Reviewerly/exHarmony huggingface dataset:
NA‐Reviewer: Reviewing the context to improve the error accumulation issue for multi‐hop QA

The multi-hop question answering (QA) task requires the machine to answer the question correctly and at the same time provide evidence clues. Some pipeline methods have achieved great results in answer accuracy and interpretability, while a obvious drawback of these pipeline methods is the error accumulation issue. In this letter, we propose a NA-Reviewer method to improve the error accumulation issue in multi-hop QA task. It consists of two parts: an NA-Discriminator and a Reviewer, where the NA-Discriminator transforms the error accumulation issue into an unanswered discrimination task, and the Reviewer corrects the error by reviewing the context according the error clues. We conducted experiments on the 2WikiMultiHopQA dataset. Compared with the previous state-of-the-art works, the proposed NA-Reviewer method have increased the F1 value by 4.27 percentage points, and compared to the ablation model without the NA-Reviewer method, it has increased the F1 value by 9.58 percentage points, which significantly improves the error accumulation issue.
End-to-End 3D Facial Shape Reconstruction From an Unconstrained Image

Three-dimensional face generation is of high importance for computer graphics and computer vision applications. The reconstruction task of human faces is challenging and face varies extensively when considering pose, expression, occlusion and illumination. Conventional face models are learned from a set of 3D face scans and represented by Principal Component Analysis(PCA), which is always constrained in linear space. Nevertheless, the shape of face and variations in images cannot be represented faithfully by these traditional models, which are very crucial for face synthesis. To address this problem, a novel architecture is proposed to learn face models in nonlinear spaces. Explicitly, our framework is a Convolutional Neural Networks(CNN) based End-to-End 3D Facial Shape Reconstruction(3DFSR-E2E) from a single-view 2D image scheme. And the variations in facial images are also coped with the model. Firstly, our Encoder is composed of new Improved Residual Blocks(IR-B) utilized in face recognition task, and Decoder consists of Fractionally-Strided Convolutions. Secondly, a UV map is estimated by the trainable Encoder-Decoder network, which is a representation of face geometry and face alignment information. Furthermore, a novel loss function is combined by a weighted Vertex-wise and a Laplacian regularization loss. The measure is employed to improve the results of reconstructed face model. Finally, the experimental results demonstrate the benefits and effectiveness of our overall method with qualitative and quantitative comparisons on public challenging face datasets.
Model of functional suitability of the process of growing fish planting material in recirculating aquaculture systems based on methods of interval data analysis

The problem of future provision of the population with food by developing and improving types of management that do not have an impact on the environment and minimize the involvement of natural resources for production is considered in the article. The model of functional suitability of the process of growing of fish planting material in recirculating aquaculture systems on the basis of methods of analysis of interval data is offered. The software complex of support of process of growing of fish planting material on the basis of the offered model is developed.
IT-based E-learning Environment Design in Web Engineering for Students: on the Experience of Regional Universities

The study verifies the efficiency effect of the concept of an E-learning environment for students, future web engineers. Modular learning derived from the following integrated components stands for the concept. They are VK social media platform used as an information hub; an automated test system for solving problems in Steach.space learning resource; Zoom Video Communications to give lectures and consult students and Google Forms service for developing test activities. A YouTube video hosting service is used to store the history of lectures and consultations combined as F.A.Q. The training assumes the methodology of gradual complication of tasks, their hands-on focus, and an individual path of student learning. The scientific research aimed at testing the efficiency effect was carried out in the regional universities in Russia and Kyrgyzstan (Sholom-Aleichem Priamursky State University, Osh State University, Southern Federal University, and Pacific National University) in the 2020-2021 academic year. Two groups of students took part in the study. The control group (146 students) took the course using social media to interact with a lecturer on all issues, including turning in paper projects. The experimental group (152 students) took advantage of the designed E-learning environment in education. The research findings proved the efficiency of the technique used: the control group improved it by 22 percent, and the participants from the experimental group improved it by 42 percent. There was an appreciable increase seen in the low-level students' academic progress when performing the assignments. Further teaching method improvement can be caused by optimizing the design of system elements to reduce an educator's work while arranging the courses.
Polling Mechanism for Video Deepfake

A polling-based mechanism is developed to identify whether a video is forged. It is important for forensic image processing. However, most of the existing video deepfake algorithms are frame-based. In other words, a learning-based method is applied to identify whether a frame is forged then the mean of the fake score for all frames is applied to determine whether the whole video is forged. In this work, we propose a polling mechanism to well integrate the deepfake score of each frame. We found that the misidentification of a deepfake algorithm usually occurs in the frame with drastic motion, a tilted head, and blinking eyes. Therefore, we determine the weights of each frame according to the frame difference, the head orientation, whether the eyes are blinking, and the accuracy rate of the validation data. With the proposed polling mechanism, the accuracy of video deepfake can be improved and whether a video is forged can be well determined using a much smaller number of frames.
Investigating the Influence of Multiple Parameter Settings on Selected Metaheuristic Algorithms

Several optimization algorithms are being proposed by researchers to provide near optimal solution to problems. Some of these algorithms are popular and require much parameter tuning while others are new and require few parameters tuning hence less stressful. There is need for a fair comparison of such optimization algorithms. This study presents the experiment procedure and results on the performance evaluation of four popular metaheuristic algorithms; Genetic Algorithm, Particle Swarm Optimization, Bat Algorithm, Firefly Algorithm, and a new metaheuristic algorithm with less parameter tuning; Symbiotic Organism Search Optimization. The metaheuristic algorithms were evaluated using twelve benchmark functions consisting of six unimodal and six multimodal functions. Experiment results showed that the Symbiotic Organism Search optimization outperformed all the compared algorithms. It is established in the study that the Symbiotic Organism Search optimization with a smaller number of parameters to tune has better performance compared to those requiring high number of parameters to tune.
Superpixel and Plane Information Based Disparity Estimation Algorithm in Stereo Matching

In stereoscopic image processing, disparity estimation helps determine the depth or distance information of objects. In this paper, a more accurate algorithm is presented to estimate the disparity map based on data processing techniques. Different from most of the existing methods, which mainly apply the features around the key points, we apply the plane information extracted from superpixels to perform disparity estimation. Since the key points, such as the scaled invariant feature transform (SIFT) points, are distributed nonuniformly and many regions may not contain any key point, using only the information of key points may not work well in some cases. This problem is well addressed by the proposed superpixel-based plane information. After carrying out entropy rate superpixel (ERS) segmentation, we perform disparity estimation for each superpixel based on the point, edge, and plane information of the current and surrounding superpixels. Experiments show that the proposed algorithm outperforms other methods and can much improve the accuracy of disparity matching.
How to promote the pragmatic awareness and avoid the fossilization phenomenon through a role play activity in English as a foreign language classes

Many people, even after spending a lifetime studying a foreign language are prone to get stuck in a certain grammatical domain no matter the efforts made and the amount of the target language input offered in the classroom. Unfortunately, the great majority who pursuits their dreams to be the closest possible to a native like speaker, the lack of ability to analyze and synthesize linguistic elements makes them persist in the same errors, establishing a phenomenon known as Fossilization in their learning process. In fact, Fossilization can occur in all learning process levels: phonological, grammatical, lexical and pragmatic and the errors made seem to be similar from individual to individual. In an attempt to avoid this Fossilization process concerning the pragmatically ability to communicate, this article aims to present a suggestion of a role-play activity involving strategies to enhance pragmatic awareness related to politeness theory.
Robot Arm Control Using Fuzzy Logic Design Integrated With Genetic Algorithm Optimization

Abstract— The main contribution of this proposed study is to control the robot's arm angle by changing the torque of the each joints .The dynamic model of the robot manipulator contain from equations, these equations are coupled differential equations , hardy nonlinear, highly complex, multiple inputs and multiple outputs (MIMO), coupled differential equations ,strong model uncertainties and time-variant . The conventional computed torque controllers are not suitable for nonlinear systems, complex, time-variant systems with delay. In this paper, the suggested control law consists of Fuzzy Logic Control (FLC) tuning via Genetic Algorithm (GA). The FLC used, because it is efficient tools for control of nonlinear and uncertain parameters systems. In this design, GA is mainly presented to find a simultaneous near optimum design of the membership functions, scaling factors ,defuzzification Method ,Inference enginee and control rules. GA with a fitness function in a form of a cumulative response error which is widely utilized as an efficient optimization technique. This paper proposed a methodology to optimize fuzzy logic parameters based on GA. The whole system is simulated using MATLAB Simulink. Through this study it is proved that the optimized fuzzy controller gives near optimum performance in the time response behavior ,When the uncertainty added to the system, it maintained roughly the same level of controller performance .
Research and Innovation in Exploration and Mining of Raw Materials: The ROBOMINERS Project

ROBOMINERS is a new project funded under the European Union&rsquo;s Research and Innovation Programme Horizon 2020, which aims at employing a bio-inspired robot, focused on the prospect of mineral exploration and extraction within Europe. ROBOMINERS&rsquo; innovative approach combines the creation of a new mining ecosystem through the development of a bioinspired robotic miner prototype, able to explore and mine mineral deposits which are currently considered uneconomic due to their small size and difficulty of access. The main objectives of the project include the creation of a European database of potentially suitable locations for the deployment of this novel technology. The building of the pan-EU mineral deposits database is considered vital for the development of the project as it will provide essential information related to deposit type and commodities, spatial and temporal distribution, and location of exploration targets. Several deposits have been reviewed and examined in Greece as potential targets suitable for the ROBOMINERS technology, after considering the specific restrictions and requirements of the project. The main targets have been determined and arranged according to the different aspects required by the applicability of the ROBOMINERS innovative technology.
Study of Chinese spam filtering Based on Improved Naive Bayesian Classification Algorithm

Abstract Spam is a growing threat to mobile communications. This paper puts forward some mitigation technologies, including white list and blacklist, challenge response and content-based filtering. However, none are perfect and it makes sense to use an algorithm with higher accuracy for classification. Bayesian classification method shows high accuracy in spam processing, so it has attracted extensive attention. In this paper, a Bayesian classification method based on annealing evolution algorithm is introduced into Chinese spam filtering to improve the accuracy of classification. Our simulation results show that the algorithm has better performance in spam filtering.
PROJECT SUCCESS AND INDIVIDUAL ENTREPRENEURIAL ORIENTATION OF PROJECT MANAGERS: RUSSIAN CONTEXT

Projects implemented in conditions of high uncertainty are sometimes called entrepreneurial projects. Success in such projects is more difficult to achieve. To successfully manage entrepreneurial projects, project managers should have entrepreneurial skills. The article explores two issues related to project success, entrepreneurial characteristics, and project uncertainty. First, the article tries to find out whether the increase in the entrepreneurial nature of the project, manifested in the increase in project goals and methods uncertainty, is accompanied by a decrease in project success. Second question is – does the entrepreneurial orientation of project managers affect project success and the relationship between the projects’ success and their entrepreneurial features. To answer these questions, data were collected from Russian project managers assessing the entrepreneurial nature of projects, project success and the individual entrepreneurial orientation of project-managers. The collected data were examined using factor and regression analysis. The findings indicate that there is a negative relationship between the entrepreneurial nature and such indicators of project success as internal efficiency, preparation for the future, and results for clients. Some indicators of success were positively associated with such indicators of entrepreneurial orientation as proactivity and risk taking. As uncertainty increased, some measures of individual entrepreneurial orientation of project managers can positively compensate negative impact on project success from uncertainty associated with projects’ entrepreneurial nature.
Surfacing Collective Harms in Privacy Sensitive Data

Privacy protections for human subject data are often focused on reducing individual harms that result from improper disclosure of personally identifiable information. However, in a networked environment where information infrastructures enable rapid sharing and linking of different datasets there exist numerous harms which abstract to group or collective levels. In this paper we discuss how privacy protections aimed at individual harms, as opposed to collective or group harms, results in an incompatible notion of privacy protections for social science research that synthesizes multiple data sources. Using the framework of Contextual Integrity we present empirical scenarios drawn from 17 in-depth interviews with researchers conducting synthetic research using one or more privacy sensitive data sources. We use these scenarios to identify ways that digital infrastructure providers can help social scientists manage collective harms over time through specific, targeted privacy engineering of supporting research infrastructures and data curation.
Using a recurrent neural network with S2 characteristics, efficient identification of localised cortical dysplasia

Focal cortical dysplasia (FCD) is an inborn anomaly in brain growth and morphological deformation in lesions of the brain which induces focal seizures. Neurosurgical therapies were performed for the detection of FCD. Furthermore, it can be overcome through the presurgical evaluation of epilepsy. The surgical result is attained basically through the output of the presurgical output. In preprocessing the process of increasing true positives with the decrease in false negatives occurs which results in an effective outcome. MRI (Magnetic Resonance Imaging) outputs are efficient to predict the FCD lesions through T1- MPRAGE and T2- FLAIR efficient output can be obtained. In our proposed work we extract the S2 features through the testing of T1, T2 images. Using RNN-LSTM (Recurrent neural network-Long short-term memory) test images were trained and the FCD lesions were segmented. The output of our work is compared with the proposed work yields better results compared to the existing system such as artificial neural network (ANN), support vector machine (SVM), and convolution neural network (CNN). This approach obtained an accuracy rate of 0.195% (ANN), 0.20% (SVM), 0.14% (CNN), specificity rate of 0.23% (ANN), 0.15% (SVM), 0.13% (CNN) and sensitivity rate of 0.22% (ANN), 0.14% (SVM), 0.08% (CNN) respectively in comparison with RNN-LSTM.
The Encryption of Electronic Professional Certificate by Using Digital Signature and QR Code

In Indonesia, there have been many certificates forgery happened. The lack of security system for the certificate and the difficulty in verification process toward the authenticity certificate become the main factor of the certificate forgery cases happen. The aim of this research is to improve the security system such digital signature and QR code to authenticate the authenticity certificate and to facilitate the user in verify their certificate and also to minimize the certificate forgery cases. The aim of this research is to improve the security system such digital signature and QR code to authenticate the authenticity certificate and to facilitate the user in verify their certificate and also to minimize the certificate forgery cases. The application is built in web system to facilitate the user to access it everywhere and any time. This research uses Research and Development method for problem analysis and to develop application using Software Development Life Cycle method with waterfall approach. Black box testing is chosen as testing method for each function in this system. The result of this research is creatcate application that’s designed to support the publishing and the verification of the electronic authenticity certificate by online. There are two main schemes in system: the scheme in making e-certificate and the scheme of verification QR Code. There is the electronic professional certificate application by applying digital signature and QR Code. It can publish e-certificate that can prevent from criminal action such certificate forgery, that’s showed in implementation and can be proven in test.
Design and Application of Model Based on Bayesian Network

Abstract At present, Bayesian networks lack consistent algorithms that support structure establishment, parameter learning, and knowledge reasoning, making it impossible to connect knowledge establishment and application processes. In view of this situation, by designing a genetic algorithm coding method suitable for Bayesian network learning, crossover and mutation operators with adjustment strategies, the fitness function for reasoning error feedback can be carried out. Experimental results show that the new algorithm not only simultaneously optimizes the network structure and parameters, but also can adaptively learn and correct inference errors, and has a more satisfactory knowledge inference accuracy rate.
Locating Restrooms for Specially-Abled People using Artificial Intelligence and Machine Learning

The overall purpose of the research is to locate restrooms and keep hygiene in consideration for those who are specially-abled. The app will be designed with the assistance of Artificial Intelligence and Machine Learning, providing navigation, every step of the way which will be susceptible to use and comprehend. People with having disabilities implies that having fundamental difficulty accomplishing aspects. They are of numerous types like physical disability in nature, due to amputation inability to walk, sensory like blindness, hearing impairment with the assistance of this app, this gap can be filled. The problems faced by specially abled people have been taken into consideration creating a balanced platform for them. Utilizing the necessary tools and functions required for enabling them to locate and understand where the restrooms are, keeping in mind hygiene and safety factors. Certain parameters are taken into consideration such as voice recognition, Maps Live feature, magnifier, sign language interpretation, hands-free settings, and many more features. They could converse or speak and reach their destination with the support of the virtual assistant of a very specialized navigation system designed especially for them with a simple user interface which is easy to interpret and work efficiently according to the needs of the user.
Evaluating DASH QoE with MPTCP Under Different MPTCP Buffer Sizes and Path Latencies

Dynamic Adaptive Streaming over HTTP (DASH) is accessible without any restrictions on most networks and can adapt playback bitrate to match available throughput, which ensures acceptable Quality of Experience (QoE) to content consumers. When DASH is used with MPTCP as transport protocol, the aggregated throughput and increased reliability due to the use of multiple interfaces for a streaming session improves QoE. There are ongoing efforts to improve QoE even further. The current literature concerning DASH on MPTCP focuses on studies related to link errors, shared bottleneck links and improvement of QoE using player level approaches. Moreover, the experimental results in current literature are obtained from software-based simulators or emulators utilizing very simple topologies which may not be consistent with real-world usage. Our work is the first evaluation of DASH QoE performance on MPTCP under different MPTCP buffer sizes and path latencies. Our work is also the first to use a real hardware testbed with a generalized Internet topology for DASH on MPTCP performance evaluation. There are four major outcomes of our experimental evaluation. First, the optimal MPTCP buffer size for the best end-user QoE is dependent on the bandwidth-delay product within a reasonable path latency limit. Second, the path latency and the video segment size that optimises QoE increase proportionally. Third, beyond the path latency limit, DASH is non-functional due to problems with HTTP/1.1 head-of-line blocking and MPTCP congestion control. Last but not the least, the MPTCP buffer size does not influence raw network throughput directly.