Reviewerly/exHarmony huggingface dataset:
MATANA: A Reconfigurable Framework for Runtime Attack Detection Based on the Analysis of Microarchitectural Signals

Microarchitectural attacks exploit target hardware properties to break software isolation techniques used by the processor. These attacks are extremely powerful and hard to detect since the determination of the program execution’s impact on the microarchitecture is at the same time not precisely understood and not easily observable at the software layer. Some approaches have attempted to benefit from existing hardware to better understand and detect the microarchitectural attacks (i.e., Hardware Performance Counters or Arm CoreSight), but such hardware was not meant to be used for cybersecurity, with reduced choice on observable signals and limited throughput of information. In this paper, we propose MATANA, an open and adaptive reconfigurable hardware/software co-designed framework. Combining fine-grained analysis of microarchitectural signals and software support, MATANA allows to design and assess detection mechanisms for attacks by characterizing their microarchitectural effects—in particular, microarchitectural attacks, but also some high-level attacks such as return-oriented programming attacks. The paper also describes a prototype implementation, built with a RISC-V softcore processor Rocket running Linux 4.15 on a Virtex-6 FPGA. We successfully used MATANA to analyze cache side-channel attacks and build attack detection logic from two different perspectives: instruction-based and memory-access-based. We also successfully detected return-oriented programming attacks by exhibiting a specific behavioral pattern on the microarchitecture.
LONTAR_DETC: Dense and High Variance Balinese Character Detection Method in <i>Lontar</i> Manuscripts

This paper proposed LONTAR_DETC, a method to detect handwritten Balinese characters in <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Lontar</i> manuscripts. LONTAR_DETC is a deep learning architecture based on YOLO. The detection of Balinese characters in <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Lontar</i> manuscripts is challenging due to the characteristics of Balinese characters in Lontar manuscripts. Balinese characters in <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Lontar</i> manuscripts are dense, overlapping, have high variance, contain noise, and classes of these characters are imbalanced. The proposed method consists of three steps, namely data generation, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Lontar</i> manuscript annotation, and Balinese character detection. The first step is data generation, in which synthetic images of original <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Lontar</i> manuscript images are generated with enhanced image quality. The second step is data annotation to build a new <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Lontar</i> manuscript dataset. As a result, we also propose the Handwritten Balinese Character of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Lontar</i> manuscript (HBCL_DETC) dataset, a novel Balinese character in <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Lontar</i> manuscripts dataset. HBCL_DETC contains 600 images that consists of more than 100,000 Balinese characters annotated by experts. Finally, the third step is training the YOLOv4 detection model using the HBCL_DETC dataset. We created this dataset specifically for the task of detecting Balinese characters in <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Lontar</i> manuscripts. To evaluate the reliability of the dataset, we experimented with three scenarios. In the first scenario, the detection model was trained using original images of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Lontar</i> manuscripts, in the second scenario the detection model was trained with the addition of augmented grayscale images, and in the third scenario the detection model was trained using HBCL_DETC. Based on the experimental results, LONTAR_DETC can detect Balinese characters with high detection rate with mAP of 99.55%.
STRATEGIES TO IMPROVE SPEAKING SKILL FOR TOURISM STUDENTS

This research was conducted with the aim of channeling information about learning strategies that educators can use to provide speaking teaching to students majoring in tourism. The instrument to collected the data in this research is by doing a library research. Through library research, researchers carry out activities by finding materials related to this research. The data collection technique used in this research is library research. This is an activity to collect materials related to research originating from books, scientific journals, literatures and other publications, which are worthy as the sources for research that the researchers examined, by describing the data through some opinions of experts. Using qualitative approach in this research is expected to provide a comprehensive facts about strategies to improve English speaking skill for tourism students. The benefit of this research is to add insight to readers, especially for the educator, regarding the role of learning strategies to increase speaking skills in the tourism sector.
Research on Dual Channel News Headline Classification Based on ERNIE Pre-training Model

The classification of news headlines is an important direction in the field of NLP, and its data has the characteristics of compactness, uniqueness and various forms. Aiming at the problem that the traditional neural network model cannot adequately capture the underlying feature information of the data and cannot jointly extract key global features and deep local features, a dual-channel network model DC-EBAD based on the ERNIE pre-training model is proposed. Use ERNIE to extract the lexical, semantic and contextual feature information at the bottom of the text, generate dynamic word vector representations fused with context, and then use the BiLSTM-AT network channel to secondary extract the global features of the data and use the attention mechanism to give key parts higher The weight of the DPCNN channel is used to overcome the long-distance text dependence problem and obtain deep local features. The local and global feature vectors are spliced, and finally passed to the fully connected layer, and the final classification result is output through Softmax. The experimental results show that the proposed model improves the accuracy, precision and F1-score of news headline classification compared with the traditional neural network model and the single-channel model under the same conditions. It can be seen that it can perform well in the multi-classification application of news headline text under large data volume.
Application of the Fuzzy Sugeno Method in a Decision Support System for Teacher Performance Assessment

&lt;p class="Abstract"&gt;The teacher is a professional educator who has essential duties, functions, and roles in the nation's intellectual life. In order for the functions and duties attached to the functional positions of teachers to be carried out following applicable regulations, it is necessary to assess teacher performance which ensures a quality learning process at all levels of education. Currently, teacher performance evaluation uses a manual assessment system, which causes the evaluation process to be relatively long. For this reason, a decision support system is needed that can take into account all the criteria that support decision making in order to assist, speed up and simplify the decision-making process. In determining teacher performance assessment, the method used is Fuzzy Takagi-Sugeno Kang (Fuzzy Sugeno). This method was chosen because the Fuzzy Sugeno method is a decision support model where the main input uses the basic concept of finding the weighted summation. The Fuzzy Sugeno method for assessing teacher performance uses three stages: the Fuzzification Stage, the Implication Function Stage, and the Defuzzification Stage. The level of validity with Sugeno's Fuzzy Inference Systems (FIS) method for assessing teacher performance is very good.&lt;/p&gt;
A Keyword Detection and Context Filtering Method for Document Level Relation Extraction

Relation extraction (RE) is the core link of downstream tasks, such as information retrieval, question answering systems, and knowledge graphs. Most of the current mainstream RE technologies focus on the sentence-level corpus, which has great limitations in practical applications. Moreover, the previously proposed models based on graph neural networks or transformers try to obtain context features from the global text, ignoring the importance of local features. In practice, the relation between entity pairs can usually be inferred just through a few keywords. This paper proposes a keyword detection and context filtering method based on the Self-Attention mechanism for document-level RE. In addition, a Self-Attention Memory (SAM) module in ConvLSTM is introduced to process the document context and capture keyword features. By searching for word embeddings with high cross-attention of entity pairs, we update and record critical local features to enhance the performance of the final classification model. The experimental results on three benchmark datasets (DocRED, CDR, and GBA) show that our model achieves advanced performance within open and specialized domain relationship extraction tasks, with up to 0.87% F1 value improvement compared to the state-of-the-art methods. We have also designed experiments to demonstrate that our model can achieve superior results by its stronger contextual filtering capability compared to other methods.
Medical Image Cryptanalysis using Adaptive, Lightweight Neural Network based Algorithm for IoT based Secured Cloud Storage

Currently available modern medical system generates large amounts of data, such as computerized patient data and digital medical pictures, which must be kept securely for future reference.Existing storage technologies are not capable of storing large amounts of data efficiently.It is a key and abrogating topic of specialized, social, and medical significance and a key and abrogating subject of general interest.The results of cars, purchasers, and Internet of Things industrybased and essential segments, sensors, and other daily objects are fused with a network of the Internet and solid information abilities that promise to alter the way we operate and live future.The suggested work demonstrates a symmetric-key lightweight technique for secure data transmission of images and text, which uses an image encryption system and a reversible data hiding system to demonstrate the program's implementation.On the other hand, cloud storage services can meet demand due to features such as flexibility and availability.Cloud computing is enabled by amazing internet innovation as well as cutting-edge electrical equipment.Even though medical images may be stored on the cloud, most cloud service providers only save client data in plain text.As part of their overall strategy, cloud users must take responsibility for protecting medical data.Because attackers' increasing computing power and creativity are opening up more and more areas in this mathematical form, most existing image encryption schemes are vulnerable to the plaintext attack of choice.This article presents an image encryption method inspired by an Adaptive IoT-based Hopfield Neural Network (AIHNN) that can resist other assaults while optimizing and improving the system through continuous learning and updating.
IDENTIFIKASI CAPAIAN INDIKATOR BIDANG PENGEMBANGAN KOGNITIF ANAK USIA 5-6 TAHUN SELAMA BELAJAR DARI RUMAH DI TK PGRI TANJUNG BATU

This research was conducted with the aim of identifying the proportion of development indicators for children aged 5 to 6 years during the implementation of BDR (learning from home) at TK PGRI Tanjung Batu. This type of research is quantitative. Observation data through and interviews. The population of this study was children in group B and a sample of 21 children who were selected based on nonprobability techniques. The results showed that the cognitive proportion of children individually included 5 children with the category developing according to expectations (24%), 1 child with the category developing according to expectations (5%), 9 children with the category starting to develop (43%) and 6 children with the category not yet. developing (28%). The percentage of the classical completeness value of 21 children was 28%.
BUILDING SOFT SKILLS IN THE PROCESS OF FUTURE CHOREOGRAPHERS’ PROFESSIONAL TRAINING

The paper is devoted to the peculiarities of the soft skills building in the students majoring in “Choreography” during the educational process. The research also indicates the impact of social skills on the future graduates’ competitiveness. The paper states the fact of the existing problem of the relationship between the future choreographers’ knowledge gained in the educational process and the skills and abilities necessary for immediate immersion in their professional activities. It is emphasized in the research that modern requirements are set for the successful implementation of the educational process in a higher educational establishment. These requirements consider taking into account the needs of the labor market, studied by analyzing requests from potential employers, research of labor market development and employment experience of graduates as a necessary condition. It is proved that the effective application of social and psychological skills (soft skills) in a balanced combination with professional knowledge (hard skills) is one of the main indicators of a highly qualified specialist. The paper presents the program learning outcomes defined by the Standard of Higher Education. The mastery and achievement of these outcomes will ensure the future choreographers’ soft skills-building for their further employment. The research offers the set of methods that contribute to the acquisition of soft skills. Among them, there are as follows: a problem presentation, a search method, pair and small group work, a case method, a brainstorming, a presentation, a project method, discussions and debates, business games, creative tasks, and improvisation, and interpretation, etc The author determines social and psychological skills, which are formed more effectively during the students’ acquisition of educational components of different cycles: the cycle of educational components aimed at the formation of general competencies; educational components of the training cycle; educational components related to pedagogical communication; the cycle of educational management components; different types of practices. The influence of quarantine restrictions on the soft skills building in students majoring in “Choreography” is determined.&#x0D; Keywords: soft skills; flexible skills; competitiveness; future choreographers; students; educational components; competence; teaching methods; forms of teaching
A Knowledge-based Expert System for Supporting Security in Software Engineering Projects

Building secure software systems requires the intersection between two engineering disciplines, software engineering and security engineering.There is a lack of a defined security mechanism for each of the software development phases, which affects the quality of the software system intensively.In this paper, the authors are proposing a framework to consider the security aspects in all the phases of the software development process from the requirements until the deployment of the software product, with three additional phases that are important to automatically produce a secure system.The framework is developed after analyzing the existing models for secure system development.The key elements of the framework are the addition of the phases like physical, training, and auditing, where they improve the level of security in software engineering projects.The authors found so a solution for the replacement of the knowledge of the security engineer through the construction of an intelligent knowledge-based system, which provides the software developer with the security rules needed in each phase of the software development lifecycle and it improves the awareness of the software developer about the securityrelated issues in each phase of the software development lifecycle.The framework and the expert system are tested on a variety of software projects, where a significant improvement of security in each phase of the software development process is achieved.
Real-Time Dance Posture Tracking Method Based on Lightweight Network

Video analysis of human motion has been widely used in intelligent monitoring, sports analysis, and virtual reality as a research hotspot in computer vision. It is necessary to decompose and track the movements in the process of movement in order to improve the training quality in dance training. The traditional motion tracking decomposition method, on the other hand, is unable to calculate the visual changes of adjacent key nodes, and the contour of 3D visual motion tracking remains ambiguous. This paper applies the human posture estimation algorithm in computer vision to the detection of key points of rectangular objects and obtains the heat map of key points of rectangular objects by adding a lightweight feature extraction network and a feature pyramid layer integrating multilayer semantic information, on the basis of summarizing and analyzing related research work at home and abroad. Because of the fusion of multilayer information, the network’s design not only reduces the amount of calculation and parameters but also improves the accuracy of the final detection result. The test results show that the proposed algorithm’s recognition accuracy has improved.
Automated Segmentation of Mass Regions in DBT Images Using a Dilated DCNN Approach

To overcome the limitations of conventional breast screening methods based on digital mammography, a quasi-3D imaging technique, digital breast tomosynthesis (DBT) has been developed in the field of breast cancer screening in recent years. In this work, a computer-aided architecture for mass regions segmentation in DBT images using a dilated deep convolutional neural network (DCNN) is developed. First, to improve the low contrast of breast tumour candidate regions and depress the background tissue noise in the DBT image effectively, the constraint matrix is established after top-hat transformation and multiplied with the DBT image. Second, input image patches are generated, and the data augmentation technique is performed to create the training data set for training a dilated DCNN architecture. Then the mass regions in DBT images are preliminarily segmented; each pixel is divided into two different kinds of labels. Finally, the postprocessing procedure removes all false-positives regions with less than 50 voxels. The final segmentation results are obtained by smoothing the boundaries of the mass regions with a median filter. The testing accuracy (ACC), sensitivity (SEN), and the area under the receiver operating curve (AUC) are adopted as the evaluation metrics, and the ACC, SEN, as well as AUC are 86.3%, 85.6%, and 0.852 for segmenting the mass regions in DBT images on the entire data set, respectively. The experimental results indicate that our proposed approach achieves promising results compared with other classical CAD-based frameworks.
Design of orthogonal filter banks using a multi-objective genetic algorithm for a speech coding scheme

In this work, we propose an optimization scheme based on a multi-objective Genetic Algorithm (GA) for the design of orthogonal filter banks for speech compression. A parameterization is adopted to assure that the resulting filter banks satisfy perfect reconstruction and have at least two vanishing moments. We search for a parameter set that optimizes the coding gain and the frequency selectivity. As the objectives are conflicting, we investigate the solution that realizes the best compromise between the objectives criteria using the Non-dominated Sorting Genetic Algorithm (NSGAIII). Experimental results have shown that the optimized filter banks provide a significant gain in coding performances when comparing with the Daubechies orthogonal filter banks for test speech signals.
The Effect of Information Technology Implementation on Supply Chain Performance through Information Sharing and Supply Chain Collaboration

The Covid-19 pandemic has resulted in a slowdown in the global economy, and the Indonesian economy has also been affected. The implementation of industry 4.0 is considered the right strategy to revitalize the manufacturing sector. This study aims to determine and understand the effect of information technology implementation on supply chain performance through information sharing and supply chain collaboration. This study uses a quantitative approach to measure an independent variable effect on mediation and dependent variables to test the predetermined hypotheses. The data was collected using a non-probability sampling technique with a purposive sampling method through a questionnaire instrument with the total number of respondents of 70 manufacturers in Surabaya. Then, the data were analyzed using Partial Least Square software and Structural Equation Modeling based on components or variants. The results indicate that the relationship between supply chain collaboration and information sharing creates competitive advantages. Consequently, the existence of precise, accurate, and qualified information can facilitate joint decision-making. Furthermore, the increasing adoption of supply chain collaboration with information technology can improve company performance. In conclusion, a manufacturing company can maintain its supply chain performance during the Covid-19 pandemic by implementing information technology, information sharing and collaboration with partners in a supply chain. This study provides insight and guidance for managers on ways to improve supply chain performance. This study can also contribute to current research in supply chain management theory.
Challenges and opportunities cryptocurrency in Iran economy &amp; e-businesses

The worlds financial markets faced a new cyberspace phenomenon. Cryptocurrencies are non-tracking by any bank or financial institution or even the state. In a digital transactions system, transactions are registered and encrypted in a general ledger book, the blockchains. Cryptocurrencies have features that make them more useful, especially in international exchange, so it is necessary to determine the advantages and challenges of using them. The study of digital currencies in Irans economy and electronic business is presented. In recent years, the use of cryptocurrencies in Iran has grown. The necessity of public policymaking in facing this emerging phenomenon of economic importance has become more critical due to the transfer, such as decentralized systems, which led to the elimination of intermediate and observer entities. Cryptocurrencies also allow Iranian businesses and governments to direct access the international markets by bypassing sanctions. In this paper, the author first considers the mechanism of cryptocurrencies in the economy and then identifies the Iranian economys challenges and opportunities. In conclusion, cryptocurrencies can allow Iranian entrepreneurs to enter the international arena and international competition. Therefore, it is vital that the government or public organizations raising awareness about cryptocurrencies will also contribute to the prosperity of businesses and prevent criminals from exploiting the conditions.