Reviewerly/exHarmony huggingface dataset:
Reinforcement and Punishment: The Concepts Teachers Need to be Carefully Watched

Abstract: Numerous acts done by teachers without proper consideration were impactful to students. This study determines the level of teacher cognition to reinforcement and punishment concept in the learning process at school. In addition, this study also aims to assign the accuracy of applying these concepts to students. Mixed-method using embedded design was implemented to address the goals. As 49 teachers responded from several schools in the District of Kepulauan Pongok, South Bangka. Besides, in-depth interviews were also conducted with five teachers who were selected. The research sample was set based on purposive sampling. The data collection techniques were a questionnaire in the form of a rating scale and an in-depth interview form with a feasibility test previously performed using the Content Validity Index. Likewise, the data obtained were processed using the calculation of the average, standard deviation, percentage as well as content analysis. The results indicated that 2.86 out of 5 or 57.2% of the teachers had good understanding towards the two concepts. Meanwhile, 3.15 or 63% of the teachers were familiar to reinforcement. Besides, 2.44 or 4.88% of the teachers were familiar to give punishment. In other words, the comprehension as well as it application are classified very moderate and needed to be carefully watched.
Kajian Pengaruh Pelebaran Sungai terhadap Debit Aliran

Flood is one of the problems that often occurs in Indonesia. In Jambi City, every rainy season often experiences flooding, especially in Jambi City or the Garuda III Jaya Complex area, Bagan Pete Village, Jambi City. Thus, this study aims to determine how much rainfall intensity is on the Kenali Besar River and how much river flood discharge has been carried out at Komplek.Perum Garuda III Jaya, Bagan Bete Village, Jambi City with a time of 10 years and so that later the Kenali Besar River channel can be channeled. can function optimally to reduce flooding that occurs in the Garuda III Jaya Complex area, Bagan Pete Village, Jambi City, so that a good and quality river is created while taking into account the safety and comfort factors for the surrounding community. In this study, the data used were secondary data in the form of daily rainfall for 10 years and cross-sectional dimension data of the Kenali Besar River Komplek.Perum Garuda III Jaya, Bagan Pete Village. The results of the calculation of the planned rain with a return period of 10 years is 246.469 mm⁄hour, while the planned discharge using the Rational method obtained a peak discharge value of 30.833 m3⁄seconds the step is continued using HEC-RAS 5.0.7 software to determine the capacity of the drainage channel by using plan debit. After being analyzed using software.
Exploration of the Counting Habit of the Hatam Tribe in Nuhuwei Papua Barat Indonesia

The Hatam tribe is one of the sub-tribes of the Arfak tribe. This study aims to explore the numeracy culture of the Hatam Tribe spoken by the Hatam Tribe community who inhabit Nuhuwei Village, Ransiki District. This research is ethnographic. The techniques used for data collection are interviews, exploration, literature study, desk study, and documentation. Interviews and explorations were conducted with several elders who spoke the Hatam language in Nuhuwei Village. Literature and documentation studies were conducted by looking for other sources of information related to the previously existing Hatam language to obtain accurate results. The Desk Study was conducted to find out the mathematical patterns of the Hatam Tribe. The results obtained from this study are that mathematically, the counting patterns spoken by the Hatam people who inhabit Nuhuwei Village have different dialects from the speech of the Hatam people who inhabit other areas. Besides that, another finding is that the pattern of mentioning numbers spoken by the Hatam tribe inhabiting Nuhuwei Village correlates with the counting pattern on the Jarimatika. We call this latest finding related to counting using the Hatam language-based Jarimatika as Himatam (Calculating Using Jarimatika in the Hatam language). This finding shows that learning numeracy for elementary students in Nuhuwei Village can be done more easily using the Himatam Method.
Intrusion and Anomaly Detection in Industrial Automation and Control Systems

In the domain of Industrial Automation and Control Systems (IACS), security was traditionally downplayed to a certain extent, as it was originally deemed an exclusive concern of Information and Communications Technology (ICT) systems. The myth of the air-gap, as well as other preconceived notions about implicit IACS security, constituted dangerous fallacies that were debunked once successful attacks become known. Ultimately, the industry started shifting away from this dangerous mindset, discussing how to properly secure those systems. In many ways, IACS security should not be treated differently from modern ICT security. For sure, IACS have distinct characteristics, assets, protocols and even priorities that should be considered – but security should never be an optional concern.In this publication, we present the main results of a PhD dissertation that proposes a holistic and data-driven framework capable of leveraging distinct techniques to increase situational awareness and provide continuous and near real-time monitoring of IACS. For such purposes, it proposes an evolution of the Security Information and Event Management (SIEM) concept, geared towards providing a unified security data monitoring solution by leveraging recent advances in the field of real-time Big Data analytics. In the same way, the most recent machine-learning-based anomaly-detection techniques (which are becoming increasingly prominent in the cybersecurity field) are also analyzed and studied to understand their benefits for developing and advancing IACS cyber-intrusion detection processes.
Finding Beauty Products Chemicals Using Token Identification and Entity Recognition Transformer Model

Transformer architectures and pre-training have facilitated building higher-capacity models and effectively utilizing this capacity for a wide variety of tasks. Various libraries consist of carefully engineered state-of-the-art Transformer architectures under a unified API. But still, there exists no such system which detects the chemicals present in the Beauty products reviews on online e-commerce websites. On the market, there are thousands of different cosmetic products, all with different combinations of ingredients. Methods such as token identification and entity recognition transformer model advancing in both model architecture and model pre-training are implemented to identify and categorize the named entities.
Do Explainable AI techniques effectively explain their rationale? A case study from the domain expert’s perspective

Artificial Intelligence (AI) systems are technologies impacting our lives. The systems learn from existing datasets that record past human decisions. Their performance is measured in terms of accuracy, precision, and recall for reproducing already-known results. Understanding the system’s rationale is crucial to check for bias and accept such technology. Explainable AI (XAI) is the area devoted to opening the AI black box, and designing guidelines to build explainable AI systems. Nevertheless, it is important to understand the user’s needs for these explanations. This paper presents an investigation of the usefulness of XAI systems in the field of cancer diagnosis from the domain expert’s (oncologist) perspective. The main findings suggest domain experts (1) understood the outcomes of the XAI systems; (2) considered XAI outcomes as informative, rather than explanatory; (3) would like to go beyond the fixed presented perspective; and (4) missed the causal relation that would reveal the system’s rationale.
Sign Language Interpretation Using Deep Learning

Sign language is used to communicate a particular message over some universally known and accepted gestures. The speech and hearing challenged people use a specific combination of hand gestures and movements to convey a message. Despite the extensive research progress in Sign Language Detection, cost effective and performance effective solutions are still need of the day. Deep learning, and computer vision can be used to provide an effective solution to the user. This can be very helpful for the hearing and speech impaired people in seamless communication with others around as knowing sign language is not something that is common to all. In this work, a sign detector is developed, which detects various signs of the Sign Language used by speech impaired people. Here, data taken as input in the form of images is extensively used for both training and testing using machine learning. A custom Convolutional Neural Network (CNN) model to identify the sign from an image frame using Open-CV is developed and sentence construction of the detected signs is accomplished. A lot of images have been used as input for the purposes of training and testing. Many of the symbols in sign language could be rightly identified. A series of gestures are translated as text to the recipient.
Self-organized and fully service-automated monitoring approach at the cloud-network slice granularity

The cloud-network slicing concept has been established to deal with the advent of the Fifth Generation (5G) of mobile networks and its enabling technologies, thus promoting softwarization and cloudification. The Novel Enablers for Cloud Slicing (NECOS) ecosystem distinguishes itself over state of the art through the definition of slicing at both cloud and network levels and by promoting a Management and Orchestration (MANO) platform that provisions features with a self-organized and full-service automation approach across multiple federated domains. In the NECOS architecture, the Infrastructure and Monitoring Abstraction (IMA) component fetches Key Performance Indicators (KPIs) associated with the constituent parts of the active cloud-network slice instances managed by the NECOS platform. However, the design of the IMA monitoring component follows a centralized approach running at the core-cloud domain. Thus, the IMA concentrates on the monitoring data fetching function, done through interaction with measurement applications. It books all of them into a database and then forwards the incoming data for targeting management applications. Our findings in the slice monitoring state of the art study and assessments in IMA central cloud monitoring suggest that the centralized cloud approach cannot make distinct monitoring technologies compatible, besides not presenting a monitoring-as-a-service perspective that allows a self-organized and fully-service automated monitoring management scheme. In this regard, this work proposes the Distributed Infrastructure and Monitoring Abstraction (DIMA) multilevel monitoring plan. DIMA can promote monitoring as a service across an edge-cloud continuum inside the NECOS domain, enabling cloud/edge-centric and distributed monitoring schemes at the granularity of cloud-network slices' constituent parts.
Co-clustering Based Methods and Their Significance for Recommender Systems

In the contemporary era, businesses are driven by Internet based web or mobile applications. In every conceivable area of research, it is indispensable for such applications to have a recommender system to expedite the interactions between customers and business entity for faster convergence. Provided this fact, there is need for leveraging such systems as they have unprecedented impact on businesses across the globe. In this regard, identification of merits and demerits in the existing methods used to realize recommender systems is to be given paramount importance. In this paper, we review literature to ascertain useful facts pertaining to different approaches to make recommender systems. Since recommender systems lubricate the process of commercial or otherwise interactions with consumers, for business entities it is imperative to have applications with built-in recommender system. The literature review made in this paper provides different aspects of recommender systems such as datasets, methods and their utility in the current business scenarios. It throws light into the research gaps that help in further research and improvement based on novel data mining approaches.
Unknown input observer for Takagi-Sugeno implicit models with unmeasurable premise variables

&lt;p&gt;Recent years have seen a great deal of interest in implicit nonlinear systems, which are used in many different engineering applications. This study is dedicated to presenting a new method of fuzzy unknown inputs observer design to estimate simultaneously both non-measurable states and unknown inputs of continuous-time nonlinear implicit systems defined by Takagi-Sugeno (T-S) models with unmeasurable premise variables. The suggested observer is based on the singular value decomposition approach and rewritten the continuous-time T-S implicit models into an augmented fuzzy system, which gathers the unknown inputs and the state vector. The exponential convergence condition of the observer is established by using the Lyapunov theory and linear matrix inequalities are solved to determine the gains of the observer. Finally, the effectiveness of the suggested method is then assessed using a numerical application. It demonstrates that the estimated variables and the unknown input converge to the real variables accurately and quickly (less than 0.5 s).&lt;/p&gt;
Creating nonlocality using geometric phases between partially distinguishable photons

The geometric (Berry-Pancharatnam) phase originates from the intrinsic geometry of the space of quantum states and can be observed in different situations, such as a cyclic evolution of a quantum system. Here, we utilize the geometric phase to obtain a surprising insight: It is possible to create nonlocal correlations in a fixed interferometer with independent photon inputs by varying the photons' internal states. In particular, we consider a cyclic interferometer that is fixed, i.e., that has no variable internal phase shifts or subsequent measurement settings. Instead, the measurement choices of the different parties correspond to the internal states of the input photons which influence the observed correlations via a collective $N$-photon geometric phase, constituting a different approach for the generation of nonlocality with respect to the usual paradigm. We observe a trade-off between the geometric phases and the visibility of the many-photon interference, impeding the generation of nonlocality. However, by making use of the dynamical quantum Zeno effect, we show that nonlocality can be created in the fixed cyclic interferometer using 12 (or more) independent photons.
Organizers and Technical supporting team

the Peer Community In (PCI) Neuroscience, etc.He published over 200 main research papers in outstanding ISI-ranked peer reviewed journals and international conference proceedings in the fields of mathematics, mathematical physics and complexity.He is a visiting professor at African, Asian, European and North American Universities.Together with his peers at the international level, he founded the International Chair in Mathematical Physics and Applications (ICMPA -UNESCO Chair) of the University of Abomey-Calavi where he created multi-university master degree and PhD programs in mathematics with connections, motivations or applications to physics, and in physics with important relations to mathematics.The best African students from over 13 French and English-speaking countries are selected to follow these graduate programs, which attract prominent and leading mathematicians and mathematical physicists worldwide who come to give lectures and supervise student's research, what has substantially increased international collaboration with African, Asian, American, European and Indian mathematicians.The ICMPA-UNESCO Chair presently hosts the international conference and school series on Contemporary Problems in Mathematical Physics, which are held every two years and each year since 1999 and 2005, respectively.These activities have led to a significant network of researchers connected with the ICMPA-UNESCO Chair, that benefits from the resources available in mathematics and mathematical physics in Africa.
DATA MINING IN ORGANIC GEOCHEMISTRY: CASE STUDY IN POTIGUAR BASIN

The amount of data from geochemical analysis using samples collected in oil wells grows simultaneously to the investment in the exploration and production sector. On the other hand, the treatment and interpretation of these results are still very dependent on experts and demand time. With the generation of extensive databases, data mining presents itself as a good alternative to explore them through statistical methods and computational algorithms, providing technological differential and agility to the system. In an experimental way, with data from 200 oils from the Potiguar Basin, these tools were implemented, with the consequent suggestion of a workflow that would, in the end, return a reasonable accuracy in predicting their genetic classification. Using multidimensional scaling (MDS) and clustering (dendrogram and k-means types) from 60 initial attributes, the optimal set was reduced to 26. Applying Machine Learning, 92.50% of median accuracy were obtained in the Decision Tree algorithm, 95.00% in Random Forest and 87.50% in Artificial Neural Network. Comparing to an analysis previously presented at the pertinent literature, the benefits in terms of efficiency can be realized with the adoption of the methodology herein proposed.&#x0D; &#x0D; Keywords: Organic geochemistry; Data Mining; Multivariate Statistics; Workflow.
Rertraction Notice: Conceptual Reflection in Its Main Historical and Philosophical Sequence. 2012; (3); 25-33.

The article Conceptual Reflection in Its Main Historical and Philosophical Sequence published in the Bulletin of Nizhnevartovsk State University of Humanities (the Bulletin of Nizhnevartovsk State University since 2013) (2012, Vol. 3) by V.I. Krasikov is a duplicate of the article by V.I. Krasikov Philosophy: Its Mental and Social Nature, Main Historical Sequences published in the Bulletin of Kemerovo State University (2012, Vol.1). That was the reason for retraction.
Deep Learning-aided Channel Estimation For Universal Filtered Multi-carrier Systems

In this paper, a novel channel estimation technique is proposed for Universal Filtered Multi-Carrier (UFMC) systems. The proposed technique employs Deep Learning (DL) models which utilize the, commonly discarded, odd-indexed samples of the received signal in order to enhance the channel estimation. Three DL models with different sets of input features are proposed. The three proposed DL models were trained and then deployed into a UFMC system to evaluate their performance. The performance metric for the training stage is the Normalized Mean Squared Error (NMSE) between the estimated channel and actual channel coefficients. For deployment stage, both NMSE and Bit Error Rate (BER) are chosen as performance metrics. The proposed models are compared versus conventional Least Square (LS) channel estimator. The results show that the proposed DL-models outperform the LS channel estimator for various Signal to Noise Ratio (SNR) even for channel models which are different from the one used for training. The SNR gains of utilizing the proposed models are 5-6dBs and 2-3dBs on average for NMSE and BER, respectively.
A Copyright Authentication Method Balancing Watermark Robustness and Data Distortion

Database watermarking plays an irreplaceable role in copyright authentication and data integrity protection, but the robustness of the watermark and the resulting data distortion are a pair of contradictory objects that cannot be ignored. To solve this problem, a reversible database watermarking method, named IGADEW, is proposed to balance the relationship between them. The biggest difference from previous research is that IGADEW synthesizes the optimization objects and obtain various parameters through genetic algorithm (GA). Second, the fitness function considers the weights of robustness and distortion, aiming to find the optimal balance between the two. IGADEW uses the Hash-based Message Authentication Code (HMAC) algorithm to encrypt the experimental parameters and uses the primary key hash algorithm for data grouping, both to ensure robustness. And the data distortion is limited with the help of threshold constraints. Finally, experiments using the UCI dataset demonstrate the effectiveness of IGADEW. Experimental results show that, compared with existing methods, IGADEW is more robust against common attacks, with lower data distortion.
TorDNS: A Novel Correlated Onion Address Generation Approach and Application

The onion service is the most important mechanism of the Tor network which enables service providers to publish anonymously various TCP services, such as web services. To access the target onion services, clients first know the 56-byte onion addresses. However, randomly generated onion addresses are difficult to memorize and can be easily used by attackers to generate phishing sites with similar onion addresses. In this paper, we propose a correlated onion address generation approach which is capable of generating a unique onion address via a customized string and a root onion address. This approach enables the generated onion addresses to be computed by clients using a human-memorable string, resulting in easier access to onion services. Based on this approach, we design and implement a Tor Domain Name System (TorDNS) that allows different service providers to register anonymously and clients to access anonymous services quickly through human-memorable pseudo-onion addresses. TorDNS is compatible with existing onion service mechanism and does not introduce additional privacy and security issues. In addition, similarity detection of pseudo-onion addresses can effectively reduce the risk of phishing sites on the Tor network.
TSFN: an Effective Time Series Anomaly Detection Approach via Transformer-based Self-feedback Network

As the scale of data on the Internet continues to increase, the management and monitoring of time series data are facing significant challenges. Efficient and stable time-series data anomaly detection methods are necessary for fields such as traffic detection, power grid operation and maintenance, financial stock market, and industry. However, there are fewer abnormal data labels in time series data, and the labeling cost is high. Traditional expert knowledge-based supervised methods have been difficult to adapt to large-scale data metric management and timely abnormal alarms. At the same time, the way based on the new neural network has an extensive time overhead when faced with massive data, and it isn’t easy to apply it in a real-time industrial environment. Therefore, we propose the TSFN model in this paper, an unsupervised method of a transformer-based self-feedback network. Which can capture timing dependencies, learn normal data distribution and improve the self-feedback ability for sensitive areas, and can be used to detect anomalies in multidimensional time series more quickly. Our experimental research on five public datasets shows that our method has fast training speed, good stability, excellent anomaly detection ability, and good generalization ability compared with the baseline method.