Reviewerly/exHarmony huggingface dataset:
Adversary Instantiation: Lower Bounds for Differentially Private Machine Learning

Differentially private (DP) machine learning allows us to train models on private data while limiting data leakage. DP formalizes this data leakage through a cryptographic game, where an adversary must predict if a model was trained on a dataset D, or a dataset D' that differs in just one example.If observing the training algorithm does not meaningfully increase the adversary's odds of successfully guessing which dataset the model was trained on, then the algorithm is said to be differentially private. Hence, the purpose of privacy analysis is to upper bound the probability that any adversary could successfully guess which dataset the model was trained on.In our paper, we instantiate this hypothetical adversary in order to establish lower bounds on the probability that this distinguishing game can be won. We use this adversary to evaluate the importance of the adversary capabilities allowed in the privacy analysis of DP training algorithms.For DP-SGD, the most common method for training neural networks with differential privacy, our lower bounds are tight and match the theoretical upper bound. This implies that in order to prove better upper bounds, it will be necessary to make use of additional assumptions. Fortunately, we find that our attacks are significantly weaker when additional (realistic)restrictions are put in place on the adversary's capabilities.Thus, in the practical setting common to many real-world deployments, there is a gap between our lower bounds and the upper bounds provided by the analysis: differential privacy is conservative and adversaries may not be able to leak as much information as suggested by the theoretical bound.
Sentiment analysis based on BiGRU information enhancement

Abstract In view of the existing sentiment analysis technology, model design is often a combination of different network models, which may not give full play to the advantages of the network. This paper proposes a data pre-training based on BERT, and input the obtained data into BiGRU neural network layer to enhance the features, and achieve the feature enhancement by stacking and repeated use. The input of the second layer is the result of the output of the first layer, and the input of the third layer is the result of the output of the second layer, and then it is calculated multiple times in different subspaces to learn relevant information. The experiment was conducted on the Twitter data set, and the results showed that in this idea, The two-layer BiGRU classification effect is better, reaching an accuracy of 82.63%, the convergence rate is faster, and it is better than other classification models, and the classification work can be completed well.
The Work‐Averse Cyberattacker Model: Theory and Evidence from Two Million Attack Signatures

The assumption that a cyberattacker will potentially exploit all present vulnerabilities drives most modern cyber risk management practices and the corresponding security investments. We propose a new attacker model, based on dynamic optimization, where we demonstrate that large, initial, fixed costs of exploit development induce attackers to delay implementation and deployment of exploits of vulnerabilities. The theoretical model predicts that mass attackers will preferably (i) exploit only one vulnerability per software version, (ii) largely include only vulnerabilities requiring low attack complexity, and (iii) be slow at trying to weaponize new vulnerabilities . These predictions are empirically validated on a large data set of observed massed attacks launched against a large collection of information systems. Findings in this article allow cyber risk managers to better concentrate their efforts for vulnerability management, and set a new theoretical and empirical basis for further research defining attacker (offensive) processes.
Video Game Interfaces and Diegesis: The Impact on Experts and Novices’ Performance and Experience in Virtual Reality

When playing action video games, an optimal experience implies the presence of a head-up display that informs players on their status in regard of their goal like health points or their localization in the environment. However, how can this type of information can be integrated in new gaming contexts like virtual reality? Should this information be integrated into the game universe (diegetic design) or stay out of it (non-diegetic design)? For this purpose, the performance, presence, and enjoyment of 41 players have been measured during a virtual reality first-person shooter game session with a diegetic and a non-diegetic interface. The results showed that diegetic integration has a positive effect on the player’s performance but not on the subjective experience (presence and enjoyment). The study also shed light on the moderator role of expertise in action games on this effect because the diegetic interface only benefited novice players.
Improving the Authenticity of Real Estate Land Transaction Data Using Blockchain-Based Security Scheme

Digital land registry systems have tried to resolve the issues of manual systems like delay in transaction, fraud, security and persistence of records. However, still, these systems are susceptible to various type of security threats due to the weaknesses of inherent issues in centralised or cloud-based systems. This paper reviews the different shortcoming with a focus on authentication threats in the traditional land registry system. Further, this research focuses on analysing how blockchain and blockchain-based authentication schemes can be applied to solve the issues related to land transaction and authentication. In last a blockchain-based authentication scheme for secure real estate and land transactions has been proposed.
A threshold hybrid encryption method for integrity audit without trusted center

Abstract Cloud storage with sharing services is increasingly popular among data owners. However, it is difficult for the users to know if the cloud server providers (CSPs) indeed protect their data. To verify data integrity and preserve data and key privacy in the group, this paper proposes a new threshold hybrid encryption for integrity auditing method without trusted center. The proposed method is developed based on the Advanced Encryption Standard (AES) and the Elliptic Curve Cryptography (ECC) with Shamir secret sharing. In this way, the key can be distributed and managed without trusted center, preserving the privacy of the key of the AES and users’ private key. Besides, we design and implement a novel integrity auditing and re-signature method which verifies the data integrity and solves the collusion question of the cloud and the revoked users. Security analysis and performance evaluation demonstrate that the proposed scheme realizes the correctness, security, and efficiency with a low communication and computation cost.
A Review of Robot Learning for Manipulation: Challenges, Representations, and Algorithms

A key challenge in intelligent robotics is creating robots that are capable of directly interacting with the world around them to achieve their goals. The last decade has seen substantial growth in research on the problem of robot manipulation, which aims to exploit the increasing availability of affordable robot arms and grippers to create robots capable of directly interacting with the world to achieve their goals. Learning will be central to such autonomous systems, as the real world contains too much variation for a robot to expect to have an accurate model of its environment, the objects in it, or the skills required to manipulate them, in advance. We aim to survey a representative subset of that research which uses machine learning for manipulation. We describe a formalization of the robot manipulation learning problem that synthesizes existing research into a single coherent framework and highlight the many remaining research opportunities and challenges.
Multi-detection and Segmentation of Breast Lesions Based on Mask RCNN-FPN

The presence of different malicious regions on a single breast reveal some necessary information for breast cancer early detection. In current computer-aided diagnosis models, different lesions contained in a single mammogram are not detected and segmented individually. Therefore, the multidetection and segmentation of the breast lesions can help the radiologists for an accurate diagnosis. This study aims to develop a model based on regional learning technique and RoI-based Convolutional neural network (CNN), which is known as Masked Regional Convolutional Neural Network embedded with Feature Pyramid Network. By using Mask RCNN-FPN, we can handle multi-detection, instance segmentation, and classification simultaneously. FPN extracts semantic features at different resolution scales and it can exhibit lesions at multiple scales. The training and testing of the model are performed on the DDSM and Inbreast respectively. In comparison, this model achieved mean average precision 0.84 for multi-detection and segmentation and 91% overall accuracy performance over SegNet and U-Net CNN encoder and decoder segmentation architecture.
Deep-HOSeq: Deep Higher Order Sequence Fusion for Multimodal Sentiment Analysis

Multimodal sentiment analysis utilizes multiple heterogeneous modalities for sentiment classification.The recent multimodal fusion schemes customize LSTMs to discover intramodal dynamics and design sophisticated attention mechanisms to discover the inter-modal dynamics from multimodal sequences.Although powerful, these schemes completely rely on attention mechanisms which is problematic due to two major drawbacks 1) deceptive attention masks, and 2) training dynamics.Nevertheless, strenuous efforts are required to optimize hyperparameters of these consolidate architectures, in particular their custom-designed LSTMs constrained by attention schemes.In this research, we first propose a common network to discover both intra-modal and inter-modal dynamics by utilizing basic LSTMs and tensor based convolution networks.We then propose unique networks to encapsulate temporal-granularity among the modalities which is essential while extracting information within asynchronous sequences.We then integrate these two kinds of information via a fusion layer and call our novel multimodal fusion scheme as Deep-HOSeq (Deep network with higher order Common and Unique Sequence information).The proposed Deep-HOSeq efficiently discovers all-important information from multimodal sequences and the effectiveness of utilizing both types of information is empirically demonstrated on CMU-MOSEI and CMU-MOSI benchmark datasets.The source code of our proposed Deep-HOSeq is and available at https://github.com/sverma88/Deep-HOSeq--ICDM-2020.
Financial development and governance: A panel data analysis incorporating cross-sectional dependence

This study investigates bidirectional causality between governance and financial development using panel data of 101 countries from 1984 to 2013. The financial development–governance nexus is explored using econometric methods robust to cross-sectional dependence, and the relationship between different levels of development and openness is analyzed. Long-run equation estimates show clear evidence that financial development positively affects governance, and this positive impact is found to be robust to three different measures of governance. Further analysis shows that improving governance quality has a positive effect on financial development, while Granger causality tests demonstrate bidirectional causality between financial development and the governance measures. Finally, the impact of financial development on governance is dependent on a country’s level of development and openness. These findings underscore the crucial role of financial development in bringing about good governance reforms and economic growth that, in turn, can further develop the financial sector. As such, a symbiotic and synergistic relationship can persist between good governance, growth, and financial development. The findings provide significant motivation for policymakers to encourage openness and financial sector development to lift the standard of living, especially in emerging economies.
Explaining Neural Scaling Laws.

The test loss of well-trained neural networks often follows precise power-law scaling relations with either the size of the training dataset or the number of parameters in the network. We propose a theory that explains and connects these scaling laws. We identify variance-limited and resolution-limited scaling behavior for both dataset and model size, for a total of four scaling regimes. The variance-limited scaling follows simply from the existence of a well-behaved infinite data or infinite width limit, while the resolution-limited regime can be explained by positing that models are effectively resolving a smooth data manifold. In the large width limit, this can be equivalently obtained from the spectrum of certain kernels, and we present evidence that large width and large dataset resolution-limited scaling exponents are related by a duality. We exhibit all four scaling regimes in the controlled setting of large random feature and pretrained models and test the predictions empirically on a range of standard architectures and datasets. We also observe several empirical relationships between datasets and scaling exponents: super-classing image tasks does not change exponents, while changing input distribution (via changing datasets or adding noise) has a strong effect. We further explore the effect of architecture aspect ratio on scaling exponents.
Revisiting the Statistics Pooling Layer in Deep Speaker Embedding Learning

The pooling function plays a vital role in the segment-level deep speaker embedding learning framework. One common method is to calculate the statistics of the temporal features, while the mean based temporal average pooling (TAP) and temporal statistics pooling (TSTP) which combine mean and standard deviation are two typical approaches. Empirically, researchers observe a big performance degradation in x-vector when removing the standard deviation. Based on this observation, in this paper, we designed a set of experiments to analyze the effectiveness of different statistics quantitatively, including the investigation and comparison on pooling functions based on standard deviation, covariance and ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</sub> -norm. Experiments are carried out on Vox-Celeb and SRE16, and the results show that the second-order statistics based pooling functions yield better performance than TAP, and only the simple standard deviation can achieve the best performance on all the evaluation conditions.
Vision-Based Human Detection Techniques: A Descriptive Review

Cameras are being used everywhere for the safety and security of citizens in different countries. Using a machine to detect humans in a photo or a video frame is a very complicated and challenging task. Various techniques have been developed for this purpose, which mainly rely on Artificial Intelligence. This article aims to provide a comprehensive review and analysis of the literatures from a descriptive perspective, which is its main differentiator from the existing survey papers in this area. Firstly, the vision-based human detection techniques and classifiers are elucidated in conjunction with the variants of feature extraction techniques. Secondly, various pros and cons of such techniques are discussed. Then, an investigation has been conducted and reported based on the state-of-the-art human detection descriptors (e.g. Log-Average Miss Rate and accuracy). Although techniques such as Viola-Jones and Speeded-Up Robust Features can detect objects in real-time and overcome Scale-Invariant Feature Transform (SIFT) limitations, they are still sensitive to illuminated conditions. Other techniques such as SIFT, Bag of Words, Orthogonal Moments, and Histogram of oriented Gradients provide other interesting benefits which include insensitivity to occlusion and clutters, simplicity, low-order element construction and invariance to illuminated conditions; nevertheless, they are computationally expensive and sensitive to image rotation. A meticulous review along similar lines revealed that the Deformable Part-based Model performs relatively better due to its ability to deal with particular pose variations and multiple views, occlusion handling (partial) and is application-free while its counterparts focus on only a single aspect. This article highlights and provides a brief description of each available data-sets for human detection research. Various use-cases of human detection systems are also elaborated. Finally, various conclusions are derived based on the conducted review followed by recommendations for future directions and possibilities to further improve the speed and accuracy of human detection systems.
Cooperative meta-heuristic algorithms for global optimization problems

This paper presents an alternative global optimization meta-heuristics (MHs) approach, inspired by the natural selection theory. The proposed approach depends on the competition among six MHs that allows generating an offspring, which can breed the high characteristics of parents since they are unique and competitive. Therefore, this leads to improve the convergence of the solutions towards an optimal solution and also, to avoid the limitations of other methods that aim to balance between exploitation and exploration. The six algorithms are differential evolution, whale optimization algorithm, grey wolf optimization, symbiotic organisms search algorithm, sine–cosine algorithm, and salp swarm algorithm. According to these algorithms, three variants of the proposed method are developed, in the first variant, one of the six algorithms will be used to update the current individual based on a predefined order and the probability of the fitness function for each individual. Whereas, the second variant updates each individual by permuting the six algorithms, then using the algorithms in the current permutation to update individuals. The third variant is considered as an extension of the second variant, which updates all individuals using only one algorithm from the six algorithms. Three different experiments are carried out using CEC 2014 and CEC 2017 benchmark functions to evaluate the efficiency of the proposed approach. Moreover, the proposed approach is compared with well known MH methods, including the six methods used to build it. Comparison results confirmed the efficiency of the proposed approach compared to other approaches according to different performance measures.
Towards multi-party targeted model poisoning attacks against federated learning systems

The federated learning framework builds a deep learning model collaboratively by a group of connected devices via only sharing local parameter updates to the central parameter server. Nonetheless, the lack of transparency in the local data resource makes it prone to adversarial federated attacks, which have shown increasing ability to reduce learning performance. Existing research efforts either focus on the single-party attack with impractical perfect knowledge setting and limited stealthy ability or the random attack that has no control on attack effects. In this paper, we investigate a new multi-party adversarial attack with the imperfect knowledge of the target system. Controlled by an adversary, a number of compromised devices collaboratively launch targeted model poisoning attacks, intending to misclassify the targeted samples while maintaining stealthy under different detection strategies. Specifically, the compromised devices jointly minimize the loss function of model training in different scenarios. To overcome the update scaling problem, we develop a new boosting strategy by introducing two stealthy metrics. Via experimental results, we show that under both perfect knowledge and limited knowledge settings, the multi-party attack is capable of successfully evading detection strategies while guaranteeing the convergence. We also demonstrate that the learned model achieves the high accuracy on the targeted samples, which confirms the significant impact of the multi-party attack on federated learning systems.
Quantum Communication Networks and Trust Management: A Survey

This paper summarizes the state of art in quantum communication networks and trust management in recent years. As in the classical networks, trust management is the premise and foundation of quantum secure communication and c... | Find, read and cite all the research you need on Tech Science Press
Local Search is a Remarkably Strong Baseline for Neural Architecture Search

Neural Architecture Search (NAS), i.e., the automation of neural network design, has gained much popularity in recent years with increasingly complex search algorithms being proposed. Yet, solid comparisons with simple baselines are often missing. At the same time, recent retrospective studies have found many new algorithms to be no better than random search (RS). In this work we consider the use of a simple Local Search (LS) algorithm for NAS. We particularly consider a multi-objective NAS formulation, with network accuracy and network complexity as two objectives, as understanding the trade-off between these two objectives is arguably among the most interesting aspects of NAS. The proposed LS algorithm is compared with RS and two evolutionary algorithms (EAs), as these are often heralded as being ideal for multi-objective optimization. To promote reproducibility, we create and release two benchmark datasets, named MacroNAS-C10 and -C100, containing 200K saved network evaluations for two established image classification tasks, CIFAR-10 and CIFAR-100. Our benchmarks are designed to be complementary to existing benchmarks, especially in that they are better suited for multi-objective search. We additionally consider a version of the problem with a much larger architecture space. While we find and show that the considered algorithms explore the search space in fundamentally different ways, we also find that LS substantially outperforms RS and even performs nearly as good as state-of-the-art EAs. We believe that this provides strong evidence that LS is truly a competitive baseline for NAS against which new NAS algorithms should be benchmarked.
Secure and trustworthy machine learning/artificial intelligence for multi-domain operations

Machine Learning (ML) algorithms and Artificial Intelligence (AI) are now regarded as very useful for data-driven applications including resilient multi-domain operations. However, ML algorithms and AI systems can be controlled, dodged, biased, and misled through flawed learning models and input data, they need robust security features and trust. Furthermore, ML algorithms and AI systems add challenges when we have (unlabeled/labeled) sparse/small data or big data for training and evaluation. It is very important to design, evaluate and test ML algorithms and AI systems that produce reliable, robust, trustworthy, explainable, and fair/unbiased outcomes to make them acceptable and reliable in mission critical multi-domain operations. ML algorithms rely on data and work on the principle of ``Garbage In, Garbage Out," which means that if the input data to learning model is corrupted or compromised, the outcomes of the ML/AI would not be optimal, reliable and trustworthy.