Reviewerly/exHarmony huggingface dataset:
DASOT: A Unified Framework Integrating Data Association and Single Object Tracking for Online Multi-Object Tracking

In this paper, we propose an online multi-object tracking (MOT) approach that integrates data association and single object tracking (SOT) with a unified convolutional network (ConvNet), named DASOTNet. The intuition behind integrating data association and SOT is that they can complement each other. Following Siamese network architecture, DASOTNet consists of the shared feature ConvNet, the data association branch and the SOT branch. Data association is treated as a special re-identification task and solved by learning discriminative features for different targets in the data association branch. To handle the problem that the computational cost of SOT grows intolerably as the number of tracked objects increases, we propose an efficient two-stage tracking method in the SOT branch, which utilizes the merits of correlation features and can simultaneously track all the existing targets within one forward propagation. With feature sharing and the interaction between them, data association branch and the SOT branch learn to better complement each other. Using a multi-task objective, the whole network can be trained end-to-end. Compared with state-of-the-art online MOT methods, our method is much faster while maintaining a comparable performance.
Mobile Phone Use in Education and Learning by Faculty Members of Technical-Engineering Groups: Concurrent Mixed Methods Design

The purpose of this study was to evaluate the Mobile Learning Acceptance among faculty members. The research methodology was a concurrent mixed methods design. Research Method in the quantitative part was descriptive-survey, and in the qualitative part a phenomenological approach was applied. In the quantitative part, the study population consisted of all female and male faculty members of technical-engineering groups at Shiraz University (N=147), where 87 participants were selected using stratified random sampling method. In the qualitative part, the participants included a small fraction of the same faculty members who had also participated in the quantitative part and were selected by purposive sampling approach with criteria technique. Research instruments consisted of a researcher-made scale of mobile learning acceptance. Upon verifying the validity and reliability of the scale, it was distributed among the subjects and the collected data were analyzed using SPSS 21 software. Qualitative data were collected from semi-structured interviews with faculty members who had an experience of mobile learning. The quantitative results indicated that in all aspects, except for Usefulness, the faculty members' acceptance of mobile learning was higher than average, and Usefulness was at a moderate level. In the qualitative part, after integrating and summarizing the data, a total of 17 basic themes and three organizing themes were extracted, including the benefits of mobile learning, the barriers and limitations of mobile learning, and the required infrastructure for effective implementation of mobile learning.
High-Fidelity Reversible Image Watermarking Based on Effective Prediction Error-Pairs Modification

In reversible watermarking for image authentication, less degradation of the marked image is always desirable. For minimum distortion, the pairwise prediction-error expansion (PEE) technique was recently proposed to modify errors jointly. Although its superiority over conventional PEE has been verified, its potential has not been fully exploited yet. In this paper, we focus on optimal modification and propose an enhanced pairwise PEE. First, it is observed in PVO-based pairwise PEE that the histogram peak varies with relative location when predicting the largest/smallest two pixels. Then, a more effective 2D mapping is proposed by content-dependently selecting the expansion bin after introducing spatial location into prediction. Next, the 2D mapping is further extended considering prediction in non-smooth region tends to produce errors with large magnitude. Finally, we also propose to flexibly define the spatial location to achieve content-dependent prediction and further enhancement. Experimental results demonstrate that the proposed scheme achieves better capacity-distortion trade-off and outperforms several state-of-the-art schemes.
E-Course as Blended Learning Digital Educational Resource in University

The article presents findings of the pilot empirical research on the performance of the ‘Mathematical Methods in Psychology’ e-course as an open digital educational resource for the implementation of blended learning using the ‘flipped classroom’ model at the university. The students' attitudes toward blended learning in the e-course format, their self-esteem of involvement in the learning process, as well as the educational results were examined. The main benefits for the students are the following: the accessibility of tutorials and information about their individual learning trajectory at any time; the convenience of self-preparation for classes. The students characterize the activity at seminars as active interaction with classmates and a teacher and involvement in the learning process. In general, the students positively assessed the use of digital resources in the blended learning educational process as a modern approach and expressed their wish to study other subjects or courses in this format (at other universities in Russia and abroad as well). No essential difficulties in taking the e-course were revealed. There is a correlation between the students’ positive evaluations of their own educational outcomes and their positive attitude to the new format (р &lt; 0,001). The educational outcomes of the students after passing the e-course statistically significantly improved (р &lt; 0,001). The blended learning group educational results are significantly higher as compared to traditional in-class education group (р &lt; 0,001). The study was conducted at the Moscow State University of Psychology and Education. The total sample size N = 387 students of psychology departments, the experimental group size N = 78. The comparison group of traditional full-time education students (N = 309) is balanced with the experimental group in the following context parameters: gender and age, major academic disciplines, same teacher, MSUPE students.
The Talavera Manifesto for Quantum Software Engineering and Programming

This paper presents the Talavera Manifesto for quantum software engineering and programming. This manifesto collects some principles and commitments about the quantum software engineering and programming field, as well as some calls for action. This is the result of the discussion and different viewpoints of academia and industry practitioners who joined at the first International Workshop on QuANtum SoftWare Engineering & pRogramming (QANSWER
Reading Habit and Students’ Attitudes Towards Reading: A Study of Students in the Faculty of Education UiTM Puncak Alam

Ability to read is crucial as reading affects intellectual and emotional growth of an individual. Those who read well have more chances in widening their mental horizons and better opportunities of success. Hence, this paper is an attempt to understand the reading habits and attitudes of the students in the Faculty of Education in Universiti Teknologi Mara, Puncak Alam. To collect the relevant data, a questionnaire was designed and distributed to 80 students. The questionnaire investigated the students’ reading habit, preferences, and attitudes towards reading. The research findings through quantitative analysis revealed that the students had an overall positive attitude towards academic and leisure reading as they read academically and leisurely daily or at least once a week. Apart from that, digital materials are considered better compared to printed texts when it comes to the students’ preference and its availability. Based on the findings, several recommendations were made to help improve students’ reading habits and attitudes. Having a national survey on Malaysian students’ reading habits is deemed necessary as it can document current trends (including the use of digital materials as a form of reading) and factors affecting students’ reading habits and attitudes so that more fruitful actions to improve students’ reading habits can be taken. Finally, teachers should also play a more significant role in motivating students to read as the findings yield that teachers are shown to have an impact on students’ reading habits and attitudes.&#x0D; Keywords: reading attitude, reading habit, students’ attitude
Modified Autoencoder Training and Scoring for Robust Unsupervised Anomaly Detection in Deep Learning

The autoencoder (AE) is a fundamental deep learning approach to anomaly detection.AEs are trained on the assumption that abnormal inputs will produce higher reconstruction errors than normal ones.In practice, however, this assumption is unreliable in the unsupervised case, where the training data may contain anomalous examples.Given sufficient capacity and training time, an AE can generalize to such an extent that it reliably reconstructs anomalies.Consequently, the ability to distinguish anomalies via reconstruction errors is diminished.We respond to this limitation by introducing three new methods to more reliably train AEs for unsupervised anomaly detection: cumulative error scoring (CES), percentile loss (PL), and early stopping via knee detection.We demonstrate significant improvements over conventional AE training on image, remote-sensing, and cybersecurity datasets.
Deep and wide feature based extreme learning machine for image classification

Extreme Learning Machine (ELM) is a powerful and favorable classifier used in various applications due to its fast speed and good generalization capability. However, when dealing with complex visual tasks, the shallow architecture of ELM makes it infeasible to have good performance when raw image data are directly fed in as input. Therefore, several works tried to make use of deep neural networks (DNNs) to extract features before ELM classification. On the other hand, when the depth of DNN is too deep, the ELM classifier may suffer from overfitting problem. To solve this issue, a novel deep and wide feature based Extreme Learning Machine (DW-ELM) has been proposed in this research work. We show that the overfitting problem can be largely remedied by employing a “widened” convolutional neural network (CNN) for feature extraction, in the sense that the number of feature maps for each convolutional layer is increased by factor of k compared to a reference model, i.e. deep residual networks (ResNets). While the wide design of residual networks has been shown to benefit image classification in terms of accuracy and efficiency, its application for feature extraction is not fully investigated. We provide an extensive experimental study in this work, showing that when combined with ELM that serves as a classifier, using wide ResNets (WRNs) for feature extraction can produce a performance leap on all benchmark datasets compared to a plain end-to-end trained network over a wide range of selections regardless of architecture choices and ELM designs, while normal ResNets as feature extractors do not provide a performance gain. The gap is even larger when fewer training iterations are employed. This indicates that a good feature extractor for ELM must be wide and deep. Experiments conducted on five benchmark datasets (CIFAR-100, CIFAR-10, STL-10, Flower-102 and Fashion-MNIST) have shown significant accuracy enhancement as well as training stability of the proposed DW-ELM. Ablation studies also demonstrate that the ELM classifier is an important component for DW-ELM which enables superior performance compared with SVM based image classification approaches.
Ranking Loss: Maximizing the Success Rate in Deep Learning Side-Channel Analysis

The side-channel community recently investigated a new approach, based on deep learning, to significantly improve profiled attacks against embedded systems. Compared to template attacks, deep learning techniques can deal with protected implementations, such as masking or desynchronization, without substantial preprocessing. However, important issues are still open. One challenging problem is to adapt the methods classically used in the machine learning field (e.g. loss function, performance metrics) to the specific side-channel context in order to obtain optimal results. We propose a new loss function derived from the learning to rank approach that helps preventing approximation and estimation errors, induced by the classical cross-entropy loss. We theoretically demonstrate that this new function, called Ranking Loss (RkL), maximizes the success rate by minimizing the ranking error of the secret key in comparison with all other hypotheses. The resulting model converges towards the optimal distinguisher when considering the mutual information between the secret and the leakage. Consequently, the approximation error is prevented. Furthermore, the estimation error, induced by the cross-entropy, is reduced by up to 23%. When the ranking loss is used, the convergence towards the best solution is up to 23% faster than a model using the cross-entropy loss function. We validate our theoretical propositions on public datasets.
Cross-lingual learning for text processing: A survey

Many intelligent systems in business, government or academy process natural language as an input during inference or they might even communicate with users in natural language. The natural language processing is currently often done with machine learning models. However, machine learning needs training data and such data are often scarce for low-resource languages. The lack of data and resulting poor performance of natural language processing can be solved with cross-lingual learning. Cross-lingual learning is a paradigm for transferring knowledge from one natural language to another. The transfer of knowledge can help us overcome the lack of data in the target languages and create intelligent systems and machine learning models for languages, where it was not possible previously. Despite its increasing popularity and potential, no comprehensive survey on cross-lingual learning was conducted so far. We survey 173 text processing cross-lingual learning papers and examine tasks, datasets and languages that were used. The most important contribution of our work is that we identify and analyze four types of cross-lingual transfer based on “what” is being transferred. Such insight might help other NLP researchers and practitioners to understand how to use cross-lingual learning for wide range of problems. In addition, we identify what we consider to be the most important research directions that might help the community to focus their future work in cross-lingual learning. We present a comprehensive table of all the surveyed papers with various data related to the cross-lingual learning techniques they use. The table can be used to find relevant papers and compare the approaches to cross-lingual learning. To the best of our knowledge, no survey of cross-lingual text processing techniques was done in this scope before.
Measuring student motivation on the use of a mobile assisted grammar learning tool

Language learning is an emerging research area where researchers have done significant contributions by incorporating technological assistantship (i.e., computer- and mobile-assistant learning). However, it has been revealed from the recent empirical studies that little attention is given on grammar learning with the proper instructional materials design and the motivational framework for designing an efficient mobile-assisted grammar learning tool. This paper hence, reports a preliminary study that investigated learner motivation when a mobile-assisted tool for tense learning was used. This study applied the Attention-Relevance-Confidence-Satisfaction (ARCS) model. It was hypothesized that with the use of the designed mobile- assisted tense learning tool students would be motivated to learn grammar (English tense). In addition, with the increase of motivation, performance outcome in paper- based test would also be improved. With the purpose to investigate the impact of the tool, a sequential mixed-method research design was employed with the use of three research instruments; Instructional Materials Motivation Survey (IMMS), a paper-based test and an interview protocol using a semi-structured interview. Participants were 115 undergraduate students, who were enrolled in a remedial English course. The findings showed that with the effective design of instructional materials, students were motivated to learn grammar, where they were positive at improving their attitude towards learning (male 86%, female 80%). The IMMS findings revealed that students’ motivation increased after using the tool. Moreover, students improved their performance level that was revealed from the outcome of paper-based instrument. Therefore, it is confirmed that the study contributed to designing an effective multimedia based instructions for a mobile-assisted tool that increased learners’ motivational attitude which resulted in an improved learning performance.
Multi-Keyword Certificateless Searchable Public Key Authenticated Encryption Scheme Based on Blockchain

As a quite attractive secure search mechanism in cloud environments, searchable encryption allows encrypted files to be searched by keyword and does not reveal any information about original data files.However, most existing searchable encryption schemes only support single keyword ciphertext retrieval, and they cannot resist against inside keyword guessing attacks.Besides, the previous schemes rarely focus on integrity verification and fair transactions without any third party.Focusing on these problems, we propose a multi-keyword certificateless searchable public key authenticated encryption scheme based on blockchain.We use certificateless cryptosystem to encrypt keywords, which avoids the problems of certificate management in traditional cryptosystem and key escrow in identity-based cryptosystem.Our scheme also supports multi-keyword search, which locates encrypted files precisely and returns the desired files.Moreover, we upload the real encrypted files to the cloud server, while the encrypted indexes are put in blockchain, which ensures the anti-tampering, integrity and traceability of the encrypted indexes.The anti-tampering of blockchain also ensures that users can receive accurate search results without any third party verification.Furthermore, we utilize smart contract to track monetary rewards, which enables fair transactions between data owners and users without any trusted third party.We prove that the proposed scheme is secure against inside keyword guessing attacks in the random oracle model.Finally, our performance evaluation shows that the proposed scheme has higher computational performance than other related schemes.
An attention‐supervised full‐resolution residual network for the segmentation of breast ultrasound images

Purpose Breast cancer is the most common cancer among women worldwide. Medical ultrasound imaging is one of the widely applied breast imaging methods for breast tumors. Automatic breast ultrasound (BUS) image segmentation can measure the size of tumors objectively. However, various ultrasound artifacts hinder segmentation. We proposed an attention‐supervised full‐resolution residual network (ASFRRN) to segment tumors from BUS images. Methods In the proposed method, Global Attention Upsample (GAU) and deep supervision were introduced into a full‐resolution residual network (FRRN), where GAU learns to merge features at different levels with attention for deep supervision. Two datasets were employed for evaluation. One (Dataset A) consisted of 163 BUS images with tumors (53 malignant and 110 benign) from UDIAT Centre Diagnostic, and the other (Dataset B) included 980 BUS images with tumors (595 malignant and 385 benign) from the Sun Yat‐sen University Cancer Center. The tumors from both datasets were manually segmented by medical doctors. For evaluation, the Dice coefficient (Dice), Jaccard similarity coefficient (JSC), and F1 score were calculated. Results For Dataset A, the proposed method achieved higher Dice (84.3 10.0%), JSC (75.2 10.7%), and F1 score (84.3 10.0%) than the previous best method: FRRN. For Dataset B, the proposed method also achieved higher Dice (90.7 13.0%), JSC (83.7 14.8%), and F1 score (90.7 13.0%) than the previous best methods: DeepLabv3 and dual attention network (DANet). For Dataset A + B, the proposed method achieved higher Dice (90.5 13.1%), JSC (83.3 14.8%), and F1 score (90.5 13.1%) than the previous best method: DeepLabv3. Additionally, the parameter number of ASFRRN was only 10.6 M, which is less than those of DANet (71.4 M) and DeepLabv3 (41.3 M). Conclusions We proposed ASFRRN, which combined with FRRN, attention mechanism, and deep supervision to segment tumors from BUS images. It achieved high segmentation accuracy with a reduced parameter number.
A Color Image Encryption Algorithm Based on One Time Key, Chaos Theory, and Concept of Rotor Machine

An innovative method proposed for encrypting color images is comprised of one-time keys and chaos theory using a distinctive concept of rotor machine. The novelty of this scheme is that the rows and columns of 2-dimensional images are converted into circular object called rotor and can be rotated at 360 degrees in clockwise or anti clockwise direction. The rotation will change the existing rotor into new one and can be used in substitution process of plain image. This process can be repeated β times and each time a new rotor is created just by a simple rotation. The rotation is performed in terms of pixels so degree of angle is converted into number of pixels. Using this method, same object with new face (after rotation) is used for encryption. The pixels of color image are permuted using the sorted index of logistic sequence. Then, three pseudo-random images are created from Piecewise Linear Chaotic Map (PWLCM). For substitution, both the permuted color channels and pseudo-random images are transformed into rotors. The angle is obtained from Chen chaotic system. The one-time keys are for chaotic maps are generated by using 512-bits hash of plain image. The simulated outcomes demonstrate that the proposed system has high quality of results and requires only single round of encryption to achieve high encryption along with high robustness against the transmission noises.
Local Correlation Consistency for Knowledge Distillation

Sufficient knowledge extraction from the teacher network plays a critical role in the knowledge distillation task to improve the performance of the student network. Existing methods mainly focus on the consistency of instance-level features and their relationships, but neglect the local features and their correlation, which also contain many details and discriminative patterns. In this paper, we propose the local correlation exploration framework for knowledge distillation. It models three kinds of local knowledge, including intra-instance local relationship, inter-instance relationship on the same local position, and the inter-instance relationship across different local positions. Moreover, to make the student focus on those informative local regions of the teacher’s feature maps, we propose a novel class-aware attention module to highlight the class-relevant regions and remove the confusing class-irrelevant regions, which makes the local correlation knowledge more accurate and valuable. We conduct extensive experiments and ablation studies on challenging datasets, including CIFAR100 and ImageNet, to show our superiority over the state-of-the-art methods.