Reviewerly/exHarmony huggingface dataset:
Document-Level Biomedical Relation Extraction Leveraging Pretrained Self-Attention Structure and Entity Replacement: Algorithm and Pretreatment Method Validation Study

Background The most current methods applied for intrasentence relation extraction in the biomedical literature are inadequate for document-level relation extraction, in which the relationship may cross sentence boundaries. Hence, some approaches have been proposed to extract relations by splitting the document-level datasets through heuristic rules and learning methods. However, these approaches may introduce additional noise and do not really solve the problem of intersentence relation extraction. It is challenging to avoid noise and extract cross-sentence relations. Objective This study aimed to avoid errors by dividing the document-level dataset, verify that a self-attention structure can extract biomedical relations in a document with long-distance dependencies and complex semantics, and discuss the relative benefits of different entity pretreatment methods for biomedical relation extraction. Methods This paper proposes a new data preprocessing method and attempts to apply a pretrained self-attention structure for document biomedical relation extraction with an entity replacement method to capture very long-distance dependencies and complex semantics. Results Compared with state-of-the-art approaches, our method greatly improved the precision. The results show that our approach increases the F1 value, compared with state-of-the-art methods. Through experiments of biomedical entity pretreatments, we found that a model using an entity replacement method can improve performance. Conclusions When considering all target entity pairs as a whole in the document-level dataset, a pretrained self-attention structure is suitable to capture very long-distance dependencies and learn the textual context and complicated semantics. A replacement method for biomedical entities is conducive to biomedical relation extraction, especially to document-level relation extraction.
An Algorithm for Computing Minimum-Length Irreducible Testors

In pattern recognition, the elimination of unnecessary and/or redundant attributes is known as feature selection. Irreducible testors have been used to perform this task. An objective of the Minimum Description Length Principle (MDL) applied to feature selection in pattern recognition and data mining is to select the minimum number of attributes in a data set. Consequently, the MDL principle leads us to consider the subset of irreducible testors of minimum length. Some algorithms that find the whole set of irreducible testors have been reported in the literature. However, none of these algorithms was designed to generate only minimum-length irreducible testors. In this paper, we propose the first algorithm specifically designed to calculate all minimum-length irreducible testors from a training sample. The paper presents some experimental results obtained using synthetic and real data in which the performance of the proposed algorithm is contrasted with other state-of-the-art algorithms that were adapted to generate only irreducible testers of minimum length.
Dual Coordinate-Descent Methods for Linear One-Class SVM and SVDD

One-class support vector machines (SVM) and support vector data description (SVDD) are two effective outlier detection techniques. They have been successfully applied to many applications under the kernel settings, but for some high dimensional data, linear rather than kernel one-class SVM and SVDD may be more suitable. Past developments on kernel and linear classification have indicated that specially designed optimization algorithms can make the training for linear scenarios much faster. However, we point out that because of some differences from standard linear SVM, existing algorithms may not be efficient for one-class scenarios. We then develop some novel coordinate descent methods for linear one-class SVM and SVDD. Experiments demonstrate their superiority on the convergence speed.
Clustering via Adaptive and Locality-constrained Graph Learning and Unsupervised ELM

In this paper an effective graph learning method is proposed for clustering based on adaptive graph regularizations. Many graph learning methods focus on optimizing a global constraint on sparsity, low-rankness or weighted pair-wise distances, but they often fail to consider local connectivities. We demonstrate the importance of locality by generalizing the Locality-constrained Linear Coding (LLC) for unsupervised learning. Each data sample is expressed as a representation of its nearest neighbors, which naturally leads to a combination of distance regularized features and a Locally Linear Embedding (LLE) decomposition. The representation enforces a locally sparse connection on the data graph that exhibits high discrimination power and is easy to optimize. To improve the learned graph structure and incorporate cluster information, a rank constraint is further imposed on the Laplacian matrix of the data graph so that the connected components match the class number. The obtained representations are smoothed via manifold regularizations on a predefined graph which serves as a prior for graph learning. Finally, we utilize unsupervised Extreme Learning Machine (US-ELM) to learn a flexible and discriminative data embedding. Extensive evaluations show that the proposed algorithm outperforms graph learning counterpart methods on a wide range of benchmark datasets.
A GitHub-Based Data Collection Method for Software Defect Prediction

With the increasing scale and complexity of software systems, the defects of software are increasing every day. Software defect data is the foundation of research and application of software reliability. Currently, the lack of software defect data, its insufficient coverage, and the limits of the software types involved have become the bottleneck of software reliability research and application. Starting from GitHub, the open-source software hosting platform, this paper analyzes software defect data in open source projects and classifies the available software data. Based on the research of the GitHub and Git repository, we propose a defect data acquisition technology based on open-source software that uses pull requests as the breakthrough point of the method. Moreover, we advanced a software defect data preliminary treatment and built a software defect big datasets collecting system that contains fix-inducing change and contextual information of defects, which solves the class imbalance problem. According to this method, a software defect big data automatic acquisition platform based on GitHub was developed to realize the automatic collection of software defect data. Finally, the efficiency of data collection, correctness of data, and validity of the dataset application were verified by experiments. The results show that the proposed method is efficient and effective.
Robust and Computationally-Efficient Anomaly Detection Using Powers-Of-Two Networks

Robust and computationally efficient anomaly detection in videos is a problem in video surveillance systems. We propose a technique to increase robustness and reduce computational complexity in a Convolutional Neural Network (CNN) based anomaly detector that utilizes the optical flow information of video data. We reduce the complexity of the network by denoising the intermediate layer outputs of the CNN and by using powers-of-two weights, which replaces the computationally expensive multiplication operations with bit-shift operations. Denoising operation during inference forces small valued intermediate layer outputs to zero. The number of zeros in the network significantly increases as a result of denoising, we can implement the CNN about 10% faster than a comparable network while detecting all the anomalies in the testing set. It turns out that denoising operation also provides robustness because the contribution of small intermediate values to the final result is negligible. During training we also generate motion vector images by a Generative Adversarial Network (GAN) to improve the robustness of the overall system. We experimentally observe that the resulting system is robust to background motion.
Approach to model human appearance based on sparse representation for human tracking in surveillance

In human tracking, sparse representation successfully localises the human in a video with minimal reconstruction error using target templates. However, the state-of-the-art approaches use colour and local appearance of a human to discriminate the human from the background regions, and hence fail when the human is occluded and appears in the varying illumination environment. In this study, a robust tracking algorithm is proposed that utilises gradient orientation and fine and coarse sparse representation of the target template. Sparse representation-based human appearance model utilises weighted gradient orientation that is insensitive to illumination variation. Coarse and fine representation of sparse code facilitates tracking under varying scales. Subspace learning from image gradient orientation is enforced with occlusion detection during the dictionary updation stage to capture the visual characteristics of the local human appearance that supports tracking under partial occlusion with lesser tracking error. The proposed human tracking algorithm is evaluated on various datasets and shows efficient human tracking performance when compared to the other state-of-the-art approaches. Furthermore, the proposed human tracking algorithm is suitable for surveillance applications.
A novel haze image steganography method via cover-source switching

In realistic outdoor scenarios, image sensors tend to suffer from various weather conditions (e.g., haze, rain, etc.),which make the images of the same scene taken at different times may be different. Therefore, one should be able to securely embed secret messages into these images by making use of the variations of the weather effects. Inspired by some recent natural steganography algorithms, this paper presents a novel haze image steganography method, which embeds messages through adjusting the weather effects of an input haze image, making it resemble the same image captured under another weather condition. The proposed steganography method consists of three parts: (1) model parameter estimation of the input haze image, (2) haze effects adjustment according to the atmospheric scattering model, (3) message embedding using the floating-point adjusted haze image. 10,000 haze images captured under different haze conditions in various scenarios were used to test the proposed steganography algorithm. The experimental results show that the proposed steganography algorithm is more secure than S-UNIWARD and HILL for steganalyzers who only have raw haze images.
Efficient Chaotic Imperialist Competitive Algorithm with Dropout Strategy for Global Optimization

The imperialist competitive algorithm combined with chaos theory (CICA) demonstrates excellent performance in global optimization problems. However, its computational complexity increases with the introduction of chaotic maps. To address this, we integrate CICA with a dropout strategy that randomly samples the dimensions of each solution at each iteration of the computation. We investigate the potential of the proposed algorithm with different chaotic maps through six symmetric and six asymmetric benchmark functions. We also apply the proposed algorithm to AUVs’ path planning application showing its performance and effectiveness in solving real problems. The simulation results show that the proposed algorithm not only has low computational complexity but also enhances local search capability near the globally optimal solution with an insignificant loss in the success rate.
Impact of Design Patterns on Software Complexity and Size

Many different factors influence the quality of software. Among the most important of these factors is software complexity. One way to improve software quality, therefore, is to minimize its complexity making it more understandable and maintainable. The design phase of the software development life cycle plays an instrumental role in fostering quality in software. Seasoned designers often use past design best practices codified in the form of design patterns to make their designs and the resultant code more elegant, robust, and resilient to change. Little work, however, has been done to empirically assess the quantitative impact of design patterns on software complexity. This research is an attempt to fill this gap. A comparative analysis of before and after versions of program pairs written without and with design patterns was performed for all twenty three GoF (Gang of Four) design patterns. These program pairs were collected (or, in some cases, developed) and compared with respect to their complexity and size. The results of this comparative analysis reveal that the cyclomatic complexity of the programs written using design patterns was less for most of the design patterns as compared to the programs written without using design patterns. However, the values of CK metrics, number of classes, and software size SLOC (Source Lines of Code) increased when design patterns were used.
Associated Attribute-Aware Differentially Private Data Publishing via Microaggregation

Releasing raw data sets with sensitive personal information will leak privacy. Therefore, various differential privacy methods have been proposed for efficient data sharing while preserving privacy. However, they focus on noise processing of all quasi-identifier attributes, which results in high time-space complexity and low data utility. In this paper, we propose a Differential Privacy Protection model considering the Correlations between Attributes, denoted DPPCA. DPPCA first computes the degree of correlations between the quasi-identifier attributes and the sensitive attributes, and determines the pair of attributes with maximal degree of correlation. Then based the attributes with the maximal degree of correlations, it uses microaggregation to partition the data set into clusters of size k (k ≥ 2) according to three types of attributes, i.e., numerical, non-numerical, and hybrid attributes, such that there are l (l <; k) values of sensitive attributes in a cluster. Finally, noise is added to each cluster separately such that each cluster satisfies ε-differential privacy. While keeping the same degree of preserving privacy, our experimental results demonstrate that DPPCA substantially reduces the amount of added noise to 11% for the Census data set and the Adult data set. Therefore, DPPCA greatly improve the data utility while reaching the same degree of differential privacy.
Association rule mining for the ordered placement of traditional Chinese medicine containers

In traditional Chinese medicine (TCM) clinics, the pharmacists responsible for dispensing the herbal medicine usually find the desired ingredients based on positions of the shelves (racks; frames; stands). Generally, these containers are arranged in an alphabetical order depending on the herbal medicine they contain. However, certain related ingredients tend to be used together in many prescriptions, even though the containers may be stored far away from each other. This can cause problems, especially when there are many patients and/or the limited number of pharmacists. If the dispensing time takes longer, it is likely to impact the satisfaction of the patients' experience. Moreover, the stamina of the pharmacists will be consumed quickly.In this study, we investigate on an association rule mining technology to improve efficiency in TCM dispensing based on the frequent pattern growth algorithm and try to identify which 2 or 3 herbal medicines will match together frequently in prescriptions. Furthermore, 3 experimental studies are conducted based on a dataset collected from a traditional Chinese medicine hospital. The dataset includes information for an entire year (2014), including 4 seasons and doctors. Afterward, a questionnaire on the usefulness of the extracted rules was administered to the pharmacists in the case hospital. The responses showed the mining results to be very valuable as a reference for the placement and ordering of the frames in the TCM pharmacies and drug stores.
DESIGN AND IMPLEMENTATION OF VIRTUAL UNIVERSITY BASED ON ICT

Virtual University is a learning system by providing an online environment based on information and communication technology. This system can overcome the problems that arise due to covid-19 not to gather together to do the learning process, but must learn from their respective places.&#x0D; This Virtual University system uses Moodle version 3.7 with PHP and MySql programming language platforms as a database server and Apache as a web server can be an innovative learning method that is an ideal solution and in accordance with existing conditions with the hope that it can improve the quality of education if used optimally and comprehensive. This system can perform the basic functions of an e-learning system in which the lecturer can upload lecture material and students can download the material online and can conduct online meetings that can replace face to face in the room.&#x0D; The general objective of this research is to apply a web-based online learning system. The system applied is software that supports the ongoing teaching and learning process for online learning through the internet. The software is installed on the server and can be accessed by participants of online learning programs from browser applications on their PC / cell, and the formation of a university that is not limited by time and space.&#x0D; The stages of this research are: determining user needs, creating a system model, design, system implementation, and testing.
The impact of different navigation speeds on cybersickness and stress level in VR

The aim of this research was to determine if the speed of movement in virtual environments has an impact on cybersickness as potentially experienced by an end user of the system. Cybersickness is a common side effect in virtual reality (VR) systems, shown to have a negative influence on user experience. It can be described as a mismatch between vestibular and oculomotor sensors, where a person has a feeling of movement even though there is none. In this research, we study the impact of different navigation speeds in VR on cybersickness, relying on both subjective ratings indicating cybersickness symptoms, and objective measures of stress level. We wanted to find out if there is a correlation between objective and subjective metrics used. For test purposes, we used the HTC Vive VR headset and scenery from the Talos Principle VR game (where subjects can easily shift between three different movement speeds). Subjective ratings were collected using a questionnaire involving 11 questions used to evaluate cybersickness symptoms. Objective metrics were collected using the Pip Biosensor. A total of 28 participants took part in the study, while 2 participants withdrew due to physical discomfort. Results obtained from the Pip device show no statistically significant difference between navigation speeds for relaxed, stressed, and steady states. Some statistically significant correlations were found between gender and stomach ache, need to vomit, and physical discomfort while wearing HMD. Furthermore, correlation was found between age and variables of nausea in transport vehicles and vertigo. Other correlations found are described in the results section of the paper.
Ethics, AI, Mass Data and Pandemic Challenges: Responsible Data Use and Infrastructure Application for Surveillance and Pre-emptive Tracing Post-crisis

As the COVID-19 health pandemic rages governments and private companies across the globe are utilising AI-assisted surveillance, reporting, mapping and tracing technologies with the intention of slowing the spread of the virus. These technologies have the capacity to amass personal data and share for community control and citizen safety motivations that empower state agencies and inveigle citizen co-operation which could only be imagined outside such times of real and present danger. While not cavilling with the short-term necessity for these technologies and the data they control, process and share in the health regulation mission, this paper argues that this infrastructure application for surveillance has serious ethical and regulatory implications in the medium and long term in relation to individual dignity, civil liberties, transparency, data aggregation, explainability and other governance challenges. To conduct this analysis, the paper presents the Singapore and China case studies, and offers a comparative description based on the many more initiatives implemented worldwide in order to understand the purpose, goal and risk of these infrastructures. The analysis looks at data protection and citizen integrity and reflects on other surveillance methods outside the health context, such as initiatives implemented in the financial sector, where similar challenges have arisen.
Rain Prediction Using Rule-Based Machine Learning Approach

Rain prediction is an important topic that continues to gain attention throughout the world. The rain has a big impact on various aspects of human life both socially and economically, for example in agriculture, health, transportation, etc. Rain also affects natural disasters such as landslides and floods. The various impact of rain on human life prompts us to build a model to understand and predict rain to provide early warning in various fields/needs such as agriculture, transportation, etc. This research aims to build a rain prediction model using a rule-based Machine Learning approach by utilizing historical meteorological data. The experiment using the J48 method resulted in up to 77.8% accuracy in the training model and gave accurate prediction results of 86% when tested against actual weather data in 2020.
Invertible Grayscale via Dual Features Ensemble

Grayscale image colorization is known as an ill-posed problem because of the imbalanced matching between intensity and color values.Even given prior hints about the original color image, existing colorization methods cannot recover the original color image from grayscale faithfully.In this paper, we propose to embed color information into an invertible grayscale, such that it can be easily recovered to the original color.However, a vanilla encoding-decoding network cannot produce rich representations of color information and thus the reconstruction quality is limited.Moreover, due to the neglect of the discrimination of color information, it cannot embed color information into visually inconspicuous patterns located in the grayscale.In this paper, we propose a novel color-encoding schema, dual features ensemble network (DFENet), for the effective embedding and faithfully reconstruction.In particular, we complement the residual representations with dense representations, to integrate the ability of local residual learning and local feature fusion.Furthermore, we propose an element-wise self-attention mechanism that highlights the discriminative features and suppresses the redundant ones generated from the dual path module.Extensive experiments demonstrate the proposed method outperforms state-of-the-art methods in terms of reconstruction quality as well as the similarity between the generated invertible grayscale and its groundtruth.