Reviewerly/exHarmony huggingface dataset:
Pengaruh Implementasi Pembelajaran Berdiferensiasi pada Pemenuhan Kebutuhan Belajar Peserta Didik Tingkat Sekolah Dasar

A learning innovation is needed that can increase students' learning motivation because of differences in their learning needs. A learning innovation called differentiated learning tries to meet the learning needs of diverse students. The purpose of this study is to describe how the application of differentiated learning has an impact on meeting the learning needs of students in elementary schools which can be seen from the increase in learning outcomes, the involvement of students in learning, and the ability to think critically of students while learning. The research technique used is known as literature review, used to collect information or sources about a particular subject and can be accessed from various sources, including journals, books, the internet, and other literature. Fulfillment of these learning needs can be seen from various aspects, such as learning readiness which refers to the ability of students to accept new material, a strong interest or desire from students with the potential they want to develop, as well as the profile of students who are influenced by learning styles, level cognitive, cultural background, and so on. According to a number of scientific journals and articles that the authors have examined, varied learning has an impact on how well students achieve their learning needs. When differentiated learning is used, students' learning outcomes improve as a result of meeting their needs and increasing their willingness to learn while engaging in the learning process.Keywords: differentiated learning, learning needs, students
STRATEGIES AND INITIATIVES OF CONTROL CYBER CRIME

Hackers in the cybercrime industry utilize the internet and computer technologies to steal sensitive information from businesses, governments, and individuals.Hackers are the cyber criminals who engage in these acts.Approaches to avoid conflict and tools for engaging in combat, supported by unified troops.Initiatives for the prevention and detection of cybercrime and development of a cybercrime strategy among the enormous number of crimes that plague humanity, cybercrime is the most recent addition.Newline as crimes committed online know no national borders and may have far-reaching consequences, the emergence of cyber law was inevitable.That's why a legislation specifically addressing the prevention of cybercrime is so crucial.Newline the term "cybercrime" is used to refer to any illegal activity that takes place using electronic means of communication, including but not limited to personal computers, tablets, smartphones, the world wide web, and cyberspace.However, these crimes are accessible even to folks who have little to no experience with technology.
Learning Accurate Performance Predictors for Ultrafast Automated Model Compression

In this paper, we propose an ultrafast automated model compression framework called SeerNet for flexible network deployment. Conventional non-differen-tiable methods discretely search the desirable compression policy based on the accuracy from exhaustively trained lightweight models, and existing differentiable methods optimize an extremely large supernet to obtain the required compressed model for deployment. They both cause heavy computational cost due to the complex compression policy search and evaluation process. On the contrary, we obtain the optimal efficient networks by directly optimizing the compression policy with an accurate performance predictor, where the ultrafast automated model compression for various computational cost constraint is achieved without complex compression policy search and evaluation. Specifically, we first train the performance predictor based on the accuracy from uncertain compression policies actively selected by efficient evolutionary search, so that informative supervision is provided to learn the accurate performance predictor with acceptable cost. Then we leverage the gradient that maximizes the predicted performance under the barrier complexity constraint for ultrafast acquisition of the desirable compression policy, where adaptive update stepsizes with momentum are employed to enhance optimality of the acquired pruning and quantization strategy. Compared with the state-of-the-art automated model compression methods, experimental results on image classification and object detection show that our method achieves competitive accuracy-complexity trade-offs with significant reduction of the search cost. Code is available at https://github.com/ZiweiWangTHU/SeerNet .
Organisational Commitment and Perceived Support as Linked by Employee Job Satisfaction in App-based Services

In this study, the relationship between perceived organisational support, organisational commitment, and front-line employees' job satisfaction is checked. The major goal of this research paper is to investigate the role that job satisfaction plays in mediating the relationship between organisational commitment and perceived organization support among front-line workers in the online app-based service industry. The survey method was adopted for the collection of data. In total 229 valid responses were used for analysing purposes. To test the proposed hypotheses, structural equation modelling (SEM) analysis is employed. The findings of this study demonstrate unequivocally that the association between perceived organisational support (POS) and organisational commitment (OC) is mediated by job satisfaction (JS). The findings also indicated a strong link between job satisfaction and perceived organisational support.
SELF CHARGEABLE E-BICYCLE USING DYNAMO

One of the greatest and most urgent needs of this world is to develop technology in the transport industry that can help to reduce the fossil fuel consumption exponentially.Our world is facing the danger of over exploitation of fossil fuels which in turns contributing a lot of pollution and global warming.Thus, working on a solution to reduce pollution and global warming, Electric bicycle is the perfect solution to reduce the causes.In E bicycle we use Lead acid battery, it is fast being recognized as a sustainable battery option because of its superior performance and life cycle.Therefore, lead acid battery based electric bikes can be major breakthrough in the transport industry.In the present work, we focus to enhance battery life while running, the Dynamo used in this setup to recycle the power for production and consumption simultaneously.
Collaborative Motion Planning for Dual Robotic Arms Based on Split Sampling Space RRT

Aiming at the problems of strong sampling randomness, difficulty in collision detection and rough paths of rapidly exploring random tree (RRT) algorithm in collaborative motion planning of dual robotic arms, we propose an RRT algorithm based on split sampling space. First, a split sampling space strategy is proposed. According to the sampling points having a fixed range in a certain axis degree, the step size of random tree generation is restricted to the respective sampling space, and combined with the hierarchical wraparound box method to achieve effective collision detection. Besides, a greedy strategy is used to speed up the growth of random trees in the respective space. Finally, the trajectory smoothing of the dual robotic arms path using the Bezier curve improves the trajectory quality while ensuring that the dual robotic arms will not collide. The feasibility and effectiveness of the algorithm are verified through simulation experiments as well as real UR experiments.
The degree of Organizational Health availability and its relationship to Job Performance among workers in the Ministry of Education in the Sultanate of Oman

The current study aimed to determine the degree of organizational health availability and its relationship to job performance among workers in the Ministry of Education in the Sultanate of Oman. And to identify whether there are statistically significant differences at the level of significance (05≥ α) between the arithmetic averages due to the study variables (gender, occupational position, and years of experience in the field of the current job). To achieve the aims of the study, the study used the correlative descriptive approach, in order to answer its questions, a study scales was developed; the first measures the degree of organizational health availability, while the second measures job performance. The results were as follows: The degree of organizational health in the Ministry of Education in the Sultanate of Oman was "large". Where the dimensions of organizational independence and organizational cohesion each scored a high degree, while the dimension of institutional integrity achieved a medium degree and the level of job performance among workers in the Sultanate of Oman was high in all its dimensions. There are no statistically significant differences at the level of significance (05≥ α) in the responses of the study sample members to the degree of organizational health, and the level of job performance due to the variables: gender, occupational position, and years of experience. The results also showed a strong positive correlation between organizational health and job performance among workers in the Ministry of Education in the Sultanate of Oman. The study recommended Preparing professional meetings and seminars among the employees of the directorates of the Ministry's general office; to discuss the dimensions of organizational health, and its impact on the job performance of teachers and enhancing the level of institutional integrity in the departments of the educational directorates in the Ministry's general office.
Wind tunnel measurements of dynamic aerodynamic coefficients using a freely rotating test bench

Purpose This paper aims to propose a dedicated measurement methodology able to simultaneously determine the stability derivative C mα̇ and the pitch damping coefficient sum C mq + C mα̇ in a wind tunnel using a single and almost non-intrusive metrological setup called MiRo. Design/methodology/approach To assess the MiRo method’s reliability, repeatability and accuracy, the measurements obtained with this technique are compared to other sources like aerodynamic balance measurements, alternative wind tunnel measurements, Ludwieg tube measurements, free-flight measurements and computational fluid dynamics (CFD) simulations. Two different numerical approaches are compared and used to validate the MiRo method. The first numerical method forces the projectile to describe a pure oscillation motion with small amplitude along the pitch axis during a rectilinear flight, whereas the second numerical approach couples the one degrees of freedom simulation motion equations with CFD methods. Findings MiRo, a novel and almost non-intrusive technique for dynamic wind tunnel measurements, has been validated by comparison with five other experimental and numerical methodologies. Despite two completely different approaches, both numerical methods give almost identical results and show that the holding system has nearly no impact on the dynamic aerodynamic coefficients. Therefore, it could be assessed that the attitude of MiRo model in the wind tunnel is very close to the free-flight one. Originality/value The MiRo method allows studying the attitude of a projectile in a wind tunnel with the least possible impact on the flow around a model.
Imagify: Reconstruction of High- Resolution Images from Degraded Images

Abstract: Image Denoising simply uses photos with noise and the U-Net architecture. In this study, we demonstrate that the unet design, which is based on convolutional and deconvolutional neural networks or transpose convolutional neural networks, is quite effective at reducing image noise. The assignment falls under abroad category of problems on distribution of posterior probabilities P(theta | X) represents the probability of theparameter theta given the evidence X. These techniques yield great results, but their practical application might be problematic due to issues like the expensive forward and adjoint operators' computations and the challenging hyper parameter selection.
Modeling Intra-class and Inter-class Constraints for Out-of-Domain Detection

Out-of-Domain (OOD) detection aims to identify whether a query falls outside the predefined intent set, which is crucial to maintaining high reliability and improving user experience in a task-oriented dialogue system. The key challenge is how to learn discriminative intent representations that are beneficial for distinguishing in-domain (IND) and OOD intents. However, previous methods ignore the compactness between instances and dispersion among categories, which limits the OOD detection performance. In this paper, we propose a novel Hybrid Contrastive Learning (HybridCL) framework to model both intra-class and inter-class constraints in OOD detection. Specifically, we first propose an intra-class constraint contrastive learning (Intra-CCL) objective, which encourages instances with the same class to be close to their prototypes, forming compact clusters. Then, we present an inter-class constraint contrastive learning (Inter-CCL) objective to effectively enlarge the discrepancy among different classes as much as possible, enforcing the strong separability for different classes in the intent embedding space. Besides, to further enhance the discriminative representation capability of the encoder, we employ an intent-wise attention mechanism to capture the relationships between the intents and the corresponding labels. Experiments and analysis on two public benchmark datasets show the effectiveness of our approach.
Axioms and Intuitions

Originally thought of as a way to build foundations, the axiomatic method was progressively used more and more to organise and expand theories, their scope and boundaries. Despite their formal content and nature, the role of axioms in mathematics cannot be understood without taking into account mathematical practice and intuition.
GhostVec: Directly Extracting Speaker Embedding from End-to-End Speech Recognition Model Using Adversarial Examples

Obtaining excellent speaker embedding representations can leverage the performance of a series of tasks, such as speaker/speech recognition, multi-speaker dialogue, and translation systems. The automatic speech recognition (ASR) system is trained with massive speech data and contains many speaker information. There are no existing attempts to protect the speaker embedding space of ASR from adversarial attacks. This paper proposes GhostVec, a novel method to export the speaker space from the ASR system without any external speaker verification system or real human voice as reference. More specifically, we extract speaker embedding from a transformer-based ASR system. Two kinds of targeted adversarial embedding (GhostVec) are proposed from features-level and embedding-level, respectively. The similarities are evaluated between GhostVecs and corresponding speakers randomly selected from Librispeech. Experiment results show that the proposed methods have superior performance in generating a similar embedding of the target speaker. We hope the preliminary discovery in this study to catalyze future downstream research speaker recognition-related topics.
Cross-domain Object Detection Model via Contrastive Learning with Style Transfer

Cross-domain object detection usually solves the problem of domain transfer by reducing the difference between the source domain and target domain. However, existing solutions do not effectively solve the performance degradation caused by cross-domain differences. To address this problem, we present the Cross-domain Object Detection Model via Contrastive Learning with Style Transfer(COCS). Our model is based on generating new samples with source domain information and target domain style. In addition, the importance of new samples feature information are aimed to match positive and negative samples for comparative learning better. So, we transfer source domain with labeled to get new samples with style of target domain. Then we employ momentum contrast learning method to maximize the similarly between positive sample pairs representations and minimize the loss function. Moreover, our model can be adapted to different style domains, which further expands the application scenarios. Experiments on a benchmark dataset demonstrate that our model achieves or matches the state-of-the-art approaches.
Logit Distillation via Student Diversity

Knowledge distillation (KD) is a technique of transferring the knowledge from a large teacher network to a small student network. Current KD methods either make a student mimic diverse teachers with knowledge amalgamation or encourage many students to do mutual/self learning free from the supervision of the teacher. Intuitively, it could be not optimal to focus on teacher diversity but ignore the teacher-student gap, or spotlight student co-learning without the guidence of the teacher. Besides, such methods mainly rely on distilling deep features from intermediate layers, thus pure logit distillation is still fully underexplored. In this paper, we propose a neat yet effective logit distillation model termed student diversity, that is, many students mimic a teacher with logit distillation, then exploit individual knowledge to collectively train a single excellent student with logit distillation again. For this aim, a multi-branch shared network as diverse students is developed to grasp the knowledge of the teacher in different degrees. Since such students share different levels of network layers, they have different yet homogeneous knowledge to pave the reliable way for bridging the teacher-student gap. To collectively train an excellent student, we fuse the semantics of all the students to pay more attention to attentive features for effective knowledge transfer. We have conducted extensive experiments on various datasets to demonstrate the effectiveness of our approach.
NEW METHOD FOR SCANNING AND RECONSTRUCTION OF 3-DIMENSIONAL SURFACES

A new structured light computer vision system has been developed to scan and reconstruct 3-dimensional geometry information of objects. After acquisition, the images are imported into ProEngineer for display and further processing. The system is composed of a dot matrix pattern laser projector, and two cameras. One of the cameras is used as the main image acquisition camera, while the other camera functions as a checking device to determine the correct image matching between the main image and the projector. The new method is based on use of epipoles and epipolar constraints. As part of this investigation, a new linear approach was developed for estimating the epipolar lines on the main camera, related to the projector. This new approach can guarantee that all points on the same epipolar line on the main image, related to the projector, have the same corresponding epipolar line on the projector. Also, two checking point equations were developed to determine the correct image matching among the main image, the checking image, and the projector. Using the methods developed here, 3-dimensional surfaces can be scanned and reconstructed accurately and rapidly. The methods developed here were verified experimentally, and the 3-dimensional information was imported into ProEngineer for visualization.
MACRE: Multi-hop Question Answering via Contrastive Relation Embedding

Multi-hop question answering over knowledge graphs (KGs) is a crucial and challenging task as the question usually involves multiple relations in the KG. Thus, it requires elaborate multi-hop reasoning with multiple relations in the KG. Two existing categories of methods, namely semantic parsing-based (SP-based) methods and information retrieval-based (IR-based) methods, either suffer from complicated logic forms for covering diverse questions or fail to offer traceable reasoning. In this paper, we propose a novel approach for multi-hop question answering over KGs via contrastive relation embedding (MACRE), powered by contrastive relation embedding and context-aware relation ranking. An adaptive beam search is developed to deliver the answer by identifying the optimal weighted inferential chain, boosting the searching efficiency and alleviating error propagation. The proposed method offers both interpretable reasoning and powerful answering ability, unifying the strengths of SP-based methods and IR-based methods. Extensive experimental results on several benchmark datasets demonstrate the effectiveness of our method.
Hierarchical Encoder-Decoder with Addressable Memory Network for Diagnosis Prediction

Deep learning methods have demonstrated success in diagnosis prediction on Electronic Health Records (EHRs). Early attempts utilize sequential models to encode patient historical records, but they lack the ability to identify critical diseases for patient health conditions. Besides, some works focus on the hierarchical structure of EHR data during learning patient representations, but neglect it in diagnosis prediction process, which leads to insufficient utilization of hierarchical information. To tackle these challenges, we propose a new Hierarchical Encoder-Decoder with Addressable Memory Network for Diagnosis Prediction named HAMNet. Specifically, we employ a hierarchical encoder-decoder framework to utilize the hierarchical structure of historical visits both during representation learning and diagnosis prediction. Furthermore, we propose the addressable memory network to distinguish core diseases towards patient health status at next visit through the well-designed addressing mechanism. It employs feed-forward layers to build the memory network, which can automatically update the memory module through gradient backpropagation without explicit read/write operations. Finally, we evaluate the performance of HAMNet on a large real-world EHR dataset MIMIC-III, and it achieves the state-of-the-art performance compared with baselines.
Statistical Data of Mineral Commodity

The advanced countries, including the United States of America, Japan, the United Kingdom, etc., struggle to secure mineral resources for their development of economy and industry themselves. We, the Republic of Korea need to respond to come up with an effective counterplan for the resource supply and demand accordingly. The Korea Institute of Geological Resources (KIGAM) is a state-designated approval statistic institution and has been building the mineral commodity statistic data of the minerals, officially defined by the mining act of the Korean government since 1986. The statistical data of mineral commodity consist of the sales volume, distribution, and production of mines, and the labor conditions for operation, etc., based on the type of ore and the production of the region, month and mine. KIGAM published the various statistical data containing the production, import, export of the mineral commodity in various forms, including annual and monthly reports and by English. This mineral commodity/mining statistics data can be viewed and downloaded from Korean Statistical Information Services(mici.kigam.re.kr), (www.kosis.kr) by Statistic Korea, Mineral Resources Statistics Portal and also bigdata environment-platform(www.bigdataenvirnment.kr).