Reviewerly/exHarmony huggingface dataset:
COUNSELING ADOLESCENTS: PRACTI CAL MODELS OF INTERACTION

The article examines the peculiarities of counseling adolescents. The main stages of counseling are outlined in the context of classic personality theories, which are the basis of modern counseling practice (psychoanalytic direction, person-centered theory, Gestalt psychology, theory of transactional analysis, rational-emotive psychology, counseling in the method of positive psychotherapy). Advantages and limitations of using the above-mentioned theories in practical activities are analyzed. The author concludes that the most effective approach in working with adolescents is an eclectic approach, which allows the consultant to combine problem-oriented and personal-oriented counseling aimed at identifying resources for solving the problem and achieving an optimal result. Typical requests of adolescents, requirements for a psychologist and tasks of a counselor working with clients are defined: activation of the client’s mental resources, integration of the client’s personality, promoting transformation of the adolescent’s behavioral reactions, development of decisionmaking skills in the face of new everyday circumstances and living conditions, development of communication and facilitation skills for the purpose of emotional self-regulation and growth of the adolescent’s personal potential. The system model of counseling adolescents is presented. The effective communication is defined as a key factor in productive interaction so it confirms the importance of developing communication skills, cognitive skills and facilitation skills. In view of the tasks, the motivational interview method, the method of relying on the client’s strengths, the three-stage model of changing thinking patterns, active listening techniques, encouragement, paraphrasing, facilitation skills development techniques are suggested to be used. The development of facilitation skills among adolescents contributes to their self-discovery, stimulates independence and activity, motivates them to be a facilitator for their peers.
Recent Advancements in Computer Science: A Comprehensive Review of Emerging Technologies and Innovations

Recent advances in computer science have led to many new technologies and inventions that continue to affect our environment. This extensive study highlights some of the most significant computer science discoveries and their possible applications. This assessment begins with AI, which has driven many recent achievements. Cutting-edge machine learning methods, deep neural networks, reinforcement learning, and explainable AI are discussed. AI and adjacent sciences like natural language processing (NLP) and computer vision have led to innovative applications in healthcare, finance, autonomous cars, and smart cities. The second part discusses quantum computing, which promises exponentially increased processing power. Qubits were created by studying quantum physics concepts like superposition and entanglement. These qubits enable quantum computers to tackle complicated problems that traditional computers cannot. The review discusses quantum hardware, error correction, and algorithms and their potential applications. Next, blockchain technology, which has changed data management and security, is examined. Cryptocurrencies and DApps and smart contracts have grown due to blockchain's decentralisation and tamper-resistance. This section discusses new consensus techniques, scalability, and blockchain's use in supply chain management and digital identification. In this study, the Internet of Things (IoT) shows how linked gadgets have changed businesses and customer experiences. IoT, cloud, and edge computing have established an environment for real-time data processing, predictive analytics, and autonomous decision-making. Privacy, security, and standards issues are discussed. Given the growing dangers from malevolent actors in a digitalized environment, the evaluation finishes with cybersecurity advances. This section discusses improved encryption, AI-driven cybersecurity, and quantum-resistant cryptography in light of quantum computing's ability to break standard cryptographic algorithms.
Strategi Pengayaan Berbasis Digital Membaca Cepat Dan Pemahaman Dengan Aplikasi Quizizz di SD Negeri Belitung Selatan 1 Banjarmasin

The development of digital-based learning media is an excellent alternative in the attempt to enhance quality of education, especially learning Indonesian language and literature in elementary schools after the Covid-19 pandemic. The study's objectives were to (1) analyze the factors that influence students' motivation for reading habituation using quizizz digital media, 2) analyze the implementation of the quizizz application in the implementation of learning enrichment in the post-Covid-19 pandemic, and 3) learn about just a variety of advantages and disadvantages obtained by teachers and students through the provision of enrichment using the quizizz application. The research method employs a qualitative approach, a descriptive qualitative method. Purposive sampling was used in the study, with the research subject being a teacher who had utilized the Quizizz application in the learning process. The speed reading ability of grade V and VI elementary school students was the focus of this study. The results of this study show that this enrichment strategy is feasible for use as a learning innovation; however, this research is limited to the enrichment stage. The findings of this study are expected to serve as a model for schools in terms of using digital enrichment media quizzes to enhance student' speed reading and comprehension skills in Indonesian courses
Parallel Implementation of PageRank Based K-Means Clustering on a Multithreaded Architecture

PageRank is the most famous algorithm used for ranking of the search results which has been developed and used by the most famous search engine Google. It is used to order the list of web pages which are specific to the query. This algorithm’s simplicity makes it generic and a very powerful tool that can be used in a broad range of various applications. PageRank was most importantly designed to use the structure of a graph to measure the importance of nodes in the graph. It is this intuition that drives it to find its application in another most widely used algorithm, K-Means clustering algorithm. K-Means algorithm uses the centroids to cluster the nodes present in the graphs. PageRank can be used to find these centroids, that is, to find the nodes that have high centrality measure to cluster the nodes. Our work uses the PageRank as the method for finding the node that has the highest centrality for a particular cluster and uses it as the centroid to find the clusters for new iteration in K-Means algorithm. It uses Voronoi cells to determine the clusters iteratively using the centroids formed by the PageRank algorithm. We use the NetworkX PageRank module to determine the K center nodes that take the NetworkX graph as the input. The NetworkX module Voronoi cells is used to find the Voronoi cells, that is, clusters of the graph, which takes the centroids and NetworkX graph as input. The datasets used in this project are social circles from Facebook, Wikipedia who-votes-on-whom network, email communication network from Enron, Gnutella peer-to-peer network from August 4, 2002, and from August 6, 2002. Our methodology implements clustering of graphs using K-Means and PageRank which greatly improved the performance over the standard K-Means clustering for graphs. Our methodology achieved a considerable speed up by implementing the algorithm in a parallel paradigm. With parallel implementation, the speed up achieved on the above datasets ranged from 1.33 to 4.87 ms.
The Effort to Increase Vocabulary Mastery through The Use of Word Cards

The purpose of this study is to increase students' vocabulary mastery and students' motivation to learn vocabulary in grade I SDN 2 Talang Bandar Lampung. The problem in this study is that whether or not the use of word card media can improve vocabulary mastery and student learning motivation. This study uses a classroom action research method which is done in three cycles. The lesson plan for each cycle is prepared by the teacher. The results of the study can be concluded as follows: first, the increase can be seen from the average score of 58.92 in the first cycle, to 67.5 in the second cycle, and to 86.60 in the third cycle. The increase of the average score that occurred is 19.1%. Second, the increasing of students' ability in mastering the vocabulary is also followed by changes in student learning behavior in a positive direction. Students become happier, active, and give a positive response to vocabulary learning.
Language Error Found on Social Media of Indonesian Celebrities

&#x0D; &#x0D; &#x0D; &#x0D; This thesis discusses the kinds of language error found on social media of Indonesian celebrities and the factors that affect the language of Indonesian celebrities. This study is designed to find out the kinds of language error is made Indonesian celebrity used in communicating on social media and to find out the factors that influence the Indonesian celebrity language. This research uses qualitative and descriptive research method. The object of this research is Indonesian celebrities. And there are 10 Indonesia celebrties who were sampled in this study, and the researcher took 3 captions from each celebrity. The research data was taken from screenshots of Indonesian celebrity captions and videos. The way of collecting data is by using documentation. The data are classified according to the kinds of language error and factors that affect language according to Ellis &amp; Brown's theory. In this study, the researcher found that there were 28 data that writer got from social media of Indonesian celebrities, such as Instagram and YouTube. From the 3 kinds of language error according to Ellis' theory, the researcher got 7 data from omission, the researcher got 14 data from misinformation, 1 data from misordering, and 6 data from combination of 2 kinds of error. Then the language error factor, the researcher found 6 data from the interlingual factor, 14 data from the intralingual factor, 3 data from the context of learning factor, and 5 data from combination of 2 factors of error.&#x0D; &#x0D; &#x0D; &#x0D; &#x0D;
The Effectiveness of Graphic Organizer As A Media in Teaching Students’ Writing Project

&#x0D; &#x0D; &#x0D; &#x0D; &#x0D; A Graphic Organizer (GO) is quite a creative media expresses the text concepts. It is an instructional tool that can help students to organize, structured the information and concepts to relate with the other concepts. Graphic organizers display information, structure, and key conceptual relationships using visual and spatial arrangements or maps. Graphic organizers often involve lines, arrows, text boxes or bubbles, pictures, and other visual depictions to represent ideas, facts, and concepts. The implementation of Graphic Organizer is aimed at increasing the students’ ability in writing narrative text for grade ninth at SMPN 1 Merlung. The previous learning used students’ task learning as method in learning writing which unmatched with the needs of students in facing challenging of writing text. The research was conducted to the ninth graders of SMPN 1 Merlung with total 62 students by giving treatment in control and experimental group. The data were the students’ writing in the pre-test and post-test.&#x0D; To count the data, the researcher used t-test and SPSS. Based on the counting, it was found that the value of t-test at the degree of freedom 60 and the level of significance 0.05 was 7.012 which is value of t-test is higher than value of t-table (7.012&gt; 2.000). It means that the Null Hypothesis is rejected and the alternative hypothesis is accepted. Thus, it proved that there is effect of using graphic organizer on students’ writing project. Since the graphic organizer media had significant effect to the students’ writing abilities, so this media can be used as one of the alternative technique for English teacher in teaching writing narrative text.&#x0D; &#x0D; &#x0D; &#x0D;
Descriptive complexity for neural networks via Boolean networks

We investigate the descriptive complexity of a class of neural networks with unrestricted topologies and piecewise polynomial activation functions. We consider the general scenario where the running time is unlimited and floating-point numbers are used for simulating reals. We characterize these neural networks with a rule-based logic for Boolean networks. In particular, we show that the sizes of the neural networks and the corresponding Boolean rule formulae are polynomially related. In fact, in the direction from Boolean rules to neural networks, the blow-up is only linear. We also analyze the delays in running times due to the translations. In the translation from neural networks to Boolean rules, the time delay is polylogarithmic in the neural network size and linear in time. In the converse translation, the time delay is linear in both factors. We also obtain translations between the rule-based logic for Boolean networks, the diamond-free fragment of modal substitution calculus and a class of recursive Boolean circuits where the number of input and output gates match.
Classification of White Blood Cells Using Machine and Deep Learning Models: A Systematic Review

Machine learning (ML) and deep learning (DL) models have been employed to significantly improve analyses of medical imagery, with these approaches used to enhance the accuracy of prediction and classification. Model predictions and classifications assist diagnoses of various cancers and tumors. This review presents an in-depth analysis of modern techniques applied within the domain of medical image analysis for white blood cell classification. The methodologies that use blood smear images, magnetic resonance imaging (MRI), X-rays, and similar medical imaging domains are identified and discussed, with a detailed analysis of ML/DL techniques applied to the classification of white blood cells (WBCs) representing the primary focus of the review. The data utilized in this research has been extracted from a collection of 136 primary papers that were published between the years 2006 and 2023. The most widely used techniques and best-performing white blood cell classification methods are identified. While the use of ML and DL for white blood cell classification has concurrently increased and improved in recent year, significant challenges remain - 1) Availability of appropriate datasets remain the primary challenge, and may be resolved using data augmentation techniques. 2) Medical training of researchers is recommended to improve current understanding of white blood cell structure and subsequent selection of appropriate classification models. 3) Advanced DL networks including Generative Adversarial Networks, R-CNN, Fast R-CNN, and faster R-CNN will likely be increasingly employed to supplement or replace current techniques.
Error-resilience Phase Transitions in Encoding-Decoding Quantum Circuits

Understanding how errors deteriorate the information encoded in a many-body quantum system is a fundamental problem with practical implications for quantum technologies. Here, we investigate a class of encoding-decoding random circuits subject to local coherent and incoherent errors. We analytically demonstrate the existence of a phase transition from an error-protecting phase to an error-vulnerable phase occurring when the error strength is increased. This transition is accompanied by R\'enyi entropy transitions and by onset of multifractal features in the system. Our results provide a new perspective on storing and processing quantum information, while the introduced framework enables an analytic understanding of a dynamical critical phenomenon in a many-body system.
Automatic Classification of Blood Cell Images Using Convolutional Neural Network

Human blood primarily comprises plasma, red blood cells, white blood cells, and platelets. It plays a vital role in transporting nutrients to different organs, where it stores essential health-related data about the human body. Blood cells are utilized to defend the body against diverse infections, including fungi, viruses, and bacteria. Hence, blood analysis can help physicians assess an individual's physiological condition. Blood cells have been sub-classified into eight groups: Neutrophils, eosinophils, basophils, lymphocytes, monocytes, immature granulocytes (promyelocytes, myelocytes, and metamyelocytes), erythroblasts, and platelets or thrombocytes on the basis of their nucleus, shape, and cytoplasm. Traditionally, pathologists and hematologists in laboratories have examined these blood cells using a microscope before manually classifying them. The manual approach is slower and more prone to human error. Therefore, it is essential to automate this process. In our paper, transfer learning with CNN pre-trained models. VGG16, VGG19, ResNet-50, ResNet-101, ResNet-152, InceptionV3, MobileNetV2, and DenseNet-20 applied to the PBC dataset's normal DIB. The overall accuracy achieved with these models lies between 91.375 and 94.72%. Hence, inspired by these pre-trained architectures, a model has been proposed to automatically classify the ten types of blood cells with increased accuracy. A novel CNN-based framework has been presented to improve accuracy. The proposed CNN model has been tested on the PBC dataset normal DIB. The outcomes of the experiments demonstrate that our CNN-based framework designed for blood cell classification attains an accuracy of 99.91% on the PBC dataset. Our proposed convolutional neural network model performs competitively when compared to earlier results reported in the literature.
DCNFIS: Deep Convolutional Neuro-Fuzzy Inference System

A key challenge in eXplainable Artificial Intelligence is the well-known tradeoff between the transparency of an algorithm (i.e., how easily a human can directly understand the algorithm, as opposed to receiving a post-hoc explanation), and its accuracy. We report on the design of a new deep network that achieves improved transparency without sacrificing accuracy. We design a deep convolutional neuro-fuzzy inference system (DCNFIS) by hybridizing fuzzy logic and deep learning models and show that DCNFIS performs as accurately as existing convolutional neural networks on four well-known datasets and 3 famous architectures. Our performance comparison with available fuzzy methods show that DCNFIS is now state-of-the-art fuzzy system and outperforms other shallow and deep fuzzy methods to the best of our knowledge. At the end, we exploit the transparency of fuzzy logic by deriving explanations, in the form of saliency maps, from the fuzzy rules encoded in the network to take benefit of fuzzy logic upon regular deep learning methods. We investigate the properties of these explanations in greater depth using the Fashion-MNIST dataset.
Taming Self-Training for Open-Vocabulary Object Detection

Recent studies have shown promising performance in open-vocabulary object detection (OVD) by utilizing pseudo labels (PLs) from pretrained vision and language models (VLMs). However, teacher-student self-training, a powerful and widely used paradigm to leverage PLs, is rarely explored for OVD. This work identifies two challenges of using self-training in OVD: noisy PLs from VLMs and frequent distribution changes of PLs. To address these challenges, we propose SAS-Det that tames self-training for OVD from two key perspectives. First, we present a split-and-fusion (SAF) head that splits a standard detection into an open-branch and a closed-branch. This design can reduce noisy supervision from pseudo boxes. Moreover, the two branches learn complementary knowledge from different training data, significantly enhancing performance when fused together. Second, in our view, unlike in closed-set tasks, the PL distributions in OVD are solely determined by the teacher model. We introduce a periodic update strategy to decrease the number of updates to the teacher, thereby decreasing the frequency of changes in PL distributions, which stabilizes the training process. Extensive experiments demonstrate SAS-Det is both efficient and effective. SAS-Det outperforms recent models of the same scale by a clear margin and achieves 37.4 AP50 and 29.1 APr on novel categories of the COCO and LVIS benchmarks, respectively.
Performance Prediction for Multi-hop Questions

We study the problem of Query Performance Prediction (QPP) for open-domain multi-hop Question Answering (QA), where the task is to estimate the difficulty of evaluating a multi-hop question over a corpus. Despite the extensive research on predicting the performance of ad-hoc and QA retrieval models, there has been a lack of study on the estimation of the difficulty of multi-hop questions. The problem is challenging due to the multi-step nature of the retrieval process, potential dependency of the steps and the reasoning involved. To tackle this challenge, we propose multHP, a novel pre-retrieval method for predicting the performance of open-domain multi-hop questions. Our extensive evaluation on the largest multi-hop QA dataset using several modern QA systems shows that the proposed model is a strong predictor of the performance, outperforming traditional single-hop QPP models. Additionally, we demonstrate that our approach can be effectively used to optimize the parameters of QA systems, such as the number of documents to be retrieved, resulting in improved overall retrieval performance.
Making Your Program Oblivious: a Comparative Study for Side-channel-safe Confidential Computing

Trusted Execution Environments (TEEs) are gradually adopted by major cloud providers, offering a practical option of \emph{confidential computing} for users who don't fully trust public clouds. TEEs use CPU-enabled hardware features to eliminate direct breaches from compromised operating systems or hypervisors. However, recent studies have shown that side-channel attacks are still effective on TEEs. An appealing solution is to convert applications to be \emph{data oblivious} to deter many side-channel attacks. While a few research prototypes on TEEs have adopted specific data oblivious operations, the general conversion approaches have never been thoroughly compared against and tested on benchmark TEE applications. These limitations make it difficult for researchers and practitioners to choose and adopt a suitable data oblivious approach for their applications. To address these issues, we conduct a comprehensive analysis of several representative conversion approaches and implement benchmark TEE applications with them. We also perform an extensive empirical study to provide insights into their performance and ease of use.