Reviewerly/exHarmony huggingface dataset:
Context-aware session recommendation based on recurrent neural networks

A session-based recommendation system that helps users get the information they are interested in is an important category of personalized recommendation systems. Traditionally, session recommendation algorithms do not take full advantage of users’ contextual information. It becomes easier to get users’ preferences and context with the rapid development of mobile devices. Under such circumstances, we proposed a novel recommendation algorithm joined session-based context-aware recommendation model. The model maps contextual information into low-dimensional real vector features and then fuses them into a recurrent neural network recommendation model based on sessions by three combinations of Add, Stack, and Multilayer Perceptron. We have verified its extensibility by combining it with the functional extension module which rest on long sequences. We conducted extensive experiments on two public datasets. The experimental results show that our model significantly outperforms state-of-the-art recommendation models in terms of recommendation performance.
Optimizing the Energy Consumption of Blockchain-Based Systems Using Evolutionary Algorithms: A New Problem Formulation

Blockchain technology has gained recognition in industrial, financial, and various technological domains for its potential in decentralizing trust in peer-to-peer systems.A core component of blockchain technology is a consensus algorithm, most commonly Proof of Work (PoW).PoW is used in blockchain-based systems to establish trust among peers; however, it does require the expenditure of an enormous amount of energy that affects the environmental sustainability of blockchain-based systems.Energy minimization, whilst ensuring trust within blockchain-based systems that use PoW, is a challenging problem.The solution has to consider how energy consumption can be minimized without compromising trust, whilst still ensuring, for instance, scalability, security, and decentralization.In this paper, we represent the problem as a subset selection problem of miners in a blockchain-based system.We formulate the problem of blockchain energy consumption as a Search-Based Software Engineering problem with four objectives: energy consumption, carbon emission, decentralization, and trust.We propose a model composed of multiple fitness functions.The model can be used to explore the complex search space by selecting a subset of miners that minimizes the energy consumption without drastically impacting the primary goals of the blockchain technology (i.e., security/trustworthiness and decentralization).We integrate our proposed fitness functions into five evolutionary algorithms to solve the problem of blockchain miners selection.Our results show that the environmental sustainability of blockchain-based systems (e.g.reduced energy use) can be enhanced with little degradation in other competing objectives.We also report on the performance of the algorithms used.
Optical conductivity and far-field radiation of silicene

Silicene is a silicon analogue of graphene. From symmetry considerations, we develop the matrix of Hamiltonian, including spin-orbit coupling in silicene. This work analyzes the heat and angular momentum radiation from silicene through the theory of far-field radiation and optical conductivity. We discuss the relation between the radiation and spin-orbit coupling parameters. Our results show the effects of different parameters, such as temperature and sublattice potential, on optical conductivity and radiation of silicene. In particular, the threshold frequency of optical conductivity causes the appearance of the second peak in energy radiation spectrum. We also find that the ratio of the perpendicular electric field to the intrinsic spin-orbit coupling is related to the strength of angular momentum radiation, which may promote the application of silicene.
Remote OpenMP offloading

OpenMP has a long and successful history in parallel programming for CPUs, and more recently GPUs through accelerator offloading.
A Scoping Review: Synthesizing Evidence on Data Management Instruction in Academic Libraries

The present scoping review examines empirical RDM instruction-related studies in academic libraries between 2010 and 2021. We searched three databases (LISTA, ERIC, and MEDLINE Complete) and two journals (Journal of eScience Librarianship and International Journal of Digital Curation) and identified 124 articles for inclusion. Cohen's kappa indicated a strong to perfect inter-rater reliability on the coding between the authors. Overall, the findings indicate an increasing trend in empirical research regarding the topic of RDM instruction across many countries and regions after 2010. Also, faculty, researchers, and librarians in the higher education field were the primary audiences for the RDM instruction with few studies addressing RDM instruction for the undergraduate and graduate levels. In terms of the RDM aspects, RDM needs assessments were investigated the most among the reviewed studies, followed by data sharing and data management plans. Additionally, the face-to-face learning context was the most popular for RDM instruction, followed by online and hybrid contexts. However, few studies used an intervention research design while delivering instruction to the target audience. This study highlights the substantial characteristics and methodological designs of the RDM instruction empirical research and provides implications for approaches and techniques used to study RDM instruction in academic libraries.
Kernel Proposal Network for Arbitrary Shape Text Detection

Segmentation-based methods have achieved great success for arbitrary shape text detection. However, separating neighboring text instances is still one of the most challenging problems due to the complexity of texts in scene images. In this article, we propose an innovative kernel proposal network (dubbed KPN) for arbitrary shape text detection. The proposed KPN can separate neighboring text instances by classifying different texts into instance-independent feature maps, meanwhile avoiding the complex aggregation process existing in segmentation-based arbitrary shape text detection methods. To be concrete, our KPN will predict a Gaussian center map for each text image, which will be used to extract a series of candidate kernel proposals (i.e., dynamic convolution kernel) from the embedding feature maps according to their corresponding keypoint positions. To enforce the independence between kernel proposals, we propose a novel orthogonal learning loss (OLL) via orthogonal constraints. Specifically, our kernel proposals contain important self-information learned by network and location information by position embedding. Finally, kernel proposals will individually convolve all embedding feature maps for generating individual embedded maps of text instances. In this way, our KPN can effectively separate neighboring text instances and improve the robustness against unclear boundaries. To the best of our knowledge, our work is the first to introduce the dynamic convolution kernel strategy to efficiently and effectively tackle the adhesion problem of neighboring text instances in text detection. Experimental results on challenging datasets verify the impressive performance and efficiency of our method. The code and model are available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/GXYM/KPN</uri> .
Self-supervised Calorie-aware Heterogeneous Graph Networks for Food Recommendation

With the rapid development of online recipe sharing platforms, food recommendation is emerging as an important application. Although recent studies have made great progress on food recommendation, they have two shortcomings that are likely to affect the recommendation performance. (1) The relations between ingredients are not considered, which may lead to sub-optimal representations of recipes and further result in the neglect of the user’s personalized ingredient combination preference. (2) Existing methods do not consider the impact of users’ preferences on calories in users’ food decision-making process. In this article, we propose a Self-supervised Calorie-aware Heterogeneous Graph Network (SCHGN) to model the relations between ingredients and incorporate calories of food simultaneously. Specifically, we first incorporate users, recipes, ingredients, and calories into a heterogeneous graph and explicitly present the complex relations among them with directed edges. Then, we explore the co-occurrence relation of ingredients in different recipes via self-supervised ingredient prediction. To capture users’ dynamic preferences on calories of food, we learn calorie-aware user representations by hierarchical message passing and compute a comprehensive user-guided recipe representation by attention mechanism. The final food recommendation is accomplished based on the similarity between a user’s calorie-aware representation and the user-guided representation of a recipe. Extensive experiment results on benchmark datasets demonstrate the effectiveness of the proposed method.
Adaptive reversible data hiding for JPEG images with multiple two-dimensional histograms

Joint photographic experts group (JPEG) can provide good quality with small file size but also eliminate extensively the redundancies of images. Therefore, hiding data into JPEG images in terms of maintaining high visual quality at small file sizes has been a great challenge for researchers. In this paper, an adaptive reversible data hiding method for JPEG images containing multiple two-dimensional (2D) histograms is proposed. Adaptability is mainly reflected in three aspects. The first one is to preferentially select sharper histograms for data embedding after K histograms are established by constructing the kth (k∈{1,2,…,K}) histogram using the kth non-zero alternating current (AC) coefficient of all the quantized discrete cosine transform blocks. On the other hand, to fully exploit the strong correlation between coefficients of one histogram, the smoothness of each coefficient is estimated by a block smoothness estimator so that a sharply-distributed 2D-histogram is constructed by combining two coefficients with similar smoothness into a pair. The pair corresponding to low complexity is selected priorly for data embedding, leading to high embedding performance while maintaining low file size. Besides, we design multiple embedding strategies to adaptively select the embedding strategy for each 2D histogram. Experimental results demonstrate that the proposed method can achieve higher rate–distortion performance which maintaining lower file storage space, compared with previous studies.
An online algorithm for scheduling big data analysis jobs in cloud environments

Cloud computing has become a popular platform for processing big data analysis jobs with its advantages of high-availability, elasticity and cost-efficiency. Many big data analysis service providers use cloud instances to process users' big data analysis job execution requests and they need efficient scheduling algorithms to improve job execution efficiency and economic benefits. This paper presents a problem of minimizing the execution time of a batch of big data analysis jobs without changing the number of cloud instances. Solving this problem can not only improve big data job execution efficiency in cloud environments and user satisfaction, but also bring higher economic benefits to big data analysis service providers. This paper proposes an online scheduling algorithm, which can make full use of the parallelism of big data analysis jobs to optimize job scheduling decisions on the premise that the job execution time cannot be accurately known. For evaluating the performance of the proposed online scheduling algorithm, a traditional two-phase scheduling algorithm is introduced as a benchmark for comparison in this paper. Theoretical analysis and extensive simulation experiments based on real datasets show that the online scheduling algorithm proposed in this paper can achieve more stable performance compared with the benchmark two-phase scheduling algorithm.
Arabic ontology extraction model from unstructured text

Ontologies enable the sharing and reusing of knowledge, allowing the interoperation and combination of information systems. Automatic ontological relation extraction from text is an important factor for representing documents and their contents in a useful computerized manner. This paper proposes a model for automatic ontology extraction from Arabic text by parsing sentences and extracting part of speech (POS). Then, the proposed rule-based model is applied to extract the triple attributes of a sentence (subject, predicate, and object) from the parsing tree. Finally, the semantic relations can be used to extract new triples inferred from the initially extracted triples. The results were evaluated using two methods: the first method was based on a comparison with manual extraction because there is no standard method for measuring Arabic triple extraction; the second method is based on translating the dataset into English and comparing the output results using the Stanford dependencies extraction web tool. The proposed model achieved an accuracy of 73.6% for Arabic triple extraction and a 35% increase in overall triples owing to new inferred triples. However, when the dataset was translated into English and tested by the Stanford dependencies extraction web tool, the accuracy was 71.8% without inferred triples.
Table detection in business document images by message passing networks

Tabular structures in business documents offer a complementary dimension to the raw textual data. For instance, there is information about the relationships among pieces of information. Nowadays, digital mailroom applications have become a key service for workflow automation. Therefore, the detection and interpretation of tables is crucial. With the recent advances in information extraction, table detection and recognition has gained interest in document image analysis, in particular, with the absence of rule lines and unknown information about rows and columns. However, business documents usually contain sensitive contents limiting the amount of public benchmarking datasets. In this paper, we propose a graph-based approach for detecting tables in document images which do not require the raw content of the document. Hence, the sensitive content can be previously removed and, instead of using the raw image or textual content, we propose a purely structural approach to keep sensitive data anonymous. Our framework uses graph neural networks (GNNs) to describe the local repetitive structures that constitute a table. In particular, our main application domain are business documents. We have carefully validated our approach in two invoice datasets and a modern document benchmark. Our experiments demonstrate that tables can be detected by purely structural approaches.
Memory forensic analysis of a programmable logic controller in industrial control systems

In industrial control systems (ICS), programmable logic controllers (PLCs) are used to automate physical processes such as nuclear plants and power grid stations, and are often subject to cyber attacks. As in conventional IT domain, the memory analysis of the PLCs can help answer important forensic questions about the attack, such as the presence of malicious firmware, injection of modified control logic (the program running on the PLC), and manipulation of I/O devices (e.g., sensors and actuators). Unlike conventional IT domain, PLCs have heterogeneous hardware architecture, proprietary firmware and control software, making it challenging to employ a unified framework for their memory forensics. For merely extracting artifacts of forensic importance, reverse-engineering the firmware is a tedious task, and the effort needs to be repeated for every PLC model. As a community, a step-wise approach to tackle this challenge is to analyze the memory of specific PLCs, and subsequently find a generic framework applicable to all PLCs. Our work is a step forward in this direction. By following a methodology that focuses on the functional layer of PLCs instead of reverse engineering the firmware, we analyze the digital forensic artifacts available in a common PLC, Allen-Bradley ControlLogix 1756-L61. Before diving into the memory dump, we analyze the PLC control software to create a list of important artifacts that are sure to exist in the PLC memory dump. The approach employs a setup where PLC control software RSLogix-5000 is connected to the PLC, and the memory dump can be obtained as and when needed. We create test cases that sequentially highlight each category of artifacts, followed by an examination of the resultant impact on memory. After attaining the listed artifacts, we employ conventional string and known data searches to extract interesting information present in this PLC's memory. The memory analysis profile, presented as a Python library and shared with the community, can help a forensic investigator to readily extract forensic artifacts from the same model's controller. The adopted approach may help researchers in creating memory profile of other PLCs, and ultimately formulating a generic PLC memory analysis framework.
NBR-Net: A Nonrigid Bidirectional Registration Network for Multitemporal Remote Sensing Images

Remote sensing image registration is the basis of change detection, environmental monitoring, and image fusion. Under severe appearance differences, feature-based methods have difficulty in finding sufficient feature matches to solve the global transformation and tackling the local deformation caused by height undulations and building shadows. By contrast, nonrigid registration methods are more flexible than feature-based matching methods, while often ignoring the reversibility between images, resulting in misalignment and inconsistency. To this end, this article proposes a nonrigid bidirectional registration network (NBR-Net) to estimate the flow-based dense correspondence for remote sensing images. We first propose an external cyclic registration network to strengthen the registration reversibility and geometric consistency by registering Image A to Image B and then reversely registering back to Image A. Second, we design an internal iterative refinement strategy to optimize the rough predicted flow caused by large distortion and viewpoint difference. Extensive experiments demonstrate that our method shows a performance superior to the state-of-the-art models on the multitemporal satellite image dataset. Furthermore, we attempt to extend our method to heterogeneous remote sensing image registration, which is more common in the real world. Therefore, we test our pretrained model in a satellite and unmanned aerial vehicle (UAV) image registration task. Due to the cyclic registration mechanism and coarse-to-fine refinement strategy, the proposed approach obtains the best performance on two GPS-denied UAV image datasets. Our code will be released at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/xuyingxiao/</uri> NBR-Net.
Hijacking of unmanned surface vehicles: A demonstration of attacks and countermeasures in the field

Abstract Driven by advances in information and communication technologies, an increasing number of industries embrace unmanned and autonomous vehicles for services, such as public transportation, shipping, mapping, and remote surveillance. Unfortunately, these vehicles are vulnerable to passive and active cyber‐physical attacks that can be used for industrial espionage and hijacking attempts. Since attackers can use hijacked vehicles as weapons in terrorist attacks, ensuring the secure operation of such vehicles is critical to prevent the attacks from causing dire financial consequences, or worse, the loss of human lives. This study is motivated by the observation that most cybersecurity studies provide superficial, high‐level descriptions of vulnerabilities and attacks, and the true impact of the described attacks remains unclear. To address this problem, we demonstrate advanced manipulation attacks against an underactuated Unmanned Surface Vehicle (USV) which results in successful hijackings. Using state‐of‐the‐art cryptography, we also show how the signal transmission can be secured to avoid hijacking attempts actively steering the vehicle off course. Through field experiments, we demonstrate how the attacks affect the closed‐loop guidance, navigation, and control system and how the proposed countermeasures prevent these attacks from being successful. Our study is unique in that we provide a complete description of the attacked USV and give a detailed analysis of how spoofed navigation estimates affect the closed‐loop behavior of the underactuated USV.
Machine learning analysis: general features, requirements and cardiovascular applications

Artificial intelligence represents the science which will probably change the future of medicine by solving actually challenging issues. In this special article, the general features of machine learning are discussed. First, a background explanation regarding the division of artificial intelligence, machine learning and deep learning is given and a focus on the structure of machine learning subgroups is shown. The traditional process of a machine learning analysis is described, starting from the collection of data, across features engineering, modelling and till the validation and deployment phase. Due to the several applications of machine learning performed in literature in the last decades and the lack of some guidelines, the need of a standardization for reporting machine learning analysis results emerged. Some possible standards for reporting machine learning results are identified and discussed deeply; these are related to study population (number of subjects), repeatability of the analysis, validation, results, comparison with current practice. The way to the use of machine learning in clinical practice is open and the hope is that, with emerging technology and advanced digital and computational tools, available from hospitalization and subsequently after discharge, it will also be possible, with the help of increasingly powerful hardware, to build assistance strategies useful in clinical practice.