Reviewerly/exHarmony huggingface dataset:
Orthos: A Trustworthy AI Framework for Data Acquisition

Information acquisition through crowdsensing with mobile agents is a popular way to collect data, especially in the context of smart cities where the deployment of dedicated data collectors is expensive and ineffective. It requires efficient information elicitation mechanisms to guarantee that the collected data are accurately acquired and reported. Such mechanisms can be implemented via smart contracts on blockchain to enable privacy and trust. In this work we develop Orthos, a blockchain-based trustworthy framework for spontaneous location-based crowdsensing queries without assuming any prior knowledge about them. We employ game-theoretic mechanisms to incentivize agents to report truthfully and ensure that the information is collected at the desired location while ensuring the privacy of the agents. We identify six necessary characteristics for information elicitation mechanisms to be applicable in spontaneous location-based settings and implement an existing state-of-the-art mechanism using smart contracts. Additionally, as location information is exogenous to these mechanisms, we design the Proof-of-Location protocol to ensure that agents gather the data at the desired locations. We examine the performance of Orthos on Rinkeby Ethereum testnet and conduct experiments with live audience.
Research and improvement of k-means parallel multi-association clustering algorithm

In this paper, k-means parallel clustering algorithm is studied. Firstly, this paper introduces the purpose and significance of k-means clustering algorithm. Secondly, we describe the process of clustering analysis, six classical clustering algorithms, the composition and operation of Hadoop Environment, and the K-MEANS algorithm in the cluster environment. In the environment of large data, the time and space complexity of k-means algorithm becomes an obstacle of k-means algorithm. Based on the research of a lot of traditional k-means algorithms, a parallel k-means algorithm is proposed, and the formula of its speed-increasing ratio is given. Experimental results show that the algorithm is correct and effective, and has a good clustering effect.
Tilde at WMT 2020: News Task Systems.

This paper describes Tilde’s submission to the WMT2020 shared task on news translation for both directions of the English-Polish language pair in both the constrained and the unconstrained tracks. We follow our submissions form the previous years and build our baseline systems to be morphologically motivated sub-word unit-based Transformer base models that we train using the Marian machine translation toolkit. Additionally, we experiment with different parallel and monolingual data selection schemes, as well as sampled back-translation. Our final models are ensembles of Transformer base and Transformer big models which feature right-to-left re-ranking.
An exploratory approach to the Parallel Corpus Filtering shared task WMT20

In this document we describe our submission to the parallel corpus filtering task using multilingual word embedding, language models and an ensemble of pre and post filtering rules. We use the norms of embedding and the perplexities of language models along with pre/post filtering rules to complement the LASER baseline scores and in the end get an improvement on the dev set in both language pairs.
Strategic Placement and Design of Microgrids using Nighttime Imagery

Natural disasters are increasingly impacting the power availability to communities around the world causing long term blackouts. Rural and remote communities are at a greater risk of suffering longer blackouts due to being lower priority or more difficult to power during the energy restoration efforts. The communities impacted by long term blackouts tend to be economically disadvantaged and remote. Fuel pipelines are unavailable and fuel delivery is interrupted as resources are diverted to higher population areas; this makes the installation of PV plus storage very attractive. This is especially true in the tropics where the solar resource is abundant and the limiting factors, also discussed here, are of a different nature. Utilities may not have a complete picture of the restoration of power across time, this is where previous work done by NASA comes in by providing a time series of the recovery process using nighttime satellite imagery. This work proposes a methodology regarding microgrid planning and critical asset clustering using GIS, census, and the nighttime imagery provided by NASA. The work presents a workflow as well as the decision criteria involved. The aftermath of Hurricane Maria in Puerto Rico is presented as the test case, with a more detailed example based in the town of Jayuya.
Numerical analysis of quadcopter in different weather conditions of regions in India

For photography, remote sensing, aerial surveillance disaster relief and motion picture filmmaking quadcopters are very useful today, and it is essential to find out its limits, so that we can predict in which atmospheric conditions, it works with perfection. And another important factor for quad copter is to know about its effect in order to run smoothly. To overcome these problems, A 3D model of the drone is created on SolidWorks and CFD is performed on Ansys. This simulation results help researchers to see the after effects of different flight condition.
Retrieval Mechanisms of Data Linked to Virtual Servers Using Metaheuristic Technique

In the present situation, the large-scaled data supports the virtual environment specifically from small to medium ICT-based business. The implementation of virtual server, along with the associated technologies in general can reduce the processing costs, hardware costs as well as evaluation and optimization of large queries in multi query environments. As the processing capabilities during processing the queries are major issues, the process in such situation acquires more time as it cannot optimize the concerned data in the whole database in the stipulated time. Practically, it is difficult to process erroneous data with virtual link. To overcome this difficulty, canonicalization concept can be adopted to increase the performance of sustained data along with safe querying. Initially, it finds the similarities of the given query with the distinct database. After that it is essential to generalize the symbols linked with query stringing and enhances the system performance. This would result in better accuracy with limited query processing time and minimum computation cost. In this context, it has been thought of analysing the performance of query plans linked with the database associated with virtual servers using metaheuristic approach.
Analysis of the security and privacy risks and challenges in smart cities’ traffic light system

Currently, the IoT network is the fastest growing network in the world that brings the smart cities revolution. The increase in the smart cities development poses several security and privacy risks. With the acceleration of times, we can now hastily observe the lack of privacy in our life. The major security and privacy issues occur because of either non-consideration of its security and privacy aspects or having inappropriate controls in place. Many of these issues could be resolved by applying advanced IoT-enabled solutions. This paper presents security and privacy risks and challenges against issues within traffic lights system, which is a complex and critical smart cities system. The paper also addresses a proposed secure and privacy-aware system for future traffic light system.
Fake Profile Detection in Facebook

The social network a crucial part of our life is plagued by online impersonation and fake accounts. Facebook, Instagram, Snapchat are the most well-known informal communities’ sites. The informal organization an urgent piece of our life is tormented by online pantomime and phony records. Fake profiles are for the most part utilized by the gatecrashers to complete malevolent exercises, for example, hurting individual, data fraud, and security interruption in online social network (OSN). Hence, recognizing a record is certified or counterfeit is one of the basic issues in OSN. Right now, propose a model that could be utilized to group a record as phony or certified. This model uses random forest method as an arrangement strategy and can process an enormous dataset of records on the double, wiping out the need to assess each record physically. Our concern can be said to be a characterization or a bunching issue. As this is a programmed recognition strategy, it very well may be applied effectively by online interpersonal organizations which have a large number of profiles, whose profiles cannot be inspected physically.
Generative Time-series Modeling with Fourier Flows

Generating synthetic time-series data is crucial in various application domains, such as medical prognosis, wherein research is hamstrung by the lack of access to data due to concerns over privacy. Most of the recently proposed methods for generating synthetic time-series rely on implicit likelihood modeling using generative adversarial networks (GANs)—but such models can be difficult to train, and may jeopardize privacy by “memorizing” temporal patterns in training data. In this paper, we propose an explicit likelihood model based on a novel class of normalizing flows that view time-series data in the frequency-domain rather than the time-domain. The proposed flow, dubbed a Fourier flow, uses a discrete Fourier transform (DFT) to convert variable-length time-series with arbitrary sampling periods into fixed-length spectral representations, then applies a (data-dependent) spectral filter to the frequency-transformed time-series. We show that, by virtue of the DFT analytic properties, the Jacobian determinants and inverse mapping for the Fourier flow can be computed efficiently in linearithmic time, without imposing explicit structural constraints as in existing flows such as NICE (Dinh et al. (2014)), RealNVP (Dinh et al. (2016)) and GLOW (Kingma & Dhariwal (2018)). Experiments show that Fourier flows perform competitively compared to state-of-the-art baselines.
Multi-word Expressions for Abusive Speech Detection in Serbian

This paper presents our work on the refinement and improvement of the Serbian language part of Hurtlex, a multilingual lexicon of words to hurt. We pay special attention to adding Multi-word expressions that can be seen as abusive, as such lexical entries are very important in obtaining good results in a plethora of abusive language detection tasks. We use Serbian morphological dictionaries as a basis for data cleaning and MWE dictionary creation. A connection to other lexical and semantic resources in Serbian is outlined and building of abusive language detection systems based on that connection is foreseen.
Research of Recommendation System Based on Deep Interest Network

Abstract With the development of e-commerce, it provides users with many choices. But how to quickly and accurately recommend goods to users is an important topic in this field. Matrix factorization recommendation model based on scoring data is widely used, but data sparsity affects the recommendation quality of the model. In the paper, a product recommendation algorithm based on deep interest network is proposed. First, the user’s purchased goods and the user’s search are embedded coded, and the sparse features are transformed into low-dimensional dense features.Then, the feature vectors of the purchased goods and the search text vectors are joined together to input the deep interest network as the feature to predict the user’s interest.Finally, the effectiveness of the model is validated by using record data. The validity of the model is verified by comparing with other models.
AUGMENTED REALITY PENGENALAN GEOGRAFI ATMOSFER BERBASIS ANDROID

The development of this augmented reality application is based on the visualization of the geographical Atmosphere learning subject and the conditions of the teaching materials applied to students there are still weaknesses in understanding the teaching materials because the knownledge obtained is still in printed books so that it creates a sense of boredom in students. The purpose of this research is to develop the level of interest and insight of students in the Atmosphere, especially on improving the memory of students through a game in the form of a quiz. The data method applied is thtough observation and interview techniques. The preparation of this application design uses the Markerless-Based Tracking method which is a method that does not require markers to display virtual objects. However, in this study the researchers limited it to the trial stage only. As for the use of Unified Modeling Language to describe how the augmented reality application works. In this study, researchers used a tool called Unity for application design and Adobe Illustrator as a design button. The results of the trials on this application can be installed on Android device and appearance unique.
Differential Encoding in Prefrontal Cortex Projection Neuron Classes Across Cognitive Tasks

Single-cell transcriptomics has been widely applied to classify neurons in the mammalian brain, while systems neuroscience has historically analyzed the encoding properties of cortical neurons without considering cell types. Here we examine how specific transcriptomic types of mouse prefrontal cortex (PFC) projection neurons relate to axonal projections and encoding properties across multiple cognitive tasks. We found that most types projected to multiple targets, and most targets received projections from multiple types, except PFC→PAG (periaqueductal gray). By comparing Ca2+-activity of the molecularly homogeneous PFC→PAG type against two heterogeneous classes in several two-alternative choice tasks in freely-moving mice, we found that all task-related signals assayed were qualitatively present in all examined classes. However, PAG-projecting neurons most potently encoded choice in cued tasks, whereas contralateral PFC-projecting neurons most potently encoded reward context in an uncued task. Thus, task signals are organized redundantly, but with clear quantitative biases across cells of specific molecular-anatomical characteristics.
An Optimized Novel Technique for Video Synopsis Using Bayesian Object Tracking Algorithm

This paper presents another analytical video description of the methodology, which is far superior in pressure and depth to previous methodologies. In preparation, the video of the reconnaissance was usually packed by moving the pushing objects alongside the time hub, which undoubtedly resulted in a real crash and ordered issue antiques between the items being pushed. The main idea in this paper is that these antiques can be reduced by using the Bayesian calculation for fragmentation, and the last approach for foundation extraction is used following the products. We offer the best way to integrate these three heterogeneous activities into a single improvement system and achieve excellent outline performance. The Calculation of Metropolis does not like past methodologies that usually use optional improvements to fathom summary improvements to find the answer for our three-variable progress problem. A range of research demonstrates the feasibility of our technology.
CREATIVITY OF STUDENTS WITH SPECIAL EDUCATIONAL NEEDS

The article presents the main results of theoretical and empirical research of the problem of development of creativity of students with special educational needs on the basis of application in educational and extracurricular activities of business games, situational tasks, trainings and creative techniques. The task of the modern education system as an inclusive system aimed at harmonious development and joint improvement of all participants is the creation of conditions for the development of students and students of HIA, including creativity, their adaptation, and socialization. At the same time, in comparison with people with HIA, whose life plans and strategies do not always need a strong creative approach, creative activity and creative approach to study, professional activity, to life in general plays an important role in the lives of students with special educational needs.. All of these techniques are based on the actualization of the life experience of students with disabilities and involve the solution of professional problems of the student. It is also noted that in modern inclusive and special education different, classical and innovative technologies of working with children, adolescents, young people and adults with HIA are needed, synthetic and flexible approaches are needed in educational, correctional, other types of activity of students and with students with disabilities. Creative, art-therapeutic activity is one of the tricks of successful work in different areas is the development of creativity and creative abilities of students and students with HIA at the levels of primary, secondary, higher professional education. In addition to pedagogical (educational and educational), psychological (correctional and developmental) effects on students and students, work in the development of creative abilities of children, adolescents, young people with HIA helps to increase tolerance and harmonize the attitude of teachers, psychologists, and others specialists, as well as societies in general.
Modeling of the Associative Verbal Network and its Cognitive Interpretation (the Conceptual Domain ЗРАДА ‘BETRAYAL’)

This paper is devoted to the modeling of the associative verbal network based on the experimental data obtained in the associative test. The indices of mutual associative relation of the investigated associative areas are determined. The results of the analysis are compared with the data obtained by means of Word2Vec Models and the corpus data.
Is Long-Serving Teaching Experience a Barrier of Transformation in Online Teaching? – An Exploration

Online teaching, an inevitable part of education in 2020, has its own advantages and disadvantages with respect to the perspectives of student and teachers.Every teacher has been in a situation to transform suddenly to the fluctuating needs of education in the COVID 19 crisis.Is this transformation quick and easy for all the teachers?This is an important question that may depend on number of factors including teachers' teaching experience, economic and social background, and skills in teaching tools, motivation and attitude.This research study looks into the parameter "Teaching experience" and attempts to find whether teaching experience stands as an influencing factor in the transformation process.As learning new is always easier than unlearning, this parameter is critically important in analysing whether the long-time experience affects the rate of transformation to online teaching.The research is conducted as a qualitative research with surveys obtained from students and faculty of the host institution after the first cycle of online teaching (after May 2020).66 teachers and 215 students from various engineering disciplines participated in the survey.Observations from the survey are analysed in different elements of online learning.This study compares the expectations of students and the preference of teachers in these elements.From the study, it has been inferred that teaching experience has a significant impact on the transformation of teachers to meet the students' expectations in the perspective of online teaching.The study also enables suitable recommendations for need based training programs.
An openCL-based speckle matching on the monocular 3D sensor using speckle projection

Single-shot speckle projection profilometry (SPP), which can build the global correspondences between stereo images by projecting a single random speckle pattern, is applicable to the dynamic 3D acquisition. However, the traditional stereo matching algorithm used in SPP has low matching accuracy and high computational cost, which makes it difficult to achieve real-time and accurate 3D reconstruction dynamically. For enhancing the performance of 3D sensing of single-shot speckle projection profilometry (SPP), in this paper, we proposed an OpenCL-based speckle matching on the monocular 3D sensor using speckle projection. In terms of hardware, our low-cost monocular 3D sensor using speckle projection only consists of one IR camera and a diffractive optical element (DOE) projector. On the other hand, an improved semi-global matching (SGM) algorithm using OpenCL acceleration was proposed to obtain efficient, dense, and accurate matching results, enabling high-quality 3D reconstruction dynamically. Since the baseline between the IR camera and the DOE projector is about 35mm, the absolute disparity range of our system is suitably set to 64 pixels to measure scenes with a depth range of 0:3m to 3m. The experiment results demonstrated that the proposed speckle matching method based on our low-cost 3D sensor can achieve fast and absolute 3D shape measurement with the millimeter accuracy through a single speckle pattern.
Research on SLAM of Large-scale Space Point Cloud Topological Robot

Abstract This paper focuses on the cutting-edge and difficult issues in the field of navigation and positioning of service robots. By exploring the technical approach to practical service robot environment modeling and positioning, a new topology SLAM method is proposed. This paper focus on the key technologies involved in 3D SLAM of robots based on large-scale spatial point clouds, including back-end optimization and closed-loop, high-precision sensor calibration technology. Further propose topological map construction strategies, and focus on designing map-generating algorithms based on graphs or tree structures. The research results will provide innovative ideas and practical solutions for environmental modeling and navigation control of service robots, and strongly support technological advances in related research fields based on three-dimensional environmental perception.
A Governance Dividend for Sub-Saharan Africa?

Countries in Sub-Saharan Africa (SSA) tend to lag those in most other regions in terms of governance and perceptions of corruption. Weak governance undermines economic performance through various channels, including deficiencies in government functions and distortions to economic incentives. It thus stands to reason that SSA countries could strengthen their economic performance by improving governance and reducing corruption.This paper estimates that strengthening governance and mitigating corruption in the region could be associated with large growth dividends in the long run. While the process would take considerable time and effort, moving the average SSA country governance level to the global average could increase the region's GDP per capita growth by about 1-2 percentage points.