Reviewerly/exHarmony huggingface dataset:
Precise Synthetic Image and LiDAR (PreSIL) Dataset for Autonomous Vehicle Perception.

We introduce the Precise Synthetic Image and LiDAR (PreSIL) dataset for autonomous vehicle perception. Grand Theft Auto V (GTA V), a commercial video game, has a large detailed world with realistic graphics, which provides a diverse data collection environment. Existing works creating synthetic LiDAR data for autonomous driving with GTA V have not released their datasets, rely on an in-game raycasting function which represents people as cylinders, and can fail to capture vehicles past 30 metres. Our work creates a precise LiDAR simulator within GTA V which collides with detailed models for all entities no matter the type or position. The PreSIL dataset consists of over 50,000 frames and includes high-definition images with full resolution depth information, semantic segmentation (images), point-wise segmentation (point clouds), and detailed annotations for all vehicles and people. Collecting additional data with our framework is entirely automatic and requires no human annotation of any kind. We demonstrate the effectiveness of our dataset by showing an improvement of up to 5% average precision on the KITTI 3D Object Detection benchmark challenge when state-of-the-art 3D object detection networks are pre-trained with our data. The data and code are available at https://tinyurl.com/y3tb9sxy
Synthesis of Emotional Speech by Prosody Modification of Vowel Segments of Neutral Speech

Background: Emotional speech synthesis is the process of synthesising emotions in a neutral speech – potentially generated by a text-to-speech system – to make an artificial humanmachine interaction human-like. It typically involves analysis and modification of speech parameters. Existing work on speech synthesis involving modification of prosody parameters does so at sentence, word, and syllable level. However, further fine-grained modification at vowel level has not been explored yet, thereby motivating our work. Objective: To explore prosody parameters at vowel level for emotion synthesis. Methods: Our work modifies prosody features (duration, pitch, and intensity) for emotion synthesis. Specifically, it modifies the duration parameter of vowel-like and pause regions and the pitch and intensity parameters of only vowel-like regions. The modification is gender specific using emotional speech templates stored in a database and done using Pitch Synchronous Overlap and Add (PSOLA) method. Results: Comparison was done with the existing work on prosody modification at sentence, word and syllable label on IITKGP-SEHSC database. Improvements of 8.14%, 13.56%, and 2.80% for emotions angry, happy, and fear respectively were obtained for the relative mean opinion score. This was due to: (1) prosody modification at vowel-level being more fine-grained than sentence, word, or syllable level and (2) prosody patterns not being generated for consonant regions because vocal cords do not vibrate during consonant production. Conclusion: Our proposed work shows that an emotional speech generated using prosody modification at vowel-level is more convincible than prosody modification at sentence, word and syllable level.
Towards deeper understanding of semi-supervised learning with variational autoencoders

Recently, deep generative models have been shown to achieve state-of-the-art performance on semi-supervised learning tasks. In particular, variational autoencoders have been adopted to use labeled data, which allowed the development of SSL models with the usage of deep neural networks. However, some of these models rely on ad-hoc loss additions for training, and have constraints on the latent space, which effectively prevent the use of recent developments in improving the posterior approximations. In this paper, we analyse the limitations of semi-supervised deep generative models based on VAEs, and show that it is possible to drop the assumptions made on the latent space. We present a simplified method for semi-supervised learning which combines the discriminative and generative loss in a principled manner. Our model allows for straightforward application of normalizing flows and achieves competitive results in semi-supervised classification tasks.
The digital technology: past, present and future

This article examines the notion of the term “digital technology”. And the authors aim to analyze and discuss the usage of technology in the past, present and future. Three main stages of using computer technologies in the teaching of foreign languages are discussed in this article. Also it is reviewed how today digital technology should be used by educators in the classroom, as a resource for teaching EFL. This discussion arose from the analysis by the second year students of the Kazakh Ablai Khan University of International Relations and World Languages (KazUIR&WL) at which the digital technology (online tool) has gained recognition of its potential by students. And types of digital technologies of future are discussed as well.
Reconstruction of past European land cover from pollen data: using spatial statistics and Crank-Nicolson Monte Carlo

Given a pollen data set from Europe over a time period, the aim is to reconstruct the past land cover by interpolating from the pollen data values to a continuous map. The data is on compositional form with three vegetation categories; coniferous forest, broadleaved forest and open land. Reconstruction will be based on a Gaussian Markov random field with separable spatio-temporal structure for the covariance matrix. The spatio-temporal covariance matrix is constructed by Kronecker products which simplifies many matrix computations. The field and parameters for the model are estimated by Markov Chain Monte Carlo, with a Crank Nicolson Langevin proposal to estimate the spatio-temporal field. Crank Nicolson Langevin method works well, although implementation could be technical with a lot of details. Convergence for some of the model parameters is slow with bad mixing. The average compositional distance for the reconstruction and the validation set was 0.71. The model was better at finding temporal structure rather than spatial. Reconstructions from this model could be used as input to other models such as \citep{Strandberg} to investigate how anthropogenic deforestation, and other changes in nature, impacts climate change. (Less)
Transport Network Slice YANG Data Model

This document describes a YANG data model for managing and controllingtransport network slices.
Pemanfaatan Energi Alternatif di SMAN 1 Labuapi

Counselling about the use of alternative energy has been carried out at SMAN 1 Labuapi as a partner. This counselling needs to be done because based on the analysis of the situation, the people around SMAN 1 Labuapi desperately need energy assistance. Meanwhile, in the area around the SMAN 1 Labuapi, there is plenty of alternative energy. Therefore, the main purpose of implementing community service is to motivate the community, especially the community of SMAN 1 Labuapi, to be able to provide their own energy. Then, it is hoped that SMAN 1 Labuapi is expected to provide a model of alternative energy utilization to meet the energy needs of the wider community of Desa Labuapi continuously. The method used in this service is the method of counselling and demonstration about the use of alternative energy. The results of service are changes in the behaviour of the people of SMAN 1 Labuapi to utilize alternative energy. One tool that had been practised was a biogas maker unit using gallons of Narmada.
DECADAL ANALYSIS AND ESTIMATION OF TEMPERATURE CHANGES IN NORTHERN SENATORIAL ZONE OF KADUNA STATE-NIGERIA

This study examined the decadal analysis and estimation of temperature changes in northern senatorial zone of Kaduna state-Nigeria. Temperature data (minimum and maximum) covering 1967 to 2016 was obtained from the archives of Nigeria Meteorological Agency (NiMet) station Zaria. These data were used to determine the decadal trends and estimate changes in minimum, maximum and average temperatures using trend analysis in Microsoft excel tool (2013) and SPSS 23.0 version. Findings indicate a total increase in average temperature of 1.72°C at the rate of 0.034°C year -1 . For the maximum temperature, a total increase of 1.68°C at the rate of 0.033°C year -1 and for minimum temperature an increase of 1.72°C the rate of 0.034 0 C year -1 , indicating increased warming in recent years. The result of Cramer’s test for the monthly average temperature analysis indicated that the tk values for the last two decades shows an increase in temperature as indicated by their positive with the months of March, April, may, June, July, August, September and October being significantly warmer at 95% confidence level than the long-term condition. The paper concludes that the study area is getting warmer in recent years.The paper recommends that government should come up with policies that will help in. managing socio- economic consequences of rising temperature and /or   planned interventions to avoid surprises and take right decision in case of unexpected periods of intense heat. Additional functioning synoptic weather stations should be provided to complement the existing one since the study area is becoming hotter in recent years.
Considerations for defining a Transport Slice NBI

The transport network is an essential component in the end-to-enddelivery of services and, consequently, with the advent of networkslicing it is necessary to understand what could be the way in whichthe transport network is consumed as a slice. This document analysesthe needs of potential transport slice consumers in order to identifythe functionality required on the North Bound Interface (NBI) of atransport slice controller for satisfying such transport slicerequests.
It’s time to break-up with SPSS! Moving to open stats software without pain

Have you ever thought SPSS was ‘so last century’? Have you ever wished using SPSS with point and click was friendlier? Are you tempted to explore Bayesian statistics but are not ready to learn R? Do you think SPSS cost too much and are you concerned that your analyses can only be read if you have access to an SPSS software?JASP© (Jeffreys’s Amazing Statistics Program) is a software originally developed by a team at University of Amsterdam, as an open-source project. It is free to use and supported by funding that will allow the team to develop the software in the long term. JASP© is further distinct from SPSS in two ways. The user interface was developed so that it is more intuitive and user-friendly. For example, JASP© computes your analysis dynamically as you choose your options; therefore, if one makes a mistake after clicking OK on their analysis, they can edit that analysis directly, instead of running it again. Secondly, JASP© not only offers the main frequentist tests SPSS allows one to do, but for most of them, it also offers users its Bayesian inference framework alternative.Alternatively, jamovi© is another open-source project, with the same dynamic features as JASP©. Unlike JASP©, jamovi© does not allow to perform Bayesian inference tests with point and click, but it allows to see the R code required by your analysis, instead of hiding it behind the scenes. It is therefore a useful tool for any researcher that would like to start learning R coding: the point-and-click software provides scaffolding, leading to a gentler learning curve.In this talk, I intend to demonstrate how researchers can use these open software options to move away from SPSS. I will demonstrate common frequentist hypothesis tests in both software, and Bayesian ones in JASP©, to articulate how and when each software is most appropriate. I will provide these demonstrations using screen-casting, so that attendees can refer back to them when they later try and explore the two software options. Seminar attendees are encouraged to download JASP© as well as jamovi© before the seminar, and to bring their laptop in the seminar so they can practice during the presentation, if they wish to.
Coordinate Routing in the Lightning Network

Blockchains such as the Bitcoin facilitate the online transaction between users without an intermediate financial institution and can serve as a global currency. However, at the moment, blockchain solutions are suffering from low transaction throughput and high delays. For instance, the bitcoin network processes at most 7 transactions per second. Blockchain second layer solutions aim to solve this transaction scaling problem with the idea of creating off-chain transactions using payment channels. For a payment channel, the blockchain network is only required to initialize and terminate the channels. Otherwise, they can finalize transactions between pairs of users instantly without using the blockchain network. The Lightning Network is one such payment channel solution that creates an overlay network on top of the blockchain and routes payments across users. Currently, the Lightning network uses source routing to route payments from a source to a destination. However, source routing does not scale to large networks. In this thesis, we begin by establishing the requirements for routing in the Lightning Network. Next, we analyze generic payment routing algorithms based on the set requirements and understand their limitations. Further, we design a routing algorithm for the Lightning Network that builds upon the key ideas found in the previous work. We, in particular, provide design solutions for computing transaction fees and communicating errors in a privacy-preserving manner. These problems were not addressed in any earlier works. Finally, we implement our algorithm in an active Lightning Network codebase. We evaluate it with regard to security, privacy, and performance requirements and also compare the same with the existing solution.
Missing Data Characteristics and the Choice of Imputation Technique: An Empirical Study

One important characteristic of good data is completeness. Missing data is a major problem in the classification of medical datasets. It leads to incorrect classification of patients, which is dangerous to health management of patients. Many imputation techniques have been employed to solve this problem, but these techniques are without recourse to the characteristics that cause the missingness. In this paper, we investigated the causes of missing data in a medical dataset and proposed multiple imputation technique to solving the problem of missing data. A 5-fold-iteration multiple imputation was employed. The whole missing values in the dataset was regenerated 100%. The imputed datasets were validated using extreme learning machine (ELM) classifier. The results show improvement on the accuracy of the imputed datasets. The work can, however, be extended to compare the accuracy of the imputed datasets with different classifiers.
A Comprehensive Survey on Graph Neural Networks

Deep learning has revolutionized many machine learning tasks in recent years, ranging from image classification and video processing to speech recognition and natural language understanding. The data in these tasks are typically represented in the Euclidean space. However, there is an increasing number of applications, where data are generated from non-Euclidean domains and are represented as graphs with complex relationships and interdependency between objects. The complexity of graph data has imposed significant challenges on the existing machine learning algorithms. Recently, many studies on extending deep learning approaches for graph data have emerged. In this article, we provide a comprehensive overview of graph neural networks (GNNs) in data mining and machine learning fields. We propose a new taxonomy to divide the state-of-the-art GNNs into four categories, namely, recurrent GNNs, convolutional GNNs, graph autoencoders, and spatial-temporal GNNs. We further discuss the applications of GNNs across various domains and summarize the open-source codes, benchmark data sets, and model evaluation of GNNs. Finally, we propose potential research directions in this rapidly growing field.
GOT-10k: A Large High-Diversity Benchmark for Generic Object Tracking in the Wild

We introduce here a large tracking database that offers an unprecedentedly wide coverage of common moving objects in the wild, called GOT-10k. Specifically, GOT-10k is built upon the backbone of WordNet structure and it populates the majority of over 560 classes of moving objects and 87 motion patterns, magnitudes wider than the most recent similar-scale counterparts. The contributions of this paper are summarized in the following: (1) GOT-10k offers over 10,000 video segments with more than 1.5 million manually labeled bounding boxes, enabling unified training and stable evaluation of deep trackers. (2) GOT-10k is by far the first video trajectory dataset that uses the semantic hierarchy of WordNet to guide class population. (3) For the first time, GOT-10k introduces the one-shot protocol for tracker evaluation, where the training and test classes are zero-overlapped. The protocol avoids biased evaluation results towards familiar objects and it promotes generalization in tracker development. (4) We conduct extensive tracking experiments with 39 typical tracking algorithms on GOT-10k and analyze their results in this paper. (5) Finally, we develop a comprehensive platform for the tracking community that offers full-featured evaluation toolkits, an online evaluation server, and a responsive leaderboard. The annotations of GOT-10k's test data are kept private to avoid tuning parameters on it. The database, toolkits, evaluation server and baseline results are available at http://got-10k.aitestunion.com.
Susceptibility to SARS-CoV-2 Infection Among Children and Adolescents Compared With Adults

The degree to which children and adolescents are infected by and transmit severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is unclear. The role of children and adolescents in transmission of SARS-CoV-2 is dependent on susceptibility, symptoms, viral load, social contact patterns, and behavior.To systematically review the susceptibility to and transmission of SARS-CoV-2 among children and adolescents compared with adults.PubMed and medRxiv were searched from database inception to July 28, 2020, and a total of 13 926 studies were identified, with additional studies identified through hand searching of cited references and professional contacts.Studies that provided data on the prevalence of SARS-CoV-2 in children and adolescents (younger than 20 years) compared with adults (20 years and older) derived from contact tracing or population screening were included. Single-household studies were excluded.PRISMA guidelines for abstracting data were followed, which was performed independently by 2 reviewers. Quality was assessed using a critical appraisal checklist for prevalence studies. Random-effects meta-analysis was undertaken.Secondary infection rate (contact-tracing studies) or prevalence or seroprevalence (population screening studies) among children and adolescents compared with adults.A total of 32 studies comprising 41 640 children and adolescents and 268 945 adults met inclusion criteria, including 18 contact-tracing studies and 14 population screening studies. The pooled odds ratio of being an infected contact in children compared with adults was 0.56 (95% CI, 0.37-0.85), with substantial heterogeneity (I2 = 94.6%). Three school-based contact-tracing studies found minimal transmission from child or teacher index cases. Findings from population screening studies were heterogenous and were not suitable for meta-analysis. Most studies were consistent with lower seroprevalence in children compared with adults, although seroprevalence in adolescents appeared similar to adults.In this meta-analysis, there is preliminary evidence that children and adolescents have lower susceptibility to SARS-CoV-2, with an odds ratio of 0.56 for being an infected contact compared with adults. There is weak evidence that children and adolescents play a lesser role than adults in transmission of SARS-CoV-2 at a population level. This study provides no information on the infectivity of children.
A Survey on Consensus Mechanisms and Mining Strategy Management in Blockchain Networks

The past decade has witnessed the rapid evolution in blockchain technologies, which has attracted tremendous interests from both the research communities and industries. The blockchain network was originated from the Internet financial sector as a decentralized, immutable ledger system for transactional data ordering. Nowadays, it is envisioned as a powerful backbone/framework for decentralized data processing and data-driven self-organization in flat, open-access networks. In particular, the plausible characteristics of decentralization, immutability, and self-organization are primarily owing to the unique decentralized consensus mechanisms introduced by blockchain networks. This survey is motivated by the lack of a comprehensive literature review on the development of decentralized consensus mechanisms in blockchain networks. In this paper, we provide a systematic vision of the organization of blockchain networks. By emphasizing the unique characteristics of decentralized consensus in blockchain networks, our in-depth review of the state-of-the-art consensus protocols is focused on both the perspective of distributed consensus system design and the perspective of incentive mechanism design. From a game-theoretic point of view, we also provide a thorough review of the strategy adopted for self-organization by the individual nodes in the blockchain backbone networks. Consequently, we provide a comprehensive survey of the emerging applications of blockchain networks in a broad area of telecommunication. We highlight our special interest in how the consensus mechanisms impact these applications. Finally, we discuss several open issues in the protocol design for blockchain consensus and the related potential research directions.
CodeBERT: A Pre-Trained Model for Programming and Natural Languages

We present CodeBERT, a bimodal pre-trained model for programming language (PL) and natural language (NL).CodeBERT learns general-purpose representations that support downstream NL-PL applications such as natural language code search, code documentation generation, etc.We develop Code-BERT with Transformer-based neural architecture, and train it with a hybrid objective function that incorporates the pre-training task of replaced token detection, which is to detect plausible alternatives sampled from generators.This enables us to utilize both "bimodal" data of NL-PL pairs and "unimodal" data, where the former provides input tokens for model training while the latter helps to learn better generators.We evaluate CodeBERT on two NL-PL applications by fine-tuning model parameters.Results show that CodeBERT achieves state-of-the-art performance on both natural language code search and code documentation generation.Furthermore, to investigate what type of knowledge is learned in CodeBERT, we construct a dataset for NL-PL probing, and evaluate in a zero-shot setting where parameters of pre-trained models are fixed.Results show that CodeBERT performs better than previous pre-trained models on NL-PL probing.