Reviewerly/exHarmony huggingface dataset:
On Tight Security Proofs for Schnorr Signatures

The Schnorr signature scheme is the most efficient signature scheme based on the discrete logarithm problem and a long line of research investigates the existence of a tight security reduction for this scheme in the random oracle model. Almost all recent works present lower tightness bounds and most recently Seurin EUROCRYPT 2012 showed that under certain assumptions the non-tight security proof for Schnorr signatures in the random oracle by Pointcheval and Stern EUROCRYPT'96 is essentially optimal. All previous works in this direction rule out tight reductions from the (one-more) discrete logarithm problem. In this paper, we introduce a new meta-reduction technique, which shows lower bounds for the large and very natural class of generic reductions. A generic reduction is independent of a particular representation of group elements. Most reductions in state-of-the-art security proofs have this property. It is desirable, because then the reduction applies generically to any concrete instantiation of the group. Our approach shows unconditionally that there is no tight generic reduction from any natural non-interactive computational problem $$\Pi $$ defined over algebraic groups to breaking Schnorr signatures, unless solving $$\Pi $$ is easy. In an additional application of the new meta-reduction technique, we also unconditionally rule out any (even non-tight) generic reduction from natural non-interactive computational problems defined over algebraic groups to breaking Schnorr signatures in the non-programmable random oracle model.
Cloud Based Virtual Reality Exposure Therapy Service for Public Speaking Anxiety

Public speaking anxiety is commonly treated using cognitive behavioral therapy. During the therapy session, the patient is either asked to vividly imagine and describe the feared stimulus or is confronted with it in the real world. Sometimes, however, it can be hard to imagine the object of fear or to create a controllable environment that contains this stimulus. Virtual reality exposure therapy (VRET) can help solve these problems by placing the patient in a simulated 3D environment. While standalone VRET applications have been investigated for more than 25 years, we are analyzing the viability of a cloud-based VRET system. In this paper, we discuss the architectural and technical choices made in order to create a mobile and lightweight solution that can be easily adapted by any psychology clinic. Moreover, we are analyzing data gathered from 30 participants who have undergone a VRET session for public speaking anxiety. Finally, the collected psychophysiological signals including galvanic skin response (GSR) and skin temperature are processed and investigated in order to evaluate our cloud-based VRET system.
Robust Computer Vision Chess Analysis and Interaction with a Humanoid Robot

As we move towards improving the skill of computers to play games like chess against humans, the ability to accurately perceive real-world game boards and game states remains a challenge in many cases, hindering the development of game-playing robots. In this paper, we present a computer vision algorithm developed as part of a chess robot project that detects the chess board, squares, and piece positions in relatively unconstrained environments. Dynamically responding to lighting changes in the environment, accounting for perspective distortion, and using accurate detection methodologies results in a simple but robust algorithm that succeeds 100% of the time in standard environments, and 80% of the time in extreme environments with external lighting. The key contributions of this paper are a dynamic approach to the Hough line transform, and a hybrid edge and morphology-based approach for object/occupancy detection, that enable the development of a robot chess player that relies solely on the camera for sensory input.
Research on distance education image correction based on digital image processing technology

Distance education is generally developed through live broadcast or video playback. Because of the influence of various factors in the process of distance education, the pixel characteristics in the original educational resource image will change. Based on this, this study is based on digital image processing technology to correct the distance education image. In addition, this study uses the layered processing model to decompose each color channel and then process the image brightness channel, which effectively reduces the computational overhead while ensuring the fusion effect. Finally, combined with comparative experimental research, the performance analysis of the algorithm is carried out. Research shows that the algorithm of this study has good performance in image correction and can provide theoretical reference for subsequent related research.
On developing handwritten character image database for Malayalam language script

The objective of this paper is to build a handwritten character image database for Malayalam language script. Standard handwritten document image databases are an essential requirement for the development and objective evaluation of different handwritten text recognition systems for any language script. Considerable research efforts for handwritten Malayalam character recognition are present in literature. Still, no public domain handwritten image database is available for the Malayalam language. The present work focuses on building an open source handwritten character image database for Malayalam language script. The unique orthographic representation of the Malayalam characters forms the different character classes, and the current version of the database contains 85 character classes frequently used in writing Malayalam text. Handwritten data samples collected from 77 native Malayalam writers. For extracting the character images from the handwritten data sheets, active contour model-based image segmentation algorithm utilized. Recognition experiments conducted on the created character image database by employing different feature extraction techniques. Among the considered feature descriptors, scattering convolutional network-based feature descriptors attain the highest recognition accuracy of 91.05%.
Simulating frustrated antiferromagnets with quadratically driven QED cavities

We propose a class of quantum simulators for antiferromagnetic spin systems based on coupled photonic cavities in the presence of two-photon driving and dissipation. By modeling the coupling between the different cavities through a hopping term with negative amplitude, we solve numerically the quantum master equation governing the dynamics of the open system and determine its nonequilibrium steady state. Under suitable conditions, the steady state can be described in terms of the degenerate ground states of an antiferromagnetic Ising model. When the geometry of the cavity array is incommensurate with the antiferromagnetic coupling, the steady state presents properties which bear full analogy with those typical of the spin-liquid phases arising in frustrated magnets.
A patch-based super resolution algorithm for improving image resolution in clinical mass spectrometry

Mass spectrometry imaging (MSI) and histology are complementary analytical tools. Integration of the two imaging modalities can enhance the spatial resolution of the MSI beyond its experimental limits. Patch-based super resolution (PBSR) is a method where high spatial resolution features from one image modality guide the reconstruction of a low resolution image from a second modality. The principle of PBSR lies in image redundancy and aims at finding similar pixels in the neighborhood of a central pixel that are then used to guide reconstruction of the central pixel. In this work, we employed PBSR to increase the resolution of MSI. We validated the proposed pipeline by using a phantom image (micro-dissected logo within a tissue) and mouse cerebellum samples. We compared the performance of the PBSR with other well-known methods: linear interpolation (LI) and image fusion (IF). Quantitative and qualitative assessment showed advantage over the former and comparability with the latter. Furthermore, we demonstrated the potential applicability of PBSR in a clinical setting by accurately integrating structural (i.e., histological) and molecular (i.e., MSI) information from a case study of a dog liver.
ACTRCE: Augmenting Experience via Teacher's Advice For Multi-Goal Reinforcement Learning

Sparse reward is one of the most challenging problems in reinforcement learning (RL). Hindsight Experience Replay (HER) attempts to address this issue by converting a failed experience to a successful one by relabeling the goals. Despite its effectiveness, HER has limited applicability because it lacks a compact and universal goal representation. We present Augmenting experienCe via TeacheR's adviCE (ACTRCE), an efficient reinforcement learning technique that extends the HER framework using natural language as the goal representation. We first analyze the differences among goal representation, and show that ACTRCE can efficiently solve difficult reinforcement learning problems in challenging 3D navigation tasks, whereas HER with non-language goal representation failed to learn. We also show that with language goal representations, the agent can generalize to unseen instructions, and even generalize to instructions with unseen lexicons. We further demonstrate it is crucial to use hindsight advice to solve challenging tasks, and even small amount of advice is sufficient for the agent to achieve good performance.
Interplay Between Optimization and Generalization of Stochastic Gradient Descent with Covariance Noise.

The choice of batch-size in a stochastic optimization algorithm plays a substantial role for both optimization and generalization. Increasing the batch-size used typically improves optimization but degrades generalization. To address the problem of improving generalization while maintaining optimal convergence in large-batch training, we propose to add covariance noise to the gradients. We demonstrate that the learning performance of our method is more accurately captured by the structure of the covariance matrix of the noise rather than by the variance of gradients. Moreover, over the convex-quadratic, we prove in theory that it can be characterized by the Frobenius norm of the noise matrix. Our empirical studies with standard deep learning model-architectures and datasets shows that our method not only improves generalization performance in large-batch training, but furthermore, does so in a way where the optimization performance remains desirable and the training duration is not elongated.
Implicit Kernel Learning

Kernels are powerful and versatile tools in machine learning and statistics. Although the notion of universal kernels and characteristic kernels has been studied, kernel selection still greatly influences the empirical performance. While learning the kernel in a data driven way has been investigated, in this paper we explore learning the spectral distribution of kernel via implicit generative models parametrized by deep neural networks. We called our method Implicit Kernel Learning (IKL). The proposed framework is simple to train and inference is performed via sampling random Fourier features. We investigate two applications of the proposed IKL as examples, including generative adversarial networks with MMD (MMD GAN) and standard supervised learning. Empirically, MMD GAN with IKL outperforms vanilla predefined kernels on both image and text generation benchmarks; using IKL with Random Kitchen Sinks also leads to substantial improvement over existing state-of-the-art kernel learning algorithms on popular supervised learning benchmarks. Theory and conditions for using IKL in both applications are also studied as well as connections to previous state-of-the-art methods.
Towards an Efficient Data Fragmentation, Allocation, and Clustering Approach in a Distributed Environment

Data fragmentation and allocation has for long proven to be an efficient technique for improving the performance of distributed database systems’ (DDBSs). A crucial feature of any successful DDBS design revolves around placing an intrinsic emphasis on minimizing transmission costs (TC). This work; therefore, focuses on improving distribution performance based on transmission cost minimization. To do so, data fragmentation and allocation techniques are utilized in this work along with investigating several data replication scenarios. Moreover, site clustering is leveraged with the aim of producing a minimum possible number of highly balanced clusters. By doing so, TC is proved to be immensely reduced, as depicted in performance evaluation. DDBS performance is measured using TC objective function. An inclusive evaluation has been made in a simulated environment, and the compared results have demonstrated the superiority and efficacy of the proposed approach on reducing TC.
Research on The Application of AR Technology Based on Unity3D in Education

Augmented Reality (AR) has developed rapidly in recent years and is widely used in education teaching due to its strong sense of immersion and interaction. In this context, this paper analyzes the key technologies of AR and designs a complete display system of human body tissues and structures with interactive functions based on Unity3D and Vuforia. Finally, this system is packaged into an android app. The running results show that the app has a strong sense of immersion and interaction, and it also has strong expansibility so that we can change the human structure model into any other models.
CluSim: a python package for calculating clustering similarity

Clustering is a primary method to reveal the structure of data (Jain, Murty, & Flynn, 1999).To understand, evaluate, and leverage data clusterings, we need to quantitatively compare them.Clustering comparison is the basis for method evaluation, consensus clustering, and tracking the temporal evolution of clusters, among many other tasks.For instance, the evaluation of a clustering method is usually achieved by comparing the method's result to a planted reference clustering, assuming that the more similar the method's solution is to the reference clustering, the better the method.Despite the importance of clustering comparison, no consensus has been reached for a standardized assessment; each similarity measure rewards and penalizes different criteria, sometimes producing contradictory conclusions.
Super-resolution recurrent convolutional neural networks for learning with multi-resolution whole slide images

We study a problem scenario of super-resolution (SR) algorithms in the context of whole slide imaging (WSI), a popular imaging modality in digital pathology. Instead of just one pair of high- and low-resolution images, which is typically the setup in which SR algorithms are designed, we are given multiple intermediate resolutions of the same image as well. The question remains how to best utilize such data to make the transformation learning problem inherent to SR more tractable and address the unique challenges that arises in this biomedical application. We propose a recurrent convolutional neural network model, to generate SR images from such multi-resolution WSI datasets. Specifically, we show that having such intermediate resolutions is highly effective in making the learning problem easily trainable and address large resolution difference in the low and high-resolution images common in WSI, even without the availability of a large size training data. Experimental results show state-of-the-art performance on three WSI histopathology cancer datasets, across a number of metrics.
Keyphrase Generation: A Text Summarization Struggle

Authors' keyphrases assigned to scientific articles are essential for recognizing content and topic aspects.Most of the proposed supervised and unsupervised methods for keyphrase generation are unable to produce terms that are valuable but do not appear in the text.In this paper, we explore the possibility of considering the keyphrase string as an abstractive summary of the title and the abstract.First, we collect, process and release a large dataset of scientific paper metadata that contains 2.2 million records.Then we experiment with popular text summarization neural architectures.Despite using advanced deep learning models, large quantities of data and many days of computation, our systematic evaluation on four test datasets reveals that the explored text summarization methods could not produce better keyphrases than the simpler unsupervised methods, or the existing supervised ones.
Multiple Image Compression in Medical Imaging Techniques using Wavelets for Speedy Transmission and Optimal Storage

Multiple image compression using wavelet based methods including Discrete Wavelet Transform (DWT) through sub band coding (SBC) and decoding are reviewed for their comparative study. True color image compression measuring parameters like compression ratio (CR), peak to signal noise ratio (PSNR), mean square error (MSE), bits per pixel (BPP) are computed using MATLAB code for each algorithm employed. Gray scale image like Magnetic Resonance Imaging (MRI) is chosen for wavelet transform to achieve encoding and decoding using multiple wavelet families and resolutions to examine their relative merits and demerits. Our main objective is to establish advantages of multiple compression techniques (compressions using multiresolution) helpful in transmitting bulk of compressed medical images via different gadgets facilitating early detection and diagnosis followed by treatments or referrals to specialists residing in different parts of the world. Contemporary compression techniques based on wavelet transform can serve as revolutionary idea in medical field for the overall benefit of humanity.
Dynamic prediction of cardiovascular disease using improved LSTM

Purpose Previous dynamic prediction models rarely handle multi-period data with different intervals, and the large-scale patient hospital records are not effectively used to improve the prediction performance. This paper aims to focus on the prediction of cardiovascular disease using the improved long short-term memory (LSTM) model. Design/methodology/approach A new model based on the traditional LSTM was proposed to predict cardiovascular disease. The irregular time interval is smoothed to obtain the time parameter vector, and it is used as the input of the forgetting gate of LSTM to overcome the prediction obstacle caused by the irregular time interval. Findings The experimental results show that the dynamic prediction model proposed in this paper obtained a significant better classification performance compared with the traditional LSTM model. Originality/value In this paper, the authors improved the LSTM by smoothing the irregular time between different medical stages of the patient to obtain the temporal feature vector.
Wide Swath and High Resolution Airborne HyperSpectral Imaging System and Flight Validation

Wide Swath and High Resolution Airborne Pushbroom Hyperspectral Imager (WiSHiRaPHI) is the new-generation airborne hyperspectral imager instrument of China, aimed at acquiring accurate spectral curve of target on the ground with both high spatial resolution and high spectral resolution. The spectral sampling interval of WiSHiRaPHI is 2.4 nm and the spectral resolution is 3.5 nm (FWHM), integrating 256 channels coving from 400 nm to 1000 nm. The instrument has a 40-degree field of view (FOV), 0.125 mrad instantaneous field of view (IFOV) and can work in high spectral resolution mode, high spatial resolution mode and high sensitivity mode for different applications, which can adapt to the Velocity to Height Ratio (VHR) lower than 0.04. The integration has been finished, and several airborne flight validation experiments have been conducted. The results showed the system’s excellent performance and high efficiency.
Knowledge-Based Method for Word Sense Disambiguation by Using Hindi WordNet

The purpose of word sense disambiguation (WSD) is to find the meaning of the word in any context with the help of a computer, to find the proper meaning of a lexeme in the available context in the problem area and the relationship between lexicons. This is done using natural language processing (NLP) techniques which involve queries from machine translation (MT), NLP specific documents or output text. MT automatically translates text from one natural language into another. Several application areas for WSD involve information retrieval (IR), lexicography, MT, text processing, speech processing etc. Using this knowledge-based technique, we are investigating Hindi WSD in this article. It involves incorporating word knowledge from external knowledge resources to remove the equivocalness of words. In this experiment, we tried to develop a WSD tool by considering a knowledge-based approach with WordNet of Hindi. The tool uses the knowledge-based LESK algorithm for WSD for Hindi. Our proposed system gives an accuracy of about 71.4%.
THE IMPACT OF CYBER THREATS ON SOCIAL NETWORKING SITES

Social networks are websites that enable people to communicate with others, express their opinions, and share their thoughts, experiences, and interests.It also contributes to job creation and facilitates the marketing of various products and services.A cyber threat is the malicious attempt to access a computer network through a data communications pathway by illegal means; they can be intended or unintended, direct or indirect, and are usually carried out by hackers, virus code writers, industrial spies, organized crime unions, vengeful employees and spiteful intruders.This paper presents the history of online social networking and classifies their types; it also discusses cyber threats on social networking websites and puts forward a policy and action plan to counter threats to social networks in the future.