Reviewerly/exHarmony huggingface dataset:
PERSEPSI MAHASISWA TERHADAP PEMANFAATAN PERPUSTAKAAN POLITEKNIK LP3I DALAM TRIDHARMA PERGURUAN TINGGI

&lt;p&gt;The three pillars of higher education for lecturers and students is an obligation. The law requires the existence of libraries in universities for those who want to organize higher education. The library, which is a sub-section of the college where it belongs, must participate in the success of the main organization's missions. The presence of the library must provide tangible benefits for academic people. The Kramat LP3I business administration students as the main actors in this study fully participated to realize the goals of the institution. They have a positive impression of the existence of the library. The results showed that the students' perceptions of the use of the Kramat LP3I Polytechnic library in the three pillars of higher education activities were approaching good either at 74.9%.&lt;/p&gt;&lt;p class="Style4"&gt;&lt;strong&gt;Key words:&lt;/strong&gt; Perception, Library, LP3I Polytechnic Students. The three pillars of higher education.&lt;/p&gt;
An Encoder-Decoder Neural Network for Indefinite Length Digit Sequences in Natural Scene Recognition

Abstract Extracting text information from raw images has always been a hot and difficult problem in computer vision research. Due to blurred image, uneven illumination and complex backgrounds, etc., the recognition of natural scene digit is difficult to achieve desired results. In this paper, an encoder-decoder neural network model is proposed to solve the problem of recognition of indefinite length digit sequences in natural scene. The encoder is convolutional neural network (CNN), and the decoder is long short-term memory (LSTM). The encoder accepts a fixed-format image and outputs a fixed-length feature vector; the decoder accepts the feature vector and outputs a predictive sequence of indefinite length. The verification based on Google Street View house number Dataset (SVHN) shows that our method has a good performance. After 10 hours of training, whose accuracy is 96.57%.
Development of Support Program for Managing Assets by Considering Regular Maintenance Cost and Statistically Expected Failure Cost

The asset management technique, which was developed in the field of financial engineering, is expected to rationalize business operations. There have been some previous investigations on introducing this technique into the maintenance divisions of electric power transmission and distribution facilities in Europe. Therefore CRIEPI (Central Research Institute of Electric Power Industry) is examining its effective introduction into Japan in the last about 15 years by referring to these investigations. In this paper, the evaluation is in terms of cost. Two types of cost, the maintenance cost in ordinary operation and the failure-handling cost including a penalty cost of electric outage, are considered in this investigation. The basic concept and a support program used to select better maintenance scenarios for an oil-immersed power transformer are described in this paper. The candidates are the successive operation of aged equipment by repairing it whenever possible or by large-scale repair (overhaul) until expected lifetime, and replacement by new, low-maintenance-cost equipment immediately or at a certain age. The cumulative maintenance cost is calculated for each scenario as the NPV (Net Present Value). In replacement scenarios, the depreciation is considered for realism. At present, the maintenance cost characteristic and the failure probability by age, which should be input to the support program as parameters, are under investigation. The method and difficulties are also described.
Finding Layers Using Hover Visualizations

In 2D digital art software, it is common to organize documents in to layers that are composited to create the final image, mimicking the traditional technique of creating an image by drawing on stacked transparent celluloid sheets. While intuitive, the layer stack suffers from problems of scale and organization. Documents with many layers are unwieldy to edit, finding a specific layer is akin to finding a needle in a haystack. This paper presents a click and hover interaction which visualizes the impact of a layer in the context of the full resolution composited image, providing an easier and faster way to identify layers in complex compositions. Through a user study, we find that users prefer to use hover visualizations in all cases, and that in compositions with many overlapping, semi-transparent layers, users are able to locate layers twice as fast with the click and hover interface.
Binary Classification of Visual Scenes Using Convolutional Neural Network

Scene classification is a dominant track in computer vision tasks as it can help in many missions such as navigation, preprocessing, big data organization, albuming systems, and recognition applications for blinds. Recently, Convolutional Neural Network (CNN) shows noteworthy performance in enhancing the results of most image processing research points. In this paper, we use CNN for indoor-outdoor classification problem with the aid of a large database. Inception-v3 model (without its top layers) is used to extract scene features and extra three layers are attached to adopt the classification task. Our approach reaches an overall classification accuracy of 98.4% which shows the robustness of CNN over the old techniques.
Genesis of the Songhung gold-polymetallic deposit in the Hoechang district, the Democratic People’s Republic of Korea: Constraints from Geology, Petrochemistry and Pb–S–C–O isotope geochemistry

The Songhung deposit is located in the southwestern part of the Yangdok Uplift Zone (YUZ) in the northern part of the Pyongnam Basin in the Sino–Korean Craton. Most of large– and small–scale gold deposits and occurrences were discovered and exploited in the western part of the YUZ. Several tectonic events in this area are the key to understanding the gold and polymetallic mineralization in the Sonhung deposit. The YUZ comprises the Neoarchean Rangnim Metamorphic Belt, Proterozoic and Early Paleozoic rocks in the central domain, and granitoid intrusions of crustal and mantle derivation (Yangdok granitoid intrusions). Two extensive NNE–trending fault zones with mylonitized rocks are interpreted to represent detachment fault and two NE–SW–trending regional rift structures represent zones of subsidence (e.g., the Tokchon–Maengsan to the west, the Kowon to the east) and the Junghwa–Samdung subsidence to the south. It is suggested that the YUZ is a metamorphic core complex (MCC), similar to those in the North China Craton (NCC). Polymetallic mineralization in the Songhung deposit is divided into early and late stages. The early mineralization was formed during compressional deformation in the Triassic accompanied by low–grade metamorphism of the sedimentary host rocks. Variations in Pb, S, C and O isotope ratios indicate that the ore–forming fluids and metals were derived from the country rocks. The Au grade is insignificant in this style of mineralization. The main gold–polymetallic mineralization in the Songhung deposit is related to tectonic extension of the region; the Pb, S, C and O isotope data indicate that these elements, as well as ore–forming fluids, could be derived from both the granitic intrusion (Yangdok granitoid) and the Proterozoic and Paleozoic country rocks.
API Prober – A Tool for Analyzing Web API Features and Clustering Web APIs

Nowadays, Web services attract more and more attentions. Many companies expose their data or services by publishing Web APIs (Application Programming Interface) to let users create innovative services or applications. To ease the use of various and complex APIs, multiple API directory services or API search engines, such as Mashape, API Harmony, and ProgrammableWeb, are emerging in recent years. However, most API systems are only able to help developers to understand Web APIs. Furthermore, these systems do neither provide usage examples for users, nor help users understand the “closeness” between APIs. Therefore, we propose a system, referred to as API Prober, to address the above issues by constructing an API “dictionary”. There are multiple main features of API Prober. First, API Prober transforms OAS (OpenAPI Specification 2.0) into the graph structure in Neo4J database and annotates the semantic concepts on each graph node by using LDA (Latent Dirichlet Allocation) and WordNet. Second, by parsing source codes in the GitHub, API Prober is able to retrieve code examples that utilize APIs. Third, API Prober performs API classification through cluster analysis for OAS documents. Finally, the experimental results show that API Prober can appropriately produce service clusters.
A constant-rate non-malleable code in the split-state model.

Non-malleable codes, introduced by Dziembowski, Pietrzak and Wichs in ICS 2010, have emerged in the last few years as a fundamental object at the intersection of cryptography and coding theory. Non-malleable codes provide a useful message integrity guarantee in situations where traditional error-correction (and even error-detection) is impossible; for example, when the attacker can completely overwrite the encoded message. Informally, a code is non-malleable if the message contained in a modified codeword is either the original message, or a completely “unrelated value”. The family which received the most attention is the family of tampering functions in the so called (2-part) split-state model: here the message x is encoded into two shares L and R, and the attacker is allowed to arbitrarily tamper with each L and R individually. In this work, we give a constant rate non-malleable code from the tampering family containing so called 2-lookahead functions and forgetful functions, and combined with the work of Dodis, Kazana and the authors from STOC 2015, this gives the first constant rate non-malleable code in the split-state model with negligible error. The full version of this paper can be found here: https://eprint.iacr.org/2019/1299.
Velocity-Based Heuristic Evaluation for Path Planning and Vehicle Routing for Victim Assistance in Disaster Scenarios

Natural and human-made disasters require effective victim assistance and last-mile relief supply operations with teams of ground vehicles. In these applications, digital elevation models (DEM) can provide accurate knowledge for safe vehicle motion planning but grid representation results in very large search graphs. Furthermore, travel time, which becomes a crucial cost optimization criterion, may be affected by inclination and other challenging terrain characteristics. In this paper, our goal is to evaluate a search heuristic function based on anisotropic vehicle velocity restrictions for building the cost matrix required for multi-vehicle routing on natural terrain and disaster sites. The heuristic is applied to compute the fastest travel times between every pair of matrix elements by means of a path planning algorithm. The analysis is based on a case study on the ortophotographic DEM of natural terrain with different target points, where the proposed heuristic is compared against an exhaustive search solution.
Efficient Object Detection in Large Images using Deep Reinforcement Learning

Traditionally, an object detector is applied to every part of the scene of interest, and its accuracy and computational cost increases with higher resolution images. However, in some application domains such as remote sensing, purchasing high spatial resolution images is expensive. To reduce the large computational and monetary cost associated with using high spatial resolution images, we propose a reinforcement learning agent that adaptively selects the spatial resolution of each image that is provided to the detector. In particular, we train the agent in a dual reward setting to choose low spatial resolution images to be run through a coarse level detector when the image is dominated by large objects, and high spatial resolution images to be run through a fine level detector when it is dominated by small objects. This reduces the dependency on high spatial resolution images for building a robust detector and increases run-time efficiency. We perform experiments on the xView dataset, consisting of large images, where we increase run-time efficiency by 50% and use high resolution images only 30% of the time while maintaining similar accuracy as a detector that uses only high resolution images.
Sentence-Based Dialect Identification System Using Extreme Gradient Boosting Algorithm

In this paper,Chittaragi, Nagaratna B. a dialect identification system (DIS) is proposed by exploring the dialect specific prosodic features and cepstral coefficients from sentence-levelKoolagudi, Shashidhar G. utterances. Commonly, people belonging to a specific region follow a unique speaking style among them known as dialects. Sentence speech units are chosen for dialect identification since it is observed that a unique intonation and energy patterns are followed in sentences. Sentences are derived from a standard Intonational Variations in English (IViE) speech dataset. In this paper, pitch and energy contour are used to derive intonation and energy features respectively by using Legendre polynomial fit function along with five statistical features. Further, Mel frequency cepstral coefficients (MFCCs) are added to capture dialect specific spectral information. Extreme Gradient Boosting (XGB) ensemble method is employed for evaluation of the system under individual and combinations of features. Obtained results have indicated the influences of both prosodic and spectral features in recognition of dialects, also combined feature vectors have shown a better DIS performance of about 89.6%.
NATIONAL MECHANISMS OF REGULATION OF CROSS-BORDER COPYRIGHT RELATIONS AIMED AT PROTECTION OF ORPHAN WORKS

The article has investigated the mechanisms of the national (both legal and non-legal) regulation of orphan works, i.e. works the holder (holders) of rights to which is (are) not identified and/or the location of the rights-holder is not established. Orphan works are supposedly protected by copyright, which means the validity of exclusive rights and the potential need to obtain permission from the copyright holder for any form of using the works under consideration, namely: reproduction including digitization, translation, processing, etc. However, in a situation where the right holder is not determined (is unavailable), the user does not have an objective opportunity to obtain such a permission, and the work actually remains unknown to the society, although it can be of artistic, cultural or historical value. Since the beginning of the new millennium, the national legal systems of a number of States have establish a special regime for the legal protection of orphan works, and about 20 states of the world have developed the foundations of such a regime so far. The article analyzes the regulation of orphan works in several states — in the EU and its member states, Great Britain, the USA, Canada, Korea, Japan, India. The authors have determined the foundations of the substantive and conflict of laws regulation of cross-border relations regulating orphan works. Features of regulation of works with an unidentified author in the era of a network society are highlighted: in particular, the need to digitize orphan works, since many of them are in a single copy on the medium ruined by time, and the fact that the digitized work can instantly spread from databases to other jurisdictions. The authors provide for the forecast of possible ways of evolution of legal regulation of relations in question with the use of mechanisms of national and international law.
Learning to Compress Using Deep AutoEncoder

A novel deep learning framework for lossy compression is proposed. The framework is based on Deep AutoEncoder (DAE) stacked of Restricted Boltzmann Machines (RBMs), which form Deep Belief Networks (DBNs). The proposed DAE compression scheme is one variant of the known fixed-distortion scheme, where the distortion is fixed and the compression rate is left to optimize. The fixed distortion is achieved by the DBN Blahut-Arimoto algorithm to approximate the N <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">th</sup> -order rate distortion approximating posterior. The trained DBNs are then unrolled to create a DAE, which produces an encoder and a reproducer. The unrolled DAE is fine-tuned with back-propagation through the whole autoencoder to minimize reconstruction errors.
Pre-service Teachers Teaching Chinese as Foreign language integration of Technological Pedagogical Didactical Social ICT Competencies

Teachers need to improve knowledge and skills to enhance, improve, and explore their teaching practices. Many of the studies on competencies of teachers focus on the teaching role of teachers in the classroom rather than teachers' competencies. Competencies are defined as "the setoff knowledge, skills, and experience necessary for future, which manifests in activities." Gupta defined competencies as "knowledge, skills, attitudes, values, motivations and beliefs people need in order to be successful in a job." ICT competencies are based on using tools and technical equipment for reaching, distributing and transferring knowledge. In recent years, the field of TCS/FL (Teaching Chinese as a Second/Foreign Language) has rapidly expanded to every corner of the world. In this paper, we will examine pre-service teachers' TCFL integration of technological pedagogical didactical social ICT competencies in their daily life in the classroom. The present study aimed to identify the competencies needed by teachers for development and implementation of ICT-based education. The survey method of research was employed for the study. A rating scale was used to know the ICT related competencies that teachers needed for instructional purposes. The rating scale consisted of 47 items based on four types of teacher competencies. Sixteen weeks of observation and coaching to pre-service TCFL with an expert in ICT was established to create a new design for teaching pre-service teachers TCFL and to improve their quality and ICT competencies.
Exploring Pervasive Making Using Generative Modeling and Speech Input

Digital manufacturing technologies, especially three-dimensional (3-D) printing, are rapidly transforming the way we experience retail. Consumer products can now be deeply customized to an individual both in terms of fit, as well as style. However, consumers typically lack the design tools and expertise to express their design intent. In this article, we explore a human-centered approach for leveraging artificial intelligence (AI) to scaffold 3-D design tasks to make them accessible to more consumers. Our sunglasses kiosk prototype allows users to iteratively express their design intent at a high-level, using voice. The AI generates new 3-D designs of sunglasses that reflect the user's refined design intent. A user study examines the attitudes and behaviors toward this AI-powered kiosk approach.
Age Estimation Based on Integrated Convolutional Neural Network

In daily life, human faces due to factors such as lifestyle, work environment, and innate genes, showing different degrees of aging. Under the existing conditions, the face age estimation efficiency is low. This paper mainly studies face age classification under unfiltered faces. Unlike traditional face classification under standard images which are collected in laboratory environment, our age classification under unfiltered faces, has more realistic meaning and application value. This paper proposes an integrated algorithm for face age estimation based on convolutional neural network. Firstly, the image is preprocessed. The Preprocessing methods are data enhancement and histogram equalization. Then we design two different convolutional neural network structures. The feature is extracted by convolutional neural network. Finally, the age estimation result is obtained by the integrated algorithm. We have done experiments on the FGNET and CACD2000 datasets. Experiments show that the integrated algorithm, the accuracy is respectively 75.8% and 75.1%. The accuracy of NET1 is respectively 73.9% and 73.6%. The accuracy of NET2 is respectively 71.7% and 70.1%. The age estimation results infer that the integrated network has greater generalization ability and better robustness than the single convolutional neural network.
Attribution of Customers’ Actions Based on Machine Learning Approach

A multichannel attribution model based on gradient boost-ing over trees is proposed, which was compared with the state of theart models: bagged logistic regression, Markov chains approach, shapelyvalue. Experiments on digital advertising datasets showed that the pro-posed model is better than the solutions considered by ROC AUC metric.In addition, the problem of probability prediction of conversion by theconsumer using the ensemble of the analyzed algorithms was solved,the meta-features obtained were enriched with consumers and offlineactivities of the advertising campaign data.
A Platform for Multidisciplinary Research in Disaster Science Through Experiences from the 2011 Tohoku Earthquake and Tsunami

Recent natural disasters, including the 2011 Tohoku earthquake and tsunami, have caused tremendous widespread damage and disruption around the world, and disaster risk reduction (DRR) has become a global priority. Tohoku University established a core research cluster, thus creating a new area of “disaster science” that consolidates four different science areas – practical, natural sciences, humanities, and social science – by adopting the disaster management cycle concept. It takes an inventory of research needs for science and technology applications in DRR and serves as a platform for discussions on strengthening multidisciplinary research on disaster science. This paper introduces the central idea driving the project and offers strategies to contribute to existing academic science and international society.
Improved Parallel Reactive Hybrid Particle Swarm Optimization using Improved Neighborhood Schedule Generation Method for the Integrated Framework of Optimal Production Scheduling and Operational Planning of an Energy Plant in a Factory

This study proposes improved parallel reactive hybrid particle swarm optimization (IPRHPSO) using an improved neighborhood schedule generation method for the integrated framework of optimal production scheduling and operational planning of an energy plant in a factory. Conventionally, in an energy plant, fixed loads of various tertiary energies have been utilized to solve the optimal operational planning of an energy plant so far. Additionally, in production lines, only the minimization of production time has been yet considered. Therefore, the secondary energy cost of a factory cannot be reduced accurately. However, in this study, the loads of various tertiary energies are calculated according to the candidates of production scheduling and the optimal operational planning of an energy plant is determined using the tertiary energies. This can explicitly reduce the secondary energy cost of a factory. The proposed method was applied to ten jobs and machine JSPs each. Accordingly, it was verified that it can minimize the secondary energy cost and production time, simultaneously, and realize fast computation through parallel computation using IPRHPSO.