Reviewerly/exHarmony huggingface dataset:
Interpreting learning in biological neural networks as zero-order optimization method

Recently, significant progress has been made regarding the statistical understanding of artificial neural networks (ANNs). ANNs are motivated by the functioning of the brain, but differ in several crucial aspects. In particular, the locality in the updating rule of the connection parameters in biological neural networks (BNNs) makes it biologically implausible that the learning of the brain is based on gradient descent. In this work, we look at the brain as a statistical method for supervised learning. The main contribution is to relate the local updating rule of the connection parameters in BNNs to a zero-order optimization method. It is shown that the expected values of the iterates implement a modification of gradient descent.
Comparison of Classification Algorithms toward Sentiment Analysis on Claim of Old Age Guaranteed

The pros and cons of the Old Age Security (JHT) claim rules contained in the Minister of Manpower Regulation No. 2 of 2022 are unavoidable in Indonesia, especially on Twitter social media. Of course, this is something that deserves to be analyzed as government policy makers so as not to make a wrong move, one of which is using sentiment analysis techniques. The stages of the research carried out include data collection, pre-processing, the model used, experimentation and model assessment, as well as validation and evaluation of results. The researcher crawled 1000 Indonesian-language tweets on the pros and cons of claims related to Old Age Security using the R programming language and carried out the Text Cleaning Process. In the next stage, pre-processing is carried out using Case Folding, Stemming, Tokenizing and Stopword Removal. This research resulted in the sentiments of the Indonesian people being very happy with the president's statement asking for the regulation of the Minister of Manpower No. 2 of 2022 to be revised, then tested using 3 classification algorithms and the application of Particle Swarm Optimization feature selection. The results of the sentiment testing of the JHT benefit claim analysis obtained the highest value on the Support Vector Machine algorithm with the addition of Particle Swarm Optimization, namely the accuracy value of 82.92%, precision of 84.53%, recall of 80.90% and AUC value of 0.892.. Keywords: Support Vector Machine, Sentiment Analysis, Old Age Guarantee d sar 0.892
Comparison of Logistic Regression and Random Forest using Correlation-based Feature Selection for Phishing Website Detection

The world is currently experiencing mass developments in information technology, especially during the current pandemic, which requires all of us to learn and even work online. They are triggered much crime in the internet world. One of them is stealing internet user data through a fake website built like the original or called a phishing website. In this research , a classification model is needed to detect phishing websites using the best performance from one of the logistic regression and random forest classification algorithms to overcome the rise of phishing websites in cyberspace. Classification performance can be improved using the correlation-based feature selection (CFS) method to select the most influential attribute in detecting web phishing . Based on the test results, applying the logistic regression and random forest classification algorithm in the classification of web phishing resulted in an accuracy of 93.035% and 96.834%. After feature selection with CFS, the accuracy was 92.718% and 97.015%, respectively. On the Testing, There was an increase in accuracy in RandomForest by 0.181% and an insignificant decrease in logistic regression. The test results prove that feature selection with CFS can eliminate redundant attributes and the resulting classification algorithm accuracy is not much different when the details are complete and Random Forest has accuracy better than after using CSF . Keywords : website phis h ing, classification, logistic regression, random forest, correlation-based
Human Control Redressed: Comparing AI-To-Human Vs. Human-To-Human Predictability in a Real-Effort Task

Predictability is a prerequisite for effective human control of artificial intelligence (AI). The inability to predict malfunctioning of AI, for example, impedes timely human intervention. In this paper, we empirically investigate how AI’s predictability compares to the predictability of humans in a real-effort task. We show that humans are worse at predicting AI performance than at predicting human performance. Importantly, participants are not aware of the differences in relative predictability of AI and overestimate their prediction skills. These results raise doubts about the human ability to effectively exercise control of AI — at least in certain contexts.
1d Split Rules for Incremental Gaussian Mixture Models Estimation

In this paper we review existing split rules for incremental estimation of Gaussian Mix- ture Models with model selection, and propose a new type of split rule based on 1D projections of the data. Split-based methods typically start with a mixture composed of a single component representing all data, and successively split and optimize components for a given model selection criteria. These algorithms are typically faster than alternatives, but depend critically in the component splitting method, since a good split rule promotes a faster convergence of the mixture optimization phase. We propose a new efficient and robust split rule that projects mixture components onto a 1D subspace and fits a two-component model to the projected data with the Expectation Maximization algorithm. The proposed approach is fast and robust to parameter tuning, being the ideal choice for applications that privilege speed while still maintaining an acceptable accuracy. We illustrate the validity of the method through a series of experiments on synthetic datasets comparing the proposed method to alternatives of the state-of-the-art in terms of efficiency, accuracy and sensitivity to parameter tuning.
PRAGMATIC STUDY ON DEIXIS ANALYSIS IN PRESIDENT JOKOWI’S STATE SPEECH

Due to the fact that deixis is important to indicate impermanent reference because it shows the phenomenon where in understanding the meaning of certain words and phrases in an utterance requires contextual information. Hence, people especially language learners should enrich themselves by understanding deixis include its elements and how deixis is used in communication. This study titled “A Pragmatic Study on Deixis Analysis in President Jokowi’s State Speech”. This study aims to identify the five types of deictic expression and analyse its functions used in President Jokowi’s State Speech when commemorating Indonesian Independence Day on August 16th 2019. This study uses the qualitative research method since the data are the recorded video of Jokowi’s speech and the writer turns it in the form of transcript. The writer uses the theory of Stephen C. Levinson (1983) as a guidance to analyse the five types of deixis - personal, spatial, temporal, discourse, social; also the functions of each types. The results of this study are that in Jokowi’s speech, there consists all five types of deixis but the most dominantly used is personal deixis. On other side, this study found that the functions are (1) person deixis is for determining the role of participants in Jokowi’s speech, (2) place deixis is for encoding place or location Jokowi means either close to him and Indonesian people (proximal) or far from them (distal), (3) temporal is for indicating the time points in which particular Jokowi’s utterances were spoken, (4) discourse is for showing the token-reflexivity of Jokowi’s utterances, (5) social is for determining the social differences that are relative to participant-roles; concerns about the relative rank, different social status, politeness and respect.
Evidential Decision Theory via Partial Markov Categories

We introduce partial Markov categories. In the same way that Markov categories encode stochastic processes, partial Markov categories encode stochastic processes with constraints, observations and updates. In particular, we prove a synthetic Bayes theorem; we apply it to define a syntactic partial theory of observations on any Markov category, whose normalisations can be computed in the original Markov category. Finally, we formalise Evidential Decision Theory in terms of partial Markov categories, and provide implemented examples.
Action Capsules: Human Skeleton Action Recognition

Due to the compact and rich high-level representations offered, skeleton-based human action recognition has recently become a highly active research topic. Previous studies have demonstrated that investigating joint relationships in spatial and temporal dimensions provides effective information critical to action recognition. However, effectively encoding global dependencies of joints during spatio-temporal feature extraction is still challenging. In this paper, we introduce Action Capsule which identifies action-related key joints by considering the latent correlation of joints in a skeleton sequence. We show that, during inference, our end-to-end network pays attention to a set of joints specific to each action, whose encoded spatio-temporal features are aggregated to recognize the action. Additionally, the use of multiple stages of action capsules enhances the ability of the network to classify similar actions. Consequently, our network outperforms the state-of-the-art approaches on the N-UCLA dataset and obtains competitive results on the NTURGBD dataset. This is while our approach has significantly lower computational requirements based on GFLOPs measurements.
Special Police Team Towards the Bright Point Brigadier Joshua's Murder Case

The obstacles faced by the police in uncovering this case are 1). Police officers have cleared the crime scene; 2). Brigadier J's HP Lost; 3). Witness Shut Up; 4). Other Obstacles regarding CCTV at the Office House Damaged; 5). Putri Candrawati is a key witness who is still alive. The bright spots for the cases that led to the death of Brigadier J include 1. The presence of Tita President Joko Widodo; 2. There is suspicion of premeditated murder; 3. It has been obtained and turned on CCTV cameras around the crime scene that are lost or dead; 4. Brigadier J's cellphone and CCTV have been examined in a forensic laboratory based on the Scientific Crime Investigation processed by a forensic laboratory; 5. There is a re-autopsy. To open the veil of Brigadier J's case based on President Joko Widodo's order, the National Police Chief finally named a suspect in the shooting case that killed Brigadier Yoshua Hutabarat, based on 1). Initial evidence is sufficient; 2). Other possible suspects; 3). The threat of punishment (Bharada E; 4). Bharata E was arrested; 5). Not self-defense; 6). Murder suspect; 7). Initial chronology.
Multi-elemental analysis of free water obtained from water in oil emulsions from Niger Delta Region using ICP-OES

Disposal of produced water obtained from crude oil could be challenging and tough because of the effect it has on the environment. Produced water contains high levels of salt and toxic substances which must be treated before re-use or disposal to reduce their impact on the environment, people and assets. Hence, the characterization of produced water for its chemical composition is essential. This study presents the determination of heavy metals (Co, Cu, Pb, Fe, Zn, Ni, and Cd) in produced water obtained from crude oil emulsion samples via inductively coupled plasma optical emission spectrometry (ICP-OES). The samples were subjected to treatment prior to analysis. The analytical curve obtained from the calibration gave excellent correlation coefficients ranging from 0.9995- 1.000 and the recovery values obtained were very good (95% - 105%). The result of the heavy metal analysis in μg/l (Co: &lt;DL to 14.3, Cu: 114.1 to 1553.2, Fe: 492.1 to 1576.4, Ni: 52.3 to 174.5, Pb: &lt;DL to 15.1, Zn: 92.4 to 432.9, Cd: 2.3 to 4.4) indicates that the concentrations of the metals evaluated in the produced water samples were above the recommended discharge limit ( Zn - 1.0 mg/L, Co - 0.02 mg/L, Fe - 1.0 mg/L, Cu - 0.02 mg/L, Cd - 0.01 mg/L, Ni - 0.03 mg/L and Pb - 0.05 mg/L).
A Notional Understanding of the Relationship between Code Readability and Software Complexity

Code readability and software complexity are considered essential components of software quality. They significantly impact software metrics, such as reusability and maintenance. The maintainability process consumes a high percentage of the software lifecycle cost, which is considered a very costly phase and should be given more focus and attention. For this reason, the importance of code readability and software complexity is addressed by considering the most time-consuming component in all software maintenance activities. This paper empirically studies the relationship between code readability and software complexity using various readability and complexity metrics and machine learning algorithms. The results are derived from an analysis dataset containing roughly 12,180 Java files, 25 readability features, and several complexity metric variables. Our study empirically shows how these two attributes affect each other. The code readability affects software complexity with 90.15% effectiveness using a decision tree classifier. In addition, the impact of software complexity on the readability of code using the decision tree classifier has a 90.01% prediction accuracy.
IGNiteR: News Recommendation in Microblogging Applications

As social media, and particularly microblogging applications like Twitter or Weibo, gains popularity as platforms for news dissemination, personalized news recommendation in this context becomes a significant challenge. We propose a diffusion and influence-aware approach, Influence-Graph News Recommender (IGNiteR), which is a content-based deep recommendation model that jointly exploits all the data facets that may impact adoption decisions, namely semantics, diffusion-related features pertaining to local and global influence among users, temporal attractiveness, and timeliness, as well as dynamic user preferences. We perform extensive experiments on two real-world datasets, showing that IGNiteR outperforms the state-of-the-art deep-learning based news recommendation methods.
English ubiquitous learning system based on big data analysis

The English ubiquitous learning system is designed on the basis of big data analysis. The English ubiquitous learning system uses big data mining technology to deeply mine students' individual differences and learning characteristics. As a teaching auxiliary means, big data technology can further integrate English teaching data information and transform English knowledge into visualization, intelligence Efficient learning mode. Data processing model can also analyze and utilize English knowledge on network resources, enrich English teaching database, provide personalized services for students' English learning, and realize the diversification of English ubiquitous learning system functions.
User Searching in Digital Repositories

This chapter provides an overview of current knowledge of user search behavior and needs regarding digital repositories. It examines the relevant research, organizes key themes, and discusses how user search behavior in digital repositories is investigated and the extent to which these investigative methods are answering key questions. This chapter explores the results of studies seeking to address the following questions that researchers and practitioners ask when analyzing and trying to predict user search behavior: Context impacting use of digital repositories: what materials do digital repositories provide access to, and which audiences do they serve? What are the user expectations toward digital repositories and the tools offered to support discovery in them? How do users interact with digital repositories? What are the typical keyword search constructions? How are system features typically used and how might interface design affect their use? How do controlled vocabularies contribute to discoverability in digital repositories? Based on this overview, the chapter concludes with recommendations of methods and tools practitioners can use to investigate user interactions with and expectations toward digital repositories and to obtain actionable data that will inform improvements in discoverability.
Implantation of Mixed Reality Tools in Design Enhancement Application

The novel Mixed Reality Tools as part of Augmented Reality Technologies enables new approaches in design, education, presentation and collaboration in all aspects of human life.Industrial revolution 4.0 gives new possibilities for the development by extending the application of Mixed Reality to be used in all steps of the design process.Integration of the digital world in the real world gives a better view during the design process and helps designers collaborate all over the world today in real time.Using Hololight AR 3S collaborative software, which provides the opportunity to see 3D models interact with the real world, gives full insight into fulfillment of ergonomic and functional requirements.AR 3S software helps understand very complex solutions and designs in great details.The possibility to use online meetings with complete interaction of all participants in Mixed Reality enables better understanding and collaboration.Using step by step guides in Mixed Reality environments provides enormous possibilities for presentation and education, letting users see holograms (stationary and animated), images and videos.Mixed reality in Microsoft Dynamics 365 Guides allows incorporate web-based information and documents in online real-time experience providing a chance to users to actively engage with the presented material.All the material within the guides is a cloud-based solution, enabling large numbers of the guide users to use it at the same time.As an educational tool, Microsoft Dynamics 365 Guides gives also feedback to the author in regards to how fast and how well the presented material is covered and understood.Using Microsoft Dynamics 365 Remote Assist allows the user to share the amazing Mixed Reality experiences with other users online when presenting concepts and designs to a large audience.All new Mixed Reality Tools will be, in the future, an integral part of education and presentation of design solutions.
Mind and matter

Andrew Zangwill’s March 2022 article (page 28) presents an insightful portrait of Philip Anderson in dynamic, human terms. I was particularly drawn in by Zangwill’s mention of Anderson’s Anglophilia and his association with leading researchers at the University of Cambridge. In that context, two of Anderson’s “four facts”—that computers will not replace scientists and that good science has aesthetic qualities—resonate with Brian Josephson’s interests in the past 20-odd years.I met Josephson at an international conference, titled Home and the World: Rabindranath Tagore at the End of the Millennium, which was held by the University of Connecticut in September 1998. Josephson spoke about the poet-philosopher Tagore (1861–1941) and science.11. B. Josephson, in Rabindranath Tagore: Universality and Tradition, P. C. Hogan, L. Pandit, eds., Fairleigh Dickinson U. Press (2003), p. 107. From my relatively brief encounter with him, I understood at the time that Josephson was especially interested in the area of mind–matter interactions, and that, of course, had some relevance to the well-known 1930 conversation that Tagore had with Albert Einstein on reality and the human mind.22. M. Popova, “When Einstein met Tagore: A remarkable meeting of minds on the edge of science and spirituality,” Marginalian (27 April 2012). Mind–matter interactions have also been an area of sustained interest for many leading scientists, including Ilya Prigogine and Roger Penrose.It is also quite noteworthy that Zangwill mentions Charles Kittel as one of Anderson’s mentors at Bell Labs. Many of us pursuing physics and engineering in India in the 1970s were introduced to Kittel’s classic textbook Introduction to Solid State Physics, which was foundational to our understanding of the subject.ReferencesSection:ChooseTop of pageReferences <<1. B. Josephson, in Rabindranath Tagore: Universality and Tradition, P. C. Hogan, L. Pandit, eds., Fairleigh Dickinson U. Press (2003), p. 107. Google Scholar2. M. Popova, “When Einstein met Tagore: A remarkable meeting of minds on the edge of science and spirituality,” Marginalian (27 April 2012). Google Scholar© 2023 American Institute of Physics.
A fast method for reducing noise in digital color images using anomaly detection and interpolation

The interest of this paper is impulse noise reduction in digital color images. The most popular noise reduction schemes are the vector median filter and its many variants that operate by minimising the aggregate distance from one pixel to every other pixel in a chosen window. The major bottleneck in these methods is the high computational complexity involved due to sequential filtering over the chosen windows. This impedes the use of vector median filters for real time applications despite their superior performance. This paper presents a fast method for reducing impulse noise in digital color images. The key idea here is to consider each row in each color component as a univariate data slice. The impulse noise appears as anomalies at the corrupted pixel indices within the data slice. These indices are identified using thresholding and then the values therein are interpolated across the two pixels left and right of the anomaly(s). This process is performed across all the slices and along all the color components. This ensures that the impulse noise is smoothed out. Since the proposed method smooths out the impulse noise by way of interpolation, it does not result in as much reduction in noise as do the conventional median filters but scales super efficiently in terms of time complexity.
Building Movie Recommender Systems Utilizing Poster's Visual Features: A Survey Study

Information overload has made it difficult for users to get needed data. In this context, recommender systems are used to filter information and provide personalized recommendations to help individuals make better choices. Providing consumers with a customized selection of movies to watch is critical for media service providers. However, sparsity and cold-start are two barriers to reliable recommendations. Recent advancements in deep learning architectures have spurred several studies to overcome recommender flaws using various informative resources, such as movie posters. In contrast to other surveys on movie recommendations, this work provides a comprehensive review of how visual data, especially movie posters, can enhance the results. Furthermore, comparing different approaches for building recommender systems is followed by in-depth surveying about investigating the movie recommender systems, including their methods, evaluation metrics, and datasets. This exhaustive analysis provides a detailed picture of the topic's popularity, gaps, and unexplored regions. It is envisaged that the proposed research and introduced possible future directions would serve as a stepping stone for researchers interested in building approvingly efficient modern movie recommender systems.
Losing and Location in the Knowledge Economy

Ths chapter looks at the economic and political dimensions of the downside of the knowledge economy. The knowledge economy both drives knowledge clusters and creaters hinterlands where those left behind by the changes in the economy dwell. Significant evidence shows the extent of grievance on the part of the left-behind, which manifests itself in nationalist populism and hostility to international law. Populists depict international law as a series of corrupt power grabs by an élite that is indifferent to the left-behind. The chapter concludes with a consideration of the possibility of Chinese hegemony substituting for international law as a way of organizing international cooperation.