Reviewerly/exHarmony huggingface dataset:
Evaluation of modular multiplication techniques for Supersingular Isogeny Schemes on ARMv8 cores

This paper focuses on the evaluation of different modular multiplication implementation techniques on 64-bit ARMv8 systems for the third-round NIST alternate candidate SIKE. The benchmarks were performed on four devices: an Orange Pi WinPlus featuring the Cortex-A53 processor, an NVIDIA Jetson Nano with a Cortex-A57, a Raspberry Pi 4 with a Cortex-A72 and a Macbook Air based on an Apple M1 chip. Throughout these platforms we observed that the two-level Karatsuba Comba method performs better on most Cortex-A processors but the Operand Scanning method presented a performance improvement ranging from 10% to 43% for the multiplication procedure and a 7% to 25% improvement for the modular reduction on the Apple M1 for all SIKE security levels, resulting in an overall improvement ranging from 8% to 28% for the SIKE KEM operations on this architecture.
Application and Practice of MVC Architecture Pattern in On-the-job Internship Management System

In View of the characteristics of the management of on-the-job internship in vocational education, such as scattered internship sites, inconsistent working hours etc., are difficult to manage. A management system of on-the-job internship based on MVC architecture pattern was constructed. First, the working principle and technical advantages of MVC architecture pattern are analyzed. Second, the functional requirements of the system are sorted out, and the framework structure of the system is designed based on the MVC architecture pattern. Third, the basic business logic of View, Control and Model are designed based on MVC architecture pattern and SpringMVC development framework. In order to better carry out the decoupling of system functions, besides the three-tier of MVC architecture pattern, another data access object is extracted to separate the system business logic and data storage. Finally, based on the above design architecture, JSP, Java language and other technologies are used to implement the system. The experimental results show that this system has obvious effect on improving the efficiency of internship management and further improving the quality of internship teaching.
Subsynchronous oscillation detection via cycle-based feature extraction and weighted kernel extreme learning machine

The fast and reliable detection of subsynchronous oscillations (SSOs) in wind farms is important for maintaining the stable operation of a power system. This paper presents a novel, SSO detection method for noisy synchrophasor data that considers the issue of detection as a binary classification (SSO and non-SSO) from the perspective of pattern recognition. The proposed algorithm easily implements cycle-based feature extraction from raw data by applying the strong period of the SSO signal, which is distinguished from noisy data. To mitigate the performance reduction of regular classifiers due to the imbalance issue caused by SSO data being substantially less than non-SSO data, a weighted kernel extreme learning machine is constructed as a classifier to implement SSO detection. Experimental studies are carried out on simulation and field data; the results show the effectiveness of the proposed algorithm for SSO detection in the case of a low SNR and of imbalance issues.
Solar Concentrator PDR with solar tracking

The reduction in conventional fuels consumption can be accomplished through the use of solar energy, transforming it into thermal energy throught a solar collector. There are a lot of solar collector kind, but the solar concentration way, specifically the parabolic dish reflector technology, or just PDR solar concentrator, offers one of the most efficient paths to achieve high quality thermal energy. Usually, the PDR solar concentrator are designed in big dimensions in order to generate electrical energy; nevertheless this technology could be used in minor scale in order to generate solar concentration high levels to achieve enough thermal energy to satisfy hot water demand in residential buildings, homes and even the industry. The prototype here presented was developed in four phases: The parabolic dish, the gear box and mechanical support device, the sun tracking control system and finally the system integration and operational tests. The four phases conjunction results in a totally operative prototype with the capability of achieve temperatures of 400 ºC, over a concentration focal point under partially cloudy day conditions.
THE IMPACT OF EMPOWERING LEADERSHIP ON THE CAREER PLATEAU: AN ANALYTICAL STUDY OF THE OPINIONS OF A SAMPLE OF EMPLOYEES IN THE GENERAL DIRECTORATE OF EDUCATION OF THE FIRST KARKH

The current research aims, though, to verify the impact of empowering leadership as an independent variable in the career plateau as a responsive variable, in the General Directorate of Education in Baghdad, Karkh First and all its departments in the city of Baghdad, and trying to come up with a set of recommendations that contribute to strengthening the practice and adoption of variables in the organizations under study. Proceeding from the importance of the research topic for society, and for the organizations surveyed and their members, the reconnaissance-analytical approach was adopted in the completion of this research, the place of application of the General Directorate of Education in Baghdad, Karkh first, while the research sample was represented by (the director general and his assistants, heads of departments, directors of people and units at the level of the directorate and its departments) as a sample Intentionally for the research community represented by the General Directorate of Education in Baghdad, Karkh First and all its departments in the city of Baghdad and in a comprehensive inventory, as the researcher distributed (282) questionnaires for the research sample, and then collected data from (275) respondents from the surveyed community by adopting the questionnaire that included (48) paragraphs, and supplemented it with interviews. Personal and field observations as tools to help strengthen it. The research adopted the program (Amos V.23, SPSS V.28) with the adoption of descriptive and inferential statistics methods (normal distribution test, confirmatory factor analysis, building models of variables, arithmetic mean, percentages, standard deviation, relative importance, coefficient of variation, correlation coefficient Pearson, simple regression coefficient, path analysis, Sobel test, Aroian test, and Godman test) to test his hypotheses. The most prominent results of the research were represented in the presence of a clear negative impact of the empowering leadership on the career plateau by granting the employees independence from bureaucratic restrictions.
Improved Knowledge Distillationwith Dynamic Network Pruning

Deploying convolutional neural networks to mobile or embedded devices is often prohibited by limited memory and computational resources. This is particularly problematic for the most successful networks, which tend to be very large and require long inference times. Many alternative approaches have been developed for compressing neural networks based on pruning, regularization, quantization or distillation. In this paper, we propose the “Knowledge Distillation with Dynamic Pruning” (KDDP), which trains a dynamically pruned compact student network under the guidance of a large teacher network. In KDDP, we train the student network with supervision from the teacher network, while applying L1 regularization on the neuron activations in a fully-connected layer. Subsequently, we prune inactive neurons. Our method automatically determines the final size of the student model. We evaluate the compression rate and accuracy of the resulting networks on an image classification dataset, and compare them to results obtained by Knowledge Distillation (KD). Compared to KD, our method produces better accuracy and more compact models.
Topological Semantic Graph Memory for Image-Goal Navigation

A novel framework is proposed to incrementally collect landmark-based graph memory and use the collected memory for image goal navigation. Given a target image to search, an embodied robot utilizes semantic memory to find the target in an unknown environment. % The semantic graph memory is collected from a panoramic observation of an RGB-D camera without knowing the robot's pose. In this paper, we present a topological semantic graph memory (TSGM), which consists of (1) a graph builder that takes the observed RGB-D image to construct a topological semantic graph, (2) a cross graph mixer module that takes the collected nodes to get contextual information, and (3) a memory decoder that takes the contextual memory as an input to find an action to the target. On the task of image goal navigation, TSGM significantly outperforms competitive baselines by +5.0-9.0% on the success rate and +7.0-23.5% on SPL, which means that the TSGM finds efficient paths. Additionally, we demonstrate our method on a mobile robot in real-world image goal scenarios.
A Multi-hop and Distributed Proxy Re-encryption Scheme with Dynamic Re-sharing

In order to achieve delegated data sharing, a reliable proxy is required for both data storage and execution of the delegated authorization. The PRE scheme is a representative technique for delegating data sharing, which involves a single proxy to transform the encryption by reencrypting algorithm with an auth-key, without knowing any knowledge about the plaintext. However, most PRE schemes are performed in a centralized environment, which means the system will crash upon the proxy is off-work. In this paper, we optimize the PRE scheme from two aspects. Firstly, the proxy acting as the key path is decentralized in a thresholdbased network, which will provide continuous PRE service when any t out of N nodes work. Moreover, considering the flexible entry and exit mechanism of the decentralized nodes, this proposal presents a re-share algorithm to ensure N live nodes. Secondly, we adopt the multi-hop re-encryption strategy for transitivity of ciphertext, so that the data owner is released from re-encryption key generation task upon user requests, and the authorized delegatees are able to retransform the encryption to designated users by using its own secret key.
Dynamics Of Intelligence And Motivation Of Primary Schoolchildren In The Process Of A Formative Experiment

The progress of society in recent years has not bypassed the educational environment. The problem of personal development of the primary generation is increasingly being discussed in psychological and pedagogical communities. Excessive requirements for schoolchildren do not always correspond to their age-related psychological characteristics, because the modern school curriculum requires schoolchildren to carry out large intellectual loads. The problem of educating personal qualities in primary schoolchildren that contribute to successful schooling remains relevant. Therefore, it is important that when attending school, children are psychologically ready for new social conditions, schooling. The article presents the results of a psychological experiment with primary schoolchildren. The study focuses on the polymodality of perception as an important aspect of the educational and cognitive process, the driving factor of which is the intelligence and motivation of schoolchildren. The practical value of the study lies in the fact that by purposefully developing perception (auditory and visual channels) through interaction with the kinesthetic modality, it is possible to increase the overall level of intelligence and motivation of primary schoolchildren.
Real-time monocular 3D sensor using infrared speckle projection and an optimized 3D measurement method based on OpenCL acceleration

For enhancing the performance of 3D imaging of speckle projection profilometry, in this paper, we propose a real-time monocular 3D sensor using infrared speckle projection and an optimized 3D measurement method based on OpenCL acceleration.
Missing values imputation in Arabic datasets using enhanced robust association rules

Missing value (MV) is one form of data completeness problem in massive datasets. To deal with missing values, data imputation methods were proposed with the aim to improve the completeness of the datasets concerned. Data imputation's accuracy is a common indicator of a data imputation technique's efficiency. However, the efficiency of data imputation can be affected by the nature of the language in which the dataset is written. To overcome this problem, it is necessary to normalize the data, especially in non-Latin languages such as the Arabic language. This paper proposes a method that will address the challenge inherent in Arabic datasets by extending the enhanced robust association rules (ERAR) method with Arabic detection and correction functions. Iterative and Decision Tree methods were used to evaluate the proposed method in an experiment. Experiment results show that the proposed method offers a higher data imputation accuracy than the Iterative and Decision Tree methods.
Super-resolution Inversion and Reconstruction for Remote Sensing Images of Bands of Interest

The super-resolution inversion and reconstruction algorithm of remote sensing image of band of interest is proposed in this paper. Based on the existing multi-resolution hyperspectral images, a spectral reflectance dictionary is constructed, according to which the objects in radiation calibrated and atmospheric corrected scene spectral reflectance images are sorted and classified using the end element extraction algorithm and the correlation distance method. Based on the classification results, a dictionary of scene feature spectral reflectance is constructed. Based on this, the sparse representation method and the secondary optimization algorithm based on image similarity are used to solve and optimize the feature distribution of the scene image. According to the feature distribution of the scene image, using the remote sensing link imaging model, super-resolution inversion reconstruction is performed to obtain a high-resolution remote sensing image of a band of interest. Experimental results show that the proposed algorithm can reconstruct low-resolution remote-sensing images of different bands into high-resolution remote-sensing images. Thus, the proposed algorithm can effectively boost image resolution, enrich image details and improve the target detection capability by the image.
SISTEM INFORMASI PEMANTAUAN PENJUALAN BARANG RONGSOKAN MENGGUNAKAN FRAMEWORK CI.4 PADA CV. SUMBER BAJA

Sumber Baja is a trading company engaged in used goods such as cans, iron, and junk which will be sent to the client. At the time of delivery the company is still making notes in the book to make a report on the delivery of goods. This system is intended for the design of sales monitoring applications that can assist in making reports on goods that have been sent to the client. Because it has several obstacles in using the system which is still recorded in the book. So the process of making reports takes a long time because of searching for data in the books, making file and document storage increasing which makes it difficult to find old data, and data loss often occurs which can make company leaders not know whether the goods sent were appropriate or not when sent. . research aims to create a sales monitoring information system on CV. Sumber Baja Palembang, so that it can help admins in making reports on sales of goods and making it easier to search for new or old data and avoid data loss. The system design technique used is the agile method and the implementation of this research uses the PHP programming language and the MYSQL database. The results of the study are expected to help admins store data on goods that have been sent and help make reports to find out the difference between goods.
Blur image detection and classification using edge detection techniques

Blur classification is important for blind image restoration. It is a challenging task to detect blur in a single image without any information. In this paper, we used edge detection techniques and deep learning convolutional neural network named ResNet 50 for the classification of blur-type images. ResNet 50 model effectively reduces gradient disappearance problem and uses skip connection to train the dataset. Generally, images are subjected to defocus and motion blur, which is caused by the improper depth of focus and movement of objects at the time of capture. Kaggle's blur dataset is used in this paper, which consists of sharp, defocus and motion blur images. Edge detection techniques are applied on images using Laplacian, Sobel, Prewitt, and Roberts filters to obtain features like mean, variance, maximum signal-to-noise ratio (SNR), which are used to train the system and classify the images using classification algorithm.
A design turn: on post-truth society and the use of design

In a world with deliberate distortion of the reality to create and shape public opinion and influence social attitudes, objective facts, and logical arguments are losing to emotions and personal beliefs. This post-truth world abides by the rules of volatility, fluctuation, and surplus value that in turn, condemn some aspects of the referentiality of language to the law of obsolescence. It is imperative to conduct how this process affects design, and uncover whether it plays a vital part since, to some extent, design involves the cultivation of emotions in the development of solutions. Literature shows that insincerity exists in the design and advertising industry and the fact that the world works in the way that we design it. Therefore, designers must uphold their social responsibility to create a better world. It is shown that emotions are critical drivers of the designs. Also, it is demonstrated that design is a public language that settles objectives, interests, instruments, and affections held individuals to create political competence and dialogue.
PEA: Practical Private Epistasis Analysis Using MPC

Due to the significant drop in prices for genome sequencing in the last decade, genome databases were constantly growing. This enabled genome analyses such as Genome-Wide Association Studies (GWAS) that study associations between a gene and a disease and allow to improve medical treatment. However, GWAS fails at the analysis of complex diseases caused by non-linear gene-gene interactions such as sporadic breast cancer or type 2 diabetes. Epistasis Analysis (EA) is a more powerful approach that complements GWAS and considers non-linear interactions between multiple parts of the genome and environment. Statistical genome analyses require large, well-curated genomic datasets, which are difficult to obtain. Hence, the aggregation of multiple databases is often necessary, but the sharing of genomic data raises severe privacy concerns and is subject to extensive regulations (e.g., GDPR or HIPAA), requiring further privacy protection for collaborative analyses. Although there has been work on private GWAS, there was a lack of attention to Private EA (PEA). In this work, we design the first secure and accurate PEA protocol, with security against passive adversaries. Our efficient PEA protocol consists of two subprotocols: (1) (optional) feature selection for filtering noisy features to reduce the input size for better efficiency and (2) finding relevant associations. For feature selection, we design two protocols based on Secure Multi-Party Computation (MPC) for Relief-F and TuRF. For finding associations, we design an MPC protocol for Multifactor Dimensionality Reduction (MDR). Our private MDR protocol is based on two novel, efficient building blocks, arithmetic greater than and arithmetic swap, which may be of independent interest. This approach omits the need for expensive conversions between sharing types in private MDR and reduces the communication by two orders of magnitude compared to a naïve design using garbled circuits. Our private MDR protocol runs in (extrapolated) three days on a practical database with 10,000 features for all two mutually combined features, i.e., considering about 50 million combinations.
What Do Developers Discuss about Code Comments?

Code comments are important for program comprehension, development, and maintenance tasks. Given the varying standards for code comments, and their unstructured or semi-structured nature, developers get easily confused (especially novice developers) about which convention(s) to follow, or what tools to use while writing code documentation. Thus, they post related questions on external online sources to seek better commenting practices. In this paper, we analyze code comment discussions on online sources such as Stack Overflow (SO) and Quora to shed some light on the questions developers ask about commenting practices. We apply Latent Dirichlet Allocation (LDA) to identify emerging topics concerning code comments. Then we manually analyze a statistically significant sample set of posts to derive a taxonomy that provides an overview of the developer questions about commenting practices. Our results highlight that on SO nearly 40% of the questions mention how to write or process comments in documentation tools and environments, and nearly 20% of the questions are about potential limitations and possibilities of documentation tools to add automatically and consistently more information in comments. On the other hand, on Quora, developer questions focus more on background information (35% of the questions) or asking opinions (16% of the questions) about code comments. We found that (i) not all aspects of comments are covered in coding style guidelines, e.g., how to add a specific type of information, (ii) developers need support in learning the syntax and format conventions to add various types of information in comments, and (iii) developers are interested in various automated strategies for comments such as detection of bad comments, or verify comment style automatically, but lack tool support to do that.
MCER: A Multi-domain Dataset for Sentence-Level Chinese Ellipsis Resolution

Ellipsis is a cross-linguistic phenomenon which can be commonly seen in Chinese. Although eliding some of the elements in the sentence that could be understood from the context makes no difference for human beings, it is a great challenge for machine in the procedure of natural language understanding. In order to promote ellipsis-related researches in Chinese language, we propose an application-oriented definition of ellipsis specifically for researches in the realm of Chinese natural language processing. At the same time, we build and release a multi-domain dataset for sentence-level Chinese ellipsis resolution following the new definition we propose. In addition, we define a new task: sentence-level Chinese ellipsis resolution, and model it with two subprocedures: 1) Elliptic position detection; 2) Ellipsis resolution. We propose several baseline methods based on pre-trained language models, as they have obtained state-of-the-art results on related tasks. Besides, it is also worth noticing that, to our knowledge, this is the first study that apply the extractive method for question answering to Chinese ellipsis resolution. The results of the experiments show that it is possible for machine to understand ellipsis within our new definition.