Reviewerly/exHarmony huggingface dataset:
Explore Voice and Foot-based Interaction Techniques to Navigate 2D Radiological Images in the Virtual Reality Operation Theatre

During surgery, the surgeons often need to interact with the 2D radiological images. Due to sterility, surgeons are unable to interact with the system and depend on duty assistant. However, communication with the substitute might be complicated and error-prone if the operators and the surgeons do not have an equal level of communication skills which might interrupt the workflow. To get rid of the dependency on substitute operator, two hands-free modalities i.e. voice and foot- based interactions have been analyzed and investigated for the surgeon to interact with 2D images. To feel like a real Operation Theatre, the Virtual Reality environment has been designed, where the hands-free modalities are deployed. 16 participants had evaluated the interaction systems. To analysis, the system usability, the qualitative and quantitative studies have been considered. Our finding indicates that both systems reached general usability rating. Wherein the observation indicated that, the voice command system is comfortable to use whereas foot- based interaction is more efficient. Nevertheless, for the short and longer interaction foot-based interaction is preferable than a voice command, however, for short interaction, voice command is more acceptable.
A Study on Heart Segmentation Using Deep Learning Algorithm for MRI Scans

Among all body organs heart is a one of the most vital of organs of human body. Dysfunction of heart function even for a couple of moments can be fatal, therefore, efficient monitoring of its physiological state is essential for the patients with cardiovascular diseases. In the recent past, various computer assisted medical imaging systems have been proposed for the segmentation of the organ of interest. However, for the segmentation of heart using MRI, only few methods have been proposed each with its own merits and demerits. For further advancement in this area of research, we analyze automated heart segmentation methods for magnetic resonance images. The analysis are based on deep learning methods that processes a full MR scan in a slice by slice fashion to predict desired mask for heart region. We design two encoder-decoder type fully convolutional neural network models (1) Multi-Channel input scheme (also known as 2.5D method), (2) a single channel input scheme with relatively large size network. Both models are evaluated on real MRI dataset and their performances are analysed for different test samples on standard measures such as Jaccard score, Youden's index and Dice score etc. Python implementation of our code is made publicly available at https://github.com/Shak97/iceest2019 for performance evaluation.
On-Off Adversarially Robust Q-Learning

This letter, presents an “on-off” learning-based scheme to expand the attacker's surface, namely a moving target defense (MTD) framework, while optimally stabilizing an unknown system. We leverage Q-learning to learn optimal strategies with “on-off” actuation to promote unpredictability of the learned behavior against physically plausible attacks. We provide rigorous, theoretical guarantees on the stability of the equilibrium point even when switching. Finally, we develop two adversarial threat models to evaluate the learning agent's ability to generate robust policies based on a distance to uncontrollability.
A Hybrid Modified Advanced Encryption Standard and Chaos Encryption Algorithm for Securing Compressed Multimedia Data

The widespread use of multimedia data makes media content security and protection increasingly urgent and necessary. In order to fully protect or maintain security and privacy of multimedia data, it must be secured through encryption before transmission.
Social Media User’s Safety Level Detection through Classification via Clustering Approach

Social media has a significant impact on our daily life, and the popularity is increasing rapidly because of the ability to be attached to people around the world and share feelings, photos, videos, etc. So, it bears a high-security concern. However, most of the social media user does not know the security level of their account, including what features of social media should consider if the account is in a risk situation. The posting, friendship, etc. sometimes brings unfortunate events like identity theft, sexual harassment, cyber-crime, etc. To overcome such kind of unexpected issues, this research proposes a classification via clustering algorithm based predictive model by which one can know his safety level in the social media. A dataset is formed through a closed-ended questionnaire. Essential features are selected via gain ration method as high dimensional data is costly to train a model. An unsupervised algorithm, hierarchical clustering, cluster the users into three groups that are labeled for further analysis. The various classification algorithm is chosen to train the predictive model. From the model evaluation result, “Logistic Regression” predicts the safety level of a social media user with high accuracy. So, this model will bring an extra dimension in social media user account safety.
Industrial Practices on Requirements Reuse: An Interview-Based Study

[Context and motivation] Requirements reuse has been proposed as a key asset for requirements engineers to efficiently elicit, validate and document software requirements and, as a consequence, obtain requirements specifications of better quality through more effective engineering processes. [Question/problem] Regardless the impact requirements reuse could have in software projects’ success and efficiency, the requirements engineering community has published very few studies reporting the way in which this activity is conducted in industry. [Principal ideas/results] In this paper, we present the results of an interview-based study involving 24 IT professionals on whether they reuse requirements or not and how. Some kind of requirements reuse is carried out by the majority of respondents, being organizational and project-related factors the main drivers. Quality requirements are the type most reused. The most common strategy is find-copy-paste-adapt. Respondents agreed that requirements reuse is beneficial, especially for project-related reasons. The most stated challenge to overcome in requirements reuse is related to the domain of the project and the development of a completely new system. [Contribution] With this study, we contribute to the state of the practice in the reuse of requirements by showing how real organizations carry out this process and the factors that influence it.
Personality Prediction System Through CV Analysis

Personality prediction is to scientifically finding out, appraising and comprehending an individual's personality based on CV. In this paper, an automated personality prediction system through CV is presented, which classifies the personality traits as well as profession of an individual naturally with the assistance of a PC framework, without the human intercession in which sets of characters to identify the personality traits related to a person, which helps in choosing the profession of that individual.
Hardware and Software Co-Verification from Security Perspective

Attacks which combine software vulnerabilities and hardware vulnerabilities are emerging security problems. Although the runtime verification or remote attestation can determine the correctness of a system, existing methods suffer from inflexible security policy setup and high performance overheads. Meanwhile, they rarely focus on addressing the threat in the RISC-V architecture, which provides an open Instruction Set Architecture (ISA) of the processsor. In this paper, we propose a comprehensive software and hardware co-verification method to protect the entire RISC-V system in the runtime. The proposed method adopts the Dynamic Information Flow Tracking (DIFT) framework to implement a new Verifier and Prover security architecture for supporting runtime software and hardware coverification. We realize a FPGA prototype on the Rocket-Chip, an RISC-V open-source processor core. The framework is implemented as a co-processor which do not change the architecture of main processor core and the new security architecture can be integrated with other RISC-V processors.
A Cross-cultural Study on Information Architecture: Culture Differences on Attention Allocation to Web Components

A well-designed web information architecture (IA) supports the findability and usability of web content to ensure efficient and effective user experience. This study aims to investigate how web visitors from different cultures allocate their attention to the four main systems (labeling, organization, navigation, and searching) in IA. We conducted a user study comprised of observation tasks, sketch sessions, and questions regarding participants' attention allocation, as well as background questionnaires regarding demographics, cultural dimensions, and personal traits. A total of 33 student participants from Taiwan (TW) and the US were recruited. Our preliminary results find that the less complicated content a website displays, the more participants are aware of IA and its components in general. We also found that US participants usually pay more attention to text labels on a webpage, whereas TW participants are more likely to evenly distribute their attention to both text and image objects. The ultimate goal of this study is to shed light on the topic of culture-specific IA in global web communities.
Effective Training End-to-End ASR systems for Low-resource Lhasa Dialect of Tibetan Language

The Lhasa dialect is the most important Tibetan dialect and has the largest number of speakers in Tibet and massive written scripts in the long history. Studying how to apply speech recognition techniques to Lhasa dialect has special meaning for preserving Tibet's unique linguistic diversity. Previous research on Tibetan speech recognition focussed on selecting phone-level acoustic modeling units and incorporating tonal information but paid less attention to the problem of limited data. In this paper, we focus on training End-to-End ASR systems for Lhasa dialect using transformer-based models. To solve the low-resource data problem, we investigate effective initialization strategies and introduce highly compressed and reliable sub-character units for acoustic modeling which have never been used before. We jointly training the transformer-based End-to-End acoustic model with two different acoustic unit sets and introduce an error-correction dictionary to further improve the system performance. Experiments show our proposed method can effectively modeling low-resource Lhasa dialect compared to DNN-HMM baseline systems.
Impact of Anonymization on Vehicle Detector Performance

Due to strict data protection regulations in the European Union, many companies face great challenges regarding the gathering, processing and storage of image data for the development of driving functions based on convolutional neural networks (CNN). To meet this challenge, de-identification can be employed to remove personal information. This paper assesses the impact of anonymization on the detection of vehicles. A dataset is compiled and anonymized with different types of de-identification. A CNN object detector is trained on the original and anonymized versions of the dataset. Evaluation shows that the impact of anonymization depends on variousfactors, including type and size of the object, de-identification method and share of the object type in the dataset. In addition, we show that anonymization with pixelization has a rather insignificant impact on the performance of the object detector. Therefore, it can be considered a feasible approach to tackle the challenge of gathering image data on the open road while complying to current data protection regulations.
Interactive versus noninteractive locally differentially private estimation: Two elbows for the quadratic functional

Local differential privacy has recently received increasing attention from the statistics community as a valuable tool to protect the privacy of individual data owners without the need of a trusted third party. Similar to the classical notion of randomized response, the idea is that data owners randomize their true information locally and only release the perturbed data. Many different protocols for such local perturbation procedures can be designed. In most estimation problems studied in the literature so far, however, no significant difference in terms of minimax risk between purely noninteractive protocols and protocols that allow for some amount of interaction between individual data providers could be observed. In this paper, we show that for estimating the integrated square of a density, sequentially interactive procedures improve substantially over the best possible noninteractive procedure in terms of minimax rate of estimation. In particular, in the noninteractive scenario we identify an elbow in the minimax rate at s=34, whereas in the sequentially interactive scenario the elbow is at s=12. This is markedly different from both, the case of direct observations, where the elbow is well known to be at s=14, as well as from the case where Laplace noise is added to the original data, where an elbow at s=94 is obtained. We also provide adaptive estimators that achieve the optimal rate up to log-factors, we draw connections to nonparametric goodness-of-fit testing and estimation of more general integral functionals and conduct a series of numerical experiments. The fact that a particular locally differentially private, but interactive, mechanism improves over the simple noninteractive one is also of great importance for practical implementations of local differential privacy.
Admiring the Great Mountain: A Celebration Special Issue in Honor of Stephen Grossberg’s 80th Birthday

This editorial summarizes selected key contributions of Prof. Stephen Grossberg and describes the papers in this 80th birthday special issue in his honor. His productivity, creativity, and vision would each be enough to mark a scientist of the first caliber. In combination, they have resulted in contributions that have changed the entire discipline of neural networks. Grossberg has been tremendously influential in engineering, dynamical systems, and artificial intelligence as well. Indeed, he has been one of the most important mentors and role models in my career, and has done so with extraordinary generosity and encouragement. All authors in this special issue have taken great pleasure in hereby commemorating his extraordinary career and contributions.
System for Speech Corpus Development

Many modern speech recognition systems now use neural networks. Although neural networks can produce acceptable results in some situations, there are a number of problems they cannot solve. Such speech recognition systems cannot properly recognize an unfamiliar dialect or speech with an accent or impairment. In addition, neural networks are a "black box", so that we cannot assess the correctness of their work or confidently know what we will get at the output, and can only observe the result. In this paper the acoustic-phonetic approach is discussed. The problem of speech segmentation and phoneme dataset collection with the description of speaker features is considered. The structure of a graph database for storing speech records, phonemes, description of speakers characteristics such as native language, dialect, presence of accent and speech disorders is presented. The speech corpus development system architecture is described.
Design and simulation of handwritten recognition system

In the field of computer science involving with the huge amount of role in artificial intelligence, the recent improvements in this have provided various effective solutions for the problems being solved by the human in the today’s world. The main aim of this paper is to describe the recognition of handwritten characters for the ease use of it by using the various machine learning algorithms. This paper involves with the use of supervised and unsupervised machine learning algorithms to provide the betterment of result in the form of accuracy level. Algorithms such as Random forest, Logistic regression, Support Vector Machine (SVM) and K-Nearest Neighbor (KNN) algorithms are compared to provide its efficiency with the result comparison. Out of all the above mentioned algorithms, KNN seems to provide the better accuracy of around 98%.
Understanding ML Driven HPC: Applications and Infrastructure

We recently outlined the vision of which captures the possibility and impact of how learning methods and traditional HPC methods can be coupled together. A primary driver of such coupling is the promise that Machine Learning (ML) will give major performance improvements for traditional HPC simulations. Motivated by this potential, the ML around HPC class of integration is of particular significance. In a related follow-up paper, we provided an initial taxonomy for integrating learning around HPC methods. In this paper which is part of the Learning Everywhere series, we discuss ``how'' learning methods and HPC simulations are being integrated to enhance effective performance of computations. This paper describes several modes --- substitution, assimilation, and control, in which learning methods integrate with HPC simulations and provide representative applications in each mode. This paper discusses some open research questions and we hope will motivate and clear the ground for MLaroundHPC benchmarks.
An Empirical Analysis of Plugin-Based Tor Traffic over SSH Tunnel

Tor is the most widely used system for anonymous low-latency communication. However, the anonymity of TOr is not invulnerable according to a large amount of researches, even with the traffic obfuscation provided by pluggable transports. Concerned about security issues such as identity leakage, users deploy fronting servers as proxies that forward traffic to the entry node of Tor, and encrypted tunneling services such as secure shell (SSH) protocol are commonly used to connect users with proxies. To quantitatively analyze the plugin-based Tor traffic over encrypted tunnels, experiments involving the traffic identification and correlation are performed. Identification aims at recognizing tunneled Tor flows among background traffic at the client side, while correlation associates outward flows of Tor at the server side with corresponding inward flows at the client side. We access to the self-built server through the SSH proxy and Tor successively, capturing data flows generated by different pluggable transports and upper applications. Then identification and correlation techniques based on various machine learning algorithms are used to break anonymity. The accuracy and F1 scores reach above 95% while false positive rates approach 0% under certain conditions. The result demonstrates that Tor traffic encrypted by tunneling protocols is also at risk of anonymity revealing when confronted with traffic analysis.
Particle Flow Particle Filter using Gromov's method

Particle flow filters obtain impressive results in challenging high dimensional, non-linear sequential state estimation problems. In contrast to a particle filter, which uses importance sampling to approximate the posterior distribution of the state, the flow based algorithms solve a differential equation to migrate the particles from the prior to the posterior distribution. However, the particles after the flow are not true samples of the posterior distribution due to strong model assumptions required for the derivation of the flow and the approximations associated with the numerical solution. This affects performance adversely in many highly non-linear, non-Gaussian filtering problems. Particle Flow Particle Filters (PFPF) adapt the particle flow procedure to construct a proposal density inside the particle filter. These techniques can outperform the underlying particle flow algorithms by compensating for the approximations in the flow calculations via update of importance weights after the flow, at the cost of a negligible increase in the computational complexity. Most of the PFPF approaches have focused on using a deterministic particle flow. In this paper, we develop a PFPF algorithm using a stochastic particle flow based on Gromov's method. Numerical simulations are conducted to examine when the proposed method offers advantages compared to existing techniques.
A Blockchain and SIFT Based System for Image Copyright Protection

Internet technology has driven the dissemination revolution of images, but also makes the illegal reproduction and unauthorized use of images extremely rampant. At present, the registration of digital copyright of images should be authorized by authoritative management agencies. There are many problems, such as long audit cycle, no substantive review, difficulty in proof, high cost, centralized storage and so on. Based on blockchain technology and SIFT local feature extraction algorithm, this paper studies and implements a new generation of image digital copyright system. SIFT algorithm is used to extract the invariant features of image such as angle of view, brightness and rotation, etc. which will constitute the local feature vector set of the image, and these local feature vector sets are regarded as the only copyright basis of the image. Even if the image is modified by scaling, rotation and brightness, local features can still be extracted correctly, which can effectively prevent copyright registration of infringing images; Furthermore, IPFS (Inter-Planetary File System) was utilized for distributed storage of images' copyright features; Finally, Hyperledger Fabric and smart contract (chaincode) are used to realize copyright registration, copyright transfer and other functions. The experimental results show that the system has the advantages of automatic similar infringement detection, decentralized storage, tamper-proof and traceability.
Design and Implementation of Gesture Recognition and Writing Text Virtually Using Finger

This paper deals with real time gesture recognition and writing alphanumeric letters over the air using fingertip. The human and machine interaction lays an important role in gesture recognition. The proposed method contains two parts: First, a video camera is used to acquire the image for gesture recognition and then the fingertip is recognized from the live video frames. In this work, the Optical Character Recognition (OCR) algorithm is applied to plot the alphanumeric characters with respect to the finger movement. As a result, we can eliminate the requirements of the input devices like keypad, mouse, stylus, stick, and glove or pen and so on. Using Python language coding, we have obtained the text on the screen. Further, recognition of gesture does not require sensors or any hardware other than a camera. Since, the proposed work is a simulation based approach; hence the developed algorithm executes the recognition task in a simple, fast and lucid manner.
Poisson Kernel-Based Clustering on the Sphere: Convergence Properties, Identifiability, and a Method of Sampling

Spherical or directional data arise in many applications of interest. Furthermore, many nondirectional datasets can be usefully re-expressed in the form of directions and analyzed as spherical data. We have proposed a clustering algorithm using mixtures of Poisson-kernel-based densities (PKBD) on the sphere. We prove convergence of the associated generalized EM-algorithm, investigate the identifiability of various forms of PKBD mixture model, and study in detail its performance via simulation and application to real data. Specifically, we discuss a method and simulate data from a variety of PKBD models, then study the performance of the algorithm in terms of adjusted Rand index, macro-precision, and macro-recall. Finally, we compare the PKBD clustering method with other algorithms for clustering data on the sphere. Supplementary materials are available online and provide proofs of the theoretical results and the associated computer code.