Reviewerly/exHarmony huggingface dataset:
Double attention based recommendation for heterogeneous information

Abstract Heterogeneous graph networks show superior performance as a network that can combine information from multiple nodes. The heterogeneous graph network is introduced in the recommendation algorithm, and the HARec algorithm is proposed. HARec is the first heterogeneous network that integrates the idea of the bilayer‐attention network with the self‐attention mechanism. The algorithm embeds item neighborhoods with abundant semantic information as auxiliary information through the bilayer‐attention network, while modeling the user‐item interactive representation by fusing the feature representations of users and items through self‐attention. The proposed algorithm, HARec, can obtain better accuracy and capture more abundant information, which greatly improves the recommendation performance. The results of experiment not only demonstrate the performance of our proposed model, but also show its potentially good interpretability.
Multi-model Neural Style Transfer (MMNST) for Audio and Image

Neural style transfer (NST) was created to give a new look for images, audios and videos through optimization and manipulation techniques. Nowadays, this specific field has picked up pace amongst various techniques that deal with neural networks and it has emerged as one of the most efficient means of producing style transfer. In order to address the shortcomings in the existing system, multi-model neural style transfer (MMNST) approach for image and audio is proposed. It focuses on two kinds of data: audio and image. The main objective of this proposed system is to create artistic imagery by separating and recombining image content and style. For the audio style transfer, we have two inputs which are broken down, optimized and enhanced and finally combined together in a fulfilling manner. Specifically, local and global features can be transferred using both parametric and non-parametric neural style transfer algorithms, which result in an outcome that has equal portions of both—content and style input as they coalesce perfectly. For experimentation, VGG-19 (CNN) and TensorFlow Lite models are used. The proposed model outperforms the existing models in terms of accuracy, execution speed and the total loss incurred during the process.
Minimum Rule-Repair Algorithm for Supervised Learning Classifier Systems on Real-Valued Classification Tasks

This paper proposes a simple rule-repair algorithm for the UCS classifier system with real-valued inputs on classification problems. Our concept is to repair inaccurate rules with a possible minimum reduction of the rule-generality in order to avoid the problematic cover-delete cycle. We identify the following two principles to achieve this purpose; 1) to repair the rule-condition to omit one incorrect input from a matching space represented by its rule-condition; 2) to repair either a lower value or an upper value for one dimension $${\boldsymbol{x_i}}$$ . Experiments confirmed the adequacy of those principles. Consequently, UCS with our rule-repair algorithm successfully boosts the performance while preventing the increase of the population size.
Grammar-Based Evolutionary Approach for Automatic Workflow Composition with Open Preprocessing Sequence

Knowledge discovery is a complex process involving several phases. Some of them are repetitive and time-consuming, so they are susceptible of being automated. As an example, the large number of machine learning algorithms, together with their hyper-parameters, constitutes a vast search space to explore. In this vein, the term AutoML was coined to encompass those approaches automating such phases. The automatic workflow composition is an AutoML task that involves both the selection and the hyper-parameter optimisation of the algorithms addressing different phases, thus giving a more comprehensive assistance during the knowledge discovery process. Unlike other proposals that predetermine the structure of the preprocessing sequence, and in some cases the size of the workflow, our proposal generates workflows made up of an arbitrary number of preprocessing algorithms of any type and a classifier. This allows returning more accurate results since its avoids the oversimplification of the solution space. The optimisation is conducted by a grammar-guided genetic programming algorithm. The proposal has been validated and compared against TPOT and RECIPE generating workflows with greater predictive performance.
An Electronic Medical Records’ Keywords Detection Using Supervised Learning for Supporting Medical Coding

Electronic medical records (EMRs) are an important record, which is recorded to describe a medical status of patient in the digital or computerized form. Most of the EMRs content contain a textual data or medical notes. One of the important usages of EMRs is to support the assignment of medical codes by medical coders. The medical code is important to make EMRs usable in term of hospital administration and research purpose. One step of medical code assignment is done by detecting keywords inside medical notes. The detection of keywords is conducted by reading through the medical notes by medical coder, which is time consuming and lack of interpretation. In order to support a keywords detection of a medical notes, we propose supervised learning algorithms to automatically detect keywords and present the keywords to the medical coder. We use Long short-term memory (LSTM) based supervised learning algorithm to detect keywords. LSTM is able to achieve good performance in term of accuracy in detecting keywords inside medical notes. LSTM achieve 80% accuracy. Based on the result, LSTM-based supervised learning is promising to be used for an automatic keywords detection of medical notes, which is one of the important steps for the medical coding.
SmartPoint Seismic Data Acquisition System

Abstract Seismic acquisition system is the most essential equipment in seismic exploration for oil and gas. Quality, efficiency, and accuracy of seismic data acquisition play the most important role in oil and gas exploration. True wireless seismic exploration is of great significance in "cost decreasing and benefit increasing" in oil and gas industrial. Seismometer and its supporting software are core equipment in seismic acquisition. With build-in features of eMBB, uRLLC and mMTC, 5G provides a good opportunity in developing wireless real-time seismic acquisition system. This paper is mainly about R&amp;D progress of SmartPoint Seismic Data Acquisition System (SmartPoint system for short). Based on 5G, SmartPoint system consists of seismographs, software system, 5G networking system and auxiliary system. SmartPoint system takes good use of industry leading technology such as 5G, AI, Big Data and Cloud Computing to solve the difficulties of high economic and labor cost in seismic acquisition. Field test of SmartPoint system has shown some very promising results. From the current test result, we are confident to deduce that with the help of advanced communication and information technology, SmartPoint system will make seismic data acquisition much more efficient, economic, and less costly.
Generalized Net Model of Computer Based Registration and Rehabilitation of Cognitive Impairments in Multiple Sclerosis

Cognitive decline occurs along with physical disability in approximately two-thirds of persons suffering from multiple sclerosis (MS) and has devastating influence on quality of life at all stages and in all subtypes of the disease. The modern immunomodulatory therapy has a satisfactory effect on the relapse rate and disease progression, but does not improve the degree of cognitive impairment. Over the last decade, the number of studies on treatment and management of cognitive impairment in people with MS are rapidly growing. A generalized net model for registration, assessment and rehabilitation of cognitive impairment in persons with MS has been developed. The assessment of cognitive status was made on the basis of results obtained through computer-based neuropsychological tests. Based on comparison of the results from MS patients with those of healthy controls, the degree of cognitive deficit was determined. The input of the generalized net was formed by the data from patient’s medical records, which were categorized according to disease duration, grade of disability, age, sex and employment. As a result of applying the InterCriteria Analysis, the output of the model provides information about the type of impaired cognitive domains and the degree of cognitive deterioration. This allows the therapist to build an appropriate rehabilitation strategy for each individual patient. The rehabilitation module contains a set of different rehabilitation games with several levels of difficulty. The frequency, as well as the degree of complexity of cognitive training is based on the patient’s results and the general cognitive reserve registered at baseline. The system is implemented in a cloud infrastructure and is planned to be provided as an IT service for neurological departments and clinics. In this way, the system is available for testing and rehabilitation of unlimited number of people (both healthy controls and persons with MS) at any time. The developed GN model provides a mechanism for description and simulation of these parallel processes. In this way, the proposed model will be useful in determining the individual approach to each patient and will help optimizing the results of cognitive rehabilitation. The analysis of the model will allow outlining the future directions for therapeutic approaches on cognitive impairments in MS, which will lead to inclusion of cognitive rehabilitation and training programs in routine clinical practice within the next decade.
Verifiable Dynamic Searchable Symmetric Encryption with Forward Privacy in Cloud-Assisted E-Healthcare Systems

The integration of Internet of Things (IoT) and cloud computing is transforming traditional healthcare systems into cloud-assisted e-healthcare systems. In a cloud-assisted e-healthcare system, patients can upload their personal health information (PHI) files to the cloud, from where different healthcare service providers can obtain appropriate information to determine the patients’ health status. However, this paradigm shift has raised many security and privacy concerns: data sharing, data tampering and information leakage. To address the above challenges, in this paper, we propose a verifiable dynamic searchable symmetric encryption (DSSE) scheme with forward privacy for e-healthcare systems, which enables different doctors to access and search PHI files in a secure and efficient manner. Forward privacy is achieved by maintaining state chains on the cloud server, while the verification of searched healthcare data comes from homomorphic MAC (HomMAC) technique. Detailed security analysis and simulations on real-world and simulated datasets demonstrate the practical efficiency of the proposed scheme in real-world e-healthcare applications.
Fido: A String-Based Fuzzy Logic Mechanism for Content Extraction from UAV Data Lakes

In this article, we propose Fido, a fuzzy-based string comparison method for extracting content from data lakes in a camera-based Internet of Drones (IoD) environment. Data lakes support data dumping in their native format, which makes them suitable for real-time deployments such as IoD. However, parsing through these unstructured databases has its concerns, particularly data extraction. Existing works on image-based content extraction focus on complex geometric and matrix mathematical operations, which are expensive in terms of both computation and time. A straightforward and fast solution is necessary for overcoming such challenges. Toward this, we limit our operations to string comparisons between the user&#x0027;s request and tags in the data lake. In particular, we adopt a fuzzy-based approach as it is capable of efficiently handling spelling variations, out-of-order words, and partial matchings, compared to conventional string comparison methods. Through lab-scale experiments, we demonstrate the efficacy of Fido with an almost 80 percent similarity ratio between the request and response images and processing time of 80 &#x00B5;s. Further, we observe minuscule deviations between the coordinates of the request and response images (0.005 units).
A Survey of Video Captioning Methods

For a human mind, each picture is perceived in a certain way, a different person may see things differently in the same scene. One might say “A dog in grass” while some other might say “A white dog in a grassy area”. All of these captions are relevant and there may be hundreds others too. this seems like a trivial task for human beings, a human being can have a glance and make captions like these with correct use of language with ease. But making a computer do this is a different thing to ask, cognitive ability is required to make captions out of a subjective scene. Without the recent developments in the field of deep neural networks, this would have been an inconceivable task to do, but with these new deep learning neural networks at our disposal, this problem can be solved. Content-based image retrieval, also known as query by image content (QBIC) and content-based visual information retrieval (CBVIR), are different image retrieval methods used to solve the problem of searching for images based on the content of the image. The purpose of this paper is to survey different papers and make a comparison among different methods, algorithms that are useful in video captioning.
A Consortium Blockchain for Smart Contracts in Oil and Gas Projects

Abstract The task of managing petroleum projects is often cumbersome and complex, as these projects involve a vast number of activities that are often conducted in remote and potentially dispersed locations. In addition, petroleum projects must reconcile the many transactions that occur between multiple stakeholders, including the developer and their contractors. Due to this nature of business, projects are subject to fraudulent activity, cyber-attacks and lost time. To alleviate these issues, this paper presents a Proof-of-Work (PoW) consortium blockchain that executes and tracks oil and gas project tasks, while providing secured and transparent documentation and time stamping for all the project activities. Historically, many companies have worked on technologies to improve scheduling and project management by implementing intelligent and digitized information system solutions. Yet, these solutions require a centralized entity to monitor the activities - leading to errors and fraud. The approach adopted here overcomes these limitations by suggesting a smart contract decentralized blockchain that can solve many of the problems preventing management efficiency. The process utilizes a web-based framework that scans record tamper-evident transactions based on peer-to-peer network involving the developer and the contractor machines. The approach also creates a web interface to allow stakeholders to interact with the blockchain and store schedule updates and completion certificates. As there is no paperwork to process, and no time wasted on reconciling errors associated with filing these documents, the proposed application demonstrates a promising tool to improve speed, efficiency, and accuracy. It can enforce the contract conditions and manage the tracking of complex oil and gas activities. Employees within the developer and contractor companies can record a transaction in the network and ensure that the data within the block is not tampered with and contain a secured timestamp. The record is stored in the multiple machines available in the network and serves as a digital signature for each activity completed in the project. However, because the network is open to all stakeholders, the process requires regulation to prevent adverse events, such as members posting without restrictions. To overcome this limitation, each member is required to have a public and private key to post in the application. The paper also discusses other potential limitations to this approach to project management, and strategies that can be employed to make this use of blockchain technology viable in commercial settings. Because the data stored in the proposed blockchain is immutable, secured, and transparent, the blockchain application has the potential to transform the traditional process by tracking and digitally time-stamping all the project deliverables required by the stakeholders involved. Nevertheless, the paper argues that there are several technical, security and legal limitations needed to be addressed before having the application as mainstream.
Research on State Modeling of Multiple Parsers Based on Attention Mechanism

Transition-based dependency parsing is a fast and effective approach for dependency parsing. When the sentence is input to the transition-based dependency parser, the parser predicts a series of parsing actions from left to right. Stack information, which plays an essential role in the parsing process. In this paper, we propose a dependency parser that is based on bidirectional-LSTMs and an approach that uses the attention mechanism for learning representations of parser states. This model simulates the global state of the parser by capturing more relevant information, we dig into the stack information, buffer information, the word information that has been parsed out of the stack, the complete historical information of the actions taken by the parser, and semantic information during the parsing process. The words popped from the stack at each time step and the predicted action are then applied to the parsing task at the next time step, which is helpful to the parsing performance of the parser.
Face Recognition and Mask Detection Technology for Surveillance Systems

Corona pandemic has affected the daily routine of life disturbing the trade and economic globally. Wearing a mask has become compulsory and a new tradition. within the close to future, several suppliers can raise the shoppers to wear masks properly. Therefore, detection of face mask has become one of the important tasks to assist the international society. This paper provides a easy and simplified approach to detect the face masks using some of the important Machine Learning packages like TensorFlow, Keras, OpenCV and Scikit-Learn. The projected methodology detects the face from the image properly and so identifies if it&#x0027;s a mask thereon or not. As a police work task performing artist, it may detect a face together with a mask in motion. the tactic gives an accurate output with an accuracy of 96.77<sup>&#x0025;</sup> on dataset. The model tendency to find the optimized values of parameters are employed using Convolutional Neural Network (CNN) model to identify whether the masks are worn properly or not while not inflicting over-fitting.
J. Goseling and M.N.M. van Lieshout's Contribution to the Discussion of ‘Gaussian Differential Privacy’ by Dong<i>et al.</i>

We congratulate Professors Dong, Roth and Su on their compelling work on Gaussian Differential Privacy. In official statistics, the p%-rule (Hundepoel et al., 2012) is widely used to protect tabular data. In recent work (Hut et al., 2020) we adapted this concept to thematic maps, for example, of energy consumption per company. Usually such maps are drawn directly from an underlying table that is protected from disclosure. The resulting colour-coded map, however, is, by construction, discretised in regions defined by the cells in the table. These geographic regions are usually large, corresponding, for instance, to municipalities. The resulting protection is very conservative, leading to a map with reduced utility. Therefore, there is a need for smooth thematic maps. One might use the Nadaraya–Watson kernel weighted average. This procedure, however, is not necessarily safe. Indeed, suppose that an attacker is able to read off the plotted, smoothed, values of the variables of interest at all measurement locations. Then their original values satisfy a linear system which in many cases (including that of a Gaussian kernel) can be solved exactly if the measurement locations are distinct.
Does Unit-Tested Code Crash? A Case Study of Eclipse

Context: Software development projects increasingly adopt unit testing as a way to identify and correct program faults early in the construction process. Code that is unit tested should therefore have fewer failures associated with it. Objective: Compare the number of field failures arising in unit tested code against those arising in code that has not been unit tested. Method: We retrieved 2 083 979 crash incident reports associated with the Eclipse integrated development environment project, and processed them to obtain a set of 126 026 unique program failure stack traces associated with a specific popular release. We then run the JaCoCo code test coverage analysis on the same release, obtaining results on the line, instruction, and branch-level coverage of 216 392 methods. We also extracted from the source code the classes that are linked to a corresponding test class so as to limit test code coverage results to 1 263 classes with actual tests. Finally, we correlated unit tests with failures at the level of 9 523 failing tested methods. Results: Unit-tested code does not appear to be associated with fewer failures. Conclusion: Unit testing on its own may not be a sufficient method for preventing program failures.
A new label ordering method in Classifier Chains based on imprecise probabilities

In Multi-Label Classification (MLC), Classifier Chains (CC) are considered simple and effective methods to exploit correlations between labels. A CC considers a binary classifier per label, in which the previous labels, according to an established order, are used as additional features. The label order strongly influences the performance of the CC, and there is no way to determine the optimal order so far. In this work, a new label ordering method based on label correlations is proposed. It uses a non-parametric model based on imprecise probabilities to estimate the correlations between pairs of labels. Then, it employs a greedy procedure that, to insert the labels in the chain, considers the correlations among the candidate labels and the ones already inserted, as well as the correlations between the candidate labels and the ones non-inserted yet. We argue that our proposal presents some advantages over the label ordering methods in CC developed so far based on label correlations. It is also shown that our proposal achieves better experimental results than the label ordering methods proposed so far that use label correlations in CC.
Exposing Data Value On a Risc-V Based SoC

Performed usually with the aim of protecting cryptographic implementations, side-channel leakage assessment is progressively carried out on the micro-architecture elements of various circuits. Hamming Distance (HD) and Hamming Weight (HW) are widely used as leakage models for such analysis. In this paper, we present a study on how to retrieve the value of any data that is crossing the interconnect bus or stored in registers by using the power consumption of the circuit. We actually consider our recorded leakage to be the direct leakage of the value of the data as opposed to its Hamming weight or distance. We show that by using a divide and conquer approach including a template attack and an exhaustive search, a 32-bit data can be retrieved with a probability of 0.96 for 200 attack traces and a remaining research cost of 2<sup>13.2</sup>. To achieve this goal, we first confirm and harness HW and HD leakage models on interconnect bus and registers of a RISC-V based ASIC SoC (28 nm bulk) through a template attack, and then we introduce a data value recovery attack on the interconnect bus.