Reviewerly/exHarmony huggingface dataset:
Hierarchical Gaussian Filtering of Sufficient Statistic Time Series for Active Inference

Active inference relies on state-space models to describe the environments that agents sample with their actions. These actions lead to state changes intended to minimize future surprise. We show that surprise minimization relying on Bayesian inference can be achieved by filtering of the sufficient statistic time series of exponential family input distributions, and we propose the hierarchical Gaussian filter (HGF) as an appropriate, efficient, and scalable tool for active inference agents to achieve this.
Amazon SageMaker Automatic Model Tuning: Scalable Black-box Optimization

Tuning complex machine learning systems is challenging. Machine learning models typically expose a set of hyperparameters, be it regularization, architecture, or optimization parameters, whose careful tuning is critical to achieve good performance. To democratize access to such systems, it is essential to automate this tuning process. This paper presents Amazon SageMaker Automatic Model Tuning (AMT), a fully managed system for black-box optimization at scale. AMT finds the best version of a machine learning model by repeatedly training it with different hyperparameter configurations. It leverages either random search or Bayesian optimization to choose the hyperparameter values resulting in the best-performing model, as measured by the metric chosen by the user. AMT can be used with built-in algorithms, custom algorithms, and Amazon SageMaker pre-built containers for machine learning frameworks. We discuss the core functionality, system architecture and our design principles. We also describe some more advanced features provided by AMT, such as automated early stopping and warm-starting, demonstrating their benefits in experiments.
Competence-oriented tasks as a means of forming natural science literacy

Abstract Science literacy is an important component of functional literacy. An effective means of forming functional literacy, that is, the ability to solve standard life tasks in various fields of activity on the basis of applied knowledge, are competence-oriented tasks. Competence-oriented tasks are successfully used in the study of disciplines for students in agricultural and engineering fields. The article provides types and examples of subject, interdisciplinary and practical competence-oriented tasks with chemical and biological content. Competence-oriented tasks differ from traditional ones in that the condition of the task is formulated as a plot, situation or problem, for the solution of which it is necessary to use knowledge that is not explicitly indicated in the text of the task.
Internet Access, Hate Speech and the First Amendment

This article deals with the problem of hate speech on the Internet. The very mode of the Internet is access and the Internet's ready access for ideas is in many ways its most attractive feature. Indeed the Internet is a harbor for all kinds of speech, including hate speech. But as recent events in Charleston, Pittsburgh and El Paso demonstrate, hate speech, such as white supremacist websites, has lethal consequences.The interactivity of a social media platform, the camaraderie that it engenders, the rage that it fosters, provides a means of radicalization unknown to the more traditional media. A federal statute, 47 U.S.C. Sec. 230(c)(1) plays a significant role here. The theme of this provision is to free online service providers from whatever harmful effects that flow from the content they transmit. Furthermore, under First Amendment law, hate speech is generally speaking, protected speech. The Supreme Court has dealt with hate speech in non-internet contexts. This case law is analyzed in determining what guidelines it provides for the new phenomenon of hate speech on the Internet. In addition, the article suggests a possible amendment to Sec. 230 (c)(1).
Progressive Domain Adaptation from a Source Pre-trained Model

Domain adaptation assumes that samples from source and target domains are freely accessible during a training phase. However, such an assumption is rarely plausible in the real-world and possibly causes data-privacy issues, especially when the label of the source domain can be a sensitive attribute as an identifier. To avoid accessing source data that may contain sensitive information, we introduce progressive domain adaptation (PrDA). Our key idea is to leverage a pre-trained model from the source domain and progressively update the target model in a self-learning manner. We observe that target samples with lower self-entropy measured by the pre-trained source model are more likely to be classified correctly. From this, we select the reliable samples with the self-entropy criterion and define these as class prototypes. We then assign pseudo labels for every target sample based on the similarity score with class prototypes. Furthermore, to reduce the uncertainty from the pseudo labeling process, we propose set-to-set distance-based filtering which does not require any tunable hyperparameters. Finally, we train the target model with the filtered pseudo labels with regularization from the pre-trained source model. Surprisingly, without direct usage of labeled source samples, our PrDA outperforms conventional domain adaptation methods on benchmark datasets. Our code is publicly available at this https URL.
Deep Learning Based Parallel Detector for MIMO Systems

Multi-input multi-output (MIMO) technology becomes one of the most popular technologies for the fifth-generation (5G) wireless networks due to the advantages in terms of spectrum efficiency and data rate. However, some existing MIMO detectors cannot achieve a good trade-off between the performance and computational complexity. In this paper, we propose a novel DL-based detector for signal detection in MIMO systems, which integrates several modified DNNs into a parallel structure. By changing the connection between layers of conventional deep neural network (DNN) detectors and introducing some differences into these modified DNNs, the diversity of the detection results of these modified DNNs can be achieved. The numerical results verify that our proposed DL-based detector can obtain better performance in MIMO systems compared with some existing linear detectors and conventional DNN detector.
Exploiting Sharing Join Opportunities in Big Data Multiquery Optimization with Flink

Multiway join queries incur high-cost I/Os operations over large-scale data. Exploiting sharing join opportunities among multiple multiway joins could be beneficial to reduce query execution time and shuffled intermediate data. Although multiway join optimization has been carried out in MapReduce, different design principles (i.e., in-memory Big Data platforms, Flink) are not considered. To bridge the gap of not considering the optimization of Big Data platforms, an end-to-end multiway join over Flink, which is called Join-MOTH system (J-MOTH), is proposed to exploit sharing data granularity, sharing join granularity, and sharing implicit sorts within multiple join queries. For sharing data, our previous work, Multiquery Optimization using Tuple Size and Histogram (MOTH) system, has been introduced to consider the granularity of sharing data opportunities among multiple queries. For sharing sort, our previous work, Sort-Based Optimizer for Big Data Multiquery (SOOM), has been introduced to consider the implicit sorts among join queries. For sharing join, additional modules have been tailored to the J-MOTH optimizer to optimize sharing work by exploiting shared pipelined multiway join among multiple multiway join queries. The experimental evaluation has demonstrated that the J-MOTH system outperforms the naive and the state-of-the-art techniques by 44% for query execution time using TPC-H queries. Also, the proposed J-MOTH system introduces maximal intermediate data size reduction by 30% in average over Hadoop-like infrastructures.
A Multimodal High Level Video Segmentation for Content Targeted Online Advertising

In this paper we introduce a novel advertisement system, dedicated to multimedia documents broadcasted over the Internet. The proposed approach takes into account the consumer’s perspective and inserts contextual relevant ads at the level of the scenes boundaries, while reducing the degree of intrusiveness. From the methodological point of view, the major contribution of the paper concerns a temporal video segmentation method into scenes based on a multimodal (visual, audio and semantic) fusion of information. The experimental evaluation, carried out on a large dataset with more than 30 video documents validates the proposed methodology with average F1 scores superior to 85%.
Permissionless Consensus based on Proof-of-Eligibility

We propose a consensus algorithm whose objective is to decide on the same union of proposed values, such that with high probability all the values proposed by the honest nodes belong to the decision. Our algorithm has been designed to cope with an asynchronous and permissionless system. By relying on a proof-of-eligibility, our algorithm is tolerant to an adversary capable of instantaneously corrupting entities. A straightforward application of our algorithm is the design of permissionless distributed ledgers.
Recurrence of optimum for training weight and activation quantized networks

Deep neural networks (DNNs) are quantized for efficient inference on resource-constrained platforms. However, training deep learning models with low-precision weights and activations involves a demanding optimization task, which calls for minimizing a stage-wise loss function subject to a discrete set-constraint. While numerous training methods have been proposed, existing studies for full quantization of DNNs are mostly empirical. From a theoretical point of view, we study practical techniques for overcoming the combinatorial nature of network quantization. Specifically, we investigate a simple yet powerful projected gradient-like algorithm for quantizing two-layer convolutional networks, by repeatedly moving one step at float weights in the negative direction of a heuristic fake gradient of the loss function (so-called coarse gradient) evaluated at quantized weights. For the first time, we prove that under mild conditions, the sequence of quantized weights recurrently visit the global optimum of the discrete minimization problem for training a fully quantized network. We also show numerical evidence of the recurrence phenomenon of weight evolution in training quantized deep networks.
Twitter library account: highlights for the users and librarians

Purpose The purpose of this paper is to explore the scholarly literature relating to the usage of Twitter in library settings. Various uses of Twitter and strategies used to promote effective utilisation of Twitter in the library are given. The library uses Twitter accounts to support various information services, interact with users and for collecting feedback from users for the services given. Design/methodology/approach The Google Scholar database was consulted to extract the desired data published in the five years (2015–2019). Then, the content analysis method was used to analyse the retrieved literature. Findings The findings revealed that Twitter is used by both the library and its users. The library uses Twitter accounts to support various information services, interact with users and for collecting feedback from users for the services given. Furthermore, the findings revealed different ways that can be implemented to improve the effective utilisation of Twitter in the library. Research limitations/implications The main limitation of this study is that the findings of the research are based on the data collected from Google Scholar in the five years only. Originality/value This study tries to explore how Twitter is used in the library settings and the strategies that when implemented can help to ensure that libraries reap the best from Twitter.
TALES: Test Set of Portuguese Lexical-Semantic Relations for Assessing Word Embeddings

Static word embeddings, like word2vec or GloVe, are of- ten assessed when solving syntactic and semantic analogies. Among the latter, we are interested in relations that one would find in lexical- semantic knowledge bases like WordNet, also covered in analogy test sets for English. This paper describes the creation of a new test for assessing Portuguese word embeddings, dubbed TALES, with an exclusive focus on lexical-semantic relations, acquired from lexi- cal resources in Portuguese. It further reports on the performance of methods previously used for solving analogies, with pre-trained Por- tuguese word embeddings, when applied to the created dataset, an experiment that revealed that TALES is challenging to solve. Results achieved are briefly discussed, with conclusions that may be useful for developing new approaches for this problem, possibly new em- beddings, as well as future versions of TALES.
Target Track Recognition from Few-Labeled Radar Data with Outliers

The frequent occurrence of “black flying” and illegal invasion make it necessary to identify radar target tracks. Existing research on the radar flight target mainly focuses on the target identification. Existing research on the recognition of target track types ignores the impact of outliers in the track data and the small number of labeled data on the target track type recognition performance. In this paper, we propose a target Track Recognition algorithm based on a Semi-Supervised approach for few-labeled radar target track data with outliers (TRSS). We first remove the outliers and fill in the missing track points caused by the outlier removal. We then extract five basic flight features (BFFs) and an advanced flight feature (AFF) from the track data to obtain a strong recognition flight feature combination (SRFFC). Finally, a semi-supervised generative adversarial network (SSGAN) model is constructed to classify flight target tracks using SRFFC as the input. Simulation results show that the proposed algorithm TRSS can effectively improve the accuracy, precision, and recall performance of target track type recognition.
Class Probability-based Visual and Contextual Feature Integration for Image Parsing

Deep learning networks have become one of the most promising architectures for image parsing tasks. Although existing deep networks consider global and local contextual information of the images to learn coarse features individually, they lack automatic adaptation to the contextual properties of scenes. In this work, we present a visual and contextual feature-based deep network for image parsing. The main novelty is in the 3-layer architecture which considers contextual information and each layer is independently trained and integrated. The network explores the contextual features along with the visual features for class label prediction with class-specific classifiers. The contextual features consider the prior information learned by calculating the co-occurrence of object labels both within a whole scene and between neighboring superpixels. The class-specific classifier deals with an imbalance of data for various object categories and learns the coarse features for every category individually. A series of weak classifiers in combination with boosting algorithms are investigated as classifiers along with the aggregated contextual features. The experiments were conducted on the benchmark Stanford background dataset which showed that the proposed architecture produced the highest average accuracy and comparable global accuracy.
Sentiment Analysis for Software Engineering Domain in Turkish

The focus of this study is to provide a model to be used for the identification of sentiments of comments about education and profession life of software engineering in social media and microblogging sites. Such a pre-trained model can be useful to evaluate students’ and software engineers’ feedbacks about software engineering. This problem is considered as a supervised text classification problem, which thereby requires a dataset for the training process. To do so, a survey is conducted among students of a software engineering department. In the classification phase, we represent the corpus by using conventional and word-embedding text representation schemes and yield accuracy, recall and precision results by using conventional supervised machine learning classifiers and well-known deep learning architectures. In the experimental analysis, first we focus on achieving classification results by using three conventional text representation schemes and three N-gram models in conjunction with five classifiers (i.e., naïve bayes, k-nearest neighbor algorithm, support vector machines, random forest and logistic regression). In addition, we evaluate the performances of three ensemble learners and three deep learning architectures (i.e. convolutional neural network, recurrent neural network, and long short-term memory). The empirical results indicate that deep learning architectures outperform conventional supervised machine learning classifiers and ensemble learners.
Research on Arm Embedded System for Football Robot

Two degrees of freedom to play a gift collision board game robot design, manufacture, and the primary control of real-time vision. The information processing and communication unit is one of the high resolution digital cameras. The robot consists of two servo motors and an impact mechanism: three significant elements will be included in the system. On the platform, the camera takes the image and sends it to the central office. Photo processing and adaptive map technology to capture important data to use the. The file will integrate the purchased product; In terms of the structure after it is forwarded via a serial interface to the microcontroller. motor drive control algorithm and orthogonal compression codec for medium distance and angles to better coordinate negative. When it is in line with the disk or the goal post, an impressive unit, and delivers, the firing pin controls the force. Players will kick the maximum power that is generated from his foot into the goal post. Intelligent meet the concept of technology entertainment, cost-effective, is to develop a user-friendly system.
Competency Profile for Software Development Team that Support Project Success

The success of software development depends very much on the competencies possessed by human resources within the development team. These competencies include hard and soft skills. In this study, a team competency model will be proposed, which consists of the minimum competency level for the project manager, analyst, and programmer job roles. The types of competencies that are focused on in this paper consist of soft competencies and hard competencies. The method used to determine the competency model is to use the Focus Discussion Group (FGD) by adopting the Behavior Event Interview (BEI) technique. The result of FGD validates with expert judgments, questionnaires, and team member interviews. This research provides the results of profiling the competency of the software development team on project manager job analysts, analysts, and programmers. This study's competencies profile stated that the average minimum level of soft competency for project managers is 3, 3 for analysts, and 2 for programmers. In comparison, the average minimum level of hard competency for a project manager is 3, analyst 2, and programmer 2. Job Role for project managers requires high enough competence both in terms of technical (hard competency) and soft competency. A job as an Analyst requires a level of soft competence higher than hard competency. At the same time, the job role programmer requires soft and hard competency at a low level, 2. The results of this paper can be useful for the recruitment team to get the right individuals to ensure the success of the project.
SYNTACTIC COMPLEXITY OF ACADEMIC TEXT: A CORPUS STUDY OF WRITTEN PRODUCTION BY LEARNERS OF ENGLISH WITH RUSSIAN L1 IN COMPARISON WITH EXPERT TEXTS OF ENGLISH AUTHORS

The paper analyses data of automated evaluation of syntactical complexity of scholarly texts from four comparable corpora – two of expert texts and two of learner texts – in two subject areas. The research applied two different computer tools – UDPipe parser and AntConc set of programs. The following conclusions were made: – Expert scholarly texts demonstrated high clausal and phrasal complexity, and its level was higher in Business Studies than in Economics. – Clausal complexity in expert texts was much higher than in student texts, while phrasal complexity was at about the same level as, or even slightly lower than that of students. The largest difference was attested for genitive construction with preposition of, which was 1.7 times as frequent in student texts in both subject areas as in expert texts. The results contradict the conclusion of the prior research that with the growth in proficiency clausal complexity decreases, while phrasal complexity grows.
Practice-centered HCI

Human-Computer Interaction is a multidisciplinary field that is taught in Computing undergraduate courses. Although HCI Education has been a theme for research both nationally and internationally, much of this existing research is focused on syllabi guidelines and the topics being taught. We propose an in-depth research so we can better understand how HCI is being taught in Computing undergraduate courses in Brazil. For that, we chose to use the UDUM method, conducting semi-structured interviews with thirteen geographically distributed HCI professors that teach introductory courses in Computing departments and courses in Brazil. Among our results, an aspect that stood out was the high value given to practice by our interviewees, which is the focus of this paper. The interviewees value conducting the course itself guided by a life cycle in HCI, walking through different phases and teaching respective methods and techniques in a practical way, where students can exercise and practice what they are learning in hands-on projects during the course.
HyperSeg: Patch-wise Hypernetwork for Real-time Semantic Segmentation

We present a novel, real-time, semantic segmentation network in which the encoder both encodes and generates the parameters (weights) of the decoder. Furthermore, to allow maximal adaptivity, the weights at each decoder block vary spatially. For this purpose, we design a new type of hypernetwork, composed of a nested U-Net for drawing higher level context features, a multi-headed weight generating module which generates the weights of each block in the decoder immediately before they are consumed, for efficient memory utilization, and a primary network that is composed of novel dynamic patch-wise convolutions. Despite the usage of less-conventional blocks, our architecture obtains real-time performance. In terms of the runtime vs. accuracy trade-off, we surpass state of the art (SotA) results on popular semantic segmentation benchmarks: PASCAL VOC 2012 (val. set) and real-time semantic segmentation on Cityscapes, and CamVid. The code is available: this https URL.