Reviewerly/exHarmony huggingface dataset:
A Statistical Approach for Dating Archaeological Contexts

This paper describes a statistical model developing from Correspondence Analysis to date archaeological contexts of the city of Tours (France) and also to obtain an estimated absolute timescale.The data set used in the study is reported as a contingency table of ceramics against contexts.But, as pottery is not intrinsically a dating indicator (a date is rarely inscribed on each piece of pottery), we estimate dates of contexts from their finds, and we use coins to attest the date of assemblages.The model-based approach uses classical tools (correspondence analysis, linear regression and resampling methods) in an iterative scheme.Archaeologists may find in the paper a useful set of known statistical methods, while statisticians can learn a way to order well known techniques.No method is new, but their gathering is characteristic of this application.
Features of the Application of the Detection Algorithm Based on the Background Subtraction Method in the Scanning Optoelectronic Surveillance System

The article considers the approach developed by the authors to adapt the detection algorithm based on the background subtraction method to work in a scanning optoelectronic surveillance system. This algorithm is the basis of the developed image processing method applied to the problem of detecting moving objects by an optoelectronic thermal imaging surveillance system. At the same time, this method provides a solution to the tasks of protecting a critical object with an optoelectronic surveillance system, which must function both in stationary and scanning mode. The use of an improved mixture of Gaussian distributions to build a background model makes it possible to adapt to periodic changes in pixel brightness from dynamic background elements of the observed scene. However, the application of this detection algorithm in a scanning optoelectronic system requires the solution of the following problematic issues: the exclusion of false highlighting of the areas of the processed image in the foreground when transferring the sensor field of view; the elimination of errors in the initial values of the parameters of a mixture of Gaussian distributions. The application of the approach proposed by the authors to solving these issues is implemented in a specialized optoelectronic surveillance system and allows achieving the same quality of the detector based on the background subtraction method in scanning mode as in stationary mode.
GRACED: A Novel Fragile Watermarking for Speech Based on Endpoint Detection

Content authentication and tampering detection of multimedia is a vital application by using digital watermarking. In this paper, we propose a novel fraGile wateRmArking of speeCh based on Endpoint Detection (namely GRACED) to verify the integrity of speech. Firstly, speech signal is framed word by word and each speech frame includes one intact nonsilence word. Subsequently, feature fusion is adopted to generate the fragile watermark which will be embedded into the coefficients of hybrid domain of discrete wavelet transform (DWT) and singular value decomposition (SVD). Finally, the tampering detection is accomplished without using any synchronous code to detect kinds of attacks. Several experiments are executed in order to quantify the performance of the proposed method. Experimental evaluation and comparisons with other schemes demonstrate that the signal-to-noise ratio of the proposed method is high with a favorable imperceptibility. Additionally, the tampering localization of various malicious attacks can be achieved without using synchronous code and the proposed scheme even can determine the attack types.
Efficient Face Mask Detection Method Using YOLOX: An Approach to Reduce Coronavirus Spread

Many lives have been put in danger since the start of the COVID-19 outbreak. According to WHO (World Health Organization)’s statements, breathing without a mask is highly dangerous in public and crowded places. Wearing a mask is essential as long as the vaccination is not widely used and does not fully protect everyone. Wearing masks does minimize the chances of becoming infected and the transmission of Coronavirus. Due to this, many public service providers may mandate customers to wear masks in order to get service.However, manually monitoring whether people are wearing a mask or not in public is inefficient and difficult. This paper proposes replacing manual inspection with a deep learning-based method using YOLOX, the most powerful objection detection algorithm. The results of the experiments show that the algorithm described in this work can efficiently detect face masks and enable more effective personnel monitoring than other YOLO series models. In addition, as compared to other models, our technique has significant benefits in terms of speed and accuracy in small and crowded areas.
Security breaches and modifications on cybersecurity disclosures

Research Question: How do firms approach their cybersecurity disclosure obligations, especially for those who experienced a cyber-attack? Prior research has found that year-after-year modification on textual disclosures adds more appreciable information that makes it more relevant. But do firms provide meaningful disclosures to promote market transparency? Motivation: Because of growing cybersecurity threats in recent years, the U.S. Securities and Exchange Commission (SEC) has issued several regulations and guidance that emphasized on the disclosure of material information on cybersecurity. Given that the mandatory risk factor disclosures in SEC Form 10-K is the first place firms are encouraged to disclose cybersecurity-related assessment, it is important to examine how firms approach their disclosure expectations. Idea: To examine whether firms respond to cyber-attacks with meaningful disclosures, we use the Vector Space Model (VSM) to calculate disclosure modifications before and after major cyber-attack incident. Data: We extracted cybersecurity breach incidents from the Data Breach Database, a centralized and global database of data breaches maintained by a leading security company. In addition, we use the SEC data depository to find firms’ 10-K disclosures. Findings: We find that firms modify their cybersecurity disclosures by increasing the quantity of disclosures, but not necessarily the quality of disclosures as measured by document similarity. Furthermore, we find partial evidence that the degree of modification is positively associated with the severity of cyber-attacks. Contribution: Our evidence suggests that firms tend to use boilerplate language to disclose cybersecurity-related issues. This finding is consistent with prior research. That is, consistent with prior literature, the information content in public company 10-Ks is limited. We find that this seems to be the case as well when it comes to cybersecurity disclosures.
AraNet: A Deep Learning Toolkit for Arabic Social Media

We describe AraNet, a collection of deep learning Arabic social media processing tools. Namely, we exploit an extensive host of publicly available and novel social media datasets to train bidirectional encoders from transformer models (BERT) to predict age, dialect, gender, emotion, irony, and sentiment. AraNet delivers state-of-the-art performance on a number of the cited tasks and competitively on others. In addition, AraNet has the advantage of being exclusively based on a deep learning framework and hence feature engineering free. To the best of our knowledge, AraNet is the first to performs predictions across such a wide range of tasks for Arabic NLP and thus meets a critical needs. We publicly release AraNet to accelerate research and facilitate comparisons across the different tasks.
INVESTIGATING LEXICAL DIVERSITY OF ONLINE ENGLISH NEWSPAPER EDITORIALS ACROSS COUNTRIES

Online media or digital media is an internet based communication technology to share and to exchange the latest information throughout the world. This study aims to investigate the lexical diversity of editorials section published in English online media from across ten countries; Egypt, UK, Indonesia, Japan, Malaysia, Russia, USA, Pakistan, Singapore and Nigeria. By using a batch mode of web-based interface to the lexical calculation proposed by Laufer and Nation (1995), namely Second Language Lexical Frequency Profile (L2LFP), this paper focused on the production of tokens, types, type-token ratio (TTR) and academic words of ten online English newspaper editorials from two native English countries and eight non-native English countries. The results show that an editorial published in The Today Online newspaper from Singapore consists the highest score of tokens (987), types (431) and academic words (52), but the least score of TTR (0.44). Nevertheless, the highest score of TTR is not produced by The Today Online. The highest score of TTR (0.60) is gained by The Pakistan Today, even though The Pakistan Today only consists 273 tokens, 223 types and 38 academic words. Despite of Laufer and Nation’s (1995) argumentation that native writer has higher TTR’s score than non-native writer, this paper showed different results. The native online newspaper: The Guardian from U.K and The New York Times from USA has TTR’s score of 0.48 and 0.47. Meanwhile, most of non-native online newspapers has higher TTR’s scores.
Penggunaan Multimedia Presentasi pada Model Pembelajaran Project Based Learning (PjBL) untuk Meningkatkan Kemampuan Menulis Peserta Didik (Studi Kasus Pada Tema Ekosistem di Kelas 5 SDN 6 Cikondang)

The research is aim to know raising student competence in writing using project based learning model based mutimedia presentation. This method research using experiment method with quantitative approach. Design is used experimental formed nonequivalence design group pretest-posttes. Research approach can summarised are: 1) Student writing competence raise after learning using projects based learning base mutimedia presentation in Bahasa Indonesia learning to student at grade five SDN 6 Cikondang and 2) there are different result of writing study in bahasa indonesia at grade five SDN 6 Cikondang between student who use project based learning base mutimedia presentation and student who don't use it.
Efficient pre-training objectives for Transformers

The Transformer architecture deeply changed the natural language processing, outperforming all previous state-of-the-art models. However, well-known Transformer models like BERT, RoBERTa, and GPT-2 require a huge compute budget to create a high quality contextualised representation. In this paper, we study several efficient pre-training objectives for Transformers-based models. By testing these objectives on different tasks, we determine which of the ELECTRA model's new features is the most relevant. We confirm that Transformers pre-training is improved when the input does not contain masked tokens and that the usage of the whole output to compute the loss reduces training time. Moreover, inspired by ELECTRA, we study a model composed of two blocks; a discriminator and a simple generator based on a statistical model with no impact on the computational performances. Besides, we prove that eliminating the MASK token and considering the whole output during the loss computation are essential choices to improve performance. Furthermore, we show that it is possible to efficiently train BERT-like models using a discriminative approach as in ELECTRA but without a complex generator, which is expensive. Finally, we show that ELECTRA benefits heavily from a state-of-the-art hyper-parameters search.
Accumulation Bit-Width Scaling For Ultra-Low Precision Training Of Deep Networks

Efforts to reduce the numerical precision of computations in deep learning training have yielded systems that aggressively quantize weights and activations, yet employ wide high-precision accumulators for partial sums in inner-product operations to preserve the quality of convergence. The absence of any framework to analyze the precision requirements of partial sum accumulations results in conservative design choices. This imposes an upper-bound on the reduction of complexity of multiply-accumulate units. We present a statistical approach to analyze the impact of reduced accumulation precision on deep learning training. Observing that a bad choice for accumulation precision results in loss of information that manifests itself as a reduction in variance in an ensemble of partial sums, we derive a set of equations that relate this variance to the length of accumulation and the minimum number of bits needed for accumulation. We apply our analysis to three benchmark networks: CIFAR-10 ResNet 32, ImageNet ResNet 18 and ImageNet AlexNet. In each case, with accumulation precision set in accordance with our proposed equations, the networks successfully converge to the single precision floating-point baseline. We also show that reducing accumulation precision further degrades the quality of the trained network, proving that our equations produce tight bounds. Overall this analysis enables precise tailoring of computation hardware to the application, yielding area- and power-optimal systems.
The Smartboard in Chemistry Classrooms: What is Its Effect on Chemistry Teaching and Learning in Selected Topics in Grade 11?

The study aimed to investigate the effect of using the smartboard on teaching and learning acid-base reactions by applying qualitative and quantitative calculations. The effect of the smartboard on knowledge retention of the concept application was examined and compared to traditional teaching methods. A quasi-experimental design with experimental and control groups using the pretest and posttest design was adapted for the study. A convenient sampling technique was used to select 284 Grade 11 students from an urban region of The Gambia. We prepared an achievement test with 15 questions to collect data. While the experimental group studied the topics using the smartboard, the control group studied using traditional teaching methods. The achievement test was prepared to measure the groups’ differences in knowledge retention and application. The same test was applied to compare the pretest and posttest to measure group differences. The independent t test results showed a significant difference (p = .000) between the experimental group (M = 34.30, SD = 18.971) and the control group (M = 28.01, SD = 13.853). Furthermore, the results of the knowledge-retention rate were higher among the experimental group participants (M = 29.23, SD = 14.232) than in the control group (M = 26.72, SD = 12.673). This leads to the conclusion that using the smartboard provides an educative contribution to technology integration in the classroom, especially innovation in teaching and learning.
Document classification as a theoretical problem of documentology

The changes and amendments to the general document classification as a theoretical problem of document studies and documentology, are discussed. The document in the meaning “recorded information” is the subject of classification. The author examines the faceted-block classification of document based on various characteristics united into six clusters: “Types of documents physical carrier”, “Types of documents by document acquisition circumstances”, “Types of documents by information representational and transfer tools (by signative component)”, “Types of documents by information reception (perceptive component)”, “Types of documents by their environmental circumstances”. The document classification within each facet is independent which enables to characterize any document in various parameters. The changes and amendments are made to the general document classification based on increasing number and weight of electronic documents including digital versions of originally non-digital documents. The refined general classification is to facilitate special classification in particular types (classes, groups) of documents. The general document classification is applicable to various scientific disciplines within documentology, and to teaching documentological disciplines.
A Predictive Coding Approach to Estimating the Complexity of Popular Music Drum Patterns

This study presents a method to estimate the complexity of popular music drum patterns based on a core idea from predictive coding. Specifically, it postulates that the complexity of a drum pattern depends on the quantity of surprisal it causes in the listener. Surprisal, according to predictive coding theory, is a numerical measure that takes large values when the perceiver’s inner model of the surrounding world fails to predict the actual stream of sensory data (i.e. when the perception surprises the perceiver), and low values if model predictions and sensory data agree. The proposed new method first approximates a listener’s inner model of a popular music drum pattern (using ideas on inculturation and a Bayesian learning process). It then quantifies the listener’s surprisal evaluating the discrepancies between the predictions of the inner model and the actual drum pattern. It finally estimates drum pattern complexity from surprisal. The method was optimised and tested using a set of forty popular music drum patterns, for which empirical perceived complexity measurements are available. The new method provided complexity estimates that had a good fit with the empirical measurements (R2=.852). This suggests that surprisal indeed captures essential aspects of complexity, and that it may serve as the basis for a general theory of perceived complexity.
Exploring the use of Image Target Based Virtual Controller for Mobile Augmented Reality

Abstract: Mobile phones have made the Augmented Reality technology easily available to large audience and people are developing applications for it but the interaction with mobile AR is limited by touchscreen gesture, hand-based gesture can be used but it needs additional sensors like depth sensor and better processing power to work properly and lower end mobile phone devices cannot handle it and also external hardware can be build but around it but it adds to additional cost and is also not affordable to everyone. In this project a prototype application is developed to demonstrate the use of image targets to design a virtual controller that can be used to interact with AR content and implement basic tasks such as touching and moving virtual objects available in the AR space with the help of virtual controller and further the paper discusses about the benefits and drawbacks of such implementation and its future usage.
Conversational linguistic features predict social network learning

Whether it is the first day of school or a new job, individuals often find themselves in situations where they must quickly and accurately learn novel social networks. However, the mechanisms through which this learning occurs remain unclear. We posit that individuals use linguistic features of conversations to identify the valence and strength of social relationships. Across three studies using a naturalistic behavioral task (57 adults; 34,735 observations), we employ novel person- and stimulus-focused approaches to investigate social network learning success, examine how distinct linguistic features predict network learning, and explore the association between semantic similarity and relationship formation. We found that participants learned similar network structures, linguistic features uniquely predicted network learning, and greater semantic similarity was associated with perceived friendship formation. These findings suggest that naturalistic conversational content is both a potential mechanism of social network learning and a promising avenue for future research on social relational inference.
Evaluating Various Tokenizers for Arabic Text Classification

The first step in any NLP pipeline is to split the text into individual tokens. The most obvious and straightforward approach is to use words as tokens. However, given a large text corpus, representing all the words is not efficient in terms of vocabulary size. In the literature, many tokenization algorithms have emerged to tackle this problem by creating subwords which in turn limits the vocabulary size in a given text corpus. Most tokenization techniques are language-agnostic i.e they don't incorporate the linguistic features of a given language. Not to mention the difficulty of evaluating such techniques in practice. In this paper, we introduce three new tokenization algorithms for Arabic and compare them to three other baselines using unsupervised evaluations. In addition to that, we compare all the six algorithms by evaluating them on three supervised classification tasks which are sentiment analysis, news classification and poetry classification using six publicly available datasets. Our experiments show that none of the tokenization technique is the best choice overall and that the performance of a given tokenization algorithm depends on the size of the dataset, type of the task, and the amount of morphology that exists in the dataset. However, some tokenization techniques are better overall as compared to others on various text classification tasks.
AI applications in smart cities’ energy systems automation

Artificial intelligence (AI) plays a significant role in energy systems transformations in smart cities. Climate change and environmental sustainability imposed utilities to shift toward renewable energy resources and technologies applications in recent decades. Renewable energy technology deployment is associated with high initial investment and integration with the existing supply and demand systems. Operation stability has been challenging to integrate renewable energy with the customary old systems. On the other hand, renewable energy ensures sustainable energy and future development with minimum loss and greenhouse gas emissions. Therefore, AI is the primary mover of power systems modernization with high accuracy of management and control. This study tried to evaluate the efficiency and performance of AI in the renewable energy sector, focusing on the European Union as the case study. This study analyzes the first renewable energy processes in the chain and energy from gross to final consumption. Afterward economic consequences of renewable energy using natural resources (solar, wind, etc.) in smart cities are discussed. Finally, the efficiency of AI in renewable energy is examined, followed by future work.
The Effect of mCSCL to Improve Student Learning Outcomes

Mobile Computer-Supported Collaborative Learning (mCSCL) is an alternative that can be used in mobile learning. In this study, mCSCL consists of three steps. First, create groups of three to five students. Second, make learning activities starting from understanding the problem to discussion. Third, facilitating group discussions starting from one-to-one discussions to dissemination. The goal of this study was to see how mCSCL affected student learning results. The participants in this study were students from Duta Bangsa University's Informatics Engineering Study Program, which comprised of three classes totaling 90 individuals. This research is a quasi-experimental study with a one-way analysis of variance. The independent variables in this study were mCSCL, mobile learning, and e-learning based on Moodle. The results show that there are differences in learning outcomes in the number of material from the three learning models carried out. The mCSCL model is more effective than using the Mobile Learning model. Furthermore, the mobile learning model is more effective than e-learning Moodle