Reviewerly/exHarmony huggingface dataset:
An Efficient Deep Video Model For Deepfake Detection

The use of deep learning technology to manipulate images and videos of people in ways that are difficult to distinguish from the real ones, known as deepfake, has become a matter of national security concern in recent years. As a result, many studies have been carried out to detect deepfake and manipulated media. Among these studies, deep video models based on convolutional neural networks have been the preferred method for detecting deepfake in videos. This study presents a novel deep video model called Sequential-Parallel Networks (SPNet) that provides efficient deepfake detection. The SPNet model consists of a simple yet innovative sequential-parallel block that first extracts spatial and temporal features sequentially, then concatenates them together in parallel. As a result, the presented SPNet possesses comparable spatiotemporal modeling abilities as most state-of-the-art deep video methods but with lower computation complexity and fewer parameters. The efficiency of the presented SPNet is demonstrated on a large-scale deepfake benchmark in terms of high recognition accuracy and low computational cost.
Beating the 3 dB quantum squeezing enhancement limit of two-mode phase-sensitive amplifier by multi-beam interference

Abstract Quantum squeezing, which makes measurement sensitivity beyond classical limit by reducing system noise, is an essential non-classical resource for quantum metrology. It is of great importance to enhance quantum squeezing since the squeezing degree directly determines the extent to which measurement sensitivity beats the classical limit. Recently, a two-mode phase-sensitive amplifier has been utilized to enhance the quantum squeezing of phase-insensitive amplifier. However, such enhancement has an intrinsic limit of 3 dB. Here we show that such limit of 3 dB can be overcome by utilizing multi-beam interference. Specifically, a quantum squeezing enhancement of about 3.67 dB is observed by direct measurement. Moreover, we find that the amount of quantum squeezing enhancement increases as the number of multi-beam interference increases, which clearly shows that beating the quantum squeezing enhancement limit of 3 dB is induced by multi-beam interference. Our results here provide an efficient way to enhance the quantum squeezing.
A CAM-Enhancing Generative Person Re-ID Method Based Global and Local Features

For GAN-based Person Re-identification (Re-ID), the key is to generate pedestrian images with higher identity consistency and meanwhile larger intra-class diversity. Generally, the main discriminative parts focus on some local regions from the foreground of each pedestrian image for Re-ID, and they should be irrelevant to the background. Whereas, most existing methods generate pedestrian images only based on global features, which difficultly achieves emphasizing crucial local regions and weakening the background. Based on this, we propose a CAM-enhancing generative Re-ID method in which the global and local features are jointly used. In the proposed method, an adaptive CAM-enhancing local encoder is designed to explore the significance of local appearances and enhance the effect of crucial local features in generations, where the foreground is divided into multiple local parts and separated from the background by pedestrian segmentation. Moreover, a new generation loss is proposed to supervise the identity consistency by reducing the inconsistency of crucial regions in foregrounds and meanwhile enrich the intra-class diversity by generating variant backgrounds. Experimental results indicate that the proposed method obtains better generation images and Re-ID performance than other methods.
Using Classifier Discrepancy for Cross-Domain Image Retrieval

In recent years, cross-domain image retrieval (CDIM) has garnered considerable interest. The primary difficulty of CDIM is the domain gap, which makes it hard for the system to retrieve two photos that belong to the same category but have distinct domains. In this paper, we provide a novel multi-branch network employing the quintuplet structure to minimize retrieval loss and classifier discrepancy to minimize domain loss. Using three public datasets, we test the proposed method for zero-shot sketch-based image retrieval, which is one of CDIM's application tasks. Experiments validated the proposed method's state-of-the-art performance on the majority of datasets.
ANALISA POLA PEMBELIAN KONSUMEN MENGGUNAKAN DATA MINING DENGAN ALGORITMA APRIORI (STUDI KASUS: EDUKITS BATAM CENTRE)

The initial research purposes is to do the analyzing of data transaction for one of the stationary stores in Batam city, namely edukits Batam Centre, because the store is a well-known business and of course many consumers are interested, of course, a lot of data has been collected so far. From transactions that have occurred so far, these data are only stored in the form of archive files without being processed or used for future business progress. To analyze this research, the a priori algorithm association rule method was used, by analysing support value and as well as the confidence value that were set so that the results of this study obtained the support value for the Printing Paper category 71%, Accessories 74%, 77%, Other Equipment 88%, Glue and Adhesive 72%. By using the Apriori algorithm, 2 rules are obtained, namely the antecedent value of stationery, the consequent accessories resulting in a support value of 65% and confidence of 84.4%. Then the second rule is the antecedent of accessories, the consequent of writing instruments with a support value of 65% and a confidence value of 88.4%.
Bidirectional sampling method for imbalanced data

Traditional over-sampling and under-sampling algorithms suffer from overfitting and high noise when unbalanced data classes are in the sample set. To improve the performance of the data classifier, this study proposes a SMOTECU algorithm combining SMOTE and ClusterCentroids under-sampling. It absorbs the advantages of both algorithms and avoids generating or rejecting excessive samples in the dataset, effectively reducing the harmful effects of overfitting and noise. We experiment with 16 unbalanced standard datasets combining three classifiers: RF, RBFNN, and RBFSVM. By comparing three evaluation metrics: F1-score, AUC, and running time, the results demonstrate that the performance of the SMOTECU-based random forest model is better, and compared with SMOTE and ClusterCentroids, SMOTECU can effectively avoid overfitting and save running time.
Hierarchical Multi-task Learning via Task Affinity Groupings

Multi-task learning (MTL) permits joint task learning based on a shared deep learning architecture and multiple loss functions. Despite the recent advances in MTL, one loss often dominates the learning optimization in multiple unrelated tasks. This often results in poor performance compared to the corresponding single task learning. To overcome the aforementioned "negative transfer", we propose a novel hierarchical framework that leverages task relations via inter-task affinity to supervise multi-task learning. Specifically, the inter-task affinity generated task sets, with low-level task set and complex task set at the bottom and top layers respectively, enables iterative multi-task information sharing. In addition, it also alleviates simultaneous image annotations for multiple tasks. The proposed framework achieves state-of-the-art results on classification, detection, semantic segmentation and depth estimation across three standard benchmarks. Furthermore, with state of the results on two benchmarks for image retrieval task, we also demonstrate that the embeddings learned using such a framework provide good generalization and robust representation learning.
Semantic-Embedded Knowledge Acquisition and Reasoning for Image Segmentation

Image segmentation is a difficult and challenging task because of the complex object appearance and diverse object categories. Traditional methods directly use visual features for segmentation but ignore the correlation between objects. We introduce a knowledge reasoning module (KRM) for external knowledge aggregation and leverage a graphic neural network to aggregate the knowledge feature, which is concatenated with a visual feature for semantic segmentation. To this end, we use word embedding of category names as semantic feature and establish the relationship between categories. Through iteration, the aggregated features can be enriched. In experiments, three well known semantic segmentation methods are used as baseline. Our experiment results outperform the baseline methods on the food dataset Food-Seg103 and Cityscapes, and demonstrate the effectiveness of our proposed method.
YOLO-G: Improved YOLO for cross-domain object detection

Cross-domain object detection is a key problem in the research of intelligent detection models. Different from lots of improved algorithms based on two-stage detection models, we try another way. A simple and efficient one-stage model is introduced in this paper, comprehensively considering the inference efficiency and detection precision, and expanding the scope of undertaking cross-domain object detection problems. We name this gradient reverse layer-based model YOLO-G, which greatly improves the object detection precision in cross-domain scenarios. Specifically, we add a feature alignment branch following the backbone, where the gradient reverse layer and a classifier are attached. With only a small increase in computational, the performance is higher enhanced. Experiments such as Cityscapes→Foggy Cityscapes, SIM10k→Cityscape, PASCAL VOC→Clipart, and so on, indicate that compared with most state-of-the-art (SOTA) algorithms, the proposed model achieves much better mean Average Precision (mAP). Furthermore, ablation experiments were also performed on 4 components to confirm the reliability of the model. The project is available at https://github.com/airy975924806/yolo-G.
Few-Shot Object Detection with Memory Contrastive Proposal Based on Semantic Priors

Few-shot object detection (FSOD) aims to detect objects belonging to novel classes with few training samples. With the small number of novel class samples, the visual information extracted is insufficient to accurately represent the object itself, presenting significant intra-class variance and confusion between classes of similar samples, resulting in large errors in the detection results of the novel class samples. We propose a few-shot object detection framework to achieve effective classification and detection by embedding semantic information and contrastive learning. Firstly, we introduced a semantic fusion (SF) module, which projects semantic spatial information into visual space for interaction, to compensate for the lack of visual information and further enhance the representation of feature information. To further improve the classification performance, we embed the memory contrastive proposal (MCP) module to adjust the distribution of the feature space by calculating the contrastive loss between the class-centered features of previous samples and the current input features to obtain a more discriminative embedding space for better intra-class aggregation and inter-class separation for subsequent classification and detection. Extensive experiments on the PASCAL VOC and MS-COCO datasets show that the performance of our proposed method is effectively improved. Our proposed method improves nAP50 over the baseline model by 4.5% and 3.5%.
Measuring responsible artificial intelligence (RAI) in banking: a valid and reliable instrument

Abstract Widespread use of artificial intelligence (AI) and machine learning (ML) in the US banking industry raises red flags with regulators and social groups due to potential risk of data-driven algorithmic bias in credit lending decisions. The absence of a valid and reliable measure of responsible AI (RAI) has stunted the growth of organizational research on RAI (i.e., the organizational balancing act to optimize efficiency and equity). To address this void, we develop a novel measurement instrument to assess RAI maturity in firms. A review of the nascent literature reveals that there is a wide distribution of RAI capabilities. The RAI instrument that we advance is based on the exhaustive review of this dispersed literature. Analyses of data from large US banks show strong evidence of validity and reliability of the RAI maturity instrument.
RANCANG BANGUN SISTEM INFORMASI GEOGRAFIS DAN PEMESANAN LAPANGAN BULU TANGKIS DI KOTA BATAM

In Indonesia, badminton is one of the most popular sports among the people, where every competition on the world stage, Indonesian people flock to watch every match and it is not uncommon for athletes from Indonesia to win in every competition. In the era of the development of information technology, especially information systems, there are still many badminton courts that still use field reservations using the manual method by taking notes on paper, ordering by telephone or WA, and so on. This can pose a risk of loss of ordering data during the recording process. For this reason, this research creates a web-based application to solve the problem of renting a badminton court in Batam City. This web-based application is created using the extreme programming method with the PHP and HTML programming languages. The purpose of this research is to make it easier for tenants to make bookings on badminton courts and make it easier for managers to collect data by utilizing the existing internet network.
Some Results of the Study on Determining Digital Competence Needs of Citizens

Nowadays, when the digitalization is happening around the world, someone who uses computers, mobile devices and other equipment well, easily browses, searches and filters data, information and is able to create digital content, is considered as a person with basic digital skills. In this context, the Government of Mongolia announced to become a "Digital Nation" and to form competitive and creative citizens as well as enhance the quality of life of Mongolian citizens. At the core of this policy, there will be citizens who have the appropriate skills of utilizing ICT. The research was carried out with the aim to identify the digital skills needed by citizens on a daily basis and determining the current state of digital skills. There is a need to review some of the competences included in the 12 competency frameworks with 3 domains developed by the Universal ICT Competency Framework Working Group, and improve and compare to competences in other countries. Newly identified competence groups need to be redefined based on the research. In order to support/develop citizens’ digital skills necessary for everyday use, special programs will be required to be implemented that reflect\consider their place of residence, age, gender, employment and household income.
Certain diagonal equations and conflict-avoiding codes of prime lengths

We study the construction of optimal conflict-avoiding codes (CAC) from a number theoretical point of view. The determination of the size of optimal CAC of prime length p and weight 3 is formulated in terms of the solvability of certain twisted Fermat equations of the form g2Xℓ+gYℓ+1=0 over the finite field Fp for some primitive root g modulo p. We treat the problem of solving the twisted Fermat equations in a more general situation by allowing the base field to be any finite extension field Fq of Fp. We show that for q greater than a lower bound of the order of magnitude O(ℓ2) there exists a generator g of Fq× such that the equation in question is solvable over Fq. Using our results we are able to contribute new results to the construction of optimal CAC of prime lengths and weight 3.
Cultural Influences in Language Learning in a Global Context

Language is one of the most important aspects in connecting people around the world. In the current era of globalisation, the ability to communicate in multiple languages has become very important, whether in the context of international business, diplomacy, or cultural exchange. This research attempts to understand the impact of culture in language learning in a global context. This research is a literature review that uses a qualitative method approach, which means it will analyse and interpret data by relying on information and texts from various sources. The results reveal that culture plays a very significant role in language learning in the current era of globalisation. Language cannot be separated from the culture that shapes it, and the language learning process is often also a journey into a deeper cultural understanding. This affects the learner's motivation, communication style, and perception of the language and culture being learnt.
One-shot Ultra-high-Resolution Generative Adversarial Network That Synthesizes 16K Images On A Single GPU

We propose a one-shot ultra-high-resolution generative adversarial network (OUR-GAN) framework that generates non-repetitive 16K (16,384×8,640) images from a single training image and is trainable on a single consumer GPU. OUR-GAN generates an initial image that is visually plausible and varied in shape at low resolution, and then gradually increases the resolution by adding detail through super-resolution. Since OUR-GAN learns from a real ultra-high-resolution (UHR) image, it can synthesize large shapes with fine details and long-range coherence, which is difficult to achieve with conventional generative models that rely on the patch distribution learned from relatively small images. OUR-GAN can synthesize high-quality 16K images with 12.5 GB of GPU memory and 4K images with only 4.29 GB as it synthesizes a UHR image part by part through seamless subregion-wise super-resolution. Additionally, OUR-GAN improves visual coherence while maintaining diversity by applying vertical positional convolution. In experiments on the ST4K and RAISE datasets, OUR-GAN exhibited improved fidelity, visual coherency, and diversity compared with the baseline one-shot synthesis models. To the best of our knowledge, OUR-GAN is the first one-shot image synthesizer that generates non-repetitive UHR images on a single consumer GPU. The synthesized image samples are presented at https://our-gan.github.io.
Mixed emotion extraction analysis and visualisation of social media text

With the widespread use of social media and accelerated development of artificial intelligence, sentiment analysis is regarded as an important way to help enterprises understand user needs and conduct brand monitoring. It can also assist businesses in making data-driven decisions about product development, marketing strategies, and customer service. However, as social media information continues to grow exponentially, and industry demands increase, sentiment analysis should no longer be limited to fundamental polarity classification of positive, neutral, and negative. Instead, it should move to more precise classification of emotions. Therefore, in this paper, we expand sentiment analysis to analysis of eight different emotions based on Plutchik's wheel of emotions, and define it as a multi-label classification task to identify complex and mixed emotions in text. We achieved an overall precision of 0.7958 for the eight emotions multi-label classification based on the attention-based bidirectional long short-term memory with convolution layer (AC-BiLSTM) model on the SemEval-2018 dataset. In addition, we proposed the introduction of the NRC emotion lexicon and emotion correlation constraints to optimise the emotion classification results. This ultimately increased the overall precision to 0.8228 demonstrating the effectiveness of our approach. Finally, we store and visualise the emotion analysis results in a graph structure, in order to achieve deductibility and traceability of emotions.
A Better Way to Teach Software Architecture

Software architecture education is a weak spot in many undergraduate programs in computer science and software engineering. While the concepts and practices used by software architects in industry are rich and varied, transferring this expertise into a university classroom has proved problematic. Bridging the gap between industry and academia requires ongoing, often heroic, effort. This is a “chicken and egg” problem: Because there is a lack of good teaching materials, architecture is seldom taught, and because it is seldom taught, there has been little incentive to create good materials. We would like to change that. Our goal is to establish guidelines for how software architecture practices should be taught—both technical and non-technical topics—and to suggest appropriate teaching methods to best prepare students to be software architects in practice.
Randomized Algorithms

Learn about probability as it is used in computer science with this rigorous, yet highly accessible, undergraduate textbook. Fundamental probability concepts are explained in depth, prerequisite mathematics is summarized, and a wide range of computer science applications is described. Throughout, the material is presented in a “question and answer” style designed to encourage student engagement and understanding. Replete with almost 400 exercises, real-world computer science examples, and covering a wide range of topics from simulation with computer science workloads, to statistical inference, to randomized algorithms, to Markov models and queues, this interactive text is an invaluable learning tool whether your course covers probability with statistics, with stochastic processes, with randomized algorithms, or with simulation. The teaching package includes solutions, lecture slides, and lecture notes for students.
INTERNATIONAL EXPERIENCE IN ENSURING THE QUALITY OF HIGHER EDUCATION

The problem of the development of the national system of higher education is the subject of research by scientists from different countries. In Kazakhstan, increasing the competitiveness of Kazakhstani universities should be achieved through: implementation of international educational programs, academic exchanges with foreign partners; invitations to attract foreign specialists to teaching; establishment of branches of leading foreign universities. The key issue is to ensure the quality of higher education. This article examines the international experience of ensuring the quality of higher education within the framework of the Targeted Financing Program "Improving the competitiveness of universities in Kazakhstan through the reengineering of the national system of quality assurance of higher education" for 2022-2024. The program is connected with research conducted for more than 10 years in the field of improving the quality of education, introducing the parameters of the Bologna process into the system of higher education, scientific and methodological support of innovations. The main idea of the article is the modernization of the national system of quality assurance of higher education based on the best experience of foreign countries, since the further development of the country depends on the quality of higher education. The result of this study will be the substantiation of approaches to improving the holistic quality assurance system of higher education aimed at the final result –the training of a competitive specialist.