Reviewerly/exHarmony huggingface dataset:
Credit Card Fraud Detection Using Advanced Machine Learning Techniques

The world is witnessing rapid growth in Ecommerce and hence in the use of plastic money and with this dramatic increase in card uses there is an explosion in credit card fraud. With the number of credit card users, fraud cases are also rising. In real case scenarios, the fraudulent transaction is intermixed with the genuine transaction so simpler ways like pattern matching are not good to detect these frauds. Execution of an accurate and effective fraud detecting system is of major importance to all financial card issuing bodies. Several ways are based on approximate reasoning, AI, Data mining, sequence alignment that is identifying regions of similarities, inheritable programming, etc, which are highly used in the detection of these credit card frauds. An innovative approach and a clear understanding of these technologies will help design better systems for credit card detection.
Research on Data Security Model of Environmental Monitoring Based on Blockchain

With the rapid development of the social economy, the problem of environmental pollution has been widely concerned. The existing environmental monitoring system adopts a hierarchical centralized management structure, which has some problems, such as data silos and the risk of data falsification. Thus, this paper proposes an environmental monitoring data security model based on the blockchain, which uses the blockchain distributed storage mode to realize the secure sharing of monitoring data and curb the behavior of data forgery. A practical Byzantine fault tolerant mechanism based on credit grouping supervision is adopted to reduce the computation and communication overhead caused by data consensus. Through the cloud chain fusion technology, the encrypted monitoring data is stored on the cloud storage server, and the monitoring data credentials are stored on the blockchain to reduce the storage pressure. And AES(Advanced Encryption Standard) combined with the RSA (Rivest-Shamir-Adleman) encryption algorithm to ensure the security of data transmission and storage. Security analysis and experiments demonstrate that our proposed scheme achieves authenticity, integrity, and security for monitored data. In addition, it effectively reduces the computation, communication, and storage overhead of the block nodes.
TEACHING ENGLISH FOR YOUNG LEARNERS IN RURAL AREA: TEACHERS’ CHALLENGES

Rural education, in comparison to its urban and suburban counterparts, is seen to face a distinct challenge. The location and implementation of an English curriculum in primary school are significant problems. As a result, the purpose of this study is to look into the difficulties that English teachers face when teaching English to young learners in rural public primary schools in Baureno, East Java. A semi-structured interview with six participants from six elementary schools in five villages (Gajah, Gunungsari, Tulungagung, Selorejo, and Blongsong) is utilized to address the question. It begins by briefly reviewing the role of English in the national primary school curriculum on the one hand, and the implementation of English programs in rural schools on the other. Throughout the research, the researcher encountered three difficulties: (i) Students’ socio-economics condition, (ii) the status of English in the school, and (iii) Covid-19 pandemic. As a result, the writer suggested that more research be done to highlight English teachers’challenges on the implementation of primary school learning. The findings of the study can be used by English teachers, stakeholders, policymakers, and institutions to develop practical strategies for teaching English in primary school.
A Prototype for Mexican Sign Language Recognition and Synthesis in Support of a Primary Care Physician

Few hearing people know and use Mexican Sign Language (MSL). Consequently, this is the main barrier between deaf and hearing people. This study proposes a system that recognizes and animates in real time a set of signs belonging to the semantic field of a general medicine consultation service. Therefore, a linkage between a hearing doctor and a deaf patient can be established in a non-intrusive way and with an easy dynamic interaction. Our main contribution is a bidirectional translator system for Mexican sign language in the context of a general medical consultation. The recognition module uses a MS Kinect sensor to obtain the sign trajectories, and images to feed hidden Markov Models (HMMs) for processing signs samples in real time. The experiments show recognition of 82 different signs from 22 participants. As a result, accuracy and F1 scores average rates of 99% and 88% were obtained.
Classical Planning as QBF without Grounding

Most classical planners use grounding as a preprocessing step, essentially reducing planning to propositional logic. However, grounding involves instantiating all rules and actions with concrete object combinations, and results in large encodings for SAT/QBF-based planners. This severe cost in memory becomes a main bottleneck when actions have many parameters, such as in the Organic Synthesis problems from the IPC 2018 competition. We provide a compact QBF encoding that is logarithmic in the number of objects and avoids grounding completely by using universal quantification for object combinations. We show that we can solve some of the Organic Synthesis problems, which could not be handled before by any SAT/QBF based planners due to grounding.
Framework and Challenges of Cyber Security in India: An Analytical Study

Cyber security threats come in a wide variety of forms, including ransomware, phishing, malware attacks, and many others. India is currently ranked 11th in the world in terms of the number of local cyberattacks and it has already experienced 2,399,692 of these incidents in the first three months of 2020. Because cyber security is a topic that is growing more and more essential, businesses are undoubtedly well aware of the hazards and threats that hackers offer to their corporation. However cybersecurity would continue to be a difficult issue for three reasons: It goes beyond a simple technological issue. Cyberspace operates under a distinct set of regulations than the real world. Law, policy, and practise in the area of cybersecurity are still in their infancy. Every firm requires a security analyst to ensure that their system is secure given the rise in cyber-attacks. These security analysts must secure private company servers; protect the confidential data of governmental organisations, and other cybersecurity-related difficulties. Research indicates that there is a significant need in India for qualified cybersecurity specialists and that this demand will continue to rise in the near future. Employers anticipate a scarcity of qualified cybersecurity specialists. The goal of the study is to analyse and explain India's cyber security framework and difficulties. The study used a combination of descriptive and analytical method to draw a result. The Thematic software tool QADMAX also used in the study to analyse the qualitative data for secondary sources
BREAST CANCER DIAGNOSIS USING WRAPPER-BASED FEATURE SELECTION AND ARTIFICIAL NEURAL NETWORK

Breast cancer is commonest type of cancers among women. Early diagnosis plays a significant role in reducing the fatality rate. The main objective of this study is to propose an efficient approach to classify breast cancer tumor into either benign or malignant based on digitized image of a fine needle aspirate (FNA) of a breast mass represented by the Wisconsin Breast Cancer Dataset. Two wrapper-based feature selection methods, namely, sequential forward selection(SFS) and sequential backward selection (SBS) are used to identify the most discriminant features which can contribute to improve the classification performance. The feed forward neural network (FFNN) is used as a classification algorithm. The learning algorithm hyper-parameters are optimized using the grid search process. After selecting the optimal classification model, the data is divided into training set and testing set and the performance was evaluated. The feature space is reduced from nine feature to seven and six features using SFS and SBS respectively. The highest classification accuracy recorded was 99.03% with FFNN using the seven SFS selected features. While accuracy recorded with the six SBS selected features was 98.54%. The obtained results indicate that the proposed approach is effective in terms of feature space reduction leading to better accuracy and efficient classification model.
Pruning Coherent Integrated Photonic Neural Networks Using the Lottery Ticket Hypothesis

Coherent integrated photonic neural network (C-IPNN) architectures enable light-speed and ultra-low-energy accelerators for rapidly growing artificial intelligence applications. Nevertheless, C-IPNNs have a large footprint and suffer from high tuning power consumption for both training and inference. Pruning C-IPNNs using model compaction to reduce the number of weight parameters can potentially alleviate these problems. However, prior attempts at pruning singular-value-decomposition-based C-IPNNs (SC-IPNNs) have shown that very few parameters can be removed without significantly degrading the network accuracy. In this paper, we present the first hardware-aware pruning method for SC-IPNNs based on the lottery ticket hypothesis (LTH). We also discuss the challenges associated with pruning SC-IPNNs and show that, in addition to the classification accuracy, model-compaction techniques should be guided by a reliability assessment of the pruned networks. As a case study, we prune a multi-layer perceptron-based SC-IPNN with two hidden layers and show that up to 89 % of the phase angles, which correspond to weight parameters in SC-IPNNs, can be pruned with a negligible loss in accuracy (smaller than 5 %) while reducing the network (i.e., tuning) power consumption by up to 86 %. Therefore, the proposed pruning method paves the way for realizing compact and energy-efficient photonic neural networks.
MCMCTS PCG 4 SMB: Monte Carlo Tree Search to Guide Platformer Level Generation

Markov chains are an enticing option for machine learned generation of platformer levels, but offer poor control for designers and are likely to produce unplayable levels. In this paper we present a method for guiding Markov chain generation using Monte Carlo Tree Search that we call Markov Chain Monte Carlo Tree Search (MCMCTS). We demonstrate an example use for this technique by creating levels trained on a corpus of levels from Super Mario Bros. We then present a player modeling study that was run with the hopes of using the data to better inform the generation of levels in future work.
Improved Model Predictive Control System Design and Implementation for Unmanned Ground Vehicles

Autonomous ground robots autonomously are being used in the places where it is very hazardous for human beings to reach and operate, such as nuclear power plants and chemical industries.The aim of the research presented here is to develop a control system that enables such ground robots navigate autonomously with various sensors as the depth camera, 2D scanning laser, 3D Lidar, GPS, and IMU.The controller uses the current position measured using the sensors on the Husky A200, given the waypoints of the destination.Then it calculates the best possible route based on the recent events provided using IMU data and GPS.The Model Predictive Control (MPC) improves the robot's motion, by using a path planner for the robot's trajectory generation.The use of global reference frame waypoints is planned to create the appropriate path and the actions required to follow the motion planner's direction.The path planner depends on the active sensor data such as locations and size of obstacles.Then, a feasible path is generated based on the sensor data.The desired trajectory consists of a set of waypoints fit in a 3 rd -order polynomial.They determine the path's feasibility for the ground robot's dynamics and a series of points generated with a certain velocity and acceleration profile.The MPC adjusts the robot's lateral, longitudinal, yaw motions and approximates a continuous trajectory with discrete paths to command behaviors.The kinematic model of a robot, Husky is used as the dynamic model for transient and steady-state characteristics.The camera captures the images and other types of data processed through the computational framework used to build machine learning models.TensorFlow is used for deep learning and to identify and classify various objects around the Husky.This research has limitations such as using the linear dynamic model as the LQR method.Also on vehicle models, the vehicle model considered in this research considers a constant value to describe the slope in the most linear region.Detailed discussion on MPC development with a major system design factor has been emphasized with logical steps in MPC.
Sign Language Video Retrieval with Free-Form Textual Queries

Systems that can efficiently search collections of sign language videos have been highlighted as a useful application of sign language technology. However, the problem of searching videos beyond individual keywords has received limited attention in the literature. To address this gap, in this work we introduce the task of sign language retrieval with free-form <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> The terminology “natural language query” is commonly used to describe unconstrained textual queries in spoken languages. However, since sign languages are also natural languages, we adopt for the term “free-form textual query” instead. textual queries: given a written query (e.g. a sentence) and a large collection of sign language videos, the objective is to find the signing video that best matches the written query. We propose to tackle this task by learning cross-modal embeddings on the recently introduced large-scale How2Sign dataset of American Sign Language (ASL). We identify that a key bottleneck in the performance of the system is the quality of the sign video embedding which suffers from a scarcity of labelled training data. We, therefore, propose SPOT-ALIGN, a framework for interleaving iterative rounds of sign spotting and feature alignment to expand the scope and scale of available training data. We validate the effectiveness of SPOT-ALIGN for learning a robust sign video embedding through improvements in both sign recognition and the proposed video retrieval task.
Truth-Telling and Trolls: Trolling, Political Rhetoric in the Twenty-First Century, and the Objectivity Norm

Trolling has received increasing scholarly attention both as an online phenomenon and as an allegedly new strategy of political communication. This article moves trolling out of the digital realm and applies ‘trolling theory’ to political communication by well-known and influential figures on the political front stage. To establish consistency between the online and the offline phenomenon, this paper will focus on a (broadly defined) style of political communication that seems to focus more on provoking outrage, establishing itself as speaking from an outsider position to defend free speech to counter an allegedly totalitarian opponent, and to trigger political sensitivities of opponents and thus cause outrage on ‘the other side.’ Contrary to the vast majority of trolling research that simply dismisses trolling as ‘antisocial,’ ‘nonnormative’ behavior, I argue that trolling in contemporary politics gains effectiveness by reproducing some of the patterns of ‘objective communication’ on a discursive and an emotional level. Political trolling embraces the notion of a universal truth, with the speakers often branding themselves as ‘disinterested’ and thus not emotionally involved and incorruptible, thereby gaining credibility by pointing to their position as discursive outsiders. When trolling rhetoric succeeds and the trolls’ claims are met with outrage by the people the trolls deem to be totalitarian and censors of free speech, the speakers can downgrade their opponents as weak, biased, emotionally involved and thus necessarily illegitimate, gaining a superior position in the conversation.
An Image Steganography Algorithm Based on PSO and IWT for Underwater Acoustic Communication

To achieve fast and secure information steganography in underwater acoustic channel scenarios, this paper proposes a PSO (Particle Swarm Optimization) and IWT (integer wavelet transform) based image steganography algorithm for underwater acoustic communication.Firstly, the underwater image to be embedded is interleaved and packed to generate the secret information code-stream to improve the detection and recovery performance of transmission errors.Secondly, the cover image is divided into non-overlapping blocks and IWT is applied to each block, which can save PSO search space and improve the optimal solution performance.Thirdly, the embedding position matrix of the high frequency sub-band coefficients is chaotic scrambled, and PSO is used to select the optimal key to obtain the optimal matching position for the secret information to produce the best stego-image quality.Finally, transmission error detection and recovery measures are applied to the extracted secret information to improve the reconstruction quality in underwater acoustic channel scenarios.Experimental results show that the proposed algorithm outperforms existing approaches in terms of information embedding capacity and steganographic visual quality, meeting the requirements of safety and real-time performance of underwater communication.Moreover, it also has the ability of error detection and recovery for harsh underwater acoustic channel.
A Personalized Approach for Developing a Snacking Detection System using Earbuds in a Semi-Naturalistic Setting

Snacking, unlike meals, can happen in short, sporadic bursts, often coupled with distractions, thereby rendering the detection of such episodes a challenging problem. Despite the importance of understanding snacking behavior to monitor the dietary habits of individuals, few eating detection systems report their efficacy for detecting snacking episodes. Furthermore, due to the nature of the study designs, most eating detection systems are suited for detecting long and continuous eating events such as meals. Through a semi-naturalistic user study in the lab with 18 participants, we present our system for detecting short and sporadic chewing episodes, which are present in snacking, passively using Nokia eSense earbuds. We find that IMU data from earbuds have limited generalizability (n=18) for detecting short chewing episodes for unseen participants. Our study unpacks the significance of personalization (n=14) in short and sporadic chewing detection and quantifies the data necessary for developing such systems using a personalized approach.
Are 3D better than 2D Convolutional Neural Networks for Medical Imaging Semantic Segmentation?

In the last decade, Deep Learning has revolutionized Computer Vision thanks to Convolutional Neural Networks (CNN), that achieved state-of-the-art results in many tasks. In the medical field, imaging techniques, like MRI and CT, are widely used to acquire 3D images of regions that need to be analyzed to identify targets or regions of interest (ROIs). In particular, semantic segmentation is a common image processing task involved in several clinical procedures. When using Deep Learning to solve this task it is possible to either apply a 2D CNN to each slice of the acquired 3D image or apply a 3D CNN to the entire volume acquired. Despite both this approaches have been investigated in the literature, there is neither yet a clear understanding of which one is better (if this is the case) nor a fair comparison of their performances on the same datasets. In this work we aim at making a first step toward to providing an empirical guidance on choosing between 2D and 3D CNNs for medical imaging segmentation. To this purpose we compared a 2D CNN and a 3D CNN based on deep residual U-Net (ResUnet) architecture on different datasets. Our results suggest that the potential benefits of using a 3D CNN are difficult to exploit due to the very limited amount of data that is typically available in medical datasets.
DeepClean: A Robust Deep Learning Technique for Autonomous Vehicle Camera Data Privacy

Autonomous Vehicles (AVs) are equipped with several sensors which produce various forms of data, such as geo-location, distance, and camera data. The volume and utility of these data, especially camera data, have contributed to the advancement of high-performance self-driving applications. However, these vehicles and their collected data are prone to several security and privacy attacks. One of the main attacks against the AV-generated camera data is location inference, in which camera data is used to extract knowledge for tracking the users. A few research studies have proposed privacy-preserving approaches for analysing AV-generated camera data using powerful generative models, such as Variational Auto Encoder (VAE) and Generative Adversarial Network (GAN). However, the related work considers a weak geo-localisation attack model, which leads to weak privacy protection against stronger attack models. In this paper, we propose DeepClean, a robust deep learning model that combines VAE and a private clustering technique. DeepClean learns distinct labelled object structures of the image data as a cluster, and generates a more visual representation of the non-private object clusters, e.g., roads. It then distorts the private object areas using a private Gaussian Mixture Model (GMM) to learn distinct cluster structures of the labelled object areas. The synthetic images generated from our model guarantee privacy and resist against a robust location inference attack by less than 4% localisation accuracy. This implies that using DeepClean for synthetic data generation, it is less likely for a subject to be localised by an attacker even when using a robust geo-localisation attack. The overall image utility level of the generated synthetic images by DeepClean is comparable to the benchmark studies.
Mining and processing of the Stránská skála-type chert during the Late Neolithic and Early Eneolithic periods

Stránská skála represents an important geological and archaeological site. It is also known for its rich deposits of Jurassic cherts that were utilized in various ways during different stages of Moravian prehistory. The substantial Early Upper Paleolithic occupation events are of great significance and have been subject to several international research projects. Archaeological excavations targeting mining and workshop areas were resumed in the last years with a focus on Late Neolithic and Early Eneolithic activities. This paper introduces this project and summarizes recent results. Trends in the chipped industry within the Stránská skála workshop area are outlined, focusing on raw material variability, technology-typology, chronology, and socioeconomic aspects. The first indicators of the location and other aspects of post-Paleolithic mining activities in the field of Stránská skála III are introduced and discussed in the context of mining and workshop activities of similar contemporary features in the Czech Republic and neighboring regions.