Reviewerly/exHarmony huggingface dataset:
More Bang for Your Buck: Natural Perturbation for Robust Question Answering

Deep learning models for linguistic tasks require large training datasets, which are expensive to create.As an alternative to the traditional approach of creating new instances by repeating the process of creating one instance, we propose doing so by first collecting a set of seed examples and then applying humandriven natural perturbations (as opposed to rule-based machine perturbations), which often change the gold label as well.Such perturbations have the advantage of being relatively easier (and hence cheaper) to create than writing out completely new examples.Further, they help address the issue that even models achieving human-level scores on NLP datasets are known to be considerably sensitive to small changes in input.To evaluate the idea, we consider a recent question-answering dataset (BOOLQ) and study our approach as a function of the perturbation cost ratio, the relative cost of perturbing an existing question vs. creating a new one from scratch.We find that when natural perturbations are moderately cheaper to create (cost ratio under 60%), it is more effective to use them for training BOOLQ models: such models exhibit 9% higher robustness and 4.5% stronger generalization, while retaining performance on the original BOOLQ dataset.
Analyzing Individual Neurons in Pre-trained Language Models

While a lot of analysis has been carried to demonstrate linguistic knowledge captured by the representations learned within deep NLP models, very little attention has been paid towards individual neurons.We carry out a neuron-level analysis using core linguistic tasks of predicting morphology, syntax and semantics, on pre-trained language models, with questions like: i) do individual neurons in pretrained models capture linguistic information?ii) which parts of the network learn more about certain linguistic phenomena?iii) how distributed or focused is the information?and iv) how do various architectures differ in learning these properties?We found small subsets of neurons to predict linguistic tasks, with lower level tasks (such as morphology) localized in fewer neurons, compared to higher level task of predicting syntax.Our study reveals interesting cross architectural comparisons.For example, we found neurons in XLNet to be more localized and disjoint when predicting properties compared to BERT and others, where they are more distributed and coupled.
EPPDA: An Efficient and Privacy-Preserving Data Aggregation Scheme with Authentication and Authorization for IoT-Based Healthcare Applications

Nowadays, IoT technology is used in various application domains, including the healthcare, where sensors and IoT enabled medical devices exchange data without human interaction to securely transmit collected sensitive healthcare data towards healthcare professionals to be reviewed and take proper actions if needed. The IoT devices are usually resource-constrained in terms of energy consumption, storage capacity, computational capability, and communication range. In healthcare applications, many miniaturized devices are exploited for healthcare data collection and transmission. Thus, there is a need for secure data aggregation while preserving the data integrity and privacy of the patient. For that, the security, privacy, and aggregation of health data are very important aspects to be considered. This paper proposes a novel secure data aggregation scheme called “An Efficient and Privacy-Preserving Data Aggregation Scheme with authentication for IoT-Based Healthcare applications” (EPPDA). EPPDA is based to verification and authorization phase to verify the legitimacy of the nodes that need to join the process of aggregation. EPPDA, also, uses additive homomorphic encryption to protect data privacy and combines it with homomorphic MAC to check the data integrity. The major advantage of homomorphic encryption is allowing complex mathematical operations to be performed on encrypted data without knowing the contents of the original plain data. The proposed system is developed using MySignals HW V2 platform. Security analysis and experimental results show that our proposed scheme guarantees data privacy, messages authenticity, and integrity, with lightweight communication overhead and computation.
International Journal of Research in Economics and Social Sciences

The Social Sciences are the fields of scholarship that study society. International Journal of Research in Social Sciences is a scholarly open access, peer-reviewed, interdisciplinary and fully refereed journal focusing on theories, methods and applications of Social Sciences. The main objective of IJRESS is to provide an intellectual platform for the international scholars. IJRESS aims to promote interdisciplinary studies in the society and become the leading journal in social science in the world. The journal publishes research papers in the fields of:=> Anthropology=> Economics=> Education=> Geography=> History=> Law=> Linguistics=> Political science=> Public administration=> Psychology=> Sociology=> Archaeology=> Area studies=> Behavioral science=> Communication studies=> Demography=> Development studies=> Environmental studies=> Information science=> International relations=> Global issues=> International education=> Journalism=> Legal management=> Library science=> Management=> Political economy=> Marketing=> Finance=> Economics=> Banking=> Accounting=> Human Resources Management=> Hotel and tourism=> Entrepreneurship development=> Business ethics
Multi-Objective Task Scheduling Optimization for Load Balancing in Cloud Computing Environment Using Hybrid Artificial Bee Colony Algorithm With Reinforcement Learning

Workload balancing in cloud computing is still challenging problem, especially in Infrastructure as a Service (IaaS) in the cloud model. A problem that should not occur during cloud access is a host or server being overloaded or underloaded, which may affect the processing time or may result in a system crash. Therefore, to prevent these problems, an appropriate schedule of access should be considered so that the system can distribute tasks across all available resources, which is called load balancing. The load balancing technique should ensure that all Virtual Machines (VMs) are used appropriately. In this paper, an independent task scheduling approach in cloud computing is proposed using a Multi-objective task scheduling optimization based on the Artificial Bee Colony Algorithm (ABC) with a Q-learning algorithm,which is a reinforcement learning technique that helps the ABC algorithm work faster, called the MOABCQ method. The proposed method aims to optimize scheduling and resource utilization, maximize VM throughput, and create load balancing between VMs based on makespan, cost, and resource utilization, which are limitations of concurrent considerations. Performance analysis of the proposed method was compared using CloudSim with the existing load balancing and scheduling algorithms: Max-Min, FCFS, HABC_LJF, Q-learning, MOPSO, and MOCS algorithms in three datasets: Random, Google Cloud Jobs (GoCJ), and Synthetic workload. The experimental results indicated that the algorithms used MOABCQ approach outperformed the other algorithms in terms of reducing makespan, reducing cost, reducing degree of imbalance, increasing throughput and average resource utilization.
Grey wolf optimizer based on Aquila exploration method

The grey wolf optimizer(GWO) is an effective meta-heuristic algorithm. However, since the update of the search agent's position often depends on the alpha wolf, it is easy to fall into a local optimal solution. Therefore, this paper proposes an improved GWO algorithm to solve the global optimization. The improved algorithm is inspired by the Aquila Optimizer(AO), which enables some wolves to have the ability to fly, expand their search range to improve the global search ability and reduce the possibility of falling into the local optimum. At the same time, a new reduction strategy is proposed, which mainly emphasizes the exploitation ability of grey wolf and the exploration ability of Aquila, which is used to balance the two stages of exploration and exploitation. In order to verify the effectiveness of the algorithm, the algorithm was benchmarked on 23 well-known test functions and compared with other popular meta-heuristic algorithms. The results show that our proposed algorithm has good performance. Finally, it is applied to four practical engineering problems, and the results show that the algorithm is suitable for challenging problems with unknown search space. Matlab codes of AO are available at https://ww2.mathworks.cn/matlabcentral/fileexchange/110410-grey-wolf-optimizer-based-on-aquila-exploration-method.
Fuzzy Group Decision Making for influence-aware recommendations

Group Recommender Systems are special kinds of Recommender Systems aimed at suggesting items to groups rather than individuals taking into account, at the same time, the preferences of all (or the majority of) members. Most existing models build recommendations for a group by aggregating the preferences for their members without taking into account social aspects like user personality and interpersonal trust, which are capable of affecting the item selection process during interactions. To consider such important factors, we propose in this paper a novel approach to group recommendations based on fuzzy influence-aware models for Group Decision Making. The proposed model calculates the influence strength between group members from the available information on their interpersonal trust and personality traits (possibly estimated from social networks). The estimated influence network is then used to complete and evolve the preferences of group members, initially calculated with standard recommendation algorithms, toward a shared set of group recommendations, simulating in this way the effects of influence on opinion change during social interactions. The proposed model has been experimented and compared with related works.
Adversarial Learning of Semantic Relevance in Text to Image Synthesis

We describe a new approach that improves the training of generative adversarial nets (GANs) for synthesizing diverse images from a text input. Our approach is based on the conditional version of GANs and expands on previous work leveraging an auxiliary task in the discriminator. Our generated images are not limited to certain classes and do not suffer from mode collapse while semantically matching the text input. A key to our training methods is how to form positive and negative training examples with respect to the class label of a given image. Instead of selecting random training examples, we perform negative sampling based on the semantic distance from a positive example in the class. We evaluate our approach using the Oxford-102 flower dataset, adopting the inception score and multi-scale structural similarity index (MS-SSIM) metrics to assess discriminability and diversity of the generated images. The empirical results indicate greater diversity in the generated images, especially when we gradually select more negative training examples closer to a positive example in the semantic space.
Optimal Transport in Reproducing Kernel Hilbert Spaces: Theory and Applications

In this paper, we present a mathematical and computational framework for comparing and matching distributions in reproducing kernel Hilbert spaces (RKHS). This framework, called optimal transport in RKHS, is a generalization of the optimal transport problem in input spaces to (potentially) infinite-dimensional feature spaces. We provide a computable formulation of Kantorovich's optimal transport in RKHS. In particular, we explore the case in which data distributions in RKHS are Gaussian, obtaining closed-form expressions of both the estimated Wasserstein distance and optimal transport map via kernel matrices. Based on these expressions, we generalize the Bures metric on covariance matrices to infinite-dimensional settings, providing a new metric between covariance operators. Moreover, we extend the correlation alignment problem to Hilbert spaces, giving a new strategy for matching distributions in RKHS. Empirically, we apply the derived formulas under the Gaussianity assumption to image classification and domain adaptation. In both tasks, our algorithms yield state-of-the-art performances, demonstrating the effectiveness and potential of our framework.
Feature Aggregation With Reinforcement Learning for Video-Based Person Re-Identification

Video-based person re-identification (re-id) matches two tracks of persons from different cameras. Features are extracted from the images of a sequence and then aggregated as a track feature. Compared to existing works that aggregate frame features by simply averaging them or using temporal models such as recurrent neural networks, we propose an intelligent feature aggregate method based on reinforcement learning. Specifically, we train an agent to determine which frames in the sequence should be abandoned in the aggregation, which can be treated as a decision making process. By this way, the proposed method avoids introducing noisy information of the sequence and retains these valuable frames when generating a track feature. On benchmark data sets, experimental results show that our method can boost the re-id accuracy obviously based on the state-of-the-art models.
Web Services Classification Based on Wide &amp; Bi-LSTM Model

With the rapid growth of Web services on the Internet, it becomes a great challenge for Web services discovery. Classifying Web services with similar functions is an effective method for service discovery and management. However, the functional description documents of Web services usually are short in their length, with sparse features and less information, which makes most topic models unable to model the short text well, consequently affecting the Web service classification. To solve this problem, a Web service classification method based on Wide & Bi-LSTM model is proposed in this paper. In this method, first, all the discrete features in the description documents of Web services are combined to perform the breadth prediction of Web service category by exploiting the wide learning model. Second, the word order and context information of the words in the description documents of Web services are mined by using the Bi-LSTM model to perform the depth prediction of the Web service category. Third, it uses the linear regression algorithm to integrate the breadth and depth prediction results of Web service categories as the final result of the service classification. Finally, compared with six Web service classification methods based on TF-IDF, LDA, WE-LDA, LSTM, Wide&Deep, and Bi-LSTM, respectively, the experimental results show that our approach achieves a better performance in the accuracy of Web service classification.
A Comprehensive Survey of Video Datasets for Background Subtraction

Background subtraction is an effective method of choice when it comes to detection of moving objects in videos and has been recognized as a breakthrough for the wide range of applications of intelligent video analytics (IVA).In recent years, a number of video datasets intended for background subtraction have been created to address the problem of large realistic datasets with accurate ground truth.The use of these datasets enables qualitative as well as quantitative comparisons and allows benchmarking of different algorithms.Finding the appropriate dataset is generally a cumbersome task for an exhaustive evaluation of algorithms.Therefore, we systematically survey standard video datasets and list their applicability for different applications.This paper presents a comprehensive account of public video datasets for background subtraction and attempts to cover the lack of a detailed description of each dataset.The video datasets are presented in chronological order of their appearance.Current trends of deep learning in background subtraction along with top-ranked background subtraction methods are also discussed in this paper.The survey introduced in this paper will assist researchers of the computer vision community in the selection of appropriate video dataset to evaluate their algorithms on the basis of challenging scenarios that exist in both indoor and outdoor environments.
Gradient-based Hierarchical Clustering using Continuous Representations of Trees in Hyperbolic Space

Hierarchical clustering is typically performed using algorithmic-based optimization searching over the discrete space of trees. While these optimization methods are often effective, their discreteness restricts them from many of the benefits of their continuous counterparts, such as scalable stochastic optimization and the joint optimization of multiple objectives or components of a model (e.g. end-to-end training). In this paper, we present an approach for hierarchical clustering that searches over continuous representations of trees in hyperbolic space by running gradient descent. We compactly represent uncertainty over tree structures with vectors in the Poincare ball. We show how the vectors can be optimized using an objective related to recently proposed cost functions for hierarchical clustering (Dasgupta, 2016; Wang and Wang, 2018). Using our method with a mini-batch stochastic gradient descent inference procedure, we are able to outperform prior work on clustering millions of ImageNet images by 15 points of dendrogram purity. Further, our continuous tree representation can be jointly optimized in multi-task learning applications offering a 9 point improvement over baseline methods.
Random Feature-based Online Multi-kernel Learning in Environments with Unknown Dynamics

Kernel-based methods exhibit well-documented performance in various nonlinear learning tasks. Most of them rely on a preselected kernel, whose prudent choice presumes task-specific prior informatio...
Two-Party ECDSA from Hash Proof Systems and Efficient Instantiations

ECDSA is a widely adopted digital signature standard. Unfortunately, efficient distributed variants of this primitive are notoriously hard to achieve and known solutions often require expensive zero knowledge proofs to deal with malicious adversaries. For the two party case, Lindell [Lin17] recently managed to get an efficient solution which, to achieve simulation-based security, relies on an interactive, non standard, assumption on Paillier’s cryptosystem. In this paper we generalize Lindell’s solution using hash proof systems. The main advantage of our generic method is that it results in a simulation-based security proof without resorting to non-standard interactive assumptions. Moving to concrete constructions, we show how to instantiate our framework using class groups of imaginary quadratic fields. Our implementations show that the practical impact of dropping such interactive assumptions is minimal. Indeed, while for 128-bit security our scheme is marginally slower than Lindell’s, for 256-bit security it turns out to be better both in key generation and signing time. Moreover, in terms of communication cost, our implementation significantly reduces both the number of rounds and the transmitted bits without exception.
Is Photorealism Important for Perception of Expressive Virtual Humans in Virtual Reality?

In recent years, the quality of real-time rendering has reached new heights—realistic reflections, physically based materials, and photometric lighting are all becoming commonplace in modern game engines and even interactive virtual environments, such as virtual reality (VR). As the strive for realism continues, there is a need to investigate the effect of photorealism on users’ perception, particularly for interactive, emotional scenarios in VR. In this article, we explored three main topics, where we predicted photorealism will make a difference: the illusion of being present with the virtual person and in an environment, altered emotional response toward the character, and a subtler response—comfort of being in close proximity to the character. We present a perceptual experiment, with an interactive expressive virtual character in VR, which was designed to induce particular social responses in people. Our participant pool was large (N = 797) and diverse in terms of demographics. We designed a between-group experiment, where each group saw either the realistic rendering or one of our stylized conditions (simple and sketch style), expressing one of three attitudes: Friendly, Unfriendly, or Sad. While the render style did not particularly effect the level of comfort with the character or increase the illusion of presence with it, our main finding shows that the photorealistic character changed the emotional responses of participants, compared to the stylized versions. We also found a preference for realism in VR, reflected in the affinity and higher place illusion in the scenario, rendered in the realistic render style.
Shredder

A wide variety of deep neural applications increasingly rely on the cloud to perform their compute-heavy inference. This common practice requires sending private and privileged data over the network to remote servers, exposing it to the service provider and potentially compromising its privacy. Even if the provider is trusted, the data can still be vulnerable over communication channels or via side-channel attacks in the cloud. To that end, this paper aims to reduce the information content of the communicated data with as little as possible compromise on the inference accuracy by making the sent data noisy. An undisciplined addition of noise can significantly reduce the accuracy of inference, rendering the service unusable. To address this challenge, this paper devises Shredder, an end-to-end framework, that, without altering the topology or the weights of a pre-trained network, learns additive noise distributions that significantly reduce the information content of communicated data while maintaining the inference accuracy. The key idea is finding the additive noise distributions by casting it as a disjoint offline learning process with a loss function that strikes a balance between accuracy and information degradation. The loss function also exposes a knob for a disciplined and controlled asymmetric trade-off between privacy and accuracy. While keeping the DNN intact, Shredder divides inference between the cloud and the edge device, striking a balance between computation and communication. In the separate phase of inference, the edge device takes samples from the Laplace distributions that were collected during the proposed offline learning phase and populates a noise tensor with these sampled elements. Then, the edge device merely adds this populated noise tensor to the intermediate results to be sent to the cloud. As such, Shredder enables accurate inference on noisy intermediate data without the need to update the model or the cloud, or any training process during inference. We also formally show that Shredder maximizes privacy with minimal impact on DNN accuracy while the tradeoff between privacy and accuracy is controlled through a mathematical knob. Experimentation with six real-world DNNs from text processing and image classification shows that Shredder reduces the mutual information between the input and the communicated data to the cloud by 74.70% compared to the original execution while only sacrificing 1.58% loss in accuracy. On average, Shredder also offers a speedup of 1.79x over Wi-Fi and 2.17x over LTE compared to cloud-only execution when using an off-the-shelf mobile GPU (Tegra X2) on the edge.