information system? An analysis using ISO/IEC/IEEE 29148 – 2011

Abstract The management citizen database is Improper and not up-to-date will results decisions that are not strategic for the village development process. Inappropriate citizen data also results in long-term ineffective local government planning processes. Such as the problem of forecasting or predicting the level of growth of citizens as well as the need for development services which are part of the medium-term long-term plans of local governments. Therefore they need requires a citizen database information system. In connection with the needs of citizen database information systems, this study provides a solution of analysing documents, namely the requirement specification software for citizen database information systems using the ISO / IEC / IEEE 29148 edition of 2011. The study was conducted with case studies of 6781 management data of residents of the village of Sumbersekar, Malang Regency, East Java. The method to execute the research consisted of data gathering stages, data flow and decision analysis, and the last is create software requirement specification. the specific requirements of the SRS documents ISO / IEC / IEEE 29148 2011 edition are external interfaces, functions, usability requirements, performance requirements, logical database requirements, design constraints, software system attributes, and supporting information.
Performance Modeling of HPC Applications on Overcommitted Systems

Recently, the use of interactive jobs in addition to traditional batch jobs are getting common in supercomputer systems. We expect overcommitting scheduling, in which multiple HPC jobs share one computational resource, to accept them in a single system and to make effective use of resources. On the other hand, the performance of a job must be affected when it is in an overcommitted state. Therefore, we need to investigate the impact of the overcommitted state. We evaluated the performance change when a single computational resource was shared by multiple HPC jobs, and built a model to predict the performance change using performance counters as input variables. The model could predict the performance in the overcommitted state with good accuracy, and the fatal mispredictions occupied only 1.4% of the total cases.
A Unified Model for Face Detection Using Multiple Task Cascaded Neural Network Prepended with Non-local Mean Denoising Algorithm

The task of face identifications has been a topic of research from the last very decades. The accuracy of the model has always been a mere challenge among researchers. This article proposes a unified model for facial identification in a noisy scenario. The model deploys a Multiple Task Cascaded Neural Network (MTCNN) deep learning model to detect the human face present in test images. The challenge of model performance in a noisy environment is encountered using non-local means denoising algorithm. The denoising is performed at the feeding end of MTCNN network to boost its performance in a noisy scenario. The experimental results justify the proposed model outperforming the prior deep learning strategies for face identification. The model achieves an accuracy of eighty percent in the Gaussian noise whose variance ranging between a hundred and two hundred.
The influence of fusion display mode on the user's sense of personal security in virtual immersion system

Abstract During the immersive virtual reality experience, because the visual senses are completely enclosed in the virtual environment, they are unable to perceive environmental changes in the real world and produce unsafe feelings, which affects their experience in virtual reality systems. In order to improve people's sense of security in the immersive virtual reality experience, the paper has designed four ways to interact with the real world in a virtual environment, so that users can get real‐world information in the virtual environment. This paper tests users' changes in their sense of security during immersive virtual reality experiences under the influence of various interaction methods and conduct psychological analysis. Twenty‐one volunteers are recruited to participate in the experiment, and their personal safety and psychological effects are tested. The experimental results show that the method of using Yolcat neural network to segment the captured images according to categories and then fuse the segmented images into the virtual environment can improve the user's sense of security without destroying the immersion.
Hybrid Recommender System Using Artificial Bee Colony Based on Graph Database

Recommender systems hoard appropriate suggestions guided by the interactions and previous choices of the users. In a world of the ever-growing amount of data, we are overpopulated with undesired information, which is making it difficult to operate or choose. Hence, we require a recommender system that is enough capable to deduce desired suggestions which can be valuable for the users. Thus, they are rising in popularity and becoming part and parcel of day-to-day activities in our general life. Here, in this paper, we implement the recommender system, which is conceptualized on a hybrid filtering algorithm that helps in dealing with limitations of both content and collaborative filtering reinforced with artificial bee colony optimization along with k- nearest neighbor for better performance. For this purpose, we used MovieLens dataset, which contains information regarding users, movies, and ratings given by the users. Here, we gathered a pre-filled user project scoring matrix and have compared multiple models of recommender systems for their precision and recall factor. We are using a recommender system based on graph database which uses graph traversal and pathfinding algorithms to establish relations and hence is more robust and faster in implementation. The thickness of the edges connecting movie nodes and the indegree of a movie node specifies the recommendation limit of the movie. The experiment results on MovieLens dataset establishes scope for future scalable models and delivers competent outcomes brought in comparison with traditional systems. Preliminary results show improvement of 9% in precision and 3% improvement in recall over traditional systems.
Underwater Image Enhancement via Color Balance and Stationary Wavelet Based Fusion

Underwater imaging is one of the most interesting and popular field for researchers and scientists now a day. Generally, underwater images are suffered from poor visibility and color shift because of absorption of different wavelength lights and scattering of water droplets. To address these problems, this paper proposed a novel framework based on fusion principle for enhancing of underwater images. Our strategy consists of four main steps. In first step, we perform color correction using white balancing based on gray world algorithm to remove the unwanted color cast. Then we processed the white balanced image using contrast enhancement techniques i.e. gamma correction of optimal value is 1.5 and sharpening. After second step, both versions of images of contrast enhancement techniques are processed through weight mapping of three categories i.e. luminance, chromatic and saliency. Finally, both versions of images are fused through stationary wavelet based fusion rule to get better quality final enhanced image. Our quantitative and qualitative analysis reveals that our approach outperforms better as compared to state-of-the-art algorithms. Our corroboration also demonstrates that the enhanced images are characterized by improved edge sharpness, global contrast and clear visible dark regions.
Detecting Subject-Weapon Visual Relationships

Computer vision-based weapon detection method- ologies and applications in safety and security have fallen behind when compared to state-of-the-art computer vision applications problems in other areas. In this paper we propose a novel visual relationship detection model trained on the Open Images V6 dataset to detect the visual relationships of “holds” and “wears” between people and objects. We also introduce an application of the proposed model to detect if weapons are being held. Weapons are an unseen object class to the network. The best proposed model achieves an accuracy of 90.01% ±2.05 % on the test set of the Open Images V6 dataset for classifying the “holds” and “wears” visual relationships.