Reviewerly/exHarmony huggingface dataset:
Secure Fog-Assisted Crowdsensing With Collusion Resistance: From Data Reporting to Data Requesting

The development and ubiquity of smart mobile devices have produced the idea of crowdsensing, where people report and request data in a community via a cloud server. Recently, fog is introduced to assist the cloud server by providing location-sensitive and latency-aware local data management. However, interaction between users and server without appropriate sanitation puts serious security threats to user' privacy (e.g., data content and preference). While existing work already has a wide range of privacy-preserving schemes, they hardly consider collusion attacks (CAs) between the server and users, let alone CAs between fog nodes and users. To solve this problem, we first define four specific CAs in fog-assisted crowdsensing and propose a novel privacy-preserving data reporting and requesting (PARE) scheme with collusion resistance. PARE is constructed by leveraging one-way hash chains, marked mix-nets, and grouping-based secure searchable encryption to securely collect users' reports and respond to users' requests under CAs. Then, we consider one extreme scenario and provide a solution by introducing a role of sentry reporter while reducing computational costs and communication overhead. Thorough security and privacy analysis shows that PARE is secure and collusion resistant and we also quantitatively measure privacy with mutual information. Extensive performance evaluation results indicate that PARE is lightweight with respect to computational cost and communication overhead. To the best of our knowledge, this paper is the first one that gives four formal definitions of CAs in fog-assisted crowdsensing and aim to defend them at the same time.
Recursive Rewarding Modified Adaptive Cell Decomposition (RR-MACD): A Dynamic Path Planning Algorithm for UAVs

A relevant task in unmanned aerial vehicles (UAV) flight is path planning in 3 D environments. This task must be completed using the least possible computing time. The aim of this article is to combine methodologies to optimise the task in time and offer a complete 3 D trajectory. The flight environment will be considered as a 3 D adaptive discrete mesh, where grids are created with minimal refinement in the search for collision-free spaces. The proposed path planning algorithm for UAV saves computational time and memory resources compared with classical techniques. With the construction of the discrete meshing, a cost response methodology is applied as a discrete deterministic finite automaton (DDFA). A set of optimal partial responses, calculated recursively, indicates the collision-free spaces in the final path for the UAV flight.
Graded hyponymy for compositional distributional semantics

The categorical compositional distributional model of natural language provides a conceptually motivated procedure to compute the meaning of a sentence, given its grammatical structure and the meanings of its words. This approach has outperformed other models in mainstream empirical language processing tasks, but lacks an effective model of lexical entailment. We address this shortcoming by exploiting the freedom in our abstract categorical framework to change our choice of semantic model. This allows us to describe hyponymy as a graded order on meanings, using models of partial information used in quantum computation. Quantum logic embeds in this graded order.
Between the Blabbering Noise of Individuals or the Silent Dialogue of Many: a Collective Response to ‵Postdigital Science and Education′ (Jandrić et al. 2018)

This article is a multi-authored response to an editorial ‵Postdigital Science and Education′ published in 2018 by Petar Jandrić, Jeremy Knox, Tina Besley, Thomas Ryberg, Juha Suoranta and Sarah Hayes in Educational Philosophy and Theory as a mission statement for the journal Postdigital Science and Education. Nineteen authors were invited to produce their sections, followed by two author-reviewers who examined the article as a whole. Authors' responses signal the sense of urgency for developing the concept of the postdigital and caution about attempts at simplifying complex relationships between human beings and technology. Whilst the digital indeed seems to become invisible, we simultaneously need to beware of its apparent absence and to avoid over-emphasizing its effects. In this attempt, authors offer a wide range of signposts for future research such as 'the critical postdigital' and 'postdigital reflexivity'; they also warn about the group's own shortcomings such as the lack of 'real' sense of collectivity. They emphasize that postdigital education must remain a common good, discuss its various negative aspects such as smartphone addiction and nomophobia, and exhibit some positive examples of postdigital educational praxis. They discuss various aspects of postdigital identities and point towards the need for a postdigital identity theory. With these varied and nuanced responses, the article opens a wide spectrum of opportunity for the development of postdigital approaches to science and education for the future.
Flag fault-tolerant error correction, measurement, and quantum computation for cyclic Calderbank-Shor-Steane codes

Flag qubits have recently been proposed in syndrome extraction circuits to detect high-weight errors arising from fewer faults. The use of flag qubits allows the construction of fault-tolerant protocols with the fewest number of ancillas known to date. In this work, we prove some critical properties of Calderbank-Shor-Steane (CSS) codes constructed from classical cyclic codes that enable the construction of a flag fault-tolerant error correction scheme. We then develop fault-tolerant protocols as well as a family of circuits for flag fault-tolerant error correction and operator measurement, requiring only four ancilla qubits and applicable to cyclic CSS codes of distance 3. The measurement protocol can be further used for logical Clifford gate implementation via quantum gate teleportation. We also provide examples of cyclic CSS codes with large encoding rates.
Motion Estimation by Hybrid Optical Flow Technology for UAV Landing in an Unvisited Area

The capability of landing on previously unvisited areas is a fundamental challenge for an unmanned aerial vehicle (UAV). In this paper, we developed a vision-based motion estimation as an aid to improve landing performance. As an alternative to the common scenarios accompanying by external infrastructures or well-defined marker, the proposed hybrid framework can successfully land on a new area without any prior information about guiding marks. The implementation was based on the optical flow technique associated with a multi-scale strategy to overcome the decreasing field-of-view during the UAV descending. Compared with a commercial Global Positioning System (GPS) through a sequence of flight trials, the vision-aided scheme can effectively minimize the possible sensing error, thus, leading to a more accurate result. Moreover, this work has potential to integrate the fast-growing image learning process and yields more practical versatility for UAV applications in the future.
MulNet: A Flexible CNN Processor With Higher Resource Utilization Efficiency for Constrained Devices

Leveraging deep convolutional neural networks (DCNNs) for various application areas has become a recent inclination of many machine learning practitioners due to their impressive performance. Research trends show that the state-of-the-art networks are getting deeper and deeper and such networks have shown significant performance increase. Deeper and larger neural networks imply the increase in computational intensity and memory footprint. This is particularly a problem for inference-based applications on resource constrained computing platforms. On the other hand, field-programmable gate arrays (FPGAs) are becoming a promising choice in giving hardware solutions for most deep learning implementations due to their high-performance and low-power features. With the rapid formation of various state-of-the-art CNN architectures, a flexible CNN hardware processor that can handle different CNN architectures and yet customize itself to achieve higher resource efficiency and optimum performance is critically important. In this paper, a novel and highly flexible DCNN processor, MulNet, is proposed. MulNet can be used to process most regular state-of-the-art CNN variants aiming at maximizing resource utilization of a target device. A processing core with multiplier and without multiplier is employed to achieve that. We formulated optimum fixed-point quantization format for MulNet by analyzing layer-by-layer quantization error. We also created a power-of-2 quantization for multiplier-free (MF) processing core of MulNet. Both quantizations significantly reduced the memory space needed and the logic consumption in the target device. We utilized Xilinx Zynq SoCs to leverage the one die hybrid (CPU and FPGA) architecture. We devised a scheme that utilizes Zynq processing system (PS) for memory intensive layers and the Zynq programmable logic (PL) for computationally intensive layers. We implemented modified LeNet, CIFAR-10 full, ConvNet processor (CNP), MPCNN, and AlexNet to evaluate MulNet. Our architecture with MF processing cores shows the promising result, by saving 36%-72% on-chip memory and 10%-44% DSP48 IPs, compared to the architecture with cores implemented using the multiplier. Comparison with the state of the art showed a very promising 25-$40\times $ DSP48 and 25-$29\times $ on-chip memory reduction with up to 136.9-GOP/s performance and 88.49-GOP/s/W power efficiency. Hence, our results demonstrate that the proposed architecture can be very expedient for resource constrained devices.
Hierarchical differential evolution algorithm combined with multi-cross operation

In expert systems, complex optimization problems are always characterized by nonlinearity, nonconvexity, multi-modality, discontinuity, and high dimensionality. Although classical optimization algorithms are mature, they readily fall into a local optimum. The differential evolution (DE) algorithm has been successfully applied to solve numerous problems with expert systems. However, balancing the global and local search capabilities of the DE algorithm remains an open issue and has attracted significant research attention. Thus, a hierarchical heterogeneous DE algorithm that incorporates multi-cross operation (MCO) is proposed in this article. In the proposed algorithm, success-history-based adaptive DE (SHADE) is implemented in the bottom layer, while MCO is implemented in the top layer. The MCO search is based on the SHADE results, but its search results do not affect the bottom layer. First-order stability analyses conducted for the presented MCO showed that the individual positions are expected to converge at a fixed point in the search space. The accuracy and convergence speed of the proposed algorithm were also experimentally compared with those of eight other advanced particle swarm optimization techniques and DE variants using benchmark functions from CEC2017. The proposed algorithm yielded better solution accuracy for 30- and 50-dimensional problems than the other variants, and although it did not provide the fastest convergence for all of the functions, it ranked among the top three for the unimodal and simple multimodal functions and achieved fast convergence for the other functions.
Quantum circuits for maximally entangled states

We design a series of quantum circuits that generate absolute maximally entangled (AME) states to benchmark a quantum computer. A relation between graph states and AME states can be exploited to optimize the structure of the circuits and minimize their depth. Furthermore, we find that most of the provided circuits obey majorization relations for every partition of the system and every step of the algorithm. The main goal of the work consists in testing efficiency of quantum computers when requiring the maximal amount of genuine multipartite entanglement allowed by quantum mechanics, which can be used to efficiently implement multipartite quantum protocols.
Two new feature selection metrics for text classification

Obtaining meaningful information from data has become the main problem. Hence data mining techniques have gained importance. Text classification is one of the most commonly studied areas of data mining. The main problem about text classification is the increase in the required time and a decrease in the success of classification because of data size. To determine the right feature selection methods for text classification is the main purpose of this study. Metrics that are used frequently for feature selection like Chi-square and Information Gain were applied over different data sets and performance was measured. In this study two feature selection metrics, which are based on filtration, are recommended as alternatives to the current ones. The first recommended metric is Relevance Frequency Feature Selection metric that was obtained by adding new parameters to Relevance Frequency method that is used for term weighting in text classification. The second one is the alternative Accuracy2 metric, which was obtained by changing the parameters of Accuracy2 metric. It was observed that the suggested Relevance Frequency Feature Selection and Alternative Accuracy2 metrics offer successful results as the current metrics used frequently.
Enhancing Reflection and Empathy Skills via Using a Virtual Reality Based Learning Framework

The last few years Virtual Reality (VR) based approaches have emerged lately as a new education paradigm. This paper addresses the possibility to cultivate reflection and empathy skills using a VR based framework targeting to maximize the professional development of teachers. Reflection and empathy are skills of paramount importance for teachers and an integral part of their professional development. The current research aims to investigate possible differences in cultivating reflection and empathy skills between participants who used a VR system and the control group who were trained in a real classroom environment. Experimental results indicate that the participants using the virtual classroom environment were able to better reflect and empathize with the students whereas participants from the control group tended to be more undecided. Moreover, the VR system gave the participants the opportunity to enter the students’ virtual body and understand the different perspectives affecting at a higher level the reflection process than the control group.
Cross-Domain Text Sentiment Analysis Based on CNN_FT Method

Transfer learning is one of the popular methods for solving the problem that the models built on the source domain cannot be directly applied to the target domain in the cross-domain sentiment classification. This paper proposes a transfer learning method based on the multi-layer convolutional neural network (CNN). Interestingly, we construct a convolutional neural network model to extract features from the source domain and share the weights in the convolutional layer and the pooling layer between the source and target domain samples. Next, we fine-tune the weights in the last layer, named the fully connected layer, and transfer the models from the source domain to the target domain. Comparing with the classical transfer learning methods, the method proposed in this paper does not need to retrain the network for the target domain. The experimental evaluation of the cross-domain data set shows that the proposed method achieves a relatively good performance.
The Structural Relationship among Digital Literacy, Learning Strategies, and Core Competencies among South Korean College Students

This study explores the structural relationship among digital literacy, learning strategies, and core competencies among South Korean college students as well as the group differences between these variables depending on individual characteristics. Data analysis was conducted by correlation analysis, independent sample t-testing, and structural equation modelling. Participants were 916 college students from 10 colleges in South Korea. The results showed that i) no difference between groups existed in digital literacy and core competencies; however, a group difference existed in learning strategy; ii) digital literacy had a direct effect on core competencies and learning strategies had an indirect effect on the influence of digital literacy on core competencies; and iii) digital literacy can enhance the effect of core competencies when mediating learning strategy. This study suggests areas for future research, including developing digital technology-enhanced, competency-driven learning resources that account for the effects of cognitive, metacognitive, and resource management; the relationship between digital literacy and core competencies of learner characteristics such as achievement goal orientation, learning style, and academic self-efficacy; the effect of individual digital technologies on core competencies based on individual learning strategies through experimental and quasiexperimental designs; and qualitative studies on employing digital technologies and learning strategies.
On visualization problems in a generalized computational experiment

Modern development of computing systems and technologies allows to organize the construction of a generalized computational experiment in modeling problems of computational gas dynamics.The construction of such an experiment is based on numerical parametric studies and the solution of optimization analysis problems.Solving such problems implies a multiple solution to the direct problem of numerical simulation of a gas-dynamic process with different input data, where different sets of defining parameters are used as input data.The defining parameters of a class of problems, such as Mach number, Reynolds number, geometric parameters, etc., vary in certain ranges with a certain partitioning step.As a result, the resulting solution is a multidimensional volume of data.To analyze this volume, visualization is necessary.The paper attempts to systematize and briefly describe some types of visualization problems arising from processing the results of a generalized computational experiment.The aim of the work is to begin a broad discussion of the tasks of visualization and methods for their solution in a generalized computational experiment.
The visual side of digital humanities: a survey on topics, researchers, and epistemic cultures

Abstract Although the digital humanities have traditionally been conceived as a text-based discipline, both digital visualization techniques as well as visual analysis are increasingly used for research in various humanities disciplines. Since there are several overlaps in epistemic cultures of visually oriented and digitally supported research in art and architectural history studies, museology, and archaeology, as well as cultural heritage, we introduce ‘visual digital humanities’ as novel ‘umbrella’ term to cover research approaches in the digital humanities that are dependent on both consuming and producing pictorial, rather than textual, information to answer their humanities research questions. This article aims to determine this particular field of research in terms of (1) research topics, (2) disciplinary standards, and (3) a scholarly culture as well as (4) researchers’ habits and backgrounds. This study is intended to highlight a scope of phenomena and aspects of relevance. Information is gathered by interviews with researchers at London universities and workshops held in Germany and Sweden.
Predictive Models of Student College Commitment Decisions Using Machine Learning

Every year, academic institutions invest considerable effort and substantial resources to influence, predict and understand the decision-making choices of applicants who have been offered admission. In this study, we applied several supervised machine learning techniques to four years of data on 11,001 students, each with 35 associated features, admitted to a small liberal arts college in California to predict student college commitment decisions. By treating the question of whether a student offered admission will accept it as a binary classification problem, we implemented a number of different classifiers and then evaluated the performance of these algorithms using the metrics of accuracy, precision, recall, F-measure and area under the receiver operator curve. The results from this study indicate that the logistic regression classifier performed best in modeling the student college commitment decision problem, i.e., predicting whether a student will accept an admission offer, with an AUC score of 79.6%. The significance of this research is that it demonstrates that many institutions could use machine learning algorithms to improve the accuracy of their estimates of entering class sizes, thus allowing more optimal allocation of resources and better control over net tuition revenue.
Characterizing Crowds to Better Optimize Worker Recommendation in Crowdsourced Testing

Crowdsourced testing is an emerging trend, in which test tasks are entrusted to the online crowd workers. Typically, a crowdsourced test task aims to detect as many bugs as possible within a limited budget. However not all crowd workers are equally skilled at finding bugs; Inappropriate workers may miss bugs, or report duplicate bugs, while hiring them requires nontrivial budget. Therefore, it is of great value to recommend a set of appropriate crowd workers for a test task so that more software bugs can be detected with fewer workers. This paper first presents a new characterization of crowd workers and characterizes them with testing context, capability, and domain knowledge. Based on the characterization, we then propose Multi-Objective Crowd wOrker recoMmendation approach (MOCOM), which aims at recommending a minimum number of crowd workers who could detect the maximum number of bugs for a crowdsourced testing task. Specifically, MOCOM recommends crowd workers by maximizing the bug detection probability of workers, the relevance with the test task, the diversity of workers, and minimizing the test cost. We experimentally evaluate MOCOM on 532 test tasks, and results show that MOCOM significantly outperforms five commonly-used and state-of-the-art baselines. Furthermore, MOCOM can reduce duplicate reports and recommend workers with high relevance and larger bug detection probability; because of this it can find more bugs with fewer workers.