Reviewerly/exHarmony huggingface dataset:
Updating the Authentication of Digital Evidence in the International Criminal Court

Abstract This paper discusses the issue of authenticity of digital evidence in the icc . While some types of digital evidence have been used at the icc , some challenges persist. icc Chambers have used old methods of verifying the authenticity of physical documents to analyze pieces of digital evidence. These practices do not fully grasp the issue of authenticity in relation to a piece of digital evidence, which may require specific practices and technologies. This paper discusses how icc judges can update its practices in order to address such issue. With an ever-growing use of digital evidence in its courtroom, it is essential for icc Judges to develop solid practices that allow them to fully examine the authenticity of a piece of digital evidence. This paper claims that such adaptations may require capacity-building for judges, as well as changes in the evidence management system and in the icc legal framework.
Learning Whenever Learning is Possible: Universal Learning under General Stochastic Processes

This work initiates a general study of learning and generalization without the i.i.d. assumption, starting from first principles. While the traditional approach to statistical learning theory typically relies on standard assumptions from probability theory (e.g., i.i.d. or stationary ergodic), in this work we are interested in developing a theory of learning based only on the most fundamental and necessary assumptions implicit in the requirements of the learning problem itself. We specifically study universally consistent function learning, where the objective is to obtain low long-run average loss for any target function, when the data follow a given stochastic process. We are then interested in the question of whether there exist learning rules guaranteed to be universally consistent given only the assumption that universally consistent learning is possible for the given data process. The reasoning that motivates this criterion emanates from a kind of optimist's decision theory, and so we refer to such learning rules as being optimistically universal. We study this question in three natural learning settings: inductive, self-adaptive, and online. Remarkably, as our strongest positive result, we find that optimistically universal learning rules do indeed exist in the self-adaptive learning setting. Establishing this fact requires us to develop new approaches to the design of learning algorithms. Along the way, we also identify concise characterizations of the family of processes under which universally consistent learning is possible in the inductive and self-adaptive settings. We additionally pose a number of enticing open problems, particularly for the online learning setting.
New computational hybrid three-wavelength-based approach of phase shifting for analyzing the stress field in photoelasticity studies

Digital photoelasticity is used for evaluating the stress in loaded bodies. However, when dynamic analyses are needed, the motions of optical elements are an experimental challenge. This new computational hybrid approach calculates the stress field by extract the phase steps from RGB color channels of a photoelastic color image. Our approach integrated the load stepping strategy with a computational hybrid phase algorithm, hence only bright field images are required. Although, our method has a lower performance than phase shifting methods evaluated, the principal advantage of this hybrid strategy is that only a color- image is required to analyze stress field, avoided capture multiple images for analyzing phase maps.
Sign in with Face Recognition and Gesture Recognition Based on Raspberry Pi

In today's society, artificial intelligence has gradually penetrated into people's daily life, and its fields have become more and more extensive, with many breakthroughs in face recognition. The system realizes a simple sign-in function. It is a sign-in system with face recognition and gesture recognition, which is based on deep learning using Raspberry Pi and Baidu Smart Cloud Platform. Raspberry Pi and python were used for research during the experiment. Python's Face_recognition Library is called and gesture recognition is achieved by using the gesture recognition API of Baidu Smart Cloud Platform. Finally, we realize sign-in with face recognition and gesture recognition.
Privacy and data protection in the time of pandemic

How does the Pandemic bring about the challenges to privacy and data protection? Is there anything we should be worried about? The isolation, restricted movement, disease control measures, tracking apps, contact tracing, work-from-home, online learning… and the list goes on. All these dots when connecting to each other will reveal that privacy concern is real and its protection can be a critical game-changer. Just in time to test the efficiency of the Personal Data Protection law. This is the gist of my keynote presented at the Sixth International Conference on Internet Applications, Protocols and Services (NETAPPS2020). This International Conference is held by the Computing School of the Universiti Utara Malaysia (UUM).
Language Complexity Trade-Offs Revisited

In this paper we revisit the results that originally appeared in Coloma (2015) and Coloma (2017), using a newly-assembled database of 50 languages for which we have the same text (which is the fable known as “The North Wind and the Sun”). Most conclusions of the original papers remain the same, especially the ones that signal the existence of language complexity trade-offs. This is particularly clear when we look at partial correlation coefficients between three linguistic ratios (phonemes per syllable, syllables per word, and words per clause), when we use simultaneous-equation regression methods, and when we estimate different versions of Menzerath law that relate phonemes per word and words per clause.
Trends of print books usage among academic communities

AbstractThe purpose of the study is to examine usage patterns of print books in terms of checked out by subject, publication date, faculty, user categories and months using ‘Transaction Log Method’. Checked out books log data for one year was collected through library information system (MLIMS) taking University of the Punjab’s central library as a sample. Findings depict that undergraduate and graduate students checked out most of the books, while academic and administrative staff checked out least number of books. Library users from the faculty of Education, Science and Law checked out most of the books. Results also showed that Literature, Social Science and Religion books were mostly checked out as compared to Natural Science and Technology books. The study is unique in Pakistani scenario as no previous study investigated use patterns of print books in a university setting using log data of the checked out books. The findings are consistent with other studies from the developed countries.
The Accuracy Improvement of Text Mining Classification on Hospital Review through The Alteration in The Preprocessing Stage

Sentiment analysis is a part of text mining used to dig up information from a sentence or document. This study focuses on text classification for the purpose of a sentiment analysis on hospital review by customers through criticism and suggestion on Google Maps Review. The data of texts collected still contain a lot of nonstandard words. These nonstandard words cause problem in the preprocessing stage. Thus, the selection and combination of techniques in the preprocessing stage emerge as something crucial for the accuracy improvement in the computation of machine learning. However, not all of the techniques in the preprocessing stage can contribute to improve the accuracy on classification machine. The objective of this study is to improve the accuracy of classification model on hospital review by customers for a sentiment analysis modeling. Through the implementation of the preprocessing technique combination, it can produce a highly accurate classification model. This study experimented with several preprocessing techniques: (1) tokenization, (2) case folding, (3) stop words removal, (4) stemming, and (5) removing punctuation and number. The experiment was done by adding the preprocessing methods: (1) spelling correction and (2) Slang. The result shows that spelling correction and Slang method can assist for improving the accuracy value. Furthermore, the selection of suitable preprocessing technique combination can fasten the training process to produce the more ideal text classification model.
How Java Works

Java is a unique programming language due to the “write once, run anywhere” mantra that was part of the original vision. How it makes this vision come to life is important to understanding how you work as a programmer.
Vyacheslav Vsevolodovich Ivanov about Semiotics, Languages of the Brain and History of Ideas

The interviews with one of the founders of the Moscow-Tartu school, Vyacheslav Vsevolodovich Ivanov (1929–2017) from August 2010 and from October 2012, describe V. V. Ivanov’s opinions on several scholars (Evgenij Polivanov, Mikhail Bakhtin, Andrej Kolmogorov, Nikolaj Marr etc.) and their work, on his relationships with his father Vsevolod Ivanov, as well as V. V. Ivanov’s views on the past and future of sciences, with some emphasis on neurosemiotics, zoosemiotics, semiotics of culture, cybernetics, history of linguistics, study and protection of small languages. The first interview also deals with V. V. Ivanov’s book Even and Odd; the second interview discusses the key notions of semiotics: sign, the very definition of semiotics, its connections with other disciplines, as well as V. V. Ivanov’s main achievements in the field of semiotics.
The Ridgelet Prior: A Covariance Function Approach to Prior Specification for Bayesian Neural Networks

Bayesian neural networks attempt to combine the strong predictive performance of neural networks with formal quantification of uncertainty associated with the predictive output in the Bayesian framework. However, it remains unclear how to endow the parameters of the network with a prior distribution that is meaningful when lifted into the output space of the network. A possible solution is proposed that enables the user to posit an appropriate Gaussian process covariance function for the task at hand. Our approach constructs a prior distribution for the parameters of the network, called a ridgelet prior, that approximates the posited Gaussian process in the output space of the network. In contrast to existing work on the connection between neural networks and Gaussian processes, our analysis is non-asymptotic, with finite sample-size error bounds provided. This establishes the universality property that a Bayesian neural network can approximate any Gaussian process whose covariance function is sufficiently regular. Our experimental assessment is limited to a proof-of-concept, where we demonstrate that the ridgelet prior can out-perform an unstructured prior on regression problems for which a suitable Gaussian process prior can be provided.
A Study on Federated Cloud Computing Environment

Federated cloud computing is the advancement in the area of the general cloud computing paradigm. In a federated cloud environment, multiple cloud service providers share their computing assets, servers, and various facilities to fulfill customer demands. Federated cloud computing terminology consists of the aggregation of services considered by interoperability characteristics and creates the integration of several cloud service providers regardless of any geographical location. It improves the performance, utilization of facilities, minimizes response time and pricing model by partial subcontracting various computing resources and facilities from the nearby cost-efficient province. Customers also get profited from service level agreements signed between the cloud service providers through intermediator cloud brokers. This work aims to survey the federated cloud environment, its various architectural types, advantages associated with the federation, challenges associated with a federated cloud environment, and future research directions in the federated cloud computing research area.
WideCaps: a wide attention-based capsule network for image classification

The capsule network is a distinct and promising segment of the neural network family that has drawn attention due to its unique ability to maintain equivariance by preserving spatial relationships among the features. The capsule network has attained unprecedented success in image classification with datasets such as MNIST and affNIST by encoding the characteristic features into capsules and building a parse-tree structure. However, on datasets involving complex foreground and background regions, such as CIFAR-10 and CIFAR-100, the performance of the capsule network is suboptimal due to its naive data routing policy and incompetence in extracting complex features. This paper proposes a new design strategy for capsule network architectures for efficiently dealing with complex images. The proposed method incorporates the optimal placement of the novel wide bottleneck residual block and squeeze and excitation Attention Blocks into the capsule network upheld by the modified factorized machines routing algorithm to address the defined problem. This setup allows channel interdependencies at almost no computational cost, thereby enhancing the representation ability of capsules on complex images. We extensively evaluate the performance of the proposed model on the five publicly available datasets, namely the CIFAR-10, Fashion MNIST, Brain Tumor, SVHN, and the CIFAR-100 datasets. The proposed method outperformed the top-5 capsule network-based methods on Fashion MNIST, CIFAR-10, SVHN, Brain Tumor, and gave a highly competitive performance on the CIFAR-100 datasets.
Religion and Financial Development and Structure

This study examines whether religion influences the financial development and structure of a country. In a sample of 46 countries between 1996 and 2015, we find that religiosity fosters financial development and a market-based financial structure, whereas religious hierarchy and moderate religious heterogeneity induce financial underdevelopment and a bank-based structure. Furthermore, these religious effects are more pronounced for countries with a weaker contractual environment, supporting the ameliorating role of social trust in a contractual environment. Overall, our study provides the first evidence that religion has a crucial impact on financial development and structure by affecting social trust. (JEL G10, G21, O16, Z12)
Examining Anti-Corruption Disclosures, Profitability and Financial Stability Among Extractive Firms in Africa

This study examines the determinants of anti-corruption disclosures and the effect of disclosures on the profitability and financial stability of extractive firms in Africa. The study also tests the presence of convergence in anti-corruption disclosure, profitability, and financial stability of the firms. The study uses an unbalanced panel data of 27 firms operating in 5 African countries covering the period 2006 to 2018. The data was collected from different corporate reports of the firms that form part of the Global Reporting Initiative (GRI) database. The study uses a composite index to measure overall disclosure and individual items are coded as binary. Profitability is measured using the return on asset and return on equity while the z-score measures financial stability. The study uses panel logistic regression for binary dependent variables and panel corrected standard error regression/random effect regression for continuous variables. The results indicate that the determinants of anti-corruption disclosure are: membership in the United Nations Global Compact (UNGC) and Extractive Industry Transparency Initiative, multinational enterprise status, corruption perception index, and human development index. Specifically, UNGC membership and multinational status enhance the disclosure on corruption analysis while countries with a high prevalence of corruption have firms that report more on corruption analysis. Disclosure on corruption training is high among firms that are UNGC signatories, countries with a high human development index, and countries with a high prevalence of corruption. There is a weak effect of firm-level, industry-level, and country-level factors on disclosures on corruption response. There is evidence that corruption disclosure reduces the financial stability of firms. Disclosures on corruption analysis and corruption training are the main factors driving the reduction in financial stability. The effect on profitability is not significant except in the case of disclosure on corruption response which also reduces profitability. There is strong statistical evidence to suggest that anti-corruption disclosure, profitability, and financial stability of extractive firms converge. The study has relevant implications for practitioners, policymakers, and the academic community. It is very beneficial to the GRI Secretariat and other sustainability standard setters. The study uses data that is skewed towards large extractive firms. This study is premier in exploring the nexus between anti-corruption disclosure, profitability, and financial stability in extractive firms in Africa. It is also unique in providing a test of both beta and sigma convergence among the firms.
Wearable Sensors: Fundamentals, Implementation and Applications, 2nd ed.

<bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">This text is</b> a well-referenced fairly comprehensive overview of the current state of the art involving wearable sensors and relevant applications in (mainly) human health monitoring in applications from medicine to sports. The text is organized into six sections containing a total of 22 chapters contributed by some 78(!) researchers from 13 countries. It is aimed at “Practicing engineers in the area of medical and wearable devices, academic researchers, undergraduate and graduate students.” A review of the text, by sections, follows.
Language Models Application in Sentiment Attitude Extraction Task

Large text can convey various forms of sentiment information including the author’s position, positive or negative effects of some events, attitudes of mentioned entities towards to each other. In this paper, we experiment with BERT based language models for extracting sentiment attitudes between named entities. Given a mass media article and list of mentioned named entities, the task is to ex tract positive or negative attitudes between them. Efficiency of language model methods depends on the amount of training data. To enrich training data, we adopt distant supervision method, which provide automatic annotation of unlabeled texts using an additional lexical resource. The proposed approach is subdivided into two stages FRAME-BASED: (1) sentiment pairs list completion (PAIR-BASED), (2) document annotations using PAIR-BASED and FRAME-BASED factors. Being applied towards a large news collection, the method generates RuAttitudes2017 automatically annotated collection. We evaluate the approach on RuSentRel-1.0, consisted of mass media articles written in Russian. Adopting RuAttitudes2017 in the training process results in 10-13% quality improvement by F1-measure over supervised learning and by 25% over the top neural network based model results.
Research on stability and accuracy of the OptiTrack system based on mean error

OptiTrack system was the most advanced optical motion capture system in recent years. Camera calibration was an indispensable part of optical motion capture. Its function was to convert 2D data obtained by each camera into spatial 3D data by multiple cameras. The mean error reflected the numerical relationship between the system coordinates and the world coordinates of the measurement area, so the mean error was related to the measurement accuracy of OptiTrack system directly. In this paper, the influence of average calibration error on measurement stability and accuracy was obtained by repeated calibration and sampling under control variables
Human Capital Investment and Economic Growth in Nigeria: 1989-2019

Human capital investment has been identified as the increase in the potential of an individual through education, knowledge, skills and health acquired overtime which enhances his ability to be more productive in the society. It is believed that the more productive a people are, the more developed the economy, having been impacted by the increased productivity of the workforce. As its main objective, this paper examined the relationship between human capital investment and economic growth with Nigeria in focus and between the period 1989 – 2019. The paper used secondary sources of information for the study. Data obtained within the scope of the study and the variables were within the period of investigation. It adopted the unit root, the dynamic ordinary least squares, as well as the Error Correction Model (ECM) to test the short and long run relationship of the variables selected for the study. Between human capital investment and economic growth in Nigeria, the results showed a strong relationship. Based on the result, the study recommended that government prioritize education and health to assure the socio-economic well being of the people. As a corollary to the above, government at all tiers should increase annual budgetary allocation to these sectors. Government should also intensify efforts in economic and social orientation to mobilize and inculcate sanitary and health culture in the people.
Pendampingan peningkatan profesionalitas guru Bahasa Indonesia melalui literasi menulis artikel di jurnal ilmiah

Teachers are currently required to produce scientific works, especially writing scientific articles. The purpose of this community service is to improve teacher professionalism literacy in writing scientific articles. The method applied in community service activities is descriptive qualitative through training and mentoring activities intended for teachers of the Indonesian Language Senior High School Subject Teacher Consultation (MGMP) in Sukoharjo. The implementation method in community service is carried out through the following stages: 1) preparation (observations and action plans); 2) implementation; 3) evaluation and feedback; 4) assistance in compiling scientific articles. The results of this training include a positive response from the trainees and an increase in the skills of teachers in literacy in writing scientific articles. The final results showed that of the 30 participants who participated in the mentoring, there were ten manuscripts of scientific articles for Indonesian language teachers in Sukoharjo adapted to the scientific journal templates to be addressed. The journals targeted include Klitika Journal; Scientific Journal of Indonesian Language and Literature Education, Univet Bantara; Literature Journal; Journal of Language Education and Teaching Sebelas Maret University; TABASA Journal; Indonesian Language Tadris Scientific Journal IAIN Surakarta; and the Linguistic Journal; PGRI Madiun University. If a percentage of 33% of participants succeeded in submitting in achieving the goal of publishing in the desired scientific journal, as many as 45% succeeded in converting the PTK report into articles, the remaining 22% were still struggling with revisions of converting reports into articles. It can be concluded that, in total, 78% of the total number has shown maximum progress in the mentoring process.