Reviewerly/exHarmony huggingface dataset:
TEEKAP: Self-Expiring Data Capsule using Trusted Execution Environment

Safeguarding privacy in data sharing is challenging, especially when data owners lose control over their data once it is passed to another party. Our work aims to build a data-sharing platform that enables data owners to regain control over their shared data. Specifically, sensitive data is first encapsulated into a data capsule. The platform regulates functional access to the data capsule, i.e., the receiver can compute a predefined function on the data with its input and learns nothing else. The platform also enforces self-expiry of the data capsule. In addition, the data capsule features a notion of “send-and-forget” wherein data owners can go offline after releasing their data capsules. As a result, data capsules can be freely circulated.
<i>S</i> <sup>3</sup> DMT-Net

We deal with the problem of multi-task learning (MTL) in the context of performing multiple related visual dense prediction tasks from single image inputs. The soft-sharing-based deep MTL Convnets (CNN) have separate networks for each of the tasks with additional constraints on the model parameters. Although such MTL models have shown convincing performances for tasks including semantic segmentation, depth estimation, and surface normal estimation from monocular images, they have two inherent bottlenecks: i) the constraints imposed on such models do not in general leverage the inter-task information which otherwise can boost the joint training, and ii) the performances of the individual task-specific networks are not explicitly optimized. We hypothesize that the MTL performance can be enhanced comprehensively if the aforesaid issues are taken into account in soft-sharing-based MTL models. To this end, we introduce a novel MTL architecture in this paper called S3 DMT-Net, where i) the task-specific networks are trained under the notion of self-distillation, which aims at inheriting the features from the deeper layers into the shallower layers, thus enriching the capacity of the network, and ii) the idea of cross-task interactions are utilized where the feature-maps of the task-specific encoders are shared amongst each other. We validate the model performance on two scenes: indoor (NYUv2 and Mini Taskonomy) and urban (CityScapes and ISPRS), where substantial improvements are recorded for all the tasks over the existing literature.
Ensemble Forecasting Frame Based on Deep Learning and Multi-Objective Optimization for Planning Solar Energy Management: A Case Study

There are many prediction models that have been adopted to predict uncertain and non-linear photovoltaic power time series. Nonetheless, most models neglected the validity of data preprocessing and ensemble learning strategies, which leads to low forecasting precision and low stability of photovoltaic power. To effectively enhance photovoltaic power forecasting accuracy and stability, an ensemble forecasting frame based on the data pretreatment technology, multi-objective optimization algorithm, statistical method, and deep learning methods is developed. The proposed forecasting frame successfully integrates the advantages of multiple algorithms and validly depict the linear and nonlinear characteristic of photovoltaic power time series, which is conductive to achieving accurate and stable photovoltaic power forecasting results. Three datasets of 15-min photovoltaic power output data obtained from different time periods in Belgium were employed to verify the validity of the proposed system. The simulation results prove that the proposed forecasting frame positively surpasses all comparative hybrid models, ensemble models, and classical models in terms of prediction accuracy and stabilization. For one-, two-, and three-step predictions, the MAPE values obtained from the proposed frame were less than 2, 3, and 5%, respectively. Discussion results also verify that the proposed forecasting frame is obviously different from other comparative models, and is more stable and high-efficiency. Thus, the proposed frame is highly serviceable in elevating photovoltaic power forecasting performance and can be used as an efficient instrument for intelligent grid programming.
Fundamentals of a Novel Debugging Mechanism for Orchestrated Cloud Infrastructures with Macrosteps and Active Control

In Infrastructure-as-a-Service (IaaS) clouds, the development process of a ready-to-use and reliable infrastructure might be a complex task due to the interconnected and dependent services that are deployed (and operated later on) in a concurrent way on virtual machines. Different timing conditions may change the overall initialisation method, which can lead to abnormal behaviour or failure in the non-deterministic environment. The overall motivation of our research is to improve the reliability of cloud-based infrastructures with minimal user interactions and significantly accelerate the time-consuming debugging process. This paper focuses on the behaviour of cloud-based infrastructures during their deployment phase and introduces the adaption of a replay, and active control enriched debugging technique, called macrostep, in the field of cloud orchestration in order to provide support for developers troubleshooting deployment-related errors. The fundamental macrostep mechanisms, including the generation of collective breakpoint sets as well as the traversal method for such consistent global states, have been combined with the Occopus cloud orchestrator and the Neo4J graph database. The paper describes the novel approach, the design choices as well as the implementation of the experimental debugger tool with a use case for validation purposes by providing some preliminary numerical results.
Mobile Game Development with Spatially Generated Reverberation Sound

In this paper, the authors describe methods of generating spatial reverberation sound for echolocation teaching game. Available audio tools and plugins generate directional first reflections and flat reverberations due to computational complexity limitation. Waypoint Acoustic Simulation (WAS) and Room Acoustic Simulation (RAS) methods are presented as solutions for preparing pre-rendered acoustic maps for game scenarios defined in virtual environments. Dedicated plugins for the Unity Engine were prepared to process the gathered acoustic data into spatialized reverberation sound. The quality of the synthesized sounds was reviewed by a group of volunteers including blind persons with high echolocation skills.
Comparative Features of MIA GMDH and Deep Feed-Forward Neural Networks

Introduction.Deep neural networks are effective tools for solving actual tasks such as data mining, modeling, forecasting, pattern recognition, clustering, classification etc.They differ with respect to the architecture design, learning methods and so on.Most simple and widely used are deep feed-forward supervised NNs. The purpose of the paper is to compare briefly main features of the deep feed-forward deterministic supervised networks with the Multilayered Iterative Algorithm of GMDH (MIA GMDH) and to formulate main ideas of constructing a new class of hybrid deep networks based on the MIA neural network.Methods.Most usable deep feed-forward supervised neural networks have been studied: multilayered perceptron, convolutional NN and some its modifications, polynomial neural networks, genetic polynomial neural network etc.Results.There was carried out a comparative analysis of main features of the MIA GMDH neural network with the characteristics of other deep deterministic supervised neural networks.The most promising approaches are identified to improve the performance of this network, particularly by hybridization with methods of computational intelligence.The main idea of building a new class of hybrid deep networks based on MIA GMDH is formulated.
Introduction to Apache Pulsar

Traditionally, most enterprise applications were developed on monolith architectures. These applications were quick to create at the early stages, but the maintenance and operational teams often encountered many challenges when working on them. Over the last decade, distributed application architecture has become the primary enterprise development strategy. It is well supported by enterprise cloud infrastructure adaption and container adaption plans. As a result, we no longer build simple applications; instead, we typically develop end-to-end platforms. These platforms consist of numerous applications that communicate using lightweight communication protocols, like REST APIs or remote procedure calls (RPC). These platforms cut across organizational boundaries and often require complex tools and architectures to deliver their intended benefits.
Detection for Transient Patterns with Unpredictable Duration using Chebyshev Inequality and Dynamic Binning

This paper proposes an online algorithm for detecting transient patterns with an unpredictable duration from an astronomical data stream. The key idea appears in the binning technique which is often useful for noise filtering. It is used to split the original data stream into bins with a fixed length. A suitable length for binning depends on the transient patterns to be detected, although their prior information is not available in advance. The proposed method addresses this problem by introducing a dynamic mechanism which enables to adjust the bins with an adequate length. Consequently, it prevents from missing transient patterns with arbitrary duration Our extensive experiments using real astronomy dataset reveals that the proposed method outperforms the existing methods with high accuracy.
Stochastic configuration networks with fast implementations

Stochastic configuration networks (SCNs) employ a supervisory mechanism to assign hidden-node parameters in the incremental construction process. SCNs offer the advantages of practical implementation, fast convergence, and better generalization performance. However, due to its high computational cost and the scalability of numerical algorithms for the least square technique, it is rather limited for dealing with enormous amounts of data. This paper proposes fast SCNs (F-SCNs), whose output weights are determined using orthogonal matrix Q and upper triangular matrix R decomposition. The network can iteratively update the output weights utilizing the output information from the predecessor node using this incremental technique. We investigated the computational complexity of SCNs and F-SCNs and demonstrated that F-SCNs are suitable for scenarios in which the hidden layer has a significant number of nodes. We evaluated the proposed method on four real-world regression datasets; experimental results show that our method has notable advantages in terms of speed and effectiveness of learning.
FST-Net: Exploiting Frequency Spatial Temporal Information for Low-Quality Fake Video Detection

Recently, state-of-the-art face manipulation algorithms have made significant progresses to forge images and videos that are able to deceive human eyes or even detection algorithms, which brings new challenges to forgery detection. In particular, the performance of detection algorithms for forgery videos is not as perfect as that for forgery images; furthermore, the difficulty of detection increases dramatically for low-quality videos. To address this challenge, we propose a novel dual stream architecture, referred to as FST-Net, for jointly mining forged features in the frequency, spatial and temporal domains. Specifically, we extract the spectral information of different frequency bands to expose intra-frame artifacts, and use the separable 3D CNN (S3D) to extract the spatio-temporal features among video frame groups. Moreover, to make the model focus on the tampered area, we add an attention layer to both backbone networks. Comprehensive experiments show that our model outperforms existing methods in video detection on challenging FaceForensics++ datasets, especially on low-quality video datasets.
Why can it be so hard to solve Bayesian problems? Moving from number comprehension to relational reasoning demands

Over the last decades, understanding the sources of the difficulty of Bayesian problem solving has been an important research goal, with the effects of numerical format and individual numeracy being widely studied. However, the focus on the comprehension of probability numbers has overshadowed the relational reasoning demand of the Bayesian task. This is particularly the case when the statistical data are verbally described since the requested quantitative relation (posterior ratio) is misaligned with the presented ones (prior and likelihood ratios). In this regard, here I develop the proposal that research on Bayesian reasoning might improve by considering the notational alignment framework of mathematical problem-solving. Specifically, this framework can help to understand the sources of the main difficulties underlying Bayesian inferences based on verbal descriptions. In essence, the present proposal supports the general claim in math education regarding the need to foster relational comprehension to avoid misleading alignments and improve problem solving.
Experimental performance of deep learning channel estimation for an X-ray communication-based OFDM-PWM system

A deep learning channel estimation scheme in orthogonal frequency division multiplexing for X-ray communication (XCOM) is studied. The scheme uses simulated and detected data obtained with different working parameters and numbers of pilots as training and testing data, respectively, for the deep neural network (DNN) model. The bit-error-rate performance of the DNN model under various system operating parameters, numbers of pilot sequences, and channel obstructions is investigated separately. Experiment results showed that the deep-learning-based approach can address the distortion of the air-scintillator channel for XCOM, giving a performance comparable to those of least-squares and minimum-mean-square error estimation methods.
Evaluation of Height and Speed Effects on the Comfort of VR Motion Picture Display

Virtual Reality (VR) technology has become ever more popular with consumers in recent years. However, dizziness brought by VR greatly affects its experience, especially VR motion sickness. Therefore, the industry needs in order to study how to improve the comfort of VR motion pictures. The subject conducts subjective evaluation experiments through the constructed virtual 3D scene. The relationship between the height and speed of the VR camera, as well as the gender of the audience and VR motion sickness, is considered by subjective evaluation experiments. Viewer’s VR motion sickness is the least obvious when the viewer is close to the height and average pace, and women are more likely to be dizzy to VR than men, which are proved by experimental results.
Express Diagnosis of COVID-19 on Cough Audiograms with Machine Learning Algorithms from Scikit-learn library and GMDH Shell tool

The ongoing COVID-19 pandemic and necessity of mass control of population makes to create inexpensive rapid diagnostic methods that could replace or complement existing methods based on clinical studies. In response to this challenge, at the end of 2020 MIT scientists proposed a way to detect COVID-19 sick patients using audio recordings of their cough. They build a binary classifier based on a trained deep neural network that provides 99&#x0025; precision in detecting sick patients on a dataset of 5000 people (the precision of detecting the healthy ones is not reported). In our study, we propose another technology, which uses: (a) a simple transformation of digital audiograms being matrices &#x2018;fequency-time&#x0027;and (b) typical machine learning algorithms from the popular scikit-learn Python library and the platform GMDH Shell. Objects of consideration are: a large unbalanced dataset (282 sick and 1595 healthy) and a small balanced dataset (174 sick and 193 healthy). In total GMDH-based algorithms demonstrated some better results with both datasets. The winners provides the following precisions of detecting sick/healthy patients [&#x0025;]: (a) 92/95 on the small dataset and 78/95 on the large data set for the algorithm SVM with a Gaussian kernel; (b) 95/97 on the small dataset and 82/96 on the large data set for the algorithm Random Forest based on GMDH. We suppose these results are promising.
Automated Generation of Illustrations for Synthetic Geometry Proofs

We report on a new, simple, modular, and flexible approach for automated generation of illustrations for (readable) synthetic geometry proofs.The underlying proofs are generated using the Larus automated prover for coherent logic, and corresponding illustrations are generated in the GCLC language.Animated illustrations are also supported.
Research on the Region-Growing and Segmentation Technology of Micro-Particle Microscopic Images Based on Color Features

Silkworm microparticle disease is a legal quarantine standard in the detection of silkworm disease all over the world. The current common detection method, the Pasteur manual microscopy method, has a low detection efficiency all over the world. The low efficiency of the current Pasteur manual microscopy detection method makes the application of machine vision technology to detect microparticle spores an important technology to advance silkworm disease research. For the problems of the low contrast, different illumination conditions and complex image background of microscopic images of the ellipsoidal symmetrical shape of silkworm microparticle spores collected in the detection solution, a region growth segmentation method based on microparticle color and grayscale information is proposed. In this method, the fuzzy contrast enhancement algorithm is used to enhance the color information of micro-particles and improve the discrimination between the micro-particles and background. In the HSV color space with stable color, the color information of micro-particles is extracted as seed points to eliminate the influence of light and reduce the interference of impurities to locate the distribution area of micro-particles accurately. Combined with the neighborhood gamma transformation, the highlight feature of the micro-particle target in the grayscale image is enhanced for region growing. Mea6nwhile, the accurate and complete micro-particle target is segmented from the complex background, which reduces the background impurity segmentation caused by a single feature in the complex background. In order to evaluate the segmentation performance, we calculate the IOU of the microparticle sample image segmented by this method with its corresponding true value image, and the experiments show that the combination of color and grayscale features using the region growth technique can accurately and completely segment the microparticle target in complex backgrounds with a segmentation accuracy IOU as high as 83.1%.
A Siamese Network for Face Verification with Depth Images

Face verification is one of the most researched and demanding tasks in the field of computer vision. The task of Face verification is to check whether two input faces are the same object. With the development of depth cameras and their reliability against light changes, face verification has become more widely used in many scenarios, for example night driving. This paper constructs a deep Siamese architecture for face verification based on two same fully convolutional networks, relying on depth images. Despite the lack of deep-oriented depth-based datasets, the network only relies on the small amount of depth data available on Pandora dataset for training and still achieves state-of-art results and real time performance, and the network also get excellent results during the variation of head pose.
International Educational Project: ELearning Results Assessment (Case Study)

The global pandemic has made it possible to test in real-time the approaches and ideas that have been developed over the past ten years. We are talking about digitalization and its transformation in various fields. The transition to online work, online shopping, and the development of online delivery services have resulted from the impact of the pandemic on different services. This influence has not been spared in the education field. Over the past 30 years, eLearning technologies and methodologies have been actively developed and implemented. However, they mostly remained related to training services. Over the past year, they came to the fore and became the main ones. There are real opportunities to assess their impact, pros, and cons on the learning outcome. This article is devoted to the results of such learning. ELearning was conducted as part of an international educational project
Assessment of the student’s educational experience as a tool for making management decisions in the university

This article draws attention to students’ educational experience at a Russian regional university. Such experience is based on two aspects: on the one hand, it is a result of abilities integration, and on the other, it is an ability to act in a context of uncertainty. The latter aspect in understanding students’ experience is the most important for the personality formation in today’s complex world. Within the context of the higher education transformation, experience as an ability to act plays a significant role in individual development. That is why this study is aimed at analyzing the educational experience of first-year students and university graduates. Their educational experience is assessed based on their involvement in educational practices in class, in research activities at the university, and in self-educational activities. The results show that the first-year students’ experience and the graduates’ one at the university have functional differences. The first-year students’ academic engagement is higher as compared to the graduate students’. The research experience of both is expressed in a low degree. The self-educational experience at the university is not strategically formed. Comparing the first-year students’ and the graduates’ educational experiences, one can conclude that the structure of the latter is more functional due to the graduates’ limited involvement in educational activities. This means that the educational experience varies during the period of studies mainly in individual competencies, which are not connected with each other. The low level of the students’ involvement in educational practice reflects the weak development of their experience as an ability to act. It is the structure of a student’s motives that can be one of the main factors of his/her involvement in the learning process. Our findings contribute to the design of educational practices at the university and suggest the strong demand for a more responsible approach to designing the educational environment and educational practices at all university levels.
Non-Interactive Zero-Knowledge Proof based Authentication

Passwords are currently the king in online authentication. But passwords come with a lot of baggage - how to use them, store them, hide them, and obfuscate them. Every year, there are plenty of cyber-attacks which involves passwords being stolen. These passwords are stored and transmitted over networks and hence, many mistakes can be made while doing so. In this paper, a relatively new form of authentication is explored - one based on the concept of Zero-Knowledge Proof (ZKP). It further explores how ZKP can provide additional security to the authentication mechanism by taking the transmission of passwords out of the picture. This paper also discusses the Replay attack vulnerability present in the non-interactive implementation of ZKP and proposes a two-fold mitigation strategy against the vulnerability.