Reviewerly/exHarmony huggingface dataset:
Search Me in the Dark: Privacy-preserving Boolean Range Query over Encrypted Spatial Data

With the increasing popularity of geo-positioning technologies and mobile Internet, spatial keyword data services have attracted growing interest from both the industrial and academic communities in recent years. Meanwhile, a massive amount of data is increasingly being outsourced to cloud in the encrypted form for enjoying the advantages of cloud computing while without compromising data privacy. Most existing works primarily focus on the privacy-preserving schemes for either spatial or keyword queries, and they cannot be directly applied to solve the spatial keyword query problem over encrypted data. In this paper, we study the challenging problem of Privacy-preserving Boolean Range Query (PBRQ) over encrypted spatial databases. In particular, we propose two novel PBRQ schemes. Firstly, we present a scheme with linear search complexity based on the space-filling curve code and Symmetric-key Hidden Vector Encryption (SHVE). Then, we use tree structures to achieve faster-than-linear search complexity. Thorough security analysis shows that data security and query privacy can be guaranteed during the query process. Experimental results using real-world datasets show that the proposed schemes are efficient and feasible for practical applications, which is at least ×70 faster than existing techniques in the literature.
Weapon Detection using Artificial Intelligence and Deep Learning for Security Applications

Security is always a main concern in every domain, due to a rise in crime rate in a crowded event or suspicious lonely areas. Abnormal detection and monitoring have major applications of computer vision to tackle various problems. Due to growing demand in the protection of safety, security and personal properties, needs and deployment of video surveillance systems can recognize and interpret the scene and anomaly events play a vital role in intelligence monitoring. This paper implements automatic gun (or) weapon detection using a convolution neural network (CNN) based SSD and Faster RCNN algorithms. Proposed implementation uses two types of datasets. One dataset, which had pre-labelled images and the other one is a set of images, which were labelled manually. Results are tabulated, both algorithms achieve good accuracy, but their application in real situations can be based on the trade-off between speed and accuracy.
Learning to Learn Adaptive Classifier–Predictor for Few-Shot Learning

Few-shot learning aims to learn a well-performing model from a few labeled examples. Recently, quite a few works propose to learn a predictor to directly generate model parameter weights with episodic training strategy of meta-learning and achieve fairly promising performance. However, the predictor in these works is task-agnostic, which means that the predictor cannot adjust to novel tasks in the testing phase. In this article, we propose a novel meta-learning method to learn how to learn task-adaptive classifier-predictor to generate classifier weights for few-shot classification. Specifically, a meta classifier-predictor module, (MPM) is introduced to learn how to adaptively update a task-agnostic classifier-predictor to a task-specialized one on a novel task with a newly proposed center-uniqueness loss function. Compared with previous works, our task-adaptive classifier-predictor can better capture characteristics of each category in a novel task and thus generate a more accurate and effective classifier. Our method is evaluated on two commonly used benchmarks for few-shot classification, i.e., miniImageNet and tieredImageNet. Ablation study verifies the necessity of learning task-adaptive classifier-predictor and the effectiveness of our newly proposed center-uniqueness loss. Moreover, our method achieves the state-of-the-art performance on both benchmarks, thus demonstrating its superiority.
Accuracy-diversity trade-off in recommender systems via graph convolutions

Graph convolutions, in both their linear and neural network forms, have reached state-of-the-art accuracy on recommender system (RecSys) benchmarks. However, recommendation accuracy is tied with diversity in a delicate trade-off and the potential of graph convolutions to improve the latter is unexplored. Here, we develop a model that learns joint convolutional representations from a nearest neighbor and a furthest neighbor graph to establish a novel accuracy-diversity trade-off for recommender systems. The nearest neighbor graph connects entities (users or items) based on their similarities and is responsible for improving accuracy, while the furthest neighbor graph connects entities based on their dissimilarities and is responsible for diversifying recommendations. The information between the two convolutional modules is balanced already in the training phase through a regularizer inspired by multi-kernel learning. We evaluate the joint convolutional model on three benchmark datasets with different degrees of sparsity. The proposed method can either trade accuracy to improve substantially the catalog coverage or the diversity within the list; or improve both by a lesser amount. Compared with accuracy-oriented graph convolutional approaches, the proposed model shows diversity gains up to seven times by trading as little as 1% in accuracy. Compared with alternative accuracy-diversity trade-off solutions, the joint graph convolutional model retains the highest accuracy while offering a handle to increase diversity. To our knowledge, this is the first work proposing an accuracy-diversity trade-off with graph convolutions and opens the doors to learning over graphs approaches for improving such trade-off.
Virtual reality storytelling as a double-edged sword: Immersive presentation of nonfiction 360°-video is associated with impaired cognitive information processing

This study examines the effects of the immersive presentation of nonfiction omnidirectional video on audiences’ cognitive processing. Participants watched a sample of 360°-video nonfiction content, presented either in a virtual reality headset or on a computer screen. Measures of heart rate variability and electrodermal activity were collected, together with self-reported ratings of presence, information recognition, and memory. The results indicate that the immersive presentation elicits higher arousal and presence, but also lower focused attention, recognition, and cued recall of information. These effects on focused attention and memory were not mediated by variations on arousal or presence. Implications are discussed in terms of the psychological effects of immersive media, as well as their relevance for media practitioners.
Intelligent Signal Classification in Industrial Distributed Wireless Sensor Networks Based Industrial Internet of Things

In industrial sensor networks, complex industrial environments may be encountered leading to a mix of signals of different types. Complicated interference caused by mixed signals on industrial equipments may significantly degrade the classification rate of signals, which may result in a long training time in order to extract features. In addition, with limited channel resources, it is difficult to make the global optimal decision in industrial distributed wireless sensor networks. To address this problem, a signal classification method using feature fusion is proposed for industrial Internet of Things in this article. In the proposed method, the received signals of nodes are processed by frequency reduction and sampling pretreatment, based on which intelligent representations of signals are obtained. Using federated learning, the data samples are trained with the feature fusion network. Moreover, the trained deep learning network is used on each sensor node to classify signals, the results of which will be transmitted to aggregation center. In the aggregation center, the improved evidence theory method is used to aggregate the recognition results of each sensor node to achieve the final classification. Simulation shows that the proposed method has excellent classification performances. Notably, it is not required for the proposed method to transmit signals from nodes to the aggregation center, which could effectively protect the privacy of industrial information.
Ensemble Learning Techniques and its Efficiency in Machine Learning: A Survey

Ensemble learning is an imperative study in the domain of machine learning. Over the previous years, ensemble learning has drawn considerable attention in the field of artificial intelligence, pattern recognition, machine learning, neural network and data mining. Ensemble learning has shown to be efficient and functional in wide area of problem domain and substantial world application. Ensemble learning, it constructs several classifiers or the set of base learners and merge their output so that the overall variance should be reduced. By merging several classifiers or the set of base learners it significantly improves the accuracy in contrast to single classifier or single base learner. In this literature we survey the various ensemble learning techniques that is prevalent in machine learning.
Fully automated carbonate petrography using deep convolutional neural networks

Carbonate rocks are important archives of past ocean conditions as well as hosts of economic resources such as hydrocarbons, water, and minerals. Geologists typically perform compositional analysis of grain, matrix, cement and pore types in order to interpret depositional environments, diagenetic modification, and reservoir quality of carbonate strata. Such information can be obtained primarily from petrographic analysis, a task that is costly, labor-intensive, and requires in-depth knowledge of carbonate petrology and micropaleontology. Recent studies have leveraged machine learning-based image analysis, including Deep Convolutional Neural Networks (DCNN), to automate description, classification and interpretation of thin sections, subsurface core images and seismic facies, which would accelerate data acquisition and reproducibility for these tasks. In carbonate rocks, this approach has been applied primarily to recognize carbonate lithofacies, and no attempt has been made to individually identify and quantify various types of carbonate grains, matrix, and cement. In this study, the applicability and performance of DCNN-based object detection and image classification approaches are assessed with respect to carbonate compositional analysis. The training data comprised of more than 13,000 individually labelled objects from nearly 4000 carbonate petrographic images. The dataset is grouped into six and nine different classes for the image classification and object detection tasks, respectively. Even with a small and relatively imbalanced training set, the DCNN was able to achieve an F1 score of 92% for image classification and 84% mean precision for object detection by combining one-cycle policy, class weight, and label mixup-smoothing methods. This study highlights the inefficiency of image classification as an approach to replicating human description and classification of carbonate petrography. By contrast, DCNN-based object detection appears capable of approaching human speed and accuracy in the area of carbonate petrography because it is able to individually locate and identify different carbonate components with greater cost-efficiency, speed, and reproducibility than conventional (human) petrographic analysis.
Relation R-CNN: A Graph Based Relation-Aware Network for Object Detection

Due to the deteriorated quality of feature in the propagation process of the neural network, it may be hard for traditional detector to identify a small object by just utilizing information within one region proposal. To overcome the limitation of the traditional object detector, we proposed a graph based relation-aware network, to capture the relation information from labels, and images. The semantic relation network is proposed to mine the global semantic relation in labels, and the spatial relation network is proposed to capture the local spatial relation in images. The feature representation is further improved by aggregating the outputs of the two networks. Instead of directly disseminating visual features in the network, the relation-aware network explores more advanced feature information. Experiments on the PASCAL VOC, and MS COCO datasets demonstrate that key relation information significantly improve the performance of object detection with better ability to detect small objects, and reasonable bounding box. The results on COCO dataset demonstrate our method can detect objects robustly, increasing the detection performance of small objects from average precision, and average recall by 31.8%, and 32.3% respectively in performance relative to Faster R-CNN.
Placing language in an integrated understanding system: Next steps toward human-level performance in neural language models

Language is crucial for human intelligence, but what exactly is its role? We take language to be a part of a system for understanding and communicating about situations. In humans, these abilities emerge gradually from experience and depend on domain-general principles of biological neural networks: connection-based learning, distributed representation, and context-sensitive, mutual constraint satisfaction-based processing. Current artificial language processing systems rely on the same domain general principles, embodied in artificial neural networks. Indeed, recent progress in this field depends on query-based attention, which extends the ability of these systems to exploit context and has contributed to remarkable breakthroughs. Nevertheless, most current models focus exclusively on language-internal tasks, limiting their ability to perform tasks that depend on understanding situations. These systems also lack memory for the contents of prior situations outside of a fixed contextual span. We describe the organization of the brain's distributed understanding system, which includes a fast learning system that addresses the memory problem. We sketch a framework for future models of understanding drawing equally on cognitive neuroscience and artificial intelligence and exploiting query-based attention. We highlight relevant current directions and consider further developments needed to fully capture human-level language understanding in a computational system.
Cloud Resource Scheduling With Deep Reinforcement Learning and Imitation Learning

The cloud resource management belongs to the category of combinatorial optimization problems, most of which have been proven to be NP-hard. In recent years, reinforcement learning (RL), as a special paradigm of machine learning, has been used to tackle these NP-hard problems. In this article, we present a deep RL-based solution, called DeepRM_Plus, to efficiently solve different cloud resource management problems. We use a convolutional neural network to capture the resource management model and utilize imitation learning in the reinforcement process to reduce the training time of the optimal policy. Compared with the state-of-the-art algorithm DeepRM, DeepRM_Plus is 37.5% faster in terms of the convergence rate. Moreover, DeepRM_Plus reduces the average weighted turnaround time and the average cycling time by 51.85% and 11.51%, respectively.
Oculus imaginaries: The promises and perils of Facebook’s virtual reality

This article explores the Oculus suite of virtual reality (VR) technologies, with a specific focus on the period following the company’s 2014 acquisition by Facebook. Through a close reading of promotional material, we first describe and analyse the ‘Oculus imaginary’ – the narrative produced by Facebook about the Oculus as integrated into and enhancing the experience of Facebook’s wider suite of social software. The purpose of this narrative, we suggest, is to construct and ‘sell’ a Facebook-specific vision of VR’s potentials – one that is appealing both to end users and platform complementors – and moreover, a vision that appears to be conducive to Facebook’s current methods for accumulating profit and power. Following on, we develop via a study of YouTube user comments posted on promotional videos for the Oculus, an anticipatory account of how the Oculus imaginary is perceived to relate to the lives and values of everyday individuals.
Online Replanning in Belief Space for Partially Observable Task and Motion Problems

To solve multi-step manipulation tasks in the real world, an autonomous robot must take actions to observe its environment and react to unexpected observations. This may require opening a drawer to observe its contents or moving an object out of the way to examine the space behind it. Upon receiving a new observation, the robot must update its belief about the world and compute a new plan of action. In this work, we present an online planning and execution system for robots faced with these challenges. We perform deterministic cost-sensitive planning in the space of hybrid belief states to select likely-to-succeed observation actions and continuous control actions. After execution and observation, we replan using our new state estimate. We initially enforce that planner reuses the structure of the unexecuted tail of the last plan. This both improves planning efficiency and ensures that the overall policy does not undo its progress towards achieving the goal. Our approach is able to efficiently solve partially observable problems both in simulation and in a real-world kitchen.
Software documentation

In theory, (good) documentation is an invaluable asset to any software project, as it helps stakeholders to use, understand, maintain, and evolve a system. In practice, however, documentation is generally affected by numerous shortcomings and issues, such as insufficient and inadequate content and obsolete, ambiguous information. To counter this, researchers are investigating the development of advanced recommender systems that automatically suggest high-quality documentation, useful for a given task. A crucial first step is to understand what quality means for practitioners and what information is actually needed for specific tasks.
Bitcoin Price Prediction and Analysis Using Deep Learning Models

Cryptocurrencies are a digital way of money in which all transactions are held electronically. It is a soft currency which doesn’t exist in the form of hard notes physically. Here, we are emphasizing the difference of fiat currency which is decentralized that without any third-party intervention all virtual currency users can get the services. However, getting services of these cryptocurrencies impacts on international relations and trade, due to its high price volatility. There are several virtual currencies such as bitcoin, ripple, ethereum, ethereum classic, lite coin, etc. In our study, we especially focused on a popular cryptocurrency, i.e., bitcoin. From many types of virtual currencies, bitcoin has a great acceptance by different bodies such as investors, researchers, traders, and policy-makers. To the best of our knowledge, our target is to implement the efficient deep learning-based prediction models specifically long short-term memory (LSTM) and gated recurrent unit (GRU) to handle the price volatility of bitcoin and to obtain high accuracy. Our study involves comparing these two time series deep learning techniques and proved the efficacy in forecasting the price of bitcoin.
Qubit coupled cluster singles and doubles variational quantum eigensolver ansatz for electronic structure calculations

Abstract Variational quantum eigensolver (VQE) for electronic structure calculations is believed to be one major potential application of near term quantum computing. Among all proposed VQE algorithms, the unitary coupled cluster singles and doubles excitations (UCCSD) VQE ansatz has achieved high accuracy and received a lot of research interest. However, the UCCSD VQE based on fermionic excitations needs extra terms for the parity when using Jordan–Wigner transformation. Here we introduce a new VQE ansatz based on the particle preserving exchange gate to achieve qubit excitations. The proposed VQE ansatz has gate complexity up-bounded to O ( n 4 ) for all-to-all connectivity where n is the number of qubits of the Hamiltonian. Numerical results of simple molecular systems such as BeH 2 , H 2 O, N 2 , H 4 and H 6 using the proposed VQE ansatz gives very accurate results within errors about 10 −3 Hartree.
The Impact of Innovation Management in SMEs Performance: A Systematic Review

This paper presents a method of evaluating innovation in small and medium enterprises, while the main purpose of the article is to examine how innovation affects business performance. Innovation is an important factor that produces quality and improves competitiveness. This study presents a comprehensive analysis of 17 research articles from 2015 to 2019. Questionnaires, surveys, and case studies were the primary methods of data collection and were undertaken in Pakistan, Mexico, Kuwait, Indonesia, Italy, respectively, among other countries. To that end, the findings in the studies offer an investigation on the circumstances the lead SMEs to adopt innovation to enhance their performance.
Correcting “PALK: Password-based anonymous lightweight key agreement framework for smart grid”

Very recently in 2020, Khan et al. proposed an authentication scheme (PALK) for the smart grid infrastructure. Based on elliptic curve cryptography (ECC), symmetric hash functions and block cipher based encryption/decryption operations, the scheme was argued to work efficiently and securely in smart grid based infrastructure. However, in this paper, we prove that PALK has incorrect login and authentication phase; mainly, due to a superficial ECC operation involving the multiplication of two points over the curve. Moreover, in the scheme of Khan et al. the responding entity without knowing any clue of the initiator, uses the public key of the initiator for the completion of the authentication process, which is also not possible in the presence of multiple communicating devices. These design flaws lead to the situation, where the smart grid entities are unable to complete even a single cycle of authentication. Finally, we propose a quick solution to fix the pertinent flaws of the PALK. The security and correctness of the proposed solution iPALK is proved using formal BAN logic, automated tool ProVerif along with a brief discussion on the correctness of the scheme. The performance comparisons also show that the iPALK not only provides the correctness, but it is more efficient in terms of computation and communication costs.