Reviewerly/exHarmony huggingface dataset:
Locally Constant Networks

We show how neural models can be used to realize piece-wise constant functions such as decision trees. Our approach builds on ReLU networks that are piece-wise linear and hence their associated gradients with respect to the inputs are locally constant. We formally establish the equivalence between the classes of locally constant networks and decision trees. Moreover, we highlight several advantageous properties of locally constant networks, including how they realize decision trees with parameter sharing across branching / leaves. Indeed, only $M$ neurons suffice to implicitly model an oblique decision tree with $2^M$ leaf nodes. The neural representation also enables us to adopt many tools developed for deep networks (e.g., DropConnect (Wan et al., 2013)) while implicitly training decision trees. We demonstrate that our method outperforms alternative techniques for training oblique decision trees in the context of molecular property classification and regression tasks.
An educational fuzzy temperature control system

Control engineering is one of the important engineering topics taught at many engineering based universities around the world in most undergraduate and postgraduate courses. The control engineering curriculum includes both the classical feedback based control theory and the state space theory. The modern control theory is based on the intelligent control algorithms utilizing the soft computing techniques, such as the fuzzy control theory and neural networks. Laboratory work is an important part of any control engineering course. The problem with the modern control theory laboratories is that it is essential to offer simple experiments to students so that they can easily put the complex theories they have learned in their courses into practice and see and understand the results. This paper describes the design of a low-cost fuzzy based microcontroller temperature control system using off the shelf products. The developed system should provide a low-cost fuzzy control experiment in the laboratories for students studying control engineering.
Robust, Extensible, and Fast: Teamed Classifiers for Vehicle Tracking in Multi-Camera Networks

As camera networks have become more ubiquitous over the past decade, the research interest in video management has shifted to analytics on multi-camera networks. This includes performing tasks such as object detection, attribute identification, and vehicle/person tracking across different cameras without overlap. Current frameworks for management are designed for multi-camera networks in a closed dataset environment where there is limited variability in cameras and characteristics of the surveillance environment are well known. Furthermore, current frameworks are designed for offline analytics with guidance from human operators for forensic applications. This paper presents a teamed classifier framework for video analytics in heterogeneous many-camera networks with adversarial conditions such as multi-scale, multi-resolution cameras capturing the environment with varying occlusion, blur, and orientations. We describe an implementation for vehicle tracking and vehicle re-identification (re-id), where we implement a zero-shot learning (ZSL) system that performs automated tracking of all vehicles all the time. Our evaluations on VeRi-776 and Cars196 show the teamed classifier framework is robust to adversarial conditions, extensible to changing video characteristics such as new vehicle types/brands and new cameras, and offers real-time performance compared to current offline video analytics approaches.
Joint Semantic Hashing Using Deep Supervised and Unsupervised Methods

Deep hashing methods have achieved impressive results due to the powerful nonlinear mapping ability brought by deep neural network. However, existing deep hashing algorithms treat label information as the only measurement for image similarity, which degenerated the task from retrieval to classification. In this paper, to address this problem, we propose a joint learning framework that learns semantic hash codes from both supervised and unsupervised information. We divide the K-bits hash codes into semantic branch and content branch. The codes from semantic branch are generated with general deep supervised training procedure, while the content branch constructs hash codes by autoencoder incorporated within the hashing model. Experimental results show that the semantic retrieval performance of our framework is compatible to the state of the art. In addition, more semantic information can be embedded into the generated hash codes, which demonstrates the effectiveness of our joint framework for CBIR tasks.
Finite models and the theory of concatenation

We propose FC, a logic on words that combines the previous approaches offinite-model theory and the theory of concatenation, and that has immediateapplications in information extraction and database theory in the form ofdocument spanners. Like the theory of concatenation, FC is built around word equations; incontrast to it, its semantics are defined to only allow finite models, bylimiting the universe to a word and all its subwords. As a consequence of this,FC has many of the desirable properties of FO[<], while being far moreexpressive. Most noteworthy among these desirable properties are sufficientcriteria for efficient model checking and capturing various complexity classesby extending the logic with appropriate closure or iteration operators. These results allows us to obtain new insights into and techniques for theexpressive power and efficient evaluation of document spanners. In fact, FCprovides us with a general framework for reasoning about words that haspotential applications far beyond document spanners.
PENGEMBANGAN MODUL ELEKTRONIK  MOTIVASI BELAJAR SISWA DENGAN MENGGUNAKAN APLIKASI KVISOFT FLIPBOOK BERBASIS ETNHOKONSTRUKTIVISME

Technological progress is inseparable from the success of an education. In the process of learning technology it is important to be integrated in teaching materials. However, while still supporting schools that do not have teaching materials using technology. Therefore, development research is carried out that makes teaching materials by utilizing technology that develops electronic modules. This type of research is a research development developed by Sugiyono. The trial was conducted by giving questionnaires to 21 students of Sridadi 55 / I public elementary schools. Based on the results of research and development figures obtained 85.6% with the category Good on the motivation of students in using electronic modules in the learning process
Depth Estimation Based on Stereo Image Using Passive Sensor

This article presents an algorithm for depth estimation using a pair of passive sensors which involves two cameras. These cameras did not produce any energy to collect the depth information. However, the depth information obtained from a camera can be produced by a matching process between two images at the same viewpoints. These images are captured from two cameras, which are also known as stereo cameras. The matching process consists of several stages, which will produce depth map. The most challenging problem for the matching process is to get an accurate corresponding point between two images. Hence, this article proposes an algorithm for stereo matching using Weighted Sum of Absolute Differences (WSAD), Median Filter (MF), and Bilateral Filter (BF) to surge up the accuracy. The WSAD will be implemented at the first stage to get the preliminary corresponding result, then the BF works as an edge-preserving filter to remove the noise from the first stage. The MF is used at the last stage to improve final depth map. A standard benchmarking dataset from the Middlebury has been used for the experimental analysis and validation. The proposed work in this article achieves good accuracy. The comparison is also conducted with some established methods where the proposed framework performs much better.
Resurfacing Historical Scientific Data: A Case Study Involving Fruit Breeding Data

<strong>Objective</strong>: The objective of this paper is to illustrate the importance and complexities of working with historical analog data that exists on university campuses. Using a case study of fruit breeding data, we highlight issues and opportunities for librarians to help preserve and increase access to potentially valuable data sets. <strong>Methods</strong>: We worked in conjunction with researchers to inventory, describe, and increase access to a large, 100-year-old data set of analog fruit breeding data. This involved creating a spreadsheet to capture metadata about each data set, identifying data sets at risk for loss, and digitizing select items for deposit in our institutional repository. <strong>Results/Discussion</strong>: We illustrate that large amounts of data exist within biological and agricultural sciences departments and labs, and how past practices of data collection, record keeping, storage, and management have hindered data reuse. We demonstrate that librarians have a role in collaborating with researchers and providing direction in how to preserve analog data and make it available for reuse. This work may provide guidance for other science librarians pursing similar projects. <strong>Conclusions</strong>: This case study demonstrates how science librarians can build or strengthen their role in managing and providing access to analog data by combining their data management skills with researchers’ needs to recover and reuse data. <em>The substance of this article is based upon a panel presentation at RDAP Summit 2019.</em>
Preferences of end-users for raw and post-edited NMT in a business environment

This paper presents an evaluation conducted with end-users of translations produced by Swiss Post's in-house Language Service. The aim is to assess whether end-users i) would rate post-edited MT more highly than raw MT; ii) would find that Swiss Post's customized NMT system produces better results than a general-purpose, off-the-shelf NMT engine (DeepL) and, lastly, when aware of translation production metadata, iii) would be willing to pay for post-edited texts. This latter aspect in particular was intended to help determine whether the customers would still value human intervention or whether they would rather accept a lower quality translation and associated risks if this means they can save on costs. Results show that the post-edited texts are preferred by the majority of the participants, even when production metadata are revealed. The in-house customized engine seems to produce better results than DeepL, since the end-users choose the raw output from our system more often than from DeepL.
A Personalized Collaborative Filtering Recommendation System of Network Document Resource Based on Knowledge Graph

The Internet has become one of the important channels for users to obtain information and knowledge. It is crucial that how to acquire personalized requirement of users accurately and effectively from huge amount of network document resource. This paper proposes a personalized collaborative filtering recommendation system for network document resource exploration using knowledge graph which can solve the problem of information overload and resource trek effectively. Extensive system test has been carried out in the field of big data application in packaging industry. The experimental results show that the proposed system recommends network document resource more accurately, and further improves recommendation quality using knowledge graph. Therefore, it can meet people’s personalized resource need more effectively.
Positioning of Post-Soviet Universities in International Ratings: Case of Entrepreneurship Education

Effective functioning of the educational system including higher education is one of actively discussed themes related to socioeconomic development of the country in a modern world. It is known that status of the country is defined by the level of realization of its educational potential, whose main indicator is considered to be the ranking of a higher educational institutions in the world ratings. The economic level of an entrepreneur is determined by the level of development of the entrepreneurial education system. Inclusion of a higher educational institution in the world ratings and having a firm position equal or comparable with leading world universities can be seen as a sign of competitiveness of higher education of the country and result of the efficiency of the conducted reforms and timeliness of innovations. Post-Soviet universities have managed to take a worthy place and have demonstrated positive dynamics, which characterizes efficiency of educational reforms in these countries. The study considers entrepreneurship education in higher educational institutions and analyses dynamics of such institutions in international ratings of higher education. The latter is in interrelation with specific features of higher education development.
Alokasi Air dan Pengembangan Prasarana  Penyediaan Air Baku di DAS Ciliman

Ciliman Watershed is one of the second largest watersheds in Banten Province and is part of the Ciliman-Cibungur River Region, besides being a rice barn in Banten Province, its development has accelerated since the development of Panimbang Airport, Tanjung Lesung Special Economic Zone, and Serang Toll Road Development –Considering. Therefore, it is necessary to regulate the water allocation for various needs and the possibility of developing water supply infrastructure. WEAP is one of the hydrological software in evaluating and planning the water balance of a region. The WEAP approach method operates with the basic principle of water balance and allows the creation of scenarios based on existing conditions to investigate the impact of policies or alternative assumptions in the use and availability of water in the future.The purpose of this study includes; (1) Analyzing the potential availability of raw water in each Ciliman Sub-watershed, (2) Analyzing the raw water needs of the Ciliman watershed, (3). Analyzing water allocation for each raw water requirement in the Ciliman watershed to be fulfilled and (4). Analyzing the potential for developing raw water infrastructure in the Ciliman watershed. The types of data used in this study are secondary data including: (1) monthly rainfall data from the Ciliman reservoir rainfall observation station, Gunung Kencana, Bojong Manik and KP3 at year 1998 - 2015, (2) population data of Ciliman watershed region, (3) the amount of raw water needs data, (4) Irrigation networks around the Ciliman watershed data (5) maps of the Ciliman watershed, (6) Tools used in this study include computer units and WEAP software. The method used in this study basically is the water allocation based on the water balance and determining the order of priority needs. The results analysis obtained that based on 5 scenarios with 3 alternative dam construction the most optimal use of water is fifth scenario with the development of  an agricultural area of 3,200 ha and an average electricity generation potential of 0.96 MW in a year.
Detection of test cheating by outlier analysis approach using probability estimation

With the rapid growth of traffic demand, the number of drivers and motor vehicle ownership is growing rapidly, but a prerequisite of driving in China is holding a valid Chinese driving license. To obtain a driving license, applicant must take a computerized theory test which tests applicant's knowledge of driving and traffic regulations. Detection and supervision of exam cheating is an issue of great importance for road safety and traffic accident prevention. With the development of information techniques, massive data recording applications' test process has been accumulated in integrated application platform of traffic management of public security. In this work, exam cheating detection is modeled as an outlier detection problem, proposing a novel probabilistic method to estimate examination venues' degree of outlier-ness. Experimental result on real dataset is given, demonstrating the effectiveness of our proposed method.
Parallel reliability-guided algorithm for digital image correlation

Digital image correlation is a non-contact optical method for measuring the displacement and strain on the surface of a material. The existing reliability-guided digital image correlation (RG-DIC) method is stable and reliable for a single image but it still needs a large calculational resource for a sequence of images. Due to the decorrelation effect, the reference image must be replaced several times to correct the measurement results for an image sequence involving a large deformation or a discontinuous deformation. Since the process must be executed sequentially, image by image, the total time required is often unacceptably large when the image sequence is long. The challenge is to find a way of improving the speed while retaining calculational reliability and measurement accuracy, which are important for the practical application of DIC. To address this problem, an improved method is proposed in this paper. The parallel bottleneck caused by the decorrelation effect is solved through improving the parallelism to increase the processing speed. This approach can be used to calculate the strain field of the surface of the material in cases of discontinuous deformation, such as in the area near to a crack. Compared with existing methods, this method not only retains the calculational reliability but also greatly improves calculation speed, especially on current multi-core computing platforms.
Development and modeling of online-course “physical and chemicals basis of nanotechnology”

This paper discusses the development and application of the course “Physical and chemical foundations of nan-otechnology” for online application and training of students majoring in “Nanoengineering”. The developed course is included to the studying program of the Department of electronic technologies in the field of mechanical engineering of Moscow state technical University named after Bauman. This course can be useful as an additional material in specialty courses. The modularity of the course makes it easy to integrate it in to sections of other disciplines. The developed course includes four modules, each of which is a complete and complete information base. As a result of the development of the modules, control measures are provided, which the student passes remotely. For the first three modules developed test items that cover all sections of the discipline. For the fourth module the topics of abstract work are proposed. Verification of such work is carried out by the teacher personally, thanks to the distance learning environment Moodle. The defense of each work takes place at online conferences, where students train to present material and defend their point of view. The distance learning environment is chosen taking into account the analysis of information sources and practical skills of the author. As a result of the development of the online course “Physical and chemical fundamentals of nanotechnology” the student acquires skills that will facilitate the development of other special disciplines.
Efficient Pairwise Penetrating-rank Similarity Retrieval

Many web applications demand a measure of similarity between two entities, such as collaborative filtering, web document ranking, linkage prediction, and anomaly detection. P-Rank (Penetrating-Rank) has been accepted as a promising graph-based similarity measure, as it provides a comprehensive way of encoding both incoming and outgoing links into assessment. However, the existing method to compute P-Rank is iterative in nature and rather cost-inhibitive. Moreover, the accuracy estimate and stability issues for P-Rank computation have not been addressed. In this article, we consider the optimization techniques for P-Rank search that encompasses its accuracy, stability, and computational efficiency. (1) The accuracy estimation is provided for P-Rank iterations, with the aim to find out the number of iterations, k , required to guarantee a desired accuracy. (2) A rigorous bound on the condition number of P-Rank is obtained for stability analysis. Based on this bound, it can be shown that P-Rank is stable and well-conditioned when the damping factors are chosen to be suitably small. (3) Two matrix-based algorithms, applicable to digraphs and undirected graphs, are, respectively, devised for efficient P-Rank computation, which improves the computational time from O ( kn 3 ) to O (υ n 2 +υ 6 ) for digraphs, and to O (υ n 2 ) for undirected graphs, where n is the number of vertices in the graph, and υ (≪ n ) is the target rank of the graph. Moreover, our proposed algorithms can significantly reduce the memory space of P-Rank computations from O ( n 2 ) to O (υ n +υ 4 ) for digraphs, and to O (υ n ) for undirected graphs, respectively. Finally, extensive experiments on real-world and synthetic datasets demonstrate the usefulness and efficiency of the proposed techniques for P-Rank similarity assessment on various networks.
Morfometría de cerámicas grises catalanas: algunas consideraciones sobre la definición de tipos en cerámica común de época medieval y postmedieval

Cooking wares fired under reducing atmosphere are one of the most common archaeological findings in medieval contexts. These are coarse, irregular and mostly coil-built vessels finished on the wheel. The great homogeneity of these products, together with the high level of breakage and the massive amount of sherds found out in many contexts are the main difficulties to deal with in order to study them. In this paper results obtained from the morphometric analysis of traditional craft greyware pottery from Quart (Girona) and medieval greywares from the site of Santa Margarida (Martorell, Barcelona) are introduced. According to the obtained results in both cases, we demonstrate the methodological validity of including vessel’s profile measurement within a multivariate statistic exploitation of morphometric data. This exploitation allowed us to obtain precise classifications that take into account the degree of breakage and to establish comparable types.
INTERDISCIPLINARY EDUCATIONAL MODULES TO SUPPORT DIGITAL ENTREPRENEURSHIP UPSKILLING IN CULTURAL AND CREATIVE INDUSTRY

INTERDISCIPLINARY EDUCATIONAL MODULES TO SUPPORT DIGITAL ENTREPRENEURSHIP UPSKILLING IN CULTURAL AND CREATIVE INDUSTRY
Extracting RDF Triples from Raw Text

This manuscript manifests the results of our work on extracting RDF triples from raw text data. We took a corpus of news articles and applied several methods for extracting subject - verb - object relationships from texts. The first method that we used is syntactic parsing and constructing triples from nsubj and obj syntactic relations. The second approach incorporates extracting semantic roles with the help of frame parser and constructing triples from agent, patient and predicate relationships. After applying the aforementioned methods, a manual evaluation was done over a small number of hand labeled samples. Our system achieved a Precision score of 0.34, Recall of 0.46 and a combined score of 0.39 for F-measure as the final result.
Developing of Digital Library Through GSDL: An Experience at GITAM

Developing of Digital Library Through GSDL: An Experience at GITAM - IJLSIT- Print ISSN No: - 2582-1555 Online ISSN No:- 2456-9623 Article DOI No:- 10.18231/j.ijlsit.2019.025, IP Indian Journal of Library Science and Information Technology-IP Indian J Libr Sci Inf Technol