Reviewerly/exHarmony huggingface dataset:
8 Rights Issues in the Digitization of Library Collections

This chapter addresses the copyright issues that might arise in the dig­itization of materials held in academic library collections. Developing a clear understanding of the copyright status of digitized materials can be complicated, frustrating, and even at times, with orphan works, impossible, leading to the copyright conundrum. Nonetheless, when curating and managing digitized library collections, a copyright review and management plan should be followed to maximize the availability of open access and public domain items and min­imize the confusion of researchers and the public when using such digitized objects. A copyright review protocol, or a map of how to proceed through the copyright analysis, will aid librarians in making decisions regarding what rights metadata to include for a digital work and will, in turn, aid patrons in determin­ing how digitized works can be effectively used.
The Expression of Hate in Hate Speech

ABSTRACT In this article, I argue that hate speech expresses hate, and I answer some objections to expressivist views. First, I briefly comment on some limitations of pragmatic accounts of harmful speech. I then present an expressive‐normative view of derogatory discourse according to which it is expressive of an affective state by presupposing it. A linguistic act expressive of an affective state inherits the normativity that is constitutive of that state, as directed to its intentional object. If the act is successful, it updates the conversational context with the normative appraisal conditions of the affective state presupposed. I argue that this model can be applied to hate speech. I rely on current research on the psychology of hate to identify the appraisal conditions, action tendencies, and motivational goals characteristic of hate. The account supplements other pragmatic accounts of hate speech.
Sensechain: Sense Contracts, Sense Currency

Time is the most important asset of any living person on our planet. The presence of a digital personal financial and economic environment, decentralized to each of its users, would significantly change the quality and standard of living of this user.The main unit of measurement of the value of an individual user of the environment should be the hours (minutes) spent by him on the execution of any sense contract.Our international team proposes a practical implementation of such an environment using the logic of the new mathematical theory for artificial intelligence Sense Theory [1].
Synthesis and application of amberlite XAD-16 diphenyl carbazide adsorbent chelating resin for solid-phase extraction of Cu (II) and Pb (II) ions from river water

Today, Heavy metals are the incontrollable question of magnitude and ecological significance due to their high toxicity and ability to accumulate in living organisms. This study is likely to assess the pollution grade of the Cikapundung River in Bandung city and the extent to which river water is exposed to heavy metals hence, the safety levels of water users. This research aimed to synthesize and apply the adsorbent AXAD-16-DPC chelating resin to copper and lead-containing solution. To determine dissolved heavy metal (Cu and Pb ions) levels in the water Cikapundung River using laboratory instruments to pre-concentrate the sample using a solid-phase extraction method. Analysis of heavy metals in the water sample was carried out using flame atomic absorption spectrometry (FAAS) Solid-phase extraction was used to pre-concentrate trace metal as it attains a high concentration factor, and potentially interfering matrices may be removed. The clear route is based on the retention of the targeted analytes on a mixed chelating organic ligand, 1,5-diphenyl carbazide (DPC) with polystyrene-divinyl benzene, a co-polymer Amberlite XAD-16 resin matrix placed inside cartridge column tube. The composite chelating resin was found to be successful as it can adsorb Cu (II) and Pb (II) ions with a high percentage of recovery around 101.20% for Pb (II) and 96% for Cu (II), and the reusability of resin is possible as RSD <5%. Fourier transform infrared (FTIR) spectroscopy was used to characterize the chelating resin, and 2 M Nitric acid was the best eluent. Moreover, water quality properties in terms of its physicochemical parameters assessed were Temperature (24.67 °C), pH (7.66) of sample solution, Conductivity 259 ɱS/cm), Turbidity (130) and DO (3.80 mg/L).
A Probabilistic Model for Effective Explainability Based on Personality Traits

It is becoming increasingly important for an autonomous system to be able to explain its actions to humans in order to improve trust and enhance human-machine collaboration. However, providing the most appropriate kind of explanations – in terms of length, format and presentation mode of explanations at the proper time – is critical to enhancing their effectiveness. Moreover, since explanation entails costs, such as the time it takes to explain and for humans to comprehend and respond, the actual improvement in human-system tasks from explanations (if any) is not always obvious, particularly given various forms of uncertainty in knowledge about the human. In this paper, we describe an approach to address this issue. The key idea is to provide a structured framework that allows a system to model and reason about human personality traits as critical elements to guide proper explanation in human and system collaboration. In particular, we focus on the two concerns of modality and amount of explanation in order to optimize the explanation experience and improve overall system-human utility. Our models are based on probabilistic modeling and analysis to determine at run time what is the most effective explanation under uncertainty. To demonstrate our approach, we introduce a self-adaptative system called the Stock Prediction Engine (SPE), which allows an automated system and a human to collaborate on stock investments. Our evaluation of this exemplar through simulation demonstrates that a human subject’s performance and overall human-system utility is improved when considering the psychology of human personality traits in providing explanations.
A Machine Intelligence Based Approach for the Classification of Human Face with Mask and without Mask

The importance of face mask (FM) is a major concern for the entire human society in the current circumstances. All peopleshould wear FM in order to lower the chance of infection due to several diseases. It is very much essential to track the peoplewho have not worn the FM in different crowded places, so that warning can be given to them to wear FM in order to lower thespread of infection of different diseases. So, the classification of human face images (HFIs) into human face with mask images(HFWMIs) and human face without mask images (HFWOMIs) types is an essential requirement in this situation. In this work, amachine intelligent (MI) based approach is proposed for the classification of HFIs into HFWMIs and HFWOMIs types. Theproposed approach is focused on the stacking (hybridization) of Logistic Regression (LRG), Support Vector Machine (SVMN),Random Forest (RFS) and Neural Network (NNT) methods to carry out such classification. The proposed method is comparedwith other machine learning (ML) based methods such as LRG, SVMN, RFS, NNT, Decision Tree (DTR), AdaBoost (ADB),Naïve Bayes (NBY), K-Nearest Neighbor (KNNH) and Stochastic Gradient Descent (SGDC) for performance analysis. Theproposed method and other ML based methods have been implemented using Python based Orange 3.26.0. In this work, 200HFWMIs and 200 HFWOMIs are taken from the Kaggle source. The performance of all the methods is assessed using theperformance parameters such as classification accuracy (CA), F1, Precision (PR) and Recall (RC). From the results, it is found that the proposed method is capable of providing better classification results in terms of CA,F1,PR and RC ascompared to other ML based methods such as LRG, SVMN,RFS, NNT,DTR,ADB, NBY, KNNHand SGD.
Improved Deep Learning Method for Intelligent Analysis of Sports Training Posture

Motion recognition based on human bones has attracted extensive attention in recent years because of its simplicity and robustness. Considering the causality of human movement, this paper proposes an improved deep learning method for posture analysis in sports training. In order to deal with the complex situation of calculating joint torques as weights, the edge weights and convolution weights of bone maps are used as auxiliary information networks according to the causality of joint distribution. Thus, the stronger driving force of joint weights in the neural network is improved, the low importance of joint attention is reduced, and the high importance of joint attention is enhanced. Experiments on three public motion recognition datasets show that the proposed method can distinguish similar motions effectively compared with the mainstream methods. Besides, experiments on a challenging UCF (University of Central Florida) sports dataset show that the proposed method can effectively enhance the motion features and improve the accuracy of recognition.
The Effect of the Internet on Inflation: A Research on ASEAN-5 Countries

Information and communication technologies have become widespread with the onset of globalization, affecting almost every facet of human life. Increasing internet usage has made accessing information swift and easy. The internet has also had a significant economic impact and provided financial benefits to nations all around the world to increase productivity and efficiency and reduce costs. Customers had been able to access products at lower prices as a result of the reduction in market entry barriers and search costs, which led to an increase in competition in the markets. The research aimed to investigate the effect of the internet on inflation in ASEAN-5 countries (Indonesia, Malaysia, Philippines, Singapore, and Thailand). Consumer prices (annual %) were used as an indicator of inflation, and individuals using the internet (% of population) were used as an indicator of internet usage. Control variables such as unemployment rate, real interest rate, energy use and money growth were also included in the research. Panel data analysis was performed using the data of ASEAN-5 countries covering the period of 1994-2014. Pooled least squares method (Pooled OLS) was applied to obtain an estimation of the model. As a result, it is found that as internet usage increases, inflation rates decrease in ASEAN-5 countries.
GAME EDUKASI PENGEMBANGAN GAME EDUKASI I’TIKAD 50 BERBASIS POWER POINT DI DAYAH AL-FURQAN ACEH BESAR

This study is to answer the problem of developing power point-based i'tikaf 50 educational games in Dayah Al-furqan Aceh Besar because students are not very interested in lecture or conventional learning systems. Therefore, an educative and interesting game was made in learning I'tiqad 50. The method used in the research was R&amp;D (Research and Development) or research development. The results of the validation test by the validator were 16 respondents with the aspect of material assessment getting a score. as much as 77.2%, and the media assessment aspect got a score of 78.2%. The results of the implementation of the I'tikad 50 educational game learning media which were applied to the control class (KK) in Dayah Al-furan obtained an average score of 89.3 and the traditional (conventional) learning outcomes were applied to the ordinary class (KB) in Dayah Al -furqan obtained an average score of 74.6.
SISTEM MONITORING JARINGAN REALTIME BERBASIS INTERNET CONTROL MESSAGE PROTOCOL

Network monitoring in the company is more extensive and complex, each device must be connected to the network so that there are no obstacles when there is receiving and sending data from various devices. Problems on computer networks cannot be predicted when they occur and continuous monitoring is needed to ensure the network is connected properly and it is difficult to troubleshoot network damage. Therefore, a network monitoring system is needed to check the network so that administrators can find out network conditions. By using the Internet Control Message Protocol to check the network continuously so that it can find out the condition of the device, taking into account the latency and data obtained by Packet Internet Gopher. Network checks and notifications use a backdoor working system or through a background process, control the process is also a network check log to find out which devices are often disconnected and to make network troubleshooting easier. In this paper, will be building a network monitoring system that displays network status and monitors the network continuously with telegram notifications, network logs to facilitate troubleshooting, Internet Protocol management and backdoor system settings by the name of SIMONIT.
Information System Design of financial bookkeeping at UD Dewi Sri

The current condition of the rice milling business is getting tougher. Price competition between rice milling companies and local rice price competition with imported rice makes companies need to improve efficiency to survive in business competition. Various ways are done to increase efficiency, including utilizing conceptual resources (information) to manage the company's physical resources. This research designs a financial bookkeeping information system which is the initial stage of the development stage of an integrated information system at UD. Dewi Sri. The first step is to search and collect company profile data for company workflows. After the objects are identified and the relationship between the objects is known, the next step is to make a data flow diagram for each process. The diagram is used to create entity relationship diagrams (Entity Relationship Diagrams) and programming flows. After writing the programming language, initial testing is carried out by entering sample data. The result of this research is a computer-based financial bookkeeping information system application design that is expected to be able to be applied in companies and can be developed toward an integrated information system.
SISTEM INFORMASI ASET SEKOLAH BERBASIS WEBSITE PADA SMK NEGERI 4 BANDA ACEH

Website-Based School Asset Information System is a system that provides reports on borrowing goods, condition of goods, number of goods and records of borrowing or transferring assets that are currently implementing a data processing system in the form of Microsoft Excel and also bookkeeping for recording borrowed goods. This can be very time consuming, effort and also costly. As technology develops, it is time to switch to manual methods of asset maintenance using technology, an Asset Information System must be developed so that assets are more easily monitored and so that if assets are lost or damaged it is easy to check them. The development of this system uses the UML (Unified Modeling Language) model, including use case diagrams, flowcharts and Table Relations. This research uses the R&amp;D method and also uses the waterfall development method, PHP programming and MySQL database.
PERANCANGAN DAN PENGEMBANGAN SISTEM INFORMASI ELECTRONIC ABSENSI (E-ABSENSI) UNTUK SISWA/SISWI DISEKOLAH MENENGAH ATAS (SMA) NEGERI 1 MEUKEK KABUPATEN ACEH SELATAN BERBASIS WEB

As a form of success to the learning system in the world of education can be further improved by the existence of an effective information system technology. As for the form of information system activities in the world of education, one of them is absenteeism activities in schools, which is a proof of attendance. However, there are some schools that still use absenteeism or attendance lists of students who are still manual. One of them in SMAN 1 Meukek still uses absenteeism manually. The purpose of designing this web-based e-absence is to be easy in the process of application takes place, so that it suits the needs in SMAN 1 Meukek. System development methods use the Waterfall method. System testing methods use the System Usability Scale (SUS) method. The results of the final assessment evaluation conducted on the E-absentee system of respondents received a score of 80.2 which has a Grade Scale B with Adjective Ratings included in the Good category. So that this web-based E-absentee system can be used easily by teachers in terms of student application.
PREDIKSI KELULUSAN JALUR MASUK PERGURUAN TINGGI BANDA ACEH (STUDY KASUS MAHASISWA BARU TAHUN AJARAN 2019 ) MENGGUNAKAN ALGORITMA SUPPORT VECTOR MACHINE (SVM)

Nowadays there are several types of entry points for new student admissions at tertiary institutions. There are many ways that every prospective student can prepare to pass the PTN entrance selection exam. Therefore the research aim to predict the graduation of tertiary entrance by classification method using the SVM algorithm (Support Vector Machine) which is assisted by WEKA machine learning, using data of new students in the 2019 school year. The final results in this study there are two variables that have the most relationship both the PTN entry selection tutoring variable and the interest pathway variable, with the PTN entry selection tutoring variable having a Pearson correlation value of -0.180 ** and a significance value of 0.002, the interest pathway having an accuracy value of 0.311 ** and a significance value of 0,000. Then based on the results of cross-validation testing and percentage split SVM algorithm has very good accuracy with an average accuracy of 99% with an AUC (Area Under Curve) value of 0.9907
Action Bar Adaptations for One-Handed Use of Smartphones

One-handed use of smartphones is a common scenario in daily life. However, use of smartphones with thumb gives limited reachability to the complete screen. This problem is more severe when targets are located at corners of the device or far from the thumb's reachable area. Adjusting screen size mitigates this issue by making screen UI to be at the reach of the thumb. However, it does not utilize available screen space. We propose UI adaptation for action bar to address this. With our results, designed adaptations are faster for non-dominant hand and provides significantly better grip stability for holding smartphones. Intriguingly, users perceived our system as faster, more comfortable and providing safer grip when compared with the existing placement of action bar. We conclude our work with video analyses for grip patterns and recommendations for UI designers.
Spatial Temporal Graph Attention Network for Skeleton-Based Action Recognition

It's common for current methods in skeleton-based action recognition to mainly consider capturing long-term temporal dependencies as skeleton sequences are typically long (>128 frames), which forms a challenging problem for previous approaches. In such conditions, short-term dependencies are few formally considered, which are critical for classifying similar actions. Most current approaches are consisted of interleaving spatial-only modules and temporal-only modules, where direct information flow among joints in adjacent frames are hindered, thus inferior to capture short-term motion and distinguish similar action pairs. To handle this limitation, we propose a general framework, coined as STGAT, to model cross-spacetime information flow. It equips the spatial-only modules with spatial-temporal modeling for regional perception. While STGAT is theoretically effective for spatial-temporal modeling, we propose three simple modules to reduce local spatial-temporal feature redundancy and further release the potential of STGAT, which (1) narrow the scope of self-attention mechanism, (2) dynamically weight joints along temporal dimension, and (3) separate subtle motion from static features, respectively. As a robust feature extractor, STGAT generalizes better upon classifying similar actions than previous methods, witnessed by both qualitative and quantitative results. STGAT achieves state-of-the-art performance on three large-scale datasets: NTU RGB+D 60, NTU RGB+D 120, and Kinetics Skeleton 400. Code is released.
An Empirical Evaluation of Competitive Programming AI: A Case Study of AlphaCode

AlphaCode is a code generation system for assisting software developers in solving competitive programming problems using natural language problem descriptions. Despite the advantages of the code generating system, the open source community expressed concerns about practicality and data licensing. However, there is no research investigating generated codes in terms of code clone and performance. In this paper, we conduct an empirical study to find code similarities and performance differences between AlphaCode-generated codes and human codes. The results show that (i) the generated codes from AlphaCode are similar to human codes (i.e., the average maximum similarity score is 0.56) and (ii) the generated code performs on par with or worse than the human code in terms of execution time and memory usage. Moreover, AlphaCode tends to generate more similar codes to humans for low-difficulty problems (i.e., four cases have the exact same codes). It also employs excessive nested loops and unnecessary variable declarations for high-difficulty problems, which cause low performance regarding our manual investigation. The replication package is available at https:/doi.org/10.5281/zenodo.6820681
A Scalable, Interpretable, Verifiable &amp; Differentiable Logic Gate Convolutional Neural Network Architecture From Truth Tables

We propose $\mathcal{T}$ruth $\mathcal{T}$able net ($\mathcal{TT}$net), a novel Convolutional Neural Network (CNN) architecture that addresses, by design, the open challenges of interpretability, formal verification, and logic gate conversion. $\mathcal{TT}$net is built using CNNs' filters that are equivalent to tractable truth tables and that we call Learning Truth Table (LTT) blocks. The dual form of LTT blocks allows the truth tables to be easily trained with gradient descent and makes these CNNs easy to interpret, verify and infer. Specifically, $\mathcal{TT}$net is a deep CNN model that can be automatically represented, after post-training transformation, as a sum of Boolean decision trees, or as a sum of Disjunctive/Conjunctive Normal Form (DNF/CNF) formulas, or as a compact Boolean logic circuit. We demonstrate the effectiveness and scalability of $\mathcal{TT}$net on multiple datasets, showing comparable interpretability to decision trees, fast complete/sound formal verification, and scalable logic gate representation, all compared to state-of-the-art methods. We believe this work represents a step towards making CNNs more transparent and trustworthy for real-world critical applications.
On the evolution of research in hypersonics: application of natural language processing and machine learning

Research and development in hypersonics have progressed significantly in recent years, with various military and commercial applications being demonstrated increasingly. Public and private organizations in several countries have been investing in hypersonics, with the aim to overtake their competitors and secure/improve strategic advantage and deterrence. For these organizations, being able to identify emerging technologies in a timely and reliable manner is paramount. Recent advances in information technology have made it possible to analyze large amounts of data, extract hidden patterns, and provide decision-makers with new insights. In this study, we focus on scientific publications about hypersonics within the period of 2000-2020, and employ natural language processing and machine learning to characterize the research landscape by identifying 12 key latent research themes and analyzing their temporal evolution. Our publication similarity analysis revealed patterns that are indicative of cycles during two decades of research. The study offers a comprehensive analysis of the research field and the fact that the research themes are algorithmically extracted removes subjectivity from the exercise and enables consistent comparisons between topics and between time intervals.