Reviewerly/exHarmony huggingface dataset:
A Blur Classification Approach Using Deep Convolution Neural Network

Computer vision-based gesture identification is designed to recognize human actions with the help of images. During the process of gesture image acquisition, images suffer various degradations. The method of recovering these degraded images is called restoration. In the case of blind restoration of such a degraded image where blur information is unavailable, it is essential to determine the exact blur type. This article presents a convolution neural network model for blur classification which categories a blur found in a hand gesture image into one of the four blur categories: motion, defocus, Gaussian, and box blur. The simulation results demonstrate the improved preciseness of the CNN model when compared to the MLP model.
Detecting entanglement by the mean value of spin on a quantum computer

We implement a protocol to determine the degree of entanglement between a qubit and the rest of the system on a quantum computer. The protocol is based on results obtained in paper [Frydryszak et al. (2017)]. This protocol is tested on a 5-qubit superconducting quantum processor called ibmq-ourense provided by the IBM company. We determine the values of entanglement of the Schr\"odinger cat and the Werner states prepared on this device and compare them with the theoretical ones. In addition, a protocol for determining the entanglement of rank-2 mixed states is proposed. We apply this protocol to the mixed state which consists of two Bell states prepared on the ibmq-ourense quantum device.
Optimization Dubins Path of Multiple UAVs for Post-Earthquake Rapid-Assessment

In the last decade, with the wide application of UAVs in post-earthquake relief operations, the images and videos of affected areas obtained by UAVs immediately after a seismic event have become an important source of information for post-earthquake rapid assessment, which is crucial for initiating effective emergency response operations. In this study, we first consider the kinematic constraints of UAV and the Dubins curve is introduced to fit the shortest flyable path for each UAV that meets the maximum curvature constraint. Second, based on the actual requirements of post-earthquake rapid assessment, heterogeneous UAVs, multi-depot launching, and targets allowed access to multiple times, the paper proposes a multi-UAV rapid-assessment routing problem (MURARP). The MURARP is modeled as the multi-depot revisit-allowed Dubins TOP with variable profit (MD-RDTOP-VP) which is a variant of the team orienteering problem (TOP). Third, a hybrid genetic simulated annealing (HGSA) algorithm is developed to solve the problem. The result of numerical experiments shows that the HGSA algorithm can quickly plan flyable paths for heterogeneous UAVs to maximize the expected profit. Finally, a case study based on real data of the 2017 Jiuzhaigou earthquake in China shows how the method can be applied in a post-earthquake scenario.
Design of an adaptive examination system based on artificial intelligence recognition model

Nowadays, with the change of the times, the traditional paper-and-pencil examination can not satisfy the students' test. For students of different levels, it is difficult to say that the best test effect can be achieved by passing the fixed test. In order to test students' level accurately, this paper proposes an adaptive test system based on item response theory, which integrates AI recognition model. By extracting test questions dynamically, the test time is shortened and the effect of adaptive test for students at different levels is realized. While researching the determination of test parameters, the algorithm of ability evaluation and the rule of examination termination, the system designed in this paper improves the strategy of topic selection. Firstly, a strategy of topic selection for cognitive diagnosis is proposed, which can effectively improve the drawbacks of the existing system using the tested items to evaluate the ability. Secondly, the combination of cognitive diagnosis and artificial intelligence recognition model improves the efficiency and accuracy of the system topic selection. Finally, the Monte Carlo simulation experiment method is used to test the system designed in this paper. Experiments show that the system has good cognitive diagnostic ability, and achieves the efficiency and accuracy of topic selection, thus effectively improving the performance of students' ability level estimation.
Federated Learning for Resource-Constrained IoT Devices: Panoramas and State of the Art

Nowadays, devices are equipped with advanced sensors with higher processing and computing capabilities. Besides, widespread Internet availability enables communication among sensing devices that results the generation of vast amounts of data on edge devices to drive Internet-of-Things (IoT), crowdsourcing, and other emerging technologies. The extensive amount of collected data can be preprocessed, scaled, classified, and finally, used for predicting future events with machine learning (ML) methods. In traditional ML approaches, data is sent to and processed in a central server, which encounters communication overhead, processing delay, privacy leakage, and security issues. To overcome these challenges, each client can be trained locally based on its available data and by learning from the global model. This decentralized learning approach is referred to as federated learning (FL). However, in large-scale networks, there may be clients with varying computational resource capabilities. This may lead to implementation and scalability challenges for FL techniques. In this paper, we first introduce some recently implemented real-life applications of FL underlying the applications that are suitable for FL-based resource-constrained IoT environments. We then emphasize the core challenges of implementing the FL algorithms from the perspective of resource limitations (e.g., memory, bandwidth, and energy budget) of client devices. We finally discuss open issues associated with FL for resource-constrained environments and highlight future directions in the FL domain concerning resource-constrained devices.
An Efficient Privacy Preserving Spectrum Sharing Framework for Internet of Things

Internet of Things (IoT) devices are forecasted to increase to 20 billion by the year 2020. Questions are arising as where the bandwidth and radio channels come from to make it all work. It is a common belief that the spectrum under 6GHz is easy to use for various IoT applications. However, currently it has been almost fully allocated to different kinds of systems, some of which suffer from an extremely low spectrum utilization ratio. To this end, spectrum sharing approach has been proposed recently, which dynamically redistributes the incumbents' underutilized spectrum to secondary users and exchanges for profit. To incentivize the incumbent to share the spectrum in efficient and profitable ways, auction based mechanisms are extensively discussed. Most of the existing researches propose to share the spectrum by utilizing truthful auction mechanism, in which the secondary users' dominant strategies are to bid their true valuations. However in reality, exposing true valuation is very risky and thus keeping user's bidding value confidential is of great importance. In this paper, we propose an efficient privacy preserving spectrum sharing framework for IoT by utilizing ElGamal cyptosystem. The proposed scheme hides the user's bid from both the auctioneer and other users. By extensive evaluation results, we show that the proposed framework could achieve good spectrum utilization efficiency and user satisfactory ratio, at the cost of acceptable communication and computation overheads.
Trivariate Lifted Codes with Disjoint Repair Groups

Guo, Kopparty, and Sudan introduced the notion of lifted Reed-Solomon codes in the context of locally correctable codes. We continue the study of lifted codes with the application for so-called t-disjoint-repair-group property (t-DRGP) codes. A code is said to have the t-DRGP if every symbol in a codeword from the code has t mutually disjoint recovering sets of coordinates in the codeword. In some parameter regimes, our proposed t-DRGP codes based on lifted codes have lower redundancy than previously known t-DRGP codes.
Kratos: A secure, authenticated and publicly verifiable system for educational data using the blockchain

Growing interest in educational data mining (EDM) and learning analytics (LA) to leverage big data and to benefit education and the science of learning has made data ownership an important focus point for institutions and students. While EDM and LA can provide important information that help enhance the quality of teaching and learning, it has become critical to ensure data privacy and student agency over data. In this paper, we introduce Kratos: an immutable and publicly verifiable data management system that enables EDM and LA, while maintaining data privacy and empowering students with a user interface for data governance and participation in school processes. The system aims to achieve data interoperability, which facilitates EDM and LA as incentives to educational stakeholders (policy makers, educators, developers of education technologies, etc.), while prioritizing student agency over their data. Our system gives students and schools an immutable log along with comprehensive access to data that is otherwise scattered across systems and vendors. The underlying set of rules of the system are defined in a set of smart contracts, codified from existing non-virtual agreements [1] between schools and education technology (edutech) vendors. We propose the smart contracts to be deployed on a public blockchain (like Ethereum or Bitcoin), for notarizing and time-stamping various interactions which users of Kratos may have with data. Third parties requesting access to school data have a unique virtual token assigned to them on the blockchain which helps keep track of data modifications, access and use.
Identification of trusted interactive behavior based on mouse behavior considering web User's emotions

Under existing network security technology, it is still possible for hackers to impersonate legitimate users and invade a system for malicious destruction. Therefore, this study constructs a user's unique mouse behavior pattern to identify a trusted interaction behavior in a real environment and quantify the effects of different emotions on mouse behavior and the accuracy of the user's trusted interaction behavior identification. First, mouse data was collected for 8 user's trusted interactions on an academic study website (AML). These data were used to construct the basic trusted interaction model by a big data analysis method called a random forest. Second, in a repeated measurement experiment, 18 participants completed tasks on the AML under different emotions, and the emotions' impact on the mouse behavior and accuracy of the user's trusted interaction identification was analyzed. In the results, the accuracy of the trusted interaction behavior identification based on mouse behavior reached 91.82%, and the error rate was lower than 8.18%. Significant differences were observed in horizontal velocity, velocity, and traveled distance under different emotions. However, there was no significant difference in the accuracy of a user's trusted interaction behavior identification under different emotions. Based on these results, the trusted interaction behavior of web users can be accurately identified based on the user's mouse behavior pattern. The user's mouse behavior differs under different emotions, but there is no significant difference on the identification of the user's trusted interaction behavior. The findings help to provide another protection layer for network information security.
Diagnosing Breast Cancer using Support Vector Machine and Multi-Classifiers

One and only primary reason of demise amid females is Breast cancer or Carcinoma. Initial identification of irregularities in breast definitely assists the radiologist to diagnose and detect the breast cancer disease. In this paper, classification of type of cancer is proposed to diagnose the breast cancer from classification dataset. Data set is given to different classifiers like Support Vector Machine, Naïve-Bayes, Simple Logistics, Neural Network-MLP, Random Forest and Decision Trees. Cross Validation performed, leading to training and testing the model. Classification accuracy is obtained and results are measured on few parameters. The results of all the classifiers obtained are evaluated. Support Vector Machine offers high accurateness and F-score in comparison with multi-classifiers.
GiantVM

In recent years, since scale-up machines are not economical and may not be affordable for small businesses, scale-out has become the standard answer to data analysis, machine learning, and many other fields. However, these frameworks introduce complex programming models that put a burden on developers. Therefore, Single System Image (SSI), which means a cluster of machines that appears to be one single system, has been proposed to hide the complexity of distributed systems. Unfortunately, due to the mature ecosystem of current mainstream Operating Systems (OSes), it might be non-trivial and even unaffordable to modify the current OS to implement SSI. With the wide use of virtualization, we believe that it is appealing to support SSI at the hypervisor, without modifying guest OSes.
SMART RESOURCE USAGE PREDICTION USING CLOUD COMPUTING FOR MASSIVE DATA PROCESSING SYSTEMS

Resource management plays the vital role in the cloud computing as the requirement for the massive data processing system such as heath sectors, business solutions and the internet of things keeps on increasing in at an exponential range. Allocation of proper and perfect resources remains as the mains reasons for the successful computation of the applications. However the conventional resources management methodologies, that totally depends on the simple heuristic based methods fails to accomplish a performance that is predictable. The appropriate resource allocation is directly related to the workload demand prediction as the would help to bring down the cost, time and power and the memory usage. The proposed method in the paper leverages the machine learning approaches to manage the resource allocation in the cloud computing for the massive data processing system, the simulation of the proposed model using the network simulator -2 enables to achieve a better performance and resources utilization at a decreased cost, time, power and memory usage.
Dynamic Colocation Policies with Reinforcement Learning

We draw on reinforcement learning frameworks to design and implement an adaptive controller for managing resource contention. During runtime, the controller observes the dynamic system conditions and optimizes control policies that satisfy latency targets yet improve server utilization. We evaluate a physical prototype that guarantees 95th percentile latencies for a search engine and improves server utilization by up to 70%, compared to exclusively reserving servers for interactive services, for varied batch workloads in machine learning.
IoT Security, Privacy and Trust in Home-Sharing Economy via Blockchain

The phenomenal growth of Internet-services has created a vibrant new domain for sharing economy. Millions of users around the world share personal services and possessions with others—often complete strangers. Such sharing schemes have been defined by a strong centralization of power and processes, which results in disadvantageous prices and deals for consumers alongside distrust. They also increase the risk of violation of one’s informational and physical privacy and security. Strangers often have to trust each other with their privacy (e.g. a surveillance camera in an Airbnb room). However, very little research has been devoted to investigate security, trust and privacy in sharing economy. In this paper, we explore the security, trust, and privacy concerns associated with contractual renting or leasing of IoT (Internet-of-Things) devices-enabled home. We propose a methodology to eliminate security, trust, and privacy threats from IoT-enabled telematics devices in a smart home via blockchain-based smart contract. For the purpose of illustration, we focus on how we can circumvent the threats from indoor surveillance IP cameras in a smart home sharing economy.
Adaptation of password strength estimators to a non-English environment—the Czech experience

Passwords are among the most commonly used methods of user authentication. Password strength estimators can significantly help users to choose passwords of reasonable strength. These estimates are, however, useful for end users and administrators only in those cases where they provide sufficiently precise password strength estimations. Tools for estimating password strength have mainly been tested against English, or in some cases Chinese or other widespread and global languages. Only very few studies can be found in the literature regarding how to adapt these tools for other, less widespread languages, and what results are produced by so adapted tools. This article presents the approach and reports the results of adapting the zxcvbn estimation engine for the Czech and Slovak languages. The results of this work – an adapted version of zxcvbn (including various dictionaries) – are available for download on GitHub as open-source software. For testing password strength estimation quality, we used a large set of leaked passwords from the Czech environment (approx. 3.1 million passwords), which we divided up into 12 categories. The main results are: (1) The password strength estimation improved for all 12 of the categories. (2) The overall size of zxcvbn did not increase significantly, thanks to adjustments and optimizations of both the original English dictionaries and the newly added Czech and Slovak ones. (3) The speed of operation increased by 4 to 12% depending on the version of the dictionaries used. (4) Besides the direct results for Czech and Slovak, the method described in the article can be utilized as a methodology for adapting zxcvbn for other less-widespread European languages.
Personalized Collaborative Filtering Recommendation Algorithm based on Linear Regression

A personalized collaborative filtering recommendation algorithm based on a linear regression model. Constructing the linear regression model based on the user label weight matrix and the user-item scoring matrix, and the gradient regression method is used to minimize the value of the linear regression cost function to obtain the item label. Then, the user and item label weight matrix are substituted into the linear regression model to obtain the user's predicted scores for all unrated items. Using the SlopeOne algorithm principle, calculate the difference between the predicted score and the actual score, and the predicted result is adjusted to obtain the final predicted score. Sort the results and recommend Top-N items to target users. Experiments show that the algorithm's recommendation accuracy is significantly improved than the traditional collaborative filtering algorithm. And the recommended results are interpretable and can meet the individual needs of users.
Detecting errors in pick and place procedures

Many human activities, such as manufacturing and assembly, are sequence-constrained procedural tasks (SPTs): they consist of a series of steps that must be executed in a specific spatial/temporal order. However, these tasks can be error prone - steps can be missed out, executed out-of-order, and repeated. The ability to automatically predict if a person is about to commit an error could greatly help in these cases. The prediction could be used, for example, to provide feedback to prevent mistakes or mitigate their effects. In this paper, we present a novel approach for real-time error prediction for multi-step sequence tasks which uses a minimum viable set of behavioural signals. We have three main contributions. The first we present an architecture for real-time error prediction based on task tracking and intent prediction. The second is to explore the effectiveness of using hand position and eye-gaze tracking for task tracking. We confirm that eye-gaze is more effective for intent prediction, hand tracking is more accurate for task tracking and that combining the two provides the best overall response. We show that using Hands and Gaze tracking data we can predict selection/placement errors with an F1 score of 97%, approximately 300ms before the error would occur. Finally, we discuss the application of this hand-gaze error detection architecture used in conjunction with head-mounted AR displays, to support industrial manual assembly.
IOS-Net: An inside-to-outside supervision network for scale robust text detection in the wild

Accurately detecting scene text is a challenging task due to perspective distortion, scale variance, varied orientations, uneven illumination. Among them, scale variance has always been a core issue and generally involves two types: various size and diverse aspect ratios of the text regions. In contrast to most existing approaches focusing on addressing one type of scale variance, this paper presents a novel inside-to-outside supervision network (IOS-Net) that can well tackle both two. Specifically, we design a hierarchical supervision module (HSM), which consists of a new inception unit with parallel asymmetric convolution and a skip-layer fusion structure. Inside the HSM, we introduce hierarchical supervision into the new inception unit to effectively capture the texts with diverse aspect ratios. Outside the HSM, we adopt multiple-scale supervision on the stacked HSMs to accurately detect the texts with various sizes. Moreover, a position-sensitive segmentation is used to enhance the representation of difficult text objects and the discrimination of adjacent ones. The proposed method achieves state-of-the-art performance on representative public benchmarks, reaching 86% F-score and 11.5 frames per second (FPS) on the ICDAR 2015 incidental text dataset, 47% F-score and 16.1 FPS on the COCO-Text dataset, 69% F-score and 11.7 FPS on the ICDAR 2013 video text dataset.
Enhancements to the Sequence-to-Sequence-Based Natural Answer Generation Models

There is a great interest shown by academic researchers to continuously improve the sequence-to-sequence (Seq2Seq) model for natural answer generation (NAG) in chatbots.The Seq2Seq model shows a weakness whereby the model tends to generate answers that are generic, meaningless and inconsistent with the questions.However, a comprehensive literature review on the factors contributing to the weakness and potential solutions are still missing.Therefore, this review article fills the gap by reviewing Seq2Seq based natural answer generation-based literature to identify those factors and proposed methods to address the weakness.This literature review identified several factors such as input question is not sufficient to determine a meaningful output, usage of cross-entropy function as the loss function during training, infrequent words in training data, language model influence which generates answers not relevant to the question, utilization of teacher forcing method during training which results in exposure bias, long sentences and inability to consider dialogue history as the factors.Additionally, this literature review also identified and reviewed the methods proposed to address the weakness such as utilizing additional embedding and encoders, using different loss functions and training approaches, as well as utilizing other mechanisms like copying source word(s) and paying attention to a certain portion of the input.For discussion, these methods are categorized into four broad categories which are Structural Modifications, Augmented Learning, Beam Search and Complementary Mechanisms.Additionally, the paper highlights unexplored areas in Seq2Seq modeling and proposes potential future works for natural answer generation.