Reviewerly/exHarmony huggingface dataset:
Comments on “Efficient Public Verification of Data Integrity for Cloud Storage Systems From Indistinguishability Obfuscation”

Recently, Zhang <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">et al.</i> proposed a novel public data integrity verification scheme for the cloud storage using indistinguishability obfuscation ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$iO$ </tex-math></inline-formula> ), and extend it to support batch verification and data dynamic operations ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">IEEE Transactions on Information Forensics and Security</i> , vol. 12, no. 3, pp. 676–688, Mar. 2017). However, we find that the scheme has two flaws: (a) the self-checking of the uploaded blocks and tags in <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Store</i> phase is not reliable, i.e., it is easy to generate invalid block-tag pairs without being detected; (b) the extended scheme for data dynamic operations suffers from a chosen message attack, i.e., if some uploaded blocks match a certain pattern, the cloud storage is able to replace any existing block by a forged one without being detected, which violates the scheme’s security model. Then, we provide solutions to these problems while preserving all the desirable features of the original scheme.
Fake (Sybil) Account Detection Using Machine Learning

Over the past few years, social networks have seen tremendous growth in users. There are 2.32 billion monthly active users (MAU) on the Facebook social network as of December 31, 2018. As compared to 2.32 billion monthly active users (MAUs) for 4th quarter 2018 (Source: Facebook 1/30/19), and it has been increase with an approx. rate of 9% each year. Almost 10 million likes and share buttons are clicked on various websites daily (Source: Facebook as of 10/2/2014). About 293,000 statuses are updated, 136,000 photos are uploaded and 510,000 comments are posted every minute. (Source: The Social Skinny). Overall, it is estimated that around 3 to 4% of the active Facebook accounts on the site are still fake. These fake accounts are called Sybil accounts, they may be human or bots operated with an ulterior motive. These Sybil accounts try to influence users views or befriend other users to gain those user’s information for ill intents. In this paper, we focus on detecting fake user on the Facebook social network. The overall contribution in this article are creation of a dataset for fake user detection and comparative analysis of different model over it.
A Proposed Load Balancing Algorithm Over Cloud Computing (Balanced Throttled)

Cloud computing refers to the services and applications that are accessible throughout the world from data centers. All services and applications are available online. Virtual machine migration is an important part of virtualization which is considered as essential part in cloud computing environment. Virtual Machine Migration means transferring a running Virtual Machine with all its applications and the operating system state as it is to target destination machine where it continues to run as if nothing happened. It makes balancing between servers. This improves the performance by redistributing the workload among available servers. There are many algorithms of load balancing classified into two types: static load balancing algorithms and dynamic load balancing algorithms. This paper presents the algorithm (Balanced Throttled Load Balancing Algorithm- BTLB). It compares the results of the BTLB with round robin algorithm, AMLB algorithm and throttled load balancing algorithm. The results of these four algorithms would be presented in this paper. The proposed algorithm shows the improvement in response time (75 µs). Cloud analyst simulator is used to evaluate the results. BTLB was developed and tested using Java.
A Critical View on CIS Controls

CIS Controls are a set of 20 controls and 171 sub-controls that were created with an idea of having a list of specific controls to implement so that organizations can increase their security. While good in theory, it is a big question of how viable this approach is in practice, and does it really help. There is only a minor number of critical views of CIS Controls and since CIS Controls are marketed by two very influential organizations they are very popular. Yet, there are alternatives published by ISO, NIST and even PCI consortium. In this paper we critically assess CIS Controls, assumptions on which they are based as well as validity of approach and claims made in its favor. The conclusion is that scientific community should be more active regarding this topic, but also that more data is necessary. This is something that CIS and SANS should support if they want to make CIS Controls viable alternative to other approaches.
What Causes the Test Error? Going Beyond Bias-Variance via ANOVA

Modern machine learning methods are often overparametrized, allowing adaptation to the data at a fine level. This can seem puzzling; in the worst case, such models do not need to generalize. This puzzle inspired a great amount of work, arguing when overparametrization reduces test error, in a phenomenon called double descent. Recent work aimed to understand in greater depth why overparametrization is helpful for generalization. This leads to discovering the unimodality of variance as a function of the level of parametrization, and to decomposing the variance into that arising from label noise, initialization, and randomness in the training data to understand the sources of the error. In this work we develop a deeper understanding of this area. Specifically, we propose using the analysis of variance (ANOVA) to decompose the variance in the test error in a symmetric way, for studying the generalization performance of certain two-layer linear and non-linear networks. The advantage of the analysis of variance is that it reveals the effects of initialization, label noise, and training data more clearly than prior approaches. Moreover, we also study the monotonicity and unimodality of the variance components. While prior work studied the unimodality of the overall variance, we study the properties of each term in variance decomposition. One key insight is that in typical settings, the interaction between training samples and initialization can dominate the variance; surprisingly being larger than their marginal effect. Also, we characterize phase transitions where the variance changes from unimodal to monotone. On a technical level, we leverage advanced deterministic equivalent techniques for Haar random matrices, that -- to our knowledge -- have not yet been used in the area. We also verify our results in numerical simulations and on empirical data examples.
Cryptanalysis of Classic Ciphers: Methods Implementation Survey

Cryptanalysis refers to the study of methods and techniques for obtaining information from encrypted texts. Nowadays, the term cryptanalysis more generally refers to the analysis of cryptographic methods with the aim of ‘breaking’ them. An essential approach for cryptanalysis includes all available information about the examined methods as well as the parameters and protected data. This information may be public, originate from plausible assumptions or be learnt specifically. The types of information available are divided into different attack scenarios and qualify the relevance of an attack or comments on security. This paper will provide a survey of the implementation of cryptanalysis methods by cryptanalysing ciphertexts through three classic types of ciphers: Caesar, transposition, and Hill. The paper will show how the algorithms remain simple enough for message to be recovered without errors. This paper will improve the understanding of researchers and students about cryptanalysis and how to use mathematical formulas for analysis to analyse secure information systems in order to detect vulnerabilities and shrouded components.
Open Relation Modeling: Learning to Define Relations between Entities

Relations between entities can be represented by different instances, e.g., a sentence containing both entities or a fact in a Knowledge Graph (KG).However, these instances may not well capture the general relations between entities, may be difficult to understand by humans, even may not be found due to the incompleteness of the knowledge source.In this paper, we introduce the Open Relation Modeling problemgiven two entities, generate a coherent sentence describing the relation between them.To solve this problem, we propose to teach machines to generate definition-like relation descriptions by letting them learn from defining entities.Specifically, we fine-tune Pre-trained Language Models (PLMs) to produce definitions conditioned on extracted entity pairs.To help PLMs reason between entities and provide additional relational knowledge to PLMs for open relation modeling, we incorporate reasoning paths in KGs and include a reasoning path selection mechanism.Experimental results show that our model can generate concise but informative relation descriptions that capture the representative characteristics of entities. 1
The Playful Potential of Shared Mealtime

In this paper, we present an annotated portfolio of speculative ideas that emerged from a co-design process where we investigated the playful potential of day-to-day mealtime. Our portfolio illustrates the learnings from our participatory engagements: it embodies ours and our participants' ideas of how technology might support increasingly playful and socio-emotionally rich experiences around food. We contribute: (1) a list of play potentials of mealtime-i.e. people's existing playful practices with food-that will point designers towards socio-emotionally desirable play-food experiences; (2) a portfolio of speculative design ideas that illustrate how mealtime technology could help to realize that playful potential; and (3) a discussion of our participants' experiences with and responses to lo-fi prototypes of our ideas. Our work will provoke designers to carefully consider the impact of food-tech innovation on the quality of people's social eating experiences and inspire them to cultivate forms of food-play that are socio-emotionally rich.?
Out-of-Scope Intent Detection on A Knowledge-Based Chatbot

Knowledge-based chatbot (KBC) has grown in popularity in recent years and has been widely used for various use cases.Building KBC from scratch using deep learning (DL) is challenging since no prior historical data exists.Meanwhile, DL systems need a vast Volume of data to be trained.This paper proposes a novel framework to create an intent classifier of the KBC used to detect in-scope (IS) and out-of-scope (OOS) intents.We introduce an automated queries generator to create IS intents employed as the training data from an ontology input.We utilize Bidirectional Encode Representations from Transformers (BERT) fine-tuning as the backbone of our DL system.Moreover, we present a Bayesian approach as an extension of the BERT to classify OOS queries with minimal OOS training data.The experiments result show that the proposed method manages to achieve an F1 score of 100% for IS intents and 86% for OOS queries.
Blind Super-resolution of Faces for Surveillance

Super-resolutionSuperresolution (SR) refersSurveillance to a class of techniques that derive a high resolutionLow/high resolution image from its low resolution (LR) counterpart. A vast amount of literature exists on SR spanning both multi and single image approaches.
Study on Corrosion Failure Characteristics of Silicone Rubber in Acidic Environment

In order to grasp the corrosion failure characteristics of silicone rubber in an acidic environment, an artificial mist box was used to artificially simulate the corrosion conditions acting on the silicone rubber, and the changes in its surface characteristics were analyzed. The research results show that under the combined action of acid mist and electric field, the physical and chemical properties of composite insulators have declined. There are holes and cracks on the surface, and the hardness increases with the corrosion time. Before flashover, if there is no obvious discharge in the withstand voltage process, the contact angle, relative dielectric constant and absorption peak area change little with the increase of corrosion time. The analysis shows that the damage rate of large-area corrosion of silicone rubber is very slow. Acidic substances will cause the main chain and side chains of the silicone rubber to break, and the aluminum hydroxide in the silicone rubber also reacts with strong acids, resulting in the destruction of the structure of the silicone rubber. The physical and chemical properties are reduced.
Entanglement of a pair of quantum emitters via continuous fluorescence measurements: a tutorial

We discuss recent developments in measurement protocols that generate quantum entanglement between two remote qubits, focusing on the theory of joint continuous detection of their spontaneous emission. We consider a device geometry similar to that used in well-known Bell state measurements, which we analyze using a conceptually transparent model of stochastic quantum trajectories; we use this to review photodetection, the most straightforward case, and then generalize to the diffusive trajectories from homodyne and heterodyne detection as well. Such quadrature measurement schemes are a realistic two-qubit extension of existing circuit QED experiments, which obtain quantum trajectories by homodyning or heterodyning a superconducting qubit’s spontaneous emission, or an adaptation of existing optical measurement schemes to obtain jump trajectories from emitters. We mention key results, presented from within a single theoretical framework, and draw connections to concepts in the wider literature on entanglement generation by measurement (such as path information erasure and entanglement swapping). The photon which-path information acquisition, and therefore the two-qubit entanglement yield, is tunable under the homodyne detection scheme we discuss, at best generating equivalent average entanglement dynamics as in the comparable photodetection case. In addition to deriving this known equivalence, we extend past analyses in our characterization of the measurement dynamics: we include derivations of bounds on the fastest possible evolution toward a Bell state under joint homodyne measurement dynamics and characterize the maximal entanglement yield possible using inefficient (lossy) measurements.
Enhanced Data Stream Classification by Optimized Weight Updated Meta-learning: Continuous learning-based on Concept-Drift

Purpose The aim of continuous learning is to obtain and fine-tune information gradually without removing the already existing information. Many conventional approaches in streaming data classification assume that all arrived new data is completely labeled. To regularize Neural Networks (NNs) by merging side information like user-provided labels or pair-wise constraints, incremental semi-supervised learning models need to be introduced. However, they are hard to implement, specifically in non-stationary environments because of the efficiency and sensitivity of such algorithms to parameters. The periodic update and maintenance of the decision method is the significant challenge in incremental algorithms whenever the new data arrives. Design/methodology/approach Hence, this paper plans to develop the meta-learning model for handling continuous or streaming data. Initially, the data pertain to continuous behavior is gathered from diverse benchmark source. Further, the classification of the data is performed by the Recurrent Neural Network (RNN), in which testing weight is adjusted or optimized by the new meta-heuristic algorithm. Here, the weight is updated for reducing the error difference between the target and the measured data when new data is given for testing. The optimized weight updated testing is performed by evaluating the concept-drift and classification accuracy. The new continuous learning by RNN is accomplished by the improved Opposition-based Novel Updating Spotted Hyena Optimization (ONU-SHO). Finally, the experiments with different datasets show that the proposed learning is improved over the conventional models. Findings From the analysis, the accuracy of the ONU-SHO based RNN (ONU-SHO-RNN) was 10.1% advanced than Decision Tree (DT), 7.6% advanced than Naive Bayes (NB), 7.4% advanced than k-nearest neighbors (KNN), 2.5% advanced than Support Vector Machine (SVM) 9.3% advanced than NN, and 10.6% advanced than RNN. Hence, it is confirmed that the ONU-SHO algorithm is performing well for acquiring the best data stream classification. Originality/value This paper introduces a novel meta-learning model using Opposition-based Novel Updating Spotted Hyena Optimization (ONU-SHO)-based Recurrent Neural Network (RNN) for handling continuous or streaming data. This is the first work utilizes a novel meta-learning model using Opposition-based Novel Updating Spotted Hyena Optimization (ONU-SHO)-based Recurrent Neural Network (RNN) for handling continuous or streaming data.
The ‘beautiful game’ in a world of hate

Daniel Kilvington and John Price examine the problems of football-related hate speech and abuse on social media in the context of sports journalism. They endeavour to explain and understand this online hate through a discussion of a number of recent examples of online abuse of English Premier League footballers and an analysis of how the British sports media has reported them. The chapter then critically assesses the response of football authorities and clubs to the problem, arguing both that the collective response to date has failed to properly address the issue and that the sports media has a role in scrutinising and challenging authorities on this topic. Finally, the chapter examines how sports journalists themselves have become the regular subject of online abuse, hate and threats and offers advice for young and aspiring reporters on how best to survive in this online world.
Efficient Computation of the Zeros of the Bargmann Transform Under Additive White Noise

We study the computation of the zero set of the Bargmann transform of a signal contaminated with complex white noise, or, equivalently, the computation of the zeros of its short-time Fourier transform with Gaussian window. We introduce the adaptive minimal grid neighbors algorithm (AMN), a variant of a method that has recently appeared in the signal processing literature, and prove that with high probability it computes the desired zero set. More precisely, given samples of the Bargmann transform of a signal on a finite grid with spacing $\delta$, AMN is shown to compute the desired zero set up to a factor of $\delta$ in the Wasserstein error metric, with failure probability $O(\delta^4 \log^2(1/\delta))$. We also provide numerical tests and comparison with other algorithms.
Generalized Dilated CNN Models for Depression Detection Using Inverted Vocal Tract Variables

Depression detection using vocal biomarkers is a highly researched area. Articulatory coordination features (ACFs) are developed based on the changes in neuromotor coordination due to psychomotor slowing, a key feature of Major Depressive Disorder. However findings of existing studies are mostly validated on a single database which limits the generalizability of results. Variability across different depression databases adversely affects the results in cross corpus evaluations (CCEs). We propose to develop a generalized classifier for depression detection using a dilated Convolutional Neural Network which is trained on ACFs extracted from two depression databases. We show that ACFs derived from Vocal Tract Variables (TVs) show promise as a robust set of features for depression detection. Our model achieves relative accuracy improvements of ~10% compared to CCEs performed on models trained on a single database. We extend the study to show that fusing TVs and Mel-Frequency Cepstral Coefficients can further improve the performance of this classifier.
Improving RNN-T ASR Accuracy Using Context Audio

We present a training scheme for streaming automatic speech recognition (ASR) based on recurrent neural network transducers (RNN-T) which allows the encoder network to learn to exploit context audio from a stream, using segmented or partially labeled sequences of the stream during training. We show that the use of context audio during training and inference can lead to word error rate reductions of more than 6% in a realistic production setting for a voice assistant ASR system. We investigate the effect of the proposed training approach on acoustically challenging data containing background speech and present data points which indicate that this approach helps the network learn both speaker and environment adaptation. To gain further insight into the ability of a long short-term memory (LSTM) based ASR encoder to exploit long-term context, we also visualize RNN-T loss gradients with respect to the input.
Rethinking End-to-End Evaluation of Decomposable Tasks: A Case Study on Spoken Language Understanding

Decomposable tasks are complex and comprise of a hierarchy of sub-tasks. Spoken intent prediction, for example, combines automatic speech recognition and natural language understanding. Existing benchmarks, however, typically hold out examples for only the surface-level sub-task. As a result, models with similar performance on these benchmarks may have unobserved performance differences on the other sub-tasks. To allow insightful comparisons between competitive end-to-end architectures, we propose a framework to construct robust test sets using coordinate ascent over sub-task specific utility functions. Given a dataset for a decomposable task, our method optimally creates a test set for each sub-task to individually assess sub-components of the end-to-end model. Using spoken language understanding as a case study, we generate new splits for the Fluent Speech Commands and Snips SmartLights datasets. Each split has two test sets: one with held-out utterances assessing natural language understanding abilities, and one with held-out speakers to test speech processing skills. Our splits identify performance gaps up to 10% between end-to-end systems that were within 1% of each other on the original test sets. These performance gaps allow more realistic and actionable comparisons between different architectures, driving future model development. We release our splits and tools for the community.
PRDP: Person Reidentification With Dirty and Poor Data

In this article, we propose a novel method to simultaneously solve the data problem of dirty quality and poor quantity for person reidentification (ReID). Dirty quality refers to the wrong labels in image annotations. Poor quantity means that some identities have very few images (FewIDs). Training with these mislabeled data or FewIDs with triplet loss will lead to low generalization performance. To solve the label error problem, we propose a weighted label correction based on cross-entropy (wLCCE) strategy. Specifically, according to the influence range of the wrong labels, we first classify the mislabeled images into point label error and set label error. Then, we propose a weighted triplet loss (WTL) to correct the two label errors, respectively. To alleviate the poor quantity issue, we propose a feature simulation based on autoencoder (FSAE) method to generate some virtual samples for FewID. For the authenticity of the simulated features, we transfer the difference pattern of identities with multiple images (MultIDs) to FewIDs by training an autoencoder (AE)-based simulator. In this way, the FewIDs obtain richer expressions to distinguish from other identities. By dealing with a dirty and poor data problem, we can learn more robust ReID models using the triplet loss. We conduct extensive experiments on two public person ReID datasets: 1) Market-1501 and 2) DukeMTMC-reID, to verify the effectiveness of our approach.