Reviewerly/exHarmony huggingface dataset:
Residual Attention Dual Autoencoder for Anomaly Detection and Localization in Cigarette Packaging

Anomaly detection and localization is an important problem in the industrial production process, such as cigarette packaging quality inspection. This task is challenging due to very few abnormal samples. This paper proposes Residual Attention Dual Autoencoder (RADAE) composed of the dual autoencoder and the attention map generator. Without the need of the abnormal training images, the proposed model can achieve anomaly detection and localization by unsupervised learning. The dual autoencoder is an encoder-decoder-encoder network that maps the input image to a lower dimension vector for reconstruction, and remaps the generated image to its latent representation. The data distribution of the normal samples is learned by training the dual autoencoder. Anomaly discrimination is realized by measuring the difference between the latent space of the original image and the generated image during detection. And the attention map generator is a CNN model, which uses the residuals of the original image and the generated image to obtain the attention map under the guidance of the attention loss function. The generated attention map is encouraged to focus on all regions in normal samples during training, and spontaneously focus on abnormal regions to achieve localization during testing. The experimental results demonstrate the reliability and superior performance of the proposed model for anomaly detection and localization in the cigarette packaging dataset.
Identity Recognition System based on Walking Posture

It is well known that identity recognition system is one of the key aeras of computer vision research. Meanwhile, the mainstream identity recognition systems often use the features of people's face or appearance to realize identification, but the features of face and appearance are very easy to be changed. On the contrary, the features of walking posture are steady for specific person, because walking posture have a strong connection with people's habit and their biological characteristics. Therefore, in this paper a deep learning algorithm based on walking posture is proposed. The proposed model use OpenPose algorithm to get a series skeleton feature pictures of people's walking posture through video data, then use CNN to extract the information of pictures and use RNN to analyze this information to get identifications.
Defeating Hardware Prefetchers in Flush+Reload Side-Channel Attack

Hardware prefetching can seriously interfere with Flush+Reload cache side channel attack. This interference is not taken into consideration in previous Flush+Reload attacks. In this paper, an improved Flush+Reload is provided which minimizes the impact of hardware prefetchers. Specifically, prefetching is analyzed based on reverse engineering and the result is used to make an evaluation model to evaluate the impact of hardware prefetching on Flush+Reload attacks. Then the model is applied to fine tune the placement of probes in Flush+Reload attack to mitigate the prefetching impact. The experiments show that the approach is effective on the Core i5 processor which is equipped with highly aggressive prefetchers.
Smart Fault Detection and Monitoring of Power Line by Drones

In this paper, we introduce a novel automatic power line inspection system based on automatic vision. This system uses UAV inspection as the main inspection method, optical images as the main data source, and deep learning as the backbone of data analysis. To facilitate the implementation of the system, we solve three major challenges of deep learning in vision-based power line inspection: (i) lack of training data; (ii) class imbalance; (iii) detection of small parts and faults. First, we create four medium-sized datasets for training component detection and classification models. Next, we apply a series of effective data enhancement techniques to balance the unbalanced classes. Finally, we propose a multi-stage component detection and classification method based on a single-shot multi-box detector and a deep residual network to detect small components and faults. The results show that the proposed system can quickly and accurately detect common failures of power line components, including the lack of a top cover, cracks on the rod and cross arm, woodpecker damage to the rod, and rot on the cross arm. Field tests show that our system has broad prospects in the Smart monitoring and inspection of power line components and the valuable addition of smart grids.
Fishing and Military Ship Recognition using Parameters of Convolutional Neural Network

Indonesia has a maritime boundary that vulnerable to illegal activities. Those activities lead to the bad loss of Indonesia income. Therefore, monitoring of every object which is passing through the maritime boundary is important. Detection of ship that is passing through the ocean is one of many ways to monitor the maritime boundary. Nowadays, there are many systems developed to detect and to recognize ship automatically especially fishing ship and military ship. The recognition adopts technology which is called CNN. CNN is deep learning algorithm that is based on image. CNN has many parameters that can be optimized the recognition system. This study investigated some parameters such as pooling layer, batch normalization and dropout parameters. For the best accuracy results on the fishing ship and military ship dataset obtained a value of 99.99% for training and 90% for validation accuracy. The best accuracy results are obtained by using the pooling layer with the max pooling type. Max pooling is more efficient used for object recognition than average pooling. The use of dropout functions can increase the level of training accuracy. Batch normalization can increase the validation accuracy value.
The Role of Artificial Intelligence and Machine Learning in Supply Chain Management and its Task Model

The purpose of the study to demonstrate newly developed logic functions by researchers that have used in the developing world, which have utilized in the supply chain management field. The hypothetical statement shows how researchers developed an optimization technique for feasibility in management operations based on AI to solve the causes of the issues in supply chain management. There are three fundamental types of AI which have overseen learning, pointless learning, and defence learning and all these have characterized in a hypothetical bit of this investigation. The deep-learning architecture will highly affect the preparing of the supply chain. This examination dependent on the supply chain the executives and explained the ideas of an item the board, stock administration and coordination. This examination found that the artificial intelligence is used in supply chain cycles and its current condition on the use of programming in the world of programming rationale in supply chain measure has demonstrated more captions of the sensible model from amazon and Amer sports. As the smart warehouse is more advanced in day to day activities, Amer sports is effectively utilizing false figuring out how to upgrade the administration and consistency of the supply chain. The strategies for subjective investigations like document analysis has implemented in this article. Electronic papers and these articles on computerized reasoning in the supply chain have studied also. This paper showed the two organizations benefited incredibly from the introduction of individual artificial intelligence requests. It has observed that automation has significantly enhanced with the implemented artificial intelligence model. Moreover, automation eliminates human intervention with a notable performance.
Dynamic ensemble based feature selection model for credit card fraud detection

Credit card fraud has increased significantly with the increased use of credit cards. So an efficient but robust system is needed to detect credit card fraud. Many machine learning algorithms have been proposed for this purpose. Among them, recently proposed dynamic ensemble classification using soft probability (DECSP) outstood other individuals as well as ensemble classifiers. However, the authors do not focus on feature selection/extraction, which can improve the model to a great extent. Hence, we propose a framework for credit card fraud detection based on DECSP. The framework presented incorporates DECSP with various feature selection/extraction techniques such as Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Principal Component Analysis (PCA) and Recursive Feature Elimination (RFE). We experimented and compared the results generated based on accuracy, precision, f1-score, and AUC. DECSP with GA outperforms other techniques on all four performance metrics. Further, we validated the results based on statistical tests.
Enabling Finger-Touch-Based Mobile User Authentication via Physical Vibrations on IoT Devices

This work enables mobile user authentication via finger inputs on ubiquitous surfaces leveraging low-cost physical vibration. The system we proposed extends finger-input authentication beyond touch screens to any solid surface for IoT devices (e.g., smart access systems and IoT appliances). Unlike passcode or biometrics-based solutions, it integrates passcode, behavioral and physiological characteristics, and surface dependency together to provide a low-cost, tangible and enhanced security solution. The proposed system builds upon a touch sensing technique with vibration signals that can operate on surfaces constructed from a broad range of materials. New algorithms are developed to discriminate fine-grained finger inputs and supports three independent passcode secrets including PIN number, lock pattern, and simple gestures by extracting unique features in the frequency domain to capture both behavioral and physiological characteristics including contacting area, touching force, and etc. The system is implemented using a single pair of low-cost portable vibration motor and receiver that can be easily attached to any surface (e.g., a door panel, a stovetop or an appliance). Extensive experiments demonstrate that our system can authenticate users with high accuracy (e.g., more than 97 percent within two trials), low false positive rate (e.g., less 2 percent) and is robust to various types of attacks.
Autonomous recognition technology of carrier robot on various terrain environment

Field carrier robot can help people carry a certain amount of necessary materials in mountaineering, exploration and other fields, and even help field soldiers carry a certain amount of armaments. However, due to the complexity of the field environment, it is difficult for a robot with a single locomotion mechanism to complete such work. A carrier robot with leg/caterpillar/wheel is proposed. The robot can recognize different terrain environment, so as to select the appropriate locomotion mechanism, and can avoid obstacles and plan the path at the same time. We optimize the traditional ORB algorithm to extract the features of the environment image. The random forest classifier is used to classify the terrain, so as to start the corresponding locomotion mechanism. In addition, the extracted ORB feature points are also used for stereo matching to achieve the depth recovery for environmental information. Based on the analysis of two path planning algorithms of DWA and artificial potential field, an artificial potential field algorithm based on DWA is proposed for robot local path planning. In order to verify the effectiveness of these methods, experiments are carried out on the switching for three locomotion mechanisms and path planning in different practical environments. The results show that when the terrain changes, the robot can effectively switch the locomotion mechanism, and can run stably in different environments.
Comparison of Homogeneous and Heterogeneous Digital Quantum Coprocessors

Quantum computers are analog and probabilistic computers and, in fact, they play the role of coprocessors in relation to classical computers. The components of quantum coprocessors are called qubits. In particular, qubits can be discrete objects - finite states machines or series of quantum gates. Such qubits will be called digital. Digital quantum coprocessor can be implemented in FPGA as set of digital quantum qubits. The PRNG is an important part of digital qubit. A homogeneous digital quantum coprocessor uses one PRNG for all digital qubits. In a heterogeneous digital quantum coprocessor, each qubit has its own pseudo random number generator (PRNG). The article compares the performance of a homogeneous and heterogeneous digital quantum coprocessor. A 32-qubit digital quantum coprocessor was selected for study; it will perform the quantum Fourier transform during the study.
A Logic for Conditional Local Strategic Reasoning

Abstract We consider systems of rational agents who act and interact in pursuit of their individual and collective objectives. We study and formalise the reasoning of an agent, or of an external observer, about the expected choices of action of the other agents based on their objectives, in order to assess the reasoner’s ability, or expectation, to achieve their own objective. To formalize such reasoning we extend Pauly’s Coalition Logic with three new modal operators of conditional strategic reasoning, thus introducing the Logic for Local Conditional Strategic Reasoning $$\mathsf {ConStR}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>ConStR</mml:mi> </mml:math> . We provide formal semantics for the new conditional strategic operators in concurrent game models, introduce the matching notion of bisimulation for each of them, prove bisimulation invariance and Hennessy–Milner property for each of them, and discuss and compare briefly their expressiveness. Finally, we also propose systems of axioms for each of the basic operators of $$\mathsf {ConStR}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>ConStR</mml:mi> </mml:math> and for the full logic.
An Intelligent System to Detect Violent Mob Activities

The Paper here discusses and further provides users with a solution to tackle the unending problem that is Mob Violence. The approach involves Human Pose estimation technique to detect human skeleton poses and Action recognition using DNN to detect violent actions, especially involving a mob. The introduced approach will aid the field of mob violence surveillance and violence detection systems by accurate pose estimation and easily distinguishable actions. Violent Behaviors are hard to trace given their nature and rapid changes in actions. Hence, taking into consideration what a violent act is, the dataset is classified into 9 different sub-actions namely 'walk, wave, punch, kick, stand, run, jump, squat, and sit'. This is because every violent action can be broken down into pieces of information that can be easily processed. The Results provided us with sufficient accuracy so as to implement the system.
Detecting Arson and Stone Pelting in Extreme Violence: A Deep Learning Based Identification Approach

Violence is an extreme activity that presents a clear danger to human lives, human properties and governing authorities . Violence emancipates from strong protests to extreme disturbing activities by the mob. Violence unfolds itself from low level violence to extreme violence. This journey has two major steps. Stone Pelting and Arson activities are the two most ferocious activities by mobs from which classify the violence as building from low level violence to extreme violence. Arson is a spontaneous activity which is executed by protestors for showing extreme emotional dissent to the governing authorities. Lead by mob, arson is a dangerous activity and constitute most ferocious form of violence. Stone pelting is again an extreme case of mob fury against governing authorities. Pelting refers to throwing number of things at someone or something very quickly. Stone pelting is thus the most feared form of crowd violence that needs to be tackled on priority. Arson and stone pelting activities generate a fear in crowd and endangers life of humans and public and private property. The paper presents an application oriented deep learning framework using transfer learning approach for identification of arson and stone pelting in the images and videos. Cities which are classified as sensitive can opt for arson and stone pelting identification scheme for the protection of people and properties. We present a 2D ConvNets based transfer learning model for classifying extreme violence of arson and stone pelting. For a proof of concept, a small dataset is curated containing arson images, stone pelting and normal images.
A New Grey Wolf Optimization Algorithm with Improved Convergence Factor and Mutation Strategy

Focus on the disadvantages of grey wolf optimization algorithm (GWO) such as premature convergence and low optimization accuracy, we propose a new GWO variant with improved convergence factor and mutation strategy (NIGWO) in this paper. To ensure the balance between exploration and exploitation abilities, a new arcsine convergence factor is presented. To further promote the ability of GWO to overcome premature convergence, an improved position mutation strategy is presented. The performance of NIGWO is investigated on solving eight benchmark functions. Compared with NHDEPSO, RMDE, GWO and WOA, the obtained results show that NIGWO can show better performance.
Nonlinear steering criteria for arbitrary two-qubit quantum systems

By employing Pauli measurements, we present some nonlinear steering criteria applicable for arbitrary two-qubit quantum systems and optimized ones for symmetric quantum states. These criteria provide sufficient conditions to witness steering, which can recover the previous elegant results for some well-known states. Compared with the existing linear steering criterion and entropic criterion, ours can certify more steerable states without selecting measurement settings or correlation weights, which can also be used to verify entanglement as all steerable quantum states are entangled.
Improved random forest classification approach based on hybrid clustering selection

The random forest algorithm is an ensemble learning method, with the decision tree as its base classifier. In the ensemble model, it is not always true that the more the base classifiers, the better the classification effect, since if there are more base classifiers with poor performance in the model, they may have negative impacts on the final classification result. In order to modify the random forest classification method under the premise of ensuring the diversity of the random forest model, based on the random forest algorithm of cluster integration selection and personal indoor thermal preference model, this paper proposes a random forest method of clustering ensemble selection with Dunn index. Considering the shortcomings of the irrevocable merging strategy of hierarchical clustering algorithm, a random forest method of hybrid clustering ensemble selection based on hierarchical clustering and k- medoids partition clustering is developed. The effectiveness of the proposed methods is verified by classifying personal indoor thermal preferences.
Subsets and Supermajorities: Optimal Hashing-based Set Similarity Search

We formulate and optimally solve a new generalized Set Similarity Search problem, which assumes the size of the database and query sets are known in advance. By creating polylog copies of our data-structure, we optimally solve any symmetric Approximate Set Similarity Search problem, including approximate versions of Subset Search, Maximum Inner Product Search (MIPS), Jaccard Similarity Search, and Partial Match. Our algorithm can be seen as a natural generalization of previous work on Set as well as Euclidean Similarity Search, but conceptually it differs by optimally exploiting the information present in the sets as well as their complements, and doing so asymmetrically between queries and stored sets. Doing so we improve upon the best previous work: MinHash [J. Discrete Algorithms 1998], SimHash [STOC 2002], Spherical LSF [SODA 2016, 2017], and Chosen Path [STOC 2017] by as much as a factor n <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0.14</sup> in both time and space; or in the near-constant time regime, in space, by an arbitrarily large polynomial factor. Turning the geometric concept, based on Boolean supermajority functions, into a practical algorithm requires ideas from branching random walks on Z <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> , for which we give the first non-asymptotic near tight analysis. Our lower bounds follow from new hypercontractive arguments, which can be seen as characterizing the exact family of similarity search problems for which supermajorities are optimal. The optimality holds for among all hashing based data structures in the random setting, and by reductions, for 1 cell and 2 cell probe data structures.
Moving Mixed Reality Game to the Cloud: A Survey on Feasibility

Mixed reality (MR) aims at combining virtual reality with the user's surrounding real environment in a way that they form one, coherent reality. Some sectors have started to use mixed reality such as factory, business, entertainment, education, medical, research, manufacture, and many more. This makes mixed reality systems evolved towards an overly complex system that requires high computational power, especially on medical and gaming. Another problem is current mixed reality devices still need more improvements in some aspects to fill the needs of gaming such as performance, latency, graphic quality, and gaming experience. A possible way to solve this problem is to utilize a cloud computing platform. Mixed reality devices today still use its own resources to do various process such as compute, graphical rendering, and another processes. Moving the gaming process such as graphics rendering towards the cloud services might reduce the processing load on the mixed reality devices such as rendering and computing. With moving those processes, also might reduce the usage of the device storage. Thus, in this paper, we explore the feasibility and potential of such an approach according to various related researches. The results of this paper are we see any feasibility and potential for implementation.
Sustainable knowledge flow and innovation in higher education: the implementation of change management in universities

One of the most difficult tasks in higher educational institutions is to implement effective and constructive changes within already functioning system. Resistance and unwillingness of the institutional members are the main barriers for an institution to implement a required change. This paper intends to describe how to facilitate the flow of knowledge and adopt an innovation mindset in the context of higher education or higher learning institutions. In addition, this paper also discusses three domains of a comprehensive implementation or change management plan: structure, culture, and strategies. The change management plan presented in this paper demonstrates how a change initiative can be undertaken in a higher educational institution in the context of knowledge flow and adoption of the innovation mindset. The main outcome of the paper is that institutional mission, vision and priorities need to be clearly understood by every member of the staff in order to support and facilitate change and innovative thinking.