Reviewerly/exHarmony huggingface dataset:
Neural Embedding Allocation: Distributed Representations of Topic Models

Abstract We propose a method that uses neural embeddings to improve the performance of any given LDA-style topic model. Our method, called neural embedding allocation (NEA), deconstructs topic models (LDA or otherwise) into interpretable vector-space embeddings of words, topics, documents, authors, and so on, by learning neural embeddings to mimic the topic model. We demonstrate that NEA improves coherence scores of the original topic model by smoothing out the noisy topics when the number of topics is large. Furthermore, we show NEA’s effectiveness and generality in deconstructing and smoothing LDA, author-topic models, and the recent mixed membership skip-gram topic model and achieve better performance with the embeddings compared to several state-of-the-art models.
Report on the 8th International Workshop on Bibliometric-Enhanced Information Retrieval (BIR 2019)

The Bibliometric-enhanced Information Retrieval (BIR) workshop series has started at ECIR in 2014 and serves as the annual gathering of IR researchers who address various information-related tasks on scientific corpora and bibliometrics. We welcome contributions elaborating on dedicated IR systems, as well as studies revealing original characteristics on how scientific knowledge is created, communicated, and used. This report presents all accepted papers at the 7th BIR workshop at ECIR 2018 in Grenoble, France.
An Empirical Study of Multi-domain and Multi-task Learning in Chinese Named Entity Recognition

Named entity recognition (NER) often suffers from lack of annotation data. Multi-domain and multi-task learning solve this problem in some degree. However, previous multi-domain and multi-task learning are often studied in English. In the other part, multi-domain and multi-task learning are often researched independently. In this manuscript, we first summarize the previous works of multi-domain and multi-task learning in NER. Then, we introduce the multi-domain and multi-task learning in Chinese NER. Finally, we explore the universal models between multi-domain and multi-task learning. Experiments show that the universal models can be used in Chinese NER and outperform the baseline model.
Local Nontermination Detection for Parallel C++ Programs

One of the key problems with parallel programs is ensuring that they do not hang or wait indefinitely – i.e., there are no deadlocks, livelocks and the program proceeds towards its goals. In this work, we present a practical approach to detection of nonterminating sections of programs written in C or C++, and its implementation into the DIVINE model checker. This complements the existing techniques for finding safety violations such as assertion failures and memory errors. Our approach makes it possible to detect partial deadlocks and livelocks, i.e., those situations in which some of the threads are progressing normally while the others are waiting indefinitely. The approach is also applicable to programs that do not terminate (such as daemons with infinite control loops) as it can be configured to check only for termination of selected sections of the program. The termination criteria can be user-provided; however, DIVINE comes with the set of built-in termination criteria suited for the analysis of programs with mutexes and other common synchronisation primitives.
Multiple Pattern Generations and Chaotic Itinerant Dynamics in Reservoir Computing

It is an open question how spontaneous or "existing" activity plays a constructive role in information processing of the central nervous systems in the brain. In this study, using the FORCE learning framework, we investigate the problem of multiple temporal pattern generations by a single recurrent neural network (RNN) pushed by appropriate combinations of input pulses. We show that weak chaos meaning that the maximal Lyapunov exponent is small but strictly positive (this is not the so-called "edge of chaos") is required as a generic property of the RNN for giving optimal performances. Furthermore, such a network exhibits intermittent switching processes among several quasi-stable patterns when there is no external inputs, which reminds us a prominent behavior of the high-dimensional nonlinear dynamical systems called chaotic itinerancy. We also characterize this itinerant dynamics in terms of the fluctuations of the finite-time Lyapunov exponents and the eigenvalue spectra of the recurrent weight matrices after learning.
Training for the Digital Economy

In this chapter, we will focus on training because the success of the digital economy is determined by knowledge, and therefore the people who create and apply knowledge. In the context of the digital economy, training has two main contexts: training with competencies that are in demand for the digital economy (regardless of the learning technology), and e-learning, in fact, implementing digital technologies in education, for training in any field. In general, the Federal project “Personnel for the digital economy” offers a fairly comprehensive approach to training. It is planned to create conditions for the implementation of personal trajectories of development and profiles of competencies of citizens, the development of the education system for the training of competent specialists in the field of digital economy, the implementation of retraining programs for the professions in demand in the digital economy, the involvement of highly-qualified foreign specialists, and the implementation of promising educational projects with the support of the venture fund.
Developing SPI Culture in the SME Arena: An Exploratory Study

The modern workplace has become gradually more complex as a result of numerous aspects, including technological and economic growth. Software development companies are not an exception; teams are implemented in order to facilitate high performance within these complex, dynamic conditions. One of the challenges they face is training. New team members are unfamiliar with the way of working and therefore require more attention in a hands-on learning environment. Likewise, training is also necessary when new ways of working are required. In this paper, authors present an exploratory study on the deployment of an approach to software improvement training in the context of a small software company. As a result, the participants felt that they improved their knowledge and most of them were more encouraged to know more about the topic. Although online training (synchronous mode) was delivered, after almost all training sessions, the levels of arousal and pleasure were incremented. Finally, the holistic view of training process in this context gives the authors the confidence in the alignment of the training goals and business goals.
Melanoma Classification Using Texture and Wavelet Analysis

Melanoma is the deadliest skin cancer that develops in melanocytes cell that produces melanin. This cancer is caused by ultraviolet light (UV) radiation. We use wavelet method (Discrete Wavelet Packet Transform (DWPT)) and texture feature extraction (Gray-Level Co-occurrence Matrix (GLCM), and Local Binary Pattern (LBP)) as feature extraction in this study to classify melanoma cancer. We divided this research into 4 phases. First, we used hair removal data and hair removal data with augmentation for 3 classes. Second, we used combination data with and without hair removal for three classes. Third, we used hair removal data and hair removal data with augmentation for 2 classes, and the last we used combination data with and without hair removal for two classes. The classification results of 2 classes (Melanoma and Non-Melanoma) were better than the results of the classification of 3 classes (Atypical Nevus, Common Nevus, and Melanoma). The system cannot distinguish between Atypical Nevus and Common Nevus, that's why the classification results of 2 classes were better than three classes. These two classes have characteristics that are almost similar to color and texture. When these two classes are combined, the classification results are better because the classes Nevus and Melanoma, have quite clear differences in terms of color and texture.
Application of Grey Correlation Analysis in Physical Fitness Assessment of Higher Vocational Students

Taking students from Sanya Aviation and Tourism College as research objects, the relationship between physical course and physical health is discussed with the method of grey relational analysis. The index with the highest degree of correlation between female students and teaching assessment was the index related to the physical core competence exercise, while the indexes with the highest degree of correlation between male students and teaching assessment were the indexes related to the maintenance of strong and healthy body shape and physical core competence. Thus, in the physical education course setting of higher vocational colleges and in the process of the integrated development between their teaching and students’ physical health, their teaching mode should aim at students’ exercise needs and promoting the quality improvement of their physical education teaching.
FINANCIAL CONSTRAINTS OF NON-FINANCIAL CORPORATIONS AS A FACTOR OF FINANCIAL STABILITY OF THE BANKING SYSTEM

Introduction. Under the conditions of an active development of financial sector and the demand for expanding its capabilities by business entities there is a need to attract additional capital. It is a reason for finding a certain balance and arrangement of mutually beneficial economic relations. The phenomenon of financial constraints as the company's low ability to attract additional external capital plays a crucial role in modern non-perfect market. The searching for ways of expansion of access to credit by the non-financial corporate sector under the requirement of financial stability support of the banking system is a basis for building sustainable economic ties on the financial market and safe growth of the country's economy without propagating the asymmetric credit cycle. For such a cycle, the stage of recovery of supply and demand in the market is faster and more stable. Purpose. The justification of theoretical basis of the phenomenon of financial constraints and its key channels of interaction with the financial performance of the banking system and its financial stability. Results. The scientific results of work are the detection of the main channels of mutual influence of the financial constraints of non-financial corporations and the financial stability of banks, identification of directions for reducing financial constraints among economic entities in order to expand the possibilities for activating their economic development, that allows to increase the comprehensiveness of decisions on expansion of access to capital of banks and determine their impact on systemic financial risks and financial stability of the banking system in overall.
Optimizing Breast Mass Segmentation Algorithms with Generative Adversarial Nets

Breast cancer is the most ordinary malignant tumor in women worldwide. Early breast cancer screening is the key to reduce mortality. Clinical trials have shown that Computer Aided Design improves the accuracy of breast cancer detection. Segmentation of mammography is a critical step in Computer Aided Design. In recent years, FCN has been applied in the field of image segmentation. Generative Adversarial Networks is also popularized for its ability on generate images which is difficult to distinguish from real images, and have been applied in the image semantic segmentation domain. We apply the Dilated Convolutions to the partial convolutional layer of the Multi-FCN and use the ideas of Generative Adversarial Networks to train and correct our segmentation network. Experiments show that the Dice index of the model D-Multi-FCN-CRF-Adversarial Training on the datasets InBreast and DDSM-BCRP can be increased to 91.15% and 91.8%.
Leveraging Ontology to Enable Indoor Comfort Customization in the Smart Home

This paper introduces the Future Home for Future Communities’ Smart Home, a semantic-based framework for indoor comfort metrics customization inside a living environment. The Smart Home merges Ambient Intelligence, Ambient Assisted Living and Context Awareness perspectives to provide customized comfort experience to the dwellers, also leveraging on a ubiquitous interface. The smart home leverages ontological representations of inhabitants’ health conditions, comfort metrics and available devices to provide dwellers with indoor temperature, humidity rate, CO2 concentration and illuminance suitable for their health conditions and to the activities they want to perform inside the house. Dwellers interactions within the Smart Home are performed via the interface, while the ontologies composing the knowledge base are reasoned and hosted on a semantic repository. Two use cases depict the framework’s functioning in two typical scenarios: adjusting indoor temperature and providing illuminance comfort while preparing a meal.
Software Evolution with a Typeful Version Control System

Agile software development comprises small evolution steps that require discipline and planning to maintain the soundness between all the components of a system. Software product lines pose similar challenges when the soundness between different branches of a product is at stake. Such challenges are usually tackled by engineering methods that focus on the development process, and not on the subject of attention, the code. The risk of code inconsistency between versions has been mostly supported by analysis of the history of releases and by evaluating feature interferences. In this paper, we propose a language-based approach to provide a certifying version control system that enables the explicit specification of the evolution steps of a software artifact throughout its life-cycle, and ensures the sane sharing of code between versions. Our model is suitable to be integrated into a smart development environment to help manage the whole code base of an application. This enables the static verification of program evolution steps, based on the correctness of state transformations between related versions, and for the stable coexistence of multiple versions at run-time. We instantiate our formal developments in a core language that extends Featherweight Java and implements the verification as a type system.
A Human-Friendly Query Generation Frontend for a Scientific Events Knowledge Graph

Recently, semantic data have become more distributed. Available datasets should serve non-technical as well as technical audience. This is also the case with our EVENTSKG dataset, a comprehensive knowledge graph about scientific events, which serves the entire scientific and library community. A common way to query such data is via SPARQL queries. Non-technical users, however, have difficulties with writing SPARQL queries, because it is a time-consuming and error-prone task, and it requires some expert knowledge. This opens the way to natural language interfaces to tackle this problem by making semantic data more accessible to a wider audience, i.e., not restricted to experts. In this work, we present SPARQL-AG, a human-Friendly front-end that automatically generates and executes SPARQL queries for querying EVENTSKG. SPARQL-AG helps potential semantic data consumers, including non-experts and experts, by generating SPARQL queries, ranging from simple to complex ones, using an interactive web interface. The eminent feature of SPARQL-AG is that users neither need to know the schema of the knowledge graph being queried nor to learn the SPARQL syntax, as SPARQL-AG offers them a familiar and intuitive interface for query generation and execution. It maintains separate clients to query three public SPARQL endpoints when asking for particular entities. The service is publicly available online and has been extensively tested.
Design Flow of Single Camera Motion Estimation Using GPU Accelerators

Autonomous navigation and localization remains a challenging task for a moving object. Computer-vision based solutions have been proven effective when estimating the location of a moving object from an on-board camera using feature points. These feature points can be created in the image where the gradients change in all directions and offer unique values. By observing the position changes of feature points, the motion of the camera can be determined. As a result, feature detection and matching play critical role when estimating the motion of camera using video. In this paper, a GPU-accelerated feature detecting, and matching motion estimation system is introduced and tested using a drone platform. During our test, the GPU-accelerated system showed a 4-times increase in speed when compared with CPU based approach.
qSMF: an Approximate Algorithm for Quotum Planted Motif Search on ChIP-Seq Data

Motif discovery is the problem of finding common substrings in a set of biological strings. Therefore, it can be applied to find Transcription Factors Binding Sites (TFBS) that have common patterns (motifs). The Quorum Planted (1, d, q) Motif Search (gPMS) is a version of Planted (1, d) motif discovery where the motif of length I occurs in at least q percent of the sequences with up to d misnialches. The recent introduction of technologies such as ChIP (chromatin immunoprecipitation) experiments poses further challenges for algorithm developers, as the outputs of such experiments contain thousands of sequences. ChIP-Seq (ChIP sequencing, to analyze protein interactions with DNA) has gained considerable attention in the field. 1be focus of this paper is to present an approximate algorithm, quorum Strong Motif Finder (gSMF) that returns up to k highest ranked (strongest) motifs in at least q percent of the data sequences. The proposed algorithm has been tested on ChIP-Seq (large) data that was sampled using the SamScicct algorithm. In comparisons with the FMotif algorithm, the experimental results show that gSMF is faster and returns predicted motifs similar to published ones in the literature and to motifs discovered by a tool that uses the established motif finding algorithms of AlignACE, MEME, MDscan, Trawler, and Weeder.
Capsule Networks for Attention Under Occlusion

Capsule Neural Networks (CapsNet) serve as an attempt to model the neural organization in biological neural networks. Through the routing-by-agreement algorithm, the attention mechanism is implemented as individual capsules that focus on specific upstream capsules while ignoring the rest. By using the routing algorithm, CapsNets are able to attend overlapping digits from the MNIST dataset. In this work, we evaluate the attention capabilities of Capsule Networks using the routing-by-agreement with occluded shape stimuli as presented in neurophysiology. We do so by implementing a more compact type of capsule network. Our results in classifying the non-occluded as well as the occluded shapes show that indeed, CapsNets are able to differentiate occlusions from near-occlusion situations as in real biological neurons. In our experiments, performing the reconstruction of the occluded stimuli also shows promising results.
Compressive Learning of Multi-layer Perceptrons: An Error Analysis

We consider the class of 2-layer feed-forward neural networks with sigmoidal activations - one of the oldest black-box learning machines - and ask the question: Under what conditions can it successfully learn from a random linear projection of the data Part of this question has been previously attempted in the literature: A high probability bound has been given on the absolute difference between the outputs of the network on the sample before and after random projection - provided that the target dimension is at least Ω(M <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> (log MN)), where M is the size of the hidden layer, and N is the number of training points. By contrast, in this paper we prove that a lower target dimension independent of both N and M suffices, not only to guarantee low distortion of the outputs but also to ensure good generalisation for learning the network on randomly projected data. We do not require a sparse representation of the data, instead our target dimension bound depends on the regularity of the problem expressed as norms of the weights. These are uncovered in our analysis by the use of random projection, which fulfils a regularisation role on the input layer weights.
Gentle AdaBoost Algorithm with Score Function Dependent on the Distance to Decision Boundary

This paper presents a new extension of Gentle AdaBoost algorithm based on the distance of the object to the decision boundary, which is defined by the weak classifier used in boosting. In the proposed approach this distance is transformed by Gaussian function and defines the value of a score function. The assumed form of transforming functions means that the objects closest or farthest located from the decision boundary of the basic classifier have the lowest value of the scoring function. The described algorithm was tested on four data sets from UCI repository and compared with Gentle AdaBoost algorithm.
An Efficient and Secure Certificateless Aggregate Signature From Bilinear Maps

Certificateless signature schemes are a very intriguing aspect in information security because of its capability of removing the well-known key escrow problem predominately in ID-based cryptography. He et al. proposed an efficient certificateless aggregate signature scheme and proved that their scheme is secure against all possible types of security attacks. However, the authors still managed to find loopholes in the form of insecurities against ‘honest but curious' and ‘malicious but passive' attacks during cryptanalysis of He et al.'s scheme. The authors propose an efficient certificateless aggregate signature scheme which fills the security gaps in He et al.'s scheme and demonstrate the security in their scheme via a mathematical proof, and reinforce the fact that their scheme is much more efficient in a thorough performance comparison of their scheme against the previous schemes.
Ghosting

Query auto-completion presents a ranked list of queries as suggestions for a user-entered prefix. Ghosting is the process of auto-completing a search recommendation by highlighting the suggested text inline within the search box. We propose the use of a behavior-based recommendation model along with customer search context to ghost on high-confidence queries. We tested ghosting on a retail production system, on over 140 million search sessions. We found that session-context based ghosting significantly increased the acceptance of offered suggestions by 6.18%, reduced misspellings among searches by 4.42%, and improved net sales by 0.14%.
A social wearable that affords vulnerability

We present 'True Colors': a social wearable prototype designed to augment co-located social interaction of players in a LARP (live action role play). We designed it to enable the emergence of rich social dynamics between wearers and non-wearers. True Colors is Y-shaped, worn around the upper body, and has front and back interfaces to distinguish between actions taken by the wearer (front), and actions taken by others (back). To design True Colors, we followed a Research-through-Design approach, used experiential qualities and social affordances to guide our process, and co-designed with LARP designers. 13 True Colors wearables were deployed in a 3-day LARP event, attended by 109 people. From all the functionalities and interactivity the device afforded, players gravitated towards ones that emphasized the social value of experiencing vulnerability as a prompt to get together. This project was recently presented in CHI '19 [1] and may offer useful insights to others in the Ubi-Comp/ISWC community who develop technology to support co-located social experience.