Reviewerly/exHarmony huggingface dataset:
PREFENDER: A Prefetching Defender against Cache Side Channel Attacks as A Pretender

Cache side channel attacks are increasingly alarming in modern processors due to the recent emergence of Spectre and Meltdown attacks. A typical attack performs intentional cache access and manipulates cache states to leak secrets by observing the victim's cache access patterns. Different countermeasures have been proposed to defend against both general and transient execution based attacks. Despite their effectiveness, they all trade some level of performance for security. In this paper, we seek an approach to enforcing security while maintaining performance. We leverage the insight that attackers need to access cache in order to manipulate and observe cache state changes for information leakage. Specifically, we propose PREFENDER,a secure prefetcher that learns and predicts attack-related accesses for prefetching the cachelines to simultaneously help security and performance. Our results show that PREFENDER is effective against several cache side channel attacks while maintaining or even improving performance for SPEC CPU2006 benchmarks.
An Improved JADE Hybridizing with Tuna Swarm Optimization for Numerical Optimization Problems

In this paper, we propose IJADE-TSO, a novel hybrid algorithm in which an improved adaptive differential evolution with optional external archive (IJADE) has been combined with the tuna swarm optimization (TSO). The proposed algorithm incorporates the spiral foraging search and parabolic foraging search of TSO into the mutation strategy in IJADE to improve the exploration ability and population diversity. Additionally, to enhance the convergence efficiency, crossover factor (CR) ranking, CR repairing, top α r1 selection, and population linear reduction strategies have been included in the algorithm. To evaluate the superiority of the proposed algorithm, IJADE-TSO has been benchmarked with its state-of-the-art counterparts using the CEC 2014 test set. Finally, to check the validity of IJADE-TSO, we apply it to photovoltaic (PV) parameter identification and compare its performance with those of other recently developed well-known algorithms. The statistical results reveal that IJADE-TSO outperforms the other compared algorithms.
PGAN framework for synthesizing sensor data privately

Some credible third parties collect and share mobile sensor data with users to enhance scientific innovation. However, since the sensor data contains sensitive attributes, it may lead to an unexpected privacy leak. Despite privacy filtering, differential privacy, and inferential privacy techniques that attempt to address this problem, the preservation of greater privacy results in a considerable loss of utility and vice versa. In this research, we tackle the privacy-preserving for sensor-based activity recognition data-sharing issue via balancing between (1) data quality perspective and (2) privacy-preserving perspective. Two novel private generative adversarial approaches, namely PGAN1 and PGAN2, are proposed, whereby we generate filtered data to be shared before revealing the raw data to a data analyst. PGAN1 and PGAN2 differ in the privacy mechanism of raw data filtering. Therefore, PGAN1 relies on a transformation algorithm, while PGAN2 relies on a synthesis algorithm. We theoretically characterize the problem and formulate new objective functions, each one as an analogy of minimax formulation among more than two networks. We evaluate the utility performance by different classifiers while the privacy is checked by overcoming the attacks. Experimental results show that PGAN1 and PGAN2 improve utility and effectively prevent privacy leaks better than previous works.
Multimedia Vision Improvement and Simulation in Consideration of Virtual Reality Reconstruction Algorithms

Due to the large noise and many discrete points of the image in the traditional image reconstruction process, the reconstruction quality of the image deviates greatly from the actual target. In this study, the virtual reality reconstruction algorithm is applied to multimedia vision, the virtual reality image is corrected by using the binocular offset positioning method, the denoising process is performed in the image reconstruction process, and the high-pass filter matrix is used to improve the image reproduction. At the same time, the three-dimensional reconstruction algorithm is used to perform correlation retrieval, the ensemble point set and the discrete point set are obtained, the maximum and minimum reconstruction degree areas are clarified, and the deviation reconstruction and peak relocation can be performed. Finally, the experimental test results show that the algorithm in this study can enhance the authenticity of image reconstruction, improve the accuracy of image corner detection, and effectively reduce the noise interference in the process of reconstructing the image.
Analysis of Home-School Collaboration Mechanism and Psychological Impact Based on MultiSource Big Data Information Fusion

Affected by the global epidemic, teachers and students need to study related courses online, which have become a new inevitable trend for the construction of online courses. Introduce new technologies and apply mature big data to the field of education. Through information fusion processing of multisource data, we can effectively integrate educational resources and improve educational level. The research results of this article show that (1) different identity roles have different needs and problems for home-school collaboration, which needs to be designed from reality. (2) <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mi>P</mi> <mo>&lt;</mo> <mn>0.01</mn> </math> indicates that there are significant differences in parents’ evaluation in different areas; <math xmlns="http://www.w3.org/1998/Math/MathML" id="M2"> <mi>P</mi> <mo>&gt;</mo> <mn>0.05</mn> </math> indicates that parents’ evaluation has nothing to do with gender, and there is no overall difference. (3) Compared with BPNDA and SOFMDA, CNNMDA has the advantages of slow growth of error rate, high efficiency of data fusion, and 7.5% reduction of energy consumption of network nodes, showing excellent performance. (4) There is no abnormality in the function of user portrait after detection, the expected effect is over 80%, and the concurrent times per second are about 441 times. The final result of the experiment is very good. Meet our design and expectations. The follow-up work can also optimize the details.
Parabolic trough field control utilizing all sky imager irradiance data – A comprehensive robustness analysis

Parabolic trough (PT) power plants focus the sun's irradiation onto an absorber tube using collector mirrors to increase the temperature of the heat transfer medium (HTF). In the power plant, the HTF is used to generate electricity. To heat up the HTF, PT solar fields are set up in large areas and pipes are used to distribute the HTF in the solar field. The control system adjusts the temperature by changing the mass flow into the solar field and the focusing of the individual collectors. Transient conditions such as cloud passage are the main challenge for the system. State of the art control schemes use information from a few irradiation measuring points in the solar field. These measurement points are precise for the individual location but cannot accurately represent the irradiation situation in the whole solar field. In this paper, a control system that uses spatially resolved irradiation information from all sky imager systems is investigated. First, the control system was developed and tested in various simulation runs under ideal conditions. The focus of this work is to investigate the robustness of the control concept under non-ideal conditions. For this purpose, the simulation conditions are adapted to real situations and the behavior of the control system is analyzed. A robust behavior of the control system is shown in the simulations under different non-ideal scenarios. Overall, an improvement in electrical yield of 1.4% can be achieved compared to a reference without access to spatially resolved irradiance data.
Estimation of diurnal patterns of global solar radiation, temperature, relative humidity and wind speed from daily datasets at a humid tropical location

The primary focus of this study is to simulate, characterize and validate diurnal patterns of global solar radiation (GSR), temperature, relative humidity and wind speed from daily ground observations from 2017 to 2020 at Obafemi Awolowo University, Ile-Ife, Nigeria. The study also estimates diurnal variations of the meteorological parameters from three daily reanalyses (NASA, NCEP/NCAR and ERA5) and compares the results with hourly station observations (using performance evaluation indices such as mean bias, BIAS; percentage mean bias, PBIAS; root-mean-square-error, RMSE; Nash-Sutcliffe coefficient, NSE; normalised standard deviation, NSD and correlation coefficient, r). Results showed that the empirical models adequately captured the observed diurnal characteristics of the meteorological variables. Good estimates of diurnal patterns of GSR (BIAS = 6.77 Wm−2; RMSE =9.20 Wm−2; PBIAS = 5.68%; NSE =0.52; NSD = 0.62; r = 0.86), temperature (BIAS = -0.79°C; RMSE = 1.90°C; PBIAS = -2.43%; NSE =0.60; NSD =1.09; r = 0.86), and humidity (BIAS = 2.81%; RMSE =6.10%; PBIAS = 2.51%; NSE =0.43; NSD =0.83; r = 0.73) were obtained. Statistics suggested very strong model fits and close agreements with observations. Large and significant discrepancies (at p ≤ 0.05), however, were obtained for diurnal simulations of wind speed (BIAS = 0.28 ms−1; RMSE =0.40 ms−1; PBIAS = 9.79%; NSE = -0.10; NSD =1.36; r = 0.65). Furthermore, the model performances for hourly disaggregation of the parameters varied amongst the reanalyses. The findings provide good basis for generating sub-daily meteorological data resources with wide range of applications in hydrology, climate modelling, and plant growth simulation.
Quantum features of nonlinear coupler with competing nonlinearity

In this work, we examine the quantum features of a multi-waveguide nonlinear coupler exploiting the second-and third-order nonlinearities. The considered system contains four identical channels, each with a single fundamental transverse mode. The essence of this type of nonlinear coupler is to examine the effect of two or more competing nonlinearities on the generated nonclassical features in this class of devices. Here, we consider the case of second harmonic generation, wherein the fundamental harmonic (FH) fields are up-converted in pairs to double-frequency second harmonic (SH) fields, which are then evanescently coupled with the fields from other Kerr nonlinear waveguides. Using the positive P representation of the phase space, the time-evolution of the density matrix could be mapped to the corresponding Fokker-Planck equation of a classical quasiprobability distribution. Using Langevin stochastic equation, an exact representation of the system in phase space led to the demonstration of sub-Poissonian property, squeezing, and entanglement. With more effective squeezing achieved in all channel waveguides, the present system with χ(2)-χ(3) interaction can be a more efficient alternative to other versions of nonlinear couplers such as the quantum optical dimer (QOD) and Kerr nonlinear coupler (KNC). Furthermore, such a structure offers more flexibility in coupled-mode interactions in the form of correlation between the modes in different waveguides. This provides a better mechanism for the generation of enhanced nonclassical effects.
Creativity Triggers: Extension and empirical evaluation of their effectiveness during requirements elicitation

Creativity helps organization to produce novel solutions to complex and sometimes enduring problems. By breaking traditional ways of looking at a given problem and facilitating the design of alternative approaches, creativity contributes to the creation of value-adding solutions within an organization. This is true for any type of problem, including the problem of specifying the requirements for a new software or system. A number of creativity methods, techniques and tools have been proposed as a way to be more creative during Requirements Engineering (RE). They are however often demanding in terms of time, human involvement and resources, thereby reducing their attractiveness for RE practitioners and their stakeholders. Our previous research has led to the proposition of a lightweight tool to support creativity in RE; the Creativity Triggers (CTs). CTs are cards to be used during requirements elicitation to foster creativity from stakeholders and help them uncover novel features of a system-to-be. This paper builds on – and extends – our early conceptualization of CTs to produce a more comprehensive and empirically grounded proposal. Our contribution is twofold; first, we conduct a large-scale and systematic exploration of the qualities underlying the CTs. The objective is to improve the completeness of the tool in order to produce a final set of CTs. Second, we conduct a validation of CTs in different contexts and with different viewpoints to evaluate its usefulness in supporting creativity during requirements elicitation. We end-up with a set of 22 CTs that provided evidence for supporting creativity during RE.
Design of the Smart Schoolhouse Self-assessment Model

The article is a continuation of the articles exploring the concept of Smart Schoolhouse which are based on the activities of the project &#x201C;Smart Schoolhouse with IoT&#x201D; and the data collected during it. We developed a school self-assessment model that addresses the interests of different stakeholders in order to map the readiness of schools to implement the Smart Schoolhouse concept.For this purpose, we used focus group interviews and literature analyses. As the literature analysis complemented the list received from the experts in the focus group interview, we focused on three aspects in selecting articles: (1) to get an overview of the models and frameworks that have been used in educational institutions, in general, in connection with the usage of technology, (2) to get an overview of the models and frameworks used to analyse the whole organisation (school), and (3) to find additional models or frameworks developed during different research which support the pedagogical and technological innovation used in the Smart Schoolhouse concept. Out of 3029 articles a total of 56 different models, 1S frameworks, 1 standard, and 4 matrices were found. Of These, 6 concepts were used as a basis for selecting the areas of the self-assessment model and another 17 for describing the levels of the criteria.Based on the benchmarking of these findings and ideas gained from the focus group interviews, a model consisting of three (pedagogical innovation, technological innovation, and change management) areas with criteria and descriptions was developed. We used the nominal group technique (NGT) to validate the created self-assessment model. After validating the model, we created a web-based environment for schools to conduct self-analysis.
Data-Aware Cache Management for Graph Analytics

Graph analytics is powering a wide variety of applications in the domains of cybersecurity, contact tracing, and social networking. It consists of various algorithms (or workloads) that investigate the relationships between entities involved in transactions, interactions, and organizations. CPU-based graph analytics is inefficient because their cache hierarchy performs poorly owing to highly irregular memory access patterns of graph workloads. Policies managing the cache hierarchy in such systems are ignorant to the locality demands of different data types within graph workloads, and therefore are suboptimal. In this paper, we conduct an in-depth data type aware characterization of graph workloads to better understand the cache utilization of various graph data types. We find that different levels of the cache hierarchy are more sensitive to the locality demands of certain graph data types than others. Hence, we propose GRACE, a graph data-aware cache management technique, to increase cache hierarchy utilization, thereby minimizing off-chip memory traffic and enhancing performance. Our thorough evaluations show that GRACE, when augmented with a vertex reordering algorithm, outperforms a recent cache management scheme by up to 1.4×, with up to 27% reduction in expensive off-chip memory accesses. Thus, our work demonstrates that awareness of different graph data types is critical for effective cache management in graph analytics.
Controlled Continuous Deployment: A Case Study From The Telecommunications Domain

Continuous deployment has become a widely used practice in web-based software applications. Deploying a new software version to production is a seamless automated process executed thousands of times per day. Continuous deployment reduces the time between a code commit and that commit is active in production. While continuous deployment promises many advantages to software development organizations, the adoption of continuous deployment in the software-intensive embedded systems industry is limited. Several empirical studies have highlighted the challenges associated with software-intensive embedded systems. However, very few studies, if any at all, have attempted to provide a practical approach to realize continuous deployment to these systems. This paper proposes a Controlled Continuous Deployment (CCD) approach, which considers the constraints software-intensive embedded systems have, such as high reliability and availability requirements, limited possibility for rollback after deployment, and the high volume of in-service systems in the market. We derived the approach by conducting a case study at Ericsson AB, focusing on three Radio Access Networks (RAN) technologies embedded software used in 3G, 4G, and 5G mobile networks.
Novel Hidden Bit Location Method towards JPEG Steganography

Image steganalysis has been widely studied, most of which can only complete the binary task of identifying the existence of hidden bits in an inquiry image. Currently, although some algorithms have been proposed to locate hidden bits, most of them mainly focus on the spatial domain, while usually ignoring the study of locating secret bits hidden in DCT domain for JPEG image. To address that challengeable problem, in this paper, towards two classical steganographic algorithms JSteg and F5, hiding bits in DCT domain, we propose a novel payload location method. The principal step of payload location is to estimate the cover image. We novelly propose to assign the different weights to the DCT coefficient residual in virtue of the texture of regions measured by the local variance, leading to the remarkable improvement of location results. Compared with the state of the art, the numerical experiments empirically verify that our proposed location method achieves the superior performance. In particular, when locating hidden bits in JSteg steganographic images with quality factor of 95 at the payload 0.1, the accuracy of location is remarkably improved from 46.18% to 90.22%.
Characterization of hv-Convex Sequences

Abstract Reconstructing a discrete object by means of X-rays along a finite set U of (discrete) directions represents one of the main task in discrete tomography. Indeed, it is an ill-posed inverse problem, since different structures exist having the same projections along all lines whose directions range in U . Characteristic of ambiguous reconstructions are special configurations, called switching components, whose understanding represents a main issue in discrete tomography, and an independent interesting geometric problem as well. The investigation of switching component usually bases on some kind of prior knowledge that is incorporated in the tomographic problem. In this paper, we focus on switching components under the constraint of convexity along the horizontal and the vertical directions imposed to the unknown object. Moving from their geometric characterization in windows and curls, we provide a numerical description, by encoding them as special sequences of integers. A detailed study of these sequences leads to the complete understanding of their combinatorial structure, and to a polynomial-time algorithm that explicitly reconstructs any of them from a pair of integers arbitrarily given.
Location-aware neural graph collaborative filtering

Collaborative filtering (CF) is initiated by representing users and items as vectors and seeks to describe the relationship between users and items at a profound level, thus predicting users’ preferred behavior. To address the issue that previous research ignored higher-order geographical interactions hidden in users’ historical behaviors, this paper proposes a location-aware neural graph collaborative filtering model (LA-NGCF), which incorporates location information of items for improving prediction performance. The model characterizes the interactions between items based on spatial decay law from a graph perspective and designs two strategies to capture the interaction effects of users and items considering node heterogeneity. An optimized loss function with spatial distances of items is also developed in the model. Extensive experiments are conducted on three publicly available real-world datasets to examine the effectiveness of our model. Results show that LA-NGCF achieves competitive performances compared with several state-of-the-art models, which suggests that location information of items is beneficial for improving the performance of personalized recommendations. This paper offers an approach to incorporate weighted interactions between items into CF algorithms and enriches the methods of utilizing geographical information for artificial intelligence applications.
Multimodal Feature Fusion Based Hypergraph Learning Model

Hypergraph learning is a new research hotspot in the machine learning field. The performance of the hypergraph learning model depends on the quality of the hypergraph structure built by different feature extraction methods as well as its incidence matrix. However, the existing models are all hypergraph structures built based on one feature extraction method, with limited feature extraction and abstract expression ability. This paper proposed a multimodal feature fusion method, which firstly built a single modal hypergraph structure based on different feature extraction methods, and then extended the hypergraph incidence matrix and weight matrix of different modals. The extended matrices fuse the multimodal abstract feature and an expanded Markov random walk range during model learning, with stronger feature expression ability. However, the extended multimodal incidence matrix has a high scale and high computational cost. Therefore, the Laplacian matrix fusion method was proposed, which performed Laplacian matrix transformation on the incidence matrix and weight matrix of every model, respectively, and then conducted a weighted superposition on these Laplacian matrices for subsequent model training. The tests on four different types of datasets indicate that the hypergraph learning model obtained after multimodal feature fusion has a better classification performance than the single modal model. After Laplace matrix fusion, the average time can be reduced by about 40% compared with the extended incidence matrix, the classification performance can be further improved, and the index F1 can be improved by 8.4%.
Will the Central African Republic's bitcoin experiment flop?

The second country to make the cryptocurrency legal tender may end up benefiting criminals and warlords most, reports Luke Taylor