Reviewerly/exHarmony huggingface dataset:
Feature Grouping–based Trajectory Outlier Detection over Distributed Streams

Owing to a wide variety of deployment of GPS -enabled devices, tremendous amounts of trajectories have been generated in distributed stream manner. It opens up new opportunities to track and analyze the moving behaviors of the entities. In this work, we focus on the issue of outlier detection over distributed trajectory streams, where the outliers refer to a few entities whose motion behaviors are significantly different from their local neighbors. In view of skewed distribution property and evolving nature of trajectory data, and on-the-fly detection requirement over distributed streams, we first design a high-efficiency outlier detection solution. It consists of identifying abnormal trajectory fragment and exceptional fragment cluster at the remote sites and then detecting abnormal evolving object at the coordinator site. Further, given that outlier detection accuracy would be damaged due to using inappropriate proximity thresholds or a few trajectory data not having sufficient neighbors at the remote sites, we extract proximity thresholds of different regions and spatial context relationship of each region from historical data to improve the precision. Built upon this is an improved version consisting of off-line modeling phase and on-line detection phase. During the on-line phase, the proximity thresholds that are derived from historical trajectories during the off-line phase are leveraged to assist in detecting abnormal trajectory fragments and exceptional fragment clusters at the remote sites. Additionally, at the coordinator site, the detection results of some remote sites can be refined by incorporating those of other remote sites with neighborhood relationship. Extensive experimental results on real data demonstrate that our proposed methods own high detection validity, less communication cost and linear scalability for online identifying outliers over distributed trajectory streams.
BP-MVSNet: Belief-Propagation-Layers for Multi-View-Stereo

In this work, we propose BP-MVSNet, a convolutional neural network (CNN)-based Multi-View-Stereo (MVS) method that uses a differentiable Conditional Random Field (CRF) layer for regularization. To this end, we propose to extend the BP layer [16] and add what is necessary to successfully use it in the MVS setting. We therefore show how we can calculate a normalization based on the expected 3D error, which we can then use to normalize the label jumps in the CRF. This is required to make the BP layer invariant to different scales in the MVS setting. In order to also enable fractional label jumps, we propose a differentiable interpolation step, which we embed into the computation of the pairwise term. These extensions allow us to integrate the BP layer into a multi-scale MVS network, where we continuously improve a rough initial estimate until we get high quality depth maps as a result. We evaluate the proposed BP-MVSNet in an ablation study and conduct extensive experiments on the DTU, Tanks and Temples and ETH3D data sets. The experiments show that we can significantly outperform the baseline and achieve state-of-the-art results.
A novel hybrid resampling algorithm for parallel/distributed particle filters

Parallel/Distributed particle filters have been widely used in the estimation of states of dynamic systems by using multiple processing units (PUs). In parallel/distributed particle filters, the centralized resampling needs a central unit (CU) to serve as a hub to execute the global resampling. The centralized scheme is the main obstacle for the improved performance due to its global nature. To reduce the communication cost, the decentralized resampling was proposed, which only conducted the resampling on each PU. Although the decentralized resampling can improve the performance, it suffers from the low accuracy due to the local nature. Therefore, we propose a novel hybrid resampling algorithm to dynamically adjust the intervals between the centralized resampling steps and the decentralized resampling steps based on the measured system convergence. We formulate the proposed algorithm and prove it to be uniformly convergent. Since the proposed algorithm is a generalization of various versions of the hybrid resampling, its proof provides the solid theoretical foundation for their wide adoptions in parallel/distributed particle filters. In the experiments, we evaluate and compare different resampling algorithms including the centralized resampling algorithm, the decentralized resampling algorithm, and different types of existing hybrid resampling algorithms to show the effectiveness and the improved performance of the proposed hybrid resampling algorithm.
Moving Object Detection and Recognition Using Optical Flow and Eigen Face Using Low Resolution Video

Background: As crime is increasing day by day, various applications are proposed to protect public places. Monitoring and tracking of video surveillance system is the most difficult task and it is prominent that human beings are not reliable and efficacious in doing this job. Objective: The prime objective of this research is to develop an automatic monitoring and inspecting system that is competent enough to detect and track the moving objects in real-time using a low-resolution video surveillance camera. Methods: Firstly, the video data acquired from a low-resolution video surveillance camera is used for generating RGB video frames which are converted into gray scale. Optical flow and Eigen face algorithms are applied to extract and match the moving object in the video sequence with the images stored in the database. Results: The proposed system is compared with the already existing systems and it is observed that this approach gives more accurate results. This system can meet the requirement of real-time tracking even when the targeted image resolution is smaller than 160x120. Conclusion: This method uses optical flow and Eigen face algorithm to track and detect the moving objects. The system gives high performance and can be used for real time object tracking. The same experiment can be applied for the human faces too.
A Study of Big Data Analytics using Apache Spark with Python and Scala

Data is generated by humans every day via various sources such as Instagram, Facebook, Twitter, Google, etc at a rate of 2.5 quintillion bytes with high volume, high speed and high variety. When this huge volume of data with high velocity is handled by the traditional approaches, it becomes inefficient and time-consuming. Apache Spark technology has been used that is an open-source in-memory clusters computing system for fast processing. This paper introduces a brief study of Big Data Analytics and Apache Spark which consists of characteristics (7V's) of big data, tools and application areas for big data analytics, as well as Apache Spark Ecosystem including components, libraries and cluster managers that is deployment modes of Apache spark. Furthermore, this also presents a comparative study of Python and Scala programming languages with many parameters in the aspect of Apache Spark. This comparative study will help to identify which programming language like Python and Scala are suitable for Apache Spark technology. As result, both Python and Scala programming languages are suitable for Apache Spark, however language choice for programming in Apache Spark is depending on the features that best suited the needs of the project since each one has its own advantages and disadvantages. The main purpose of this paper is to make things easier for the programmer in the selection of programming languages in Apache Spark-based on their project.
ORCA Optimization Algorithm: A New Meta-Heuristic Tool for Complex Optimization Problems

In this paper, a new meta-heuristic algorithm called Orca Optimization Algorithm (OOA) is proposed for complex and nonlinear optimization problems, especially practical engineering problems. This developed method is based on unique wave-washing orcas hunting technique. In the OOA, different random candidates (orcas) implement the cunning tactic of regularly hunting in packs and building waves to wash seals off drifting piece of ice. Application of the proposed algorithm on some benchmark functions including Schaffer, Griewank, Ackley, and Rosenbrock proved its excellent capability in comparison with other similar algorithms such as Genetic Algorithm (GA), Flower Pollination Algorithm (FPA), Harris Hawk Optimization (HHO) algorithm, and Imperialist Competitive Algorithm (ICA). Moreover, the performance of the proposed optimization algorithm applied on real engineering problems. In this experiment, OOA applied for online parameter estimation of photovoltaic modules equivalent circuit and optimal design of brushless direct current (BLDC) motor. The numerical analysis and obtained simulation results of the experiments showed the excellent performance of OOA.
Validating Feature Models With Respect to Textual Product Line Specifications

Feature models (FM) are a valuable resource in the analysis of software product lines (SPL). They provide a visual abstraction of the variation points in a family of related software products. FMs can be manually created by domain experts or extracted (semi-) automatically from textual documents such as product descriptions or requirements specifications. Nevertheless, there is no way to measure the accuracy of a FM with respect to the information described in the source documents.
The improvement and comparison of diffuse radiation models in different climatic zones of China

Diffuse radiation is vital for climatology, sustainable energy, agriculture and biological activities. It is often estimated based on some meteorological factors but seldom takes total cloud cover into consideration. In this study, cloud cover data are used to establish models, compared with other regular models for estimating diffuse radiation in different climatic zones of China during 1992–2015. The results showed that the performance of the new model based on sunshine fraction (n/N), clearness index (kt), total cloud cover (CI), air temperature (Ta), relative humidity (RH), wind speed and the day of the year outperformed other models and further improved the predictive accuracy. Generally, the kd models based on the single kt was generally more precise compared with single n/N-based or single CI-based models. And models on the basis of multiple factors performed better than the single factor-based model. Comparisons between these models indicated that neural network models (with the largest R and smallest RMSD values) provided better overall accuracy than other models. The parameter of CI as the co-factor can improve the prediction of diffuse radiation. The models developed and evaluated in this study can contribute to developing and utilizing solar energy in China, especially in areas without diffuse radiation records.
QCCA-Secure Generic Key Encapsulation Mechanism with Tighter Security in the Quantum Random Oracle Model

Xagawa and Yamakawa (PQCrypto 2019) proved the transformation \(\mathsf {SXY}\) can tightly turn \(\mathsf {DS}\) secure \(\mathsf {PKE}\)s into \(\mathsf {IND}\text {-}\mathsf{qCCA}\) secure \(\mathsf {KEM}\)s in the quantum random oracle model (QROM). But transformations such as \(\mathsf {KC,\ TPunc}\) that turn PKEs with standard security (\(\mathsf {OW}\text {-}\mathsf{CPA}\) or \(\mathsf {IND}\text {-}\mathsf{CPA}\)) into \(\mathsf {DS}\) secure \(\mathsf {PKE}\)s still suffer from quadratic security loss in the QROM. In this paper, we give a tighter security reduction for the transformation \(\mathsf {KC}\) that turns \(\mathsf {OW}\text {-}\mathsf{CPA}\) secure deterministic \(\mathsf {PKE}\)s into modified \(\mathsf {DS}\) secure \(\mathsf {PKE}\)s in the QROM. We use the Measure-Rewind-Measure One-Way to Hiding Lemma recently introduced by Kuchta et al. (EUROCRYPT 2020) to avoid the square-root advantage loss. Moreover, we extend it to the case that underlying \(\mathsf {PKE}\)s are not perfectly correct. Combining with other transformations, we finally obtain a generic \(\mathsf {KEM}\) from any \(\mathsf {IND}\text {-}\mathsf{CPA}\) secure \(\mathsf {PKE}\). Our security reduction has roughly the same tightness as the result of Kuchta et al. without any other assumptions and we achieve the stronger \(\mathsf {IND}\text {-}\mathsf{qCCA}\) security. We also give a similar result for another \(\mathsf {KEM}\) transformation achieving the same security notion from any \(\mathsf {OW}\text {-}\mathsf{CPA}\) secure deterministic \(\mathsf {PKE}\).
Collision Avoidance Dynamic Window Approach in Multi-agent System

In multi-agent system formation switching, one common mission is to exchange positions of two agents. This scenario is prone to become a local minimum, since for each agent the direction to target and the direction of another agent movement are col-linear and opposite. Collision will occur if collision avoidance method fails to solve local minimum. Artificial Potential Field (APF) and Dynamic Window Approach (DWA) are classical collision avoidance methods. However, neither can be directly used in this mission. APF easily causes agent trapped in local minimum, which is unpredictable especially in dynamic environment. While DWA has collision avoidance term in objective function, inappropriate weigh parameters may block collision avoidance behavior. To cope with this mission, this paper proposes a new DWA variant called Collision Avoidance Dynamic Window Approach (CADWA). It is divided into collision risk detection part and collision avoidance part. Contributions of CADWA include: i) Neighbor set describes neighboring agents and its derivatives detect potential collision; ii) The objective function only focuses on reducing collision risk, so no local minimum will appear; iii) Besides preserving DWA merits like easiness to understand and fast reaction, CADWA does not need parameter tuning; iv) It is convenient to integrate CADWA into formation control algorithm which does not consider collision between agents. Details of CADWA algorithm are described. Simulation results show that two agents finish formation switching without collision.
Web service discovery based on maximum weighted bipartite graphs

Web service discovery is the process that service requestor or user finds services to meet their requirements, its core technologies include Web service description language and the service matching algorithm. Duo to services with more ambiguity, using service-oriented computing in the process of finding service without strong restriction can cause some error services which have the same background but not satisfy the initial request returned. In order to eliminate the effect of this kind of situation in service-oriented computing, we present a novel approach for Web service retrieval based on the evaluation of similarity between Web service interfaces. The algorithm automatically distributes the keyword weights for the request service, in order to weaken the weight of some keywords in request service have the same background in services set, which is to help the request service find the more accurate results. The experiments show that our solution outperforms some searching methods.
A recurrent cycle consistency loss for progressive face-to-face synthesis

This paper addresses a major flaw of the cycle consistency loss when used to preserve the input appearance in the face-to-face synthesis domain. In particular, we show that the images generated by a network trained using this loss conceal a noise that hinders their use for further tasks. To overcome this limitation, we propose a “recurrent cycle consistency loss” which for different sequences of target attributes minimises the distance between the output images, independent of any intermediate step. We empirically validate not only that our loss enables the re-use of generated images, but that it also improves their quality. In addition, we propose the very first network that covers the task of unconstrained landmark-guided face-to-face synthesis. Contrary to previous works, our proposed approach enables the transfer of a particular set of input features to a large span of poses and expressions, whereby the target landmarks become the ground-truth points. We then evaluate the consistency of our proposed approach to synthesise faces at the target landmarks. To the best of our knowledge, we are the first to propose a loss to overcome the limitation of the cycle consistency loss, and the first to propose an “in-the-wild” landmark guided synthesis approach.
Low-Cost Anti-Copying 2D Barcode by Exploiting Channel Noise Characteristics

In this paper, for overcoming the drawbacks of the prior approaches, such as low generality, high cost, and high overhead, we propose a Low-Cost Anti-Copying (LCAC) 2D barcode by exploiting the difference between the noise characteristics of legal and illegal channels. An embedding strategy is proposed, and for a variant of it, we also make the corresponding analysis. For accurately evaluating the performance of our approach, a theoretical model of the noise in an illegal channel is established by using a generalized Gaussian distribution. By comparing with the experimental results based on various printers, scanners, and a mobile phone, it can be found that the sample histogram and curve fitting of the theoretical model match well, so it can be concluded that the theoretical model works well. For evaluating the security of the proposed LCAC code, besides the direct-copying (DC) attack, the improved version, which is the synthesized-copying (SC) attack, is also considered in this paper. Based on the theoretical model, we build a prediction function to optimize the parameters of our approach. The parameters optimization incorporates the covertness requirement, the robustness requirement and a tradeoff between the production cost and the cost of illegally-copying attacks together. The experimental results show that the proposed LCAC code with two printers and two scanners can detect the DC attack effectively and resist the SC attack up to the access of 14 legal copies.
DRIL: Descriptive Rules by Interactive Learning

Analyzing data is increasingly a part of jobs across industry, science and government, but data stakeholders are not necessarily experts in analytics. The human-in-the-loop (HIL) approach includes semantic interaction tools, which leverage machine learning behind the scenes to assist users with their tasks without engaging them directly with algorithms. One widely applicable model for how humans under-stand data is descriptive rules, which can characterize important attributes and simultaneously their crucial values or ranges. In this paper, we introduce an approach to help with data understanding via interactively and automatically generated rules. Our approach makes discerning the behavior of groups of interesting data efficient and simple by bridging the gap between machine learning methods for rule learning and the user experience of sensemaking through visual exploration. We have evaluated our approach with machine learning experiments to confirm an existing rule learning algorithm performs well in this interactive context even with a small amount of user input, and created a prototype system, DRIL (Descriptive Rules by Interactive Learning), to demonstrate its capability through a case study.
Vietnamese Question Answering System f rom Multilingual BERT Models to Monolingual BERT Model

A question answering (QA) system based on natural language processing and deep learning gets more attention from AI communities. Many companies and organizations are interested in developing automated question answering systems which are being researched widely. Recently, the new model named Bidirectional Encoder Representation from Transformer (BERT) was proposed to solve the restrictions of NLP tasks. BERT achieved the best results in almost tasks that include QA tasks. In this work, we tried applying the multilingual BERT models (multilingual BERT [1], DeepPavlov multilingual BERT, multilingual BERT fine-tuned on XQuAD) and the language-specific BERT model for Vietnamese (PhoBERT). The obtained result has shown that the monolingual model outperforms the multilingual models. We also recommend multilingual BERT fine-tuned on XQuAD model as an option to build a Vietnamese QA system if the system is built from a multilingual BERT based model.
Interpretable Visualizations of Deep Neural Networks for Domain Generation Algorithm Detection

Due to their success in many application areas, deep learning models have found wide adoption for many problems. However, their black-box nature makes it hard to trust their decisions and to evaluate their line of reasoning. In the field of cybersecurity, this lack of trust and understanding poses a significant challenge for the utilization of deep learning models. Thus, we present a visual analytics system that provides designers of deep learning models for the classification of domain generation algorithms with understandable interpretations of their model. We cluster the activations of the model's nodes and leverage decision trees to explain these clusters. In combination with a 2D projection, the user can explore how the model views the data at different layers. In a preliminary evaluation of our system, we show how it can be employed to better understand misclassifications, identify potential biases and reason about the role different layers in a model may play.
Virtual Reality 360 UTM Campus Tour with Voice Commands

Virtual Reality (VR) is the way for the human to interact with their dream world through computer technology by simulating the experience of a realistic world. This paper presents the development of Virtual Reality 360 Universiti Teknologi Malaysia (UTM) Campus Tour with voice commands using the Head Mounted Display (HMD) as the medium to experience the simulated artificial world. This technology was designed for smartphone users to maximize the lower cost budget VR and affordable by everyone. Besides, the application was developed by using Unity3D, VR software development kit (SDK), and IBM Watson Speech Platform application programming interface (API). The VR application presents some of the interesting places and schools of UTM as the main attraction in VR world with the 360-degree view. The existing problem is that some of the students and visitors are not familiar with the school and the places around UTM. Hence, this project was developed to assist them to understand UTM better and promote the beauty of this university. This paper discussed the progress of the VR technology for campus tour from the basic overview, the methodology until the user testing stage. The result was the complete prototype of a virtual reality 360 campus tour which can promote UTM Skudai as one of the beautiful campuses to visit.