Reviewerly/exHarmony huggingface dataset:
CSIMH: Design of an Efficient Security-Aware Customized Sidechaining Model via Iterative Meta-Heuristics

AbstractBlockchains are a secure alternative for long-length data storage & security deployments. Though blockchains are unbounded, their quality of service (QoS) performance reduces after adding a certain number of blocks. Thus, sidechains have become essential for making the system decentralized, secure, and effective for use thereby improving their scalability & QoS performance. Sidechains reduce data storage and extraction delays. However, all sidechains of a single blockchain are the same in size, capacity, and security. This limits their application to real-world use cases that require dynamic security. To overcome this limitation, the authors propose a meta-heuristic approach to design a system that produces customized sidechains based on the quantity and quality of data stored on the chain. The model uses a machine-learning approach to find the best possible sidechain configuration for different data types. It makes the system fast and scalable and improves storage & memory efficiency. The proposed model is tested on multiple data sets and compared with various state-of-art sidechain deployments. An improvement of 18% in terms of mining speed, 27% in terms of energy efficiency, 10% with regards to throughput, and 8% concerning packet delivery ratio is observed. The model is tested under various attacks and faulty nodes to validate its security performance. A consistent QoS performance is observed for the proposed model under these attack types, thereby validating its resiliency for different attacks. This enhancement in performance makes the proposed model eligible for deployment in high-speed, low-energy, and high-security applications like IoT, mobile ad-hoc networks, and sensor networks.Keywords: Blockchainsidechainthroughputenergy consumptionQoSmeta-heuristic
Research on New Word Recognition Based on Weibo Information and SVM

In order to effectively recognize Weibo new words, this paper proposes a Weibo new word recognition method based on Weibo information and SVM. Firstly, positive and negative samples are extracted from the Weibo corpus and the training corpus annotated with part of speech. Next, the word features of these samples are vectorized and trained through support vector machines to obtain support vectors for new word classification. Finally, it is vectorized and inputted into the trained support vector machine classifier to obtain the recognized Weibo new words. By comparing the experimental results, the optimal fusion feature combination and SVM kernel function are obtained.
Developing a Think-Pair-Share Cooperative Learning-Based E-book Module in Improving Students’ Mathematical Communication Skills in Elementary School

Based on the pre-research activities covering interviews, observation and data documentation which were conducted in the fifth-grade of State Elementary School Pandeanlamper 05 Semarang (SD Negeri Pandeanlamper 05 Semarang), it was found that students encountered difficulty in comprehending and communicating mathematics questions, particularly with the measurement material. The underlying cause of this problem is the transition from online to offline learning. During the process of online learning, students tend to use gadgets, while during face-to-face learning, they do not. Moreover, students’ interest towards learning tends to decrease, because the modules used by the teachers are still limited and not various. Teachers have not integrated technology into learning activities optimally, which is supposed to be implemented to succeed in the improvement of students' mathematical communication skills in elementary school. Therefore, this research aims to develop and examine the effectiveness of developing a think-pair-share cooperative learning-based e-book module for mathematics subject in improving students’ mathematical communication for the fifth-grade students of SDN Pandeanlamper 05 Semarang. This research used the research and development (R&amp;D) method and adapted the ADDIE model postulated by Branch. The instrument of data collection of this research was validation sheet questionnaires that were given to several expert validators, and the effectiveness tests were conducted in the control class and experimental class. The results of analysis from the media expert, material expert, and learning expert were obtained through the analysis of the quantitative data by using criteria guidelines with the assessment categories to determine the quality of the product that was being developed and to examine its effectiveness by using the independent sample t-test. The validation result taken from the media expert obtained a percentage score of 92% which can be categorized as "very feasible", the result taken from the material expert obtained a percentage score of 94,3% which can be categorized as "very feasible", and the result taken from learning expert obtained a percentage score of 95% which can be categorized as "very feasible", and also the result of t-test showed that the significance value of two-tailed test with t &lt; 0,05 has a meaning that H0 is rejected and Ha is accepted. Based on the collected data, it can be concluded that a think-pair-share cooperative learning-based e-book module in improving the students' mathematical communication skills of the fifth-grade students of SDN Pandeanlamper 05 Semarang is feasible to be used. Keywords: E-book Module, Think Pair Share, Mathematical Communication, Elementary School
Hybrid deep learning model-based human action recognition in indoor environment

Abstract Human activity recognition (HAR) is an emerging challenge among researchers. HAR has many possible uses in various fields, including healthcare, sports, and security. Furthermore, there are only a few publicly accessible datasets for classifying and recognizing physical activity in the literature, and these datasets comprise fewer activities. We created and compared our dataset with available datasets, that is, NTU-RGBD, UP-FALL, UR-Fall, WISDM, and UCI HAR. The proposed dataset consists of seven activities: eating, exercise, handshake, situps, vomiting, headache, and walking. The activities were collected from 20 people between the ages of 25 and 40 years using Kinect V2 sensor at 30 FPS. For classification, we use deep learning architectures based on convolutional neural network (CNN) and long short-term memory (LSTM). Additionally, we developed a novel hybrid deep learning model by combining a CNN, a bidirectional LSTM unit, and a fully connected layer for activity identification. The suggested model builds unique guided features using the preprocessed skeleton coordinates and their distinctive geometrical and kinematic aspects. Results from the experiment are contrasted with the performance of stand-alone CNNs, LSTMs, and ConvLSTM. The proposed model’s accuracy of 99.5% surpasses that of CNN, LSTM, and ConvLSTM, which have accuracy rates of 95.76%, 97%, and 98.89%, respectively. Our proposed technique is invariant of stance, speed, individual, clothes, etc. The proposed dataset sample is accessible to the general public.
Implementation of Deep Learning Model-based Korean Sentence Syntactic Complexity Assessment Model

This study developed a method to assess the text level automatically regarding syntactic complexity. The new method was developed by improving the method of measuring the syntactic complexity of large-scale texts with various types. We implemented a Korean sentence syntactic complexity assessment model based on the deep learning models, especially the Korean BERT models. In particular, the KcBERT-based model, fine-tuned through the “National Institute of Korean Language Dependency-Parsed Corpus (v.2.0)”, showed excellent performance with an accuracy of 0.949. This model is expected to contribute to establishing an integrated model to assess the text level as the sub-factor model. By segmenting the text assessment model by factors, it could overcome the limitations of the existing research using unexplainable deep learning models to provide a direction for more sophisticated educational treatment.
POBO: Safe and optimal resource management for cloud microservices

Resource management in microservices is challenging due to the uncertain latency–resource relationship, dynamic environment, and strict Service-Level Agreement (SLA) guarantees. This paper presents a Pessimistic and Optimistic Bayesian Optimization framework, named POBO, for safe and optimal resource configuration for microservice applications. POBO leverages Bayesian learning to estimate the uncertain latency–resource functions and combines primal–dual and penalty-based optimization to maximize resource efficiency while guaranteeing strict SLAs. We prove that POBO can achieve sublinear regret and SLA violation against the optimal resource configuration in hindsight. We have implemented a prototype of POBO and conducted extensive experiments on a real-world microservice application. Our results show that POBO can find the safe and optimal configuration efficiently, outperforming Kubernetes’ built-in auto-scaling module and the state-of-the-art algorithms.
Modeling a reconnaissance operation in an urban environment using a swarm of UAVs

Military operations are increasingly taking place in urban environments; the effective reconnaissance of such environments is critical to their success. This paper deals with modeling the reconnaissance of a built-up area using a swarm of heterogeneous cooperating unmanned aerial vehicles. The model consists of two phases. In the first, a set of waypoints is generated from which the reconnaissance of the walls and roofs of buildings and roads located in the area of intelligence responsibility is performed; the waypoints are deployed in such a manner that the comprehensive reconnaissance of all objects takes place while keeping their number as small as possible. In the second phase, the flight trajectories of the available unmanned aerial vehicles are planned with the purpose of minimizing the overall operation time; the intention is thus to load each vehicle evenly. The proposed model is validated on a set of six scenarios with varying complexity; each scenario is based on typical tactical situations. This paper is a contribution to the research being intensively conducted in the field of robotic systems for civilian and military applications.
Fuzzy classification with distance-based depth prototypes: High-dimensional unsupervised and/or supervised problems

Supervised and unsupervised classification is crucial in many areas where different types of data sets are common, such as biology, medicine, or industry, among others. A key consideration is that some units are more typical of the group they belong to than others. For this reason, fuzzy classification approaches are necessary. In this paper, a fuzzy supervised classification method, which is based on the construction of prototypes, is proposed. The method obtains the prototypes from an objective function that includes label information and a distance-based depth function. It works with any distance and it can deal with data sets of a wide nature variety. It can further be applied to data sets where the use of Euclidean distance is not suitable and to high-dimensional data (data sets in which the number of features p is larger than the number of observations n, often written as p>>n). In addition, the model can also cope with unsupervised classification, thus becoming an interesting alternative to other fuzzy clustering methods. With synthetic data sets along with high-dimensional real biomedical and industrial data sets, we demonstrate the good performance of the supervised and unsupervised fuzzy proposed procedures.
Uncertainty-based knowing how logic

Abstract We introduce a novel semantics for a multi-agent epistemic operator of knowing how, based on an indistinguishability relation between plans. Our proposal is, arguably, closer to the standard presentation of knowing that modalities in classical epistemic logic. We study the relationship between this new semantics and previous approaches, showing that our setting is general enough to capture them. We also study the logical properties of the new semantics. First, we define a sound and complete axiomatization. Second, we define a suitable notion of bisimulation and prove correspondence theorems. Finally, we investigate the computational complexity of the model checking and satisfiability problems for the new logic.
Risks of educational space digitalization as digital demarcators of modern university corporate environment

The article deals with the problem of replication of educational space digitalization risks in modern higher education institutions by representatives of university subcorporations. It has been presented that modern higher education institution is gradually turning into a quasi-corporation, in which employees and students have a specific corporate consciousness. The article is based on the idea that digital risks arising in the process of educational space digitalization in ­modern higher education institutions act as attitude demarcators to the ongoing transformations on the part of university subcorporations representatives, the main of which are administrative staff, professors, researchers, and students. The results of the author’s sociological research conducted in the period from 2019 to 2022, expert surveys and focus group interviews have been used, in the course of which valid and representative data had been obtained. Depending on perception of the education system digitalization strategy, representatives of higher education subcorporations have been conditionally divided into four groups: “consistent supporters”, “moderate pragmatists”, “skeptics”, and “undecided”. Each of them, except for the last one, has been characterized by specific risk dispositions (risk-dispositions). The definition of risk dispositions is conditional: “risk progressors”, “risk managers”, “risk oppositionists”. The article defines the demarcators regulating the dispositions of university subcorporations representatives.
Predictive Prompts with Joint Training of Large Language Models for Explainable Recommendation

Large language models have recently gained popularity in various applications due to their ability to generate natural text for complex tasks. Recommendation systems, one of the frequently studied research topics, can be further improved using the capabilities of large language models to track and understand user behaviors and preferences. In this research, we aim to build reliable and transparent recommendation system by generating human-readable explanations to help users obtain better insights into the recommended items and gain more trust. We propose a learning scheme to jointly train the rating prediction task and explanation generation task. The rating prediction task learns the predictive representation from the input of user and item vectors. Subsequently, inspired by the recent success of prompt engineering, these predictive representations are served as predictive prompts, which are soft embeddings, to elicit and steer any knowledge behind language models for the explanation generation task. Empirical studies show that the proposed approach achieves competitive results compared with other existing baselines on the public English TripAdvisor dataset of explainable recommendations.
Dissimilarity-based indicator graph learning for clustering

Although learning a similarity graph with a 0-1 value is regarded as an important issue for clustering tasks, this topic has been scarcely reported in the literature since its NP-hard. Along these lines, this issue is addressed in the work by introducing a novel concept. Concretely, the indicator graph clustering (IGC) method was proposed. Based on the dissimilarity and coordinate descent method, IGC is able to learn an indicator graph with a 0-1 value, which is just the similarity graph with a 0-1 value. Meanwhile, the clustering result can be directly obtained by the indicator graph. From the experimental results on several datasets, it was verified that IGC performs faster than k-means, especially on high-dimensional datasets. Furthermore, IGC was extended to a multi-view version, named indicator graph clustering for multi-view (IGCMV). Our model fused the clustering indicator matrices from different views, which can be obtained by IGC, into the final clustering indicator matrix by a projection matrix. To the best of our knowledge, this work is the first attempt to learn a consensus indicator graph from indicator graphs of all views. In addition different from most of the existing multi-view clustering algorithms, the proposed IGCMV enables the production of the final clustering result, as well as each view’s clustering result simultaneously. This is helpful for measuring the quality of each view. IGCMV can be efficiently optimized by an alternative iteration strategy. Extensive experiments were also carried out on several benchmark multi-view datasets demonstrating the effectiveness of the developed algorithm to the state-of-the-art multi-view clustering algorithms.
The Importance of Life Skills Education

The present paper focuses on the importance of life skills education and the benefits of imparting life skill education in our curriculum i.e. developing social, emotional &amp; thinking skills in students, as they are the important building blocks for a dynamic citizen, who can cope up with future challenges, and survive well. Adolescence is a period when the intellectual, physical, social, emotional and all the capabilities are very high, but, unfortunately, most of the adolescents are unable to utilize their potential to maximum due to various reasons. They face many emerging issues such as global warming, famines, poverty, suicide, population explosion as well as other issues like alcoholism, drug abuse, sexual abuse, smoking, juvenile delinquency, anti-social acts, etc. that have an adverse effect on them and others too, to a large extent. The cut-throat competition, unemployment, lack of job security, etc. are some of the major concerns for the educated and as a result, they are caught in the mad race. This new challenge requires immediate and effective responses from a socially responsible system of education. ‘Education’ is important, but education to support and live life better is more important. It has been felt that life skills education bridges the gap between basic functioning and capabilities. It strengthens the ability of an individual to meet the needs and demands of the present society and helps in dealing with the above issues in a manner to get desired behavior practical. Imparting life skill training through inculcating life skill education will help youth to overcome such difficulties in life.
Hybrid approaches based on Singular Spectrum Analysis and <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si145.svg" display="inline" id="d1e958"><mml:mi>k</mml:mi></mml:math>- Nearest Neighbors for clearness index forecasting

With the increase in solar energy penetration in electric power systems, it is necessary to predict future solar radiation. With this, the solar power generation capacity can be predicted, allowing accurate scheduling for other sources. The solar radiation or the clearness index (CI) prediction was already presented using artificial intelligence, hybrid algorithms, among other approaches. In this paper, two hybrid approaches for clearness index forecasting are proposed. The first one combines batch singular spectral analysis and k-nearest neighbours (k-NN), and is called SSA-B + k-NN, whereas the second associates recursive singular spectral analysis and k-NN, and is named SSA-R + k-NN. The first one performs the SSA-B algorithm for the time series decomposition using a more usual approach, in which the eigenvalues and eigenvectors are calculated in batch. The second one performs the SSA-R algorithm for the time series decomposition, in which the eigenvalues and eigenvectors are updated recursively . The computational costs and mean square errors (MSE) were compared. The results were also compared to the ones obtained with other methods based on k-NN published in the literature. The results illustrate that the hybrid approaches proposed in this paper obtained a lower MSE compared to the results previously presented.
Toward extracting and exploiting generalizable knowledge of deep 2D transformations in computer vision

Existing deep learning models suffer from out-of-distribution (o.o.d.) performance drop in computer vision tasks. In comparison, humans have a remarkable ability to interpret images, even if the scenes in the images are rare, thanks to the generalizability of acquired knowledge. This work attempts to answer two research questions: (1) the acquisition and (2) the utilization of generalizable knowledge about 2D transformations. To answer the first question, we demonstrate that deep neural networks can learn generalizable knowledge with a new training methodology based on synthetic datasets. The generalizability is reflected in the results that, even when the knowledge is learned from random noise, the networks can still achieve stable performance in parameter estimation tasks. To answer the second question, a novel architecture called “InterpretNet” is devised to utilize the learned knowledge in image classification tasks. The architecture consists of an estimator and an identifier, in addition to a classifier. By emulating the “hypothesis-verification” process in human visual perception, our InterpretNet improves classification accuracy by 21.1%.
A generic sketch for estimating super-spreaders and per-flow cardinality distribution in high-speed data streams

For a high-speed network, it is an important task to process the IP packet stream using limited memory and measure its statistical metrics of interest. While many algorithms have been proposed to estimate the cardinality of a single data stream (i.e., the number of distinct elements), it remains a great challenge when a stream contains numerous sub-streams, called flows. In this paper, we focus on a problem of designing a generic data structure to measure multiple types of per-flow statistics in a high-speed stream, including per-flow cardinality, top-K super-spreading flows with the greatest cardinalities, per-flow cardinality moments and per-flow cardinality distribution. Previous solutions for generic measurement mainly focus on the frequency-related statistics measurement, while this paper makes a step forward to support deduplication, i.e., cardinality-related measuring. To address this new problem, we propose a generic sketch named M2D. The challenge is that the per-flow cardinality distribution is often highly skewed with a small proportion of super-spreaders. To tame the skewness, we adopt the adjustable progressive sampling technique, which samples subsets of flows by an exponentially decreasing probability according to their cardinalities. Based on the sampled super-spreaders, we estimate the moments of per-flow cardinalities with different orders. We finally apply the method of moments to reconstruct the per-flow cardinality distribution with no priori knowledge about its formula. Our experiments show M2D’s high memory efficiency (average savings of 38%) and satisfactory distribution estimation accuracy (2% to 98% improvement) than other algorithms.