Reviewerly/exHarmony huggingface dataset:
Learning Motion Regularity for Temporal Video Segmentation and Anomaly Detection

Detecting anomalous events in a long video sequence is a challenging task due to the subjective definition of “anomalous” as well as the duration of such events. Anomalous events are usually short-lived and occur rarely. We propose a semi-supervised solution to detect such events. Our method is able to capture the video segment where the anomaly happens via the analyses of the interaction between the spatially co-located interest points. The evolution of their motion characteristics is modeled and abrupt changes are used to temporally segment the videos. Spatiotemporal and motion features are then extracted to model standard events and identify the anomalous segments using a one-class classifier. Quantitative and qualitative experiments on publicly available anomaly detection dataset capturing real-world scenarios show that the proposed method outperforms state-of-the-art approaches.
A Scalable Graph Analytics Framework for Programming with Big Data in R (pbdR)

Many disciplines such as biology, economics, engineering, physics, and the social sciences represent their data as graphs to capture patterns, trends, and associations. There are are many commercially available graph libraries in different programming languages to analyze these complex graphs. But there is no distributed graph library package in R - the popular statistical programming language to analyze graphs that bigger than a single machine's memory. Many domain experts prefer R over the numerous other alternatives. Towards this, we present a distributed graph analytics framework for R called programming with big graph using R (pBGR.) Our proposed framework leverages the Programming with Big Data in R (pbdR) ecosystem that provides scalable R packages for distributed computing in data science. We present an early prototype implementation of this framework using the distributed-memory parallel graph library CombBLAS and evaluate the framework's performance on leadership class computing platforms. Our experimental results demonstrate that the proposed framework is capable of performing large-scale parallel graph mining through the easyto-use R language. This enhanced graph processing capability coupled with other statistical tools already available in R, should be valuable to many domain experts.
Improved Gamma Corrected Layered Adaptive Background Model

This paper proposes a method for pixel-based background subtraction with improved gamma correction and a layered adaptive background model (IGLABM). The main problems of background subtraction are background oscillation and shadow. To solve these problems, we have proposed the gamma corrected layered adaptive background model (GLABM), however the performance of GLABM is not sufficient for real scenes. We hence improve the gamma estimation and prepossessing step of GLABM in this study using the covariance matrix of each pixel. We demonstrate the performance of the proposed improved method by comparing it with GLABM and other pixel-based background subtraction methods.
Think That Attackers Think: Using First-Order Theory of Mind in Intrusion Response System

The intrusion response system is dedicated to automatically respond to sophisticated network intrusions, which is a sequential decision-making problem for autonomous agents. The current Markov decision process (MDP) or stochastic games based solutions suffer from several weaknesses: (i) The MDP- based approach is unable to explicitly model the opponents; (ii) The Nash equilibrium approach of stochastic games cannot handle the condition with multi equilibria. Existing studies have not considered the cognitive ability of the agent and lack of explicit opponent modeling. Inspired by recursive reasoning, this paper introduces a theory of mind (ToM)-based stochastic game-theoretic approach to reason about the beliefs and behaviors of the attackers. Each agent maintains different order ToM beliefs concerning his opponent with explicit opponent modeling. In order to accurately predict the attacker's action with nested beliefs, we utilize the Bayesian attack graph (BAG) to model multi-step attacks scenarios. In addition, the agent is allowed to learn from new information to adjust his beliefs and learning speed. Simulation results validate that ToM modeling performs well in the intrusion response system than random defense actions. Besides, a defender with first-order ToM beliefs always wins an attacker with zero-order ToM beliefs.
Some Possible Trajectory Planning Methodologies for an Articulated Deep Soil Mixing Equipment with Limited Degrees-of-Freedom

An innovative concept of mixing soil around obstacles via a newly developed equipment with articulated joints is currently being developed by a research group in NUS. To assist and supervise the movement of articulated joints in the real site, a supervisory software for trajectory mapping for the hardware of articulated joints is required. This study compares four approaches of controlling and optimizing for the subterranean trajectory of a three-jointed articulated deep mixing equipment, based on a proposed mathematical model, which is used to trace coordinates of joints in each step. Through the understanding of the equipment's behaviour, the programs of deep soil mixing trajectory calculation using different methodologies, are developed and improved to achieve more functions and better performance. Comparison and analysis of all the feasible methodologies are included to obtain the optimal program for real world applications.
Designing an Immersive Environment from the User's Perspective

This review examines Casey Fictum's "VR UX: 100 Pages of VR UX, Design, Sound, Storytelling, Movement &amp; Controls." Fictum creates a fast-paced, concise guide for readers looking to create virtual reality environments centered around the consumers who use the technology. The book interlaces technical information, imaginative storytelling, testing protocols, checklists, and amusing stories. This gives the reader perspective through the lens of a true expert in the fields of virtual reality, experience creation, head-mounted displays, and field-testing the technology. The review evaluates the strengths of the book and analyzes the author's process in creating and evaluating meaningful content that is comfortable for the targeted virtual reality user.
Time-dependent spontaneous localization processes

Time dependent Lindblad generators have mostly been studied for discrete variable open quantum systems. We hereby initiate the study of the complete positivity of a continuous one-dimensional quantum system subjected to time-dependent spatial localizations with back-flow of information.
Template-Based Space-Saving Approach for SMS Storage in A Server

Nowadays, SMS based communication is common in almost every sector. Private and government organizations deliver and receive millions of SMSs every year. This huge amount of SMSs can be used later in several useful ways. However, the storage space required for storing these huge amount SMSs creates so much complexity for the SMS service providing organizations that they cannot save the details of every SMS. Therefore, in this paper, we introduce a template-oriented SMS storage system based on a graph-theoretic approach, which reduces storage space requirements significantly. We achieve the storage space reduction through considering the fixed parts of SMSs as graph nodes and variable parts as graph edges. We perform experimentation on real SMS data collected from different telecom operators of Bangladesh. Our experimentation confirms that our proposed system substantially reduces the storage space requirement. To the best of our knowledge, we are the first to propose such a space-saving system for storing SMSs.
Philosophical and Cybernetic Approaches to the Definition of the Term «Virtual Reality»

In modern scientifi c Russian discourse, there are two approaches to the study of virtual reality and the terms associated with its technologies: philosophical and cybernetic. The problems arising for researchers of virtual reality in the context of the educational process are often due to the inability to identify which of the provisions is based on a particular approach, which leads to diffi culties in solving the tasks. Philosophical conclusions about the nature of reality will be less suitable for solving applied problems in the fi eld of information technology, and vice versa, technical and quantitative measurements of the cybernetic approach will less likely answer ethical questions on the use of virtual reality. Based on the analysis of the above approaches and the identifi cation of their diff erences, a conclusion is drawn on the need for a cybernetic approach in organizing virtual reality software and hardware systems for the educational process.
Accelerating the performance of Sequence Alignment using High Performance Multicore GPU

The computation grows quadratically with increase in sequences that needs to be aligned. The sequence alignment needs improvement in execution speed with the increase in database size. Dynamic programming approach if applied to search databases then it can be computationally expensive so it needs hardware-based acceleration like GPU which helps in searching databases quickly. Instead of using single threaded CPU based computing if computing capability of CPU and GPU are combined together then this hybrid approach results in much faster computation than the sequential approach. This improvement utilizes the parallel architecture of GPU for high performance computing. This improved strategy outperforms single threaded CPU based computing. Both performances are compared with different lengths of sequences. This paper also compares the parallel strategy by comparing different types of available GPU, types of memory access and different types of alignment algorithms on different benchmarks.
Subject-Oriented Data Retrieval and Analysis on Sina Weibo

The goal of this project is to timely identify and characterize hot topics discussed on the Chinese microblogging website, Sina Weibo. First, we develop a subject-oriented crawler to retrieve Weibo posts. Second, a probabilistic topic model is applied to the dataset to identify prominent topics and keywords. A comparison on hot topics, Word Cloud, and Latent Dirichlet Allocation results are discussed, followed by future research directions.
How context affects perception: judging distress &amp; linguistic content in forensic audio recordings

In order to assess the role of context on the perception of speech sounds, extracts of speech material from authentic forensic cases were played to two groups of listeners (experienced vs inexperienced forensic phoneticians) in two conditions (with and without sequential context).Listeners were asked to categorise each extract according to a four-way scale of perceptible distress and rate each extract according to a five-point scale of perceptible linguistic content.Findings show that extracts played without context are perceived as reflecting higher degrees of distress and lower levels of linguistic content.Experienced phoneticians performed more consistently as a group and were less prone to changing their responses across the two conditions.
Contextual Price Features for e-Commerce Search Ranking

Motivation The price of an item is one of the most useful attributes for e-commerce search. A prominent feature used by the machine learned ranker at eBay search measures, for each query, the deviance of the price of a candidate item to be ranked from the typical price distribution of the clicked items for that query. Like most e-commerce websites, in eBay search, users can restrict their search queries to certain specific categories, where category refers to nodes in the product catalog structure. As an example, a user could search for mens shirts and restrict results to either T-Shirts or Dress Shirts. In its most simple form, the price deviance feature is calculated in a category context agnostic fashion. However, the distribution from which the query price statistics (e.g. median, variance) are drawn varies significantly for different categories for the same query. For example, the query iphone x will have very different price distribution among clicked items in the Cell Phones Smartphones category as opposed to the Cell Phone Accessories category. The motivation of this work stems from accounting for category context for user queries while deriving and leveraging such distributional statistics. The core idea is extendable to other features used in the current search ranker besides item price, such as query-title similarity measure, and also to other contexts besides category, such as structured key value aspects. Problem Statement Given a query and a set of matching items, the machine learned search ranking model uses an array of diverse features to rank items. The problem addressed in this work is to enhance one such feature capturing item price deviance from query level price statistics to be sensitive to the category context of queries. Concretely, the ranker should be able to use different price distributions for the same query depending on the category context in which the query was issued. This problem has intriguing challenges in two major areas: first, since we have a hierarchical category tree structure, we need to figure out an effective way to propagate the price distribution across the tree; second, selecting the right statistic to measure the price deviation of candidate items from the typical price distribution for the query.
Supervised Interactive Co-segmentation Using Histogram Matching and Bipartite Graph Construction

The identification and retrieval of images of same or similar objects finds application in various tasks that are of prime importance in Image Processing and Computer Vision. Accurate and fast extraction of the object of interest from several images is essential for the construction of 3D models, image retrieval applications. The joint partitioning of multiple images having same or similar objects of interest into background and foreground parts is referred to as co-segmentation. This article proposes a novel and efficient interactive co-segmentation method based on the computation of a global energy function and a local smooth energy function. Computation of global energy function from the scribbled regions of the images is based on histogram matching. This is used to estimate the probability of each region belonging either to foreground or background region. The local smooth energy function is used to estimate the probability of regions having similar colour appearance. To further improve the quality of the segmentation, a bipartite graph is constructed using the segments. The algorithm has been implemented on iCoseg and MSRC benchmark data sets. The extensive experimental results show significant improvement in performance compared to many state-of-the-art unsupervised co-segmentation and supervised interactive co-segmentation methods, both in computational time and accuracy.
Wavelet Denoising of Remote Sensing Image Based on Adaptive Threshold Function

Aiming at the problem of edge feature loss caused by conventional threshold function in wavelet transform, a new adaptive threshold function denoising algorithm is proposed based on improved threshold. The algorithm takes advantages of the improved threshold functions, and takes the scale of the current wavelet decomposition as a function adjustment factor, so that the function can be adjusted by adaptive scale transformation, which is more in line with the actual distribution of noise in each scale. A few noisy remote sensing images are tested and the simulation results of MATLAB confirm the merits of the proposed denoising technique compared with other wavelet-based techniques by measuring evaluation metrics such as signal-to-noise ratio and mean square error. Furthermore, the improved threshold function can obtain better visual effects which ensures the detail features in remote sensing images are better preserved.
Privacy Preserving Opinion Publishing System for Opinion Poll

Public opinion is the belief or thought of a group of people regarding a particular topic, especially on politics, religion, social issues, demographic diversity, etc. The opinion of the individual may be sensitive since it reflects the perception, understanding, belief, conflict, and desire of that individual. The traditional mechanisms of collecting public opinions (e.g., survey, questionnaire, interviewer, mailing) are often compromising individual's privacy. Those mechanisms do not anonymize the individual's identity. Therefore people are often abstained from expressing their rational opinions. The electronic opinion collection systems are designed with a central server that keeps all the user's profiles, which may be linked the opinions, thereby compromises the privacy. In this paper, we present a multiparty opinion collecting and publishing protocol that preserves the user's identity. The protocol does not rely on any trusted third party (e.g. central server). It rather forms quorums to execute the process. Party independently cannot learn the individual's opinions. Moreover, the protocol publishes the opinions anonymously, thereby preserves the privacy of the individuals.
Bayesian transfer learning between Gaussian process regression tasks

Bayesian knowledge transfer in supervised learning scenarios often relies on a complete specification and optimization of the stochastic dependence between source and target tasks. This is a critical requirement of completely modelled settings, which can often be difficult to justify. We propose a strategy to overcome this. The methodology relies on fully probabilistic design to develop a target algorithm which accepts source knowledge in the form of a probability distribution. We present this incompletely modelled setting in the supervised learning context where the source and target tasks are to perform Gaussian process regression. Experimental evaluation demonstrates that the transfer of the source distribution substantially improves prediction performance of the target learner when recovering a distorted nonparametric function realization from noisy data.
Chaos-Based Gary Image Encryption Using Two Coupled Competition Type Maps

In this chapter, we present analytic study of nonlinear dynamics that exist in two coupled competition maps. The possible existence of various types of bifurcation in the model including flip bifurcation, pitchfork bifurcation and Neimark–Sacker bifurcation are explored. The presence of chaotic dynamics in behavior of the map has been detected for large domain of parameters. Then, numerical simulations are performed to prove theoretical analysis. Finally, a chaos-based encryption algorithm for gray images is presented as an application for the competition map. The proposed cryptography algorithm is based on the coupled competition map. The implementation of the algorithm is introduced and some aspects of security analysis such as NIST SP 800-22 statistical tests, sensitivity to mismatch in parameters, correlation coefficients for pixels, entropy analysis, and immunity to differential attacks are examined. Comparison with other related works are carried out. The numerical results given in this chapter elucidate that the introduced scheme has a large security keys space, high sensitivity to tiny variations in critical parameters and negligible small coefficients of correlation between adjacent pixels in cipher images.
An arithmetic interpretation of intuitionistic verification

Abstract Intuitionistic epistemic logic introduces an epistemic operator to intuitionistic logic, which reflects the intended Brouwer–Heyting–Kolmogorov (BHK) semantics of intuitionism. The fundamental assumption concerning intuitionistic knowledge and belief is that it is the product of verification. The BHK interpretation of intuitionistic logic has a precise formulation in the logic of proofs and its arithmetical semantics. We show here that this interpretation can be extended to the notion of verification upon which intuitionistic knowledge is based, providing the systems of intuitionistic epistemic logic based on verification with an arithmetical semantics too. This confirms that the conception of verification incorporated in these systems reflects the BHK interpretation.
Multi Facet Face Construction

To generate a multi-faceted view, from a single image has always been a challenging problem for decades. Recent developments in technology enable us to tackle this problem effectively. Previously, Several Generative Adversarial Network (GAN) based models have been used to deal with this problem as linear GAN, linear framework, a generator (generally encoder-decoder), followed by the discriminator. Such structures helped to some extent, but are not powerful enough to tackle this problem effectively. In this paper, we propose a GAN based dual-architecture model called DUO-GAN. In the proposed model, we add a second pathway in addition to the linear framework of GAN with the aim of better learning of the embedding space. In this model, we propose two learning paths, which compete with each other in a parameter-sharing manner. Furthermore, the proposed two-pathway framework primarily trains multiple sub-models, which combine to give realistic results. The experimental results of DUO-GAN outperform state of the art models in the field.
Delegation of Computation Using FV Cryptosystem

Homomorphic encryption is a very promising cryptosystem for industrial. However, it is impossible to take a decision based on homomorphic cipher texts comparison. In this paper we provide a method to do a comparison between homomorphic cipher texts, using a secure element. We give a detailed description of an IoT use case where homomorphic encryption is used to ensure security, privacy, anonymity, and aggregation. This use case shows the necessity of cipher texts comparison to turn a real use case in an IoT environment.