Reviewerly/exHarmony huggingface dataset:
Blockchain-Based Public Auditing Scheme for Shared Data

With the rapid development of cloud computing, an increasing number of organizations and individuals store and share their data on untrusted clouds. Therefore, shared data auditing has become an important issue in the field of cloud storage, attracting the attention of researchers. However, the state-of-the-art schemes can not fully meet the performance and security requirements. Therefore, in this paper we propose a public audit shared data protocol for cloud storage by using blockchain and Rank-based Merkle AVL tree (RB-MHT) to achieve privacy preserving and batch auditing to reduce system overhead as well as keeping the security of modification record in this blockchain-based scheme. For privacy-perserving, the auditing signature is only related to group management during the audit process and data will be blind by a random value. Furthermore, we prove the security of our scheme and evaluate the performance of it through implementation. The results demonstrate that the proposed scheme is provably secure and efficient.
Equilibrium-Based Force and Torque Control for an Aerial Manipulator to Interact with a Vertical Surface

SUMMARY In this paper, a force and torque controller for aerial manipulation is developed using an unmanned aerial vehicle equipped with a robotic arm to interact near or on a vertical surface such as a wall. Control of aerial manipulators interacting with the environment is a challenging task due to dynamic interactions between aerial vehicles, robotic arms, and environment. To achieve this, modeling of aerial manipulators is first investigated and presented considering interaction with the environment. Nonlinear models of generic aerial manipulators, as well as of a prototype aerial manipulator composed of a hexacopter with a three-joint robotic arm, are established. An equilibrium-based force and torque controller is developed to conduct tasks that require the aerial manipulator to exert forces and torques on a wall. Simulations and experiments validate the performance of the controller that successfully applies desired forces and torques to an object fixed on a wall while flying near the wall.
Truthful Double Auction Based VM Allocation for Revenue-Energy Trade-Off in Cloud Data Centers

With the advances in virtualization technologies, cloud has emerged as a flexible and cost-effective service paradigm by provisioning on-demand VM resources to users via a pay-per-use business model. In cloud data centers, effective resource provisioning is required with the aim of minimizing energy consumption and maximizing cloud provider's revenue. However, the existing mechanisms have either focused on the optimization of energy, or the profit of cloud service provider (CSP) while incurring inefficient resource allocation. Thus to address these fundamental research challenges and to balance the trade-off between energy and revenue, we propose a Vickrey-Clarke-Groves (VCG) based truthful double auction mechanism (TDAM). In this paper, first, we have formulated a joint optimization problem and prove it NP-hard by reducing it to a multi-dimensional bin-packing problem. Then we design TDAM, a truthful double auction scheme and propose an efficient winning bid algorithm for VM allocation and a VCG based mechanism for calculating payment of each bid. Being a double auction, TDAM allows both the buyers (VMs) and the sellers (PMs) to submit their bids and asks respectively, and performs allocation based on the energy consumption, while upholding truthfulness, in order to avoid falsification of the submitted bid or ask values. Through theoretical analysis and extensive experiments we show that the TDAM makes a significant contribution while maintaining truthfulness, individual rationality, economic efficiency, and has polynomial time complexity.
On the Security of Multikey Homomorphic Encryption

Multikey fully homomorphic encryption (MFHE) scheme enables homomorphic computation on data encrypted under different keys. To decrypt a result ciphertext, all the involved secret keys are required. For multi decryptor setting, decryption is a protocol with minimal interaction among parties. However, all prior schemes supporting the protocol are not secure in public channel against a passive external adversary who can see any public information not joining the protocol. Furthermore, the possible adversaries have not been defined clearly. In this paper, we revisit the security of MFHE and present a secure one-round decryption protocol. We apply it to one of existing schemes and prove the scheme is secure against possible static adversaries. As an application, we construct a two round multiparty computation without common random string.
Single image rain removal model using pure rain dictionary learning

A novel single image rain removal model is proposed. Based on the gradient magnitude and direction of rain streaks, the pure rain region can be extracted from the high-frequency component of rainy image. To ensure proper rain removal, a pure rain dictionary is learned from the extracted pure rain region, and the learned rain dictionary is used to reconstruct the rainy mask from the decomposed high-frequency component. To adaptively remove rain pixels and preserve more details of non-rain pixels, a rainy mask is incorporated into the model. In the proposed model, an improved bilateral filter is only used to handle rain pixels. The experimental results show that the proposed model is superior to existing models in resolving the problems of over-smoothing and rain-streak remains in synthetic and real-world rainy images. Consequently, a better quantitative index and visual quality can be achieved.
Sediment Hosted Stratiform Copper (SSC) Mineralization in Bhudoli-Basari Area, North Delhi Fold Belt, Mesoproterozoic Delhi Supergroup, Rajasthan

The rocks of the Kushalgarh Formation of the Delhi Supergroup in northeast Rajasthan, host low-temperature prospect generally known as sediment hosted stratiform copper (SSC) mineralization in Bhudoli-Basari area, district Sikar, Rajasthan. Mineralization is about 3km in length with variable width of up to 600m and sulphides occur mainly as fine dissemination as well as veinlets within the steeply dipping carbonate rocks, which were remobilized along the quartz-calcite veins during compaction and subsequent deformation. The prospect is product of low-temperature aqueous brines. The brines leached, transported, and deposited copper (and associated metals) as per physical and chemical stabilities. The mineralization in the Bhudoli-Basari SSC consists mainly of bedding-parallel disseminations of fine-grained sulphides, typically zoned. Copper is leached from poorly sorted first-cycle clastic sediments of footwall redbeds, associated meta/volcanics of the Raialo Group and basement rocks during their progressive diagenetic alteration by moderate-temperature oxygen rich brines. Ore mineral zoning from chalcocite to pyrite in the Bhudoli-Basari area is archetypal to other SSC deposits viz. the Kupferschiefer of Poland-Germany and the Central African Copper belt of Zambia.
Seeking coherence between ‘mobile learning’ applications and the everyday lives of medical residents

The role of technology in health professions education has received increased research attention. Research has examined the interaction between humans and technology, focusing on the mutual influence between people and technology. Little attention has been given to the role of motivation and incentives in how learning technologies are used in relation to daily activities. This research aims to understand the relationship between medical-learning technology and its users.A mixed-method case study of a new medical-learning mobile application (app) for family medicine residents was undertaken at a Canadian university hospital. The Information Assessment Method is a custom-made app to help residents prepare for the College of Family Physicians of Canada licensing examination. Residents' use of the app was tracked over a 7-month period and individual, semi-structured interviews were conducted with users. Data were thematically analyzed and correlated with app use data.Factors identified as shaping residents' mobile app use for learning, included: efficiency, mobility and resonance with life context; credibility of information retrieved; and relevance of content. Most influential was stage of residency. Second-year residents were more selective and strategic than first-year residents in their app use.An emphasis on coherence between self-directed learning and externally dictated learning provides a framework for understanding the relationship between users and mobile-learning technology. This framework can guide the design, implementation and evaluation of learning interventions for healthcare professionals and learners.
PhAttApp

Technology has grown rapidly since the end of the last century. Thousands of businesses from different major industries are transforming into information organizations and offering online services. The industrial and enterprise of Internet of Things (IoT) is growing at an exponential rate. Incident Command Systems (ICS) and Supervisory Control and Data Acquisition (SCADA), which were once known to be untouchable by malware as they were usually available offline, are now facing security challenges. These systems become more vulnerable as their online availability increases to enable integration with other systems. Technology allows organizations to provide greater value to customers, expand their businesses beyond physical boundaries, and compete with other businesses. However, technology also allows attackers from all over the world to attack organizations and consumers. Ransomware, a type of malware, is a growing cybersecurity threat. It mainly targets home users and businesses for financial gain. Ransomware attacks often start with a delivery phase in which attackers deliver malicious content. Attackers often use multiple threat vectors for ransomware enablement such as emails, instant messages, and drive-by downloads, exploiting the vulnerabilities of a network or application. Among these attack vectors, email is the top threat vector, which most ransomware attackers attempt to use. This study proposes the use of a phishing detector application, PhAttApp. This application offers numerous features to detect and prevent ransomware delivery through phishing channels and thus reduces the risk of ransomware infection.
Securing Distributed Gradient Descent in High Dimensional Statistical Learning

We consider unreliable distributed learning systems wherein the training data is kept confidential by external workers, and the learner has to interact closely with thoseworkers to train a model. In particular, we assume that there exists a system adversary that can adaptively compromise some workers; the compromised workers deviate from their local designed specifications by sending out arbitrarily malicious messages.
Using Personas to Model Museum Visitors

It is widely agreed that museums and other cultural heritage venues should provide visitors with personalised interaction and services such as personalised mobile guides, although currently most do not. Since museum visitors are typically first-time visitors and since their visit is for a relatively short session, personalisation should use initial interaction data to associate the user with a particular persona and thereby infer other facts about the user's preferences and needs. In this paper we report a questionnaire-based study carried out with 105 visitors of a Science and Technology Centre to examine the minimal features needed to identify visitor personas. We find that museum visitors can be clustered by their visit motivation and perceived success factors; these clusters are found to correspond both with Falk's visitor categorisation and a prior classification of exploration styles. Consequently, these two features can be used to reliably identify the visitor persona, and therefore, can be used for user modeling.
A comparison of elaborated and restricted feedback in LogEx, a tool for teaching rewriting logical formulae

Abstract This article describes an experiment with LogEx, an e‐learning environment that supports students in learning how to prove the equivalence between two logical formulae, using standard equivalences such as DeMorgan. In the experiment, we compare two groups of students. The first group uses the complete learning environment, including hints, next steps, worked solutions, and informative timely feedback. The second group uses a version of the environment without hints or next steps, but with worked solutions, and delayed flag feedback. We use pretest and posttest to measure the performance of both groups with respect to error rate and completion of the exercises. We analyse the loggings of the student activities in the learning environment to compare its use by the different groups. Both groups score significantly better on the posttest than on the pretest. We did not find significant differences between the groups in the posttest, although the group using the full learning environment performed slightly better than the other group. In the examination, which took place 5 weeks after the experiment, the group of students who used the complete learning environment scored significantly better than a control group of students who did not participate in the experiment.
Public perceptions of Internet‐based health scams, and factors that promote engagement with them

The prevalence of health scams in Canada is increasing, facilitated by the rise of the Internet as a mass communication medium. However, little is known about the nature of this phenomena. Building on previous work exploring the nature of Internet health scams (IHS), this project sought to better understand the reasons why people engaged with IHS, and if contemporary psychosocial theory can help explain IHS engagement. A mixed-methods study, involving a web-based survey incorporating qualitative questions and the Susceptibility to Persuasion-II Brief psychometric scale (STP-II Brief), were administered (N = 194) in British Columbia, Canada, in 2017. Results (n = 156) demonstrated that 40% of participants had ever engaged with IHS, but only 1% reported to have actually lost money to a deceptive product/service. Associations between scam engagement, participant demographics and STP-II Brief scores were explored, with Sex and Employment Status both found to have a significant effect on odds of IHS engagement. STP-II Brief scores were positively correlated with a likelihood of engagement with IHS, even when adjusting for demographic characteristics. The types of IHS most frequently engaged with were those related to body image products, and social influence appeared to be a dominant psychosocial factor promoting engagement. Participants reported that claims of products being 'natural', the result of scientific breakthroughs, use of pseudoscientific language, use of testimonials, and celebrity or professional endorsement could lead them to engage with a product. These findings can help inform health professionals' understanding of public health-seeking behaviours with respect to deceptive marketing.
Dynamic Weight Alignment for Temporal Convolutional Neural Networks

In this paper, we propose a method of improving temporal Convolutional Neural Networks (CNN) by determining the optimal alignment of weights and inputs using dynamic programming. Conventional CNN convolutions linearly match the shared weights to a window of the input. However, it is possible that there exists a more optimal alignment of weights. Thus, we propose the use of Dynamic Time Warping (DTW) to dynamically align the weights to the input of the convolutional layer. Specifically, the dynamic alignment overcomes issues such as temporal distortion by finding the minimal distance matching of the weights and the inputs under constraints. We demonstrate the effectiveness of the proposed architecture on the Unipen online handwritten digit and character datasets, the UCI Spoken Arabic Digit dataset, and the UCI Activities of Daily Life dataset.
Implementing positive-operator-valued-measurement elements in photonic circuits for performing minimum quantum state tomography of path qudits

Manipulation of qudits in optical tables is a difficult and nonscalable task. The use of integrated optical circuits opens new possibilities for the generation, manipulation, and characterization of high dimensional states besides the ease of transmission of these states through an optical fiber. In this work we propose photonic circuits to perform minimum quantum state tomography of path qudits and show how to determine all the constituents parameters of these circuits (beam splitters and phase shifters). Our strategies were based on the symmetries of the involved POVMs (positive operator-valued measures) suggested for minimum tomography and allowed us to obtain interferometers smaller than those obtained by other already known methods. The calculations of the transmittances and reflectivities of the beam splitters were made using the definition of probability operators in extended Hilbert spaces and the application of Naimark's theorem. The employment of equidistant states for the definition of the POVM elements allowed us to develop a recipe applicable to the tomography of qudits of any dimension, generalizing our scheme.
Hybrid Microgenetic Analysis

Tacit knowledge is a type of knowledge often existing in one's subconscious or embodied in muscle memory. Such knowledge is pervasive in creative practices yet remains difficult to observe or codify. To better understand tacit knowledge, we introduce a design method that leverages time-series data (interaction logs, physical sensor, and biosignal data) to isolate unique actions and behaviors between groups of users. This method is enacted in Eluent, a tool that distills hundreds of hours of dense activity data using an activity segmentation algorithm into a codebook - a set of distinct, characteristic sequences that comprise an activity. The results are made visually parsable in a representation we term process chromatograms that aid with 1) highlighting distinct periods of activity in creative sessions, 2) identifying distinct groups of users, and 3) characterizing periods of activity. We demonstrate the value of our method through a study of tacit process within computational notebooks and discuss ways process chromatograms can act as a knowledge mining technique, an evaluation metric, and a design-informing visualization.
Equality‐constrained state estimation for hybrid systems

The authors investigate the problem of state estimation for stochastic hybrid dynamical systems with state equality constraints. They divided this problem into three categories depending on the linearity or non-linearity of equality constraints as well as on the dependence of constraints on the operating mode. For the mode-independent equality-constrained linear case, they present sufficient conditions on process dynamics and filter initial conditions so that the classical interacting multiple model (IMM) algorithm yields state estimates satisfying the linear equality constraint for all subsequent times. For both linear and non-linear systems, the mode-dependent equality constraints must be enforced over time by the filter. They present a modified version of the IMM filter to enforce the equality constraints in such cases. Their numerical results show that the proposed methods provide more accurate estimates than the IMM unconstrained estimates.
Automatic chocolate vending machine

This paper proposes the design of Automatic chocolate vending machine. The primary point of the thought is to dispatch new innovation application in the public eye. Vending machine that sales different type of products. Here we uses Radio frequency Identification (RFID) along with Arduino, to overcome the coin based vending machine which does not returned the balance amount if no change available. The customer can select the product before the RFID is read, after card is scanned the product can be collected at the chocolate collector. There are three units are present in the machine, the cashless payment provided by the RFID in the firstunit. In secondunit, Arduino UNO executed the programming section and Finally, the machine delivers the product and displays the information on the display. This paper made solution to the machine that do not returns the balance amount in a coin based vending machine.
Application of digital watermarks in the problem of operating signal hidden transfer in multi-agent robotic system

The relevant problem of development of the concept of safe control of intelligent robots and coalition of robots is considered by authors. Such robotic complexes have a wide range of application, including they are used for prospecting and rescue tasks. Creation of the corresponding algorithms and protocols of the protected interaction will allow to solve problems of creation of uniform approach to management of robotic complexes and also increases in efficiency of this process. In this article are provided the offered approach, algorithmic providing and data of computing experiments directed to forming of mechanisms of the protected interaction between agents of robotic system based on application of steganographic methods for concealment of managing signals.
Exploring Teachable Humans and Teachable Agents: Human Strategies Versus Agent Policies and the Basis of Expertise

In this research, we explore how expertise is shown in both humans and AI agents. Human experts follow sets of strategies to complete domain specific tasks while AI agents follow a policy. We compare machine generated policies to human strategies in two game domains, using these examples we show how human strategies can be seen in agents. We believe this work can help lead to a better understanding of human strategies and expertise, while also leading to improved human-centered machine learning approaches. Finally, we hypothesize how a continuous improvement system of humans teaching agents who then teach humans could be created in future intelligent tutoring systems.
Evaluation of the Context-Free Path Querying Algorithm Based on Matrix Multiplication

Recently proposed matrix multiplication based algorithm for context-free path querying (CFPQ) offloads the most performance-critical parts onto boolean matrices multiplication. Thus, it is possible to achieve high performance of CFPQ by means of modern parallel hardware and software. In this paper, we provide results of empirical performance comparison of different implementations of this algorithm on both real-world data and synthetic data for the worst cases.
A Recurrent Model with Self-attention for Product Repurchase Recommendation

With the further development of e-commerce, new retail platforms have emerged. In the new retail platform, users have obvious cyclical characteristics for the purchase of fresh goods. In order to improve the efficiency of consumers' shopping, it is necessary to model user periodic purchase rules and recommend products for repurchase. At present, most of the recommendation models for product repurchase are based on feature engineering. These models have many problems, such as large amount of engineering, incomplete feature extraction and so on. In this work, we first propose a product repurchase recommendation model based on the Long Short Term Memory Network, which can automatically mine the temporal information in the user's shopping records to model the user periodic purchase rules. To capture the evolution of the user periodic purchase rules, we further introduce self-attention into the LSTM-based model and propose the AttRNN model. Detailed experiments on Instacart datasets are carried out to verify the effectiveness of AttRNN.