Reviewerly/exHarmony huggingface dataset:
Collaborative Filtering-Based Music Recommendation in Spark Architecture

The use of recommendation algorithms to recommend music MOOC resources is a method that is gradually gaining ground in people’s lives along with the development of the Internet. The often used ALS collaborative filtering algorithm has an irreplaceable role in personalised recommender systems via the Spark MLlib platform. In the study, it is investigated how Spark can be used to implement efficient music system recommendations. The collaborative filtering algorithm based on the ALS model in the Spark architecture is currently the most widely used technique in recommendation algorithms, allowing for the analysis and optimisation of computational techniques. The project-based collaborative filtering algorithm used in the article enables the recommendation of music by avoiding personal information about the user. More accurate user recommendations are achieved by predicting the user’s preferences and focusing on the top ranked and highly preferred music recommendations. The method improves the performance of the recommendation algorithm, which is optimised by Spark shuffle on top of resource optimisation, and its performance improved by 54.8% after optimisation compared to when there is no optimisation.
Semi-automatic generation of textual exercises for software engineering education

Creating class diagrams from given texts is an important skill for software engineering students in higher computer science education. Choosing the right diagram components and arranging them correctly regularly challenges students. Therefore, a thoughtful teaching approach is required from educators. Creating such exercises for different courses with different contexts is time-consuming for educators. In preparation for an upcoming exam, students are limited to few available exercise texts. To address these challenges, a deep learning model was trained to automatically generate textual exercises for software engineering education. To verify whether the model is suitable for educational purposes or not, a study with software engineering students has been conducted. The results show, that the texts are well understood according to grammar and sentence structure. Also, students found the created exercise useful and would like to use comparable exercises for exam preparation.
A Blockchain-Based Privacy Protection Application for Logistics Big Data

Logistics business is generally managed by logistics orders in plain text, and there is a risk of disclosure of customer privacy information in every business link. In order to solve the problem of privacy protection in logistics big data system, a new kind of logistics user privacy data protection scheme is proposed. First of all, an access rights management mechanism is designed by combining block chain and anonymous authentication to realize the control and management of users' access rights to private data. Then, the privacy and confidentiality protection between different services is realized by dividing and storing the data of different services. Finally, the participants of the intra-chain private data are specified by embedding fields in the logistics information. The blockchain node receiving the transaction is used as the transit node to synchronize the intra-chain privacy data, so as to improve the intra-chain privacy protection within the business. Experimental results show that the proposed method can satisfy the privacy requirements and ensure considerable performance.
Comments on and Corrections to “When Is the Chernoff Exponent for Quantum Operations Finite?”

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A systematic review of multi-slice and multi-frame descriptors in cardiac MRI exams

Computer-Aided Diagnosis systems have been developed to help medical professional in their decision making routines towards a more accurate diagnosis. These systems process medical exams such as Magnetic Resonance (MRI) in order to quantify meaningful features. These can be used with similarity-measuring techniques in a Content-Based Image Retrieval context, or inputted into a machine learning classifier in order to support early disease detection. For cardiac MRIs, single slice descriptors have been proposed in the two-dimensional domain, shape descriptors have been proposed in the three-dimensional domain, and previous reviews have mapped these two descriptor categories. Nonetheless, no systematic review on these descriptors have looked at full cardiac MRI images sets. We have reviewed the literature by searching for descriptors that consider the whole slice set (multi-slice) or frames (multi-frame) in cardiac MRI exams. We discuss descriptors and techniques, the datasets that were used, and the different evaluation metrics. Finally, we highlight literature gaps and research opportunities.
A secure visual framework for multi-index protection evaluation in networks

Mining the core value of Industrial Internet of Things (IIoT) data safely and reducing the risk of malicious attacks are the inherent requirements of industrial data visualization. Visualization technology has become the main tool for data aggregation, mining and analysis of IIoT data through graphical representation. However, visualization technology still has two shortcomings in big data calculation and analysis scenarios. On the one hand, visual results will lead to the disclosure of sensitive privacy. On the other hand, most visualization tools can't provide an interactive framework for users to select the suitable solutions. To address these problems, we present an open accessible Visual framework based on Differential Privacy theory (VisDP), which provides Multi-index Quantitative comprehensive Evaluation technology (MQE) for data mining results. Considering the advantages of interactive mechanism, VisDP provides rich optional schemes, including the operating web, calling API and the downloading SDK. Finally, we verify the availability and privacy of MQE through mathematical proofs, analyze the hospital medical waste detection system that actually applies the framework, and the experimental results have showed the effectiveness and practicality of the proposed platform.
Reversible Data Hiding in Encrypted Domain Based on the Error-Correction Redundancy of Encryption Process

The existing reversible data hiding methods in encrypted domain separate image encryption from information embedding and do not make full use of the redundant space in the process of image encryption. In order to improve the performance of reversible data hiding by using the technical characteristics of image encryption, a reversible data hiding method based on McEliece error correction is proposed. Firstly, the segmentation position of bit plane is determined according to the embedding requirement and texture characteristic, and the image is divided into high and low significant bits. Secondly, because of the error-correcting characteristic of McEliece encryption, reversible data embedding can be realized while encrypting low significant bits. Then, the high significant bits are compressed to reserve space for the ciphertext extension of the low significant bits. Finally, the whole high significant bits information is stream-encrypted. As long as the image receiver has the decryption key, the image can be restored without distortion. By concealing the relationship between error correction and secret information mapping, the concealment of secret information transmission can be realized. In addition, due to different processing for different pixels, it can be efficiently transmitted with low computational complexity for applications that only need general images. The simulation results show that this scheme can not only realize the separable operation of information extraction and image recovery but also resist the noise attack to a certain extent. The maximum embedding rates of 10 standard images from USC-SIPI and 50 standard images from BOSS-BASE are 2.228 and 2.323 bpp, respectively.
Interactive Regression and Classification for Dense Object Detector

In object detection, enhancing feature representation using localization information has been revealed as a crucial procedure to improve detection performance. However, the localization information (i.e., regression feature and regression offset) captured by the regression branch is still not well utilized. In this paper, we propose a simple but effective method called Interactive Regression and Classification (IRC) to better utilize localization information. Specifically, we propose Feature Aggregation Module (FAM) and Localization Attention Module (LAM) to leverage localization information to the classification branch during forward propagation. Furthermore, the classifier also guides the learning of the regression branch during backward propagation, to guarantee that the localization information is beneficial to both regression and classification. Thus, the regression and classification branches are learned in an interactive manner. Our method can be easily integrated into anchor-based and anchor-free object detectors without increasing computation cost. With our method, the performance is significantly improved on many popular dense object detectors, including RetinaNet, FCOS, ATSS, PAA, GFL, GFLV2, OTA, GA-RetinaNet, RepPoints, BorderDet and VFNet. Based on ResNet-101 backbone, IRC achieves 47.2% AP on COCO test-dev, surpassing the previous state-of-the-art PAA (44.8% AP), GFL (45.0% AP) and without sacrificing the efficiency both in training and inference. Moreover, our best model (Res2Net-101-DCN) can achieve a single-model single-scale AP of 51.4%.
A 39pJ/label 1920x1080 165.7 FPS Block PatchMatch Based Stereo Matching Processor on FPGA

Depth is a fundamental information for lots of computer vision applications. Stereo matching is a commonly used depth estimation method that mimics the human binocular vision system. Most stereo matching systems suffer when attempt to strike a balance between accuracy and computational complexity because of two reasons: 1) It is usually assumed that all the surfaces are fronto-parallel, meaning that neighbors share the same disparity. But a lot of real-world situations are slant surfaces like roads and walls. 2) Conventional methods usually utilize winner takes all (WTA) strategy [1]–[4], where aggregated costs in all disparity levels (usually 128 or 256) must be calculated. But there is only one true guess for each pixel position, such that most of the calculated costs are meaningless. To solve the above issues, in this work, a block PatchMatch-based FPGA accelerator for stereo matching is proposed. PatchMatch introduces random search strategy and slant label, where rectangle superpixels called blocks are used as the basic computing element. Main improvements of this work are: 1) Utilized random search strategy and block level computation can save massive computation. 2) Closer-to-reality slant label improves accuracy. Moreover, plane slant is also helpful for following tasks like 3D reconstruction [5], but none of the existing hardware accelerators can provide this information. 3) Algorithm-hardware co-optimized 6-points Census feature and multi-scale propagation (MSP) are proposed. 4) Based on the testing results on industrial-level KITTI dataset, the real-time performance and energy efficiency of the proposed design outperform state-of-the-art FPGA and ASIC designs, with comparable accuracy.
Large-scale photovoltaic system in green building: MPPT control based on deep neural network and dynamic time-window

Under the background of smart city, the concepts of “green building” and “net-zero energy building” become more and more popular for reducing the building power consumption. As a result, the technologies related to the design and intelligent control of building integrated green energy system develop rapidly in recent years. In this study, the topological structure of large-scale building integrated photovoltaic (BIPV) system is analyzed, and a novel data-driven maximum power point tracking (MPPT) methodology is developed. To be specific, several characteristic-variables for achieving efficient MPPT of large-scale BIPV system are proposed, and the data-driven MPPT model based on deep neural network (DNN) is developed. Then, the developed characteristic-variables and DNN model are verified by a comprehensive set of numerical experiments. The optimal DNN structure is also verified in detail in this study. In addition, in order to dynamically track the degradation of photovoltaic module and overcome its influence on DNN model, the time-window mechanism of BIPV knowledge-base is introduced, and the optimal length of time-window for different DNN structures is verified by numerical experiments. Experimental results show that the DNN model with developed characteristic-variables and time-window mechanism achieves accurate and robust forecasting performance on the MPPT of large-scale BIPV system.
Counteract Side-Channel Analysis of Neural Networks by Shuffling

Machine learning is becoming an essential part in almost every electronic device. Implementations of neural networks are mostly targeted towards computational performance or memory footprint. Nevertheless, security is also an important part in order to keep the network secret and protect the intellectual property associated to the network. Especially, since neural network implementations are demonstrated to be vulnerable to side-channel analysis, powerful and computational cheap countermeasures are in demand. In this work, we apply a shuffling countermeasure to a microcontroller implementation of a neural network to prevent side-channel analysis. The countermeasure is effective while the computational overhead is low. We investigate the extensions necessary for our countermeasure, and how shuffling increases the effort for an attack in theory. In addition, we demonstrate the increase in effort for an attacker through experiments on real side-channel measurements. Based on the mechanism of shuffling and our experimental results, we conclude that an attack on a commonly used neural network with shuffling is no longer feasible in a reasonable amount of time.
Joint Model, Task Partitioning and Privacy Preserving Adaptation for Edge DNN Inference

Deep Neural Networks (DNNs) have been widely used in everyday life owing to their impressive performance in complex machine learning tasks. The performance however comes at the cost of high computational complexity, which hinders the application of many DNN models in resource-constrained Internet-of-Things (IoT) and mobile devices. Device-edge collaborative DNN inference (referred to as co-inference) is an effective way to address the issue. However, it requires non-trivial algorithmic design, since the compound performance indicators including the inference efficiency and accuracy, and the data privacy have to be jointly considered. In this paper, we extend the degree of flexibility of the classical co-inference schemes, and propose a joint model, partitioning point and privacy differential intensity adaptation framework for co-inference, which comprises of the offline and online phases. In the offline phase, we train the co-inference model set that consists of a series of sub-models with different complexities, and profile the necessary performance of the sub-models. On that basis, we design an efficient algorithm for the online phase to promptly choose the sub-model, partitioning point and privacy differential intensity to meet the latency constraint and achieve the optimal accuracy-privacy tradeoff. Finally, extensive evaluations are carried out to demonstrate the effectiveness of our proposed framework.
Work-In-Progress: Converting textual software engineering class diagram exercises to UML models

Class diagram exercises are an important part in the development of software engineering students in higher computer science education. Generating textual exercises with sample solutions for such courses is time-consuming for educators, especially with multiple courses and different contexts. According to literature, the automatic generation of diagrams from structured text is possible. However, students often do not receive template based exercise texts but descriptions in natural language, which is still not a closed research topic. To address this problem, this paper discusses a model that analyses real exercise texts used for software engineering education, considers each individual sentences components and provides a class diagram. Due to the complexity of natural language, the model does not deliver perfect results so far, but is a great work in progress for the attempt to generate sample solutions for given exercise texts.
Internet of Things, Security of Data, and Cyber Security

Diverse forms of cyber security techniques are at the forefront of triggering digital security innovations, whereas cybersecurity has become one of the key areas of the internet of things (IoT). The IoT cybersecurity mitigates cybersecurity risk for organizations and users through tools such as blockchain, intelligent logistics, and smart home management. Literature has not provided the main streams of IoT cyber risk management trends, to cross referencing the diverse sectors involved of health, education, business, and energy, for example. This study aims to understanding the interplay between IoT cyber security and those distinct sector issues. It aims at identifying research trends in the field through a systematic bibliometric literature review (LRSB) of research on IoT cyber and security. The results were synthesized across current research subthemes. The results were synthesized across subthemes. The originality of the paper relies on its LRSB method, together with extant review of articles that have not been categorized so far. Implications for future research are suggested.
Machine Learning to the Rescue: ML-Assisted Framework for Equity-Driven Education

Data Science has recently experienced a significant surge in producing undergraduate degree/certificate and course enrollments. This growth has resulted in straining program resources at many institutions and causing concern about how to most effectively respond to the rapidly growing demand and provide equal opportunities for all students to pursue their degree. The primary goal of this work is to provide an intelligent solution to automatically identify biases related to race and gender in the content of Data Science related courses such as Machine Learning (ML), Natural Language Processing (NLP), Data Mining, Data Analytics, and Knowledge Discovery just to name a few. While machine learning allows to build powerful predicting tools, it hasn&#x2019;t been used sufficiently to detect biases rooted in gender and race in educational material. Hence, using effective machine learning techniques we aim to enhance the success rate of undergraduate students in Data Science education and programs while promoting engagement through identifying and mitigating potential gender and racial biases. To this end, we propose a novel bias detection approach which uses a combination of gender identification and sentiment classification for characterization of gender and race in educational contents. Our proposed framework can help educators and course developers to mitigate race and gender biases in their course materials and create an equitable learning experience for students of minority and under-represented groups.
Hierarchical convolutional recurrent neural network for Chinese text classification

Text classification is the most fundamental and essential task in natural language processing. The last decade has seen a surge of research in this area due to the unprecedented success of deep learning. Previous models assume that all classes are equally difficult to distinguish and treat all of them exclusively. But in fact, the property of general-to-specific category ordering often exists between classes. In this paper, we exploit the prior knowledge of class hierarchical structure to enforce the network to learn human-understandable concepts in different blocks and propose a new model named H-CRNN, which combines TextCNN and Bi-LSTM to construct a hierarchical structure. We test our proposed model on the THUCNews dataset, and experiments show that our proposed H-CRNN model achieves the best results than other methods.
Learning-Based Text Image Quality Assessment with Texture Feature and Embedding Robustness

The quality of the input text image has a clear impact on the output of a scene text recognition (STR) system; however, due to the fact that the main content of a text image is a sequence of characters containing semantic information, how to effectively assess text image quality remains a research challenge. Text image quality assessment (TIQA) can help in picking a hard sample, leading to a more robust STR system and recognition-oriented text image restoration. In this paper, by arguing that the text image quality comes from character-level texture feature and embedding robustness, we propose a learning-based fine-grained, sharp, and recognizable text image quality assessment method (FSR–TIQA), which is the first TIQA scheme to our knowledge. In order to overcome the difficulty of obtaining the character position in a text image, an attention-based recognizer is used to generate the character embedding and character image. We use the similarity distribution distance to evaluate the character embedding robustness between the intra-class and inter-class similarity distributions. The Haralick feature is used to reflect the clarity of the character region texture feature. Then, a quality score network is designed under a label–free training scheme to normalize the texture feature and output the quality score. Extensive experiments indicate that FSR-TIQA has significant discrimination for different quality text images on benchmarks and Textzoom datasets. Our method shows good potential to analyze dataset distribution and guide dataset collection.
Optimal social welfare: A many-to-many data transaction mechanism based on double auctions

With the development of Big Data and the Internet of Things (IoT), the data value is more significant in both academia and industry. Trading can achieve maximal data value and prepare data for smart city services. Due to data's unique characteristics, such as dispersion, heterogeneity and distributed storage, an unbiased platform is necessary for the data trading market with rational trading entities. Meanwhile, there are multiple buyers and sellers in a practical data trading market, and this makes it challenging to maximize social welfare. To solve these problems, this paper proposes a Social-Welfare-Oriented Many-to-Many Trading Mechanism (SOMTM), which integrates three entities, a trading process and an algorithm named Many-to-Many Trading Algorithm (MMTA). Based on the market scale, market dominated-side and market fixed-side, simulations verify the convergency, economic properties and efficiency of SOMTM.