Reviewerly/exHarmony huggingface dataset:
BSP-based Swarm Behaviour Implementation for Underwater Multiagent Environmental Low-frequency Sensing

The environmental chemical conditions - in terms of substances with positive and negative impacts on the sustainability of local ecological networks - of confined environments with weak currents change over time in probabilistically predictable ways. We are developing a reproducible and measurable approach to the deployment of complex adaptive networks of underwater gliders, ROVs and fixed or mobile sensors deployed on the sea floor. The approach exploits Voronoi maps, multisensory fusion of different kind of chemical and non chemical sensors framed by a multi agent Belief Space Planning methodology. In this paper we report the early results of the study of the swarm behaviour implementation of a set of gliders or ROVs that can work autonomously (in the case of ROVs semi-autonomously or autonomously for limited time frames). In particular we describe how we were able to implement a swarm behaviour by means of a novel multiagent implementation of a BSP control scheme that we have already studied in the context of the Project CADDY (7th Framework Program (2014)).
Improved Crypto Algorithm for High-Speed Internet of Things (IoT) Applications

Modern technologies focus on integrated systems based on the Internet of Things (IoT). IoT based devices are unified with various levels of high-speed internet communication, computation process, secure authentication and privacy policies. One of the significant demands of present IoT is focused on its secure high-speed communication. However, traditional authentication and secure communication find it very difficult to manage the current need for IoT applications. Therefore, the need for such a reliable high-speed IoT scheme must be addressed. This proposed title introduces an enhanced version of the Rijndael Cryptographic Algorithm (Advanced Encryption Standard – AES) to obtain fast-speed IoT-based application transmission. Pipeline-based AES technique promises for the high-speed crypto process, and this secure algorithm targeted to fast-speed Field Programmable Gate Array (FPGA) hardware. Thus, high-speed AES crypto algorithms, along with FPGA hardware, will improve the efficiency of future IoT design. Our proposed method also shows the tradeoff between High-Speed communications along with various FPGA platforms.
Visual Sentiment Analysis by Leveraging Local Regions and Human Faces

Visual sentiment analysis (VSA) is a challenging task which attracts wide attention from researchers for its great application potentials. Existing works for VSA mostly extract global representations of images for sentiment prediction, ignoring the different contributions of local regions. Some recent studies analyze local regions separately and achieve improvements on the sentiment prediction performance. However, most of them treat regions equally in the feature fusion process which ignores their distinct contributions or use a global attention map whose performance is easily influenced by noises from non-emotional regions. In this paper, to solve these problems, we propose an end-to-end deep framework to effectively exploit the contributions of local regions to VSA. Specifically, a Sentiment Region Attention (SRA) module is proposed to estimate contributions of local regions with respect to the global image sentiment. Features of these regions are then reweighed and further fused according to their estimated contributions. Moreover, since the image sentiment is usually closely related to humans appearing in the image, we also propose to model the contribution of human faces as a special local region for sentiment prediction. Experimental results on publicly available and widely used datasets for VSA demonstrate our method outperforms state-of-the-art algorithms.
A Proposed Framework for Interactive Virtual Reality In Situ Visualization of Parallel Numerical Simulations

As computer simulations progress to increasingly complex, non-linear, and three-dimensional systems and phenomena, intuitive and immediate visualization of their results is becoming crucial. While Virtual Reality (VR) and Natural User Interfaces (NUIs) have been shown to improve understanding of complex 3D data, their application to live in situ visualization and computational steering is hampered by performance requirements. Here, we present the design of a software framework for interactive VR in situ visualization of parallel numerical simulations, as well as a working prototype implementation. Our design is targeted towards meeting the performance requirements for VR, and our work is packaged in a framework that allows for easy instrumentation of simulations. Our preliminary results inform about the technical feasibility of the architecture, as well as the challenges that remain.
Fitting Circles to Data Around Multivariable Calculus

Answering a call by the Common Vision for instructional material to “establish stronger connections with other disciplines” and “work with professional-level technology tools,” the present article shows how various aspects of fitting circles to data fit in existing courses from basic calculus to advanced calculus.
Unveiling Internationalization Decision-Making of Burmese Managers: A Strategic Cognition Perspective

Building on concepts from cognitive science, we explored how managers in the transitional economy of Myanmar undergo an international market entry strategic dilemma. We investigated potential framing effects and the thought process in making such decisions. Three-hundred-and-five Burmese managers participated in one of three separate studies. A two-by-two experimental design and the cognitive mapping were used to examine biases in the internationalization decision-making process. Data were analyzed using non-parametric tests and content analyses. Results reveal that strategic choice was not associated with framing bias, personal characteristics, or sense-making approach. However, different risk preferences were detected between different business contexts.
Towards Adaptive Ontology Visualization — Predicting User Success from Behavioral Data

Ontology visualization plays an important role in human data interaction by offering clarity and insight for complex structured datasets. Recent usability studies of ontology visualization techniques have added to our understanding of desired features when assisting users in the interactive process. However, user behavioral data such as eye gaze and event logs have largely been used as indirect evidence to explain why a user may have carried out certain tasks in a controlled environment, as opposed to direct input that informs the underlying visualization system. Although findings from usability studies have contributed to the refinement of ontology visualizations as a whole, the visualization techniques themselves remain a one-size-fits-all approach, where all users are presented with the same visualizations and interactive features. By contrast, this paper investigates the feasibility of using behavioral data, such as user gaze and event logs, as real-time indicators of how appropriate or effective a given visualization may be for a specific user at a moment in time, which in turn may be used to inform the adaptation of the visualization to the user on the fly. To this end, we apply established predictive modeling techniques in Machine Learning to predict user success using gaze data and event logs. We present a detailed analysis from a controlled experiment and demonstrate such predictions are not only feasible, but can also be significantly better than a baseline classifier during visualization usage. These predictions can then be used to drive the adaptations of visual systems in providing ad hoc visualizations on a per user basis, which in turn may increase individual user success and performance. Furthermore, we demonstrate the prediction performance using several different feature sets, and report on the results generated from several notable classifiers, where a decision tree-based learning model using a boosting algorithm produced the best overall results.
Spline Trajectory Planning and Guidance for Fixed-Wing Drones

Automatic operation of fixed-wing drones plays an important role in research and commercial applications. Accurate planning and precise tracking of trajectories is a key aspect for flight in challenging environments with dense obstacles, e.g., urban areas. Within this paper, we propose a spline-based trajectory planner alongside an associated guidance framework. Compared to basic path descriptions such as consecutive straight lines, the spline representation allows to consider fixed-wing aircraft limitations and thus ensures feasible trajectories. Moreover, feed-forward controls can be deduced from the derivatives of the splines. Anticipating the control action necessary to follow the desired trajectory enables precise path tracking, with a nonlinear guidance scheme ensuring consistent system behavior and global stability. Flight test results are discussed to verify both the feasibility and performance of the approach proposed.
A non-Hermitian quantum approach to reliability of a two-state system

We explore the concept of the reliability theory in the quantum domain, by giving a Hilbert structure to the system states, then utilize a non-Hermitian Schrodinger equation to describe the state evolution and finally derive two new lifetime distributions. Beyond this, the alternative density matrix approach implies that the degradation term can be separated into two parts, one only related to the system properties and the other only influenced by the environment. Applications on six real data sets further show that the proposed models can outperform the existing well-known distributions, which verifies the validity of the proposed method.
SAR image change detection method based on intuitionistic fuzzy C-means clustering algorithm

OBJECTIVE:The purpose of this study is to realize the precise detection of Synthetic Aperture Radar (SAR) image changes. METHODS:In this study, an intuitionistic fuzzy C-means clustering algorithm is used to accurately detect the target changes in SAR images. The change of SAR image is detected by the constructed intuitionistic fuzzy C-means clustering algorithm. Then, the effect of intuitionistic fuzzy C-means clustering algorithm, block principal component analysis (PCA) and logarithmic ratio method is compared and analyzed in the aspects of stability, accuracy, image extraction, restoration, error and work efficiency of the algorithm. RESULTS:Compared with block PCA and logarithmic ratio methods, intuitionistic fuzzy C-means clustering algorithm has obvious advantages in stability, with standard deviation of 0.010 and other two algorithms of 0.014 and 0.017. In terms of detection accuracy and error, the algorithm in this study also has a good performance, and the detection accuracy can reach 92.4%. In addition, the intuitionistic fuzzy C-means clustering algorithm is clear and efficient for SAR image target extraction and restoration. Compared with the other two algorithms, the algorithm in this study improves by at least 20% in operation speed. There is no significant difference in the detection results of the proposed algorithm for SAR images with different targets, such as objects, people, geographical environment, etc. CONCLUSION:In this study, based on intuitionistic fuzzy C-means clustering algorithm, target changes in SAR images are detected, and the operation of the algorithm is studied. The algorithm used in this study shows a relatively comprehensive and good result, and also shows that the algorithm is a comprehensive result, which requires a good operation at many levels. This research greatly improves the recognition of intuitionistic fuzzy C-means clustering algorithm and SAR image.
A Style Transfer Approach to Source Separation

Training neural networks for source separation involves presenting a mixture recording at the input of the network and updating network parameters in order to produce an output that resembles the clean source. Consequently, supervised source separation depends on the availability of paired mixture-clean training examples. In this paper, we interpret source separation as a style transfer problem. We present a variational auto-encoder network that exploits the commonality across the domain of mixtures and the domain of clean sounds and learns a shared latent representation across the two domains. Using these cycle-consistent variational auto-encoders, we learn a mapping from the mixture domain to the domain of clean sounds and perform source separation without explicitly supervising with paired training examples.
STATIC MODELING OF THE RESERVOIR FOR ESTIMATE OIL IN PLACE USING THE GEOSTATISTICAL METHOD

Three-dimensional simulation using geostatistical methods in terms of the possibility of creating multiple realizations of the reservoir, in which heterogeneities and range of variables changes are well represented, is one of the most efficient methods to describe the reservoir and to prepare a 3D model of it and the results have been used as acceptable results in the calculations due to the high accuracy and the lack of smoothing effect in small changes compared to the results of Kriging estimation. The initial volumetric tests of the Hendijan reservoir in southern Iran were carried out according to the construction model and the petrophysical model prepared by the software and according to the fluid contact levels, and the ratio of net thickness to total thickness in different reservoir zones. The calculations can be distinguished based on the zoning of the reservoir and also on the basis of type of facies. Accordingly, the average volume of fluid in place of the field is calculated in different horizons. The results of the simulation showed that the Ghar reservoir rock has gas and Sarvak Reservoir has the largest amount of oil in place.
A public key cryptosystem and signature scheme based on numerical series

A public key cryptosystem and signature scheme based on a proposed key exchange algorithm are introduced. The key exchange algorithm is based on the difficulty of calculating the nth partial sum of infinite numerical series where no exponentiation computation is required to share a secret key between two parties as in the Diffie–Hellman algorithm. With the proposed public key cryptosystem ciphering and deciphering of messages, online signing documents and verifying signatures do not require exponentiation computation while providing higher security level than the state-of-the-art cryptosystems that depend on the difficulty of discrete logarithmic problem or factorizing large prime numbers. The proposed cryptosystem and signature scheme do not depend on elliptic curve cryptography or RSA cryptography that are computationally too slow, this makes the proposed cryptosystem and signature scheme computationally faster, easier to implement and more practical to be used in online transactions. It also provides a higher level of security as it provides forward secrecy and using large size symmetric keys to encrypt and decrypt messages in a significantly short time. Moreover, the proposed signature scheme can be used as a cryptographic hash function as the hash value significantly changes when a single letter in the document is changed. In comparison with the state-of-the art the experimental results show the superiority of the proposed key exchange algorithm and public key cryptosystem.
More Secure Collaborative APIs Resistant to Flush+Reload and Flush+Flush Attacks on ARMv8-A

With the popularity of smart devices such as mobile phones and tablets, the security problem of the widely used ARMv8-A processor has received more and more attention. Flush+Reload and Flush+Flush cache attacks have become two of the most important security threats due to their low noise and high resolution. In order to resist Flush+Reload and Flush+Flush attacks, researchers proposed many defense methods. However, these existing methods have various shortcomings. The runtime defense methods using hardware performance counters cannot detect attacks fast enough, effectively detect Flush+Flush or avoid a high false positive rate. Static code analysis schemes are powerless for obfuscation techniques. The approaches of permanently reducing the resolution can only be utilized on browser products and cannot be applied in the system. In this paper, we design two more secure collaborative APIs-flush operation API and high resolution time API-which can resist Flush+Reload and Flush+Flush attacks. When the flush operation API is called, the high resolution time API temporarily reduces its resolution and automatically restores. Moreover, the flush operation API also has the ability to detect and handle suspected Flush+Reload and Flush+Flush attacks. The attack and performance comparison experiments prove that the two APIs we designed are safer and the performance losses are acceptable.
Using Deep Learning to Predict User Behavior in the Online Discussion

Popularity of social networks makes them an attractive field for analysis of users' behavior, for example, based on the intention analysis of their posts and comments. In the linguistic theory only 25 types of intentions exist and can be joined in 5 supergroups. We use the dataset that contains directed oriented graphs which nodes store information about the author intention, text of the post in the social network "Vkontakte" etc. Each graph is split in a linked list of nodes (a sequence, 13156 sequences in our dataset) from root to each leaf so that the intention prediction becomes the sequence prediction. We have analyzed traditional and neuronet approaches that address this task and proposed to solve it with the original modifications of CNN and RNN architectures. It was decided to translate all posts to the embeddings which are then used as inputs for our neural network. According to the benchmarking experiments, we have identified that the proposed RNN architecture outperforms other alternatives. Also, predicting supergroups is done more accurately. Finally, we found out, that the context in the dialogs is lost quickly that allows to decrease the algorithm context size while keeping accuracy at the appropriate level.
Parallel Computations for Evolutionary Induction

Top-down decision tree inducers are very fast, and even if the obtained decision structures are just good and not really close to the possible optimal solutions, it is often enough for practitioners who are interested in solving specific problems. The implementations of the most popular greedy algorithms are available in every data mining commercial system, and they can be very easily applied without any profound awareness of the parameter settings or running details. Moreover, knowledge of the existence of alternative induction methods is still limited to a narrow group of researchers who are working on this topic.
Industrial Case Study on Time Series Analysis of Metrics Changes Based on GQM Models

It is difficult for companies to evaluate whether their activities have achieved the intended goals or to improve their goals in quantifiable ways. Herein we propose a method to evaluate quantitatively the results of a company's activities related to goals based on the GQM (Goal Question Metrics) method. Our proposed method uses successive statistical analysis and periodic statistical analysis. The former evaluates two groups of projects based on project release date, and the latter evaluates projects separated by six-month intervals. An empirical case study for a cloud service, which supports real estate businesses with e-Seikatsu, is used to evaluate our method. The target data contain project quality evaluation results for three years. We evaluated 73 project quality evaluation results using the proposed method. Successive statistical analysis reveals four of the five metrics have statistical significance of mean differences between groups, whereas periodic statistical analysis indicates three of the five metrics are statistically significant.
Action Co-localization in an Untrimmed Video by Graph Neural Networks

We present an efficient approach for action co-localization in an untrimmed video by exploiting contextual and temporal feature from multiple action proposals. Most existing action localization methods focus on each individual action instances without accounting for the correlations among them. To exploit such correlations, we propose the Graph-based Temporal Action Co-Localization (G-TACL) method, which aggregates contextual features from multiple action proposals to assist temporal localization. This aggregation procedure is achieved with Graph Neural Networks with nodes initialized by the action proposal representations. In addition, a multi-level consistency evaluator is proposed to measure the similarity, which summarizes low-level temporal coincidences, features vector dot products and high-level contextual features similarities between any two proposals. Subsequently, these nodes are iteratively updated with Gated Recurrent Unit (GRU) and the obtained node features are used to regress the temporal boundaries of the action proposals, and finally to localize the action instances. Experiments on the THUMOS’14 and MEXaction2 datasets have demonstrated the efficacy of our proposed method.
Privacy-Preserving Stochastic Gradual Learning

It is challenging for stochastic optimization to handle large-scale sensitive data safely. Duchi et al. recently proposed a private sampling strategy to solve privacy leakage in stochastic optimization. However, this strategy leads to a degeneration in robustness, since this strategy is equal to noise injection on each gradient, which adversely affects updates of the primal variable. To address this challenge, we introduce a robust stochastic optimization under the framework of local privacy, which is called Privacy-pREserving StochasTIc Gradual lEarning (PRESTIGE). PRESTIGE bridges private updates of the primal variable (by private sampling) with gradual curriculum learning (CL). The noise injection leads to similar issue from label noise, but the robust learning process of CL can combat with label noise. Thus, PRESTIGE yields “private but robust” updates of the primal variable on the curriculum, that is, a reordered label sequence provided by CL. In theory, we reveal the convergence rate and maximum complexity of PRESTIGE. Empirical results on six datasets show that PRESTIGE achieves a good tradeoff between privacy preservation and robustness over baselines.
Drug Audit Based on Bisecting K-Means Clustering Algorithm

To quickly discover suspicious data in massive data and facilitate the implementation of audit work, we need to use scientific experimental methods to conduct in-depth analysis of the collected data. This experiment uses bisecting k-means clustering algorithm which is an unsupervised learning method to realize in-depth analysis on the delivery data of 20 kinds of drugs from one hospital in 2018. During this experiment, three data features of drug delivery quantity, retail price and total price are selected as the clustering indexes, that is, the coordinate axes of the three-dimensional clustering diagram, and the optimal cluster number is determined according to SSE elbow rule. The experimental results show that the bisecting k-means algorithm can avoid falling into the local optimal state, it can be clearly seen that the suspicious phenomenon of some delivery drugs, so that auditors audit and verify the relevant departments and confirm the rectification. This experiment realized the drug audit based on bisecting k-means algorithm, and effectively solved the problem of multi-index visual analysis of complex data. Compared with the current audit, which uses audit software or database query statements to find suspicious clues, it innovates audit thinking and improves audit efficiency.