Reviewerly/exHarmony huggingface dataset:
Metrics and Evaluations of Time Series Explanations: An Application in Affect Computing

Explainable artificial intelligence (XAI) has shed light on enormous applications by clarifying why neural models make specific decisions.However, it remains challenging to measure how sensitive XAI solutions are to the explanations of neural models.Although different evaluation metrics have been proposed to measure sensitivity, the main focus has been on the visual and textual data.There is insufficient attention devoted to the sensitivity metrics tailored for time series data.In this paper, we formulate several metrics, including max short-term sensitivity (MSS), max long-term sensitivity (MLS), average short-term sensitivity (ASS) and average long-term sensitivity (ALS), that target the sensitivity of XAI models with respect to the generated and real time series.Our hypothesis is that for close series with the same labels, we obtain similar explanations.We evaluate three XAI models, LIME, integrated gradient (IG), and SmoothGrad (SG), on CN-Waterfall, a deep convolutional network.This network is a highly accurate time series classifier in affect computing.Our experiments rely on data-, metric-and XAI hyperparameter-related settings on the WESAD and MAHNOB-HCI datasets.The results reveal that (i) IG and LIME provide a lower sensitivity scale than SG in all the metrics and settings, potentially due to the lower scale of important scores generated by IG and LIME, (ii) the XAI models show higher sensitivities for a smaller window of data, (iii) the sensitivities of XAI models fluctuate when the network parameters and data properties change, and (iv) the XAI models provide unstable sensitivities under different settings of hyperparameters.
Unsupervised Deep Pairwise Hashing

Although unsupervised deep hashing is potentially very useful for tackling many large-scale tasks, its performance is still far below satisfactory. Additionally, its performance might be significantly improved by effectively exploiting the pair similarity relationship among training data, but the attained similarity matrix usually contains noisy information, which often largely decreases the model performance. To alleviate this issue, in this paper, we propose a novel unsupervised deep pairwise hashing method to effectively and robustly exploit the similarity information between training samples and multiple anchors. We first create an ensemble anchor-based pairwise similarity matrix to enhance the robustness of similarity and dissimilarity relations between training samples and anchors. Afterwards, we propose a novel loss function to directly and robustly take advantage of the similarity and dissimilarity information via a weighted cross-entropy loss, and make use of a square loss to reduce the gap between latent binary vectors and binary codes, and another square loss to form consensus predictions of latent binary vectors. Extensive experiments on large-scale benchmark databases demonstrate the effectiveness of the proposed method, which outperforms recent state-of-the-art unsupervised hashing methods with significantly better ranking performance.
Prefixed-Threshold Real-Time Selection for Free-Space Sending-or-Not Twin-Field Quantum Key Distribution

As a variant of the twin-field quantum key distribution (TF-QKD), the sending-or-not twin-field quantum key distribution (SNS TF-QKD) is famous for its higher tolerance of misalignment error, in addition to the capacity of surpassing the rate-distance limit. Importantly, the free-space SNS TF-QKD will guarantee the security of the communications between mobile parties. In the paper, we first discuss the influence of atmospheric turbulence (AT) on the channel transmittance characterized by the probability distribution of the transmission coefficient (PDTC). Then, we present a method called prefixed-threshold real-time selection (P-RTS) to mitigate the interference of AT on the free-space SNS TF-QKD. The simulations of the free-space SNS TF-QKD with and without P-RTS are both given for comparison. The results showed that it is possible to share the secure key by using the free-space SNS TF-QKD. Simultaneously, the P-RTS method can make the free-space SNS TF-QKD achieve better and more stable performance at a short distance.
Improved Extractors for Small-Space Sources

We study the problem of extracting random bits from weak sources that are sampled by algorithms with limited memory. This model of small-space sources was introduced by Kamp, Rao, Vadhan and Zuckerman (STOC'06), and falls into a line of research initiated by Trevisan and Vadhan (FOCS'00) on extracting randomness from weak sources that are sampled by computationally bounded algorithms. Our main results are the following. 1) We obtain near-optimal extractors for small-space sources in the polynomial error regime. For space <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$s$</tex> sources over <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$n$</tex> bits, our extractors require just <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$k\geq s. \text{polylog} (n)$</tex> entropy. This is an exponential improvement over the previous best result, which required entropy <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$k\geq s^{1,1}\cdot 2^{\log^{0.51}n}$</tex> (Chattopadhyay and Li, STOC'16). 2) We obtain improved extractors for small-space sources in the negligible error regime. For space <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$s$</tex> sources over <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$n$</tex> bits, our extractors require entropy <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$k &gt; n^{1/2+\delta}\cdot s^{1/2-\delta}$</tex> , whereas the previous best result required <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$k &gt; n^{2/3+\delta}\cdot s^{1/3-\delta}$</tex> (Chattopadhyay, Goodman, Goyal and Li, STOC'20). To obtain our first result, the key ingredient is a new reduction from small-space sources to affine sources, allowing us to simply apply a good affine extractor. To obtain our second result, we must develop some new machinery, since we do not have low-error affine extractors that work for low entropy. Our main tool is a significantly improved extractor for adversarial sources, which is built via a simple framework that makes novel use of a certain kind of leakage-resilient extractors (known as cylinder intersection extractors), by combining them with a general type of extremal designs. Our key ingredient is the first derandomization of these designs, which we obtain using new connections to coding theory and additive combinatorics.
On-Policy Reinforcement Learning via Ensemble Gaussian Processes with Application to Resource Allocation

Reinforcement learning (RL) is an interactive decisionmaking tool with well documented merits for resource allocation tasks in uncertain environments, such as those emerging with Internet-of-Things. While they can attain state-of-the-art performance in several application domains, RL using deep neural networks can be less attractive when the training datasets involved are prohibitively large. Aiming at sample efficiency, this contribution adopts nonparametric value function models using Gaussian processes (GPs). Relying on the temporal-difference update rule, a novel GP-SARSA approach is developed, where the action selection is guided by Thompson sampling to balance exploration and exploitation. Targeting also computational scalability, the advocated approach leverages random features that replace GP-SARSA's nonparametric function learning with a parametric approximate model. Adaptation to unknown dynamics is accomplished through an ensemble (E) of GP-SARSA learners, whose weights are updated in a data-driven fashion. Performance of the proposed (E)GP-SARSA is evaluated on a practical resource allocation problem.
Enhanced attribute based encryption technique for secured access in cloud storage for personal health records

Abstract Recent development of medical information technology uses personal health record (PHR) system, which allows the patients to create, store and share their own health information with doctors, nurses, health insurance providers, and family members. However, it has the security and privacy issues. The secret key generation is the major task by performing different algorithms on attribute based encryption (ABE) technique. This method helps to identify the trusted authority using secret key verification on the decryption process. The revocation scheme is performed to reduce the attributes for improving the performance. The drawback of existing method is overcome by the proposed system in which the PHR file is securely accessed by performing enhanced encryption and decryption of ABE technique. Here the matrix based lattice algorithm is proposed with bit plane transformation of decomposition matrices. Attribute reduction and secret key encryption is performed to improve the result of reliability, security, and scalability. The plain text is created to cipher text as key and it holds the encrypted PHR file on the cloud storage, which is retrieved only by the trusted authority. The proposed method is analyzed and compared with the existing work and achieves greater results than most of the recent related literature.
Context-aware vehicle and fleet security combining a Knowledge Graph and an object-oriented model

A growing number of attacks and the introduction of new security standards, e.g. ISO 21434, are increasingly shifting the focus of industry and research to the cybersecurity of vehicles. Being cyber-physical systems, compromised vehicles can pose a safety risk to occupants and the environment. Updates over the air and monitoring of the vehicle fleet over its entire lifespan are therefore established in current and future vehicles. Elementary components of such a strategy are security sensors in the form of firewalls and intrusion detection systems, for example, and an operations center where monitoring and response activities are coordinated. A critical step in defending against, detecting, and remediating attacks is providing knowledge about the vehicle and fleet context. Whether a vehicle is driving on the highway or parked at home, what software version is installed, or what security incidents have occurred affect the legitimacy of data and network traffic. However, current security measures lack an understanding of how to operate in an adjusted manner in different contexts. This work is therefore dedicated to a concept to make security measures for vehicles context-aware. We present our approach, which consists of an object-oriented model of relevant context information within the vehicle and a Knowledge Graph for the fleet. With this approach, various use cases can be addressed, according to the different requirements for the use of context knowledge in the vehicle and operations center.
Scalable Online Planning for Multi-Agent MDPs

We present a scalable tree search planning algorithm for large multi-agent sequential decision problems that require dynamic collaboration. Teams of agents need to coordinate decisions in many domains, but naive approaches fail due to the exponential growth of the joint action space with the number of agents. We circumvent this complexity through an approach that allows us to trade computation for approximation quality and dynamically coordinate actions. Our algorithm comprises three elements: online planning with Monte Carlo Tree Search (MCTS), factored representations of local agent interactions with coordination graphs, and the iterative Max-Plus method for joint action selection. We evaluate our approach on the benchmark SysAdmin domain with static coordination graphs and achieve comparable performance with much lower computation cost than our MCTS baselines. We also introduce a multi-drone delivery domain with dynamic coordination graphs, and demonstrate how our approach scales to large problems on this domain that are intractable for other MCTS methods. We provide an open-source implementation of our algorithm at https://github.com/JuliaPOMDP/FactoredValueMCTS.jl.
Computing Schematic Layouts for Spatial Hypergraphs on Concentric Circles and Grids

Abstract Set systems can be visualized in various ways. An important distinction between techniques is whether the elements have a spatial location that is to be used for the visualization; for example, the elements are cities on a map. Strictly adhering to such location may severely limit the visualization and force overlay, intersections and other forms of clutter. On the other hand, completely ignoring the spatial dimension omits information and may hide spatial patterns in the data. We study layouts for set systems (or hypergraphs) in which spatial locations are displaced onto concentric circles or a grid, to obtain schematic set visualizations. We investigate the tractability of the underlying algorithmic problems adopting different optimization criteria (e.g. crossings or bends) for the layout structure, also known as the support of the hypergraph. Furthermore, we describe a simulated‐annealing approach to heuristically optimize a combination of such criteria. Using this method in computational experiments, we explore the trade‐offs and dependencies between criteria for computing high‐quality schematic set visualizations.
Combining Bert Representation and POS Tagger for Arabic Word Sense Disambiguation

In this paper, we address the problem of Word Sense Disambiguation of Arabic language where the objective is to determine an ambiguous word’s meaning. We model the problem as a supervised sequence to sequence learning. We propose recurrent neural network models, BERT based models and combined POS-BERT models for Arabic word sense Disambiguation. We achieve 96.33% accuracy with the POS-BERT system.
Fuzzy-Weighted Echo State Networks

A novel echo state network (ESN), referred to as a fuzzy-weighted echo state network (FWESN), is proposed by using the structural information of data sets to improve the performance of the classical ESN. The information is incorporated into the classical ESN via the concept of Takagi–Sugeno (TS) models/rules. We employ the fuzzy c-mean clustering method to extract the information based on the given data set. The antecedent part of the TS model is determined by the information. Then, we obtain new fuzzy rules by replacing the affine models in the consequent part of each TS rule with a classical ESN. Consequently, the output of the proposed FWESN is calculated through inferring these new fuzzy rules by a fuzzy-weighted mechanism. The corresponding reservoir is consisted of the sub-reservoirs of the new fuzzy rules. Furthermore, we prove that the FWESN has an echo state property by setting the largest spectrum radium of all the internal weight matrices in the sub-reservoirs less than one. Finally, a nonlinear dynamic system and five nonlinear time series are employed to validate the FWESN.
Simplified effective method for identifying semantic relations from a knowledge graph

Semantic relations have been adopted in many research fields, including the semantic web, information retrieval, and Q&amp;A systems. The aim of the semantic relations is to remove conceptual and terminological confusion. This is achieved by specifying a set of general concepts that characterize domains and their definitions and interrelationships. This research describes how to detect semantic relations, including synonyms, hyponyms, and hypernym s based on WordNet and entities of a knowledge graph (KG). This KG was built from two resources: ACM Digital Library and Wikipedia. We used natural language processing and the deep learning approach for processing data before generating the KG with an effective algorithm. We chose five of 245 categories in the ACM Digital Library to evaluate the proposed method. The generated results show that our system has excellent performance.
Integrating single-shot Fast Gradient Sign Method (FGSM) with classical image processing techniques for generating adversarial attacks on deep learning classifiers

Deep learning architectures have emerged as powerful function approximators in a broad spectrum of complex representation learning tasks, such as, computer vision, natural language processing and collaborative filtering. These architectures bear a high potential to learn the intrinsic structure of data and extract valuable insights. Despite the surge in the development of state-of-the-art intelligent systems using the deep neural networks (DNNs), these systems have found to be vulnerable to adversarial examples produced by adding a small-magnitude of perturbations. Such adversarial examples are adept at misleading the DNN classifiers. In the past, different attack strategies have been proposed to produce adversarial examples in the digital, physical, and transform domain, but the likelihood to generate perceptually realistic adversarial examples require more research efforts. In this paper, we present a novel approach to produce adversarial examples by combining the single-shot fast gradient sign method (FGSM) and spatial, as well as, transform domain image processing techniques. The resulted perturbations neutralize the impact of low-intensity based regions, thus, instilling the noise only in the selective high-intensity regions of the input image. While combining the customized perturbation with one-step FGSM perturbation in an un-targeted black-box attack scenario, the proposed approach successfully fools state-of-the-art DNN classifiers with 99% adversarial examples being misclassified on the ImageNet validation dataset.
Fish-Inspired Heuristics: A Survey of the State-of-the-Art Methods

Abstract The collective behaviour of fish schools, shoals and other swarms in nature has long inspired researchers to develop solutions for optimization problems. Instinct influences the behaviour of fish to group into schools to increase safety, enhance foraging success, and promote breeding. According to these instinctive behaviours, several fish-inspired algorithms have been introduced to solve hard problems. This paper presents a comprehensive survey of fish-inspired heuristics, exploring their evolution within the context of general optimization problems. To our knowledge, this survey is the first to cover both main fish-inspired heuristics in the literature, namely, the artificial fish swarm algorithm (AFSA) and Fish school search (FSS), in addition to other algorithms inspired by specific fish species. The review covers more than 50 papers published in the Web of Science and IEEE databases since 2000. We first review the basic fish heuristics, highlighting their advantages and drawbacks, and then detail attempts in the literature to improve their behaviour to solve complex, multi-objective and high-dimensional problems in several domains. Our work is intended to provide guidance for researchers and practitioners for the purpose of further advancing research in the area of fish-inspired heuristics. We aspire to encourage their utilization in various fields for global optimization and in real-life applications. The survey findings indicate that fish-inspired heuristics are very alive in recent literature and still have great potential. Several challenges and future research directions are also identified among the findings of this survey, which can help to enhance this vibrant line of research.
Exploring Linear Feature Disentanglement for Neural Networks

Non-linear activation functions, e.g., Sigmoid, ReLU, and Tanh, have achieved great success in neural networks (NNs). Due to the complex non-linear characteristic of samples, the objective of those activation functions is to project samples from their original feature space to a linear separable feature space. This phenomenon ignites our interest in exploring whether all features need to be transformed by all nonlinear functions in current typical NNs, i.e., whether there exists a part of features arriving at the linear separable feature space in the intermediate layers, that does not require further non-linear variation but an affine transformation instead. To validate the above hypothesis, we explore the problem of linear feature disentanglement for neural networks in this paper. Specifically, we devise a learnable mask module to distinguish between linear and non-linear features. Through our designed experiments we found that some features reach the linearly separable space earlier than the others and can be detached partly from the NNs. The explored method also provides a readily feasible pruning strategy which barely affects the performance of the original model. We conduct our experiments on four datasets and present promising results.
GraphEye: A Novel Solution for Detecting Vulnerable Functions Based on Graph Attention Network

With the continuous extension of the Industrial Internet, cyber incidents caused by software vulnerabilities have been increasing in recent years. However, software vulnerabilities detection is still heavily relying on code review done by experts, and how to automatically detect software vulnerabilities is an open problem so far. In this paper, we propose a novel solution named GraphEye to identify whether a function of C/C++ code has vulnerabilities, which can greatly alleviate the burden of code auditors. GraphEye is originated from the observation that the code property graph of a non-vulnerable function naturally differs from the code property graph of a vulnerable function with the same functionality. Hence, detecting vulnerable functions is attributed to the graph classification problem. GraphEye is comprised of VecCPG and GcGAT. VecCPG is a vectorization for the code property graph, which is proposed to characterize the key syntax and semantic features of the corresponding source code. GcGAT is a deep learning model based on the graph attention networks, which is proposed to solve the graph classification problem according to VecCPG. Finally, GraphEye is verified by the SARD Stack-based Buffer Overflow, Divide-Zero, Null Pointer Deference, Buffer Error, and Resource Error datasets, the corresponding F1 scores are 95.6&#x0025;, 95.6&#x0025;, 96.1&#x0025;, 92.6&#x0025;, and 96.1&#x0025; respectively, which validate the effectiveness of the proposed solution.
IncShrink: Architecting Efficient Outsourced Databases using Incremental MPC and Differential Privacy

In this paper, we consider secure outsourced growing databases (SOGDB) that support view-based query answering. These databases allow untrusted servers to privately maintain a materialized view. This allows servers to use only the materialized view for query processing instead of accessing the original data from which the view was derived. To tackle this, we devise a novel view-based SOGDB framework, Incshrink. The key features of this solution are: (i) Incshrink maintains the view using incremental MPC operators which eliminates the need for a trusted third party upfront, and (ii) to ensure high performance, Incshrink guarantees that the leakage satisfies DP in the presence of updates. To the best of our knowledge, there are no existing systems that have these properties. We demonstrate Incshrink's practical feasibility in terms of efficiency and accuracy with extensive experiments on real-world datasets and the TPC-ds benchmark. The evaluation results show that Incshrink provides a 3-way trade-off in terms of privacy, accuracy and efficiency, and offers at least a 7,800x performance advantage over standard SOGDB that do not support view-based query paradigm.