Reviewerly/exHarmony huggingface dataset:
Multi-view Incremental Discriminant Analysis

In recent years, the use of multi-view data has attracted much attention resulting in many multi-view batch learning algorithms. However, these algorithms prove expensive in terms of training time and memory when used on the incremental data. In this paper, we propose Multi-view Incremental Discriminant Analysis (MvIDA), which updates the trained model to incorporate new data samples. MvIDA requires only the old model and newly added data to update the model. Depending on the nature of the increments, MvIDA is presented as two cases, sequential MvIDA and chunk MvIDA. We have compared the proposed method against the batch Multi-view Discriminant Analysis (MvDA) for its discriminability, order independence, the effect of the number of views, training time, and memory requirements. We have also compared our method with single-view Incremental Linear Discriminant Analysis (ILDA) for accuracy and training time. The experiments are conducted on four datasets with a wide range of dimensions per view. The results show that through order independence and faster construction of the optimal discriminant subspace, MvIDA addresses the issues faced by the batch multi-view algorithms in the incremental setting.
Review of Educational Courses Based on Augmented Reality Technologies

The research relevance is due to the growing interest of teachers in introducing Augmented Reality (AR) technologies into the course of Engineering and Computer Graphics (ECG). The research aims to study the current state of knowledge and practice of existing courses using AR-technologies, including those for geometric-graphic training of high educational institutions students. The research object was the subject readiness of engineering students to master graphic disciplines based on the AR-concept. More than 200 scientific papers published between 2005 and 2020 were selected to review the qualitative and quantitative AR-research in the education area. For this purpose, the following factors were taken into account: category of educational institution and student, publication year, academic discipline, AR-technologies. Have been presented examples for AR-technologies implementation at different levels of education, such as preschool, school, higher and special ones. Special focus has been on consideration of teachers’ experience related to AR-technologies introducing in geometric training courses. It has been revealed that at present the AR-concept has gained popularity not only among designers, but also among schoolteachers, as well as among engineering high educational institutions’ lecturers. The lack of sufficient amount of scientifically based and tested programs and training materials for student teaching on ECG using AR has been proved. The necessity for further scientific research in the area of AR application to teach students on ECG has been justified. The research base was St. Petersburg Mining University. The paper materials may be useful for high educational institutions’ lecturers, schoolteachers, and parents.
Survey2Survey: a deep learning generative model approach for cross-survey image mapping

During the last decade, there has been an explosive growth in survey data and deep learning techniques, both of which have enabled great advances for astronomy. The amount of data from various surveys from multiple epochs with a wide range of wavelengths, albeit with varying brightness and quality, is overwhelming, and leveraging information from overlapping observations from different surveys has limitless potential in understanding galaxy formation and evolution. Synthetic galaxy image generation using physical models has been an important tool for survey data analysis, while deep learning generative models show great promise. In this paper, we present a novel approach for robustly expanding and improving survey data through cross survey feature translation. We trained two types of neural networks to map images from the Sloan Digital Sky Survey (SDSS) to corresponding images from the Dark Energy Survey (DES). This map was used to generate false DES representations of SDSS images, increasing the brightness and S/N while retaining important morphological information. We substantiate the robustness of our method by generating DES representations of SDSS images from outside the overlapping region, showing that the brightness and quality are improved even when the source images are of lower quality than the training images. Finally, we highlight several images in which the reconstruction process appears to have removed large artifacts from SDSS images. While only an initial application, our method shows promise as a method for robustly expanding and improving the quality of optical survey data and provides a potential avenue for cross-band reconstruction.
A Proposed Ranked Clustering Approach for Unstructured Data from Dataspace using VSM

Now a day's huge amount of data is available in an unstructured format, users need useful information related to query or phrase that has been written in search engines. Search engine rank and indexed the data as per the nature of data and documents like structure (SQL data), unstructured (e-books, PPT, text, Streamed Data, songs, movies, research data), or semi-structured (XML). Indexing and ranking is the main issue in Information retrieval system to retrieve the appropriate results of the query from Dataspace due to heterogeneity. Indexing of unstructured data can reduce the processing time of query for fast retrieval of data. This paper proposed a ranked cluster approach using Modified cosine similarity and Vector space model (VSM) which may be replaced with the traditional cosine similarity approach for better results on the dataset. Here we applying the vector space model, Document term matrix, and TF-IDF weights for indexing and ranking of the heterogeneous data. Consequently, the documents which match the query the most are displayed first and ranking of the documents is done according to similarity with the query of unstructured data over Dataspace.
Quantum signature for designated verifier with strong security

Most of the classical designated verifier signature schemes are insecure against quantum adversary. In this paper, a quantum signature scheme for the designated verifier is proposed. In our scheme, during the initialization phase, the partners share secret keys by performing the quantum key distribution protocol. On the other hand, by performing the quantum direct communication protocol, the key generator center shares secret keys with the signer and the designated verifier, respectively. The key generator center generates a particle sequence of Bell state and distributes the particles between the signer and the designated verifier. During the signature generation phase, the signer encrypts the particle sequence by the secret keys and Hardmard operators. After that, the signer performs the controlled unitary operations on the encrypted particle sequence so as to generate the quantum signature. The designated verifier can simulate the quantum signature by performing the same symmetric signing steps as that performed by the original signer. Hence, the quantum signature signed by the true signer is the same as the one simulated by the receiver, which makes our scheme possess the designated properties. During the signature verification phase, the designated verifier performs the controlled unitary operations on the quantum signature and obtains the quantum ciphertexts. After that, the designated verifier decrypts the quantum ciphertexts by the symmetric secret keys and Hardmard operators so that the quantum signature can be verified. Our signature is secure against forgery attack, inter-resending attacks and Trojan horse attack. Because the trace distance between the density operators of different quantum signatures is zero, the information-theoretical security of our quantum signature scheme can be proved. The unconditionally secure quantum key distribution protocol and the one-time pad encryption algorithm can guarantee the security of the secret keys shared by the partners. What is more, the security assumption about the key generation center is weak. That is, it is not necessary to assume that the key generation center should be fully trusted. On the other hand, in our scheme, the quantum one-way function is not used. To generate a quantum signature, the signer need not prepare for entangled particle sequence. To verify a quantum signature, the verifier need not apply any state comparison to the received particles. The qubit efficiency is 100%. Therefore, our scheme has the advantages in the security and efficiency over the other quantum signature schemes for the designated verifier.
Deep Neural Network Trained Punjabi Children Speech Recognition System Using Kaldi Toolkit

Despite the number of developed Automatic Speech Recognition (ASR) systems for different languages, still no work has been done on children's speech of Punjabi language. Due to the unavailability of children's speech corpus for Punjabi Language, it is a challenging task to collect speech data. In our current work, efforts have been made to collect Punjabi children's speech corpus and build Children ASR system for Indian regional Punjabi language. The recognition rate of ASR systems is observed to be improved drastically by the emergence of Deep Neural Networks (DNN). In our work, the DNN acoustic model has been implemented by varying number of hidden layers. Approximately four hours of Punjabi children's speech corpus has been collected and several experiments have been performed using the DNN modeling technique. Experimental results have revealed that the system has attained 87% accuracy.
Joint image-to-image translation with denoising using enhanced generative adversarial networks

Impressive progress has been made recently in image-to-image translation using generative adversarial networks (GANs). However, existing methods often fail in translating source images with noise to target domain. To address this problem, we joint image-to-image translation with image denoising and propose an enhanced generative adversarial network (EGAN). In particular, built upon pix2pix, we introduce residual blocks in the generator network to capture deeper multi-level information between source and target image distribution. Moreover, a perceptual loss is proposed to enhance the performance of image-to-image translation. As demonstrated through extensive experiments, our proposed EGAN can alleviate effects of noise in source images, and outperform other state-of-the-art methods significantly. Furthermore, we experimentally indicate that the proposed EGAN is also effective when applied to image denoising.
Exploring the evolution of software practices

When software products and services are developed and maintained over longer time, software engineering practices tend to drift away from both structured and agile methods. Nonetheless, in many cases the evolving practices are far from ad hoc or chaotic. How are the teams involved able to coordinate their joint development?This article reports on an ethnographic study of a small team at a successful provider of software as a service. What struck us was the very explicit way in which the team adopted and adapted their practices to fit the needs of the evolving development. The discussion relates the findings to the concepts of social practices and methods in software engineering, and explores the differences between degraded behavior and the coordinated evolution of development practices. The analysis helps to better understand how software engineering practices evolve, and thus provides a starting point for rethinking software engineering methods and their relation to software engineering practice.
Full-pose estimation using inertial and magnetic sensor fusion in structurized magnetic field for hand motion tracking

The individual differences and flexible muscular tissue upon bone make it difficult to realize high accuracy hand motion tracking. A full-pose estimation method is proposed by fusing measurements from an Inertial and Magnetic Measurement Unit (IMMU) in structurized magnetic field derived from a marked permanent magnet. Rather than eliminating the non-geomagnetic field, we realize spatial hand motion estimation (including both the location and orientation) by introducing structurized magnetic field to provide increased physical field dimensions without involving field coupling. The IMMU orientation is derived by using the pose matrix expressed by quaternion with complementary filter. Then, 3D position is localized by solving the magnetic inverse problem with derived orientation. Experiments on a developed platform show that the mean individual orientation error of roll, pitch and yaw attitude angle is 0.61°, 0.52° and 0.92°, respectively. In continuous path tracking, the mean localization error and mean orientation error around Z-axis motion and around X-axis motion is (1.56 mm, 0.12°) and (1.2 mm, 0.10°), respectively. The mean full-pose estimation process time is 1.6 ms. This method provides high-precision full-pose estimation for hand gesture recognition.
Zero norm based analysis model for image smoothing and reconstruction

Abstract The sparsity-based approaches have demonstrated promising performance in image processing. In this paper, for better preservation of the salient edge structures of images, we propose an ℓ 0 + ℓ 2 -norm based analysis model， which requires solving a challenging non-separable ℓ 0 -norm related minimization problem, and we also propose an inexact augmented Lagrangian method with proven convergence to a local minimum. Extensive experiments in image smoothing, including texture removal and context smoothing, show that our method achieves better visual results over various sparsity-based models and the CNN method. Also, experiments on sparse view CT reconstruction further validate the advantage of the proposed method.
The effects of immediate versus delayed feedback on complex concept learning

We report three experiments that examine whether immediate versus delayed feedback produce differential concept learning. Subjects were shown hypothetical experiment scenarios and were asked to determine whether each was a true experiment. Correct-answer feedback was used for all three experiments; Experiments 2 and 3 also included detailed explanations. In all three experiments, subjects who received immediate feedback were shown the correct answer after each response. In Experiments 1 and 2, subjects in the delayed feedback condition were shown feedback after responding to all of the scenarios. All subjects then completed a posttest with novel scenarios. Experiment 3 was three parts (each session was 2 days apart). Subjects in the immediate feedback condition completed the posttest on the second session; subjects in the delayed feedback condition were given feedback on the second session and completed the posttest on the third session. Although no posttest differences were observed between the feedback conditions in Experiments 1 and 2, a delayed feedback advantage was found in Experiment 3. We propose that longer intervals in delayed feedback (relative to shorter intervals) might allow learners to forget the incorrect hypotheses they form during learning, which might thereby enhance the processing of feedback.
Layer-Wise Data-Free CNN Compression

We present a computationally efficient method for compressing a trained neural network without using real data. We break the problem of data-free network compression into independent layer-wise compressions. We show how to efficiently generate layer-wise training data using only a pretrained network. We use this data to perform independent layer-wise compressions on the pretrained network. We also show how to precondition the network to improve the accuracy of our layer-wise compression method. We present results for layer-wise compression using quantization and pruning. When quantizing, we compress with higher accuracy than related works while using orders of magnitude less compute. When compressing MobileNetV2 and evaluating on ImageNet, our method outperforms existing methods for quantization at all bit-widths, achieving a +0.34% improvement in 8-bit quantization, and a stronger improvement at lower bit-widths (up to a +28.50% improvement at 5 bits). When pruning, we outperform baselines of a similar compute envelope, achieving 1.5 times the sparsity rate at the same accuracy. We also show how to combine our efficient method with high-compute generative methods to improve upon their results.
Bubble storytelling with automated animation: a Brexit hashtag activism case study

Hashtag data are common and easy to acquire. Thus, they are widely used in studies and visual data storytelling. For example, a recent story by China Central Television Europe (CCTV Europe) depicts Brexit as a hashtag movement displayed on an animated bubble chart. However, creating such a story is usually laborious and tedious, because narrators have to switch between different tools and discuss with different collaborators. To reduce the burden, we develop a prototype system to help explore the bubbles' movement by automatically inserting animations connected to the storytelling of the video creators and the interaction of viewers to those videos. We demonstrate the usability of our method through both use cases and a semi-structured user study.
Connected solitude: Mobile phone use by Spanish transhumant livestock farmers

From time immemorial, transhumant livestock farmers have continued to work in the countryside, living an intense and symbolic form of social distancing. Their work consists of moving around with their herd or flock, mainly sheep, so they can provide their animals with the best pastures. This research study, using ethnographic techniques, located and interviewed a group of livestock farmers to understand how the use of smartphones has changed their professional and social habits. Even now in modern times, this group still lives in isolation, working alone in the mountains. However, they have also been undergoing profound change due to the development of virtual social networks and information channels, mainly through groups in online applications. This enables them to keep fully up to date with all the topics they are interested in. This new connectivity has allowed them to make better-informed financial decisions based on more and better information. It has also meant they can produce proposals for joint action when faced with problems that affect them as a group. Lastly, the widespread use of mobile phones has increased their ability to communicate with friends and family, breaking previously held social taboos and barriers.
Output-Sensitive Avatar Representations for Immersive Telepresence

In this article, we propose a system design and implementation for output-sensitive reconstruction, transmission and rendering of 3D video avatars in distributed virtual environments. In our immersive telepresence system, users are captured by multiple RGBD sensors connected to a server that performs geometry reconstruction based on viewing feedback from remote telepresence parties. This feedback and reconstruction loop enables visibility-aware level-of-detail reconstruction of video avatars regarding geometry and texture data, and considers individual and groups of collocated users. Our evaluation reveals that our approach leads to a significant reduction of reconstruction times, network bandwidth requirements and round-trip times as well as rendering costs in many situations.
Time-chirp Distribution for Detection and Estimation of LPI Radar Signals

One of the primary challenges for an Electronic Support (ES) receiver is interception and analysis of low probability of intercept (LPI) radar signals, which for example are linear frequency-modulated (LFM) signals. In the paper detection and estimation of LFM waveform parameters based on the extended forms of the standard cubic phase function (CPF) is proposed. Originally the CPF function was introduced for instantaneous frequency rate (IFR) estimation. Extended forms are created by summation operations or multiplication operations, or integration operations of the time slices of the standard CPF function. The CPF function and its extended forms concentrate energy of LFM waveforms along a IFR line in the time-chirp domain just as the short time Fourier transform (STFT) and Wigner-Ville distribution (WVD) concentrates energy of LFM signals along an instantaneous frequency (IF) line in time-frequency domain. Based on the extended forms of the standard CPF function, test statistics have been proposed and intensively evaluated in Monte-Carlo simulations. Selected results of investigations on parameter estimation and high detection efficiency obtained by the proposed methods are presented.
Cross-VM Network Channel Attacks and Countermeasures Within Cloud Computing Environments

Cloud providers attempt to maintain the highest levels of isolation between Virtual Machines (VMs) and inter-user processes to keep co-located VMs and processes separate. This logical isolation creates an internal virtual network to separate VMs co-residing within a shared physical network. However, as co-residing VMs share their underlying VMM (Virtual Machine Monitor), virtual network, and hardware are susceptible to cross VM attacks. It is possible for a malicious VM to potentially access or control other VMs through network connections, shared memory, other shared resources, or by gaining the privilege level of its non-root machine. This research presents a two novel zero-day cross-VM network channel attacks. In the first attack, a malicious VM can redirect the network traffic of target VMs to a specific destination by impersonating the Virtual Network Interface Controller (VNIC). The malicious VM can extract the decrypted information from target VMs by using open source decryption tools such as Aircrack. The second contribution of this research is a privilege escalation attack in a cross VM cloud environment with Xen hypervisor. An adversary having limited privileges rights may execute Return-Oriented Programming (ROP), establish a connection with the root domain by exploiting the network channel, and acquiring the tool stack (root domain) which it is not authorized to access directly. Countermeasures against this attacks are also presented
The Accountability and Transparency of Whistleblowing Platforms Issues of Networked Journalism and Contested Boundaries

WikiLeaks has often been criticized for being an organization seeking transparency without being transparent and accountable itself. The paper aims to shed light on how whistleblowing platforms understand transparency and accountability with regard to their own activities and how and whether they implement online-based practices of accountability and transparency. Drawing on the conceptual model of online media accountability developed by Domingo and Heikkilä, the paper analyzes four whistleblowing platforms: the Hungarian MagyarLeaks, the Dutch PubLeaks, the Italian IrpiLeaks and the German Briefkasten of the weekly newspaper Die Zeit. This study is based on a two-step methodological approach, applying first a document analysis of publicly accessible information on online practices of accountability and transparency; second, we present findings from in-depth interviews with selected editors from each whistleblowing platform. The study critically discusses the evidence of specific challenges with regard to actor and process transparency relating to the platform’s rationale. In addition, responsiveness does not appear to be a core practice, given that interaction with the audience is generally left to the news media partners, where the leaked material is published. The findings show that whistleblowing platforms have developed unevenly in terms of accountability and transparency.