Reviewerly/exHarmony huggingface dataset:
Mineralogical and granulo-chemical characterization of veins 4 and 10, of Ain Mimoun baryte ore mine

Purpose. The purpose of the carried out granulo-chemical and mineralogical study, realised on barite of two veins 4 and 10 of Ain Mimoun deposit (Algeria), is the identification of the barite ore in order to be able to choose a reliable processing method due to its complicated structure. Methodology. The investigation was carried out by X-Ray Diffractions (XRD), Scanning Electron Microscope (SEM), X-Ray Fluorescence (XRF), particle size analysis and microscopic observations. Findings. Using the investigation devices, predominance of barite minerals was found at 51%, quartz in the vicinity of 34% and calcite at 9%, with a homogeneous distribution in all the fractions observed. Adding to that, the main compounds (oxides) present are SiO2, CaO, Fe2O3, Al2O3, P2O5, Na2O, K2O, TiO2, and MnO. Originality. For the first time the characterization of veins 4 and 10, in which the barite particles are embedded in silica, has been realised for the purpose of proving the possibility of their processing. Practical value. The obtained results confirm, on the one hand, that veins 4 and 10 are rich in barite and, on the other hand, barite particle release from silica particles is possible. Thus, we suggest, for a better and diversified use of the ore of the veins in question, the application of the flotation process, since the latter makes it possible to obtain high-quality concentrates, so it can be used not only in the petroleum field but also in the pharmaceutical and other industries.
Role of Knowledge Management in Small and Medium Enterprise Performance

Aim-The aim of this study is to bring a unified framework that integrates Knowledge Management Process (KMP), Innovation Capability(IC), Internal Social Capital(ISC), and Organizational Performance(OP) and empirically explore the framework in the context of SMEs. These small and medium enterprises are highly relevant because of their economic and social importance globally, as they constitute nearly 90% of all firms in OECD countries, 70% of jobs, and 50-60 % of value creation. Design/ methodology/ approach- The study addressed the CEO and managers of Small and medium enterprises with the help of a designed questionnaire. The study being exploratory in nature, and employs Partial Least Square-Structural equation modeling (PLS-SEM) using Smart PLS. Practical Implications- SMEs, compared to large firms' esp. in developing economies, require different strategies to sustain and perform in the long term. Therefore, it is imperative that these enterprises begin processing knowledge management for the sake of overall organizational performance. Originality/ Value- The existing research has explored the phenomenon in the context of large enterprises, and this study explored the comprehensive framework in SMEs. The study extends the previous literature by understanding the role of Internal Social Capital in enhancing organizational performance through the knowledge Management Process.
Proceedings End-to-End Compositional Models of Vector-Based Semantics

We propose a mathematical framework for a unification of the distributional theory of meaning in terms of vector space models, and a compositional theory for grammatical types, namely Lambek's pregroup semantics.A key observation is that the monoidal category of (finite dimensional) vector spaces, linear maps and the tensor product, as well as any pregroup, are examples of compact closed categories.Since, by definition, a pregroup is a compact closed category with trivial morphisms, its compositional content is reflected within the compositional structure of any non-degenerate compact closed category.The (slightly refined) category of vector spaces enables us to compute the meaning of a compound well-typed sentence from the meaning of its constituents, by 'lifting' the type reduction mechanisms of pregroup semantics to the whole category.These sentence meanings live in a single space, independent of the grammatical structure of the sentence.Hence we can use the inner-product to compare meanings of arbitrary sentences.A variation of this procedure which involves constraining the scalars of the vector spaces to the semiring of Booleans results in the well-known Montague semantics.
Forecasting Pneumonia Toddler Mortality Using Comparative Model ARIMA and Multilayer Perceptron

Pneumonia is an inflammatory lung disease that causes the second largest number of deaths in Indonesia after Dengue Hemorrhagic Fever (DHF). In 2021, there was an increase in cases of 7.8% compared to the previous year, and was exacerbated by the Covid-19 pandemic. Predictive methods were needed to predict and compare the ARIMA and MLP methods, where the results of the best methods were selected for long-term forecasting. The research data used was from January 2014 – December 2021, with a total of 96 data. In choosing the best method, the basic error calculations used were Mean Absolute Deviation, Mean Squared Error, and Mean Absolute Percentage Error. This study aims to build a predictive model for the next period of pneumonia under-five mortality. These results can be used for government policy-making related to mortality prevention for the next period. The results showed that the MLP method was superior to ARIMA. Testing 28 mortality rate data using the final test result showed that the best method was MLP, with a hidden layer value of 2.2, a learning rate of 0.3, and an error percentage of 1.27%. The prediction results of the overall mortality rate of pneumonia under five in 2022 was predicted to be 136 people.&#x0D;
Blockchain-enabled Internet of Things (IoTs) platforms for the healthcare sector

In the last decade, world has changed gradually from mechanical to smart digital world. Defense, supply chain, industries, finance, healthcare, education, automation, and more applications are automated by the use of sensors. The world is built like a small village with inclusion of smart devices. Also, the latest developments in information technology and blockchain technology have enhanced market of the electronic healthcare sector. In healthcare, Internet of Things (IoT) provides real-time sensory data from patients to process and analyze, which requires either centralized or decentralized computation, processing or storage. For various sectors including finance, defense, healthcare, and many more, the security concerns during transmission or processing needs are very important. Security concerns include single points of failure, distrust, data tampering, and privacy evasion of IoT platforms. For healthcare systems in particular, maintaining the privacy of data is most important. Hence, security problems require resolution without relying on trusted third parties. Blockchain provides security by exchanging digital tokens and transparency of data among health sector members in a peer-to-peer network, avoiding the requisite of trusting centralized third-party entity. In this chapter, we will survey solutions to security related issues of healthcare data in IoT platforms. Future context of IoT platforms analysis for sensor data scenarios consider different healthcare applications with Blockchain. Finally, we discuss future scope and challenges in the health sector.
Blockchain technology for international trade: beyond the single window system

This chapter considers how supply chains can be made more efficient in order to achieve the objective of maximising the stakeholders’ economic profitability, while meeting the diverse range of customer requirements. The underlying discussion pertains to the role of emergent blockchain technology which is beginning to influence how firms and IT service suppliers improve supply chain agility and flexibility using blockchain. It goes on to discuss the role of blockchain in global supply chain management and cross-border trade and explores the future of supply chains underpinned by blockchain.
Incremental software product line verification

This extended abstract summarizes our eponymous journal paper [1] published first in Empirical Software Engineering (EMSE).
The Economic Impact of Bit Coin Halving Events in The United States

Bit Coin was established as a computerized resource in January 2009, following the overall monetary emergency of 2007-2008. Over 10 years after the fact, there is still no arrangement among market controllers on the characterization, use cases, strategies, and financial ramifications of bitcoin. In any event, there is a rising longing for advanced cash as a government issued money substitute, which would advance monetary development and inclusivity. The standards overseeing advanced resources in a few countries are investigated in this review. It additionally makes sense of how Bit Coin can be utilized to alleviate monetary gamble and work with cross-line trade. Bitcoin purchasers ought to know about this foundational highlight also, since a dividing frequently accompanies a lot of choppiness for the cryptographic money. Sergei Khitrov, organizer and CEО at Listing.Help, noticed that "Coindesk examiners highlighted a flood in Bitcoin exchanging volumes quickly a month prior and a month after each splitting." Bitcoin dividing happens, the stockpile of accessible Bitcoins decreases, in this manner expanding the worth of the Bitcoins yet to be mined. Furthermore, with those changes come the opportunity to benefit.
A Survey on Real Time Sign Language Detector using ML

Abstract: With the rapid growth in the field of object detection, developing a sign language detector as it is the main instrument of correspondence for physically challenged individuals has invoked the need for such a system. Communication via gestures is a shelter for the genuinely provoked individuals to communicate their contemplations and feelings. With the assistance of computer vision and neural organizations, we can recognize the signs and show the respective text as the output. This paper is a survey based on a broad collection of research papers on the related domain to propose an efficient and accurate model with the core functioning of a better response time and real-time detection using various machine-learning algorithms.
Identifying Vulnerabilities and Reducing Cyber Risks and Attacks Using a Cyber Security Lab Environment

Abstract: In the present digital world where everyone is trying to digitalize their data and assets, cyber-attacks and cyber-crimes have made their presence felt much more than when compared to the previous period. During the pandemic days, the number of state sponsored and nation sponsored threat actors who pose the threat of attacking our organizational assets has increased rapidly. Organizations, especially which fall under the critical information infrastructure are becoming an increasingly common target for cyber-attacks. The rate has alarmingly gone up that the national and state government have started to give more attention and focus to cyber security and cyber-crimes. Specific bodies have been assigned by the government to notify about threats/attacks and also to suggest possible techniques to mitigate these threats/attacks. Most devices that are used in these organization are either using older versions of devices or the devices used are not properly cyber secured. This vulnerability is most often taken advantage of by the attacker when trying to access and compromise the organization. Another factor leading to cyber breaches and attacks is the limited or low competency level of employees who work in these organizations. Employees with weak cybersecurity knowledge are the main entry point for online fraud, viruses, and other threats. This creates a situation where business owners and managers should focus on teaching employees the importance of strong digital security. This report describes different techniques and tools which can be used to detect vulnerabilities or threats present in our system, the different cyber-attacks that can take place exploiting these identified vulnerabilities: How to generate an attack, How to identify an attack and How to prevent an attack. Further on, how we can create an environment, where individuals can have hands-on experience on how to detect different cyber threats or vulnerabilities present on a device which makes them confident enough to identify a cyber-attack or threat at an early stage and deploy preventive measures against it is also discussed.
Extended ArmSpeech: Armenian Spoken Language Corpus

The first paper of ArmSpeech presented an annotative native Armenian speech corpus, its data collection, preprocessing and annotation processes, corpus structure and statistics. The main reason for ArmSpeech creation is to increase Armenian language research resources because according to research there are no free or paid Armenian speech corpora for speech-to-text, text-to-speech and language research. From an NLP perspective, the Armenian language is a low-resourced language despite the fact that The Armenian language is an independent branch of the Indo-European language family and the native language of 12-15 million people. ArmSpeech corpus can be used in natural language processing (NLP) research. The first release of the corpus mainly contains audio clips extracted from free-to-use audiobooks. The total duration of audio clips is 11.77 hours. ArmSpeech’s first release corpus includes 6206 audio clips of multiple speakers of any age, gender and accent. This paper intends to present the ArmSpeech extended version, which is a continuation of the previous work, includes an annotated Armenian speech, and the recording process is based on the volunteer’s voice donation principle. The paper also introduces necessary data collection, pre-processing, recording and annotation stages, final results and statistics of the corpus. The material (text) needed for the recording was collected from the articles on Armenian news websites about lifestyle, culture, sport and politics․ Recording was done by 1 female and 3 male volunteers whose native language is Armenian. The total duration of the data included in the second release is approximately 4 hours and along with the first release, the ArmSpeech corpus becomes 15.7 hours.
Distribution and Randomness of the Numbers in √2

This study investigated the distribution and randomness of numbers in square root 2. Rstudio was used in extracting the square root of 2 up to 1,000 digits. The Chi-square test is used for testing the uniformity of the numbers, and the runs test is used for testing the randomness of the numbers. Results show that the distributions of the generated numbers for n = 100 and for n = 1,000 are uniform. The sequence of numbers generated for n = 100 and n = 1,000 appears to be random.
Influence of Emotional Factors (Positive, Negative) on the Usefulness of Product Reviews Based on Big Data

In the era of prosperous online shopping, product reviews play a decisive role in users’ decision to purchase products. At the same time, it can also help businesses understand the corresponding deficiencies, to make targeted improvements. However, users’ comments are full of emotional colors, and these comments with strong emotional tendencies have a greater impact on consumers than ordinary comments, especially those with negative emotions. At present, most text sentiment analysis is oriented at the chapter and sentence level, and there are few sentiment analysis refined to product-specific attributes. Based on this, this paper is aimed at exploring the influencing factors of the emotional tendency of product reviews, analyzing a large number of reviews of a certain mobile phone product, and extracting the keywords of the influencing factors. This article summarizes six key influencing factors. Through the constructed conceptual model of emotional tendencies, it is concluded that the impact of negative emotional comments on consumption is much greater than that of positive emotional comments. Then, there is a further conceptual model of negative affective tendencies. This paper explores the causes of the influencing factors of negative affective tendencies. In this paper, the influencing factors of negative emotional tendencies are subdivided into 10 secondary factors. Through the reliability test, all secondary factors are above 87%. Then, after scoring the negative comment text code, a regression analysis was performed, and it was found that 10 secondary factors were significant, and the corresponding regression model was obtained.
Blockchain as a Solution of Information Security and Data Privacy Issues: Review

The growth of technology has seen development of smart devices that are connected to each other giving rise to device-mesh technology. This has given rise to many owners of these devices sharing data through various web applications such as online marketplaces. The protection of data is paramount for every organization dealing with such data. An evaluation of Blockchain technology as a solution to data privacy is studied. The study concludes that though blockchain is the technology to pursue for securing and protection data, it has numerous challenges and limitations towards data privacy. More research is needed to guarantee an absolute data privacy protection.
Rank-GCN for Robust Action Recognition

We present a robust skeleton-based action recognition method with graph convolutional network (GCN) that uses the new adjacency matrix, called Rank-GCN. In Rank-GCN, the biggest change from previous approaches is how the adjacency matrix is generated to accumulate features from neighboring nodes by re-defining &#x201C;adjacency.&#x201D; The new adjacency matrix, which we call the rank adjacency matrix, is generated by ranking all the nodes according to metrics including the Euclidean distance from the nodes of interest, whereas the previous GCNs methods used only 1-hop neighboring nodes to construct adjacency. By adopting the rank adjacency matrix, we find not only performance improvements but also robustness against swapping, location shifting and dropping of certain nodes. The fact that the human-made rank adjacency matrix wins against the deep-learning-based matrix, implies that there are still some parts that need touch of humans. We expect our Rank-GCN can make performance improvements especially when the predicted human joints are less accurate and unstable.
A Fuzzy Training Framework for Controllable Sequence-to-Sequence Generation

The generation of music lyrics by artificial intelligence (AI) is frequently modeled as a language-targeted sequence-to-sequence generation task.Formally, if we convert the melody into a word sequence, we can consider the lyrics generation task to be a machine translation task.Traditional machine translation tasks involve translating between cross-lingual word sequences, whereas music lyrics generation tasks involve translating between music and natural language word sequences.The theme or key words of the generated lyrics are usually limited to meet the needs of the users when they are generated.This requirement can be thought of as a restricted translation problem.In this paper, we propose a fuzzy training framework that allows a model to simultaneously support both unrestricted and restricted translation by adopting an additional auxiliary training process without constraining the decoding process.This maintains the benefits of restricted translation but greatly reduces the extra time overhead of constrained decoding, thus improving its practicality.The experimental results show that our framework is well suited to the music lyrics generation and restricted machine translation tasks, and that it can also generate language sequence under the condition of given restricted words without training multiple models, thereby achieving the goal of green AI.
Two PKs to Protect LSB Method of Data Steganography

LSB method of data steganography is very popular and easy method of data hiding, but it si not secure. In this research paper a simple and easy modifications will be added to LSB method to increase the security level of data hiding. Two PKs will be used to protect the hidden secret message, the first PK1 will be generated using a selected parameter values to run CLMM, this key will be converted to form a lockup table to apply message encryption-decryption. The second PK2 will be used to apply bits’ substitution of the character binary matrix, the two keys will provide a necessary key space, which can resist hacking attacks, the two keys will be sensitive to any minor changes in the PKs contents, any changes in the PKs values in the message retrieving phase will be considered as a hacking attempt. The proposed method will be implemented using various messages and various covering images, the obtained results will be analyzed, several types of results analysis will be performed to prove the quality, performance, security and sensitivity of the proposed method.
Interactive Media Applications as Learning Aids for Food Preservation Practice Courses for Deaf Vocational Students

This study aims to develop an interactive learning application that helps in providing direction on food preservation practices which are then expected to be a means of delivering material using video tutorials, text and equipped with sign language interpreter videos so that learning becomes more communicative for deaf students. The research method used in this study is the Research and Development (R&amp;D) method. The development procedure used in this research is the Luther-Sutopo method. Subjects in this study focused on deaf students who took food preservation courses. The form of the instrument used in this research is a questionnaire. The results of the study show that at stage The results of the study show that at stage (1) The interactive learning application can help in providing direction on food preservation practices for deaf students. This application contains a means of delivering material in the form of learning tutorial videos equipped with text and added with translator videos using sign language. This application is declared feasible in all aspects either by media experts or deaf students. (2) The results of application trials from media experts obtained the most in the aspect of assessing media integration. The results of the trial obtained from deaf students obtained the highest score was on the artistic and aesthetic aspects and on the aspects of overall functioning.
Utilization of Educational Technology in The Learning Process in The Sub-District of Tebas, Sambas Regency

This study aims to determine the use of educational technology in the learning process for educators in Tebas District, Sambas Regency, the theoretical use of research to contribute to the development and use of educational technology and practically to be able to apply the development of educational technology today. The variables in this study are Utilization of educational technology in the learning process" with indicators of learning models, learning strategies, learning methods, learning media and learning outcomes. The research method uses descriptive verification method in the form of case study research. The results of the study showed that the models, strategies, methods and learning media as well as learning resources most of the educators had mastered and could be applied, and it should be noted that the facilities and infrastructure that can support the use of educational technology such as internet facilities, wifi and equipment to support the implementation of learning when they want to implementing educational technology in delivering lesson messages and the lack of availability of learning resources in supporting the smooth process of using technology other than the teacher concerned.