Reviewerly/exHarmony huggingface dataset:
Self-supervised Visual Feature Learning and Classification Framework: Based on Contrastive Learning

Due to the high (human) cost of image annotation, lack of large-scale annotation data prevents computer vision models from fully solving image classification tasks. On the other hand, the Bert model [1] has shown that training large scale models with non-annotation text data is possible. Inspired by this, we propose a Self-supervised Visual Feature Learning and Classification framework (SVFLC) that can be applied to large scale training data without annotation. This approach is based on the contrastive predictive learning (CPL) method. By refining CPL using special data augmentation and new contrastive learning mechanisms, learning shape-biased features can be emphasized. In the next step, these features are used to produce pseudo labels via a clustering algorithm. Inspired by the recent research on noisy labels, we proceed to employ distance of the sample from cluster centers to eliminate low-confidence labels, and use soft triplet loss and classification loss jointly for achieving robust performance in the final classification. In this unsupervised learning paradigm, on the Imagenet dataset, our framework outperforms commonly used approaches. The unsupervised performance is lower than supervised and semi-supervised learning approaches, but our proposed framework is more suitable for general cases, and serves as a baseline algorithm for future improvement to the unsupervised learning paradigm.
MTLB-STRUCT @Parseme 2020: Capturing Unseen Multiword Expressions Using Multi-task Learning and Pre-trained Masked Language Models

This paper describes a semi-supervised system that jointly learns verbal multiword expressions (VMWEs) and dependency parse trees as an auxiliary task. The model benefits from pre-trained multilingual BERT. BERT hidden layers are shared among the two tasks and we introduce an additional linear layer to retrieve VMWE tags. The dependency parse tree prediction is modelled by a linear layer and a bilinear one plus a tree CRF architecture on top of the shared BERT. The system has participated in the open track of the PARSEME shared task 2020 and ranked first in terms of F1-score in identifying unseen VMWEs as well as VMWEs in general, averaged across all 14 languages.
A shortest path algorithm of long-period cyclic fully connected layer graph based on Dijkstra algorithm

For the shortest path problem of cyclic directed fully connected layer graph in several cycles, there is no excellent general solution algorithm in the literature. In this paper, we address this challenge by proposing a long-period decomposition algorithm based on the Dijkstra algorithm. This method employs periodic search primitives for continuous unidirectional path distance optimization. Thus the global shortest path of the long-period cyclic directed fully connected layer graph can be obtained by selecting primitives and decomposing repetitive cycles.. Experimental results demonstrate that state-of-the-art performance has achieved by our method in solving the optimal energy consumption of repeated picking and placing operations of industrial robots. Moreover, it can significantly reduce the computational complexity of the optimization process compared to the global solution using Dijkstra algorithm.
Complexity of the Self-сonsciousness of Science

The widespread use of bibliometric indicators in science management, associ­ated with the ideology of pseudo-economic positivism, is not only a specific tool for evaluating the work of scientists, but also a serious challenge to the self-con­sciousness of the scientific community. After a while this simplified language becomes the usual language of a scientist’s “self-observation”. At the same time, the expansion of this language meets with increasing resistance that is mani­fested not only in publications of individuals but also in declarations of authori­tative organizations. Science should find a way to give appropriate response to the challenges of the “audit society”. Philosophy of complexity considers the self-consciousness of science as a complex organism, as a system containing a lot of subsystems, that develops under influence of many internal and external factors, is necessary for this purpose. It implies the development of “science of science” (SciSci) as a complex discipline within there would be a real interac­tion between the philosophy of science, history of science, sociology of science, scientometrics, psychology of scientific creativity, economy of science, studies in policy in science an legal regulation and legal aspects of scientific activity.
GIS as a Bridge Across the Digital Divide: Engaging Participatory Methods to Build Capacity in Research Communities

Abstract While access to information-communication technology (ICT) infrastructure such as smartphones and Geographic Information Systems (GIS) web applications has steadily increased in many middle- and upper-income regions of the world, the use and impacts of these resources often remain limited by lack of exposure and training. I draw on ethnographic fieldwork conducted under disaster recovery conditions in Dominica to describe how the process of spatial data collection can be leveraged to help bridge this specific type of digital divide. Incorporating participatory elements into a research design can enable the transfer of valuable GIS skills and knowledge to local practitioners. These new capabilities can then be mobilized to address community problems. Results indicate that this approach has the potential to amplify the power of GIS data by making it more accessible, useable, and impactful to local communities who participate in research.
AUTOMATICAL PRETRANSLATION OF SQL-QUERIES TO A REGULAR PLAN IN CLUSTERIX-LIKE DBMS

Previously completed designs for pretranslating SQL queries did not bring significant success. Nevertheless, such a pretranslator is necessary for the possibility of using Clusterix-like database management systems (DBMSs) by a wide circle of specialists. The article proposes an original approach to pretranslating SQL queries without writing operations to the regular plan for Clusterix-like DBMSs. The possibility of implementing such a pretranslator is discussed. The concepts of a hard and a simple SQL query are given. The basis of the pretranslator is a library for parsing arbitrary grammars antlr4, which is used to build a SQL query tree. A software-implemented pretranslator uses it to build queries that meet the regular plan.
FBK@SMM4H2020: RoBERTa for Detecting Medications on Twitter

This paper describes a classifier for tweets that mention medications or supplements, based on a pretrained transformer. We developed such a system for our participation in Subtask 1 of the Social Media Mining for Health Application workshop, which featured an extremely unbalanced dataset. The model showed promising results, with an F1 of 0.8 (task mean: 0.66).
Hierarchical and Pairwise Document Embedding for Plagiarism Detection

The rapid development of the Internet, especially the application of search engines and machine translation, makes it easier to copy texts. Most existing text plagiarism detection methods are not capable of dealing with the increasing number of plagiarism sources and the increasingly ambiguous plagiarized texts. In this paper, we pay attention to the task of large-scale text deduplication, and propose a multi-level distributed text computing model, which improves the checking speed through multi-level latent semantic analysis, and combines BERT to judge plagiarized text more accurately. In order to further verify the model, we also combined the latest fuzzy plagiarism technology to construct a three-level data set. The experimental results show that our model performs well when plagiarism data increases and plagiarism ambiguity increases.
Informatization construction of human resource management in retail industry based on SaaS platform

The informatization construction of human resource management in retail industry based on SaaS service mode is based on the research of SaaS service comprehensive support technology and SaaS general management service related technology. SaaS service integrated support platform with high scalability, high security, high performance and platform. The requirements of SaaS platform providers and SaaS service providers provide general management services in SaaS mode. At the same time, the SaaS operation management platform supporting the deployment and delivery of SaaS services, standardized management, information sharing, quality monitoring and credit evaluation is studied. Through the research of SaaS support platform and related products, the technical level of SaaS service in China has been improved. It promotes the process of SaaS Service Industrialization in China.
Oltre gli abbonamenti. Che cosa sono i contratti trasformativi

This essay intends to provide the definition and an initial analysis of the transformative agreements, a modern type of deals between scientific publishers and one or more institutions (universities, library systems, etc.). These agreements, which are joining the traditional subscriptions to the scientific journals are not only as a recent phase of the contractual relationship between publishers and research world, but must be considered as one of the phenomena that most characterize the current period of the open access (OA). A phase in which open access records an increase in consensus (even among publishers) but also in criticism as it would be turning into one of the paid publishing options.
ALET (Automated Labeling of Equipment and Tools): A Dataset for Tool Detection and Human Worker Safety Detection

Robots collaborating with humans in realistic environments need to be able to detect the tools that can be used and manipulated. However, there is no available dataset or study that addresses this challenge in real settings. In this paper, we fill this gap with a dataset for detecting farming, gardening, office, stonemasonry, vehicle, woodworking, and workshop tools. The scenes in our dataset are snapshots of sophisticated environments with or without humans using the tools. The scenes we consider introduce several challenges for object detection, including the small scale of the tools, their articulated nature, occlusion, inter-class invariance, etc. Moreover, we train and compare several state of the art deep object detectors (including Faster R-CNN, Cascade R-CNN, YOLOv3, RetinaNet, RepPoint, and FreeAnchor) on our dataset. We observe that the detectors have difficulty in detecting especially small-scale tools or tools that are visually similar to parts of other tools. In addition, we provide a novel, practical safety use case with a deep network which checks whether the human worker is wearing the safety helmet, mask, glass, and glove tools. With the dataset, the code and the trained models, our work provides a basis for further research into tools and their use in robotics applications.
Youth language in virtual space in Nigeria: Multimodal affordance, indexicality and youth identities

Abstract Virtual space expressions contribute to the proliferation of African youth lingo within the Nigerian environment. However, beyond the sheer accumulation of lingual features, virtual space in Nigeria is also a site for the projection of both modern and indigenous youth identities, and for the deployment of sundry communication codes and multimodal affordances. This article explores this twin deployment, first as a way of broadening the sociolinguistic profile of African urban and youth languages, and second as a platform for examining aspects of youth identity formations in the Nigerian setting and how these intersect with the larger linguistic and sociolinguistic environment. Employing the methodology of content analysis, the article establishes youth language practice in virtual space in the Nigerian environment as not only multimodal, but also as indexical. Data samples are drawn from multiple discussion threads on Nairaland , a Nigerian youth news and entertainment blog that is hugely representative of the virtual space youth speech community in the country. The article concludes that virtual space and multimodal affordances enable the projection of hybridized and sometimes nondescript youth identities and alter-egos, as well as a display of communicative usages that appear distinctively African.
Sistem Informasi Absensi Sumber Daya Manusia PT. Rajawali Nusindo

State-owned enterprises (SOEs) are companies that are mostly owned by the state government. Limited Liability Company (PT) Rajawali Nusindo is a state-owned company (SOEs) engaged in trade distribution. The management of human resources, especially those whose employees are absent at Limited Liability Company (PT) Rajawali Nusindo, still has shortcomings because it does not have a permit and leave application feature. The systems operating today are also desktop based with outdated software. The use of outdated software means that the stored data cannot be backed up because the format is no longer compatible with today's modern applications. Based on these conditions, this study aims to develop a web-based attendance information system. The system development method used is the Waterfall method and applies the Codeigniter framework. Based on the results of the Black Box test, it shows that all system functionality is running well. The results obtained from this study is a web-based information system that can be managed by employee attendance data and permits in anintegrated system.
Universal Dependencies for Albanian

In this paper, we introduce the first Universal Dependencies (UD) treebank for standard Albanian, consisting of 60 sentences collected from the Albanian Wikipedia, annotated with lemmas, universal part-of-speech tags, morphological features and syntactic dependencies. In addition to presenting the treebank itself, we discuss a selection of linguistic constructions in Albanian whose analysis in UD is not self-evident, including core arguments and the status of indirect objects, pronominal clitics, genitive constructions, prearticulated adjectives, and modal verbs.
Efficient Objects Identification for a KidSize Humanoid Soccer Robot: Using Feedforward Neural Networks with Image Segmentation

One of the biggest problems faced in robot soccer tournaments is related to object identification in the soccer field. The ball, field marks, crossbars of the goal, the opponent players or teammates are considered objects and should be identified by the robot soccer player in the category of KidSize RoboCup Humanoid League. The main target of this paper is to present a robotic architecture composed of the Software and Hardware Modules responsible for identifying these objects. The Software Module was composed of a DataBase (DB) with images of the objects to be identified and a Feedforward Neural Network (FNN) trained by the Neural Network Toolbox (NNT) of the MATrix LABoratory (MATLAB). In order to integrate the identification process with the Hardware Module, it was necessary to develop the NeuralNet library. This library was implemented in JAVA, making it compatible with multiple platforms. The purpose of this library was to transfer the FNN already trained by the NNT to the Raspberry Pi 3B. The Raspberry Pi 3B was responsible for processing the images captured by the Vision System of the robotic player. Also, all the objects in the field were identified through the trained FNN and the Open Source Computer Vision (OpenCV) library. The results showed an efficiency of about 82% in ball identification, 92% in field marks, 81% in crossbars of the goal, and 93% in opponent players or teammates.
LITERASI MEDIA SOSIAL PADA PEMUDA DI DESA SORO KABUPATEN BIMA

The research purpose is to outlines and analyzes social media literacy rates on youth in Bima District. The research was conduct in Soro village. This research classified as a quantitative study with a survey approach. Sampling determined by the probability sampling method. Data collection using questionnaires and document studies. The data analysis technique used is the scoring method with the Likert scale. The results showed that in general the level of social media literacy in youth in Lariti Beach tourism area precisely in the Soro Village classified as a medium level which is the ability of Technical skills already relatively high, the ability of critical understanding is medium, and the ability of communicative abilities classified as medium.
Chinese Grammatical Error Detection Based on BERT Model

Automatic grammatical error correction is of great value in assisting second language writing. In 2020, the shared task for Chinese grammatical error diagnosis(CGED) was held in NLP-TEA. As the LDU team, we participated the competition and submitted the final results. Our work mainly focused on grammatical error detection, that is, to judge whether a sentence contains grammatical errors. We used the BERT pre-trained model for binary classification, and we achieve 0.0391 in FPR track, ranking the second in all teams. In error detection track, the accuracy, recall and F-1 of our submitted result are 0.9851, 0.7496 and 0.8514 respectively.
Separating Argument Structure from Logical Structure in AMR

The AMR (Abstract Meaning Representation) formalism for representing meaning of natural language sentences puts emphasis on predicate-argument structure and was not designed to deal with scope and quantifiers. By extending AMR with indices for contexts and formulating constraints on these contexts, a formalism is derived that makes correct predictions for inferences involving negation and bound variables. The attractive core predicate-argument structure of AMR is preserved. The resulting framework is similar to the meaning representations of Discourse Representation Theory employed in the Parallel Meaning Bank.
Tencent Submissions for the CCMT 2020 Quality Estimation Task

This paper presents our submissions to CCMT 2020 Quality Estimation (QE) sentence-level task for both Chinese-to-English (ZH-EN) and English-to-Chinese (EN-ZH). We propose new methods based on the predictor-estimator architecture. For the predictor, we propose XLM-predictor and Transformer-predictor. XLM-predictor novelly produces two kinds of contextual token representation, i.e., mask-XLM and non-mask-XLM. For the estimator, both RNN-estimator and Transformer-estimator are conducted and two novel strategies, i.e. top-K strategy and multi-head attention strategy, are proposed to enhance the sentence feature representation. We also propose new effective ensemble technique for sentence-level predictions.
Experimental Study on Visual Variables Influencing Icon Similarity

The study on icon similarity can provide a powerful reference for designing more unique and highly recognizable icons, to help people interact with machines better with the help of a graphical user interface. This paper extracted a series of important icon visual variables and studied their influence on icon similarity judgment. Through the experiment of icons classification tasks, the strategy adopted by subjects to classify similar icons was obtained, and it was found that the subjects gave priority to the visual variables of the background and then paid attention to the visual variables of the icon body in the classification process. Through the comparison behavior experiment, the order of the four visual variables of icon body whose influence degree on icon similarity judgment was obtained. Combined with eye movement index of the subjects during the comparison experiment, it is concluded that the icons with high similarity receive more attention during the comparison. The results are of great significance for research on similarity recognition of icons.
Fast and Guaranteed Safe Controller Synthesis for Aerial Vehicle Models

We address the problem of synthesizing a controller for nonlinear aerial vehicle models with reach-avoid requirements. Our controller consists of a reference controller and a tracking controller which drives the actual trajectory to follow the reference trajectory or waypoints. We employ a machine learning-based framework to learn a tracking controller that can track any reference trajectory and simultaneously learn a tracking certificate such that the tracking error between the actual trajectory of the closed-loop system and the reference trajectory can be bounded. Moreover, such a bound on the tracking error is independent of the reference trajectory. Using such bounds on the tracking error, we propose a method that can find a reference trajectory by solving a satisfiability problem over linear constraints. Our overall algorithm guarantees that the resulting controller can make sure every trajectory from the initial set of the aerial vehicle satisfies the given reach-avoid requirement.
Agile or waterfall development: The Clementon Company dilemma

This case study documents the challenges faced by Iota Consultancy Services, an IT Service organization, as it simultaneously developed and deployed an IT Services Management software product for one of its premier clients, The Clementon Company. A leading market research firm, The Clementon Company had its footprints across the globe. Its recent acquisition of several smaller research agencies had created a complex IT landscape, in terms of the technologies adopted and the processes followed. In Iota Consultancy Services’ 10-year-old history, this engagement with The Clementon Company was a significant milestone, consisting of two parts—(1) Streamlining and standardizing The Clementon Company’s IT processes and (2) maintaining The Clementon Company’s organization-wide IT infrastructure. Iota Consultancy Services began this crucial assignment with an initial study to identify a product for the The Clementon Company’s IT department. Iota Consultancy Services, a rapidly growing player in the sector, had indigenously developed a few IT products, as well. It had an IT Services Management product called Helpdesk Management. Being the sole service provider for maintaining The Clementon Company’s entire IT infrastructure, Iota Consultancy Services felt that Helpdesk Management could be deployed as a single tool across the organization. However, until now, Helpdesk Management had never been deployed as a comprehensive IT Services Management product for any organization. This meant that Iota Consultancy Services needed to simultaneously enhance its Helpdesk Management tool by developing new features, and also deploy it for The Clementon Company. This was Iota Consultancy Services’ chance to earn a reputation as a successful product company, which would result in increased revenue. However, if things did not go well, Iota Consultancy Services could lose face forever. Iota Consultancy Services’ dilemma was whether it should propose Helpdesk Management as a tool for The Clementon Company, or not. At this juncture, everything depended on whether Iota Consultancy Services could successfully customize the Helpdesk Management tool to suit The Clementon Company’s requirements.
Unsupervised Machine Translation Quality Estimation in Black-Box Setting

Machine translation quality estimation (Quality Estimation, QE) aims to evaluate the quality of machine translation automatically without golden reference. QE is an important component in making machine translation useful in real-world applications. Existing approaches require large amounts of expert annotated data. Recently, there are some trials to perform QE in an unsupervised manner, but these methods are based on glass-box features which demands probation inside the machine translation system. In this paper, we propose a new paradigm to perform unsupervised QE in black-box setting, without relying on human-annotated data or model-related features. We create pseudo-data based on Machine Translation Evaluation (MTE) metrics from existing machine translation parallel dataset, and the data are used to fine-tune multilingual pre-trained language models to fit human evaluation. Experiment results show that our model surpasses the previous unsupervised methods by a large margin, and achieve state-of-the-art results on MLQE Dataset.