Reviewerly/exHarmony huggingface dataset:
A dynamic trust consensus model based on personalized adjustment mechanism under multi-granular probabilistic linguistic environment

Purpose This study aims to propose a consensus model that considers dynamic trust and the hesitation degree of the expert's evaluation, and the model can provide personalized adjustment advice to inconsistent experts. Design/methodology/approach The trust degree between experts will be affected by the decision-making environment or the behavior of other experts. Therefore, based on the psychological “similarity-attraction paradigm”, an adjustment method for the trust degree between experts is proposed. In addition, we proposed a method to measure the hesitation degree of the expert's evaluation under the multi-granular probabilistic linguistic environment. Based on the hesitation degree of evaluation and trust degree, a method for determining the importance degree of experts is proposed. In the feedback mechanism, we presented a personalized adjustment mechanism that can provide the personalized adjustment advice for inconsistent experts. The personalized adjustment advice is accepted readily by inconsistent experts and ensures that the collective consensus degree will increase after the adjustment. Findings The results show that the consensus model in this paper can solve the social network group decision-making problem, in which the trust degree among experts is dynamic changing. An illustrative example demonstrates the feasibility of the proposed model in this paper. Simulation experiments have confirmed the effectiveness of the model in promoting consensus. Originality/value The authors presented a novel dynamic trust consensus model based on the expert's hesitation degree and a personalized adjustment mechanism under the multi-granular probabilistic linguistic environment. The model can solve a variety of social network group decision-making problems.
SciSpot: Scientific Computing On Temporally Constrained Cloud Preemptible VMs

Scientific computing applications are being increasingly deployed on cloud computing platforms. Transient servers such as EC2 spot instances and Google Preemptible VMs, can be used to lower the costs of running applications on the cloud by up to <inline-formula><tex-math notation="LaTeX">$10\times$</tex-math></inline-formula> . However, the frequent preemptions and resource heterogeneity of these transient servers introduces many challenges in their effective and efficient use. In this paper, we develop techniques for modeling and mitigating preemptions of transient servers, and present SciSpot, a software framework that enables low-cost scientific computing on the cloud. SciSpot deploys applications on Google Cloud Preemptible Virtual Machines that exhibit temporally constrained preemptions: VMs are always preempted in a 24 hour interval. Our empirical analysis shows that the preemption rate is generally bathtub shaped, which raises multiple fundamental challenges in performance modeling and policy design. We develop a new reliability model for temporally constrained preemptions, and use statistical mechanics to show why the bathtub shape is generally exhibited. SciSpot’s design is guided by our observation that many emerging scientific computing applications that integrate machine learning with simulations, can be deployed as “bags” of jobs, which represent multiple instantiations of the same computation with different physical model parameters. For a bag of jobs, SciSpot finds the optimal transient server on-the-fly, by taking into account the price, performance, and preemption rates of different servers. SciSpot reduces costs by <inline-formula><tex-math notation="LaTeX">$5\times$</tex-math></inline-formula> compared to conventional cloud deployments, and reduces makespans by up to <inline-formula><tex-math notation="LaTeX">$10\times$</tex-math></inline-formula> compared to conventional high performance computing clusters.
Machine Learning Assisted Prediction for Free-Space Continuous Variable Quantum Teleportation

Satellite-based quantum communication is critical in establishing a global quantum network for its ability to achieve thousand-kilometer-level tasks without the requirement of quantum relay.The free-space channel, however, is affected by diffraction, regular refraction, and atmospheric turbulence, which induce random wandering and broadening of the transmitted beam, resulting in the transmittance of fluctuation.In such a scenario, the ability to predict the unknown situation has become a problem worth considering.In this paper, we suggest a machine learning (ML) assisted prediction of continuous-variable quantum teleportation (CVQT) over satellite-ground channel.We apply the prediction algorithm, which involves k-nearest neighbor (KNN) algorithm and decision tree (DT) algorithm, to the squeezing parameter and the satellite altitude.Numerical analysis shows that the predicted parameter can reflect the actual derivation and calculation under certain circumstances within the allowable range of error.Moreover, we apply the prediction algorithm to the case affected by the fluctuating parameter transmittance.The results show that the ML-assisted prediction results are consistent with the actual data when zenith angles, turbulence strengths, and wavelengths are taken into account.It demonstrates the high identifying accuracy without increasing the complexity of the system.
Determinants of Informal Competition Among Firms in Ethiopia

The study investigated intensity of informal competition among firms in Ethiopia, using primary data source from the 2015 World Bank Enterprise Surveys for Ethiopia (Ethiopia -Enterprise Survey 2015).The estimated linear probability model (LPM) revealed that the probability prevalence (intensity) of informal competition among firms in Ethiopia is about 38.5%.It indicated that informality is a key problem in Ethiopia.The study also found that prevalence of highest corruption, burden of tax rate and credit access constraint are found to be positively and significantly affecting informal competition of firms in Ethiopia.On the contrary, firm size are found to be negatively and significantly affecting informal competition of firms.Correspondingly, the study revealed that regular inspection and communication of tax officials with firms could not contribute in reducing intensity of informality even if most studies indicated that increase in government enforcement on tax code lead to reduce informality.As many studies indicated that experience of top managers are contributing a lot for reducing informality in an establishment; however, this study exhibited that it does not save firms from informality in Ethiopia.In a nut shell, more research should be conducted as to why tax inspections and experience of top manager could not save firms from informality in Ethiopia.
Analisis Akurasi Prediksi Perubahan Aktivitas Pada Sistem Monitoring Aktivitas Jarak Jauh Pasien Isolasi Mandiri Berbasis IOT

Patients who contract the disease should avoid contact with other people. One way to do this is to self-isolate at home. The family of the patient who cares for the activities that are carried out in self-isolation to find out the condition of the patient's condition, his condition is improving or deteriorating. To avoid direct contact, the patient's activity, independently, can be monitored by remotely predicting changes in patient activity using an Internet of Things-based remote monitoring system for self-isolating patient activities. This cellular-based monitoring system uses an accelerometer sensor to retrieve data on changes in patient activity and analyzes the effect of several variations in the number of data samples and sliding-windows on the accuracy of the system in predicting changes in patient activity. Variations in the number of N samples tested were 4,6,8,10,20,30,40,50,60,70,80,90 and 100 samples, while the sliding-window N variation tested was 1 ,2,3,4,5,6,7,8,9 and 10 samples where there is a change in activity every 30 seconds for 330 seconds (10 changes in activity) for each number of N samples and N sliding windows. The results shown are N sample data = 6 providing the highest activity change prediction accuracy, amounting to 90.15%, while N sliding window data = 6 providing the highest activity change prediction accuracy, amounting to 92.72%.
Special Issue on Deep Learning-Based Action Recognition

Human action recognition (HAR) has gained popularity because of its various applications, such as human–object interaction [...]
Geomorphology and Geology

This chapter introduces the geomorphic and geologic settings of the epicentral area of the 2016 Kumamoto earthquake sequence. The earthquake ruptured a part of the southern boundary faults of the Beppu-Shimabara graben zone, a tectonic depression characterized by active volcanism and normal faulting in central Kyushu. Main geomorphic features and geologic units in and around the Kumamoto Plain and Aso Volcano are described.
Advancing research on the Human Factor in Cybercrime

This work introduces the special edited issue on the human factor in cybercrime research.
On cryptographic properties of cubic and splitting Boolean functions

Abstract The weight, balancedness and nonlinearity are important properties of Boolean functions, but they can be difficult to determine in general. In this paper, we study how to compute them for two classes of functions where these problems are more tractable. In particular, we study functions of degree three and the so-called “splitting” functions. The latter are functions that can be written as the sum of two functions defined over disjoint sets of variables. We show how, for splitting functions, studying these properties reduces to the study of simpler functions. We provide then a procedure to compute the weight of a cubic Boolean function. We show computationally that, for a cubic Boolean function with limited number of terms, this procedure is on average significantly more efficient than some other methods.
Legal and technical questions of file system reverse engineering

Reverse engineering of file systems is indispensable for tool testing, accurate evidence acquisition, and correct interpretation of data structures by law enforcement in criminal investigations. This position paper examines emerging techno-legal challenges from the practice of reverse engineering for law enforcement purposes. We demonstrate that this new context creates uncertainties about the legality of tools and methods used for evidence acquisition and the compliance of law enforcement with obligations to protect intellectual property and confidential information. Further identified are gaps between legal provisions and practice related to disclosure and peer-review of sensitive digital forensic methodology, trade secrets in investigations, and governmental vulnerability disclosure. It is demonstrated that reverse engineering of file systems is insufficiently addressed by legislators, which results in a lack of file system interpretation and validation information for law enforcement and their dependence on tools. Outlined are recommendations for further developments of digital forensic regulation.
Syntactic conditions on cumulative readings of German jeder ‘every’ DPs

Abstract English every -DPs can have cumulative readings relative to plural DPs, but only under severe syntactic constraints. This paper discusses different potential formulations of these constraints for the German correlate of every -DPs, jed -DPs. We argue that existing ‘surface-oriented’ hypotheses face empirical problems and propose a new hypothesis, namely that the availability of a cumulative reading relative to another plural X is sensitive to all positions in the derivational chain of X. We then spell out one possible semantic analysis of this pattern and sketch the consequences of this hypothesis for future empirical work.
Gerber File Parsing for Conversion to Bitmap Image–The VINCI7D Case Study

The technological market is increasingly evolving as evidenced by the innovative and streamlined manufacturing processes. Printed Circuit Boards (PCB) are widely employed in the electronics fabrication industry, resorting to the Gerber open standard format to transfer the manufacturing data. The Gerber format describes not only metadata related to the manufacturing process but also the PCB image. To be able to map the electronic circuit pattern to be printed, a parser to convert Gerber files into a bitmap image is required. The current literature as well as available Gerber viewers and libraries showed limitations mainly in the Gerber format support, focusing only on a subset of commands. In this work, the development of a recursive descent approach for parsing Gerber files is described, outlining its interpretation and the renderization of 2D bitmap images. All the defined commands in the specification based on Gerber X2 generation were successfully rendered, unlike the tested commercial parsers used in the experiments. Moreover, the obtained results were comparable to those parsers regarding the commands they can execute as well as the ground-truth, emphasizing the accuracy of the proposed approach. Its top-down and recursive architecture allows easy integration with other software regardless of the platform, highlighting its potential inclusion and integration in the production of electronic circuits.
RL_QOptimizer: A Reinforcement Learning Based Query Optimizer

With the current availability of massive datasets and scalability requirements, different systems are required to provide their users with the best performance possible in terms of speed. On the physical level, performance can be translated into queries&#x2019; execution time in database management systems. Queries have to execute efficiently (i.e. in minimum time) to meet users&#x2019; needs, which puts an excessive burden on the database management system (DBMS). In this paper, we mainly focus on enhancing the query optimizer, which is one of the main components in DBMS that is responsible for choosing the optimal query execution plan and consequently determines the query execution time. Inspired by recent research in reinforcement learning in different domains, this paper proposes A Deep Reinforcement Learning Based Query Optimizer (RL&#x005F;QOptimizer), a new approach to find the best policy for join order in the query plan which depends solely on the reward system of reinforcement learning. The experimental results show that a notable advantage of the proposed approach against the existing query optimization model of PostgreSQL DBMS.
An efficient enhanced prefix hash tree model for optimizing the storage and image deduplication in cloud

Summary The popularity of the cloud storage space mainly attracted organizations to store their data in them. Therefore, the avoidance of duplicate data contents is unavoidable and several users share the cloud storage space for data storage, and sometimes this makes higher storage space utilization. Because of the extremely high duplicate copy, memory wastage arises in the case of multimedia data. Identifying the final duplicate copies in the cloud takes more time. To overcome this problem, we employ a significant storage optimization model for deduplication. The digital data hash value is stored by requiring an additional memory space. This study proposed an enhanced prefix hash tree (EPHT) method to optimize the image and text deduplication system to reduce the overhead caused by this procedure. The efficiency of the proposed approach is compared with the interpolation search technique using different levels of tree height (2, 4, 2, 8, 16) in terms of space and time complexity. The proposed EPHT technique shows improvements in terms of speed and space complexity when the number of levels in the EPHT increases.
Shape Prior Based Myocardial Segmentation with Anatomically Motivated Pose Model

We extend the shape-prior based geometric approach developed for myocardial segmentation in cardiac CT imagery by incorporating minimal user input in the form of anatomical constraints to guide the segmentation process resulting in significantly improved results. The shape-prior based geometric approach involves estimating coefficients of a low-dimensional principal component analysis based shape representation along with a set of rigid 3D pose parameters of three separate surfaces corresponding to left (LV)/right (RV) ventricles and Epicardium (Epi) by optimizing a novel Chan-Vese like image appearance model with gradient descent. We enhance this framework by allowing experienced clinical users to identify the centers of the three anatomies in apical slices and a common anatomical cardiac base slice. We integrate this minimal user input as anatomical constraints in the segmentation process by replacing the rigid 3D pose model with a novel blended model which incorporates rigidity only within 2D slices while incorporating non-rigid effects of shear and torsion along the third (axial) dimension. With this new formulation we achieved significantly improved segmentation results in terms of average symmetric surface-to-surface distances (mm): LV 1.05 ± 0.27; RV 1.7 ± 0.40; Epi 1.22 ± 0.56 compared to LV 2.3 ± 0.50; RV 1.13 ± 0.21; Epi 3.3 ± 0.50 with rigid 3D pose model.
Bringing Attention to Image Anomaly Detection

Detecting anomalies in images is a task with several relevant real-world applications, e.g. industrial inspection. Building on the existing RIAD (Reconstruction by Inpainting for visual Anomaly Detection) framework, we introduce an attention-based component to improve the model performance. Furthermore we propose a different approach to image masking which leverages the selection of multiple random patches at a single scale in the original images. Through the provided experimental results we show how the novelties introduced by this work consistently improve the performance of the baseline approach over the various classes of the heterogeneous MVTec benchmark dataset across all the metrics considered.
Automatic Indexing of Virtual Camera Features from Japanese Anime

The position, orientation, and the distance of the camera in relation to the subject(s) in a movie scene, namely camera level, camera angle, and shot scale, are essential features in the film-making process due to their influence on the viewer’s perception of the scene. Since animation techniques exploit drawings or computer graphics objects for making films instead of camera shooting, the automatic understanding of such “virtual camera” features appears harder if compared to live-action movies. Exploiting a dataset of animated movies from popular directors such as Hayao Miyazaki, Hideaki Anno and Mamoru Oshii, we finetune pre-trained convolutional neural networks and use One Cycle Learning Rate to reach convergence in a limited number of epochs. While some difficulties are revealed and discussed in classifying complex features, like camera angle and camera level, showing F1 scores of respectively 61% and 68%, the classification of the shot scale reaches a score of about 80%, which is comparable with state-of-the-art methods applied on live-action movies. The developed models will be useful in conducting automated movie annotation for a wide range of applications, such as in stylistic analysis, video recommendation, and studies in media psychology. The database and a demo of the classifiers are available at https://cinescale.github.io/anime
Fine-grained regression for image aesthetic scoring

There are many tasks on image aesthetic assessment, such as aesthetic classification, scoring, score distribution prediction, and captions. Due to the distribution of the aesthetic score is unbalanced, the assessment models always output scores near the mean score. In this paper, we propose a fine-grained regression method for aesthetics score regression and combine position and channel attention mechanisms to enhance the aesthetic feature fusion. And by training the regression network separately from the classification network, we make the classification task a complement to the regression task. Besides, the researchers are used to using Mean Square Error (MSE) as the main evaluation metric which is inadequate in measuring the error of each interval. In order to fully consider the images of the various aesthetic score segments, instead of focusing on the intermediate aesthetic score segments because of the imbalance of the aesthetic datasets, we propose a new evaluation metric called Segmented Mean Square Errors (SMSE) to prove the advantages of the model. We divide the entire AADB dataset into 10 equal parts based on the aesthetic scores and the experiments were carried out on each of the segmented AADB datasets. In this way, images for each aesthetic score segment are fairly considered. The experimental results reveal that our method outperforms all the state-of-the-art methods on both MSE and SMSE. The dual attention modules of position and channel also make the activation maps more reasonable. Our methods make the aesthetic scoring go beyond laboratories to real life applications. Because computational visual aesthetics is a very interesting and challenging task in the field of computer vision, and computer vision is also one of the key areas of focus of this journal, the method proposed in this paper is closely related to the field covered by the journal.
TACKLING THE CHALLENGES OF SOFTWARE PROVISION

Abstract. In this perspective, we investigate the less documented topic of software provision, aimed at bridging the community of software developers to the one of software end-users. We outline aspects of the circular flow of software development, starting from the source code, software packaging, the target platform, licensing, program documentation, and feedback. Next, we highlight challenges and opportunities of these aspects and how they contribute to the overall success and adoption of a software application. Finally, we exemplify and illustrate how these aspects were addressed with the provision of the software GWB on FAO’s cloud computing platform SEPAL. The outlined reflections on software provision are of generic nature and, depending on a given software, may include many more, or different aspects. Yet, we hope that this perspective may trigger more interest and dedication to the topic of software provision and its integral function to promote and improve software development.
Marginal samples for knowledge distillation

Previous work like Category Structure Knowledge Distillation proposes to construct category-wise relations for knowledge distillation by introducing intra-category and inter-category relations based on category centers. However, category centers may be unreliable when feature representations of wrongly classified samples are used to form category centers. Besides, inter-category relations based on category centers are coarse-grained. In this paper, we propose a Marginal Sample Knowledge Distillation (MSKD) to construct reliable category centers and fine-grained inter-category relations by introducing label filtering and marginal samples. Label filtering removes feature representations of wrongly classified samples from the calculation of category centers to create unbiased and reliable category centers. Marginal samples are defined as the correctly classified samples close to category boundaries. Marginal samples contain information of other categories and form fine-grained category boundaries for knowledge distillation. Extensive experiments on different datasets and teacher-student architecture settings show that our method has excellent performances when compared with closely related methods.