Reviewerly/exHarmony huggingface dataset:
The Influence of Student Activity in Organizations on Students’ Readiness in Entering the World of Work

In the current era of globalization, human resources are required to further highlight their strengths in each field of resources in order to be able to compete at an international level.In addition, the era of globalization also creates a high level of competition in the labor market indirectly.Indonesia is one of the countries that cannot be separated from problems related to human resources (HR) such as: the low quality of the workforce, the high unemployment rate and less extensive job opportunities due to the increasing number of people who do not have work skills in Indonesia and this is also the case.This will result in Indonesia becoming one of the main markets for foreign products as well as a job market for foreign workers.Active organization is one of the external factors in influencing student academic achievement, where organizing is a direct activity in the campus environment, the students who are active in organizations will have a high influence on student achievement.By actively organizing students are required to be able to manage their time as well as possible, both time for lectures and for organizing.Academic achievement of students who can manage their time well between lectures and active organizations will be better than students who cannot manage their time at all.This research is a quantitative research using associative descriptive approach.Primary data and secondary data are the types of data used in this study.Primary data is data obtained from students of the Faculty of Economics, Universitas Negeri Padang using a questionnaire.This study uses data analysis techniques, namely simple regression analysis and hypothesis testing to see how much influence exogenous variables have on endogenous variables.The conclusion from the results of this research is that students of the Faculty of Economics are active in organizing both at the Faculty level and at the University level able to improve academic achievement.For example, being actively organized can add insight, increase Soft Skill maturity, and have a lot of experience, especially in the field of organizations that are followed, and get various kinds of information both on campus and off campus.So that it can make it easier for students of the Faculty of Economics to adjust to the new environment because the organization is a place to train themselves to work together.The higher the academic achievement of the students of the Faculty of Economics, the higher the readiness of the students of the Faculty of Economics to enter the world of work.
Inverse-Designed Multi-Level Diffractive Doublet for Wide Field-of-View Imaging

Wide field-of-view (WFOV) imaging is extensively used in fields as diverse as macro photography, virtual reality, machine vision, and microscopy. Conventional WFOV cameras are bulky since they require multiple refractive lenses. Here, we design, fabricate, and characterize a 100° FOV camera with a total-track length of ∼2 mm, composed of an inverse-designed multi-level diffractive doublet with an input-aperture diameter of 0.204 mm, a back-focal length of 1 mm, and operating at λ = 850 nm (bandwidth ∼ 34 nm). Using careful experiments, we characterize the focusing and imaging performance as well as demonstrate de-vignetting based on experimental calibration. The spatial frequency at 10% modulation-transfer-function contrast was measured at 150 and 38 lp/mm for 0 and 50° incident angles, respectively. Our approach can be scaled to larger aperture sizes and smaller f/#s, potentially leading to thin, lightweight WFOV cameras for portable applications.
Knowledge Management in Indian Higher Education – Issues and Challenges

Purpose : The current research focused on knowledge management in Indian higher education. Knowledge management is an important aspect of higher education, as it helps institutions to effectively manage and share knowledge to enhance teaching, learning, and research outcomes. In the context of Indian higher education, there are several policy implications that can help to improve the effectiveness of knowledge management. Methodology : The research paper was prepared based on secondary data. These include creating a comprehensive knowledge management framework, encouraging collaboration among institutions, investing in technology infrastructure, fostering a culture of knowledge sharing, and supporting open access to research publications and educational resources. Findings : I found that knowledge management in Indian higher education requires a multi-faceted approach that involves policy support, technological investments, and a culture of collaboration and knowledge sharing. As the Government of India welcomes foreign universities, the significance of knowledge management becomes even more vital. Implementing these policy implications can help to improve the effectiveness of KM in Indian higher education and support the development of a more robust knowledge-based economy. Practical Implications : It is recommended that the government framework should also include guidelines for the development and implementation of knowledge management systems in higher education institutions. Collaboration among institutions is essential for effective knowledge management. The government should encourage institutions to collaborate with each other to share knowledge and resources. This can be done by creating networks or consortia of institutions that work together on specific projects or initiatives. Originality : Unlike prior research on knowledge management, the current work is to provide a comprehensive understanding of KM in higher education across various mediums.
A catalogue of game-specific anti-patterns based on GitHub and Game Development Stack Exchange

With the ever-increasing use of games, game developers are expected to write efficient code and support several aspects such as security, maintainability, and performance. However, the need for frequent updates in game development may lead to quick-fix solutions and bad coding practices. Though unintentional, these bad practices may lead to poor program comprehension and can cause several issues during software maintenance. The quick-fix solutions might lead to technical debts due to the presence of anti-patterns and code smells, which may affect the functional and non-functional requirements of the game. To avoid such instances, game developers may need some guidelines to refer to during the game development process. Thus, to aid developers and researchers, in our previous work, we had presented an initial catalogue of anti-patterns in the domain of game development. To broaden the scope of the catalogue and diversify the instances of anti-patterns, we analyzed additional data from a Q&A platform. We present 15 game-specific anti-patterns based on thematic analysis of 189 issues, 892 commits, 104 pull requests from 100 open-source GitHub game repositories, and 971 questions from Game Development Stack Exchange. We see the catalogue as an effort towards improving the development and quality of the games. The catalogue containing a detailed description of every anti-pattern with the context, problem, solution, example(s), and their occurrences on GitHub and Game Development Stack Exchange is available at https://rishalab.github.io/Catalog-of-Game-Antipatterns/.
К вопросу использования технологии Moodlе в организации учебного процесса в вузе.

The ways of modernization of education through digital technologies are discussed. An example of using MOODL technology is given.
No-regret dynamics in the Fenchel game: a unified framework for algorithmic convex optimization

We develop an algorithmic framework for solving convex optimization problems using no-regret game dynamics. By converting the problem of minimizing a convex function into an auxiliary problem of solving a min–max game in a sequential fashion, we can consider a range of strategies for each of the two-players who must select their actions one after the other. A common choice for these strategies are so-called no-regret learning algorithms, and we describe a number of such and prove bounds on their regret. We then show that many classical first-order methods for convex optimization—including average-iterate gradient descent, the Frank–Wolfe algorithm, Nesterov's acceleration methods, the accelerated proximal method—can be interpreted as special cases of our framework as long as each player makes the correct choice of no-regret strategy. Proving convergence rates in this framework becomes very straightforward, as they follow from plugging in the appropriate known regret bounds. Our framework also gives rise to a number of new first-order methods for special cases of convex optimization that were not previously known.
Matched Wavelet Pooling for Efficient Light-Weight CNN Network Architectures with Application to Image Classification

This research addresses the challenge of evaluating the capability of an emerging pooling method, namely, wavelet pooling, to increase the data efficiency in deep neural networks. Specifically, we focus on light-weight network architectures. The volume of training data required to achieve a particular performance level, e.g., recognition accuracy, is what we refer to as data efficiency. Recently, wavelet pooling has been attracting an increasing research attention due to its capacity to overcome the spatial information loss that is inherent in traditional pooling, by capitalizing on localized spectral information in the wavelet domain. We proposed a new wavelet pooling approach recently which has shown significant promise specifically on MobileNets. This approach chooses which wavelet band(s) to include during training by matching the input images (to be recognized) to specific wavelet sub-bands, hence the name-Matched Wavelet Pooling (MWP). How does the performance of MobileNets differ when MWP is adopted compared to the performance of baseline MobileNets and MobileNets with non-matched wavelet pooling? The principal contribution of this research is addressing this research question. We hypothesize that MWP when used on MobileNets requires a smaller volume of training, than baseline MobileNets and non-matched wavelet pooling, to achieve the same recognition accuracy. On two popular benchmarks, namely, CINIC-10 and STL-10 datasets, we report consistent data savings (achieved by MWP) that approaches 30% compared to the baseline MobileNet model and the non-matched wavelet pooling while achieving higher recognition accuracy.
A Reputation System based on Blockchain and Deep Learning in Social Networks

Existing social networks, such as Twitter and Facebook, are rife with inaccurate and damaging information that is bad for society. Most existing solutions usually use deep learning models for disinformation detection in addition to artificial recognition. However, the result is easily tampered with by people. At the same time, if we strictly manage public opinions, freedom of speech will also cause controversy. In order to solve the above problems and maintain a good social network environment, we propose a new reputation mechanism based on blockchain and deep learning. To assess the reputation of message senders, our proposed mechanism utilizes smart contracts that automate programs without human intervention. Our approach avoids unduly restricting users’ freedom of expression and instead employs deep learning models for rumor detection and sentiment analysis to identify and label messages. By controlling the dissemination of messages based on labels of messages and the sender’s reputation, we aim to balance freedom of speech with social stability. Finally, we analyze the usability and performance of our proposed system.
Defending Poisoning Attacks in Federated Learning via Loss Value Normal Distribution

As an emerging distributed machine learning paradigm, federated learning (FL) only shares model updates between clients and servers, and training data are secured on each client’s device. Due to local data privacy, FL is vulnerable to poisoning attacks, which causes serious degradation in the model performance. In this paper, we analyze the targeted poisoning attacks of FL and reveal that when the data held by each client of FL is independent and identically distributed (iid), the loss of all non-poisoned models on some clean dataset agrees with a normal distribution (ND), while the loss value of the poisoned model deviates from this distribution. Based on this observation, we propose a method named ND_defense to defend FL targeted poisoning attacks. We set a small clean test dataset on the server, compute the loss of each client model on it, detect the abnormal ones with the proposed loss-value-based inspection algorithm, and then only aggregate the benign clients’ models to get the global model. We conducted experiments on three benchmark datasets and compared several state-of-the-art defense methods. The results show that our NDdefense is effective in defending against targeted poisoning attacks.
Dynamic Privacy-Enhanced Federated Learning Algorithm Based on Gradients Perturbation

Federated learning (FL) is an emerging distributed learning framework that can reduce privacy leakage risks by not explicitly sharing private data, but FL still has privacy leakage issues. Recently, many FL security schemes were proposed to improve the protection capabilities of FL, but they cannot realize a desirable tradeoff between privacy and model performance. In this work, we propose a dynamic privacy-enhanced federated learning algorithm based on gradients perturbation to solve above issue. First, instead of directly using original feature of training data, we generate new feature of training data by maximizing the distance between training data and resulting data that inverse mapping by new feature, and minimizing the distance between original and new feature. Next, the dynamic mixed gradients are obtained by combining gradients generated by original and new feature with variable weights. The dynamic mixed gradients-based model training can effectively prevent the original training data leakage and adjust privacy preservation strength adaptively. Furthermore, to make iDLG difficult to infer labels of training data, we perturb mixed gradients by transforming part of positive gradients into negative gradients. Finally, the experiments demonstrate that the algorithm proposed in this paper can effectively resist iDLG attack, and it has significant advantages in protection effect compared with other security schemes.
Attention based Dual Stream Modality-aware model for Visible-Infrared Person Re-identification

Differing from conventional person Re-ID task, Visible-Infrared person re-identification aims to retrieve and match the same identity’s images from both visible and infrared modalities. This task can address the limitation of RGB-based conventional person Re-ID in dark environments. The channel and modality variance between RGB and infrared (IR) images are two key challenges for Cross-modality person re-identification. Existing works mainly focus on using single-stream network learning modality-shared features by embedding different modalities into the same feature space, which fits modality features to increase intra-class cross-modality similarity. However, these methods can not effectively learn the same-modality inter-class differences. In this paper, we propose an Attention-based Dual Stream Modality-aware method (ADSM) to solve this problem. Our method contains two parts: 1) the attention mechanism is introduced in our method to learn the inter-classes differences under the same modality, 2) a dual stream network based on attention and Resnet is designed to fuse the cross-modality information. Extensive experiments have been made on two public cross-modality person Re-ID datasets SYSU-MM01 and RegDB, experimental results show that the performance of our method outperforms the current state-of-the-art methods by a wide margin, which confirms the superiority of our model.
CLIP-HASH: A Lightweight Hashing Network for Cross-Modal Retrieval

Cross-modal hashing has attracted great interest in the past decades. Due to the traditional hashing retrieval model requiring hand-crafted feature extraction, much research on the end-to-end deep hashing models has been explored. However, this training process of deep models is complex and needs a large number of samples. To address the above issues, we propose a lightweight hashing retrieval network with the pre-trained CLIP model, which is called CLIP-Hash. It can obtain better hash features via the pre-training CLIP model and provide a lightweight fine-tune model which is easily trained and only few training samples are required. First, we obtain the powerful feature representations by the visual and textual encodes. Then, we propose a simple hash encoding module, which is a lightweight network and only few parameters are needed to be trained. With the proposed hash encoding module, we can obtain efficient binary hash codes that can align the images and texts. We conduct experiments on MIRFlickr and NUS-WIDE two benchmark datasets. The results show that the proposed simple method can outperform the SOTA hashing methods.
Improving the Quality of Textual Adversarial Examples with Dynamic N-gram Based Attack

Natural language models have been widely used for their impressive performance in various tasks, while their poor robustness also puts critical applications at high risk. These models are vulnerable to adversarial examples, which contain imperceptible noise that leads the model to wrong predictions. To ensure such malicious examples are imperceptible to humans, various word-level attack methods have been proposed. Previous works on word-level attacks attempt to generate adversarial examples by substituting words in sentences. They utilize different candidate substitution selection methods and substitution strategies to improve attack effectiveness and the quality of generated examples. However, previous works are all unigram-based attack methods, which ignore the connection between words. The unigram nature of these methods downgrades fluency, increases grammatical errors, and biases the semantics of adversarial examples, making adversarial examples easier to be detected by humans. In this paper, to improve the quality of textual adversarial examples and makes the adversarial example more imperceptible to human, we propose a black-box word-level attack method called Dynamic N-Gram Based Attack (DyGram). DyGram tokenizes the entire sentence into multiple n-gram units, rather than individual words as in previous works, and substitutes words in a sentence in descending order of n-gram unit importance. Extensive experiments demonstrate that DyGram achieves higher attack success rates than previous attack methods and improves the quality of generated adversarial examples in terms of the number of perturbed words, perplexity, grammatical correctness, and semantic similarity.
Reinforcement Learning Driven Moth-flame Optimization Algorithm for Solving Numerical Optimization Problems

Moth-flame optimization (MFO) algorithm has received a lot of attention recently, due to its simple structure and easy coding. Researchers have demonstrated that the original MFO algorithm suffers from the drawbacks of insufficient variety, slow convergence speed, and readily sliding into local optimum, which are brought about by the imbalance between local and global search. Reinforcement learning driven moth-flame optimization (RLMFO) algorithm is designed to correct these issues. Opposition learning is employed to broaden the variety of the initial population. Reinforcement learning is introduced to direct the local and global search process of the algorithm. A strategy pool containing Gaussian mutation (GM), Cauchy mutation (CM), Lévy mutation (LM), and elite strategy (ES) is created to hold strategies with various functions. RLMFO is verified on the benchmark test suite in CEC 2017. RLMFO performs better than cutting-edge algorithms according to experimental findings.
Long-Short Terms Frequency: A Method for Encrypted Video Streaming Identification

Nowadays, with the vigorous development of self-media services, more and more individual users upload videos freely. While bringing goodness, it also inevitably brings evil. Therefore, it is particularly necessary to identify and supervise illegal videos through network stream. However, many video streaming services, such as YouTube, have applied encryption to protect users’ privacy, which makes it more difficult to analyze network stream. Many researches show that DASH (Dynamic Adaptive Streaming over HTTP) will leak information about video segmentation, which is related to the video content. Consequently, it is possible to analyze the content of encrypted video stream without decryption. Previous studies have proposed a series of encrypted video identification methods based on this. However, most of them need to wait for a long video playback time, such as more than 10s, or even wait for the entire video playback to complete the identification. In this paper, we propose a fast, lightweight, and accurate method named Long-Short Terms Frequency(LSTF) for online encrypted video identification. Experiments have proved that compared with the state-of-the-art, our method has advantages in both speed and accuracy, and even if CDN switching occurs during the video playback, it still has a high identification accuracy.
SeAuNet: Semi-Autonomous Encrypted Traffic Classification and Self-labeling

With the more attention to user privacy and communication security, encrypted traffic has expanded substantially, which has brought huge challenges to traditional traffic classification methods. Deep learning knowledge has great advantages in processing encrypted traffic classification. However, it is difficult for researchers to realize unknown encrypted traffic classification in time, due to the complex parameters optimization process. In order to solve the problems mentioned above, we propose a semi-autonomous encrypted traffic classification and self-labeling scheme to (i) automatically and fast achieve architecture search for known encrypted traffic classification based on simulated annealing and particle swarm optimization, (ii) accomplish unknown encrypted traffic self-labeling based on siamese network, and build a corresponding training dataset, and (iii) update encrypted traffic classifier with transfer learning. Specifically, to validate the feasibility and robustness of the proposed scheme, four specific scenarios are tested based on an open dataset. The results demonstrate that our proposed scheme accomplishes neural architecture search with an average detection rate of up to 99%, provides correct labels for unknown encrypted traffic, and generates the latest dataset. Then, the classifier is updated successfully with the self-labeling encrypted traffic dataset.
Momentum Contrastive Learning for Sequential Recommendation

Contrastive self-supervised learning (SSL) based Sequential Recommendations (SR) have recently achieved significant performance improvements in addressing the data sparsity problem, which hinders learning high-quality user representations. However, current contrastive SSL based models ignore the importance of consistency between sample pairs. Consistency means the similarity degree between the feature representation of encoded sample pairs, and the higher the consistency, the better the feature learning. To figure out the benefits of consistency and utilize it effectively, Momentum Contrastive Learning for Sequential Recommendation (MCL4SRec) is designed. Existing experiments on four public datasets demonstrate the superiority of MCL4SRec, which achieves state-of-the-art performance over existing baselines.