Reviewerly/exHarmony huggingface dataset:
Mapping the Bentham Corpus: Concept-based Navigation

British philosopher and reformer Jeremy Bentham (1748-1832) left over 60,000 folios of unpublished manuscripts. The Bentham Project, at University College London, is creating a TEI version of the manuscripts, via crowdsourced transcription verified by experts. We present here an interface to navigate these largely unedited manuscripts, and the language technologies the corpus was enriched with to facilitate navigation, i.e Entity Linking against the DBpedia knowledge base and keyphrase extraction. The challenges of tagging a historical domain-specific corpus with a contemporary knowledge base are discussed. The concepts extracted were used to create interactive co-occurrence networks, that serve as a map for the corpus and help navigate it, along with a search index. These corpus representations were integrated in a user interface. The interface was evaluated by domain experts with satisfactory results , e.g. they found the distributional semantics methods exploited here applicable in order to assist in retrieving related passages for scholarly editing of the corpus.
Effective Implementation of "Kuznyechik" Block Cipher on FPGA with OpenCL Platform

Growing amounts of information transmitted over computer networks require high-performance implementations of cryptographic functions. At speeds above 10 Gbit/s, it is no longer possible to use the CPU for cryptographic packet processing. The ideal solution is to shift the task of cryptographic packet processing to FPGA. This requires a high-performance, and preferably low-cost implementation of cryptographic functions. Also, since the development of such accelerators in hardware description languages is still quite an expensive task, the programming language OpenCL, supported by all major FPGA manufacturers, can be used to reduce development time. This paper presents a high-performance and low-cost FPGA implementation of the block cipher Kuznyechik in OpenCL for FPGA. The resulting implementation is able to encrypt data at a speed of 41 Gbit/s on FPGA Arria 10 10AX115S2F45I1SG, while occupying no more than 10% of FPGA resources.
Porosity Estimation in Carbonate Rock Based on Voronoi Diagram and 2D Histogram Segmentation in HSV Color Space

In this work, we propose a new segmentation method for color image in HSV color space as it was demonstrated to be a perceptual color space closely associated with the way human eyes perceived colors. Voronoi diagram (VD) are widely used in image processing but it costs an important execution task if applied directly on the image. In this paper, we propose to apply Voronoi diagram on some selected points extracted from an improved 2D histogram in HSV color space. The results are then exploited in porosity estimation in carbonate rocks using thin section image.
Investigation and Optimization of Power Based Smart Home Module Integrated with Automatic Solar Tracking System and MPPT Technique

Currently, the world has a lot of research and practical application of intelligent building systems integrated with intelligent power systems. Because Vietnam is a country with potential for solar energy, the integrator of solar energy is being strongly developed. However, the research result of the optimization of electrical energy used by the intelligent type solar integration is rare. This paper presents the design and structure of the module of intelligent control and monitoring via wireless network integrated with the automatic solar concentration system. The system allows easy connection and operation of all electrical power sources including the dispersal solar power to ensure the efficient and lower power consumption. In addition, the solar cell system is applied the Maximum Power Point Tracking technique (MPPT), which helps to stabilize and improve the power generation efficiency of the PV panels. The test results on the module showed absorption performance of automatic solar-cell flat plate systems is raised by 20-30% and power consumption in small households reduced approximately 30%.
A web-based multiresolution viewer to measure expert search patterns in large microscopy images: towards machine-based dimensionality reduction (Conference Presentation)

In traditional histopathology images, and emerging ex vivo microscopy techniques that generate large (gigapixel) image data, new strategies are needed to efficiently search the images to identify salient areas for human recognition or computer-aided diagnosis, to aid in rapid and efficient review workflows. One strategy is to learn from pathologists to develop model observers that can predict the most significant areas of an image for human review or application of computationally-expensive diagnostic algorithms. To get further understanding of the pathologists’ perception and cognition, we developed a custom web-based multiresolution viewer based on OpenSeadragon, which can record view coordinate, zoom level, coordinate dwell time, and coordinate scan path of users viewing images, under the assumption that the view coordinate represents the real-time visual area. We conducted experiments on two types of data with multiple reviewers: 1) traditional histopathology images of radical prostatectomy specimens, and 2) whole surface images of prostate margins collected on intact organs using structured illumination microscopy. Overall error rate, viewing pattern, saliency maps, dwell time and zoom level on fixation clusters were analyzed on normal tissue and cancer loci/positive margins. The result of the pilot experiments demonstrates that saliency maps correspond well with known areas of tumor in histopathology images and residual cancer in tumor margin images. These data will be used to predict saliency maps on new images based on low-level image features and to test accuracy vs. expert reviewers. This tool has promise for automated image dimensionality reduction and diagnosis of histopathology images.
Identification of the Stage of the Life Cycle of the Scientific Direction with the Help of the Research Front

The article considers the directions of research from the point of view of determining the stages of the life cycle of scientific directions. Much attention is paid to the practices of assessing promising research areas and forecasting the development of scientific direction. The influence of the research front on the phase of the life cycle of the scientific direction is investigated.
Motion Planning Algorithms Using DISC

Motion planning is a process of dividing movement task from start to goal positions into discrete motions, which do not intersect with given obstacles. Most of motion planning algorithms are based on special data structure called visibility graph or roadmap. Each roadmap node represents possible position of robot in space, each edge represents visibility between nodes. The start and goal positions of the robot are connected to the graph and the shortest path between them is searched. Autonomous robots are one of the motion planning application areas. They require high performance and low power consumption in motion planning. These requirements can be met by using DISC (Discrete Mathematics Instruction Set Computer), because it gives good speedup on graph operations and has low power consumption. This paper presents using DISC in several motion planning algorithms and describes their implementation.
Image Analysis by Hahn Moments and a Digital Filter

In this paper, we propose a new method of the fast and stable computation of the discrete orthogonal moments of Hahn. In this method, we have combined two main concepts. The first concept is the digital filters based on the Z-transform to accelerate the calculation process and their inverses of the Hahn moments. The second concept is the partitioning of the image into a set of blocks of fixed sizes where each block is processed independently. The first concept to accelerate the computation time and the second to improve the quality of images reconstruction. Experimental results show that both the proposed algorithms to compute Hahn moments and inverse perform better than existing methods in term of computation speed and the quality of images reconstruction.
An Extension of Delsarte, Goethals and Mac Williams Theorem on Minimal Weight Codewords to a Class of Reed-Muller Type Codes

In 1970 Delsarte, Goethals and Mac Williams published a seminal paper on generalized Reed–Muller codes where, among many important results, they proved that the minimal weight codewords of these codes are obtained through the evaluation of certain polynomials which are a specific product of linear factors, which they describe. In the present paper we extend this result to a class of Reed–Muller type codes defined on a product of (possibly distinct) finite fields of the same characteristic. The paper also brings an expository section on the study of the structure of low weight codewords, not only for affine Reed–Muller type codes, but also for the projective ones.
Type errors for the IDE with Xtext and Xsemantics

Abstract Providing IDE support for a programming language or a DSL (Domain Specific Language) helps the users of the language to be more productive and to have an immediate feedback on possible errors in a program. Static types can drive IDE mechanisms such as the content assist to propose sensible completions in a given program context. Types can also be used to enrich other typical IDE parts such as the Outline and theHovering pop-ups. In this paper, we focus on statically typed imperative languages, adopting some form of type inference. We present a few general patterns for implementing efficient type systems, focusing on type error recovery. This way, the type system is able to type as many parts of the program as possible, keeping a good IDE experience. Type error messages will be placed on the important parts of the program, avoiding cascading errors that can confuse the user. We show two case studies:we apply the presented patterns to implement the type system of two statically typed DSLs, a simple expression language and a reduced Java-like language, with OOP features. We use Xtext as the language workbench for implementing the compiler and the IDE support andXsemantics, a DSL for implementing type systems using a syntax that mimics formal systems. The patterns shown in the paper can be reused also for implementing languages with other language frameworks.
Energy-Efficient Independent Task Scheduling in Cloud Computing

The high scientific applications which contain thousands of tasks are usually executed in virtulized cloud for many benefits. With the increment of the processing capability of the cloud system, the computation energy is significantly consumed along. Thus efficient energy consumption methods are quite necessary to save the energy cost. In this paper, the independent task scheduling problem in a cloud data center is considered. It is a big challenge to achieve the tradeoff between the minimization of computation energy and user-defined deadlines. A heuristic is proposed which consist of an energy efficient task sequencing method and a virtual machine searching strategy. Experimental results show that the proposed heuristic clearly outperforms the other algorithms.
Hybrid Machine Translation Oriented to Cross-Language Information Retrieval: English-Spanish Error Analysis

The main objective of this study focuses on analysing the automatic translation of questions (intended as query inputs to a Cross-Language Information Retrieval System) and on the creation of a taxonomy of translation errors present in hybrid machine translation (HMT) systems. An analysis of translations by HMT systems was carried out. From these, there is a proposal of a type 1, 2 or 3 error taxonomy weighted according to their level of importance. Results indicate that post-editing is an essential task in the automatic translation process.
New music theatre and theories of embodied cognition

This chapter explains new music theatre through the lens of theories of ‘embodied cognition’, according to which musical perception is governed by the ‘mimetic hypothesis’, namely that listening involves an actual or imaginary attempt to imitate the sound-producing actions. It discusses their relevance for an understanding of modernist and experimental opera and music theatre, closing with an analysis of two case studies, Mauricio Kagel’s String Quartet II and Luciano Berio’s Sequenza V. One of the distinguishing features of theories of embodied cognition is their basis in experimental findings in the neurosciences. An analytical method for the study of opera and other multimodal art forms involving music based on theories of embodied cognition would have to develop ways of describing and theorising levels and forms of congruence. An analysis of musical gesture in accordance with theories of embodied cognition is most usefully executed in experimental music theatre, more than in operatic forms.
Concept Drift Based Multi-dimensional Data Streams Sampling Method

A summary can immensely reduce the time and space complexity of an algorithm. This concept is considered a research hotspot in the field of data stream mining. Data streams are characterized as having continuous data arrival, rapid speed, large scale, and cannot be completely stored in memory simultaneously. A summary is often formed in the memory to approximate the database query or data mining task. A sampling technique is a commonly used method for constructing data stream summaries. Traditional simple random sampling algorithms do not consider the conceptual drift of data distributions that change over time. Therefore, a challenging task is sampling the summary of the data distribution in multi-dimensional data streams of a concept drift. This study proposes a sampling algorithm that ensures the consistency of the data distribution with the data streams of the concept drift. First, probability statistics is used on the data stream cells in the reference window to obtain data distribution. A probability sampling is performed on the basis of this distribution. Second, the sliding window is used to continuously detect whether the data distribution has changed. If the data distribution does not change, then the original sampling data are maintained. Otherwise, the data distribution in the statistical window is restated to form a new sampling probability. The proposed algorithm ensures that the data distribution in the data profile is continually consistent with the population distribution. We compare our algorithm with the state-of-the-art algorithms on synthetic and real data sets. Experimental results demonstrate the effectiveness of our algorithm.
Co-seismic and post-seismic changes in groundwater discharge: first results from the epicentral region of the Central Italy earthquake 2016

Short and mid-term effects of earthquakes on groundwater flow were documented in several studies.The hydrogeological response of groundwater flow systems to earthquakes is very complex and due to significant changes in permeability. Such hydrologic changes may occur even at great distances from the epicentre and their magnitude may be related to the proximity to the earthquake focus.Central Italy has been hit by several large earthquakes since August 2016. With reference to the carbonate aquifers located in the epicentral region, the following short term effects were observed: a significant increase of the spring discharges and the disappearance of several perched springs.The deep-seated fault movements and fluid redistribution may cause water-level fluctuations as well as changes in groundwater discharge and composition. This study describes a preliminary characterization of such processes.
A Psychological Resource of Personality as an Integral Eco-Psychological Characteristic (The Interrelationship of Personal Development and Quality of Human Life)

This article presents the results of the study of personality resources as an integral characteristic. The definition of the concept of harmonious development and quality of life of an individual has been given. Three components have been identified in the systemic determination structure of personality: individual properties as prerequisites for harmony of personality; lifestyle and quality of life as an indicator of harmony of an individual and joint activities as the foundation to realize the individual’s life in the system of social relations. A personality resource is expressed in four areas: spirituality; contacts (relations); achievements; the future (dreams, ideals). The psychological resource of personality is determined by: the formation of the intellectual, emotional and behavioral spheres of a person’s life activity; the balance (proportionality) of the development of intellectual, emotional and behavioral spheres of a person’s life activity; integration (close interrelation and internal consistency) of the intellectual, emotional and behavioral spheres of a person’s life activity. In addition, the sphere of personal activity is determined by the manifestation of complexes of psychological personality traits occurring in intra- and interpersonal aspects. The identification of intellectual, emotional and behavioral aspects allowed us to describe the characteristic manifestations of personality in these spheres of life. The foundation of the external, intrapersonal aspect in each of the areas is the presence of diverse experience, enshrined in the relevant skills. The foundation of the interpersonal aspect in each of the spheres is the availability of experience in the realization of internal psychological resources by a person when interacting with the outside world.
Analisis Hasil Implementasi Pengembangan Aplikasi Mobile Pendekatan Hybrid Pada Aplikasi Family Tracking

Family Tracking is a mobile application that utilizes GPS technology to find out the location of each family member. There are several features such as the activity schedule, group chat, and the supervision area added by parents to the child's activity schedule. This application was developed with hybrid approach using Ionic 3 Framework development tools with support of Firebase backend. With the hybrid approach can produce multi-platform applications using the same source code. In this research the application was developed for the Android and iOS platforms. Based on functionality testing in accordance with ISO 9126-2 standards, that all features and functions contained in the application run well on both platforms. Then based on the application performance testing on each device that has the same benchmark value, the application can run stablen iOS devices but has the highest memory usage compared to Android devices with an average difference of 33.81mb. And then applications on iOS devices have lower CPU usage than Android devices, with differences in usage averaging 18.79%. Then based on the results of interviews to get user feedback, the results show that the application can running well and function as expected on Android and iOS devices.
Ubiquitous Rehabilitation Combining Inertial Measurement System with Smartphone and Supported by Visual and Voice Feedback

The positive influencePędrys, Bartłomiej of physicalJosiński, Henryk activityWojciechowski, Konrad on people’s health and well-being is no longer a doubt. Instead of “do we need to exercise?” people ask, “how to exercise?” what is noticeable in growing interest in active lifestyle websites. Following the trend, additionally considering the popularity of smartphone applications, affordability and still decreasing size of inertial measurement unit based motion capture solutions, a new system prototype has been proposed. Smartphone connected with inertial measurement sensors will allow users to perform an independent, ubiquitous rehabilitation.
Robust Semi-supervised Representation Learning for Graph-Structured Data

The success of machine learning algorithms generally depends on data representation and recently many representation learning methods have been proposed. However, learning a good representation may not always benefit the classification tasks. It sometimes even hurt the performance as the learned representation maybe not related to the ultimate tasks, especially when the labeled examples are few to afford a reliable model selection. In this paper, we propose a novel robust semi-supervised graph representation learning method based on graph convolutional network. To make the learned representation more related to the ultimate classification task, we propose to extend label information based on the smooth assumption and obtain pseudo-labels for unlabeled nodes. Moreover, to make the model robust with noise in the pseudo-label, we propose to apply a large margin classifier to the learned representation. Influenced by the pseudo-label and the large-margin principle, the learned representation can not only exploit the label information encoded in the graph-structure sufficiently but also can produce a more rigorous decision boundary. Experiments demonstrate the superior performance of the proposal over many related methods.
Integrating Topic Model and Heterogeneous Information Network for Aspect Mining with Rating Bias

Recently, there is a surge of research on aspect mining, where the goal is to predict aspect ratings of shops with reviews and overall ratings. Traditional methods assumed that aspect ratings in a specific review text are of the same level, which equal to the corresponding overall rating. However, recent research reveals a different phenomenon: there is an obvious rating bias between aspect ratings and overall ratings. Moreover, these methods usually analyze aspect ratings of reviews with topic models at textual level, while totally ignore potentially structural information among multiple entities (users, shops, reviews), which can be captured by a Heterogeneous Information Network (HIN). In this paper, we present a novel model integrating Topic model and HIN for Aspect Mining with rating bias (called THAM). Firstly, a phrase-level LDA model is designed to extract topic distributions of reviews by using textual information. Secondly, making full use of structural information, we constructs a topic propagation network, and propagate topic distributions in this heterogeneous network. Finally, by setting review as the sharing factor, the two parts are integrated into a uniform optimization framework. Experimental results on two real datasets demonstrate that THAM achieves significant performance improvement, compared to the state of the arts.
Guided Super-Resolution as a Learned Pixel-to-Pixel Transformation.

Guided super-resolution is a unifying framework for several computer vision tasks where the inputs are a low-resolution source image of some target quantity (e.g., perspective depth acquired with a time-of-flight camera) and a high-resolution guide image from a different domain (e.g., a gray-scale image from a conventional camera); and the target output is a high-resolution version of the source (in our example, a high-res depth map). The standard way of looking at this problem is to formulate it as a super-resolution task, i.e., the source image is upsampled to the target resolution, while transferring the missing high-frequency details from the guide. Here, we propose to turn that interpretation on its head and instead see it as a pixel-to-pixel mapping of the guide image to the domain of the source image. The pixel-wise mapping is parameterised as a multi-layer perceptron, whose weights are learned by minimising the discrepancies between the source image and the downsampled target image. Importantly, our formulation makes it possible to regularise only the mapping function, while avoiding regularisation of the outputs; Thus producing crisp, natural-looking images. The proposed method is unsupervised, using only the specific source and guide images to fit the mapping. We evaluate our method on two different tasks, super-resolution of depth maps and of tree height maps. In both cases we clearly outperform recent baselines in quantitative comparisons, while delivering visually much sharper outputs.