Reviewerly/exHarmony huggingface dataset:
Unsupervised word translation for English-Hindi with different retrieval techniques

Word translation or incorporation of bilingual dictionaries is an important capability that impacts many multilingual language processing tasks. For translation from one language to another language, we either relied on parallel data or bilingual dictionaries. In this paper, we address this problem and generate best cross-lingual word embedding for English-Hindi language pair. Here, we neither use an aligned document or sentence aligned corpus, nor any bilingual dictionary. We are following the assumption of intra lingual similarity distribution that for the most frequent word the distribution graph is similar between Hindi and English corpus and embeddings are isometric. These cross-lingual words embedding can be used for unsupervised neural machine translation and cross-lingual transfer learning. Different retrieval techniques nearest neighbour, inverted nearest neighbours retrieval, inverted Softmax, and cross-lingual word scaling are performed and compared for the bi-lingual embedding of English-Hindi, which is trained for unsupervised and semi-supervised ways by passing seed dictionary. Bi-lingual word embedding is tested on generated English-Hindi dictionary.
Weaving Security into DevOps Practices in Highly Regulated Environments

In this article, the authors discuss enhancing a DevOps implementation in a highly regulated environment (HRE) with security principles. DevOps has become a standard option for entities seeking to streamline and increase participation by all stakeholders in their Software Development Lifecycle (SDLC). For a large portion of industry, academia, and government, applying DevOps is a straight forward process. There is, however, a subset of entities in these three sectors where applying DevOps can be very challenging. These are entities mandated by security policies to conduct all, or a portion, of their SDLC activities in an HRE. Often, the reason for an HRE is protection of intellectual property and proprietary tools, methods, and techniques. Even if an entity is functioning in a highly regulated environment, its SDLC can still benefit from implementing DevOps as long as the implementation conforms to all imposed policies. A benefit of an HRE is the existence of security policies that belong in a secure DevOps implementation. Layering an existing DevOps implementation with security will benefit the HRE as a whole. This work is based on the authors extensive experience in assessing and implementing DevOps across a diverse set of HREs. First, they extensively discuss the process of performing a DevOps assessment and implementation in an HRE. They follow this with a discussion of the needed security principles a DevOps enhanced SDLC should include. For each security principle, the authors discuss their importance to the SDLC and their appropriate placement within a DevOps implementation. They refer to a security enhanced DevOps implementation in an HRE as HRE-DevSecOps.
Defects Detection System of Medical Gloves Based on Deep Learning

In industrial production, medical gloves with tear, stain and other defects will be produced. In traditional manual mode, the efficiency and accuracy of defect detection depend on the proficiency of spot-check workers, which results in uneven glove product quality. In this paper, a surface defect detection system of medical gloves based on deep learning is designed for the automatic detection with high efficiency and accuracy. According to the industrial requirements of high real-time, the system adopts a cache scheme to improve the data reading and writing speed, and an Open Neural Network Exchange (ONNX) to effectively improve the speed of model reasoning. For the demands of high detection accuracy, the system designs a dual model detection strategy, which divides texture detection and edge detection into two steps. The advantage of this strategy is to remove most useless information while ensuring the effective information of the image. Furthermore, two auxiliary models are used to promote the accuracy of detection based on classification methods. Finally, experiments are proposed to verify the functional indicators of the system. After the on-site test of the production line in the medical glove factory, the system has the ability to detect the gloves of two production lines with high real-time. The product missed detection rate is less than 2%, and the product mistakenly picked rate is less than 5/10000. Verified by the industry of gloves, the system can be put into production line.
Inferential Statistical Analysis in E-Learning University Education in Latin America in Times of COVID-19

Since the appearance of COVID-19, the teaching-learning processes in higher education have changed. This article shows a focus on university education and e-learning, performing a statistical analysis on university students in Ecuador, obtaining significant evidence that the use of ICTs improves academic performance in the subject of statistics. In the first case, two third semester courses are taken, the experimental group is made up of 23 students, to which e-learning is applied and an application developed in Scilab that shows the resolution process for descriptive and inferential statistics; while the control group is made up of 14 students, in which only e-learning and traditional teaching are used. In the second case, 2 courses are taken, the first is formed by 14 students and the second by 22 students, using e-learning and traditional teaching. First, the Shapiro Test is used to determine if the population has a normal distribution, then the Student’s T test is applied in the hypothesis test of difference of means to determine if academic performance is improved with the use of ICTs. Finally, for $$ \alpha = 0.05$$ , it is verified that the developed application improves academic performance. Another important finding is that only using traditional teaching with e-learning does not significantly change academic performance.
Neural Adaptive IoT Streaming Analytics with RL-Adapt

The emerging IoT stream processing is a key enabling technology for the time-critical IoT applications, which often require high accuracy and low latency. Existing stream processing engines are insufficient to meet these requirements, since they could not integrate and respond timely to variable network conditions in the dynamic wireless environment. Recent efforts focusing on adaptive streaming support user-specified policies to adapt to the variable network conditions. However, those manual-policies can hardly achieve optimal performance across a broad set of network conditions and quality of experience (QoE) objectives. In this paper, we present a Reinforcement Learning-based Adaptive streaming system (RL-Adapt) that is capable of generating adaption policies using RL-strategy and providing declarative APIs for efficient development. RL-Adapt trains a neural network model that can automatically select the optimal policy based on the observed network conditions. RL-Adapt does not rely on pre-defined models or assumptions on the environment. Instead, it learns to make decisions solely through observations of the resulting performance of past decisions. We implemented RL-Adapt and evaluated its performance extensively in three representative real-world IoT applications. Our results show that RL-Adapt outperforms the state-of-the-art scheme, with 20% improvements on average QoE.
Cryptography Tools

This chapter presents two of the most popular software applications and tools for cryptography that are great for learning and practice purposes: CrypTool (CT) [1] and OpenSSL [2].
Cybersecurity in the Era of Artificial Intelligence

In this chapter some concepts of cybersecurity are reviewed. Common cyberattacks such as malware, botnets, and phishing are explained. The hypothetical usage of AI for attacks on healthcare systems or for defensive purposes is explored.
Multilingual Information Retrieval Chatbot

Today, we live in an information, communication, and technology era. Information and data have become a major part of our life. Studies have shown that people are spending over 8 h on electronic gadgets that are a world full of information in themselves. But this available information is scattered all over the internet and if a user wants an accurate solution for his quibble, the user needs to read a lot of articles and blogs which is tedious and time consuming. Also, this information is generally available only in English language making it difficult to understand for the non-English speakers. Keeping this in mind, we have developed a multilingual chatbot that will give a response to any query in a fraction of seconds. The response will be given in the same language the query was searched or we can also select the language from the pool. Natural Language Processing (NLP) is used for language-related processing. We have used TF-IDF for document retrieval, BM-25 model for passage extraction and spaCy model for answer extraction. Finally, we have developed the proposed system as an android application as well as a website to make it device independent.
How to Relate Synchronized Static Videos between Each Other in an Active Learning Set-Up?

In the context of investigations, relationships between videos, such as knowing that they have overlapping fields of view, can be useful to efficiently navigate through videos and find hints. Manually finding such links requires checking whether the backgrounds are similar and ensuring that the same objects appear in different videos over time which is very time consuming. Fully automating such a process is a hard task as human knowledge is often required to recognize places or objects over different viewpoints. In this article, we present an active learning approach to make human intelligence and the machine collaborate. More precisely, we introduce a video descriptor based on object category detec tion, as well as a semantic background video descriptor to characterize both the dynamic parts and the static parts of a video file. Then, an algorithm for find ing overlapping links between videos is presented, based on descriptor similarity. Regarding the similarity values, the algorithm can either automatically classify the videos as having overlapping fields of view, not having overlapping fields of view, or needing the expert knowledge. In this case, parameters of the proposed algorithm are updated in such a way that the algorithm decides more and more by itself if the answer matches its expectation, and that the algorithm more often predicts the other class if the answer does not match its expectation. The approach is evaluated and compared with other active learning set-ups on a set of 63 real videos from public multi-view datasets and show promising results.
Target-based Sentiment Analysis in Finance with Domain Knowledge

In order to apply the target-based sentiment analysis to finance domain, this paper constructs a corpus with eight types of entities and four types of emotions. Compared to previous works, the corpus proposed in this paper is more fine-grained. Furthermore, we propose a complete and universal framework for target-based sentiment analysis, which contains two subtasks, i.e. named entity recognition and entity-level sentiment classification. We incorporate the pre-trained language model with financial domain knowledge and achieve significant performance improvement.
Research on the Auxiliary Application of Computer Big Data Technology in the Creation of Literary Works

The current virtual simulation system mainly revolves around physics, chemistry experiments and mathematical formulas, and there are few simulation cases of literary works. Therefore, currently using deep learning models to process Chinese natural language input is a current research hotspot. The article conducts research on Chinese morphology and syntax, and analyzes the influence of different parts of speech and dependencies on the deep logic of sentences through experiments. We distill the sentences input by people using natural language into main components with words as the unit, and transform them into entities and logical relationships that are easy for computers to understand knowledge. Finally, we designed and constructed a library system based on the SWF generation technology of SWF Tools and the dynamic file preview technology based on Flex paper. The system provides users with a complete reading platform for literary works, and at the same time can share writing experience, so as to create a good reading and communication environment for all kinds of users.
Design and Performance Analysis of High Throughput and Low Power RNS-Based FIR Filter Design on FPGA

A cost-effective finite impulse response (FIR) filter is introduced in this research work through Residue Number System (RNS). The moduli set selected provides the same benefit as that of the shift and add method. The implementation Residue Number System with reduced computational complexity, as well as high-performance finite impulse response filters that employ advanced Vivado Design Suite &amp; Artix-7 field-programmable logic (FPL) devices, are presented in this research work. For a specified 64-tap FIR filter, a classical modulo adder tree is substituted by a binary adder with enhanced accuracy pursued by a single modulo reduction stage and as a result reducing the area constraints by approximately 18%. When compared to the three-multiplier-per-tap two's complement filter, the index arithmetic complex FIR filter that is based on the Quadratic Residue Number System outperforms by approximately 75% and at the same time involving some LEs for filters with more than 8 taps. When compared to the traditional design, a 64-tap filter requires only 41% LEs.
Machine Learning Model for Breast Cancer Tumor Risk Prediction

Cancer is one of the most complex diseases and the most widespread in the world, especially lung cancer and breast cancer. Breast cancer is one of the most common diseases especially among women, which causes an increase in the death rate for them. Studies have revealed the importance of early detection of breast cancer as it increases the likelihood of cure and reduces mortality. Due to the difficulty of manually diagnosing the disease in addition to the lack of efficiency of automated systems that diagnose the disease, therefore we need to develop an automated system that facilitates the diagnostic process with high efficiency. In this paper, the risk prediction of breast cancer is carried out through the use of machine learning techniques that use the previous data to train the network and can predict the new input data through the use of classification algorithms. So that it can classify benign and malignant tumors. Results of multiple machine learning algorithms will be compared to obtain high classification accuracy.
An Approach To Secure Sensitive Attributes Stored On HDFS Using Blowfish

These days security is important while storing our sensitive data in PC or over the web. Hadoop is a distributed data storage and analysis framework. Organizations store sensitive data on HDFS and analyse the data using MapReduce. As a result, data encryption is frequently employed to prevent the leakage of important information. Our research work find the sensitive attributes from the file stored in HDFS , then symmetric encryption scheme BlowFish is applied on sensitive attributes only and whole file stored in HDFS to find the comparison. The sensitive attributes encryption/decryption is found to be much better than the whole file encryption/decryption in terms of performance evaluation. To validate the capabilities, different file sizes are used and certain experimental studies are conducted. Since Hadoop lacks in customized encryption, proposed approach will definitely enhance the performance in better way.
E-Commerce System for Anywhere Fitness PH With Sentiment Analysis

As people remain confined in their homes, more and more turn to the internet and social media daily for support, comfort, opportunities, and access to information. This presents an opportunity for businesses and e-commerce platforms to harness their own data and reach wide audiences through social media. An online store, Anywhere Fitness PH, took this opportunity which was launched to bring gym equipment to the comfort and safety of the homes of its consumers. However, the client, Anywhere Fitness PH, struggled in customer reviews and difficulties with its current e-commerce platforms. The researchers proposed a web application system that will provide their client an e-commerce platform that will utilize data analytics and sentiment analysis for its customer reviews and provide further improvements for the overall business operations of the client. The system passed for both evaluation of Customer Interface and Admin Interface with means of 4.27 and 4.49 respectively, making the Overall Evaluation have a mean of 4.3S. All means are interpreted as “Strongly Agree” which means that the admins, the non-IT, and the IT staff strongly agree that the system passed Functionality, Usability, Reliability, Performance, Security, pertaining that the system is now ready for the use of the client.
Application of Lagrange Variational Principle to Accurate Solution of Statistical Kalman Filtration Problem in Generalized Conditions

New method is proposed for restoring the trajectories of random processes under observational conditions corresponding to Kalman filtering, but with arbitrary distributions of the initial state. The approach is based on theoretical mechanics in the variational interpretation of Lagrange. It is noted that the proposed approach makes it possible to reduce the variance of errors up to two times at intermediate points of the observation interval and provides accurate solutions for a wide set of classes of apriori distributions of the initial state. Examples of solutions for cases of multimode and multidimensional distributions of the initial state, which do not allow separation of variables, are given. The proposed approach allows for a wide list of initial distributions to form optimal (according to the criterion of maximum posteriori probability) estimates of the starting positions of the trajectories of stochastic processes, in conditions when the dynamics and measurements correspond to the Kalman model.
Multidimensional scaling in the analysis of linguistic information

Up to this day, the dialogue with a system in a natural language as a service is not available at a mass level. It's noted there are a lot of dialects and they are constantly changing and new ones appear at the same time. A more effective way to solve the problem of inputting oral speech is to use artificial intelligence, which enables quick creation of linguistic knowledge base for any dialect and using it for fast and accurate translation of local language tools. The report presents linguistic information of various dialects and the dynamics of their changes. The developed software is a web application that allows users to interact with a voice database located on a remote server via a browser. A special interface was developed to download audio recordings in .wav format and mark up phonemes. The interface is a widget that creates a visualization of the downloaded audio recording (cast) and the forms that need to be filled out to describe the announcer's phonemes. Multidimensional scaling is applied to data collected using categorization procedures. The findings of this analysis comprise identification of hidden characteristics that underlie the ranking of dialects. The article shows how important is the availability of additional data on the estimated linguistic parameters to researchers interpreting outcomes resulting from application of multidimensional scaling methods.
One-class Temporal Graph Attention Neural Network for Dynamic Graph Anomaly Detection

Nowadays, the application of dynamic graphs in the modeling of complex systems has made a great achievement, which has aroused people's attention to anomaly detection of dynamic graphs. As an unsupervised learning task, anomaly detection is target at identifying the abnormal data that is different from the majority. One-class support vector machine, one of the classic anomaly detection algorithms, has been widely applied to find the outliers for it's stability, robustness and convenience. However, traditional anomaly detection algorithms always lose their effectiveness when applied to dynamic graph anomaly detection task. In order to solve the above problem, we design one-class temporal graph attention neural network (OCTGAT) for anomaly detection on dynamic graph. OCTGAT aims to integrate the powerful representation capabilities off temporal graph neural networks and the classical one-class objective. Compared with the given benchmarks, OGTGAT achieves significant improvements in the experiments.
Re-ranking Search results based on Relevancy weight: Approach and Evaluation

With the availability of large amounts of information and the variety of data it becomes difficult for the end user to obtain the required data. There is therefore a need to customize all customer searches in order to obtain significant results during the search. This paper gives an approach to construct a personalized web search system for a machine where more than one user accessing search results with variation in interest. To top rank, the relevant outcome based on one's attention, User Identification Based Personalization technique, is adopted where the results obtained from any search engine are re-ranked based on relevancy rate. Re-ranking not only improves the search engine's hit rate but also helps the user to find the desired material more quickly.Further this paper performs the evaluation of the model by comparing it earlier search results. The system proved to be efficient and secure.
More Ada in non-Ada systems

This article is based on the industrial presentation "More Ada in non-Ada systems" which was given at the 2021 Ada-Europe virtual conference.
How to Identify Changing Contexts of Use with Creativity Workshops – An Experience Report

Several software systems struggle with different contexts of use, e.g., due to a huge variety of end-user groups or various application scenarios. In some cases, the use of software systems may change due to external factors such as a pandemic. However, in other cases, software systems are known to be used in various contexts even before they are developed. In this case, it is crucial to consider as many different contexts of use as possible right from the beginning in order to avoid costly changes later on and, above all, user dissatisfaction. However, even when it is known that the software system will be used in different contexts, these contexts are often neither obvious nor easy to identify. We propose to use workshops with stakeholders as a mean to identify different contexts of use. These workshops can be conducted at different points in time, but in this paper, we present a workshop conducted before starting the development phase, that is, during the requirements elicitation phase (to identify the different contexts of use as early as possible). We conducted such a workshop in a large interdisciplinary research project with different institutions from research and practice. In this paper, we present the workshop structure and its results, our experiences, as well as take-aways highlighting how (online) workshops might support the requirements elicitation when different contexts of use are to be expected.