Reviewerly/exHarmony huggingface dataset:
Bidirectional Adversarial Domain Adaptation with Semantic Consistency

Unsupervised domain adaptation (DA) aims to utilize the well-annotated source domain data to recognize the unlabeled target domain data that usually have a large domain shift. Most existing DA methods are developed to align the high-level feature-space distribution between the source and target domains, while neglecting the semantic consistency and low-level pixel-space information. In this paper, we propose a novel bidirectional adversarial domain adaptation (BADA) method to simultaneously adapt the pixel-level and feature-level shifts with semantic consistency. To keep semantic consistency, we propose a soft label-based semantic consistency constraint, which takes advantage of the well-trained source classifier during bidirectional adversarial mappings. Furthermore, the semantic consistency has been first analyzed during the domain adaptation with regard to both qualitative and quantitative evaluation. Systematic experiments on four benchmark datasets show that the proposed BADA achieves the state-of-the-art performance.
Semi-Coupled Convolutional Sparse Learning for Image Super-Resolution

In the convolutional sparse coding-based image super-resolution problem, the coefficients of low- and high-resolution images in the same position are assumed to be equivalent, which enforces an identical structure of low- and high-resolution images. However, in fact the structure of high-resolution images is much more complicated than that of low-resolution images. In order to reduce the coupling between low- and high-resolution representations, a semi-coupled convolutional sparse learning method (SCCSL) is proposed for image super-resolution. The proposed method uses nonlinear convolution operations as the mapping function between low- and high-resolution features, and conventional linear mapping can be seen as a special case of the proposed method. Secondly, the neighborhoods within the filter size are used to calculate the current pixel, improving the flexibility of our proposed model. In addition, the filter size is adjustable. In order to illustrate the effectiveness of SCCSL method, we compare it with four state-of-the-art methods of 15 commonly used images. Experimental results show that this work provides a more flexible and efficient approach for image super-resolution problem.
Automated Classroom Lecture Note Generation Using Natural Language Processing and Image Processing Techniques

Teachers use different teaching methods to deliver the course content to the learners.Smart learning environments helps to reform of the ways of teaching and learning through advancing current methods.These environments help learners to enhance their knowledge and skills by engaging in different learning activities.These different methods can be use of power point, white board drawings, and online learning platforms, audio and video inputs.Teacher's main aim is to maximize the learner engagement in developing knowledge and skills.The teachers' expectation is that the learner's to gather knowledge though the variety of lesson materials.This paper describes a research approach on how the learning materials, smart white board input and the lectures' voice input can be combined in generation of compiled lecture note.In developing the solution it has used the voice input, white board diagrams and other earning materials used in the classroom, materials given in the Learning Management System and any internet materials.The study has adapted Natural Language Processing and Image Processing techniques.Main components of the research were text segmentation, text summarization, topic modeling, and diagram recognition.Outcome of the research can be used by the teachers and learners in day today work.
What is Semantic Search? And why is it important?

Semantic Search" is a term used to describe a variety of approaches 1 to search using techniques beyond (or in addition to) traditional text-and keyword-matching functionality for information retrieval.This short article undertakes to explicate some of the sundry strategies encompassed under this rubric and, further, to briefly explain their potential advantages.Of particular interest is the application of these strategies in scholarly publishing, as various aspects of Semantic Search are suited to tackle the specific retrieval problems presented by a large and complex corpus.In particular, examples from scholarly publishing platforms (as well as search engines and other examples) will be used to illustrate the problems -and solutions -particular to the learned publishing industry.
Research on Vehicle Detection and Tracking Algorithm for Intelligent Driving

To develop the vehicle detection function for preceding car, this paper chooses the method based on machine vision learning for vehicle detection. The LBP and Haar features of cars are used as descriptors, and the positive and negative samples of the vehicle are trained by AdaBoost network, to achieve the detection network. The preceding car video is detected by the training network. Then, Kalman filter technology is introduced to solve the problem of CamShift which is prone to heel-and-miss when the moving state of the target changes. At the same time, the improved Mixture Gauss model and the designed tracking matrix list are used to realize the full automatic multi-target tracking based on CamShift algorithm. The simulation results show that the proposed scheme has fast detection speed, low tracking time complexity and good effect, which also has good comprehensive performance compared with similar algorithms.
READING HABITS AMONGST UNIVERSITY STUDENTS IN THE REPUBLIC OF SERBIA

Reading habits can be described as continuous lifelong need for gaining new knowledge, and are perceived by individuals as both a necessity as well as a source of satisfaction. The reading habits are the subject of this paper, from the perspective of the students themselves as well as to their frequencies of reading books, newspapers, magazines, and the innovations in the reading process (the use of electronic vs. printed formats). The main reason for the stance that reading is both, a necessity and a source of satisfaction can be found in the fact that reading enables enhancement of personal development, as well as the advancement of the society as a whole. After presenting the results of the secondary research, the results of the empirical research conducted during the months of May and June 2018 will be presented, in particularly as to the descriptive and comparative statistics on the reading habits amongst university students in the Republic of Serbia.
Context-Aware Neural Machine Translation Decoding

This work presents a decoding architecture that fuses the information from a neural translation model and the context semantics enclosed in a semantic space language model based on word embeddings.The method extends the beam search decoding process and therefore can be applied to any neural machine translation framework.With this, we sidestep two drawbacks of current document-level systems: (i) we do not modify the training process so there is no increment in training time, and (ii) we do not require document-level annotated data.We analyze the impact of the fusion system approach and its parameters on the final translation quality for English-Spanish.We obtain consistent and statistically significant improvements in terms of BLEU and ME-TEOR and observe how the fused systems are able to handle synonyms to propose more adequate translations as well as help the system to disambiguate among several translation candidates for a word.
Recognizing UMLS Semantic Types with Deep Learning

Entity recognition is a critical first step to a number of clinical NLP applications, such as entity linking and relation extraction.We present the first attempt to apply state-of-theart entity recognition approaches on a newly released dataset, MedMentions.This dataset contains over 4000 biomedical abstracts, annotated for UMLS semantic types.In comparison to existing datasets, MedMentions contains a far greater number of entity types, and thus represents a more challenging but realistic scenario in a real-world setting.We explore a number of relevant dimensions, including the use of contextual versus non-contextual word embeddings, general versus domain-specific unsupervised pre-training, and different deep learning architectures.We contrast our results against the well-known i2b2 2010 entity recognition dataset, and propose a new method to combine general and domain-specific information.While producing a state-of-the-art result for the i2b2 2010 task (F1 = 0.90), our results on MedMentions are significantly lower (F1 = 0.63), suggesting there is still plenty of opportunity for improvement on this new data.
LEARN model of career trajectories: Application to the STEM postdoctoral scholar

Postdoctoral scholars constitute a sizeable population within the academic workforce.Given the intended role of a postdoc position as a time of advanced training and professional development for a future academic career, particularly in science, technology, engineering, and math (STEM) fields, it is important to understand whether and how well the career-related needs of postdocs are being met.The purpose of this research was to understand STEM postdoctoral scholars' career trajectories, with particular attention to the influences on their career-related decision making, by applying the Life Experiences and Role Negotiations (LEARN) model to qualitative data from interviews with 19 STEM postdocs.Findings reveal the multiple influences on postdocs' career-related decisions and how these shape postdocs' career trajectories.Based on our findings, we offer three suggestions for how institutional and national policies and programs can best support the career development of postdocs.
Evaluasi dan Uji Kualitas Website dengan Metode Webqual (Studi Kasus: STMIK Sumedang)

nformation and interaction in an information media is very important in delivering information to users. This information will be well received if there is continuous interaction, and it becomes an indicator of the quality of the information media. The research was conducted aimed at testing information media in the form of websites, especially the STMIK Sumedang website and evaluating the quality of the STMIK Sumedang web from user responses in the form of usability, information quality and interaction quality. This research uses WebQual 4.0 which is compiled based on three qualities, namely ease of use (usability), information quality (information quality) and quality of interaction (interaction quality). The results of this study are that the STMIK website is good enough but there are still procedures in the web menu that are not working. From this study it can be concluded the most influential in the quality of the web is the usability variable can be seen from the following results Y = - 7.174 + 0.479 X1 + 0.182 X2 + 0.060 X3, X1 = usabiliti, X2 = quality of information, X3 = quality of interaction.
MusicKG: Representations of Sound and Music in the Middle Ages as Linked Open Data

The World Wide Web is one of the main ways of accessing knowledge in cultural heritage. Recently, several projects in digital humanities have emerged; however only a few are specialized in musicology. In this paper, we present MusicKG, a multilingual knowledge graph about medieval musicology and musical iconography. A specific ontology has been designed to integrate data from several iconographic and musicology databases. In addition, MusicKG is connected to the Linked Open Data cloud with a significant part of its classes, properties and instances being linked to Wikidata, Getty Vocabularies, MIMO, Iconclass and GeoNames. MusicKG is accessible and reusable by three means: a downloadable RDF dump, a Virtuoso faceted browser and a public SPARQL endpoint. Some representative SPARQL query examples are given to illustrate the scope of MusicKG and to show the potential impact on the research work in medieval musicology.
Generating images of partial face using landmark based k-nearest neighbor

&lt;p&gt;One of the most common approaches to address the partial face recognition challenge is to crop the full face image into segments. The problem is how the full face image must be cropped in a uniform way to generate informative segments. The un-blindly strategy was applied in this paper to generate informative segments, it depends on localizing the facial landmarks and selecting the more informative facial points as a key points, as more as the k-nearest neighbor concept was explored to select the k nearest landmark points to the key points. Two landmark localization techniques were experimented, the suitable technique resulted in segments which are overlapped due to the supervised clustering technique that explored in this paper to cover important biometric face regions, not repeated and covered most probabilities in which it is possible to distinguish the query face from the available part of it.&lt;strong&gt;&lt;em&gt;&lt;/em&gt;&lt;/strong&gt;&lt;/p&gt;
Uncertain Motion Tracking Combined Markov Chain Monte Carlo and Correlation Filters

To address the uncertain motion tracking problem, a tracking method based on the Markov Chain Monte Carlo and correlation filters is proposed. Firstly, multi-scope marginal likelihood (MSML) strategy is introduced to Wang-Landau Monte Carlo (WLMC) tracking method for increasing the acceptance ratio of samples in the promising regions and obtaining a more reliable distribution of density-of-states (DOS). Secondly, in order to raise the efficiency of the tracker, DOS is used to mark the region of interest. Then correlation filters are used to simplify the iterative optimizing operation of the subregions, and eventually target positioning is achieved by maximum response in the promising regions. Finally, a unified tracking framework is designed to enable correlation filters and WLMC with MSML strategy to exploit and complement each other to cope with uncertain motion tracking. Extensive experimental results on uncertain Motion sequences and benchmark datasets demonstrate that the proposed method performs favorably against the state-of-the-art methods.
Cross-domain Attention Network with Wasserstein Regularizers for E-commerce Search

Product search and recommendation is a task that every e-commerce platform wants to outperform their peels on. However, training a good search or recommendation model often requires more data than what many platforms have. Fortunately, the search tasks on different platforms share the common underlying structure. Considering each platform as a domain, we propose a cross-domain learning approach to help the task on data-deficient platforms by leveraging the data from data-abundant platforms. In our solution, the importance of features in different domains is addressed by a domain-specific attention network. Meanwhile, a multi-task regularizer based on Wasserstein distance is introduced to help extract both domain-invariant and domain-specific features. Our model consistently outperforms the competing methods on both public and real-world industry datasets. Quantitative evaluation shows that our model can discover important features for different domains, which helps us better understand different user needs across platforms. Last but not least, we have deployed our model online in three big e-commerce platforms namely Taobao, Tmall, and Qintao, and observed better performance than the production models for all the platforms.
Low-cost limit of classical communication with restricted quantum measurements

Abstract We consider a communication scenario where classical information is encoded in an ensemble of quantum states that admit a power series expansion in a cost parameter and converge to a single zero cost state with vanishing cost. For a given measurement scheme, we derive an approximate expression for mutual information in the leading order of the cost parameter. The general results are applied to selected problems in optical communication, where coherent states of light are used as input symbols and the cost is quantified as the average number of photons per symbol. We show that for an arbitrary individual measurement on phase shift keyed (PSK) symbols, the photon information efficiency is upper bounded by 2 nats of information per photon in the low-cost limit, which coincides with the conventional homodyne detection bound. The presented low-cost approximation facilitates a systematic analysis of few-symbol measurements that exhibit superadditivity of accessible information. For the binary PSK alphabet of coherent states, we present designs for two- and three-symbol measurement schemes based on linear optics, homodyning, and single photon detection that offer respectively 2.49% and 3.40% enhancement relative to individual measurements. We also show how designs for scalable superadditive measurement schemes emerge from the introduced low-cost formalism.
The Use of Human Voice and Speech in Language Technologies: The EU and Russian Intellectual Property Law Perspectives

The article evaluates whether the Russian and EU copyright laws are mutually consistent in their treatment of voice and speech when used as input to the development of language technologies. The discussion is aimed at determining whether there are potential obstacles and legal risks in this regard for co-operation between language-technology developers from Russia and the EU, which could have an adverse impact on such collaboration.
Modification of Stemming Algorithm Using A Non Deterministic Approach To Indonesian Text

Natural Language Processing is part of Artificial Intelegence that focus on language processing. One of stage in Natural Language Processing is Preprocessing. Preprocessing is the stage to prepare data before it is processed. There are many types of proccess in preprocessing, one of them is stemming. Stemming is process to find the root word from regular word. Errors when determining root words can cause misinformation. In addition, stemming process does not always produce one root word because there are several words in Indonesian that have two possibilities as root word or affixes word, e.g.the word “beruang”.To handle these problems, this study proposes a stemmer with more accurate word results by employing a non deterministic algorithm which gives more than one word candidate result. All rules are checked and the word results are kept in a candidate list. In case there are several word candidates were found, then one result will be chosen.This stemmer has been tested to 15.934 word and results in an accurate level of 93%. Therefore the stemmer can be used to detect words with more than one root word.
Query Embedding Learning for Context-based Social Search

Recommending individuals through keywords is an essential and common search task in online social platforms such as Facebook and LinkedIn. However, it is often that one has only the impression about the desired targets, depicted by labels of social contexts (e.g. gender, interests, skills, visited locations, employment, etc). Assume each user is associated a set of labels, we propose a novel task, Search by Social Contexts (SSC), in online social networks. SSC is a kind of query-based people recommendation, recommending the desired target based on a set of user-specified query labels. We develop the method Social Query Embedding Learning (SQEL) to deal with SSC. SQEL aims to learn the feature representation (i.e., embedding vector) of the query, along with user feature vectors derived from graph embedding, and use the learned query vectors to find the targets via similarity. Experiments conducted on Facebook and Twitter datasets exhibit satisfying accuracy and encourage more advanced efforts on search by social contexts.
Discourse-Based Approach to Involvement of Background Knowledge for Question Answering

We introduce a concept of a virtual discourse tree to improve question answering (Q/A) recall for complex, multisentence questions.Augmenting the discourse tree of an answer with tree fragments obtained from text corpora playing the role of ontology, we obtain on the fly a canonical discourse representation of this answer that is independent of the thought structure of a given author.This mechanism is critical for finding an answer that is not only relevant in terms of questions entities but also in terms of interrelations between these entities in an answer and its style.We evaluate the Q/A system enabled with virtual discourse trees and observe a substantial increase of performance answering complex questions such as Yahoo!Answers and www.2carpros.com.
Multi-User Immersive Virtual Reality Prototype for Collaborative Visualization of Microscopy Image Data

This paper examines the creation of the Multi-User Cell Arena (MUCA), a novel 3d data visualization work emphasizing virtual co-location via customizable VR avatars. The work explores the utility of building collaborative virtual-reality spaces using consumer VR hardware and the importance of virtual user embodiment. The workflow from raw microbiological scan data to VR representation is documented, noting the unique design challenges of multi-user spaces. Related works in the field are compared, and the limitations of the mesh-cache shell workflow are discussed along with possible future improvements to the multi-user avatar system.
Locking Mechanism for Concurrency Conflicts on Hyperledger Fabric

Hyperledger Fabric is a popular permissioned blockchain platform and has great commercial application prospects. However, the limited transaction throughput of Hyperledger Fabric hampers its performance, especially when transactions with concurrency conflicts are initiated. In this paper, we focus on transactions with concurrency conflicts and propose a novel method LMLS, which contains the following two components, to optimize the performance of Hyperledger Fabric. Firstly, we design a locking mechanism to discovery conflicting transactions at the beginning of the transaction flow. Secondly, we optimize the ledger storage based on the locking mechanism, where the database indexes corresponding to conflicting transactions are changed and temporally stored in ledger to improve the processing efficiency. Extensive experiments conducted on three datasets demonstrate that the proposed novel methods can significantly increase transaction throughput in the case of concurrency conflicts, and maintain high efficiency in transactions without concurrency conflicts.
LinkedSaeima: A Linked Open Dataset of Latvia’s Parliamentary Debates

This paper describes the LinkedSaeima dataset that contains structured data about Latvia’s parliamentary debates from 1993 until 2017. This information is published at http://dati.saeima.korpuss.lv as Linked Open Data. It is a part of the Corpus of Saeima (the Parliament of Latvia) released as open data for multidisciplinary research. The data model of LinkedSaeima follows the data structure of the LinkedEP dataset with a few modifications. The dataset is augmented with links to the Wikidata knowledge base that provide additional information about the speakers and named entities mentioned in the corpus.