Reviewerly/exHarmony huggingface dataset:
Computing Must Pay Attention to Outcomes to Achieve Equity

There is a push right now across ACM that is gathering momentum. This push is for increased attention to cultural competency [16] in the training of computing professionals. In fact, the CS202X: ACM / IEEE-CS / AAAI Computer Science Curricula Taskforce [5] has a knowledge area subcommittee devoted to SEP or Society, Ethics, and Professionalism [6]. This subcommittee is currently proposing a new set of student learning outcomes under the heading of Justice, Equity, Diversity, and Inclusion. These new learning foci include the identification of underlying assumptions and values built into system designs and the recognition and description of the impact of diversity and under-representation of identities in the computing profession. The emphasis on underlying values and on describing impacts is not accidental. As many have come to recognize, computing has a legacy of compartmentalizing social concerns [3] (and social costs), and this segregation of intellectual concerns is often facilitated by the distance between those who design [10] and those who experience the impacts of those designs. This new curricular push within ACM and IEEE is about equipping future computer science graduates with the cultural competencies that they need to connect the values informing local design practices to the public impacts of those designs.
CS202X and SEP-KA Update

In a nutshell, CS202X refers to the current effort to revise the curricular guidelines for undergraduate degree programs in computer science. These revisions are being managed by a Steering Committe comprised of representatives from the ACM, IEEE-Computer Society, and AAAI. Following the previous curricular revisions in 2001 (CS'01) and 2013 (CS'13), the proposed guidelines are broadly partitioned into categories referred to as Knowledge Areas (KAs). Each KA has an associated sub-committee consisting of a Chair and members from the Steering Committee, as well as subject matter experts from the computing community. SEP refers to the Society, Ethics, and Profesionalism KA within the proposed CS202X curricular report.
Can the Compositional Nature of Compositional Data Be Ignored by Using Deep Learning Approaches?

The application of non-compositional methods to compositional data representing parts of a whole should be avoided from a theoretical point of view. For example, one can show that Pearson correlations applied to compositional data are biased to be negative. Moreover, almost all statistical methods lead to biased estimates when applied to compositional data. One way out is to analyze data after representing them in log-ratio coordinates. However, several implications arise, such as interpretation in log-ratios and dealing with zeros and non-detects where log-ratios are undefined. When focusing on settings where only the prediction and classification error is important rather than an interpretation of results, one might argue to rather use non-linear methods to avoid those implications. Generally, it is known that misclassification and prediction errors are lower with a log-ratio approach when using machine learning methods that model the linear relationship between variables. However, is this also true when training a neural network who may learn the inner relationships between parts of a whole also without representing the data in log-ratios? This paper gives an answer of this matter based on applications with multiple real data sets, leading to the recommendation to use a compositional treatment of compositional data in any case. Misclassification and prediction errors are lower when nonlinear methods, and in particular deep learning methods, are applied together with a compositional treatment of compositional data. The compositional treatment of compositional data therefore remains very important even in the context of focusing on prediction errors using deep artificial neural networks or other nonlinear methods.
Anne Teresa De Keersmaeker's Choreographies to Steve Reich's Music, the Re-creation of Musical Principles through Dance

The Belgian choreographer Anne Teresa de Keersmaeker has numerous times used the music by Steve Reich as a source for her choreographies. In her dance pieces she has transmitted the compositional principles of Reich's music into movement. She is not simply visualising or translating different musical parameters into motion, rather she is re-creating compositional principals in a different medium. De Keersmaeker used less complex pieces by Reich at the beginning of her career, for example, in her choreography Fase, Four Movements to the Music of Steve Reich (1982), she uses the following compositions by Steve Reich: Piano Phase (1967), Come Out (1966), Violin Phase (1967), Clapping Music (1972). Later she creates more complex choreography like Drumming (1998) to Drumming (1970-1971) and Rain (2001) to Music for 18 Musician (1974-76). De Keersmaeker, like Reich is influenced by the radical transformations which took place in the arts in the 1960s. Traditional strategies of composition were questioned which resulted in new approaches such as, for example, minimalism. Here the endless repetition of small cells and phrases creates an emotional potential through intensity, which replaces the representation through gesture and tone painting, but nevertheless affects us at the interface between the visual and the acoustic. The examples chosen from Drumming shall demonstrate which physical equivalents for the compositional principles of the music are found by de Keersmaeker, how are principles like phase shifting, pattern layering, and accumulation re-created in movement and made visually transparent? How close is the choreography to the music and when does the choreographer decide to allow smaller or larger discrepancies between music and movement? How does the interdependence between music and movement influence the intensities emerging through repetition?
LIP-TO-SPEECH SYNTHESIS USING MACHINE LEARNING

Lip reading technology uses analysis of lip movement to record the speaker's message.It is used widely in many aspects of daily life.The performance of the entire lip-reading system is impacted by the dataset's quality.As a result, this study investigates the dataset for lipreading.Scikit video is used to extract frames from the source video.Idlib is then used to conduct facial detection.Lip cropping is accomplished by processing the feature points to obtain lip pictures.The dataset is then expanded by doing data augmentation.33 voices are included in the collection, and each speaker's lips are represented by 7,000 images.A technique for creating datasets is suggested.Prior to decomposing the treated films in the Scikit-Video library.
Students’ improvement in writing skill using column comment on Facebook group

The objective of this case study is to investigate the effect of using the comment section on a Facebook group on increasing the writing ability of students at MA Pondok Pesantren Nurul Islam Seri Bandung. The study aims to determine whether utilizing the comment section can improve students' performance in writing and specifically, the material used is recount text. The participants of this study were all pupils in grade 10, with a total of 49 students. A sample of 15 students was selected through purposive sampling for the treatment class. Data was collected through a writing test consisting of 1 item and analyzed using the paired sample t-test in SPSS 22. The average pre-test score was 51.313, and the average post-test score was 85.167. The results of the data analysis showed that using the comment section on a Facebook group was significantly successful in enhancing the writing skills of students at MA Pondok Pesantren Nurul Islam Seri Bandung. Therefore, the writerss accept the alternative hypothesis (Ha) and reject the null hypothesis (Ho) as the sig. (2-tailed) = 0.000 is less than the significance level (=0.05), indicating that the use of the comment column on a Facebook group was significantly beneficial in enhancing students' ability to write recount texts at MA Pondok Pesantren Nurul Islam Seri Bandung.&#x0D; Keywords: Facebook; writing skill
Interpreting the Relevance of Readability Prediction Features

Text readability is one of the main research areas widely developed in several languages but highly limited when dealing with the Arabic language. The main challenge in this area is to identify an optimal set of features that represent texts and allow us to evaluate their readability level. To address this challenge, we propose in this study various feature selection methods that can significantly retrieve the set of discriminating features representing Arabic texts. The second aim of this paper is to evaluate different sentence embedding approaches (ArabicBert, AraBert, and XLM-R) and compare their performances to those obtained using the selected linguistic features. We performed experiments with both SVM and Random Forest classifiers on two different corpora dedicated to learning Arabic as a foreign language (L2). The obtained results show that reducing the number of features improves the performance of the readability prediction models by more than 25% and 16% for the two adopted corpora, respectively. In addition, the fine-tuned Arabic-BERT model performs better than the other sentence embedding methods, but provided less improvement than the feature-based models. Combining these methods with the most discriminating features produced the best performance.
A Deep Decision Forests Model for Hate Speech Detection

Detecting and controlling propagation of hate-speech over social media platforms is a challenge. This problem is exacerbated by extreme fast flow, readily available audience, and relative permanence of information on social media. The objective of this research is to propose a model that could be used to detect political hate speech that is propagated through social media platforms in Kenya. Using Twitter textual data and Keras TensorFlow Decision Forests (TF-DF), three models were developed i.e., Gradient Boosted Trees with Universal Sentence Embeddings(USE), Gradient Boosted Trees, and Random Forest respectively. The Gradient Boosted Trees with USE model exhibited a superior performance with an accuracy of 98.86%, recall of 0.9587, precision of 0.9831, and AUC of 0.9984. Therefore, this model can be utilized for detecting hate speech on social media platforms.
Self-optimization examination system based on improved particle swarm optimization

Abstract Artificial intelligence has been applied to many fields successfully and saved many human and material resources. The intelligent examination system is a typical application case, which makes teachers can not only master the study situation of every candidate at any time but also design further study plans with the help of the examination system. A self-optimization examination system is shown in this paper, which is carried out by an improved particle swarm optimization. The intelligent examination system can surmount two difficulties shown in the construction of the traditional examining system, one is the setting of the attributes of the examination questions, and another is the maintenance of the database of the examination questions. The experiment shows that the novel method can not only optimize the attributes of the questions in the examination database intelligently but also maintain the examination database effectively through massive training.
Implications of blockchain technology in Industry 4.0

The term “Industry 4.0” is used to describe the current technological revolution that tends to digitalize manufacturing and production. Recent achievements in robotics, IoT and machine learning have radically changed the production workflows in multiple domains. However, a number of issues related to cybersecurity arise, due to the increased attack surface that results from digitalizating operational technology. On the other hand, blockchain technology has been one of the most significant advancements for computer science in the recent years and it has been used in different use cases. Bringing blockchain technology to bear in addressing security-related issues associated with Industry 4.0 applications brings many benefits, but also presents challenges. Therefore, the integration of blockchain technology into the Industry 4.0 process should proceed only after careful consideration of the expected pros and cons. In this chapter, we present the implications of blockchain technology in the pillars of Industry 4.0 that it applies to. We discuss opportunities that arise, along with challenges and limitations. The chapter aims at emphasizing the effective and valid use of blockchain technology that will drive Industry 4.0 forwards.
Cognitive agility for improved understanding and self-governance: a human-centric AI enabler

Artificial Intelligence (AI) is a catalyst for innovation activity. This chapter looks at developers and entrepreneurs as key human actors forming a performance triad with the AI. Successful exploitation of AI capabilities in entrepreneurial contexts requires humans to successfully collaborate, prevent communication errors and maintain control over the AI. To achieve this, human enablers require cognitive agility. This skill is founded upon metacognition and relies on heightened domain cognisance and the ability to govern one’s own actions. These attributes can be trained through adaptive non-standards based forms of education. Failure to develop the necessary human factor attributes risks increasing the likelihood of compromising the triad and the catalytic effect of AI. Developing cognitive expertise can mitigate ethical issues, power imbalances, bias, and adversarial factors that present significant challenges to technological solutions.
Evaluating citizen sensing applications: how simulation of human mobility can contribute to the search of missing children

By the first decade of the century, the developments in mobile computing, including GPS receivers and cameras, have sparked the participation of citizens in solving real-life problems. Humans can move around, observe, and process the captured information based on their intellect. Therefore, citizen sensing applications that use mobile technology to harness the collective intelligence of citizens provide enhanced situational awareness that no machine has been able to offer. However, such applications intrinsically depend each on the engagement of at least a minimum number of citizens subject to their function, a threshold which cannot be easily calculated. This work presents an approach for meeting this challenge using a reconfigurable simulation environment. We focus on the problem of missing children and the engagement of mobile users for their investigation. The goal is to use this simulation environment as an evaluation tool on the effectiveness of such applications depending on their users.
Big Data Analysis of Public Perceptions of Face-to-Face Learning in COVID-19 Era

This study began on January 2, 2022, to determine public opinion on face-toface learning in Indonesia.The study takes public opinion from Twitter on January 2nd and 3rd, 2022, using the naive-bayes approach.Drone Emprit Academy provided the data.According to the data, there are 1,026 tweets with negative sentiment or 65 percent.Concerns about the spread of the COVID-19 virus, weariness, and fear of change are among the causes of bad feelings.There are also a few favorable feelings (24 percent), which might be attributed to the enthusiasm for face-to-face learning.
List of Reviewers 2022

Free AccessList of Reviewers 2022Published Online:February 21, 2023https://doi.org/10.1027/1618-3169/a000573PDF ToolsAdd to favoritesDownload CitationsTrack Citations ShareShare onFacebookTwitterLinkedInReddit SectionsMoreIn addition to the Editors and the Editorial Board, the following colleagues assisted in the peer review of manuscripts processed between December 16, 2021 and December 31, 2022 for Experimental Psychology. We are extremely grateful for their support.Marlene AbadieMagdalena AbelAmit AlmorSumyah A. AlnajashiUlrich AnsorgeSahar Bahrami-KhorshidJonathan BakdashHunter BallArthur BaroodyErik BenauSharon BertschLisi BeyersmannAngel BlanchJens BlechertChristine BlumeGary BoddaertArndt BröderKatherine BursonPiotr BystranowskiMarica CassarinoEmanuele CastanoYoojin ChangHsuan-Fu ChaoBrett A. CochraneNicole ConradMichael DoddRichard DrakeYue DuWolfgang EllermeierEmily M. ElliottDavid EngelJohn FranchakDavid J. FrankZaifeng GaoJana HasenaeckerMargit HoeflerMateusz HoholCrystal HoytAmelia HuntRoland ImhoffErin IsbilenJason IvanoffMadeline JalbertZhao KeAndrea KieselJoachim I. KruegerVanessa LoaizaJuan LupiáñezStephen LupkerSean MacKinnonHarvey MarmurekHeath MathesonIan M. McDonoughRobert McIntoshAbbie MillettRamesh K. MishraDaniel MochonAriana Modirrousta-GalianToby MordkoffEmily MrozPaul MulcahyKristi MulthaupThomas MussweilerDezso NemethAustin L. NicholsAdam OsthSiegmar OttoMarc OuelletSteven PanFrank PapenmeierBernhard PastötterRobert W. ProctorRalph ReddenMichel RegenwetterTimothy RickardEvan F. RiskoSteven SamuelStacey M. SchaeferKathryn SchertzThomas SchmidtStefan SchulreichSilvia SerinoSamuel ShakiAndrew SmithDaniel SmithJoshua SnellGiacomo SpinelliJie SuiAgnes SzollosiKarl SzpunarAlessandro TavanoTobias TempelSergio C. TorresChristian UnkelbachEric VanmanKalif VaughnDaniel WeissmanMichael WenzelMarc WilliamsBenjamin WilsonFuyun WuChunliang YangSascha ZuberFiguresReferencesRelatedDetails Volume 69Issue 6November 2022ISSN: 1618-3169eISSN: 2190-5142 Published onlineFebruary 21, 2023 InformationExperimental Psychology (2022), 69, pp. 354-354 https://doi.org/10.1027/1618-3169/a000573.© 2022Hogrefe PublishingPDF download
IMPLEMENTASI ALGORITMA A STAR DALAM PENCARIAN RUTE TERPENDEK (SHORTEST PATH PROBLEM) PADA SISTEM PENCARIAN KANTOR POS DI KOTA PEKANBARU

With the advancement of information technology today, there are several solutions that can facilitate the search for the shortest path (Shortest Path Problem) by using various algorithms such as the djiktra algorithm, A star algorithm, floyd warshall algorithm, prim algorithm and others. Algorithm A* (A star) is one of the algorithms included in the category of search methods that have information (informed search method). This algorithm is very good as a solution to the path finding process where this algorithm looks for the distance of the fastest route that will be taken by an initial point (starting point) to the destination object. The search technique used in this simulation is using the A* Algorithm with the manhattan distance heuristic function. Path Finding is one of the most important materials in Artificial Intelligence. Path Finding is usually used to solve problems on a graph. This study aims to provide a solution in finding the shortest route, so as to reduce operational costs that must be incurred by the company and also with this new system, it can be known the distance from one point to another without using manual calculations.
A Deep De-noising Neural Network to Mitigate Noise in Computed Tomography Images

Liver cancer is a cause of high mortality worldwide. Early detection can save lives and help the medical practitioners to plan the treatment. Blood tests, biopsy and imaging techniques like CT, MRI are widely used in the diagnosis of the liver cancer.Noise contaminated CT Image suffers with low resolution and often leads to wrong diagnosis which may lead to improper treatment.Deep denoising Neural Network is used to mitigate the effects of noise on CT images. Deep denoising neural network outperform other filtering methods like mean, median, wiener filters on CT images of liver from cancer imaging archive (TCIA)
Novel Perspectives in Forensics Aware Internet of Things

The Internet of Things involves connecting various things with several communication standards and technologies. While Internet of Things opens accessible opportunities in many fields, it announces new threats in the sphere of forensics investigations. The present day procedures and forensic tools cannot fit the widely distributed infrastructure of the IoT. Forensics investigators will experience threats determining, analyzing and collecting that evidence. This paper comes with the working solution of IoT forensics and consistently evaluates the IoT forensics area to examine the threats and issues in this peculiar field of forensics. We recommend a Forensics-aware IoT (FAIoT) model for approving reliable forensics investigations in the IoT environment.
IT in Court: The Evidence Does Not Speak for Itself

Abstract Presenting digital evidence in court is an onerous and complex process. Dr Ian Kennedy FBCS CITP MCSFS outlines the processes, rules and explores the future of digital forensics.
Healthcare’s Biggest Cyber Attacks

Abstract Frank Plummer MBCS, cyber security consultant, explores the CIA triad – confidentiality, integrity and availability – and its impact on three recent cyber attacks in healthcare.
Supervised Learning for Automatic Source Type Discrimination of Seismic Events in Sweden

Distinguishing small earthquakes from man-made blasts at construction sites, in quarries and in mines is a non-trivial task during automatic event analysis and thus typically requires manual revision. We have developed station-specific classification models capable of both accurately assigning source type to seismic events in Sweden and filtering out spurious events from an automatic event catalogue. Our method divides all three components of the seismic records for each event into four non-overlapping time windows, corresponding to P-phase, P-coda, S-phase and S-coda, and computes the Root-Mean-Square (RMS) amplitude in each window. This process is repeated for a total of twenty narrow frequency bands. The resulting array of amplitudes is passed as inputs to fully connected Artificial Neural Network classifiers which attempt to filter out spurious events before distinguishing between natural earthquakes, industrial blasts and mining-induced events. The distinction includes e.g. distinguishing mining blasts from mining induced events, shallow earthquakes from blasts and differentiating between different types of mining induced events. The classifiers are trained on labelled seismic records dating from 2010 to 2021. They are already in use at the Swedish National Seismic Network where they serve as an aid to the routine manual analysis and as a tool for directly assigning preliminary source type to events in an automatic event catalogue. Initial results are promising and suggest that the method can accurately distinguish between different types of seismic events registered in Sweden and filter out the majority of spurious events.
<i>Rare Earth: Crystalline Treasures</i> by Robert L. Lavinsky, Monica Kitt, Eugene Meieran, and Thomas P. Moore. Published by The Arkenstone; order from iRocks.com. 85 pages; 2022; $60 plus shipping and handling (hardbound).

"Rare Earth: Crystalline Treasures by Robert L. Lavinsky, Monica Kitt, Eugene Meieran, and Thomas P. Moore. Published by The Arkenstone; order from iRocks.com. 85 pages; 2022; $60 plus shipping and handling (hardbound).." Rocks & Minerals, 98(2), p. 196
<i>The Mineralogy of Franklin and Ogdensburg, New Jersey</i>, 3 volumes, edited by Vandall T. King, Nathaniel E. King, Philip P. Betancourt, Richard C. Bostwick, Peter Chin, Tema J. Hecht, Steven M. Kuitems, Harold Moritz, Janet D. Nemetz, Anthony J. Nikischer, Stephen Sanford, James A. Van Fleet, and Earl R. Verbeek. Order from Franklin Mineral Museum, info@franklinmineralmuseum.com. Vol. 1 466 pp., Vol. 2 502 pp., Vol. 3 432 pp.; 2021; $185 plus $35 shipping and handling ($125 to Europe) (…

"The Mineralogy of Franklin and Ogdensburg, New Jersey, 3 volumes, edited by Vandall T. King, Nathaniel E. King, Philip P. Betancourt, Richard C. Bostwick, Peter Chin, Tema J. Hecht, Steven M. Kuitems, Harold Moritz, Janet D. Nemetz, Anthony J. Nikischer, Stephen Sanford, James A. Van Fleet, and Earl R. Verbeek. Order from Franklin Mineral Museum, info@franklinmineralmuseum.com. Vol. 1 466 pp., Vol. 2 502 pp., Vol. 3 432 pp.; 2021; $185 plus $35 shipping and handling ($125 to Europe) (hardbound).." Rocks & Minerals, 98(2), pp. 195–196