Reviewerly/exHarmony huggingface dataset:
CityServices

In this covid pandemic situation, small-scale entrepreneurs suffered due to lockdown as well as common people were unable to get most of the services at their doorstep. In the current scenario, we people are so into technology that we don't want to make too many efforts to get things done we always wanted an easy way to get things done in lesser time as well. Suppose any of your appliances breaks down, let's say your cooler breaks down in hot summer then it would be a very tedious task to go out in summer and find a repairing person to overcome such situation our application “CityService” will came into the picture. So this project is all about providing the household services at your doorstep by finding the nearest professional at your fingertips. Similarly, the app would help small-scale entrepreneurs to grow faster and reach out to many customers in the city.
EFL TEACHERS’ PERCEPTIONS AND CHALLENGES OF USING MOBILE LANGUAGE LEARNING APPLICATIONS

This paper explores English language teachers’ perceptions of mobile applications and challenges in using them. The study uses data from 10 English languages teachers working in secondary schools and universities in Kazakhstan. Thematic analysis is employed. The findings suggest that teachers value availability and student engagement offered by mobile apps. The challenges of using mobile apps tend to be beyond teachers’ control and include internet issues or lack of equipment. Policy implications for teacher training and professional development are discussed.
Cryptocurrency News Website with Prediction

We analyze the predictability of the bitcoin market across prediction horizons ranging from 1 to 60 min. In doing so, we test various machine learning models and find that, while all models outperform a random classifier, recurrent neural networks and gradient boosting classifiers are especially well-suited for the examined prediction tasks. We use a comprehensive feature set, including technical, blockchain-based, sentiment-/interest-based, and asset-based features. Our results show that technical features remain most relevant for most methods, followed by selected blockchain- based and sentiment-/interest-based features. Additionally, we find that predictability increases for longer prediction horizons. Although a quantile-based long-short trading strategy generates monthly returns of up to 39% before transaction costs, it leads to negative returns after taking transaction costs into account due to the particularly short holding periods. Cryptocurrencies, which the Bitcoin is the most remarkable one, have allured substantial awareness up to now, and they have encountered enormous instability in their price. While some studies utilize conventional statistical and econometric ways to uncover the driving variables of Bitcoin's prices, experimentation on the advancement of predicting models to be used as decision support tools in investment techniques is.
Property Unlearning: A Defense Strategy Against Property Inference  Attacks

During the training of machine learning models, they may store or "learn" more information about the training data than what is actually needed for the prediction or classification task. This is exploited by property inference attacks which aim at extracting statistical properties from the training data of a given model without having access to the training data itself. These properties may include the quality of pictures to identify the camera model, the age distribution to reveal the target audience of a product, or the included host types to refine a malware attack in computer networks. This attack is especially accurate when the attacker has access to all model parameters, i.e., in a white-box scenario. By defending against such attacks, model owners are able to ensure that their training data, associated properties, and thus their intellectual property stays private, even if they deliberately share their models, e.g., to train collaboratively, or if models are leaked. In this paper, we introduce property unlearning, an effective defense mechanism against white-box property inference attacks, independent of the training data type, model task, or number of properties. Property unlearning mitigates property inference attacks by systematically changing the trained weights and biases of a target model such that an adversary cannot extract chosen properties. We empirically evaluate property unlearning on three different data sets, including tabular and image data, and two types of artificial neural networks. Our results show that property unlearning is both efficient and reliable to protect machine learning models against property inference attacks, with a good privacy-utility trade-off. Furthermore, our approach indicates that this mechanism is also effective to unlearn multiple properties.
HyperAid: Denoising in hyperbolic spaces for tree-fitting and  hierarchical clustering

The problem of fitting distances by tree-metrics has received significant attention in the theoretical computer science and machine learning communities alike, due to many applications in natural language processing, phylogeny, cancer genomics and a myriad of problem areas that involve hierarchical clustering. Despite the existence of several provably exact algorithms for tree-metric fitting of data that inherently obeys tree-metric constraints, much less is known about how to best fit tree-metrics for data whose structure moderately (or substantially) differs from a tree. For such noisy data, most available algorithms perform poorly and often produce negative edge weights in representative trees. Furthermore, it is currently not known how to choose the most suitable approximation objective for noisy fitting. Our contributions are as follows. First, we propose a new approach to tree-metric denoising (HyperAid) in hyperbolic spaces which transforms the original data into data that is ``more'' tree-like, when evaluated in terms of Gromov's $\delta$ hyperbolicity. Second, we perform an ablation study involving two choices for the approximation objective, $\ell_p$ norms and the Dasgupta loss. Third, we integrate HyperAid with schemes for enforcing nonnegative edge-weights. As a result, the HyperAid platform outperforms all other existing methods in the literature, including Neighbor Joining (NJ), TreeRep and T-REX, both on synthetic and real-world data. Synthetic data is represented by edge-augmented trees and shortest-distance metrics while the real-world datasets include Zoo, Iris, Glass, Segmentation and SpamBase; on these datasets, the average improvement with respect to NJ is $125.94\%$.
Library cloud storage of published thesis and research paper with laravel security in taguig city university

This study is conducted to create a better platform for Taguig City University Library where students can access it in just a few clicks using their mobile phones, desktop, or any smart devices. Innovation is inevitable, as the world progress to advancements school services like libraries should also innovate to provide accessible and better learning experiences to students. Library Cloud Storage of Published Thesis and Research Paper with Laravel Security in Taguig City University is created to provide Taguig City University its own online library that will benefit not just the students but also the library staff and faculty members.The ANOVA F-TEST was used in this study to see if there was a significant difference between the three groups of respondents' perceptions of the system's efficiency and effectiveness. The generated result of ANOVA computation to get the significant difference at 0.05 level of significance of the assessment of the three groups of respondents for the criteria set by ISO 25010 namely functional suitability, compatibility, usability, security, and maintainability of the system. The computed value is F and the F crit is the tabulated value. The F value is less than the F critical value that leads to the acceptance of the null hypothesis that is "There is no significant difference between the assessment of the three groups", which means that the three groups of respondents have almost the same assessment on the feature of the system.
Deep Learning for Computer Vision: A Brief Overview of YOLO

Inspired by the brain, deep neural networks (DNN) are thought to learn abstract representations through their hierarchical architecture. However, at present, how this happens is not well understood. Deep learning has been overwhelmingly successful in computer vision (CV), natural language processing, and video/speech recognition. In this paper, our focus is on the CV. We provide a critical review of recent achievements and methods of in terms of techniques and applications. We identify eight emerging techniques, investigate their origins and updates, and finally emphasize their applications in four key scenarios, including recognition, visual tracking, semantic segmentation, and image restoration. A brief account of their history, structure, advantages, and limitations is given, followed by a description of their applications in various computer vision tasks, such as object detection, face recognition, action and activity recognition, and human pose estimation. Finally, a brief overview is given of future directions in designing deep learning schemes for computer vision problems and the challenges involved therein.
LANBIQUE: LANguage-based Blind Image QUality Evaluation

Image quality assessment is often performed with deep networks that are fine-tuned to regress a human provided quality score of a given image. Usually, this approach may lack generalization capabilities and, while being highly precise on similar image distribution, it may yield lower correlation on unseen distortions. In particular, they show poor performances, whereas images corrupted by noise, blur, or compression have been restored by generative models. As a matter of fact, evaluation of these generative models is often performed providing anecdotal results to the reader. In the case of image enhancement and restoration, reference images are usually available. Nevertheless, using signal based metrics often leads to counterintuitive results: Highly natural crisp images may obtain worse scores than blurry ones. However, blind reference image assessment may rank images reconstructed with GANs higher than the original undistorted images. To avoid time-consuming human-based image assessment, semantic computer vision tasks may be exploited instead. In this article, we advocate the use of language generation tasks to evaluate the quality of restored images. We refer to our assessment approach as LANguage-based Blind Image QUality Evaluation (LANBIQUE). We show experimentally that image captioning, used as a downstream task, may serve as a method to score image quality, independently of the distortion process that affects the data. Captioning scores are better aligned with human rankings with respect to classic signal based or No-reference image quality metrics. We show insights on how the corruption, by artefacts, of local image structure may steer image captions in the wrong direction.
Defense of the Ancients (DOTA 2)-Draft Recommendation System

In the last decade, Dota 2 has gradually developed by attracting a lot of players globally and has recently hosted its 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">th</sup> international tournament for professional players. Though the data from these matches are easily available on various sites, they cannot be used to implement data mining and deep learning techniques directly. After sufficient data engineering, one can use the data and unleash the benefits that can be created for both, the existing and the new players, which is the objective of our project. The Dota2 game is played in 2 stages: (i) The Drafting Phase (ii) The Playing Phase. The drafting phase is analogous to the team selection phase of the game which has a huge impact on the outcome of the game i.e. a stronger team draft has a better chance of winning. Both teams have to choose and ban heroes/characters in a chronological order and the aim is to build a team that synergizes well with its own team and counters the opponent team. However, it is observed that most players are often unable to pick the best team lineup to compete. The aim is to solve this problem and in this research paper, a draft recommendation system based on the NLP (Natural Language Processing) technique of finding the next word is proposed and thus word2vec algorithm, more specifically the CBOW (Continuous Bag Of Words) algorithm in tandem with LSTM (Long Short-Term Memory) model is used to give out the best suggestions.
Blockchain Computing Technology: Challenging Security and Privacy Issues

Blockchain Technology (BT) has gained attention from various sectors like education, medicine, finance, and manufacturing units due to its component and characteristics such as block header, secure transactions, distributed database, and peer authentication. Hash code generation is part of the succeeding generation in transactional technology due to its security feature that is suitable to the investigation epoch. However, security and privacy aspects need to be incorporated into the complete architecture of blockchain technology. In particular, it is required to consider variables while designing security solutions like endpoint vulnerabilities, untested code, and third-party risk. In this section, there is a discussion on the notion of BT and the challenging security and privacy issues, along with available BT research solutions. In addition, there is a discussion about how to acclimatize security issues in blockchain security in detail concerning the Internet of Things (IoT) and cloud computing.
Web Application based Digital Vehicle Monitoring System

Road transport may be divided into two categories: goods transportation and people transportation. The separation of the two industries is enforced in many countries by licensing requirements and safety rules. Bicycles, automobiles, trucks, and animals such as horses and oxen may all travel on roadways. Training and education are the foundations of road safety programs in practically every country on the planet. The impact of educational and training programs on traffic safety is still restricted due to the variety and scope of evaluation methodologies. IT has been in the automobile industry for a long time. Beyond the fundamental function of the automotive head unit, it delivers a range of information and satisfaction. People are familiar with JavaScript since it is simple to create. As a result, online applications will tear down the obstacles to entry for the closed automobile industry in the terms of accuracy and continuity. Even though there are numerous tools, build rules, and open sources that make it “simple to produce,” it is a fantastic product to promote in the conservative and closed automobile business. Hence, our research aims to reproduce a web application that is an essential congregation of everything related to a vehicle, its owner, Traffic admin to manipulate, add, delete and regulate smooth traffic in a country.
PROTOCOL –Availability of Open Access Metadata from Open Journals – A case study in DOAJ and Crossref v2

This protocol is for the research about the availability of Metadata from Open Journals in the DOAJ in Crossref. The goal is to find out how many papers from DOAJ journals are available on Crossref, whether their metadata is available and the origin of their references' DOIs. This will provide us a clearer picture about the state of Open Access research. Our research involves the articles from Open Access journals in DOAJ and their data on Crossref. We scouted the availability of these articles’ reference lists, the presence of IDs such as DOIs and the entities responsible for their specification. This analysis was carried using DOAJ article metadata dump, then modified and enriched thought Crossref’s APIs DOI requests. The data will be further analysed to verify the distribution of the provenance of the articles' DOIs.
Intelligent Chatbot

A Chatbot is a software application that replaces a live human agent to conduct a conversation via text or text to speech. It is designed to behave like a human would behave in that conversation. In this system, we demonstrate a chatbot that uses Artificial Intelligence to produce dynamic responses to online client enquiries. This web-based platform provides a vast intelligent base that can help humans to solve problems. The chatbot recognises the user's context, which prompts an intended response. Because this is a dynamic response, the user's desired response will be generated. This also uses a machine-learning algorithm to learn the chatbot by experiencing various requests and responses. Chatbots come to use in numerous fields of our daily life. Because AI enhances the human touch in every communication, chatbots are becoming increasingly robust.. It triggers accurate responses after understanding a user's query. Its objective is to reduce human dependency in every organisation and reduce the need for different systems for different processes.
Memory and Round-Efficient MPC Primitives in the Pre-Processing Model from Unit Vectorization

In this paper, we propose memory- and round-efficient protocols for securely evaluating arithmetic primitives. We focus on secure two-party computation over the ring ℤ2k that achieves security against semi-honest adversaries and works in the pre-processing model. Our protocols rely on the unit vectorization technique introduced by Boyle et al. (TCC 2019). The unit vectorization technique provides online-optimal protocols for several fundamental operations in the pre-processing model. However, a relatively large memory cost for correlated randomness is required, which might become an obstacle in a large-scale application. In order to achieve both memory and communication efficiency, we propose a size reduction method that uses unit vectorization only for short-length inputs, and based on this, construct two-round protocols for equality test, detecting the most significant non-zero bit, detecting wrap-around, and less-than comparison. In addition, as applications of these results, we provide practically efficient protocols for integer division, integer square root, integer logarithm, and modular exponentiation.
Behavioral Determinants of Routine Health Information System Data Use in Senegal: A Qualitative Inquiry Based on the Integrated Behavioral Model

Routine health information system (RHIS) data are essential in driving decision making and planning in health systems as well as health programs. However, despite their importance, these data are underutilized, and the underlying individual-level facilitators and barriers to use remain understudied. In this research, we applied the Integrated Behavior Model (IBM) to examine how attitudes toward RHIS data, perceived norms concerning RHIS data use, and the ability to use RHIS data influence the demand and use of RHIS data among stakeholders in Senegal. Using data from interviews with respondents working at national levels of malaria, HIV, and TB control programs in Senegal, we used a framework analysis approach to apply the IBM behavioral constructs and identify their linkages to RHIS data use. We found that attitudes about the quality, availability, and relevance of RHIS data for decision making were important in driving data use among respondents. Institutional expectations, organizational protocols, policies, and practices around RHIS data ultimately shape social norms around the use of the data. Although we found that perceived ability and self-efficacy to use RHIS data were not barriers to RHIS data use among stakeholders at the strategic levels of their respective organizations, these were reported to be barriers at lower levels of the health system. Low perceived control of the RHIS data production process ultimately reduced RHIS data use for decision making among the strategic-level respondents. We recommend context-specific reexamination of existing RHIS interventions with a renewed emphasis on behavioral aspects of data use. The IBM can help guide practitioners, policy makers, and academics to address multiple socioecological factors that influence data use behavior when recommending RHIS and data use solutions.
Credit Card Fraud Detection System

Since the growth in online shopping, an increasing number of people are paying online using modes of payment like credit cards to pay their bills, and like every other online interface, Credit cards are also subject to hacking. online businesses and financial companies need to detect these fraudulent activities to save their clients from getting charged for items they didn’t purchase. For this purpose, banks and payment companies can implement algorithms to detect fraudulent behavior. The Credit Card Fraud Detection System includes taking into consideration past credit card transactions with the data of the ones that were fraud. This system can be used to identify a fraudulent transaction beforehand. Our objective is to identify all of the fraudulent transactions while also minimizing the incorrect fraud classifications. In this technique we first examine and pre-process the Data set to address unbalanced data, then we train our model using a Logistic regression algorithm to detect fraud.
A Unified Experiment Design Approach for Cyclic and Acyclic Causal Models

We study experiment design for unique identification of the causal graph of a simple SCM, where the graph may contain cycles. The presence of cycles in the structure introduces major challenges for experiment design as, unlike acyclic graphs, learning the skeleton of causal graphs with cycles may not be possible from merely the observational distribution. Furthermore, intervening on a variable in such graphs does not necessarily lead to orienting all the edges incident to it. In this paper, we propose an experiment design approach that can learn both cyclic and acyclic graphs and hence, unifies the task of experiment design for both types of graphs. We provide a lower bound on the number of experiments required to guarantee the unique identification of the causal graph in the worst case, showing that the proposed approach is order-optimal in terms of the number of experiments up to an additive logarithmic term. Moreover, we extend our result to the setting where the size of each experiment is bounded by a constant. For this case, we show that our approach is optimal in terms of the size of the largest experiment required for uniquely identifying the causal graph in the worst case.
Optical crosstalk mitigation methods for a trapped ion qubit array

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