Reviewerly/exHarmony huggingface dataset:
Static Analysis for Data Scientists

Big data analytics has revolutionized the world of software development in the past decade. Every day, data scientists develop computer programs to gather, triage, and process data, in order to ultimately help us make data-driven decisions. As we rely more and more on such data-manipulating software, we become increasingly vulnerable to poor choices, wrong assumptions, or other (programming or technical) mistakes made during software development. Mistakes that do not cause software failures can have serious consequences, since they give no indication that something went wrong along the way. In safety-critical applications, such mistakes can be deadly. In this chapter, we will present ongoing work to develop an abstract interpretation-based static analysis framework for data scientists. In particular, we will focus on issues arising from unexpected data and describe the challenges involved in designing and developing a practical static analysis that infers necessary expectations on the data read and manipulated using Jupyter notebooks, an increasingly popular development environment among data scientists.
DARE: Diver Action Recognition Encoder for Underwater Human–Robot Interaction

With the growth of sensing, control and robotic technologies, autonomous underwater vehicles (AUVs) have become useful assistants to human divers for performing various underwater operations. In the current practice, the divers are required to carry expensive, bulky, and waterproof keyboards or joystick-based controllers for the supervision and control of AUVs. Therefore, diver action-based supervision is becoming increasingly popular because it is convenient, easier to use, faster, and cost effective. However, various environmental, diver, and sensing uncertainties make the underwater diver action recognition problem challenging. In this regard, this paper presents DARE, a diver action recognition encoder, which is robust to underwater uncertainties and classifies various diver actions including sixteen gestures and three poses with high accuracy. DARE is based on the fusion of stereo-pairs of underwater camera images using bi-channel convolutional layers for feature extraction followed by a systematically designed decision tree of neural network classifiers. DARE is trained using the Cognitive Autonomous Diving Buddy (CADDY) dataset, which consists of a rich set of images of different diver actions in real underwater environments. DARE requires only a few milliseconds to classify one stereo-pair, thus making it suitable for real-time implementation. The results show that DARE achieves up to 95.87% overall accuracy and 92% minimum class accuracy, thus verifying its robustnesss and reliability. Furthermore, a comparative evaluation against existing deep transfer learning architectures reveals that DARE improves the performance of baseline classifiers by up to 3.44% in the overall accuracy and 30% in the minimum class accuracy.
Parsimonious Mixtures of Matrix-Variate Shifted Exponential Normal Distributions

Finite mixtures of matrix-variate distributions constitute a powerful model-based clustering device. One serious issue of these models is the potentially high number of parameters to be estimated. Thus, in this work we introduce a family of 196 parsimonious mixture models based on the matrix-variate shifted exponential normal distribution, an elliptical heavy-tailed generalization of the matrix-variate normal distribution. Parsimony is introduced in a twofold manner: (i) by using the eigendecomposition of the components scale matrices and (ii) by allowing the components tailedness parameter to be tied across the groups. A further characteristic of the proposed models relies on the more flexible tail behavior with respect to existing parsimonious matrix-variate normal mixtures, thus allowing for a better modeling of datasets having atypical observations. Parameter estimation is obtained by using an ECM algorithm. The proposed models are then fitted to a real dataset along with parsimonious matrix-variate normal mixtures for comparison purposes.
Deep Learning based facial recognition using H aar cascade technique

The face identification method depends on these technological [Face detection applications using digital pictures. Systems for biometric identification, search, and security all use this application. Computer vision combines machine learning and artificial intelligence. It can extract information from photos and videos using computer-based methods. Numerous earlier studies developed face identification apps using a variety of techniques and coding languages. Object detection is the most important component of computer vision. Locating the face is the primary step in computer vision to detect the face part in the input image. The Open-Source Computer Vision Library (OpenCV) is a free and open-source Java library for object detection. One method for object detection is the Haar cascade classifier. This technique makes it simple to convert an object by counting the square photos on an image. The usage of the OpenCV package for image processing and the Haar Cascade Classifier for face detection in digital photos are both described in this study. ] document is a "dynamic" template and already defines the components of your paper [title, text, heads, etc.] in its style sheet. This methodology enables the better investigation accuracy of the results in input images. *CRITICAL: The paper's title or abstract must not contain symbols, special characters, footnotes, or math.
Using CNN and OpenCV, Emotion Recognition with Facial Feature Approach

In the field of human-computer interaction, continuing research on the detection of emotions through the identification of face features may be found (HCI). Humans are capable of communicating a diverse range of feelings and emotions by the expressions on their faces and the way they carry themselves physically. In this project, we will utilize a technique that permits the computers to self-identify the face acknowledgment of person feelings. This will be done so that we can determine the person's true feelings from their facial expression and gesture. Sentiment Analysis is only a composite of data gathered from several types inside the end. The existing divide between computer systems and humans will start to close as soon as computers can recognise more human emotions. In this study paper, we will demonstrate how to interpret the frontface expression of a person in order to accurately detect a range of emotions, including neutral, pleased, sad, shocked, angry, fearful, and contemptuous expressions.
System Cursor Control Using Human Eyeball Movement

This paper aims to develop a system for controlling the computer cursor using the movement of the user’s eyeballs, providing a hands-free alternative to the traditional mouse. We developed a program using image processing techniques and machine learning algorithms in Python to obtain the eyeball movements and blink and translate them respectively into cursor movements and click actions. Our system was able to achieve a high level of accuracy in tracking the user’s eye movements. Users were easily able to adapt to the new input method. This system has great potential to improve the accessibility and usability of computers for individuals with motor impairments or disabilities. This hands-free control method has great potential in the area of applications in gaming and virtual reality environment etc.
Role of Cyber security in Gaming technology

Internet addiction has become a popular entertainment for many youthful people in the modern era due to the Internet's quick development. However, never-ending streams of cyber security concerns have surfaced in online games in recent years, catching the eye of many game developers. The game's common cyber security issues include information leaks and cyberattacks. These issues will cost players money, either directly or indirectly. In order to improve the gaming user experience, many gaming companies are strengthening the safety and stability of their networks or gaming systems. By introducing the background, a few prevalent cyber security threats, and a potential latent solution, this article has investigated the cybersecurity risks in online games. We looked into various types of threats to gauge the degree of cyber security risk related to gaming. The main objective of the study is role of cyber security in Gaming Technology. This research report includes statistics gathered from the Antivirus Security Network, which handles cyber security data from Kaspersky product customers. These numbers show how frequently and how many of our product's users came into contact with gaming-related cybersecurity threats during in the reporting period.
Finding Good Attribute Subsets for Improved Decision Trees Using a Genetic Algorithm Wrapper; a Supervised Learning Application in the Food Business Sector for Wine Type Classification

This study aims to provide a method that will assist decision makers in managing large datasets, eliminating the decision risk and highlighting significant subsets of data with certain weight. Thus, binary decision tree (BDT) and genetic algorithm (GA) methods are combined using a wrapping technique. The BDT algorithm is used to classify data in a tree structure, while the GA is used to identify the best attribute combinations from a set of possible combinations, referred to as generations. The study seeks to address the problem of overfitting that may occur when classifying large datasets by reducing the number of attributes used in classification. Using the GA, the number of selected attributes is minimized, reducing the risk of overfitting. The algorithm produces many attribute sets that are classified using the BDT algorithm and are assigned a fitness number based on their accuracy. The fittest set of attributes, or chromosomes, as well as the BDTs, are then selected for further analysis. The training process uses the data of a chemical analysis of wines grown in the same region but derived from three different cultivars. The results demonstrate the effectiveness of this innovative approach in defining certain ingredients and weights of wine’s origin.
The Application of Image Processing for Conversion of Handwritten Mathematical Expression

Abstract Optical character recognition (OCR) is the conversion of printed or written text from a scanned document or image file into a machine-readable form to be used for data processing like editing. Handwriting has been a way of communication for centuries, but modern technology has made it easier with the introduction of modern computers. While people have adapted to typing out words using a keyboard, formulas and mathematical expressions requires additional add-ons installed in the word processor. The process can be time consuming and tedious. Therefore, an alternative method is proposed in this paper in which handwritten mathematical formulas are converted into computer readable text. Horizontal and vertical projection is used for segmentation while convolutional neural network for character recognition is used to increase the recognition accuracy. The proposed method was able to segment out handwritten mathematical equations from lined papers as well as extract out and identify each character written. The handwritten equation was then successfully converted to a digital format.
Learning Modality Complementary Features with Mixed Attention Mechanism for RGB-T Tracking

RGB-T tracking involves the use of images from both visible and thermal modalities. The primary objective is to adaptively leverage the relatively dominant modality in varying conditions to achieve more robust tracking compared to single-modality tracking. An RGB-T tracker based on a mixed-attention mechanism to achieve a complementary fusion of modalities (referred to as MACFT) is proposed in this paper. In the feature extraction stage, we utilize different transformer backbone branches to extract specific and shared information from different modalities. By performing mixed-attention operations in the backbone to enable information interaction and self-enhancement between the template and search images, a robust feature representation is constructed that better understands the high-level semantic features of the target. Then, in the feature fusion stage, a modality shared-specific feature interaction structure was designed based on a mixed-attention mechanism, effectively suppressing low-quality modality noise while enhancing the information from the dominant modality. Evaluation on multiple RGB-T public datasets demonstrates that our proposed tracker outperforms other RGB-T trackers on general evaluation metrics while also being able to adapt to long-term tracking scenarios.
The Employee Performance Evaluation by using the Hybrid Multi-Criteria Decision-Making Approach

Objectives: This study is aimed to develop a multi-criteria decision-making model for evaluating employee performance and provide organizations with a guideline on how to quantify employee performance.Methodology: A hybrid approach of AHP and WSM was used in the development of the employee performance model. The AHP approach was employed to obtain the weight of each criterion. The obtained weight value is then used to calculate the performance score.Finding: Based on the literature review and interviews, nine criteria were used, namely the achievement of work targets, job knowledge, initiative, work discipline, responsibility, teamwork, integrity, communication, and cleanliness. Based on weight calculations, it was found that achievement of the work target has the highest importance weight, followed by job knowledge, initiative, work discipline, responsibility, teamwork, integrity, communication, and cleanliness.Conclusion: This study developed a multi-criteria decision-making model and a scoring guideline for evaluating employee performance. According to the results of the performance score, the assessed employee would fall into five categories i.e. unacceptable performance, performance is slightly under the minimum expectation, performance meets the standard and expectation, performance exceeds the standard and expectation, and extraordinary performance. The model can be used to help managers to make decisions regarding compensation which is to be given to employees by linking the employee performance category with compensation criteria.
A Voting System Using Block Chain A Deep Survey

The authenticity and transparency of voting methods must be guaranteed in today's digitally linked society. The power of blockchain technology and sophisticated optimization methods are combined in this research article to suggest a fresh strategy for overcoming these difficulties. The suggested voting system uses blockchain's decentralized structure to provide a safe and impenetrable platform for holding elections. Blockchain's built-in immutability and cryptographic capabilities are used by the proposed system to ensure voting process secrecy and transparency. The whole blockchain acts as an auditable and verifiable ledger, with each vote being recorded as a transaction inside a block. The system optimizes the distribution of resources, such as polling booths and staff, to save costs and maximize efficiency. It does this by using cutting-edge optimization algorithms. On the basis of a test dataset, a thorough simulation study is carried out to assess the efficacy of the suggested solution. The results show the system's capacity to maintain vote integrity while enhancing resource allocation generally and lowering the risk of fraud or manipulation. By offering a safe, open, and effective method, the research's results advance the area of voting systems. The suggested approach might completely alter how elections are run, fostering faith and confidence in the democratic system.
Extended HD-MAABE Scheme Supporting Delegation and Revocation for Cloud-based Organizational Data Sharing

Cloud computing facilitates data exchange and outsourcing. Any business or organization may exchange data for societal and individual benefit. Organizations lose control of their data when they upload files to the cloud. As a method of restricting access to data in a cloud setting, attribute-based encryption (ABE) has gained popularity. However, current ABE schemes have some limitations. First, contemporary ABE techniques are incapable of depicting the real-world situations of corporate data exchange, particularly when the companies are hierarchically structured. Second, they have scalability constraints when it comes to key management. Third, it is necessary to streamline the revocation methods in order to accommodate hierarchical data sharing. In this paper, we extend our multi-authority scheme HD-MAABE, by incorporating a delegation service between the root master authorities and the domain master authorities, allowing for greater security and scalability. Using mediation server and keeping an attribute revocation list on the cloud server makes the revocation procedure simple. The scheme supports the LSSS policy structure’s adaptability. Proof of security is based on q-parallel BDHE security assumptions. We show that the proposed method is secure as well as computationally and functionally efficient through the use of cost analysis, feature comparison and security analysis.
SSHTDNS: A Blockchain-Based Secure, Scalable, and High-Throughput Domain Name System

The Internet's crucial domain name system is in charge of translating domain names into IP addresses. However, because of its dispersed structure with a hub, it is subject to significant security concerns. Since the creation of Bitcoin, blockchain has drawn a lot of attention owing to its decentralized, trustworthy, and secure features. In this article, we suggest SSHTDNS, a brand-brand-new domain name system built on the blockchain. SSHTDNS selects the consortium chain form to ensure the management of Top-Level Domain names, and these consortium nodes use linkable ring signature technology for identity anonymity and vote fairness. Shading protocol is used in multi-chain structures to increase the system's scalability and throughput. A domain name system that combines these methods can be safe, scalable, and high-throughput.
Improved and Accurate Face Mask Detection Using Machine Learning in the Crowded Places

Face detection while wearing a mask is used to see whether there are any faces in a photo or video. Face recognition is a technique for recognizing or validating an individual's identification based on their facial features. Face recognition algorithms come in a variety of forms, but their accuracy varies. This page explains how we use deep learning to do facial recognition. For example, nowadays the majority of individuals wear masks. It is quite difficult to recognize somebody wearing a mask.During the difficult period of Covid-19, there was a sudden increase in criminal activity, and most of the time the victim was unable to portray the criminal's facial structure. As a result, security cameras installed throughout the area that work on the principle of face detection can easily identify an individual whether or not they are wearing a mask. This system's testing results show that this technique can identify the face mask using a multi-feature combination effectively and precisely. As a result, identifying the person behind the mask is simple with the aid of this programme.
A 28nm 77.35TOPS/W Similar Vectors Traceable Transformer Processor with Principal-Component-Prior Speculating and Dynamic Bit-wise Stationary Computing

This paper proposes an energy-efficient Transformer processor exploiting dynamic similarity in global attention computing. It has three features: 1) A principal-component-prior speculation unit (PCSU) removes 28.4% of redundant computations. 2) A similar-vector tracked computing engine (STCE) saves 42.2% of multiplications. 3) A bit-wise stationary processing element (BSPE) reduces multiplication energy by $1.47\times$. The proposed processor achieves a peak energy efficiency of 77.35TOPS/W. It reduces energy by $2.81\times$ and offers $3.71\times$ speedup compared with the state-of-the-art Transformer processor.
Construction of Trust Management using Blockchain Technology

Recent events have brought to the forefront concerns over the dependability of decentralized social networks (DOSN). Malicious individuals may engage in such activities in order to artificially inflate their trustworthiness in the network and, as a result, increase their chances of being selected by other users, which presents a risk of untrustworthy connections and partnerships in a decentralized social networking system. This risk is caused by the presence of individuals who may engage in such activities. Under these circumstances, customers won't be able to accurately evaluate the credibility of the recommendations for social services they have been provided. As a consequence of this, a trust management model is necessary in order to defend the integrity of the system against attacks and to direct users toward trustworthy social services. To this aim, we propose an innovative strategy for trust management as a means of protecting against attacks motivated by a lack of enough confidence. In light of this, the strategy that we have recommended takes use of a cutting-edge invention known as blockchain in order to accomplish this objective. At this time, Blockchain only considers physical security; thus, we wish to incorporate cognitive security based on the recognized trust-related flaws. The findings of the assessment indicate that our methodology is effective.
Gaggle: Genetic Algorithms on the GPU using PyTorch

PyTorch has profoundly impacted the machine learning (ML) community by allowing researchers of all backgrounds to train models efficiently. While PyTorch is the de facto standard in ML, the evolutionary algorithms (EA) community instead relies on many different libraries, each with low adoption in practice. In an effort to provide a standardized library for EA, packages like LEAP and PyGAD have been developed. However, these libraries fall short in either scalability or usability. In particular, neither of these packages offers efficient support for neuroevolutionary tasks. We argue that the best way to develop a PyTorch-like library for EAs is to build on the already solid foundation of PyTorch itself. We present Gaggle, an efficient PyTorch-based EA library that better supports GPU-based tasks like neuroevolution while maintaining the efficiency of CPU-based problems. We evaluate Gaggle on various problems and find statistically significant improvements in runtime over prior work on problems like training neural networks. In addition to efficiency, Gaggle provides a simple single-line interface making it accessible to beginners and a more customizable research interface with detailed configuration files to better support the EA research community.
Maelstrom: An Accelerator-compatible GP Framework

This work presents the Maelstrom framework for strong-typed tree GP. Maelstrom is a Python library designed to facilitate rapid prototyping and exploration of weak-typed tree GP, strong-typed tree GP, island model, and coevolution with flexibility that enables support for accelerator frameworks such as JAX. The architecture and features of Maelstrom are discussed alongside several example applications employing Gym environments and an accelerated version of predator-prey.
A 28 nm 66.8 TOPS/W Sparsity-Aware Dynamic-Precision Deep-Learning Processor

The required precision for deep neural network (DNN) models strongly depends on sparsity and compactness. This paper presents a heterogeneous DNN accelerator performing dynamic-precision computing adapted to sparsity. Simulation shows that the proposed dynamic precision computing successfully covers EfficientNets and Transformers with a negligible accuracy loss. The accelerator, fabricated in a 28nm LP CMOS, achieves a peak energy efficiency of 66.8 TOPS/W with a peak performance of 4.2 TOPS.