Reviewerly/exHarmony huggingface dataset:
Efektivitas Humas dan Wartawan dalam Menyosialisasikan Program Website Layanan Aspirasi Diskominfo Labuhan Batu

Communication is a need that cannot be separated from humans. In the develop of mass communication there was a development regarding mass media when the internet was discover. The aim to achieved through this research is: "to determine the effectiveness of public relations and journalists in socializing the aspiration service website program at Diskominfo Labuhan Batu". The type of research carried out in this research is qualitative research, namely research based on post-positivist philosophy, used to examine the condition of natural objects. The results of this research show that the uses of the Labuhan Batu Diskominfo website had been effective, because people with technological developments now demand that ASN/PNS are media literate. So automatically, previously the website might have been widely used by the private sector, now the government is using the website and social media too. Now people are more concern, especially government public relations, who are increasingly concerned that a website can be more representative or competent of the agency's image. The advantages of using the Labuhan Batu Diskominfo website include: we can better share data or public policy information by the Labuhan Batu Diskominfo, it can be use to image the Labuhan Batuc Diskominfo, the information provided is always up to date or the latest.
No. 53867. International Bank for Reconstruction and Development and Nigeria

Indemnity Agreement (Nigeria – Azura-Edo Independent Power Project) between the Federal Republic of Nigeria and the International Bank for Reconstruction and Development in respect of the IBRD Guaranteed Loans (with schedule and International Bank for Reconstruction and Development General Conditions for Loans, dated 12 March 2012). Abuja, 21 August 2015
Enhancing Security in Islanded AC Microgrid: Detecting and Mitigating FDI Attacks in Secondary Consensus Control through AI-Based Method

This paper presents a successfully developed secondary consensus control system to ensure accurate voltage regulation among distributed generators in an islanded AC microgrid. The communication network is built using Graph theory and Laplacian matrix techniques. The impact of False Data Injection (FDI) attacks on the communication link, particularly on consensus control measurement data, is analyzed to observe the resiliency of the consensus controller. Timely mitigation of FDI attacks is crucial for maintaining the system’s stability. To address this, we propose a novel Artificial Neural Network (ANN) based observer, capable of estimating the system’s state under load changes and attack scenarios. The proposed AI-based detection method effectively identifies and mitigates FDI attacks swiftly, preventing further deterioration of the system. The whole system is modeled in MATLAB to validate the effectiveness of the proposed method, showcasing its ability to safeguard the microgrid against cyber threats. The study highlights the resilience of the consensus controller and emphasizes the significance of quick FDI attack detection and mitigation for ensuring the stability and reliability of the entire microgrid system.
Selective Harmonic Elimination of PUC-5 MLI Using Machine Learning

In this paper, an artificial neural network technique is introduced for the application of Selective Harmonic Elimination (SHE) for a five-level packed U cell inverter. SHE is a low-frequency modulation approach for multilevel converter control and harmonic elimination. The proposed ANN-SHE method involves computing optimal switching angles through a system of nonlinear equations to reduce total harmonic distortion (THD) based on Fourier series analysis. The proposed method is based on a multilayer perceptron algorithm, which is a type of ANN that is well-suited for solving nonlinear problems. The MLP algorithm was trained on a dataset of switching angles and modulation indices, and it was able to learn the relationship between these two variables. The change in switching angles for all possible values of the modulation index could then be estimated using the trained ANN. The paper elaborates the proposed ANN's programming procedures utilizing the MATLAB/Simulink environment. The implemented prototype of inverter tested in real time with various modulation indexes using a DSP embedded board i.e., TMS320F28379D. The simulation results and experimental data are compared and presented, demonstrating the close match between the two. The ANN model offers swift and accurate estimation of switching angles for each of the modulation index, making it an efficient alternative to traditional SHE methods.
Application and Evaluation of Soft-Actor Critic Reinforcement Learning in Constrained Trajectory Planning for 6DOF Robotic Manipulators

In the field of robotic manipulator operations, precise trajectory planning for the end-effector's position and orientation is crucial, especially in tasks such as grasping a bottle by its neck. This paper presents a novel approach utilizing Reinforcement Learning to address this issue. Specifically, we employed the Soft Actor-Critic and Hindsight Experience Replay algorithm to train a UR5e manipulator in a simulated environment, incorporating a unique design for state, action, and reward. Through comparative analysis with other reward function designs, we found that our trained Reinforcement Learning model generated a more efficient trajectory and achieved a significantly higher success rate. This study underscores the potential of our approach for enhancing the trajectory planning of robotic manipulator operations.
Dialectical approach towards the renewal of the pedagogical paradigm

The relevance of the advanced updating of pedagogical paradigm, the possibility giving it a functional, con-structive and unambiguous character is substantiated.In the study, in order to update the pedagogical para-digm, universal abstract-concrete coordinates, Hegel's dialectical method, a speculative language of function-al schematic images and a reflexive “workbench”were used. As the simplest requirements for understanding the picture of the world, it is proposed to use external and internal signs of harmony of living and inanimate nature. On their basis, the “golden principles”of nature-like thinking and activity are derived. Logical, dialec-tical relations between paired categories are established. This allows us to solve the problem of their synony-my. To build a conceptual pedagogical paradigm, it is recommended to use speculative, oral and written lan-guages. Due to this, concepts under construction or being updated acquire a functional, meaningful, visible and constructive appearance. The technological cycle of dialectical reconstruction and updating of pedagogi-cal paradigm wasgiven, which provides for the consistent derivation of concepts from the cycle individual human activity. This ensures continuity and compliance of the concepts of pedagogical paradigm with the cri-teria of morality, functionality, logic, consistency, constructiveness and unambiguity.It is argued that the re-newal and subsequent development of the pedagogical paradigm will significantly increase the leveloffunc-tional literacy of teachers, students andall subjects of professional activity. Students' reproduction of func-tional models of various professional activities using the constructed paradigm will contribute to their con-scious choice of profession, self-determination in conditions of volatile market relations
Formation of entrepreneurialcompetencies in higher education institutions in the context of university development 3.0

The focus of this article is on the development of entrepreneurial competencies in Universities 3.0. The objec-tive of the article is to provide an overview of the current status of entrepreneurial education in domestic uni-versities and assess the preparedness of students for entrepreneurial endeavors. It also examines the satisfac-tion level of students with the educational program and evaluates the entrepreneurial competencies acquired during their university experience. The article includes a review of relevant literature by foreign researchers, exploring various perspectives and approaches to the study of entrepreneurial competencies. The article pre-sents the results of a survey conducted at the universities of Almaty, which revealed the self-assessment of students' entrepreneurial competencies formed as a result of teaching entrepreneurship, the ability to entre-preneurship, interest in entrepreneurial education at the university and outside the university. The study re-vealed strengths and weaknesses, as well as opportunities and threats, in the establishment of entrepreneurial universities. It also examined students' attitudes towards entrepreneurial education, including an evaluation of their competencies. The value of the study is the study of the little-studied area of the formation of entrepre-neurial competencies in Kazakhstani universities with an entrepreneurial direction, bringing the problematic issues of entrepreneurial education, which can favorably influence the solution of problems in this area and the further development of entrepreneurial universities.
COMPARATIVE STUDY OF CLINICAL AND LABORATORY PROFILE IN SPECIES SPECIFIC MALARIAL INFECTION: OBSERVATIONS FROM A TERTIARY CARE HOSPITAL FROM AN ENDEMIC REGION OF INDIA

Background: Malaria continues to be an issue of public importance in Indian subcontinent. This study was conducted to observe the difference in hematological and hepato-renal profile in malaria patients affected by Plasmodium falciparum, Plasmodium vivax and both the parasites admitted in the tertiary care hospital. As far as the authors are aware of this is the first study that attempted at understanding the clinical and laboratory profile to ascertain the difference in the clinic-pathogenesis from a malaria endemic region of India. Methods: This was a retrospective observational study conducted at a tertiary care hospital between January 2016 to July 2017. All patients aged above 18 years diagnosed with malaria were included in the study. The demographic, clinical and laboratory parameters were entered in Microsoft excel and subjected to statistical analysis. Results: A total of 262 patients were diagnosed with malaria during the study period. Majority of the patients were male (208, 79.39%) and patients of age between 18-30 years accounted for 82 (31.3%). Among the 262 patients, 93 (35.5%) were diagnosed with Plasmodium vivax, 85 (32.4%) mixed (P.vivax and P. falciparum) and 84 (32%) Plasmodium falciparum infection. In the present study, anemia, leucopenia and thrombocytopenia were seen among hematological parameters. The liver enzymes, total bilirubin were raised and total protein, S.albumin were reduced among the hepatic profile and raised Blood urea, S.creatinine and dyselectrolytemia were seen among the renal profile in the malaria patients. Conclusion: Malaria is known to cause heamato-hepato-renal dysfunction. In Mixed malaria, anemia and deranged hepato-renal parameters; in falciparum patients, lymphocytopenia, thrombocytopenia, and raised ALT; and in vivax patients, deranged granulocytes and various RBC volume were seen.
Multi-view Cross-Modality MR Image Translation for Vestibular Schwannoma and Cochlea Segmentation

In this work, we propose a multi-view image translation framework, which can translate contrast-enhanced $$\text {T}_1$$ (ce $$\text {T}_1$$ ) MR imaging to high-resolution $$\text {T}_2$$ (hr $$\text {T}_2$$ ) MR imaging for unsupervised vestibular schwannoma and cochlea segmentation. We adopt two image translation models in parallel that use a pixel-level consistent constraint and a patch-level contrastive constraint, respectively. Thereby, we can augment pseudo-hr $$\text {T}_2$$ images reflecting different perspectives, which eventually lead to a high-performing segmentation model. Our experimental results on the CrossMoDA challenge show that the proposed method achieved enhanced performance on the vestibular schwannoma and cochlea segmentation.
Review-Incorporated Model-Agnostic Profile Injection Attacks on Recommender Systems

Recent studies have shown that recommender systems (RSs) are highly vulnerable to data poisoning attacks. Understanding attack tactics helps improve the robustness of RSs. We intend to develop efficient attack methods that use limited resources to generate high-quality fake user profiles to achieve 1) transferability among black-box RSs 2) and imperceptibility among detectors. In order to achieve these goals, we introduce textual reviews of products to enhance the generation quality of the profiles. Specifically, we propose a novel attack framework named R-Trojan, which formulates the attack objectives as an optimization problem and adopts a tailored transformer-based generative adversarial network (GAN) to solve it so that high-quality attack profiles can be produced. Comprehensive experiments on real-world datasets demonstrate that R-Trojan greatly outperforms state-of-the-art attack methods on various victim RSs under black-box settings and show its good imperceptibility.
HINT: Healthy Influential-Noise based Training to Defend against Data Poisoning Attacks

While numerous defense methods have been proposed to prohibit potential poisoning attacks from untrusted data sources, most research works only defend against specific attacks, which leaves many avenues for an adversary to exploit. In this work, we propose an efficient and robust training approach to defend against data poisoning attacks based on influence functions, named Healthy Influential-Noise based Training. Using influence functions, we craft healthy noise that helps to harden the classification model against poisoning attacks without significantly affecting the generalization ability on test data. In addition, our method can perform effectively when only a subset of the training data is modified, instead of the current method of adding noise to all examples that has been used in several previous works. We conduct comprehensive evaluations over two image datasets with state-of-the-art poisoning attacks under different realistic attack scenarios. Our empirical results show that HINT can efficiently protect deep learning models against the effect of both untargeted and targeted poisoning attacks.
Tree-based Kendall’s τ Maximization for Explainable Unsupervised Anomaly Detection

We study the problem of building a regression tree with relatively small size, which maximizes the Kendall’s tau coefficient between the anomaly scores of a source anomaly detection algorithm and those predicted by our regression tree. We consider a labeling function which assigns to each leaf the inverse of its size, thereby providing satisfactory explanations when comparing examples with different anomaly scores. We show that our approach can be used as a post-hoc model, i.e. to provide global explanations for an existing anomaly detection algorithm. Moreover, it can be used as an in-model approach, i.e. the source anomaly detection algorithm can be replaced all together. This is made possible by leveraging the off-the-shelf transparency of tree-based approaches and from the fact that the explanations provided by our approach do not rely on the source anomaly detection algorithm. The main technical challenge to tackle is the efficient computation of the Kendall’s tau coefficients when determining the best split at each node of the regression tree. We show how such a coefficient can be computed incrementally, thereby making the running time of our algorithm almost linear (up to a logarithmic factor) in the size of the input. Our approach is completely unsupervised, which is appealing in the case when it is difficult to collect a large number of labeled examples. We complement our study with an extensive experimental evaluation against the state-of-the-art, showing the effectiveness of our approach.
Multiple Hypothesis Testing for Anomaly Detection in Multi-type Event Sequences

Anomaly detection in multi-type event sequences is a crucial and challenging problem with important applications in various domains, including cybersecurity, finance and healthcare. Temporal point process has emerged as a powerful technique for modeling event sequences and has gained considerable attention in the field of anomaly detection. However, existing temporal point process approaches are either inapplicable to multi-type event sequence data or incur the loss of valuable information in subsequences associated with specific event types. To this end, we propose a novel Multiple Hypothesis Testing based Anomaly Detection method (MultiAD) to detect anomalous multi-type event sequences. The basic idea of MultiAD is to capture the underlying distribution of normal sequences using a neural multivariate point process, based on which the original hypothesis testing problem can be converted into a multiple hypothesis testing using the multivariate time rescaling theorem. By conducting multiple hypothesis tests on the time-rescaled subsequences, MultiAD makes full use of the valuable information contained within individual subsequences. Moreover, we claim that the existing test statistic ignores the sequential information of inter-event time intervals and propose new statistics to address this shortcoming. Finally, we employ the kernel method to obtain a smooth estimator of the distribution of the proposed statistics under the null hypothesis. This ensures a more accurate and reliable computation of the p-value, providing robust statistical inference. Extensive experimental results demonstrate that MultiAD significantly outperforms the state-of-the-art methods on both synthetic and real-world data.
Theoretical Evaluation of Asymmetric Shapley Values for Root-Cause Analysis

In this work, we examine Asymmetric Shapley Values (ASV), a variant of the popular SHAP additive local explanation method. ASV proposes a way to improve model explanations incorporating known causal relations between variables, and is also considered as a way to test for unfair discrimination in model predictions. Unexplored in previous literature, relaxing symmetry in Shapley values can have counter-intuitive consequences for model explanation. To better understand the method, we first show how local contributions correspond to global contributions of variance reduction. Using variance, we demonstrate multiple cases where ASV yields counter-intuitive attributions, arguably producing incorrect results for root-cause analysis. Second, we identify generalized additive models (GAM) as a restricted class for which ASV exhibits desirable properties. We support our arguments by proving multiple theoretical results about the method. Finally, we demonstrate the use of asymmetric attributions on multiple real-world datasets, comparing the results with and without restricted model families using gradient boosting and deep learning models.
Design and Experiments Involving a Mechanism-Based Artificial Tongue Prosthesis

Abstract Oral cancer can result in the loss of tongue through total glossectomy, and patients have difficulty with speech, deglutition, and mastication. Current tongue prostheses lack the ability to move for mastication and deglutition and are mainly for cosmetic purposes. The goal of this research was to investigate and develop a self-contained prosthetic tongue that could aid in deglutition by moving bolus from the front of the mouth to the back. A review of existing designs and alternatives led to the development of a linkage system mounted in a silicone cast to move the bolus. The development of this new prosthetic tongue design entailed creating silicone molds, 3D printing linkage systems and jaws, and developing new circuits. The circuit used a TinyDuino microcontroller and MOSFET. A functional mandibular Hawley retainer was added to hold the tongue to increase ease of use. The final circuit used an EMG sensor to trigger actuation and a single servo motor with a gear train to actuate the tongue. The prosthetic tongue succeeded in moving bolus from the front of the mouth to the back when lying at a 5-degree angle and actuating between 1.8–2.31cm in height. The paper will describe the development process, challenges, and future work.
A Systematic Review on Employee Performance: Scopus Database

According to recent HR studies, employees play vital role to the success of organizations. In recent years, employee performance has gained popularity as a research issue in HR studies, attracted researchers to pinpoint its various determinants. To identify the areas where employee performance could be improved, researchers have undertaken a number of studies over a decade on the different perspectives of performance. The objective of this review is to discuss the dimensions of employee performance, recent advancements in the employee performance areas, the influence of a few key factors on employee performance, as well as its prospects in improving employee performance.
Optimizing Science Question Ranking through Model and Retrieval-Augmented Generation

This paper delves into the challenges of discerning optimal answers from science-based questions generated by large language models (LLM), particularly emphasizing the intricate task of ranking. Employing the MAP@3 evaluation metric and drawing from the OpenBookQA dataset, the study explores modeling strategies and highlights the exceptional performance of the Platypus2-70B model. Equipped with a state-of-the-art text encoder, Platypus2-70B achieves an impressive score of 0.909904, setting a benchmark for excellence in future large language model competitions. The paper goes beyond a mere description of model architectures and experimental results, offering a comprehensive journey that envisions the transformative impact of large-scale language models on the landscape of natural language understanding, especially within the intricate domains of scientific exploration.