Reviewerly/exHarmony huggingface dataset:
Comparison of Neural Network–Based Adaptive Controllers Using Hypercomplex Numbers for Controlling Robot Manipulator

This study investigates an adaptive controller by applying a neural network, in which all the network parameters, states, signals and functions are expressed using hypercomplex numbers and algebras; its application to dynamics control of a robot manipulator. To design hypercomplex–valued neural networks where each neural network is a multilayer feedforward network with a split–type activation function of neurons using a tapped–delay–line input, we consider the following four types of hypercomplex numbers: complex, hyperbolic, bicomplex and quaternion numbers. In the control system, we utilise a feedback error–learning scheme to conduct the training of the network through a back–propagation algorithm. In the computational experiments, we explore a hypercomplex–valued neural network–based controller as a trajectory control problem of a three–link robot manipulator, in which the position of the end–effector follows to the desired trajectory in a 3–dimensional space. The simulation results validate the feasibility and effectiveness of the quaternion neural network–based controller for this task.
An Efficient Algorithm for Mining High Utility Quantitative Itemsets

Mining high utility quantitative itemsets (HUQIs) is now a novel research topic in data mining field, which consists of discovering sets of items having a high utility (e.g. high profit) and providing information about quantities of items in each itemset. In market analysis, it could supply for decision-makers that shopping behavior could bring high profit to the company. For example, the customers purchase M to N units of a product A and purchase P to Q units of a product B at the same time. However, mining HUQIs using existing algorithms remains very computationally expensive and makes the results hard to be utilized by users. In view of this, we propose a novel algorithm named HUQI-Miner (High Utility Quantitative Itemsets Miner) for efficiently mining HUQIs in databases. Experimental results on both real and synthetic datasets show that HUQI-Miner outperforms the state-of-the-art algorithms in terms of both execution time and memory usage.
Graph Convolutional Networks-Hidden Conditional Random Field Model for Skeleton-Based Action Recognition

Recently, Graph Convolutional Network(GCN) methods for skeleton-based action recognition have achieved great success due to their ability to preserve structural information of the skeleton. However, these methods abandon the structural information in the classification stage by employing traditional fully-connected layers and softmax classifier, leading to sub-optimal performance. In this work, a novel Graph Convolutional Networks-Hidden conditional Random Field (GCN-HCRF) model is proposed to solve this problem. The proposed method combines GCN and HCRF to retain the human skeleton structure information during the classification stage. The proposed model is trained end-to-end by utilizing the message passing from the belief propagation algorithm on the human structure graph. To further capture spatial and temporal information, we propose a multi-stream framework that takes the relative coordinates of the joints and bone direction as two static feature streams and the temporal displacements as the dynamic feature stream. Experimental results on two challenging benchmarks (NTU RGB+D, N-UCLA) show the superior performance of the proposed model over state-of-the-art models.
On the Author Profiling and Deception Detection in Arabic shared task at FIRE

This paper summarises the Author Profiling and Deception Detection in Arabic (APDA) shared task at [email protected] 2019. Two have been the main aims of this year's task: i) to profile the age, gender and native language of a Twitter user; ii) to determine whether an Arabic text is deceptive or not in two different genres: Twitter and news headlines. For this purpose we have created three corpora in Arabic. Altogether, the approaches of 13 participants are evaluated.
Data-Transfer-Bottleneck-Less Architecture for FPGA-Based Quantum Annealing Simulation

Quantum Annealing (QA) is a classical probabilistic algorithm that provides a heuristic to find the globally optimal solution for a combinatorial optimization problem by using quantum tunneling processes. Quantum annealing simulation can be implemented on Field Programmable Gate Arrays (FPGAs) using Quantum Monte Carlo (QMC) simulation in the transverse Ising model. Since input data of the QMC simulation increases exponentially with the problem size, we have to use the DRAM of an FPGA board to store these data. However, storing data in DRAM causes two problems. One is the limited data access bandwidth, and the other is the limitation of DRAM capacity. We propose a data-transfer-bottleneck-less FPGA-based accelerator for quantum annealing simulation and apply it to implement number partitioning problem, which is one of the combinatorial optimization problems. The critical idea of our architecture is not storing but computing the large data in FPGA kernels and eliminating the burden on data transfer. We implement the proposed architecture on Stratix 10 FPGA and achieve up to 39.6 times speed-up compared to CPU-based quantum annealing simulation. We also achieve up to 2.8 times speed-up and implement 262,144 spins, which is 64 times increase compared to the most recent FPGA implementation.
Jordan surface theorem for simple closed SST-surfaces

The present paper proposes a new type of Jordan surface theorem for simple closed SST-surfaces. To do this work, we use space set topological (referred to as SST-, for short) structures on compact or non-compact surfaces in Rn. Indeed, the SST-structure is a special kind of Alexandroff topological structure and further, it is indeed a locally finite (LF-, for brevity) topological one. Owing to the LF-topological structure, it can efficiently be used in studying objects from the viewpoints of pure and applied topology such as discrete geometry, combinatorial topology, computational topology, digital topology, digital geometry and so on. The present paper first establishes an SST-structure of a subdivided abstract cell (SAC-, for brevity) complex on X(⊂Rn) using a generalized face to face tessellation of X⊂Rn. Second, the paper introduces a strong SST-homeomorphism to classify SST-surfaces. Finally, unlike the typical Jordan surface theorem under Hausdorff topology or polyhedral geometry, the current theorem is formulated by using Alexandroff topological or SST-structure. Thus this theorem has strong advantages being compared with those associated with Euclidean topology, digital topology and polygonal geometry. Besides, based on the SST-structure on an SAC-complex, the paper suggests some remarks on digital manifolds, Khalimsky manifolds, (n−1)-dimensional polyhedral pseudomanifolds and so on.
Verifying Arithmetic in Cryptographic C Programs

Cryptographic primitives are ubiquitous for modern security. The correctness of their implementations is crucial to resist malicious attacks. Typical arithmetic computation of these C programs contains large numbers of non-linear operations, hence is challenging existing automatic C verification tools. We present an automated approach to verify cryptographic C programs. Our approach successfully verifies C implementations of various arithmetic operations used in NIST P-224, P-256, P-521 and Curve25519 in OpenSSL. During verification, we expose a bug and a few anomalies that have been existing for a long time. They have been reported to and confirmed by the OpenSSL community. Our results establish the functional correctness of these C implementations for the first time.
In-loop Frame Super-resolution in AV1

AV1 is a recently standardized royalty-free video codec from the industry consortium Alliance for Open Media. One of the most innovative coding tools supported in AV1 is an in-loop frame super-resolution mode, that allows an encoder to code any frame at a horizontally reduced spatial resolution by one of several levels, followed by upsampling and super-resolving to full resolution, before replacing reference buffers. This mode is partly enabled by a feature in AV1 that natively allows the motion compensated prediction loop to operate across scales between a coded frame and the available references, thereby allowing on-the-fly resolution change mid-stream within a sequence. For the actual super-resolving process a normative upscaler is followed by an in-loop restoration tool that recovers some of the high frequency information lost in the downsampling process. On-the-fly resolution change capability in conjunction with the frame-superresolution mode in AV1 opens up a new dimension for codec bitrate and quality optimization that has not been possible to explore in any prior standardized video codec with (soon expected) decoding hardware support. This paper provides an overview of how the relevant tools work in AV1, but unlocking them with intelligent encoder decisions to extract real-world benefit, is largely left as future work.
IDDSAM: An Integrated Disease Diagnosis and Severity Assessment Model for Intensive Care Units

People are admitted to intensive care units (ICUs) because they need complete support for failing organ systems, constant monitoring, routine nursing care, and treatment. A critical or intensive illness is different from conventional or chronic diseases that most people are likely to have previously encountered. Such an illness is often unexpected and without warnings and can suddenly strike the previously fit. High levels of treatment and support are generally required to prevent life-threatening complications for the patents. Two of the most noticeable actions during an ICU stay are disease diagnosis and severity assessment of the patients. Unlike the majority of previous approaches where diagnosis and severity assessment are studied separately, we treat these actions as two tasks in an integrated procedure that clinicians must be able to quickly and accurately conduct such that patients are given the best possible chance for therapeutic success. In this paper, we propose an integrated disease diagnosis and severity assessment model (IDDSAM) to diagnose and assess diseases. Moreover, accompanying the prediction, we also provide an evidence-based explanation. IDDSAM is a multisource multitask model that is based on an attention mechanism and utilizes shareable information from laboratory tests, bedside monitoring, and complications to support patients' severity assessment and in-hospital disease diagnoses. We use 50,430 ICU cases consisting of 46,520 patients from 50 kinds of diseases over nine classifications to evaluate our proposed model. The experimental results demonstrated that our model outperforms the existing state-of-the-art mortality and diagnosis prediction framework by 3.79% on average in terms of accuracy for the mortality prediction tasks and by 14.51% on average for the diagnosis tasks.
Design of array multiplier circuit using reversible logic approach with optimized performance parameters

The approach of designing digital circuits using reversible logic concept is gaining more and more attention of the researchers due to its characteristics of generating low-loss or ideally loss-less systems. This area of circuit designing is gaining wide application in the field of low power computing, optical computing, quantum computing, low power VLSI designs etc. Researchers are working in the field of designing various sequential as well as combinational subsystems of the computer using reversible logic concept. Here we have presented a design approach for array multiplier using Reversible logic approach. This circuit can be further modified and explored to further design various low-loss computing devices.
Recent Developments in Halftone Based Image Watermarking

In today's world, as the multimedia content is growing at an exponential rate, the security of multimedia content has become a very important issue. Nowadays, there are two major types of technologies which can provide solutions to the security problem. One is encryption and the other is watermarking. This paper presents a review on the recent trends of image watermarking with a focus on a special kind of watermarking method called halftone based image watermarking. The process of displaying an image with a lesser number of colours or grey levels is called halftoning. It is a crucial step in printing, especially when a printer can handle only a limited number of ink colours or grey levels.
Fingertips Detection in Egocentric Video Frames using Deep Neural Networks

In recent years, there has been much advancement in Augmented Reality technologies. Also, there has been a rise in the usage of wearable cameras. These technologies allow us to interact with the virtual world and the real world simultaneously. Hand gestures or finger gestures can be used to provide input instructions replacing conventional tools like a keyboard or a mouse. This paper introduces an improvement over the YOLSE (You Only Look what You Should See) model towards multiple fingertip position estimation. We propose a regression-based technique to locate fingertip(s) in a multi-gesture condition. First, the hand gesture is segmented from the scene using a deep neural network (DNN) based object detection model. Next, fingertip(s) positions are estimated using MobileNetv2 architecture. It is difficult to use direct regression when the varying number of visible fingertips are present in different egocentric hand gestures. We used the multi-label classification concept to identify all the visible extended fingers in the image. Average errors on RGB image with a resolution of 640 × 480 is 6.1527 pixels. The processing time of 9.072 ms is achieved on Nvidia GeForce GTX 1080 GPU.
Statistical Analysis of Target Tracking Algorithms in Thermal Imagery

In the current scenario as we all know that target tracking is important aspect in almost every area of real life such as medical, ATM surveillance, border security, quality checking, weather forecasting, defence security, sea shore security and monitoring moving objects. For this reason, there is a great need to develop effective and efficient algorithm for target tracking which will be used in many research areas. To fulfil the need of current time, in this paper, a statistical analysis is performed using different detection algorithm with tracking algorithm like Kalman filter with single targets in infrared imagery. This will give a great help to researcher for developing an efficient algorithm in the context of target tracking.
Enclave-based oblivious RAM using Intel’s SGX

Oblivious RAM (ORAM) schemes exist in order to protect the access pattern of data in a data store. Under an ORAM algorithm, a client accesses a data store in such a way that does not reveal which item it is interested in. This is typically accomplished by accessing multiple items each access and periodically reshuffling some, or all, of the data in the data-store. While many recent schemes make the ORAM computation complexity feasible, the performance of practical implementations is still largely limited by computational and storage limitations of the client as well as the bandwidth available between the client and the data store. In a cloud computing environment, where it is commonly assumed that the client is underpowered and you must pay by the gigabyte for data transfer, traditional ORAM methods are not optimal. Intel’s Software Guard Extensions (SGX) provide a new opportunity for ORAM implementations that can safely outsource the computational and bandwidth requirements along with the data itself, meaning that the client can be very limited and still attain high performance. In this work, we develop efficient techniques for constructing ORAMs that takes advantage of the SGX enclave technology. We demonstrate implementations of multiple ORAM schemes (linear, square root, and path ORAM) using Intel’s SGX. We discuss the limitations of SGX as they pertain to implementing ORAM, and discuss alterations to the standard algorithms to overcome these limitations. We then evaluate the performance of our techniques.
Semantic Provenance Based Trustworthy Users Classification on Book-Based Social Network using Fuzzy Decision Tree

As web-based social network allows anyone to post the content without any restriction, the trustworthiness of the content creator plays an important role before using the content. An effiective way to find the trustworthiness is, by analyzing the web resources related to the content creator. Therefore the trustworthiness is assessed using the provenance based ontological model called W7 model. Since it is a real time data, the computed trust for each reviewer using the ontological model is uncertain and vague. An appropriate way to classify such data is using the fuzzy logic with gradual trust level. As the computed trust data are feature-based and non-symbolic, the classification ambiguity need to be reduced greatly. This is achieved with the fuzzy decision tree approach, which is a fusion of fuzzy sets with decision tree. The truth of the rule is crucial in trustworthy user classification, as highly truthful rules really increase the credibility of the user in their domain. Therefore, in the proposed model, degree of truth is used as a pruning criteria that classifies the users with minimum number of fuzzy evidence or knowledge. This paper proposes a semantic provenance based gradual trust model to classify the trustworthy reviewers in a book-based social networks using fuzzy decision tree approach. Performance analysis of the proposed model in the terms of classifier accuracy, precision, recall, the number of rules generated and its time complexity are discussed. The analysis shows that the proposed learning model outperforms other classification models. This method is also applied to other data sets and the performance of the classifier is assessed.
Improved parallel construction of wavelet trees and rank/select structures

Existing parallel algorithms for wavelet tree construction have a work complexity of O(nlog⁡σ). This paper presents parallel algorithms for the problem with improved work complexity. Our first algorithm is based on parallel integer sorting and has either O(nlog⁡log⁡n⌈log⁡σ/log⁡nlog⁡log⁡n⌉) work and polylogarithmic depth, or O(n⌈log⁡σ/log⁡n⌉) work and sub-linear depth. We also describe another algorithm that has O(n⌈log⁡σ/log⁡n⌉) work and O(σ+log⁡n) depth. We then show how to use similar ideas to construct variants of wavelet trees (arbitrary-shaped binary trees and multiary trees) as well as wavelet matrices in parallel with lower work complexity than prior algorithms. Finally, we show that the rank and select structures on binary sequences and multiary sequences, which are stored on wavelet tree nodes, can be constructed in parallel with improved work bounds, matching those of the best existing sequential algorithms for constructing rank and select structures.
Artificial Intelligence Based System for Preliminary Rounds of Recruitment Process

The process of hiring is extremely crucial for any organization. It’s existing methodology, from resume screening, preliminary rounds, face to face interviews, all of it involves huge manual effort. Thus, it is crucial to implement a hiring process that reduces human intervention, screens candidates on consistent and standard criteria, makes the system flexible and trackable thus enhancing the mode of operation of the current system and consequently reducing human effort. We propose an Artificial Intelligence (AI) based recruiting system that serves as a decision support system for recruiters, especially useful for preliminary or initial screening rounds. AI for recruiting is the application of artificial intelligence to the process of recruitment, that is to automate the preliminary rounds of the recruiting system and to filter candidates from a large applicant pool with minimal human intervention. We have implemented a real-time AI based assessment system with a closed domain intelligent question answering (henceforth mentioned as QA) system in the backend and incorporated an adaptive question generation algorithm for making the process equivalent to a real interview environment.
A Multi Modal People Tracker for Real Time Human Robot Interaction

Tracking people in the surroundings of interactive service robots is a topic of high interest. Even if image based detectors using deep learning techniques have improved the detection rate and accuracy a lot, for robotic applications it is necessary to integrate those detections over time and over the limited ranges of individual sensors into a global model. That data fusion enables a continuous state estimation of people and helps reducing the false decisions taken by individual detectors and increasing the overall range. In this paper, we present a tracking framework with a new distance measure for data association and a proper consideration of individual sensors' accuracies. By means of that, we could deal with high false detection rates of laser-based leg detectors without introducing further heuristics like a background model. The proposed system is compared to other tracking approaches from the state of the art. Furthermore, we present a novel manually annotated benchmark dataset for multi sensor person tracking from a moving robot platform in a guide scenario, which will be made publicly available.
Simulations of Photonic Quantum Networks for Performance Analysis and Experiment Design

This work models metropolitan-scale photonic quantum networks that use time bin encoding for quantum key distribution and quantum state teleportation. We develop and validate theoretical models by comparing them with prior experimental results. We use our newly developed simulator of quantum network communication, called SeQUeNCe, to perform simulations at the individual photon level with picosecond resolution. The simulator integrates accurate models of optical components including light sources, interferometers, detectors, beam splitters, and telecommunication fiber, allowing studies of their complex interactions. Optical quantum networks have been generating significant interest because of their ability to provide secure communication, enable new functionality such as clock synchronization with unprecedented accuracy, and reduce the communication complexity of certain distributed computing problems. In the past few years experimental demonstrations moved from table-top experiments to metropolitan-scale deployments and long-distance repeater network prototypes. As the number of optical components in these experiments increases, simulation tools such as SeQUeNCe will simplify experiment planning and accelerate designs of new network protocols. The modular design of our tool will also allow modeling future technologies such as network nodes with quantum memories and quantum transducers as they become available.
Boosting Neural Commit Message Generation with Code Semantic Analysis

It has been long suggested that commit messages can greatly facilitate code comprehension. However, developers may not write good commit messages in practice. Neural machine translation (NMT) has been suggested to automatically generate commit messages. Despite the efforts in improving NMT algorithms, the quality of the generated commit messages is not yet satisfactory. This paper, instead of improving NMT algorithms, suggests that proper preprocessing of code changes into concise inputs is quite critical to train NMT. We approach it with semantic analysis of code changes. We collect a real-world dataset with 50k+ commits of popular Java projects, and verify our idea with comprehensive experiments. The results show that preprocessing inputs with code semantic analysis can improve NMT significantly. This work sheds light to how to apply existing DNNs designed by the machine learning community, e.g., NMT models, to complete software engineering tasks.