Reviewerly/exHarmony huggingface dataset:
SmartHandle

Over recent years, smart locks have evolved as cyber-physical devices that can be operated by digital keypads, physiological biometrics sensors, smart-card readers, or mobile devices pairing, to secure door access. However, the underlying authentication schemes, i.e., knowledge-based (e.g., PIN/passwords), possession-based (e.g., smartphones, smart cards), or physiological biometric-based (e.g., fingerprint, face), utilized in smart locks, have shown several drawbacks. Studies have determined that these authentication schemes are vulnerable to various attacks as well as lack usability. This paper presents SmartHandle - a novel behavioral biometric-based transparent user authentication scheme for smart locks that exploits users' hand-movement while they rotate the door handle to unlock the door. More specifically, our solution models the user's hand-movement in 3-dimensional space by fetching the X, Y, and Z coordinates from 3 sensors, namely, accelerometer, magnetometer, and gyroscope corresponding to the hand-movement trajectory, to generate a user-identification-signature. We validated our solution for a multi-class classification scenario and achieve a True Acceptance Rate (TAR) of 87.27% at the False Acceptance Rate (FAR) of 1.39% with the Linear Discriminant Classifier (LDC) on our collected dataset from 11 users. The solution can be easily deployed at the main entrance of homes and offices offering a secure and usable authentication scheme to their legitimate users.
A comparative study on google translate: An error analysis of Turkish-to English translations in terms of the text typology of Katherina Reiss

Machine Translation (MT) has become one of the&#x0D; important topics of public interest especially with the advent of technology&#x0D; and the blooming need for translation. Google&#x0D; Translate, as an MT, provides quick translations; however, the quality of&#x0D; the texts often remains unsatisfactory. This study aims to analyze the&#x0D; translation errors of Google Translate&#x0D; outputs conducted from Turkish into English. The errors are classified into&#x0D; four major categories: Lexical Errors,&#x0D; Morphological Errors, Syntactic Errors, Semantic and Pragmatic Errors,&#x0D; which include subcategories. In parallel with the aim of the study, a text from&#x0D; each of the three text types put forward by Katherina Reiss (1971), was chosen&#x0D; to be translated by Google Translate and to be analyzed. These text types are Informative Texts, Expressive Texts, and Operative Texts. In the study, firstly&#x0D; it is aimed to explore which of the main text types has more translation&#x0D; errors, secondly, whether the translation error types vary by the main text&#x0D; types or not. In order to deal with&#x0D; this, both quantitative and qualitative analyses are utilized in the study. The&#x0D; data analysis revealed that the main text type that has more translation errors&#x0D; is the translation of operative text and expressive text,&#x0D; respectively. It is also observed that the error pattern between the text types&#x0D; was different. The informative text mainly includes lexical errors,&#x0D; whereas operative and expressive mainly include semantic and pragmatic&#x0D; errors. Summing up the results, it can be concluded that although&amp;nbsp;Google Translate&amp;nbsp;provides&#x0D; much quicker translations among a large number of languages, there is still a&#x0D; need for human assistance.
Statistically Significant Discriminative Patterns Searching

In this paper, we propose a novel algorithm, named SSDPS, to discover patterns in two-class datasets. The SSDPS algorithm owes its efficiency to an original enumeration strategy of the patterns, which allows to exploit some degrees of anti-monotonicity on the measures of discriminance and statistical significance. Experimental results demonstrate that the performance of the SSDPS algorithm is better than others. In addition, the number of generated patterns is much less than the number of the other algorithms. Experiment on real data also shows that SSDPS efficiently detects multiple SNPs combinations in genetic data.
An Efficient Hardware Architecture of Codec2 Low Bit-rate Speech Decoder

Speech coding algorithms have been developed for years to digitalize human voice to a few binary bits as possible while maintaining reasonable quality. Codec2 vocoder algorithm is one of an efficient sinusoidal coding with very high compression rate down to 450 bit/s. In this paper, an efficient hardware architecture of Codec2 decoder is proposed to increase the performance of voice decoding process and reduce comprehensive tasks from a host processor. Although the sinusoidal decoding algorithm is complicated with many arithmetic operations such as the arithmetic of complex numbers, FFT, FIR filter, division, trigonometry, exponential and logarithm functions, several techniques were explored to optimize and parallelize a datapath of the proposed hardware. The implementation on Xilinx Artix-7 FPGA revealed that the proposed architecture could reduce the processing time up to 20 times, compared to the conventional Cortex-M4 CPU running with the original software.
The Influence of Computer Assisted Language Learning (Call) to Improve English Speaking Skills

The use of technology in learning has grown rapidly. One of them is learning English using Computer Assisted Language Learning or CALL. CALL is a computer-aided learning media used in the field of education which easy to access, spread and stored. Afterwards, this study tried to determine the influence of CALL media with lecture method and discussion to improve the cadets’ English speaking ability. This study used quantitative approach and descriptive quantitative analysis that revealed the problems occurred during the English learning. Statistical analysis is used by reviewing the learning process which were involving 50 cadets in both class A and B. The findings showed a significant difference of the use of CALL media in lecture method and discussion on English speaking skill.
Local Unsupervised Learning for Image Analysis

Local Hebbian learning is believed to be inferior in performance to end-to-end training using a backpropagation algorithm. We question this popular belief by designing a local algorithm that can learn convolutional filters at scale on large image datasets. These filters combined with patch normalization and very steep non-linearities result in a good classification accuracy for shallow networks trained locally, as opposed to end-to-end. The filters learned by our algorithm contain both orientation selective units and unoriented color units, resembling the responses of pyramidal neurons located in the cytochrome oxidase 'interblob' and 'blob' regions in the primary visual cortex of primates. It is shown that convolutional networks with patch normalization significantly outperform standard convolutional networks on the task of recovering the original classes when shadows are superimposed on top of standard CIFAR-10 images. Patch normalization approximates the retinal adaptation to the mean light intensity, important for human vision. We also demonstrate a successful transfer of learned representations between CIFAR-10 and ImageNet 32x32 datasets. All these results taken together hint at the possibility that local unsupervised training might be a powerful tool for learning general representations (without specifying the task) directly from unlabeled data.
Archival gamma ray logs standardization by nondestructive core measurements of the low-radioactivity rocks

The present study analyzes spectral gamma ray data of low-radioactivity rock cores from Rotliegend Basin. The main goal of this research was standardization of archival gamma ray logs measured in counts per minute to American Petroleum Institute units. Selected core intervals, log data, and additional crushed samples covered three different boreholes: J-1, L-7, and K-2 from Zielona Góra Basin, localized in western Poland, where J-1 was a reference borehole. Core measurements were performed on the sandstone and conglomerate formations by Gamma Logger GT spectrometer, which uses two scintillation detectors containing bismuth germanate crystals. In the cores, real concentrations of potassium, uranium, and thorium were acquired what enabled to calculate total content of radioactive elements and total uninfluenced by uranium. Delivered new total GR_GLGT curve from cores allowed accurate standardization of archival GR curves from L-7 and K-2 boreholes. Presented results showed a very good correlation between logs, core data, and laboratory analyses. These data allow precise shale volume reinterpretation.
PERAN MEDIA PEMBELAJARAN MENINGKATKAN HASIL BELAJAR PESERTA DIDIK

This paper describes the use of media in learning as an intermediary to facilitate the delivery of learning material. Learning media is designed as well as possible by educators so that students can more easily absorb learning material and also increase the stimulus of students to learn. As time goes on, learning media are also increasingly sophisticated from those using traditional media to modern ones like now. The use of instructional media also helps students to absorb the same material as others, giving rise to the same perception among students with one another and helping students to understand the material conveyed by students.
Simulating Search and Rescue Operations Using Swarm Technology to Determine How Many Searchers Are Needed to Locate Missing Persons/Objects in the Shortest Time

The use of multiple computer-based agents formed into a swarm has found potential applications in a number of tasks [1]. Because of its flexibility and scalability, by using collaborative behavior [2], a swarm system is able to complete tasks. One area that is of particular interest is the usage of a swarm system in the search element of search and rescue missions [3]. The success of a search mission depends on the deployment of a number of assets and as human driven assets are not only expensive, in severe conditions, they can put the operators at risk. Hence, utilising unmanned autonomous assets such as swarms is preferable for future developments. However, as the cost of the autonomous assets is relatively low, a question arises as to how many agents the swarm should be composed of. Little work has been done in this area with the assumption being made that the more assets the better. Thus, the aim of this research is to investigate, through simulation, the effects the number of agents has on the need for information.
Isogeny graphs with maximal real multiplication

An isogeny graph is a graph whose vertices are principally polarizable abelian varieties and whose edges are isogenies between these varieties. In his thesis, Kohel describes the structure of isogeny graphs for elliptic curves and shows that one may compute the endomorphism ring of an elliptic curve defined over a finite field by using a depth-first search (DFS) algorithm in the graph. In dimension 2, the structure of isogeny graphs is less understood and existing algorithms for computing endomorphism rings are very expensive. In this article, we show that, under certain conditions, the problem of determining the endomorphism ring can also be solved in genus 2 with a DFS-based algorithm. We consider the case of genus-2 Jacobians with complex multiplication, with the assumptions that the real multiplication subring has class number one and is locally maximal at ℓ, for ℓ a fixed prime. We describe the isogeny graphs in that case, by considering cyclic isogenies of degree ℓ, under the assumption that there is an ideal l of norm ℓ in K0 which is generated by a totally positive algebraic integer. The resulting algorithm is implemented over finite fields, and examples are provided. To the best of our knowledge, this is the first DFS-based algorithm in genus 2.
Aggregating Bidirectional Encoder Representations Using MatchLSTM for Sequence Matching

Bo Shao, Yeyun Gong, Weizhen Qi, Nan Duan, Xiaola Lin. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). 2019.
Survey and Proposal of an Adaptive Anomaly Detection Algorithm for Periodic Data Streams

Real-time anomaly detection of massive data streams is an important research topic nowadays due to the fact that a lot of data is generated in continuous temporal processes.There is a broad research area, covering mathematical, statistical, information theory methodologies for anomaly detection.It addresses various problems in a lot of domains such as health, education, finance, government, etc.In this paper, we analyze the state-of-the-art of data streams anomaly detection techniques and algorithms for anomaly detection in data streams (time series data).Critically surveying the techniques' performances under the challenge of real-time anomaly detection of massive high-velocity streams, we conclude that the modeling of the normal behavior of the stream is a suitable approach.We evaluate Holt-Winters (HW), Taylor's Double Holt-Winters (TDHW), Hierarchical temporal memory (HTM), Moving Average (MA), Autoregressive integrated moving average (ARIMA) forecasting models, etc. Holt-Winters (HW) and Taylor's Double Holt-Winters (TDHW) forecasting models are used to predict the normal behavior of the periodic streams, and to detect anomalies when the deviations of observed and predicted values exceeded some predefined measures.In this work, we propose an enhancement of this approach and give a short description about the algorithms and then they are categorized by type of prediction as: predictive and non-predictive algorithms.We implement the Genetic Algorithm (GA) to periodically optimize HW and TDHW smoothing parameters in addition to the two sliding windows parameters that improve Hyndman's MASE measure of deviation, and value of the threshold parameter that defines no anomaly confidence interval [1].We also propose a new optimization function based on the input training datasets with the annotated anomaly intervals, in order to detect the right anomalies and minimize the number of false ones.The proposed method is evaluated on the known anomaly detection benchmarks NUMENTA and Yahoo datasets with annotated anomalies and real log data generated by the National education information system
Constrained Sequence-to-sequence Semitic Root Extraction for Enriching Word Embeddings

In this paper, we tackle the problem of "root extraction" from words in the Semitic language family.A challenge in applying natural language processing techniques to these languages is the data sparsity problem that arises from their rich internal morphology, where the substructure is inherently non-concatenative and morphemes are interdigitated in word formation.While previous automated methods have relied on human-curated rules or multiclass classification, they have not fully leveraged the various combinations of regular, sequential concatenative morphology within the words and the internal interleaving within templatic stems of roots and patterns.To address this, we propose a constrained sequence-tosequence root extraction method.Experimental results show our constrained model outperforms a variety of methods at root extraction.Furthermore, by enriching word embeddings with resulting decompositions, we show improved results on word analogy, word similarity, and language modeling tasks.
Terminology Translation in Low-Resource Scenarios

Term translation quality in machine translation (MT), which is usually measured by domain experts, is a time-consuming and expensive task. In fact, this is unimaginable in an industrial setting where customised MT systems often need to be updated for many reasons (e.g., availability of new training data, leading MT techniques). To the best of our knowledge, as of yet, there is no publicly-available solution to evaluate terminology translation in MT automatically. Hence, there is a genuine need to have a faster and less-expensive solution to this problem, which could help end-users to identify term translation problems in MT instantly. This study presents a faster and less expensive strategy for evaluating terminology translation in MT. High correlations of our evaluation results with human judgements demonstrate the effectiveness of the proposed solution. The paper also introduces a classification framework, TermCat, that can automatically classify term translation-related errors and expose specific problems in relation to terminology translation in MT. We carried out our experiments with a low resource language pair, English–Hindi, and found that our classifier, whose accuracy varies across the translation directions, error classes, the morphological nature of the languages, and MT models, generally performs competently in the terminology translation classification task.
dpSmart: A Flexible Group Based Recommendation Framework for Digital Repository Systems

Digital Repository Systems have been used in most modern digital library platforms. Even so, Digital Repository Systems often suffer from problems such as low discoverability, poor usability, and high drop-off visit rates. With these problems, the majority of the content in the digital library platforms may not be exposed to end users, while at the same time, users are desperately looking for something which may not be returned from the platforms. The recommendation systems for digital libraries were proposed to solve these problems. However, most recommendation systems have been implemented by directly adopting one specific type of recommender like Collaborative-Filtering (CF), Content-Based Filtering (CBF), Stereotyping, or hybrid recommenders. As such, they are either (1) not able to accommodate the variation of the user groups, (2) require too much labor, or (3) require intensive computational complexity. In this paper, we design and implement a new recommendation system framework for Digital Repository Systems, named dpSmart, which allows multiple recommenders to work collaboratively on the same platform. In the proposed system, a user-group based recommendation strategy is applied to accommodate the requirements from the different types of users. A user recognition model is built, which can avoid the intensive labor of the stereotyping recommender. We implement the system prototype as a sub-system of the FIU library site (http://dpanther.fiu.edu) and evaluate it on January 2019 and February 2019. During this time, the Page Views have increased from 8,502 to 10,916 and 10,942 to 12,314 respectively, compared to 2018, demonstrating the effectiveness of our proposed system.
On the Use of Non-technical Requirements for the Evaluation of FOSS Software Components

Modern enterprises rely on Information Systems specifically designed to manage the increasing complexity of their operation. In the usual case, they are built as hybrid systems which integrate several software components of different nature and origins e.g.; legacy systems, web services, commercial components (typically referred as COTS) and, Free and/or Open Source Software (FOSS). The evaluation of individual software components is highly relevant in this kind of system and is usually conducted with the support of software Quality Models. However, these artifacts usually consider only the evaluation of technical quality requirements, in detriment of non-technical ones (e.g. costs, legal and quality of suppliers) which can be just as critical, particularly in the selection of COTS and FOSS. In this paper, we propose an extension to preexisting software Quality Models, intended to deal with technical and non-technical quality requirements in a homogeneous and holistic way. The relevance of the approach is illustrated in relation to four industrial FOSS adoption processes.
FRS: A simple knowledge graph embedding model for entity prediction

Entity prediction is the task of predicting a missing entity that has a specific relation-ship with another given entity. Researchers usually use knowledge graphs embedding(KGE) methods to embed triples into continuous vectors for computation and perform the tasks of entity prediction. However, KGE models tend to use simple operations to refactor entities and relationships, resulting in insufficient interaction of components of knowledge graphs (KGs), thus limiting the performance of the entity prediction model. In this paper, we propose a new entity prediction model called FRS(Feature Refactoring Scoring) to alleviate the problem of insufficient interaction and solve information incom-pleteness problems in the KGs. Different from the traditional KGE methods of directly using simple operations, the FRS model innovatively provides the procedure of feature processing in the entity prediction tasks, realizing the alignment of entities and relationships in the same feature space and improving the performance of entity prediction model. Although FRS is a simple three-layer network, we find that our own model outperforms state-of-the-art KGC methods in FB15K and WN18. Through extensive experiments on FRS, we discover several insights. For example, the effect of embedding size and negative candidate sampling probability on experimental results is in reverse.
Decision Support System to Choose Private Higher Education Based on Service Quality Model Criteria in Indonesia

Abstract Ministry of Research and Technology of Higher Education in 2017 centralized universities in Indonesia to establish a foundation for classification and ranking of universities in Indonesia in the context of continuous improvement and health of the organization. The clustering is based on criteria 1) Quality of Human Resources, 2) Quality of Management, 3) Quality of Student Activities, and 4) Quality of Research &amp; Publication. The following are the results of the clustering. Cluster 1 is a university whose input, process and output are in a very good category, cluster 2 is in good category, cluster 3 is categorized quite well, cluster 4 is in a poor category, and cluster 5 is very poorly categorized, Input, for example, is lecturer quality, while process is, for example, institutional accreditation and accreditation of the study program and the last output is seen from the many students who received various awards (Kemenristekdikti, 2017). The College Clustering System is very influential on prospective students in making decisions to choose universities to continue their studies. According to [1] higher education is the service industry and service quality is an important determinant of the success of higher education. According to [2] the quality of service is one of the main goals of universities to attract students. Based on that, this study will create a support system for the selection of universities from the 5 clusters based on the dimensions of Service Quality (SerQual), namely 1) Reliability, 2) Responsiveness, 3) Assurance, 4) Empathy, 5) Tangibles. According to [3] decision support systems are popular tools that help make decisions in an organization. One method in the Multi-Criteria Decision Making (MCDM) is AHP (Analytic Hierarchy Process). According to [4] this AHP is quite effective in simplifying and accelerating the decision-making process by solving the problem in its parts. After a pairwise comparisonby decision makers for each criterion and alternative to obtaining the value of the level of importance of the element in the form of a qualitative opinion. Relative comparison values are then processed according to AHP steps to determine the relative rank of all alternatives. Based on the processing and analysis of data that has been done in the previous section, it can be concluded that universities in cluster 2 are selected based on the criteria of Reliability, Responsiveness, Assurance, Empathy, and Tangibles with a value of 0.261.