Reviewerly/exHarmony huggingface dataset:
Joint Distribution Alignment and Transfer Subspace Learning for Unsupervised Domain Adaptation

Transfer subspace learning plays an important role in solving unsupervised domain adaptation, which utilizes source information to label target data. To preserve data structure, the traditional techniques adopt the convex trace norm and its variant to approximate low-rank similarity structure between source and target domain. However, this approximation is too slack, which may seriously deviate from the original optimum of rank function. Additionally, these traditional methods only use feature information to align subspaces of different domains while ignoring the alignment of their semantic information of various classes. To solve these problems, we propose a novel joint distribution alignment and transfer subspace learning method named DATSL. Specifically, a non-convex regularizer is adopted to better approximate low-rank constraints by minimizing the top k minimum singular values of reconstruction matrix. Moreover, we align category information of data by reducing the joint distribution discrepancy between the source and target domains to promote better linear representation for target samples. Besides, an efficient alternative algorithm IALM is carefully developed to solve its non-convex objective function. Experimental results demonstrate the effectiveness of DATSL compared with other state-of-the-art methods.
Hybrid algorithm of invasive weeds and particle swarm optimization

Aiming at the disadvantages of low optimization accuracy and easy to fall into local optimum of particle swarm optimization algorithm, a hybrid algorithm combining the invasive weed algorithm and particle swarm optimization algorithm is proposed. The main structure of the hybrid algorithm is PSO, and IWO makes the algorithm avoid falling into local optimum. The simulation test is carried out with 11 benchmark test functions, and through comparison with the other two improved PSO algorithms, it is concluded that the hybrid algorithm in this paper is not easy to fall into local optimization and has high optimization accuracy.
Mongolian Medicine Named Entity Recognition via Dictionary-Based Synonym Generalization

The task of named entity recognition in Mongolian medicine poses challenges due to the diversity and complexity of terminology and herbal names. Variations in names used across different literature sources and regions hinder accurate recognition, as traditional dictionary-based methods often fail to encompass all synonyms and variants. To address this issue, this paper presents a lexicon-based synonym generalization method. By leveraging lexical resources in Mongolian, this approach extends the synonyms of common herbs and terms, incorporating additional synonyms and related words into the named entity recognition system. Consequently, the system becomes capable of recognizing a wider range of names and variants. In this study, the authors construct the Mongolian Medicine Named Entity Recognition Datasets (MMNER), comprising five entity types and 44,968 entities. The datasets are developed using the proposed synonym generalization framework. Subsequently, a series of deep learning models are employed to conduct experimental comparisons on the MMNER datasets. The experimental results demonstrate that by enhancing synonym coverage, the recognition of Mongolian medicine entities in text achieves a significantly improved accuracy, with an F1 value of 93.66%. These findings signify a noteworthy enhancement in the performance of Mongolian medicine named entity recognition.
Importance Guided Query Focused Long-Input Summarization

Query focused long-document summarization aims to generate a summary depending on both the given query and the given long text. Previous end-to-end method inputs the query and long text into a single model, and generates the summary. However, it’s a difficult task for the end-to-end model to recognize which parts of the long text are more important for answering the query. Besides, the length of the query is too small, so the information of the query is diluted after concatenating with the long text. As a result, many summarized sentences are irrelevant to the query. In this paper, we propose IGQFS (Importance Guided Query Focused long-input Summarization), which predicts an importance score for each sentence as an auxiliary task, and simultaneously generates the summary. To this end, we design an improved cross-attention module for the decoder according to the predicted utterance importance scores. Experimental results on QMSum dataset show that the auxiliary task and improved cross attention module generate summaries more related to the queries, and our method performs better than the end-to-end baseline model.
A Two-stage Clustering Undersampling for Class-overlapped Imbalanced Classification

Undersampling is one of the mainstream techniques in handling class-imbalance problem. The past half year, numerous undersampling methods have been proposed to deal with imbalanced data. However, most of them focus on selecting a representative majority subset, the relationship between class overlapping and undersampling is seldom considered. In this paper, we propose a two-stage clustering undersampling for class-overlapped imbalanced classification. To be specific, we first perform clustering on the training data to detect the candidate data subset of overlapped majority samples according to the majority purity of clusters. Then we further evaluate the majority samples within the candidate data subset by the global similarities of both homogenous samples and heterogeneous samples. After that, we design a data cleaning process to remove the majority samples which have negative impact on the learnability of subsequent balanced data. Finally, we undersample the remaining majority samples with the clustering undersampling technique to obtain a balanced dataset. Extensive experiments on the KEEL datasets show the effectiveness of our method with regard to both data cleaning and imbalanced undersampling.
Multi-level perception fusion dehazing network

Image dehazing models are critical in improving the recognition and classification capabilities of image-related artificial intelligence systems. However, existing methods often ignore the limitations of receptive field size during feature extraction and the loss of important information during network sampling, resulting in incomplete or structurally flawed dehazing outcomes. To address these challenges, we propose a multi-level perception fusion dehazing network (MPFDN) that effectively integrates feature information across different scales, expands the perceptual field of the network, and fully extracts the spatial background information of the image. Moreover, we employ an error feedback mechanism and a feature compensator to address the loss of features during the image dehazing process. Finally, we subtract the original hazy image from the generated residual image to obtain a high-quality dehazed image. Based on extensive experimentation, our proposed method has demonstrated outstanding performance not only on synthesizing dehazing datasets, but also on non-homogeneous haze datasets.
RayBench: An Advanced NVIDIA-Centric GPU Rendering Benchmark Suite for Optimal Performance Analysis

This study aims to collect GPU rendering programs and analyze their characteristics to construct a benchmark dataset that reflects the characteristics of GPU rendering programs, providing a reference basis for designing the next generation of graphics processors. The research framework includes four parts: GPU rendering program integration, data collection, program analysis, and similarity analysis. In the program integration and data collection phase, 1000 GPU rendering programs were collected from open-source repositories, and 100 representative programs were selected as the initial benchmark dataset. The program analysis phase involves instruction-level, thread-level, and memory-level analysis, as well as five machine learning algorithms for importance ranking. Finally, through Pearson similarity analysis, rendering programs with high similarity were eliminated, and the final GPU rendering program benchmark dataset was selected based on the benchmark’s comprehensiveness and representativeness. The experimental results of this study show that, due to the need to load and process texture and geometry data in rendering programs, the average global memory access efficiency is generally lower compared to the averages of the Rodinia and Parboil benchmarks. The GPU occupancy rate is related to the computationally intensive tasks of rendering programs. The efficiency of stream processor execution and thread bundle execution is influenced by branch statements and conditional judgments. Common operations such as lighting calculations and texture sampling in rendering programs require branch judgments, which reduce the execution efficiency. Bandwidth utilization is improved because rendering programs reduce frequent memory access and data transfer to the main memory through data caching and reuse. Furthermore, this study used multiple machine learning methods to rank the importance of 160 characteristics of 100 rendering programs on four different NVIDIA GPUs. Different methods demonstrate robustness and stability when facing different data distributions and characteristic relationships. By comparing the results of multiple methods, biases inherent to individual methods can be reduced, thus enhancing the reliability of the results. The contribution of this study lies in the analysis of workload characteristics of rendering programs, enabling targeted performance optimization to improve the efficiency and quality of rendering programs. By comprehensively collecting GPU rendering program data and performing characteristic analysis and importance ranking using machine learning methods, reliable reference guidelines are provided for GPU design. This is of significant importance in driving the development of rendering technology.
Looking Into the Future With AI for Special Education

Advancements in AI revolutionize learning, automate tasks, and provide insights. During COVID-19, AI connected educators and learners globally, but special education suffered. This chapter aims to define disabilities, explore AI-based diagnosis tools, and envision personalized learning and assistive technology for inclusive education. The lack of affordable and convenient assistive devices and technology combined with taboos stemming from deeply ingrained misconceptions and prejudices, perpetuating stereotypes and marginalizing individuals with disabilities often forces them to live a life devoid of the proper means to engage in schooling for the purpose of attaining an independent lifestyle. The primary objective of the chapter is to define various types of disabilities and consider various AI-based tools for early disability diagnosis, while also looking to the future with personalized learning and AI-based assistive technologies, assisting both learners and educators in achieving the highest level of education.
High dynamic range 3D shape measurement based on crosstalk characteristics of a color camera

Fringe projection profilometry (FPP) has been widely used in many fields due to its fast speed, high accuracy and full-field characteristics. However, it is still a challenging problem to deal with high dynamic range (HDR) objects for traditional FPP, which utilizes a single exposure time or a single projection intensity. Overexposure will occur in areas with large reflectivity, which exceeds the maximum capturing capacity of camera sensors, resulting in the failure to obtain the accurate intensity, absolute phase and three-dimensional (3D) data. In this paper, a uniform blue image is projected to divide object surface into three areas with different reflectivity by using different intensity responses of RGB channels of color images. Crosstalk coefficient function is applied to obtain intensity of overexposed areas, and then the optimal exposure time of areas is calculated by the linear photometric response of the camera. Finally, three sets of blue fringe patterns with optimal exposure time are synthesized into the fused HDR images to calculate the absolute phase. Experimental results confirm that the proposed method can accurately measure HDR objects with large variation range of reflectivity.
Relationships between Variables Related to College Students' News Literacy in Korea

This study examines the variables that are college students’ ability to discern fact from opinion, critical thinking skills, and need for cognition. The ability to differentiate between fact and opinion appears to be a key part of being able to evaluate fake news. In addition, critical thinking plays an important role in the process of fact-checking or determining the credibility of news outlets. According to the dissertation study on college students’ fake news discernment in the U.S. (Bak, 2022), critical thinking was a variable that was positively correlated with the fact and opinion discernment, as well as the need for cognition respectively. Based on the dissertation study, the researcher will collect the survey data from college students in Korea with further developed measures. The scale that measures the ability to discern fact and opinion will include borderline statements that have both factual and opinion statements. In addition, the survey will collect information about their general news reading practices. The researcher will discuss differences in statistical models between students in the U.S. and Korea.
Competency levels and influential factors of college students’ mobile learning readiness in Thailand

Abstract One of the key successes of learning through mobile technology comes from the competencies of learners. This study aimed to investigate the overall competency levels of mobile learning readiness and four influential factors (connectivist learners, technology readiness, self-directed learning, and netiquette) that may impact college students’ mobile learning readiness in Thailand. The surveys were given to 583 Thai college students who came from various fields such as Social Science, Agriculture, Engineering and Computer Science, and Science. Descriptive statistical procedures were used to generate overall mobile learning readiness and the confirmatory factor analysis was used to determine whether the competencies of connectivist learners, technology readiness, self-directed learning, and netiquette influence the mobile learning readiness of college students in Thailand. The results showed that overall mobile learning readiness among Thai college students was at a high level in which learners’ competency in technology readiness was the most influential factor related to their mobile learning readiness, followed by the competencies of self-directed learning, netiquette, and connectivist learners. The results of this study could provide useful guidance for instructors in designing effective instruction, activities, and learning materials that encourage learners to be successful in the mobile learning environment. The future study may explore other related factors such as learners’ cultural backgrounds, educational systems, or government supports that may influence mobile learning readiness among learners in higher education in different countries.
Comparative and Preventive Analysis of Dictionary Attacks

Hacking is one of the most widespread issues that the general public faces today. Hackers essentially use some social engineering techniques, combined with the publicly available information, to crack open the social media accounts, banking accounts, or cloud storage accounts. As a result, security and protection becomes the main motive for organizations running these platforms. The severity of threats linked to such incidents is tantamount to those linked with data leaks, security breaches, and security lapse events of their likes. Among others, dictionary attacks, a strategy employed for password cracking by trying every common-use word present in an extant wordlist, are the most common. Even though nowadays password strength classifiers are being used extensively almost everywhere which tends to keep these attacks at bay, they are still prone to hyper-personalized dictionary attacks as people are inclined to choose a password which revolves around their personal details itself. Consequently, the necessary awareness for the prevention of dictionary attacks is called for and is provided by performing an analysis of the time taken by different types of dictionary attacks and what type of password or otherwise is safe or not.
A Matrix Factorization Algorithm for Movie Recommendation

In this paper, we build various movie recommender system ranging from simple recommender systems to hybrid recommender system. We used content as well as a collaborative filtering technique. To establish the similarity relation between users or movies we have used cosine similarity measures. We analyse the used of user-user similarity matrix as well as a movie-movie matrix in building our recommender system which plays a very crucial role in determining the accuracy rate and computational time consumption. In content-based recommender system, for deriving similarities between movies from their description corpus we have used the Term frequency-Inverse document frequency measures. Next we move on to build collaborative filtering using various models like K-nearest neighbours, K-nearest neighbours with mean, Singular value decomposition and Singular value decomposition ++ and compare them in terms of their accuracy in Root Mean Square Error with the least time consumption. Prior to all, we utilize the Internet Movie Database weighted rating formula to build a simple recommender system.
Design and Evaluation of Speech Processing Systems for Meetei/Meitei Mayek

The paper focuses on Manipuri, an under-resourced language, and is written in a way that will help eager researchers build a robust speech processing system. To improve the quality of speech processing and language processing systems, the tools and models need to be properly designed and evaluated. Various metrics are considered in the areas of syllabification, speech recognition, speech synthesis, machine translation, etc. Multiple works have demonstrated that authors value accuracy, precision, and scores. Tests are designed to assess data perplexity, naturalness of speech, and intelligibility. Furthermore, the challenges and difficulties associated with the development of various systems are compared. The evaluation metrics, both subjective and objective, are thoroughly discussed, as are various modifications designed to address specific issues arising during data collection, design, and evaluation, mainly in speech recognition and speech synthesis.
Resource theory of nonabsolute separability

We develop a resource theory for non-absolutely-separable states (non-AS) in which absolutely separable states (AS) that cannot be entangled by any global unitaries are recognized as free states and any convex mixture of global unitary operations can be performed without incurring any costs. We employ two approaches to quantify nonabsolute separability (NAS)---one based on distance measures and the other one through the use of a witness operator. We prove that both of the NAS measures obey all the conditions that should be followed by a ``good'' NAS measure. We demonstrate that NAS content is equal and maximal in all pure states for a fixed dimension. We then establish a connection between the distance-based NAS measure and the entanglement quantifier. We illustrate our results with a class of non-AS states, namely Werner states.
Securing Secret Information

The art of information concealment has become a crucial subject since information security has grown to be a primary concern in the online world. Using cryptography and steganography plays a significant role in secure data transfer. Steganography is a covert writing technique; the message is contained behind a cover medium. Cryptography encrypts messages to hide their contents. Two-dimensional bar codes called QR codes can store text strings. They can encode data both vertically and horizontally, encoding more information. By combining the ideas of cryptography, steganography, and QR codes, an innovative method for covert communication is proposed in this paper. Statistical and security analyses of the proposed method show that the proposed method provides better imperceptibility, security, and robustness.
Comparative Study on YOLOv2 Object Detection Based on Various Pretrained Networks

Object detection is an important topic in computer vision. You only look once (YOLO) is a fast and effective object detection algorithm. In this work, various object detection models are constructed by using different types of pretrained networks and YOLOv2 detection layers. The pretrained network portion of the model is used for the feature extraction and the YOLOv2 portion for the object detection. Training, testing, and validation of these object detection models are performed with a common vehicle dataset. A comparison is presented in terms of precisions and model sizes for the various detection models at the end of the research. The YOLOv2 detection model based on MobileNetv2 has the highest precision of 81.64% among the eight models. The ShuffleNet-based detection model has the smallest model size of 6.16 MB which is suitable for platforms with limited resources.
Intelligent Speaker Identification System Under Multi-Variability Speech Conditions

Speech is the natural source of information for human identification in most biometrics, forensics, and access control systems. Mismatch in speech data is one of the biggest challenges preventing speaker identification systems from being employed in real-world scenarios. This research explores how intelligently speaker identification tasks are affected by degraded speech. Our preliminary investigation into mismatch effects in conversational style telephonic speech conditions utilizing the IIT-G database includes mismatch in sensor, environment, language, and conversational style. Convolutional neural networks (CNNs) have surpassed traditional techniques in speaker identification (SI) systems in recent years. This paper proposes a novel architecture based on a VGG-like network for an end-to-end speaker identification system. The proposed architecture outperforms the statistical methods with an improvement of 7% accuracy in identifying the speakers. The results show that the suggested approach is more accurate than state-of-the-art speaker identification techniques and notable performance deterioration compared to the matched scenario.
Tracing the Use of Cognitive Strategies by Non-English Students in Learning English

The objectives of the current research are to identify and quantify the cognitive strategies non-English students use when working on assignments or solving problems of the English language they are learning and seek to find out non-English students’ motivation and background knowledge of English as sustaining factors related to the use of numerous cognitive strategies leading to the success of learning. This research employed a case study qualitative approach. Data were taken from observations and interviews and were analyzed using thematic analysis. Results of this research show that non-English students treated as the subject of this research employed the so-called cognitive strategies in doing all tasks, including auditory representation, repetition, note-taking, resourcing, inferring, translation, and transfer. Although most students had only the so-called instrumental motivation, weak interest, and also, they lacked knowledge of English, yet it is proven that the cognitive strategies they employed have enabled them to do all given tasks. An important implication of this study is that learning should include training and supporting the students to understand the importance of learning strategy.