Reviewerly/exHarmony huggingface dataset:
A Distributed Automatic Video Annotation Platform

In the era of digital devices and the Internet, thousands of videos are taken and share through the Internet. Similarly, CCTV cameras in the digital city produce a large amount of video data that carry essential information. To handle the increased video data and generate knowledge, there is an increasing demand for distributed video annotation. Therefore, in this paper, we propose a novel distributed video annotation platform that explores the spatial information and temporal information. Afterward, we provide higher-level semantic information. The proposed framework is divided into two parts: spatial annotation and spatiotemporal annotation. Therefore, we propose a spatiotemporal descriptor, namely, volume local directional ternary pattern-three orthogonal planes (VLDTP–TOP) in a distributed manner using Spark. Moreover, we developed several state-of-the-art appearance-based and spatiotemporal-based feature descriptors on top of Spark. We also provide the distributed video annotation services for the end-users so that they can easily use the video annotation and APIs for development to produce new video annotation algorithms. Due to the lack of a spatiotemporal video annotation dataset that provides ground truth for both spatial and temporal information, we introduce a video annotation dataset, namely, STAD which provides ground truth for spatial and temporal information. An extensive experimental analysis was performed in order to validate the performance and scalability of the proposed feature descriptors, which proved the excellence of our proposed approach.
Alert System for Drivers based on Traffic Signs, Lights and Pedestrian Detection

The research on this topic was important primarily because of the high rate of accidents due to negligence of the traffic signs present. This led to finding ways of creating an alert system for each vehicle which would ensure the safety of those driving the vehicle. Not only should it ensure the safety of the drivers, but also of all the pedestrians and animals through their detection. Many methods were studied including SSD, R-FCN, orthodox neural networks but the implementation of YOLO (You Only Look Once) was carried out because of the efficiency in the image processing rate and the accuracy of the object detection even with custom datasets. The datasets considered include GTSRB, COCO and Bosch for Traffic Signs, Human Detection and Traffic Light detection respectively. The model is trained along with a pre-trained YOLO dataset comprising of 80 classes. The execution results in an accuracy of 91.12% at an image processing speed of 30 fps beating all the methods present including SSD, R-FCN and R-CNN. The above accuracy is the average accuracy obtained on combining the 3 custom datasets, which independently has a much higher accuracy when trained on YOLO.
Image compression for quality 3D reconstruction

A 3D mesh can be reconstructed from multiple viewpoint images or from a single structured light image. Lossy compression of such images by standard techniques such as JPEG at high compression ratios lead to 3D reconstruction being adversely affected by artifacts and missing vertices. In this paper we demonstrate an improved algorithm capable of high compression ratios without adversely affecting 3D reconstruction and with minimum data loss. The compression algorithm starts by applying block DCT over the input image, and the transformed data being quantized using an optimized quantization matrix. The quantized coefficients of each block are arranged as a 1D array and saved with other block’s data in a larger matrix of coefficients. The DC coefficients are subject to a first order difference whose values are referred to as residual array. The AC coefficients are reduced by eliminating zeros and saving the non-zero values in a reduced coefficients array using a mask of 0 (for a block of zeros) and 1 (for a block of non-zeros). Finally, arithmetic coding is applied to both coefficients and residual arrays. At decompression stage, the coefficients matrix is regenerated by scanning the coefficients array and examining the headers to substitute zero and non-zero data. This matrix is then added to the residual array to obtain the original DC values. The IDCT is then applied to obtain the original image. The proposed algorithm has been tested with images of varying sizes in the context of 3D reconstruction. Results demonstrate that our proposed algorithm is superior to traditional JPEG at higher compression ratios with high perceptual quality of images and the ability to reconstruct the 3D models more effectively, both for structured light images and for sequences of multiple viewpoint images.
Raspberry Pi Based Wearable Reader For Visually Impaired People with Haptic Feedback

Visually Impaired people report numerous difficulties with accessing printed text. The current scenario of reading for blind people is with the help of the Braille system, which is a code-system of dots represent letters of an alphabet. Not all books are written in Braille, thus a visually impaired person is limited to a countable number of books. Hence, there is a need to make a reading device that enables better manageable eyes-free operation (reading). The current work proposes a wearable reader that captures real-time images of printed text from a book using a high-resolution miniature camera. The images of the printed text are processed to convert it as a computerized text using raspberry pi microcontroller. Vibration motors were embedded in the device that guides the user to orient with the direction of reading in case they get deviated from the current text-line. The computerized text can be heard as a voice by the user. The device can be worn in finger and gain access to a various number of learning resources and can be widely used by the blind people for their studies.
KV-Fresh: Freshness Authentication for Outsourced Multi-Version Key-Value Stores

Data outsourcing is a promising technical paradigm to facilitate cost-effective real-time data storage, processing, and dissemination. In such a system, a data owner proactively pushes a stream of data records to a third-party cloud server for storage, which in turn processes various types of queries from end users on the data owner's behalf. This paper considers outsourced multi-version key-value stores that have gained increasing popularity in recent years, where a critical security challenge is to ensure that the cloud server returns both authentic and fresh data in response to end users' queries. Despite several recent attempts on authenticating data freshness in outsourced key-value stores, they either incur excessively high communication cost or can only offer very limited real-time guarantee. To fill this gap, this paper introduces KV-Fresh, a novel freshness authentication scheme for outsourced key-value stores that offers strong real-time guarantee. KV-Fresh is designed based on a novel data structure, Linked Key Span Merkle Hash Tree, which enables highly efficient freshness proof by embedding chaining relationship among records generated at different time. Detailed simulation studies using a synthetic dataset generated from real data confirm the efficacy and efficiency of KV-Fresh.
Parsing with zippers (functional pearl)

Parsing with Derivatives (PwD) is an elegant approach to parsing context-free grammars (CFGs). It takes the equational theory behind Brzozowski's derivative for regular expressions and augments that theory with laziness, memoization, and fixed points. The result is a simple parser for arbitrary CFGs. Although recent work improved the performance of PwD, it remains inefficient due to the algorithm repeatedly traversing some parts of the grammar. In this functional pearl, we show how to avoid this inefficiency by suspending the state of the traversal in a zipper. When subsequent derivatives are taken, we can resume the traversal from where we left off without retraversing already traversed parts of the grammar. However, the original zipper is designed for use with trees, and we want to parse CFGs. CFGs can include shared regions, cycles, and choices between alternates, which makes them incompatible with the traditional tree model for zippers. This paper develops a generalization of zippers to properly handle these additional features. Just as PwD generalized Brzozowski's derivatives from regular expressions to CFGs, we generalize Huet's zippers from trees to CFGs. Abstract The resulting parsing algorithm is concise and efficient: it takes only 31 lines of OCaml code to implement the derivative function but performs 6,500 times faster than the original PwD and 3.24 times faster than the optimized implementation of PwD.
Work-in-Progress—Converging Virtual Reality, Robots, and Social Networks to Support Immersive Learning

This work-in-progress paper describes the development of a conceptually led Cyber-Physical-Social EcoSystem (CPSeS) that seamlessly blends the real with the digital worlds using VR, Robots and Social Networking technologies to support immersive learning. The proposed CPSeS can bring students together by merging the real with virtual social spaces, enabling them to interact with each other, and with real and digital environments through physical and virtual robots. The framework used to design this system provides opportunities for developing innovative interactive systems and educational approaches to support and enhance immersive learning.
Blind Image Clustering for Camera Source Identification via Row-Sparsity Optimization

Given a set of images with the number of cameras providing those images unknown, how to blindly identify the sources of the images has been a critical problem in digital forensics. Although state-of-the-art methods have achieved impressive results, they have failed at suppressing outliers. When they deal with a noisy dataset, the performance is significantly degraded. To address this issue, we propose an optimization approach with sparsity constraints to simultaneously handle the how-many subproblem ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e.</i> , the number of cameras) and the which-from-which subproblem ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e.</i> , the image–camera relationship). In our approach, we first formulate the blind camera source clustering as a row-sparsity optimization problem, in which the representation errors are minimized and the outliers caused by noisy features are suppressed. Then, a new two-stage refinement method based on inter- and the intra-class differences is proposed to achieve a more accurate estimation of the number of cameras. Because strong sparsity constraints have been adopted and the interactive relationship among data points can be fully explored to distinguish the images originated from different cameras, the proposed method can effectively handle outliers. Extensive experiments on the popular Dresden dataset show that the proposed method outperforms existing methods in both identification accuracy and efficiency.
TARDIS: Rolling Back The Clock On CMS-Targeting Cyber Attacks

Over 55% of the world's websites run on Content Management Systems (CMS). Unfortunately, this huge user population has made CMS-based websites a high-profile target for hackers. Worse still, the vast majority of the website hosting industry has shifted to a "backup and restore" model of security, which relies on error-prone AV scanners to prompt users to roll back to a pre-infection nightly snapshot. This research had the opportunity to study these nightly backups for over 300,000 unique production websites. In doing so, we measured the attack landscape of CMS-based websites and assessed the effectiveness of the backup and restore protection scheme. To our surprise, we found that the evolution of tens of thousands of attacks exhibited clear long-lived multi-stage attack patterns. We now propose TARDIS, an automated provenance inference technique, which enables the investigation and remediation of CMS-targeting attacks based on only the nightly backups already being collected by website hosting companies. With the help of our industry collaborator, we applied TARDIS to the nightly backups of those 300K websites and found 20,591 attacks which lasted from 6 to 1,694 days, some of which were still yet to be detected.
Directional Sampling-Based Generalized Shape Expansion for Accelerated Motion Planning in 2-D Obstacle-Cluttered Environments

Recently proposed Generalized Shape Expansion (GSE) algorithm for planning of shortest collision-free path in 2-D environments has shown significant promise in improvement of its performance over several seminal algorithms from existing literature. Recognizing that a suitable directional sampling feature could potentially enhance the performance of the GSE algorithm further, this letter proposes two sampling schemes - basic and augmented directional sampling - and presents GSE-D and GSE-AD algorithms, respectively, as expansion over the GSE. These algorithms, by default, enjoy the advantages of the GSE. Both the directional sampling schemes enable drawing random sample points with more preference towards the direction of the Goal leading to lower cost of computed shortest path on an average. While the basic directional sampling strategy faces a drawback in computational time when obstacle density in the direction towards the Goal is high, the augmented directional sampling scheme is free of this limitation. Probabilistic analysis and extensive numerical simulation studies show the effectiveness of the GSE-D and GSE-AD in performance in terms of computational time efficiency and shortest path cost when compared with the GSE, other seminal and existing directional algorithms.
Two-Stage Adaptive Constrained Particle Swarm Optimization Based on Bi-Objective Method

For the sake of better balancing the relationship between diversity and convergence when handling constrained optimization problems, a two-stage adaptive constrained particle swarm optimization algorithm based on bi-objective method (TABC-PSO) is proposed. In accordance with different phases of the constraint process, the target-constraint space derived from the angle is partitioned adaptively, and simultaneously the global best particle is selected and the external archive set is safeguarded. In the first stage, the whole space is divided adaptively in term of the angular distribution of individual, and the feasible region is explored comprehensively. In the second stage, local regions are adaptively compartmentalized and in-depth exploitation is carried out. Primary and secondary external archive sets are established to maintain population diversity and accelerate convergence. The two phases are switched adaptively in light of the storage status of the two external archive sets. We evaluated TABC-PSO algorithm on the benchmark functions in CEC 2006 and CEC 2017. The experimental results show that TABC-PSO algorithm compared with other state-of-the-art algorithms can be superior to applied to test functions with different types of constraints and possesses a competitive search capability.
KGGen: A Generative Approach for Incipient Knowledge Graph Population

Knowledge graph is becoming an indispensable resource that offers structured information for numerous AI applications. However, the knowledge graph often suffers from its incompleteness. Building a complete, high-quality knowledge graph is time-consuming and requires significant human annotation efforts. In this paper, we study the Knowledge Graph Population task, which aims at extending the scale of structured knowledge, with a special focus on reducing data preparation and annotation efforts. Previous works mainly based on discriminative methods build classifiers and verify candidate triplets that are extracted from texts, which heavily rely on the quality of data collection and co-occurrance of entities in the text. However, such methods fail to generalize on entity pairs that are not highly co-occurred, and fail to discover entity pairs that are not co-occurred at all in the given text corpus. We introduce a generative perspective to approach this task and define each relationship by learning the data distribution that embodies the core common properties for relational reasoning. A generative model <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">KGGen</small> is proposed, which samples from the learned data distribution for each relation and can generate triplets regardless of entity pair co-occurrence in the text corpus. To further improve the generation quality while alleviate human annotation efforts, adversarial learning is adopted to not only encourage generating high quality triplets, but also give model the ability to automatically assess the generation quality. Quantitative and qualitative experimental results conducted on two real-world generic knowledge graphs show that the proposed model <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">KGGen</small> generates novel and meaningful triplets with improved efficiency and less human annotation comparing with the state-of-the-art approaches.
Game Theoretic Approach for Secure and Efficient Heavy-Duty Smart Contracts

Smart contracts in the blockchain systems such as Ethereum are usually executed or verified by all nodes, and thus inefficient for heavy-duty computation. This paper addresses the limitation by proposing, implementing, and evaluating a practical and efficient solution based on a game theoretic approach. The solution defines a template of heavy-duty smart contract (HDSC); recruits only a small number of executors to execute heavy-duty tasks; employs a game theoretic scheme to enforce economically-rational executors to individually or collectively perform the execution correctly. Extensive game theoretic analysis has been conducted to show the security and computational efficiency of the solution even in face of collusion among the executors. As a proof of concept, the proposed solution has been implemented and experimented to demonstrate its practicality and compatibility with Ethereum.
Advanced Data Balancing Method with SVM Decision Boundary and Bagging

In the field of data mining applications, the most challenging research work is to classify imbalanced data in supervised learning. From the beginning of the machine learning era, a huge number of researches has done by inventing a lot of successful data balancing methods, however, these are not yet fully compatible to learn from imbalanced data. The key reason is data is producing in every moment and randomly by various sources, like humans, machines, sensors, robots, and so on. To date, the existing machine learning algorithms mostly biased to the majority class instances and neglecting the minority class instances, therefore they have a crucial effect on the prediction capacity and exactness on the final results. To deal with imbalanced data the most popular classifiers are under-sampling and over-sampling, cost-sensitive learning and ensemble learning. In this paper, we have presented a novel data balancing method by employing clustering technique with SVM especially decision boundary instances for creating balanced data. The proposed approach selects the most informative majority class and minority class support vectors nearest to the decision boundary or hyperplane, and finally, combines them to establishes a novel method. We have experimented with the performance of our proposed technique with some popular single classifiers such as C4.5 Decision Tree (DT) and Naive Bayes (NB) classifier and ensemble classifiers such as Random Forest and AdaBoost on thirteen standard real-life imbalanced datasets. The experimental result shows apparently, that the proposed idea generates improved accuracy when classifying both the minority and majority class instances compared to other existing methods.
An Exploration of Cyber Symbology

The following topics are dealt with: data visualisation; data analysis; security of data; data mining; pattern classification; Web sites; computer network security; Internet; invasive software; and sentiment analysis.
Stability analysis of dynamic interval type‐2 fuzzy control systems in frequency domain

Abstract The present paper aims to analyze the stability of dynamic Interval Type‐2 fuzzy control systems (IT2 FCSs) with adjustable parameters by investigating the existence of limit cycles based on the describing function (DF) method. To simplify the stability analysis, the following structured procedure is described. First, to avoid complex computation, a simple architecture of the IT2 FCS using two embedded Type‐1 fuzzy control systems (T1 FCSs) is proposed. Then, the DF of IT2 FCS is obtained based on the DFs of embedded T1FCSs. Subsequently, integrating the stability equation and parameter plane approaches provides a solution to identify the limit cycle and the asymptotically stability regions. Moreover, particle swarm optimization (PSO) technique is utilized to minimize the limit cycle region. Other focus of this paper is on the stability margins analysis for robust design. For this purpose, a gain‐phase margin tester is applied to determine the minimum Gain Margin ( G M m i n ) and Phase Margin ( P M m i n ) when limit cycles can arise. Finally, two simulation examples are given to verify the effectiveness of the proposed approach.
On the Differences Between Practical and Cognitive Presumptions

Abstract The study of presumptions has intensified in argumentation theory over the last years. Although scholars put forward different accounts, they mostly agree that presumptions can be studied in deliberative and epistemic contexts, have distinct contextual functions (guiding decisions vs. acquiring information), and promote different kinds of goals (non-epistemic vs. epistemic). Accordingly, there are “practical” and “cognitive” presumptions. In this paper, I show that the differences between practical and cognitive presumptions go far beyond contextual considerations. The central aim is to explore Nicholas Rescher’s contention that both types of presumptions have a closely analogous pragmatic function, i.e., that practical and cognitive presumptions are made to avoid greater harm in circumstances of epistemic uncertainty. By comparing schemes of practical and cognitive reasoning, I show that Rescher’s contention requires qualifications. Moreover, not only do practical and cognitive presumptions have distinct pragmatic functions, but they also perform different dialogical functions (enabling progress vs. preventing regress) and, in some circumstances, cannot be defeated by the same kinds of evidence. Hence, I conclude that the two classes of presumptions merit distinct treatment in argumentation theory.
Takagi-Sugeno-Kang fuzzy PID control for DC electrical machines

This paper deals with Takagi-Sugeno-Kang (TSK) type fuzzy PID controllers in the context of nonlinear dynamic systems. The design framework for tuning the controller gains depends on particle swarm optimization (PSO), assuming the nonlinear system approximated by an artificial neural network, leading to an overall robust control methodology based on TSK fuzzy PID control. Obtained data and information from simulations and experimental tests considering a nonlinear dynamic process including a DC electrical machine confirm the effectiveness of the control strategy.
Discovery of Elsagate: Detection of Sparse Inappropriate Content from Kids Videos

Elsagate refers to kids videos containing inappropriate content for children but difficult to filter thus often shown up on kids channels. Most of the mainstream kids channels have powerful filters. However, when the inappropriate content is sparse, the filters often fail to detect the inappropriateness. In this paper, we introduce our work in progress, a scheme to detect Elsagate videos based on Sparse Linear Discrimination (SLD), an effective way to help detect and classify these kinds of unsafe videos and enrich better user experience.