Reviewerly/exHarmony huggingface dataset:
Manipulating 3D Anatomic Models in Augmented Reality: Comparing a Hands-Free Approach and a Manual Approach

Many AR and VR task domains involve manipulating virtual objects; for example, to perform 3D geometric transformations. These operations are typically accomplished with tracked hands or hand-held controllers. However, there are some activities in which the user's hands are already busy with another task, requiring the user to temporarily stop what they are doing to perform the second task, while also taking time to disengage and reengage with the original task (e.g., putting down and picking up tools). To avoid the need to overload the user's hands this way in an AR system for guiding a physician performing a surgical procedure, we developed a hands-free approach to performing 3D transformations on patient-specific virtual organ models. Our approach uses small head motions to accomplish first-order and zero-order control, in conjunction with voice commands to establish the type of transformation. To show the effectiveness of this approach for translating, scaling, and rotating 3D virtual models, we conducted a within-subject study comparing the hands-free approach with one based on conventional manual techniques, both running on a Microsoft HoloLens and using the same voice commands to specify transformation type. Independent of any additional time to transition between tasks, users were significantly faster overall using the hands-free approach, significantly faster for hands-free translation and scaling, and faster (although not significantly) for hands-free rotation.
Novel email spam detection method using sentiment analysis and personality recognition

Abstract Unsolicited email campaigns remain as one of the biggest threats affecting millions of users per day. During the past years several techniques to detect unsolicited emails have been developed. This work provides means to validate the hypothesis that the identification of the email messages’ intention can be approached by sentiment analysis and personality recognition techniques. These techniques will provide new features that improve current spam classification techniques. We combine personality recognition and sentiment analysis techniques to analyse email content. We enrich a publicly available dataset adding these features, first separately and after in combination, of each message to the dataset, creating new datasets. We apply several combinations of the best email spam classifiers and filters to each dataset in order to compare results.
An Extensive Review on Cloud Computing

Cloud computing is one of the most sought after fields in the world of computing and IT. This paper is an extensive survey intended to highlighting the importance of cloud computing in the business world and how it has benefited major companies as well as the potential ones. The different service models and key features along with database services play a major role. Virtualization, its features, and its role in making cloud computing possible are extensively discussed. However, like any other technology, even the cloud technology faces a vast array of issues that need to be attained. Some of the major issues like resource scheduling, security, and interoperability pertaining to cloud are reviewed here, along with possible ways of tackling them. A comprehensive study on the different fields to be explored in the study of cloud computing is presented here. We have done a systematic literature review of 62 selected papers and believe that this paper will help the new researchers in cloud computing area.
Resonant Quantum Kicked Rotor as A Continuous-Time Quantum Walk

We analytically investigate the analogy between a standard continuous-time quantum walk in one dimension and the evolution of the quantum kicked rotor at quantum resonance conditions. We verify that the obtained probability distributions are equal for a suitable choice of the kick strength of the rotor. We further discuss how to engineer the evolution of the walk for dynamically preparing experimentally relevant states. These states are important for future applications of the atom-optics kicked rotor for the realization of ratchets and quantum search.
A Smart Environments Architecture (Search)

We report on a system architecture, SEArch, and its associated methods and tools we have been developing, testing, and extending for several years through a number of innovation processes in the field of Smart Environments. We have developed these infrastructure in a bottom-up fashion directed by the needs of the different projects as opposed to an ideal one which projects have to conform to. In this sense is practical and although necessarily incomplete, it has significant versatility and reasonable efficiency. Projects developed using this architecture have been funded by different companies and funding bodies in Europe. The different components of the architecture are explained through the software supporting those aspects of the system and through the functionality they exhibit in different practical scenarios, extracted from some of the projects implemented with SEArch.
Digital Learning Environment of Ukrainian Universities: The Main Components to Influence the Competence of Students and Teachers

Needs of digital transformation requires specific flexibility from modern universities to ensure the society demands implementation through innovative teaching and IC-technologies. Modern universities create a digital learning environment to support studying activities. This research presents an experts' estimate of the current condition and perspectives of universities' digital studying environments in Ukraine. We verified the theoretical model structure of the university digital studying environments by means of the empirical data factor analysis. We studied the components of the existing learning environment and enabling environment and compared them to the results of our previous research. We estimated the survey reliability and analyzed the influence of the digital learning environment formation on the respondents' digital competence. We proved the digital learning environment theoretical model was correct. We proved that visions of students and teachers correspond to the key trends accelerating higher education technology adoption. We assume the digital learning environment development benefits overcoming significant challenges impeding higher education technology adoption and increases the level of digital competence. We assume these tendencies to have an impact on the students' and teachers' growing competitiveness.
Securing Medical Data Transmission Systems Based on Integrating Algorithm of Encryption and Steganography

The awareness to secure medical data has significantly increased. Steganographic has binged an important topic especially in this area since it has the capability to avoid medical data breach. This paper proposes a new steganography scheme based on Bit Invert System (BIS) using three control random parameters. The random selection process is performed based on Henon Map Function (HMF). In order to increase the security level, affine cipher and Huffman method is used for encryption as well as to minimize the encrypt data prior to the embedding for high payload ability. This integration is effective due to two main reasons: first, checking, and mapping to determine 0- and 1-bits during embedding, and second, segmenting the secret data to track and map every bit in stego image. The results showed that the presented scheme can assure confidentiality and security of the medical data while maintaining the image quality.
Object Grounding via Iterative Context Reasoning

In this paper, we tackle the problem of weakly-supervised object grounding. For an image and a set of queries extracted from its description, the goal is to localize each query in the image. In a weakly-supervised setting, ground-truth query groundings are not accessible at training time. We propose a novel approach for weakly-supervised object grounding through iterative context reasoning in which we update query representations and region representations iteratively conditioning on each other. Such iterative contextual refinement gradually resolves ambiguity and vagueness in the queries and regions, thus helping to resolve challenges in grounding. We show the effectiveness of our proposed model on two challenging video object grounding datasets.
Test usability guidelines and follow conventions. Useful recommendations from web developers

Usability measures the quality of use when a user interacts with a website. There are many guidelines and recommendations to improve the user experience on websites. The purpose of this study is to analyze the degree of compliance with the well-known recommendation "follow conventions." The behavior of 20 IT engineers is analyzed to obtain application data for each of the associated guidelines. This test is carried out with web developers without training in web usability. The purpose of the analysis is to know if there are guidelines that are applied innate. As web developers are also users, it is thought that there may be some innate behavior when applying these guidelines. The purposes of the paper are: (1) Make a list of the most forgotten recommendations by web developers to think about the importance of offering specific training in this field. (2) Know the most important recommendations according to web developers. To get the study purposes the research is carried out as follows. User tests are carried out to evaluate the most forgotten and applied proposals by the developers. Examinations are also done to determine the level of compliance since the participants lack experience in web usability. Interviews with web developers are carried out to evaluate the recommendations considered necessary. It is expected to find results that help to understand if a web developer without training in usability can innately apply guidelines on conventions. And if this native application of the guidance it is useful. We want to show that there are guidelines that are used intuitively. And that there are guidelines that do not apply, and the reason for each situation. The results indicate that the guidelines considered essential and those most applied by web developers without training have something in common. All of them focus on design and seek to design and adjust to known standards that facilitate user navigation.
Emission spectrum and geometric phase in deformed Jaynes-Cummings model

The emission spectrum of a qubit (two-level atom) system that interacts with a field in the framework of parity deformations is investigated in this paper. The model consists of a qubit coupled to a single-mode field within the parity deformed Jaynes-Cummings model (PDJCM) based on the λ-analog of the quantum harmonic oscillator algebra. We numerically evaluate the atomic emission spectrum (AES), by considering the influence of the deformed parameter and half-band-width of the spectrometer. Moreover, the dependence of the spectrum peaks on the detuning parameter is discussed. Finally, we study the variation of the geometric phase of the whole system state modelled by the PDJCM in terms of the main physical parameters.
An improved algorithm for semantic segmentation of remote sensing images based on DeepLabv3+

Remote sensing image segmentation is a more and more popular topic currently, and obviously it depends on the improvement of semantic segmentation. Encoder-decoder structure is an effective architecture in semantic segmentation. Since the encoder network helps to obtain various scale feature from the deep convolution layers, and the decoder network often help to recover the spatial resolution and location information in detail. Drawing on this idea, Google proposed the DeepLabv3+ [9] after DeepLabv3[8], which reused DeepLabv3 as its encoder, and added the decoder part to help the network to recover accurate location information. However, the decoder part of DeepLabv3+ is simple and sometimes it's difficult to obtain enough details from the encoder, and achieves not so good results on remote sensing images. Therefore, we design our decoder by adding more skip connections and convolution layers, which improves the result of building detection in the dataset SpaceNet [1].
Joint-learning segmentation in Internet of drones (IoD)-based monitor systems

Object segmentation of monitor systems based on the Internet of drones plays an important role in the practical applications of wide-area smart-city intelligent monitoring systems. It is an important step for extracting objects from remote-sensing images, and provides a reliable theoretical basis for key property monitoring, environmental monitoring, disaster monitoring, and agricultural monitoring. To improve the accuracy of object segmentation and to solve the problem of inadequate edge recognition, a joint-learning segmentation scheme was designed that combines the conditional random field (CRF) model with an improved U-net model. It employs the improved U-net model as the front-end model of the joint-learning framework for feature fusion and the CRF model as the back-end of the joint-learning framework for transforming to gradient optimization-based recurrent neural networks. The joint-learning framework enables the front and back parts to interact with each other to obtain the location of the target and its classification information accurately. The joint-learning framework was realized on open datasets and compared with state-of-the-art remote-sensing image segmentation algorithms. The experiment results show that the accuracy of the ground object segmentation improved to 86.1%, which is an encouraging improvement.
Political Articles Categorization Based on Different Naïve Bayes Models

Sentiment analysis plays an important role in most of human activities and has a significant impact on our behaviours. With the development and use of web technology, there is a huge amount of data that represents users opinions in many areas such as politics and business. This paper applied Naïve Bayes (NB) to analyse the opinions by exploring categories from a text and classified it to the right class (Reform, Conservative and Revolutionary). It investigates the effect of using two feature extraction i.e. Term Frequency (TF) and Term Frequency-Inverse Document Frequency (TF-IDF) methods with Naïve Bayes classifiers (Gaussian, Multinomial, Complement and Bernoulli) on the accuracy of classifying Arabic articles. Precision, recall, F1-score and number of correct predict have been used to evaluate the performance of the applied classifiers. The results reveal that, using TF with TF-IDF improved the accuracy to 96.77%. The Complement was deemed the most suitable for our model.
On the distribution of quadratic residues and non-residues modulo composite integers and applications to cryptography

We develop exact formulas for the distribution of quadratic residues and non-residues in sets of the form a+X={(a+x)modn∣x∈X}, where n is a prime or the product of two primes and X is a subset of integers with given Jacobi symbols modulo prime factors of n. We then present applications of these formulas to Cocks’ identity-based encryption scheme and statistical indistinguishability.
Partially observable Markov decision process to generate policies in software defect management

Bug repositories are dynamic in nature and as new bugs arrive, the old ones are closed. In a typical software project, bugs and their dependencies are reported manually and gradually using a issue tracking system. Thus, not all of the bugs in the system are available at any time, creating uncertainty in the dependency structure of the bugs. In this research, we propose to construct a dependency graph based on the reported dependency-blocking information in a issue tracking system. We use two graph metrics, depth and degree, to measure the extent of blocking bugs. Due to the uncertainty in the dependency structure, simply ordering bugs in the descending order of depth and/or degree may not be the best policy to prioritize bugs. Instead, we propose a Partially Observable Markov Decision Process model for sequential decision making and Partially Observable Monte Carlo Planning to identify the best policy for this sequential decision-making process. We validated our proposed approach by mining the data from two open source projects, and a commercial project. We compared our proposed framework with three baseline policies. The results on all datasets show that our proposed model significantly outperforms the other policies with respect to average discounted return.
Augmented Reality as a Medium for Human-Robot Collaborative Tasks

This paper presents a novel augmented reality (AR) interaction method that allows a robot to perform manipulation of unknown physical objects in a human-robot collaborative working environment. A mobile AR application is developed to determine and communicate, in real-time, the position, orientation, and dimension of any random object in a robot manipulator's workspace to perform pick-and-place operations. The proposed method is based on estimating the pose and size of the object by means of an AR virtual element superimposed on the live view of the real object. In particular, a semi-transparent AR element is created and manipulated through touch screen interactions to match with the pose and scale of the physical object to provide the information about that object. The resulting data is communicated to the robot manipulator to perform pick-and-place tasks. In this way, the AR virtual element acts as a medium of communication between a human and a robot. The performance of the proposed AR interface is assessed by conducting multiple trials with random objects, and it is observed that the robot successfully accomplishes tasks communicated through the AR virtual elements. The proposed interface is also tested with 20 users to determine the quality of user experience, followed by a poststudy survey. The participants reported that the AR interface is intuitive and easy to operate for manipulating physical objects of various sizes and shapes.
Revealing the traces of nonaligned double JPEG compression in digital images

JPEG standard has been widely applied in many image acquisition devices and image editing tools. Identifying the authenticity of JPEG images is receiving significant attention in image forensics. In many JPEG image splicing forgeries, the forged regions have always experienced the JPEG compression twice with inconsistent block segmentations (hereinafter referred to as nonaligned double JPEG compression). In this paper, an effective method to reveal the traces of the nonaligned double JPEG compression is proposed. The high-order co-occurrence statistics are utilized to model the underlying intra-block and inter-block dependencies of JPEG quantized DCT coefficients. The entropies of the high-order co-occurrence matrices are computed and treated as the discriminative features to identify the presence of nonaligned double JPEG compression. The experiment results have shown that the proposed approach outperforms several existing methods for low resolution (small size) images.
Visual Feedback for Core Training with 3D Human Shape and Pose

In this paper, we propose a visual feedback system for core training using a monocular camera image. To support the user in maintaining the correct postures from target poses, we adopt 3D human shape estimation for both the target image and input camera video. Because it is expensive to capture human pose using depth cameras or multiple cameras using conventional approaches, we employ the skinned multi-person linear model of human shape to recover the 3D human pose from 2D images using pose estimation and human mesh recovery methods. We propose a user interface for providing visual guidance based on the estimated target and current human shapes. To clarify the differences between the target and current postures of 3D models, we adopt markers for visualization at ten body parts with color changes. From user studies conducted, the proposed visual feedback system is effective and convenient in performing core training.
Pooled Mining is Driving Blockchains Toward Centralized Systems

The decentralization property of blockchains stems from the fact that each miner accepts or refuses transactions and blocks based on its own verification results. However, pooled mining causes blockchains to evolve into centralized systems because pool participants delegate their decision-making rights to pool managers. In this paper, we established and validated a model for Proof-of-Work mining, introduced the concept of equivalent blocks, and quantitatively derived that pooling effectively lowers the income variance of miners. We also analyzed Bitcoin and Ethereum data to prove that pooled mining has become prevalent in the real world. The percentage of pool-mined blocks increased from 49.91% to 91.12% within four months in Bitcoin and from 76.9% to 92.2% within five months in Ethereum. In July 2018, Bitcoin and Ethereum mining were dominated by only six and five pools respectively.
A CNN-based In-loop Filtering Approach for AV1 Video Codec

In-loop filter using Convolutional Neural Network (CNN) has lately attracted lots of attention in video coding. CNN models may be trained to learn how to restore degradation introduced by compression in pictures, and hence effectively help improve the coding efficiency. State-of-the-art work in this field generally employs a single network to enhance reconstructed frames mainly in intra coding. In this paper, we develop a depth-variable network handling both intra and inter coding. The depth of our network is varied with the distortion levels of reconstructed frames. Moreover, we leverage a skip enhancing strategy for inter coding, which improves both the coding efficiency and the resulting visual quality, while maintaining low computational complexity. We apply our approach to AV1, a newly released video coding standard from AOM. Experimental results show that our approach achieves an average BD-rate reduction of 7.27% and 5.57% for intra and inter modes, respectively, compared to AV1 anchor. The code and model of our approach are published in our Github website [1].
Unsupervised representation learning by discovering reliable image relations

Learning robust representations that allow to reliably establish relations between images is of paramount importance for virtually all of computer vision. Annotating the quadratic number of pairwise relations between training images is simply not feasible, while unsupervised inference is prone to noise, thus leaving the vast majority of these relations to be unreliable. To nevertheless find those relations which can be reliably utilized for learning, we follow a divide-and-conquer strategy: We find reliable similarities by extracting compact groups of images and reliable dissimilarities by partitioning these groups into subsets, converting the complicated overall problem into few reliable local subproblems. For each of the subsets we obtain a representation by learning a mapping to a target feature space so that their reliable relations are kept. Transitivity relations between the subsets are then exploited to consolidate the local solutions into a concerted global representation. While iterating between grouping, partitioning, and learning, we can successively use more and more reliable relations which, in turn, improves our image representation. In experiments, our approach shows state-of-the-art performance on unsupervised classification on ImageNet with 46.0% and competes favorably on different transfer learning tasks on PASCAL VOC.