Reviewerly/exHarmony huggingface dataset:
Adversarial-Aware Deep Learning System Based on a Secondary Classical Machine Learning Verification Approach

Deep learning models have been used in creating various effective image classification applications. However, they are vulnerable to adversarial attacks that seek to misguide the models into predicting incorrect classes. Our study of major adversarial attack models shows that they all specifically target and exploit the neural networking structures in their designs. This understanding led us to develop a hypothesis that most classical machine learning models, such as random forest (RF), are immune to adversarial attack models because they do not rely on neural network design at all. Our experimental study of classical machine learning models against popular adversarial attacks supports this hypothesis. Based on this hypothesis, we propose a new adversarial-aware deep learning system by using a classical machine learning model as the secondary verification system to complement the primary deep learning model in image classification. Although the secondary classical machine learning model has less accurate output, it is only used for verification purposes, which does not impact the output accuracy of the primary deep learning model, and, at the same time, can effectively detect an adversarial attack when a clear mismatch occurs. Our experiments based on the CIFAR-100 dataset show that our proposed approach outperforms current state-of-the-art adversarial defense systems.
Research on Curriculum Reform and Laboratory Construction through Academia-industry Integration in Quantitative Investment

The "Academia-industry Integration" is the key to solve the problem that the theory and teaching method are detached from practice in industry in higher education institutions.Therefore, discussing and exploring the curriculum reform through academia-industry integration as well as the research on the related laboratory construction is a very important and meaningful thing.This article takes the field of quantitative investment as a case study and conducts a deep analysis of the curriculum reform system and laboratory construction in terms of four steps: Firstly, the article introduces the background of the academia-industry integration in higher education institutions; then we discuss the method of curriculum reform analysis in terms of necessity and feasibility of curriculum reform as well as the laboratory construction and analyze their feasibility level comparing with other existing cases; then we logically sort out the framework of the construction of quantitative investment laboratories; finally, we analyze the benefits of curriculum reform and laboratory construction and clarifies their importance for the future construction and development of higher education institutions.
Centralised Application Development for College Notices

The aim of our project is to replace traditional notice boards with an E-notice board system. With the "BMIT App" installed on Android devices, users can easily access all college-related notices anytime and anywhere. The system includes a notification manager feature, through which the admin can send notifications to faculty and students after verification. These notifications cover various topics such as scholarships, admissions, exams, staff meetings, holidays, events, timetables, and achievements. The system enhances organization and communication between instructors, faculty, and students, saving time and effort. By leveraging the latest technologies and connecting to the educational database of the university, the Android application streamlines information dissemination. The system also eliminates the need for printing papers or making individual calls or messages to inform staff about meetings with the principal. The web-based admin application and the Android application provide a simple interface for maintaining and accessing information. This project report comprehensively addresses the various tasks carried out by the college, providing staff with detailed information about their wards, including results, important notices, event details, and teacher information
The Construction of Identity in Nigerian Universities' Vision and Mission Statements

This chapter unpacks contextual contents of the Nigerian universities' vision and mission statements which have largely been neglected in the scholarship concerning identity construction, especially from the angle of promotional discourse. Ninety universities were selected, thirty each from federal, state, and private universities for equal and holistic distribution and genralisation. The data were analysed with the theoretical help of the notion of indexicality principles and construction grammar theory. The findings reveal two broad categories of identity: social-informed and education-informed identities. These identities were constructed through infinitival clauses, implicit and explicit legitimatization, metphoraisation and lexicalization. The chapter concludes beyond advertising, the Nigerian universities' vision and mission statements provide, they equally positioned Nigerian universities as tolls of development. Hence, funding them should be the utmost priority of the government.
A Virtual Linguistic Landscape Analysis of Higher Education Institutions and Their Use of Pronouns of Address in the Hispanic and Lusophone World

The use of space, both physical and virtual, has been extensively studied in relation to language, with many researchers considering it a determining factor. This chapter investigates the virtual linguistic landscape (VLL) of universities in ten Hispanic countries and five Portuguese-speaking countries to examine the use of pronouns of address (PoA) on their digital platforms. With the exponential increase in communication through digital means in recent years, computer-mediated communication has become a relevant topic in understanding human interaction through virtual devices. In this study, the authors specifically analyze the use of second-person singular pronouns in the VLL of higher education institutions in Hispanic and Portuguese-speaking countries, focusing on their social media platforms. The findings reveal that tú is the predominant PoA in Spanish-speaking institutions, whereas você prevails in Portuguese-speaking institutions on their social media.
Image based agorithm for automatic generation of chinese couplets

Chinese couplet is one of the important forms of expression in Chinese and even world literature, with its own unique charm and beauty. In order to meet the needs of users who only need one image to obtain corresponding couplets, realize the function of computer automatically writing couplets with images, and improve the literary expression ability of couplets to images, this paper proposes an image based intelligent generative model of couplets. The model first outputs corresponding descriptions based on image extraction features, and then extracts keywords through an improved hybrid algorithm according to the descriptions. Then, based on the keywords, the Chinese GPT-2 model automatically expands the first line of a couplet, and finally generates the second line of a couplet from the first line of a couplet through the encoding and decoding framework. Through experiments, it has been shown that the generated couplets of the model meet the requirements for image description, and the effectiveness of the model has been confirmed by manual evaluation results.
Multi-aspect features of items for time-ordered sequential recommendation

The key to sequential recommendation modeling is to capture dynamic users’ interests. Existing sequential recommendation methods (e.g., self-attention mechanism) have achieved extraordinary success in modeling users’ interests. However, these models ignore that users have different levels of preferences for different aspects of items, failing to capture users’ most concerning aspects. In addition, they are highly dependent on the quality of training data, which may lead to overfitting of the model when the training data is insufficient. To address the above issues, we propose a novel sequence-aware model (Multi-Aspect Features of Items for Time-Ordered Sequential Recommendation, MFITSRec), which combines the features of items with user behavior sequences to learn more complex item-item and item-attribute relationships. Moreover, the model uses a self-attention network based on an absolute time relationship, which can better represent the changes in users’ interests and capture users’ preferences for particular aspects of items. Extensive experiments on five datasets demonstrate that our model outperforms various baseline models. In particular, the model’s prediction accuracy has been significantly improved on sparse datasets.
Quantum Computing: Principles and Applications

People are witnessing quantum computing revolutions nowadays. Progress in the number of qubits, coherence times and gate fidelities is happening. Although quantum error correction era has not arrived, the research and development of quantum computing have inspired insights and breakthroughs in quantum technologies, both in theories and in experiments. In this review, we introduce the basic principles of quantum computing and the multilayer architecture for a quantum computer. There are different experimental platforms for implementing quantum computing. In this review, based on a mature experimental platform, the Nuclear Magnetic Resonance (NMR) platform, we introduce the basic steps to experimentally implement quantum computing, as well as the common challenges and techniques.
Sentiment Analysis of the Consumer Review Text Based on BERT-BiLSTM in a Social Media Environment

In this paper, a BERT-BiLSTM-based consumer review text sentiment analysis method in the e-commerce big data field is proposed. First, the unlabeled text is trained using the BERT training model for the language introduced in the deep learning, and then the pre-training model of the text data is delivered by the learning textual features and data to extract deeper vectors. Second, the BiLSTM model is applied to simultaneously obtain contextual information so as to illustrate optimal textual features. Finally, a corresponding sentiment analysis model relative to the consumer review text is constructed by combining the BERT model with BiLSTM to better merge the context for classifying sentiment and improving the final feature vector accuracy for the sentiment classification results. Simulated by experiments, the method proposed in this paper was compared with another three methods using the same data set. The results obtained indicate that the proposed method has the highest precision, recall, and F1-Measure, and the values reach 92.64%, 90.32%, and 91.46%, respectively.
Domain knowledge-guided adversarial adaptive fusion of hybrid breast ultrasound data

Contrast-enhanced ultrasound (CEUS), which provides more detailed microvascular information about the tumor, is always taken by radiologists in clinic diagnosis along with B-mode ultrasound (B-mode US). However, automatically analyzing breast CEUS is challenging due to the difference between the CEUS video and the natural video, e.g., sports or action videos, where the CEUS video has no positional displacements. Additionally, most existing methods rarely use the Time Intensity Curve (TIC) information of CEUS and non-imaging clinical (NIC) data. To address these issues, we propose a novel breast cancer diagnosis framework that learns the complementarity and correlation across hybrid modal data, including CEUS, B-mode US, and NIC data, by an adversarial adaptive fusion method. Furthermore, to fully exploit the CEUS information, the proposed method, inspired by the clinical processing of radiologists, first extracts the TIC parameters of CEUS. Then, we select a clip from CEUS using a frame screening strategy and finally get spatio-temporal features from these clips through a critical frame attention network. To our knowledge, this is the first AI system to use TIC parameters, NIC data, and ultrasound imaging in diagnoses. We have validated our method on a dataset collected from 554 patients. The experimental results demonstrate the excellent performance of the proposed method. The result shows that our method can achieve an accuracy of 87.73%, which is higher than that of uni-modal approaches by nearly 5%.
Doubts on the reliability of parallel corpus filtering

Parallel corpus filtering (PCF) aims to filter out low-quality data residing in parallel corpora. Recently, deep learning-based methods have been employed to assess the quality of sentence pairs in a parallel corpus, along with rule-based filtering that filters out noisy data depending on the pre-defined error types. Despite their utilization, to the best of our knowledge, a comprehensive investigation into the practical applicability and interpretability of PCF techniques remains unexplored. In this study, we raise two doubts on deep learning-based PCF: (i) Can deep learning-based PCF extract high-quality data? and (ii) Are scoring functions of PCF reliable? To answer these questions, we conduct comparative experiments on various PCF techniques with four datasets on two language pairs, English–Korean, and English–Japanese. Through the experiments, we demonstrate that the performance of the deep learning-based PCF highly depends on the targeting parallel corpus, and shows fluctuating adaptability depending on their characteristics. In particular, we figure out that high-scored sentences derived by the PCF technique do not necessarily guarantee high-quality results, rather it shows unintended preference.
Sentiment Distribution of Topic Discussion in Online English Learning

Online English teaching resources have recently surged, highlighting the exigency for efficient organization and categorization. This manuscript introduces an innovative strategy to classify university-level English teaching resources, employing a sophisticated density clustering algorithm. Initially, student discourse was mined within a teaching platform comment section, and in-depth textual analysis was conducted. Subsequently, the term frequency-inverse document frequency (TF–IDF) feature extraction algorithm was enhanced, while emotive attributes were seamlessly integrated into the textual manifestation layer during the classification procedure. This enabled the distribution of topics and emotions to be acquired for each comment, facilitating subsequent analyses of emotion feature extraction and model training. An improved weight calculation was designed based on TF–IDF to evaluate the importance of feature items for each corpus file. The simulation results demonstrate the proposed scheme's effectiveness. The algorithm facilitates faster scholarly access to educational resource information and effectively classifies data for high research adaptability.
Multi-level attention for referring expression comprehension

Referring expression comprehension aims to locate a target object in an image described by a referring expression, where extracting semantic and discriminative visual information plays an important role. Most existing methods either ignore attribute information or context information in the model learning procedure, thus resulting in less effective visual features. In this paper, we propose a Multi-level Attention Network (MANet) to extract the target attribute information and the surrounding context information simultaneously for the target object, where the Attribute Attention Module is designed to extract the fine-grained visual information related to the referring expression and the Context Attention Module is designed to merge the context information of surroundings to learn more discriminative visual features. Experiments on various common benchmark datasets show the effectiveness of our approach.
Teaching and Training with Open Science: From Classroom Teaching Tool to Professional Development

In order for open science to reach its full potential to reduce barriers in science and increase access to the field, data and tools need to be accompanied by training and other supporting resources to make them truly accessible to prospective and new users. Further, these educational resources must be tailored to meet the needs of many different user profiles, such as neuroscientists of many specialties, computational scientists, and educators. In this article, we will describe some of the approaches the Allen Institute has taken to support working scientists, students, and educators at multiple career stages and paths to use our open science resources.
PIMnet: A quality enhancement network for compressed videos with prior information modulation

In this paper, we propose a quality enhancement network for compressed videos, named as PIMnet, which can effectively use the spatio-temporal information of multiple frames to improve the video quality. The main idea of PIMnet is to use the Quantization Parameter (QP) and Delta Picture Order Count (ΔPOC) of multiple input frames to modulate the network, where QP can reflect the quality of frames and ΔPOC can reflect the temporal distance between neighboring frames and the current frame. In PIMnet, the modulated deformable convolution (DCNv2) is performed to align and fuse multiple input frames. The offsets of DCNv2 for alignment are obtained by the flow-guided offset prediction module and the masks of DCNv2 for fusion are obtained by the mask prediction module. The offset and mask prediction modules are modulated by prior information. Afterwards, the features obtained by DCNv2 are further used by the QE module to compute the enhanced result. Extensive experiments demonstrate that the proposed PIMnet can achieve superior performance in quality enhancement.
Drone Technologies and Applications

Unmanned aerial vehicles, previously used for military purposes, have started to be used for civilian purposes since the 2000s. With the widespread use of unmanned aerial vehicles, mostly used for defense purposes, they have turned into flying vehicles called drones. Today, it is used for different purposes such as taking pictures, taking images from inaccessible places, competitions, and having a good time. However, it continues to evolve, playing an important role in social media and inter-company competition. In recent years, studies have continued on small drones that can fly autonomously, especially in closed areas, and assist people. The shrinkage and cost reduction of electronic components such as microprocessors, sensors, batteries, and wireless communication units shows that drones smaller than 1 kg will soon occur in many areas of our daily life at much more affordable prices. This rapid change, development, and proliferation of drones also raise privacy and security concerns. While obtaining images of people and institutions in supposedly private environments with such technological devices emphasizes individual security, its use in urban areas and airports draws attention to social security. The rapid developments in this area necessitate the necessary regulations and controls to prevent accidents and other problems due to the growth in use and loss of control.
Russian cloud office application packages in the educational process of universities

The article aims to introduce the author’s developments in the use of online office applications MyOffice and P7-Office, included in the Unified Register of Russian programs for electronic computers and databases, to the institutions of higher education in Russia. Based on the analysis of scientific, scientific-methodical, and educational literature, comprehension of own pedagogical work, analysis educational activities of student construction of new educational content, the author defines the subject of the research in solving the problem of educational process transfer at Russian universities to domestic software, picks out directions of using cloud office programs in the educational process, gives examples of using text and table online editors MyOffice and P7-Office in educational purposes, and provides the rationale for the choice to study this particular software in the courses “Informatics” and in the block of academic disciplines on the application of information technology in professional activities for socio-economic and humanitarian areas of higher education. The relevance of the article is determined by the need to implement the methodological recommendations of the Ministry of Science and Higher Education of the Russian Federation on the transition of educational institutions to preferential use of domestic software, in particular, the need to replace the foreign cloud software Microsoft Office 365 and online office applications of Google corporation, which has been widely used until recently. The author formulated a number of suggestions for the developers of Yandex.Disk and Cloud Mail.ru on how to improve and use Russian cloud services. To save money allocated for the purchase of licensed domestic office applications, the author suggests using the free full-featured online versions in the educational process. The novelty of the results presented in this article lies in the absence of scientific articles based on research on the use of domestic online office applications in the educational process of Russian universities in the RSCI database, and the lack of training manuals on the use of these applications in the catalogs of leading Russian electronic library systems. Considering the fact that even the strategic partners of the United States are attempting to switch to their cloud products, the author believes that the experience of using Russian online office applications described in this article may be useful for university teachers in the Eurasian Economic Union member states.
Hidden Markov Models for Pattern Recognition

Hidden Markov Models (HMMs) are the most popular recognition algorithm for pattern recognition. Hidden Markov Models are mathematical representations of the stochastic process, which produces a series of observations based on previously stored data. The statistical approach in HMMs has many benefits, including a robust mathematical foundation, potent learning and decoding techniques, effective sequence handling abilities, and flexible topology for syntax and statistical phonology. The drawbacks stem from the poor model discrimination and irrational assumptions required to build the HMMs theory, specifically the independence of the subsequent feature frames (i.e., input vectors) and the first-order Markov process. The developed algorithms in the HMM-based statistical framework are robust and effective in real-time scenarios. Furthermore, Hidden Markov Models are frequently used in real-world applications to implement gesture recognition and comprehension systems. Every state of the model can only observe one symbol in the Markov chain. In contrast, every state in the topology of a Hidden Markov Model can see one symbol emerging from a particular gesture. The matrix representing the observation probability distribution contains the likelihood of observing a symbol in each state. As an illustration, the probability that a symbol will emit is determined by its observation probability in the first state. In the recognition task, the emission distribution is another name for the observation probability distribution. For the following reasons, HMM states are also referred to as hidden. First, choosing to emit a symbol denotes the second process. Second, an HMM’s emitter only releases the observed symbol. Finally, since the current states are derived from the previous states, the emitting states are unknown. HMMs are well-known and more flexible in the field of gesture recognition because of their stochastic nature.