Reviewerly/exHarmony huggingface dataset:
Tracking Applications: A Factor of Mithridatism of Personal Data and Privacy in the Post-COVID-19 Era

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A Nuclei Segmentation Method Based on Optimal Fuzzy Clustering Using Salp Swarm Algorithm for Histopathological Images

Automatic segmentation of nuclei in the H&E stained histopathological images is an open-ended research problem. In this paper, a new salp swarm algorithm based fuzzy clustering method is proposed which is used for nuclei segmentation in the histopathological images. The salp swarm algorithm finds the optimal clusters by the objective function defined over intra-cluster distances or compactness. The performance of the proposed segmentation method is evaluated in terms of F1 score and aggregated jaccard index on the histopathological image dataset of TNBC patients. The experimental results depict the efficacy of the proposed method over the other considered clustering-based segmentation methods, namely K-means and Fuzzy C-means.
Motivational Company’s Characteristics to Secure Software

Software companies' acceptance to adopt security practices during the software development life cycle differs from one company to another. It can be said that one of the reasons for these differences is the varied characteristics of each company, such as its size, resources, and field. This paper aims to study the characteristics of companies that affect the adoption of security practices during software development. We studied eight company characteristics, some of which are classified as primary characteristics, including company size, type of developed software, and type of company. Meanwhile, the remaining five characteristics are classified as secondary characteristics, including company field, years of experience, methodology, presence of security experts, and approach to adopting security practices. A survey method was designed to find company characteristics that impact security adoption during software development. Results show that large companies, especially generic companies, adoption security practices more than other types of companies.
Visual Analysis of Eye Movements During Game Play

Eye movements indicate visual attention and strategies during game play, regardless of whether in board, sports, or computer games. Additional factors such as individual vs. group play and active playing vs. observing game play further differentiate application scenarios for eye movement analysis. Visual analysis has proven to be an effective means to investigate and interpret such highly dynamic spatio-temporal data. In this paper, we contribute a classification strategy for different scenarios for the visual analysis of gaze data during game play. Based on an initial sample of related work, we derive multiple aspects comprising data sources, game mode, player number, player state, analysis mode, and analysis goal. We apply this classification strategy to describe typical analysis scenarios and research questions as they can be found in related work. We further discuss open challenges and research directions for new application scenarios of eye movements in game play.
Improved Scheduling Algorithm for Instance-Intensive Cloud Workflow

Aimed at optimizing the efficiency of resource scheduling problem in cloud environment, an improved scheduling algorithm is proposed for instance-intensive cloud workflow. It used the direct acyclic graph to model the workflow process, and then the clustering method is employed to classify the cloud service resources, and optimize the resource strategy to match the execution time and cost of the entire workflow instance. The experimental results show that the algorithm can effectively improve the efficiency and reduce the cost of using cloud resources for scheduling the instance-intensive cloud workflows.
A Novel and Fast Memory Perturbation Method to Increase Exploration in Particle Swarm Optimization Algorithm

Abstract Particle Swarm Optimization (PSO), one of the versatile nature-inspired optimization algorithm, continue to suffer from premature convergence despite the numerous amount of research trying to improve this algorithm. Many research had tried to address this issue but often use a complex algorithm which tax on computational time and complexity. This research introduced a novel perturbation method to mitigate premature convergence / to increase exploration while keeping the computational cost at a minimum. The particles’ memories (i.e the position of personal and global best) are modified by a random multiplier which in turn will ‘perturb’ the particles’ velocity. The implementation of this novel perturbation method in early iterations had resulted in 100% success rate in finding global optima in multimodal benchmark tests including the Rastrigin problem - whereas the original PSO failed in all benchmark tests - without adding a significant amount of computational complexity and time.
An Area-Efficient Implementation of Recurrent Neural Network Core for Unsupervised Anomaly Detection

Toward on-device anomaly detection for time-series data, in this paper, we analyze Echo State Network (ESN), which is a simple form of Recurrent Neural Networks (RNNs), and propose its area-efficient implementation. It is evaluated in terms of the anomaly detection capability and area. (Keywords: On-device learning, Machine learning, and Anomaly detection).
3D Deep Residual Encoder-Decoder CNNS with Squeeze-and-Excitation for Brain Tumor Segmentation

Segmenting brain tumors from multimodal MR scans is thought to be highly beneficial for brain abnormality diagnosis, prognosis monitoring, and treatment evaluation. Due to the highly heterogeneous appearance and shape, segmentation of brain tumors in multimodal MRI scans is a challenging task in medical image analysis. In recent years, many segmentation algorithms based on neural network architecture are proposed to address this task. Observing the previous state-of-the-art algorithms, not only did we explore multimodal brain tumor segmentation in 2D space, 2.5D space and 3D space respectively, we also made a lot of attempts in attention block to improve the segmentation result. In this paper, we describe a 3D deep residual encoder-decoder CNNS with Squeeze-and-Excitation block for brain tumor segmentation. In order to learn more effective image features, we have utilized an attention module after each Res-block to weight each channel, which emphasizes useful features while suppresses invalid ones. To deal with class imbalance, we have formulated a weighted Dice loss function. We find that 3D segmentation network with attention block which can enhance context features can significantly improve the performance. In addition, the results of data preprocessing have a great impact on segmentation performance. Our method obtained Dice scores of 0.70, 0.85 and 0.80 for segmenting enhancing tumor, whole tumor and tumor core, respectively on the testing data set.
Decision Support System Museum Ambassadors Using Topsis Method

Abstract Generally, museum ambassador is based on various criteria from several assessment stages. The evaluation criteria for the audition stage are: interview, communication, appearance. The assessment criteria for the quarantine stage are: creativity, ability, liveliness, loyalty, responsibility, and written test. The final stage assessment criteria are: ideas, communication, and appearance. In the selection process, the committee used manual method so it was prone to errors. The decision support system used TOPSIS method. SDLC (System Development Life Cycle) was used as a design method. The research showed a score of 100 which means that the system is accepted by users. Meanwhile the user testing to four respondents obtained a percentage of 89.24% which means that the system is worth using. The system was developed in the web. So that is need more research to be developed in mobile.
Robust principal component analysis with projection learning for image classification

In this paper, we propose a robust subspace learning method, based on RPCA, named Robust Principal Component Analysis with Projection Learning (RPCAPL), which further improves the performance of feature extraction by projecting data samples into a suitable subspace. For Subspace Learning (SL) methods in clustering and classification tasks, it is also critical to construct an appropriate graph for discovering the intrinsic structure of the data. For this reason, we add a graph Laplacian matrix to the RPCAPL model for preserving the local geometric relationships between data samples and name the improved model as RPCAGPL, which takes all samples as nodes in the graph and treats affinity between pairs of connected samples as weighted edges. The RPCAGPL can not only globally capture the low-rank subspace structure of the data in the original space, but also locally preserve the neighbor relationship between the data samples.
SAFE: A Secure and Efficient Long-Term Distributed Storage System

Secret sharing-based distributed storage systems are one approach to provide long-term protection of data even against quantum computing. Confidentiality is provided because the shares of data are renewed periodically while integrity is provided by commitment schemes. However, this solution is prohibitively costly and impractical: share renewal requires an information-theoretically secure channel between any two nodes and long-term confidential commitment schemes are computationally impractical for large files. In this paper, we present SAFE, a secret sharing-based long-term secure distributed storage system that leverages a Trusted Execution Environment (TEE) to overcome the above limitations. Share generation and renewal are performed inside the TEE and the shares are securely distributed to the storage servers. We prototype SAFE protocols using a TEE instantiation, and show their efficiency, even for large files, compared to existing schemes.
Moodle mobile development in enjoyable learning in computer system subjects

Abstract The mobility of students in the world of education is displaced by social media which is a concern for educators to utilize, create or innovate in the world of Education. This study aims to utilize moodle mobile in the development of learning material as an online learning media that approaches social media platforms. The media application development method uses Waterfall, consisting of (1) requirements, (2) analysis, (3) system design, (4) Coding, (5) Testing system, (6) Acceptance Test. Sample testing on media experts, Acceptance Test uses a response assessment consisting of 5 aspects namely Perceived ease of use (PEOU), Perceived usefulness (PU), Perceived Enjoyment (PE), and Intention to use (INT). From the analysis of the Acceptance Test study for 130 students Perceived ease of use (PEOU) of 92.30%, Perceived usefulness (PU) of 86.34, Perceived Enjoyment (PE) of 88%, and Intention to use (INT) of 87, 10% Then from the two classes using independent T-Test two samples showed that the value of the T-test reached 0.046 where the value was below the significance value of 0.05, indicating that H0 was rejected and accepted H1, and so the result was a difference between learning outcomes using the method conventional and learning using Moodle Mobile.
KL-cuts influence on optimization of polymorphic circuits based on PAIG rewriting

The synthesis process of complex polymorphic circuits represents a very demanding task in which the existing approaches, frequently based on various techniques known from the conventional digital circuits domain, fail to deliver acceptable results. This fact initiated the development of a scalable, rigid and multilevel-oriented synthesis flow to tackle the existing shortcomings. However, the conducted experiments have revealed a need to carry out certain modifications to improve the efficiency of the initially proposed polymorphic circuits optimization concept. Therefore, the main attention in this article is given to the investigation of KL-cuts and their impact on the resulting circuitry structure produced by the polymorphic and-inverter graphs rewriting technique introduced earlier. The principle of using KL-cuts within the synthesis process will be explained. Besides, a discussion of the obtained results and their comparison with the previously reported approach will be provided for a better clarification of the potential benefits.
Competitive Deep Reinforcement Learning over a Pokémon Battling Simulator

Pokémon is one of the most popular video games in the world, and recent interest has appeared in Pokémon battling as a testbed for AI challenges. This is due to Pokémon battling showing interesting properties which contrast with current AI challenges over other video games. To this end, we implement a Pokémon Battle Environment, which preserves many of the core elements of Pokémon battling, and allows researchers to test isolated learning objectives. Our approach focuses on type advantage in Pokémon battles and on the advantages of delayed rewards through switching, which is considered core strategies for any Pokémon battle. As a competitive multi-agent environment, it has a partially-observable, high-dimensional, and continuous state-space, adheres to the Gym de facto standard reinforcement learning interface, and is performance-oriented, achieving thousands of interactions per second in commodity hardware. We determine whether deep competitive reinforcement learning algorithms, WPLθ and GIGAθ, can learn successful policies in this environment. Both converge to rational and effective strategies, and GIGAθ shows faster convergence, obtaining a 100% win-rate in a disadvantageous test scenario.
Application of discrete artificial bee colony algorithm for cloud task optimization scheduling

The performance of task scheduling algorithm in cloud computing determines the performance of the cloud system. This study mainly analyzed the application of the artificial bee colony (ABC) algorithm in the cloud task scheduling. In order to solve the problem of cloud task scheduling, the ABC algorithm was discretized to get the discrete artificial bee colony (DABC) algorithm. Then the mathematical model of cloud task scheduling was established and solved by the DABC algorithm. Finally, the simulation experiment was carried out, and the performance of first-come-first-served (FCFS), MIN–MIN, ABC and DABC algorithms under different cloud tasks was compared to verify the performance of the proposed algorithm. The results showed that the user waiting time of the DABC algorithm was 1210s, the load balance degree was 0.01, and the user payment fee was 1688 yuan when the number of cloud tasks was 500; compared with other algorithms, the user waiting time of the DABC algorithm was shorter, the resource load balance degree was higher, and the overall performance was better. The research results verify the effectiveness of the DABC algorithm in solving the problem of cloud task optimal scheduling, and it can be further extended and applied in practice.
Research on Multi-feature Adaptive Fusion Face Tracking Algorithm

Abstract Under the premise of fixed computer performance, it is necessary to take into account the accuracy and real-time of face tracking. For this detection requirement, the image processing method of skin color segmentation is incorporated into AdaBoost’s face detection algorithm to accurately and quickly locate the face position. However, in the previous stage of face detection, there is a case where the real-time detection does not respond fast enough and fails the detection. In this case, a particle filter tracking algorithm based on multi-feature adaptive fusion is proposed, which uses the face area detected in the first frame as the tracking target, and achieves face detection by self-adjusting CS-LBP and the weight of how the skin color influences tracking effect, in which way the computational efficiency of face detection between frames is improved when the detection accuracy is maintained. And it is also robust to various complex external factors. It has been proved by experiments.
Sparse Representation and Dictionary Learning for Network Traffic Anomaly Detection

In this article we present the use of sparse representation of signal and dictionary learning method for solving the anomaly detection problem. The signals analysed in the article represented selected features of the network traffic. In the learning process we used modified Method of Optimal Directions in order to find a dictionary resembling correct structures of the network traffic deprived of the influence of possible outlying observations (outliers). A dictionary defined in such a way constituted basis for sparse representation of the analysed signal. Anomaly detection is realised by parameter estimation of the analysed signal and its comparative analysis to network traffic profiles. Efficiency of our method is examined with the use of extended set of test traces from real network traffic. The received experimental results confirm effectiveness of the presented method.
Designing android-based learning media and assessment in improving students achievement

Mobile phone with android platform is one of the electronic devices that experienced rapid development from other electronic devices. Therefore, in this paper the researcherstried to explain about how 1) Designing and developing learning media and android-based assessment onthe computer electronics course in STMIK AKBA 2) Increasing the learning result of STMIK AKBA students on the computer electronics course through the application of instructional media and android-based platform assessment based. The type of research used in this paper is Research & Development (R & D) whilethe data analysis method is done by using descriptive and inferential analysis technique to reveal the result of study that has been done. The results of the study found that the learning media can run appropiately with the desired where the Main Menu, Material Form, Material Edit Form, Admin Form, Login Form and Evaluation Form can work very well and based on the results of inferentia analysis, it can be reported that there is a significant increasement in student learning achievements.
Application Research of Mixed Reality (MR) Technology in Urban Historical Features Display

The historical features of the city has valuable artistic value, displaying the historical features of the city can help the public better understand the process of urban change and development, enhance people's sense of cultural belonging and pride, and promote the integration and development of urban culture and tourism, which is of great significance to the protection and inheritance of urban history and culture. In this paper, the mixed reality technology is innovatively introduced into the historical display of the city, breaking the traditional two-dimensional display mode, constructing the urban historical appearance display system composed of the historical changes of the urban environment, the historical architectural space, and the important urban historical space. Then introduce the three-dimensional spatial perception into the historical display of the city, which can bring people the scene of virtual and real combination. So that the participants can interact with urban historic buildings and environments freely, which provide new methods for the display of urban historical features.
Formation of project environmental thinking in the training of engineers

Abstract At transition of civilization to the sixth technological way and the Industry 4.0 the speed of new technologies emergence, which influence on the environment can be different from former, increases. The created means of environmental protection can be insufficiently effective for new technologies. In these conditions the formation of project environmental thinking is important when training engineers to fully consider ecological requirements directly during the development of new technologies. In this work the possibilities of the theory of inventive problem solving (TIPS) use as effective means of modern engineering thinking for project ecological thinking formation are considered. The theoretical analysis and systemic synthesis of the TIPS methods and the didactic system of innovative education "TIPS pedagogics" are applied. The possibilities of TIPS in innovative thinking formation and its highest form, such as sustainable thinking, including ecological project thinking, modern ecological outlook and noosphere thinking, starting since school level, are shown. The possibility of a collaborative activity in interactive training, using the method of innovative projects, allows to find new problems solutions, including in ecological education.
Optimal bounding box and Grabcut for weakly supervised segmentation

Weakly supervised semantic segmentation is a challenging problem in computer vision filed. In this paper, we propose a novel method to perform weakly supervised semantic segmentation based on fast regional convolutional neural network (Faster R-CNN) and Grabcut. To deal with the imprecise location of Faster R-CNN detection, we provide a method to optimize the bounding box by building the undirected region adjacency graph on the image to search for the regions which belong to the object outside the bounding box. Then the obtained optimal bounding box is applied to initialize Grabcut algorithm. These accelerate the convergence of the Grabcut algorithm and no extra user interaction is needed. Our approach is validated on the PASCAL VOC 2012 and MSRC-21 datasets. The experimental results demonstrate that our method can provide efficient results in comparison with some state-of-the-arts.
Speaker verification system based on articulatory information from ultrasound recordings

Current state-of-the-art speaker verification (SV) systems are known to be strongly affected by unexpected variability presented during testing, such as environmental noise or changes in vocal effort. In this work, we analyze and evaluate articulatory information of the tongue's movement as a means to improve the performance of speaker verification systems. We use a Spanish database, where besides the speech signals, we also include articulatory information that was acquired with an ultrasound system. Two groups of features are proposed to represent the articulatory information, and the obtained performance is compared to an SV system trained only with acoustic information. Our results show that the proposed features contain highly discriminative information, and they are related to speaker identity; furthermore, these features can be used to complement and improve existing systems by combining such information with cepstral coefficients at the feature level.