Reviewerly/exHarmony huggingface dataset:
Effects of Background Complexity and Viewing Distance on an AR Visual Search Task

Information in augmented reality (AR) consists of virtual and real contexts and is provided to the AR environment at different distances for each user. Therefore, it is important to understand how the integration of two different information influences the user's AR experience. However, little has been studied regarding the complexity and viewing distance of a real space. Our study investigates how the complexity of the physical environment and viewing distance influence workload and performance in a visual search task in an AR environment. We conducted an experiment in which participants performed conjunction search under three different levels of background complexity, at both near (1.5m) and far (3m) distances. The results indicated that as the complexity of the background increased, the users' performance time and workload were negatively impacted. In addition, when the distance between the user and the background was greater, search time increased. From the results of the study, we derived some recommendations for the design of AR interfaces. Our research contributes to the design of interfaces by demonstrating the necessity to consider the complexity of background and viewing distance.
Security of Binary Modulated Continuous Variable Quantum Key Distribution Using Optimally Displaced Threshold Detection

Continuous variable quantum key distribution attracts increasing attention in recent years due to its potential high secret key rates and the compatibility with existing communication infrastructure. This letter derives the secret key rate for a binary modulated continuous variable quantum key distribution using an optimally displaced threshold detection and post-selection. To improve the secret key rate, we optimize the post selected area and the coherent amplitude for different transmission efficiency of the channel under different thermal noises. Both the individual and collective beamsplitter attacks are considered. Numerical results show that the secret key rate using the optimally displaced threshold detection can surpass that using the classical detection scheme under different transmission efficiencies and different thermal noises. Besides, more improvement on the secret key rate can be obtained when the transmission efficiency is low.
Real-time object detection and face recognition system to assist the visually impaired

Abstract The application system presented here is for use in an Android based mobile phone by the visually impaired for detecting objects in their vicinity, which will help them move around safely, without crashing into objects. The detection of objects is done from a real time video taken from the mobile phone camera. People and objects are detected from this video using OpenCV, YOLO and FaceNet. If a human is detected, the system identifies the person. His identity will then be converted into audio and presented to the user. Similarly, objects detected in the vicinity will be presented in audio format to the user.
BP-DIP: A Backprojection based Deep Image Prior

Deep neural networks are a very powerful tool for many computer vision tasks, including image restoration, exhibiting state-of-the-art results. However, the performance of deep learning methods tends to drop once the observation model used in training mismatches the one in test time. In addition, most deep learning methods require vast amounts of training data, which are not accessible in many applications. To mitigate these disadvantages, we propose to combine two image restoration approaches: (i) Deep Image Prior (DIP), which trains a convolutional neural network (CNN) from scratch in test time using the given degraded image. It does not require any training data and builds on the implicit prior imposed by the CNN architecture; and (ii) a backprojection (BP) fidelity term, which is an alternative to the standard least squares loss that is usually used in previous DIP works. We demonstrate the performance of the proposed method, termed BP-DIP, on the deblurring task and show its advantages over the plain DIP, with both higher PSNR values and better inference run-time.
Revisiting Test Impact Analysis in Continuous Testing From the Perspective of Code Dependencies

In continuous testing, developers execute automated test cases once or even several times per day to ensure the quality of the integrated code. Although continuous testing helps ensure the quality of the code and reduces maintenance effort, it also significantly increases test execution overhead. In this paper, we empirically evaluate the effectiveness of test impact analysis from the perspective of code dependencies in the continuous testing setting. We first applied test impact analysis to one year of software development history in 11 large-scale open-source systems. We found that even though the number of changed files is small in daily commits (median ranges from 3 to 28 files), around 50 percent or more of the test cases are still impacted and need to be executed. Motivated by our finding, we further studied the code dependencies between source code files and test cases, and among test cases. We found that 1) test cases often focus on testing the integrated behaviour of the systems and 15 percent of the test cases have dependencies with more than 20 source code files; 2) 18 percent of the test cases have dependencies with other test cases, and test case inheritance is the most common cause of test case dependencies; and 3) we documented four dependency-related test smells that we uncovered in our manual study. Our study provides the first step towards studying and understanding the effectiveness of test impact analysis in the continuous testing setting and provides insights on improving test design and execution.
Data-Efficient Training Strategies for Neural TTS Systems

India is a country with thousands of languages and dialects spoken across a billion-strong population. For multi-lingual content creation and accessibility, text-to-speech systems will play a crucial role. However, the current neural TTS systems are data-hungry and need about 20 hours of clean single-speaker speech data for each language and speaker. This is not scalable for the large number of Indian languages and dialects. In this work, we demonstrate three simple, yet effective pre-training strategies that allow us to train neural TTS systems with just about one-tenth of the data needs while also achieving better accuracy and naturalness. We show that such pre-trained neural TTS systems can be quickly adapted to different speakers across languages and genders with less than 2 hours of data, thus significantly reducing the effort for future expansions to the thousands of rare Indian languages. We specifically highlight the benefits of multi-lingual pre-training and its consistent impact across our neural TTS systems for 8 Indian languages.
Finding Optimal Manipulator Arm Shapes to Avoid Collisions in a Static Environment

In situations of a confined workplace with a lot of obstacles and a complicated required trajectory of the endpoint of an industrial or collaborative robot, it may be impossible to find a suitable robot and its position within the workplace to fulfill the given task. In some cases, it could be favorable to design a custom manipulator arm with an unusual kinematic structure or shapes of some of its links. This article presents a novel way of finding the optimal lengths and shapes of two crucial links of a manipulator arm, where the target lengths are as short as possible to reduce mass, and the shape in the form of a Bézier curve is chosen to avoid collisions. The chosen type of kinematic structure of the manipulator arm is fixed and is based on the most typical structure of existing industrial robots, with six degrees of freedom. Two algorithm variants were proposed; one method uses iterations to find the solution based on in-depth collision analysis, and the second method uses the particle swarm optimization algorithm. Both methods were implemented in a simulation system and verified in several testing workplaces.
Lightweight Authentication and Data Encryption Scheme for IoT Applications

Internet-of-Things (IoT) can also be defined as interconnection of “factual-and-virtual” objects placed across the globe that are attracting attentions of both “makers-and-hackers”. IoT devices are self-organizing and self-adaptive that communicate with each other over the public channel. These communications are vulnerable to security and privacy attacks of user and data. Hence to provide lightweight crypto-solution, a hybrid authentication and data integrity scheme is proposed using elliptic curve cryptography based digital signature and encryption scheme. The experiment is performed on a Raspberry Pi-3 based client-server network. Through the experimental analysis, it can be inferred that the proposed method has shown improvement in the time of about 28.3-33.3% in the signature phase and 15.6 % in the verification phase than existing ECDSA based schemes. Also, the use of cBLAKE2b has shown less time in encryption and decryption of 11.73-5.45% and 7.11-5.63%, respectively.
Image Localized Style Transfer to Design Clothes Based on CNN and Interactive Segmentation

In recent years, image style transfer has been greatly improved by using deep learning technology. However, when directly applied to clothing style transfer, the current methods cannot allow the users to self-control the local transfer position of an image, such as separating specific T-shirt or trousers from a figure, and cannot achieve the perfect preservation of clothing shape. Therefore, this paper proposes an interactive image localized style transfer method especially for clothes. We introduce additional image called outline image, which is extracted from content image by interactive algorithm. The interaction consists simply of dragging a rectangle around the desired clothing. Then, we introduce an outline loss function based on distance transform of the outline image, which can achieve the perfect preservation of clothing shape. In order to smooth and denoise the boundary region, total variation regularization is employed. The proposed method constrains that the new style is generated only in the desired clothing part rather than the whole image including background. Therefore, in our new generated images, the original clothing shape can be reserved perfectly. Experiment results show impressive generated clothing images and demonstrate that this is a good approach to design clothes.
Comparison of Gradient Boosting and Extreme Boosting Ensemble Methods for Webpage Classification

Web page classification is an important task in various areas like web content filtering, contextual advertising and maintaining or expanding web directories etc. Machine Learning methods have been found to perform well to classify web pages, and ensemble models have been used to improve the results obtained from single classifiers. The Gradient Boosting and Extreme Boosting ensemble models are used in this work for binary classification. The dataset containing URLs of web pages have been collected manually. The comparison between the two boosting algorithms validated the improvement in accuracy and speed obtained through Extreme boosting. Extreme boosting has been found to be around ten times faster than Gradient boosting and also shows improvement in accuracy. The effect of three preprocessing techniques; lemmatization, stop words removal and regular expressions shows that these preprocessing techniques improves the accuracy of the results but not significantly.
Tamper detection technique for document images using zero watermarking in wavelet domain

Existing tamper detection techniques for document images use the textual information of the image to create a zero watermark. These techniques cannot authenticate a document image if the image is tampered with in non-textual regions. In order to resolve this problem, a tamper detection technique for document images is proposed using the concept of zero watermarking. The input document image is transformed by applying lifting wavelet transform to get its subbands. These subbands are further partitioned into non-overlapping blocks of the same size. Features of each blocks are extracted to create a zero watermark. This watermark is combined with the key watermark in order to create a meaningful watermark. This meaningful watermark is stored by a trusted authority. On the receiver side, the meaningful watermark is compared to the watermark generated using tampered image to check the authenticity of the cover image. The tampered regions of the cover image can also be localized using this technique. The experimental results demonstrate the effectiveness of the technique in comparison to the existing authentication techniques.
A novel optimal path-planning and following algorithm for wheeled robots on deformable terrains

An immense body of research has focused on path-planning and following of wheeled robots in unstructured surfaces. Nonholonomic robots traveling over deformable terrains together with complex operating conditions, however, pose further challenges in terms of a higher demand for robustness and optimality. In this paper, a Chaos-enhanced Accelerated Particle Swarm Optimization (CAPSO) algorithm is employed for planning an optimal path of a wheeled robot, so as to ensure shortest path from the starting point to the target location together with safety through guaranteed avoidance of collisions with static and dynamic obstacles. The fundamental terramechanics concepts are employed to derive essential forces and moments acting on the wheeled robot. Subsequently, a kineto-dynamic model of the robot is developed for designing a novel robust control algorithm based on an exponential-integral-sliding mode (EISMC) scheme and a RBF-NN approximator. The results revealed that the proposed algorithm is responsive and robust to withstand adverse effects of structured and unstructured uncertainties by using the designed adaptation law according to the Lyapunov stability theorem. The effectiveness of the proposed algorithm is also validated against several reported frameworks.
Tactile information counteracts the attenuation of rubber hand illusion attributable to increased visuo-proprioceptive divergence

Sense of body ownership is an immediate and distinct experience of one's body as belonging to oneself. While it is well-recognized that ownership feelings emerge from the integration of visual and somatosensory signals, the principles upon which they are integrated are still intensely debated. Here, we used the rubber hand illusion (RHI) to examine how the interplay of visual, tactile, and proprioceptive signals is governed depending on their spatiotemporal properties. For this purpose, the RHI was elicited in different conditions varying with respect to the extent of visuo-proprioceptive divergence (i.e., the distance between the real and fake hands) and differing in terms of the availability and spatiotemporal complexity of tactile stimulation (none, simple, or complex). We expected that the attenuating effect of distance on illusion strength will be more pronounced in the absence of touch (when proprioception gains relatively higher importance) and absent in the presence of complex tactile signals. Additionally, we hypothesized that participants with greater proprioceptive acuity-assessed using an elbow joint position discrimination task-will be less susceptible to the illusion, but only under the conditions of limited tactile stimulation. In line with our prediction, RHI was attenuated at the farthest distance only when tactile information was absent or simplified, but the attenuation was effectively prevented by the use of complex tactile stimulation-in this case, RHI was comparably vivid at both distances. However, passive proprioceptive acuity was not related to RHI strength in either of the conditions. The results indicate that complex-structured tactile signals can override the influence of proprioceptive signals in body attribution processes. These findings extend our understanding of body ownership by showing that it is primarily determined by informative cues from the most relevant sensory domains, rather than mere accumulation of multisensory evidence.
A Post-Quantum Secure Discrete Gaussian Noise Sampler

While the notion of achieving “quantum supremacy” may be debatable, rapid developments in the field of quantum computing are heading towards more realistic quantum computers. As practical quantum computers start becoming more feasible, the requirement to have quantum secure cryptosystems becomes more compelling. Due to its many advantages, lattice based cryptography has become one of the key candidates for designing secure systems for the post-quantum era. The security of lattice-based cryptography is governed by the small error samples generated from a Gaussian distribution. Hence, the Gaussian distribution lies at the core of these cryptosystems. In this paper, we present the hardware design implementation of three different sampling algorithms including rejection, Box-Muller, and the Ziggurat method for the Gaussian Sampler. Our goal is to provide concrete recommendations for future use and adoption in various cryptosystems based on sampling efficiency, hardware cost and throughput. The key feature of our design implementation is that it performs high-precision sampling to meet the NIST's recommended security level of 112-bits or higher for the postquantum era, which most existing hardware implementations fail to do. Furthermore, our design implementation is highly optimized for FPGA-based implementation and is also generic so that it can be seamlessly integrated into most cryptosystems. Synthesis results are obtained using Vivado design suite for a Xilinx Zynq-7010 CLG400ACX1341 FPGA board.
Study on Hand Gesture Recoginition by using Machine Learning

Artificial Intelligence [AI] and Machine Learning [ML] play a vital role in the health care industries and other such scientific applications. In continuation with Artificial Intelligence, so many researches are attracted towards the Hand Gesture Recognition (HGR). HGR is the simple method used to interact with machines. Hand gesture recognition is performed by using machine learning algorithm. It consists of two approaches, they are appearance based approach and model based approach. In appearance based method, the hand image is reconstructed by using image properties. But in model based method, different models such as volumetric, geometric etc., are used to reconstruct the image. The complications in machine learning algorithm and processing time are remaining as the major challenge in gesture recognition. The performance of HGR system is evaluated based on its accuracy. This paper presents the detailed study on various methods and algorithms used in gesture recognition.
Image Transformation Network for Privacy-Preserving Deep Neural Networks and Its Security Evaluation

We propose a transformation network for generating visually-protected images for privacy-preserving DNNs. The proposed transformation network is trained by using a plain image dataset so that plain images are transformed into visually protected ones. Conventional perceptual encryption methods have a weak visual-protection performance and some accuracy degradation in image classification. In contrast, the proposed network enables us not only to strongly protect visual information but also to maintain the image classification accuracy that using plain images achieves. In an image classification experiment, the proposed network is demonstrated to strongly protect visual information on plain images without any performance degradation under the use of CIFAR datasets. In addition, it is shown that the visually protected images are robust against a DNN-based attack, called inverse transformation network attack (ITN-Attack) in an experiment.
Analyzing the Human Rights Impact of Increased Digital Public Health Surveillance during the COVID-19 Crisis.

The COVID-19 pandemic has led policy makers to expand traditional public health surveillance to take advantage of new technologies, such as tracking apps, to control the spread of SARS-CoV-2. This article explores the human rights dimensions of how these new surveillance technologies are being used and assesses the extent to which they entail legitimate restrictions to a range of human rights, including the rights to health, life, and privacy. We argue that human rights offer a crucial framework for protecting the public from regulatory overreach by ensuring that digital health surveillance does not undermine fundamental features of democratic society. First, we describe the surveillance technologies being used to address COVID-19 and reposition these technologies within the evolution of public health surveillance tools and the emergence of discussions concerning the compatibility of such tools with human rights. We then evaluate the potential human rights implications of the surveillance tools being used today by analyzing the extent to which they pass the tests of necessity and proportionality enshrined in international human rights law. We conclude by recommending ways in which the harmful human rights effects associated with these technologies might be reduced and public trust in their use enhanced.
Differential Fault Attack on TWINE Block Cipher with Nibble

TWINE cipher is a generalized Feistel structure algorithm, which can be applied to Internet of things terminals with limited computing resources because of its good performance. Since it was proposed, it has been widely studied by academia and industry. This paper analyses the permutation law of TWINE cipher, and proposes a differential fault attack method with its differential property. By selecting 33 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">th</sup> ,34 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">th</sup> and 35 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">th</sup> rounds for the nibble differential fault attack, the original 80-bit key can be recovered from 24 nibble fault ciphertext, with the computational complexity and data complexity of 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">10.47</sup> and 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">10.58</sup> , respectively. The results show this method has great advantages over other public literatures about the cryptanalysis of TWINE cipher.