Reviewerly/exHarmony huggingface dataset:
Detecting Deepfakes with Self-Blended Images

In this paper, we present novel synthetic training data called self-blended images (SBIs) to detect deepfakes. SBIs are generated by blending pseudo source and target images from single pristine images, reproducing common forgery artifacts (e.g., blending boundaries and statistical inconsistencies between source and target images). The key idea behind SBIs is that more general and hardly recognizable fake samples encourage classifiers to learn generic and robust representations without overfitting to manipulation-specific artifacts. We compare our approach with state-of-the-art methods on FF++, CDF, DFD, DFDC, DFDCP, and FFIW datasets by following the standard cross-dataset and cross-manipulation protocols. Extensive experiments show that our method improves the model generalization to unknown manipulations and scenes. In particular, on DFDC and DFDCP where existing methods suffer from the domain gap between the training and test sets, our approach outperforms the baseline by 4.90% and 11.78% points in the cross-dataset evaluation, respectively.
AB/BA analysis: A framework for estimating keyword spotting recall improvement while maintaining audio privacy

Evaluation of keyword spotting (KWS) systems that detect keywords in speech is a challenging task under realistic privacy constraints. The KWS is designed to only collect data when the keyword is present, limiting the availability of hard samples that may contain false negatives, and preventing direct estimation of model recall from production data. Alternatively, complementary data collected from other sources may not be fully representative of the real application. In this work, we propose an evaluation technique which we call AB/BA analysis. Our framework evaluates a candidate KWS model B against a baseline model A, using cross-dataset offline decoding for relative recall estimation, without requiring negative examples. Moreover, we propose a formulation with assumptions that allow estimation of relative false positive rate between models with low variance even when the number of false positives is small. Finally, we propose to leverage machine-generated soft labels, in a technique we call Semi-Supervised AB/BA analysis, that improves the analysis time, privacy, and cost. Experiments with both simulation and real data show that AB/BA analysis is successful at measuring recall improvement in conjunction with the trade-off in relative false positive rate.
The libraries of Syrian higher education institutions: Current state and ways of development

Syria is one of the countries that was in the process of development at all levels, including education, but the war that began in 2011 led to the decline in the level of education and academic libraries activities. The features of Syrian higher education institutions and their libraries are described, with the focus on the four largest state universities (Damascus, Aleppo, Tishrin and Al-Ba'ath). The main characteristics including library collections of 8 public and 22 private higher education institutions are given. The organization of the libraries of Syrian educational institutions differs: a universitywide library plus the faculty (college) libraries, while the private universities have only one library. Modified and Arabicized Dewey decimal classification used in all libraries for indexing, catalogs are maintained on paper cards, modern information technologies are not implemented, there are still unsuccessful attempts to introduce ALIS Koha and Horizon. The authors show the prerequisites for designing conceptual, information and functional models of networked information system of university libraries to upgrade library and information processes to the modern level.
Enhancing Non-Mass Breast Ultrasound Cancer Classification with Knowledge Transfer

Much progress has been made in the deep neural network (DNN) based diagnosis of mass lesions breast ultrasound (BUS) images. However, the non-mass lesion is less investigated because of the limited data. Based on the insight that mass data is sufficient and shares the same knowledge structure with non-mass data of identifying the malignancy of a lesion based on the ultrasound image, we propose a novel transfer learning framework to enhance the generalizability of the DNN model for non-mass BUS with the help of mass BUS. Specifically, we train a shared DNN with combined non-mass and mass data. With the prior of different marginal distributions in input and output space, we employ two domain alignment strategies in the proposed transfer learning framework with the insight of capturing domain-specific distribution to address the issue of domain shift. Moreover, we propose a cross-domain semantic-preserve data generation module called CrossMix to recover the missing distribution between non-mass and mass data that is not presented in training data. Experimental results on an in-house dataset demonstrate that the DNN model trained with combined data by our framework achieves a 10% improvement in AUC on the malignancy prediction task of non-mass BUS compared to training directly on non-mass data.
EFFECTIVENESS OF PSYCHOLINGUISTIC DIRECTION OF DEVELOPMENT OF KAZAKH LANGUAGE AS A SECOND LANGUAGE

Language is a means of communication that brings people and nations closer together and allows them to understand. Language teaching is one of the most difficult tasks. Therefore, the teaching of the Kazakh language to other nations requires a psychological basis. The psychological characteristics of a particular nation play an important role in learning another language. Therefore, when planning the teaching of a language as a foreign language, in the selection of educational materials, first of all, age and psychological characteristics are taken into account. The study of psychological problems of teaching common languages as a foreign language dates back to the last century. As psychology is a branch of science that studies human behavior, it is closely related to the fields related to each methodology. In this regard, in the 50s and 60s of the 20th century, the doctrine of psycholinguistics emerged, which considers the relationship between psychology and linguistics, human speech, the importance of mastering the native language, a foreign language. When a person learns another language, he experiences various psychological processes. Therefore, the teacher should closely monitor the psyche of the student and help him to master the language without any harm. Therefore, the learning process is a process of interaction between teacher and students, because learning, by its psychological nature, is the result of interaction between teachers and students. There is no doubt that teaching a teacher only when he is well-versed in psychology and takes great responsibility for his profession will give certain results. The article considers the methods of teaching the Kazakh language as a second language, psychological features, the fact that the teacher is a leading specialist in language teaching, ways to improve the abilities of the student. Mental types of language learners are identified
Digital emotion analytics: a pilot study of human emotion recognition using mobile device sensors

Affective computing is a rapidly developing area at the intersection of psychology and the development of artificial intelligence systems. At the moment, systems for recognising human emotions from photos and videos (facial expressions), voice recordings (intonation), gestures, posture, gait and other data using various machine learning algorithms are actively developed. This research project is devoted to the recognition of emotions and mental states of a person through the sensors of mobile devices (accelerometer, gyroscope, etc.), which record the features of micro- and macro-hand motor skills characteristic of the states under study. The pilot study considered the possibility of fixing and differentiating psychoemotional states according to the readings of sensors of mobile devices (tablet, smartphone), using machine learning models. As a result, it was possible to obtain models that determine, according to the readings of the sensors of a mobile device in the hands of a person, whether it is in a neutral emotional state or under stress. Moreover, it was possible to differentiate the state of stress according to two modalities – stress caused by psychological reasons ("fulfillment of obligations") and psychophysiological reasons (unpleasant noise in the headphones). The statistically significant differences, as well as the relatively high accuracy of the constructed machine learning model, allow us to speak about the reliability of the results obtained, and they confirm the hypothesis about the possibility of identifying and classifying emotional states using the sensors of mobile devices.
BTranspose: Bottleneck Transformers for Human Pose Estimation with Self-Supervised Pre-Training

The task of 2D human pose estimation is challenging as the number of keypoints is typically large (~ 17) and this necessitates the use of robust neural network architectures and training pipelines that can capture the relevant features from the input image. These features are then aggregated to make accurate heatmap predictions from which the final keypoints of human body parts can be inferred. Many papers in literature use CNN-based architectures for the backbone, and/or combine it with a transformer, after which the features are aggregated to make the final keypoint predictions [1]. In this paper, we consider the recently proposed Bottleneck Transformers [2], which combine CNN and multi-head self attention (MHSA) layers effectively, and we integrate it with a Transformer encoder and apply it to the task of 2D human pose estimation. We consider different backbone architectures and pre-train them using the DINO self-supervised learning method [3], this pre-training is found to improve the overall prediction accuracy. We call our model BTranspose, and experiments show that on the COCO validation set, our model achieves an AP of 76.4, which is competitive with other methods such as [1] and has fewer network parameters. Furthermore, we also present the dependencies of the final predicted keypoints on both the MHSA block and the Transformer encoder layers, providing clues on the image sub-regions the network attends to at the mid and high levels.
UNDERGRADUATE STUDENTS’ VOICES OF LEARNING ENGLISH THROUGH ONLINE APPLICATION : ZOOM MEETING DISCUSSION

Online learning that is carried out by students with lecturers as part of the learning process during this pandemic makes students study harder. The online learning process which is different from face-to-face classroom learning makes students experience several obstacles and difficulties in participating in online learning. This study will try to voice the opinions and experiences of students while carrying out online learning through the online zoom meeting application during this pandemic. Through the questionnaire and interview methods, this narrative inquiry research will find the results of what students have felt so far. The obstacles found by the researchers were one of them that occurred in the student environment such as the internet connection network to the factor of the lecturers in the inaccuracy of time in teaching so that they spent learning time which made students experience some difficulties in living it. On the other hand, students also admit that online learning is positive, which is a solution to flexibility and time effectiveness during online learning through the online zoom meeting application. This online learning does have positive and negative sides from the views of students that lecturers and others need to know.
UID2021: An Underwater Image Dataset for Evaluation of No-reference Quality Assessment Metrics

Achieving subjective and objective quality assessment of underwater images is of high significance in underwater visual perception and image/video processing. However, the development of underwater image quality assessment (UIQA) is limited for the lack of comprehensive human subjective user study with publicly available dataset and reliable objective UIQA metric. To address this issue, we establish a large-scale underwater image dataset, dubbed UID2021, for evaluating no-reference UIQA metrics. The constructed dataset contains 60 multiply degraded underwater images collected from various sources, covering six common underwater scenes (i.e. bluish scene, bluish-green scene, greenish scene, hazy scene, low-light scene, and turbid scene), and their corresponding 900 quality improved versions generated by employing fifteen state-of-the-art underwater image enhancement and restoration algorithms. Mean opinion scores (MOS) for UID2021 are also obtained by using the pair comparison sorting method with 52 observers. Both in-air NR-IQA and underwater-specific algorithms are tested on our constructed dataset to fairly compare the performance and analyze their strengths and weaknesses. Our proposed UID2021 dataset enables ones to evaluate NR UIQA algorithms comprehensively and paves the way for further research on UIQA. Our UID2021 will be a free download and utilized for research purposes at: https://github.com/Hou-Guojia/UID2021.
The Impact of Critical Factors for the Successful Software Process Improvement

Software enterprises should develop high-quality products to thrive in the competitive markets amid continuous changes in requirements and situations while adapting to changes in the operational environment while improving the software development process.Most organizations embrace diverse strategies, including gamification, human factors, relevant skills, experts, and organizational structure, to enhance the quality of products and sustainability of corporate operations.In this regard, human factors' lack of sufficient understanding of the software improvement processes makes it challenging to achieve the desired software process improvement (SPI) results.The researcher in this project identified SPI as one of the critical initiatives for enhancing product quality and ensuring organizations accomplish software goals.However, the attainment of effective SPI requires a set of activities and factors initiated in the software development environment.The factors constitute critical success factors (CSFs), which are actions and activities completed by the project team to achieve established goals through effective implementation of SPI.In a literature analysis, the researcher identified SPI's main setbacks in software development because organizations and projects lack root causes analysis on why projects fail, review of continuous improvement models, and inputs of experienced project managers.The embedded survey affirmed that CSFs constitute the SPI solution in modern software development processes and projects.
Development of Streaming Media Security using Geolocation

Video streaming is digital data that is shared over the internet network. Streaming video through security protection techniques creates a security that is expected to protect intellectual property rights because there is data transfer through the internet. The use of password-based and token-based authentication is a security technology widely used in exchanging data and information between the client and server. However, this method is currently not sufficient to represent a security system using a Geographic Information System. Geolocation is a system or designation of geographic location in a world on an object that is connected via the internet network. IP geolocation can be used to define a particular IP address based on the geographic location from which the device is connected to the internet. The benefit of applying this technology is that each individual or organization can identify the location of a device connected to the internet. The study will use applied research and quantitative data with the implementation of a Geographic Information System that includes the creation of a security architecture on video streaming using Geolocation and test variables that can be used as a basis for analysis to describe the results of the testers. The test results with a random access server system from 50 countries with a blacklist and whitelist grouping mapping system show a success rate of 80%. Based on the results obtained from the use of live streaming security with Geolocation, it can be implemented to improve the security system.
Reliability Evaluation of Individual Predictions: A Data-centric Approach

At the same time that artificial intelligence (AI) and machine learning are becoming central to human life, their potential harms become more vivid. In the presence of such drawbacks, a critical question to address before using individual predictions for critical decision-making is whether those are reliable. Aligned with recent efforts on data-centric AI, this paper proposes a novel approach, complementary to the existing work on trustworthy AI, to address the reliability question through the lens of data. Specifically, it associates data sets with distrust quantification that specifies their scope of use for individual predictions. It develops novel algorithms for efficient and effective computation of distrust values. The proposed algorithms learn the necessary components of the measures from the data itself and are sublinear, which makes them scalable to very large and multi-dimensional settings. Furthermore, an estimator is designed to enable no-data access during the query time. Besides theoretical analyses, the algorithms are evaluated experimentally, using multiple real and synthetic data sets and different tasks. The experiment results reflect a consistent correlation between distrust values and model performance. This highlights the necessity of dismissing prediction outcomes for cases with high distrust values, at least for critical decisions.
Noisy detectors with unmatched detection efficiency thwart identification of single photon emitters via photon coincidence correlation

Single photon emitters (SPEs) are often identified with a Hanbury Brown and Twiss intensity interferometer (HBTII) consisting of a 50:50 beamsplitter and two time-correlated single photon counters. For an SPE, the cross-correlation of photon arrival time between the two detectors, $g^{(2)}(\tau)$, shows a hallmark dip to zero at $\tau=0$. One common heuristic for identifying SPEs is by measuring a system to have $g^{(2)}(0)<1/2$. Here in, we use stochastic methods to simulate the case of a SPE observed with non-ideal detectors and optics. We show that identification of SPEs is thwarted when the detectors have asymmetric detection efficiency, and when detector background noise is more than half the true SPE signal rate.
A Proposed Model for Card Fraud Detection Based on CatBoost and Deep Neural Network

Abstract Rapid development of technology has digitized customers' payment behavior towards a cashless society. To some extent, this has created a feast for miscreants committing fraud. According to Nilson’s Report (2020), the global fraud loss is projected to reach over $35 billion in 2025. Thus, the need for a novel method to prevent this menace is undisputed. Studies in fraud detection in cards abound with various machine learning-based techniques. However, many of them were conducted on modest-sized sample with a few familiar features and mainly focused on transaction-based approach to detect fraud. These models fail to satisfy low false alarm rates when being fed with large-scale data streams. In this paper, an innovative model was proposed to improve classification accuracy based on the idea of user separation, in which we divided users into old and new people before applying CatBoost and Deep Neural Network on each category, respectively. The experimental result shows that our model achieved better as we obtained AUC score of 0.97 (CatBoost) and 0.84 (Deep Neural Network).
Opportunities for the development of communication skills in preschoolers by means of art in an inclusive education

The article presents the main results of the research work devoted to the description of the activity of a teacher-psychologist of an inclusive preschool educational organization. The combination of scientific and theoretical substantiation and empirical research allowed us to reveal the resources of various types of art for the formation of communication skills in preschoolers in an inclusive practice. The psychological and pedagogical possibilities of using a poly-artistic approach, art therapy resources and other methods and techniques are characterized. The results of a diagnostic study of the levels of formation of communication skills in older preschool children in a group of combined orientation, in which half of preschoolers have severe speech disorders, are presented. A project of activity of a teacher-psychologist for the development of children’s communication skills by means of art in an inclusive education is proposed, including a description of the thematic content, forms and methods of work. In the course of the study, it is proved that a teacher-psychologist will be able to successfully carry out work on the development of communication skills in older preschoolers (including those with speech disorders) if conditions for genuine inclusion are created in a preschool educational organization, team cooperation of specialists is implemented and the developing potential of various types of art is correctly used.
Technologies of Foreign-Language Information Competence Formation among Students of a Technical University

The purpose of the study is to identify the features characterising the technologies of concept-based learning, inquiry-based learning, project-based learning as a component of the methodological system of teaching students of non-linguistic universities in the process of mastering foreign-language information competence. The author substantiates the need to use technologies of foreign-language information processing. The paper considers and analyses the main principles and content bases of specially selected and adapted learning technologies during the implementation of foreign-language information activities. Scientific novelty lies in developing and describing a set of technologies aimed at foreign-language information competence formation among students of a technical university. As a result, the author characterises the main stages of the implementation of the presented technologies, clarifies their role in foreign-language information competence formation among students of a technical university.
Mining Data Streams using Clustering Techniques

This research highlights the significant aspects to consider when building a framework for mining the data streams.The main aspects include the methods of data summarizing and creating synopsis.Besides, the main approaches of clustering the data synopses.The research also shows some real applications and tools for mining the streaming data.The main goal is to present the ClusKmeans model as an example for data stream clustering, and to study its performance with different scenarios of data speed, noise levels, and horizon ranges.
CILDA: Contrastive Data Augmentation using Intermediate Layer Knowledge Distillation

Knowledge distillation (KD) is an efficient framework for compressing large-scale pre-trained language models. Recent years have seen a surge of research aiming to improve KD by leveraging Contrastive Learning, Intermediate Layer Distillation, Data Augmentation, and Adversarial Training. In this work, we propose a learning based data augmentation technique tailored for knowledge distillation, called CILDA. To the best of our knowledge, this is the first time that intermediate layer representations of the main task are used in improving the quality of augmented samples. More precisely, we introduce an augmentation technique for KD based on intermediate layer matching using contrastive loss to improve masked adversarial data augmentation. CILDA outperforms existing state-of-the-art KD approaches on the GLUE benchmark, as well as in an out-of-domain evaluation.