Reviewerly/exHarmony huggingface dataset:
Local Local Reasoning: A BI-Hyperdoctrine for Full Ground Store

Abstract Modelling and reasoning about dynamic memory allocation is one of the well-established strands of theoretical computer science, which is particularly well-known as a source of notorious challenges in semantics, reasoning, and proof theory. We capitalize on recent progress on categorical semantics of full ground store , in terms of a full ground store monad , to build a corresponding semantics of a higher order logic over the corresponding programs. Our main result is a construction of an (intuitionistic) BI-hyperdoctrine , which is arguably the semantic core of higher order logic over local store. Although we have made an extensive use of the existing generic tools, certain principled changes had to be made to enable the desired construction: while the original monad works over total heaps (to disable dangling pointers), our version involves partial heaps ( heaplets ) to enable compositional reasoning using separating conjunction. Another remarkable feature of our construction is that, in contrast to the existing generic approaches, our BI-algebra does not directly stem from an internal categorical partial commutative monoid.
The Development of Electronic Book with QR Code Technology and Interactive Exhibition Entitled “Media Literacy” for Undergraduate Students

This research was aimed 1) to survey the needs, 2) to develop electronic book with QR code technology and interactive exhibition entitled &amp;ldquo;Media Literacy&amp;rdquo; for undergraduate students, 3) to examine the quality, 4) to examine the learning achievement and 5) to examine the satisfaction. The tools in this study consisted of needs survey form, electronic book with QR code technology and interactive exhibition entitled &amp;ldquo;Media Literacy&amp;rdquo; for undergraduate students, quality evaluation forms for contents and media presentation, learning achievement test, and satisfaction questionnaire. The sampling group in this study consisted of 40 third-year undergraduate students who enrolled in the ETM 314 Professional Experiences in Educational Technology and Mass Communication, Department of Educational Communications and Technology, Faculty of Industrial Education and Technology, King Mongkut&amp;rsquo;s University of Technology Thonburi in the first semester of the academic year 2019. They were chosen using purposive sampling method. There were 9 experts in contents, media presentation as well as measurement and evaluation. The statistical methods were percentage, mean score, standard deviation and t-test. The research findings showed that the sampling group expressed the highest level of needs ( = 4.68, S.D.= 0.47). The quality evaluation results showed that the quality of the contents was at a very good level ( = 4.73, S.D. = 0.42) and the quality of the media presentation was at a good level ( = 4.42, S.D. = 0.40). The learning achievement test results showed that their average post-test score was higher than their average pre-test score with statistical significance at the .05 level (t-test = 7.23). The sampling group expressed the highest level of satisfaction towards the media ( = 4.68, S.D. = 0.49). Therefore, the developed media could be distributed for further use.
Mind Your Inflections! Improving NLP for Non-Standard English with Base-Inflection Encoding

Morphological inflection is a process of word formation where base words are modified to express different grammatical categories such as tense, case, voice, person, or number. World Englishes, such as Colloquial Singapore English (CSE) and African American Vernacular English (AAVE), differ from Standard English dialects in inflection use. Although comprehension by human readers is usually unimpaired by non-standard inflection use, NLP systems are not so robust. We introduce a new Base-Inflection Encoding of English text that is achieved by combining linguistic and statistical techniques. Fine-tuning pre-trained NLP models for downstream tasks under this novel encoding achieves robustness to non-standard inflection use while maintaining performance on Standard English examples. Models using this encoding also generalize better to non-standard dialects without explicit training. We suggest metrics to evaluate tokenizers and extensive model-independent analyses demonstrate the efficacy of the encoding when used together with data-driven subword tokenizers.
3-Dimensional Bag of Visual Words Framework on Action Recognition

Human motion recognition plays a crucial role in the video analysis framework. However, a given video may contain a variety of noises, such as an unstable background and redundant actions, that are completely different fr... | Find, read and cite all the research you need on Tech Science Press
Building Proactive Voice Assistants: When and How (not) to Interact

Voice assistants have recently achieved remarkable commercial success. However, the current generation of these devices is typically capable of only reactive interactions. In other words, interactions have to be initiated by the user, which somewhat limits their usability and user experience. We propose, that the next generation of such devices should be able to proactively provide the right information in the right way at the right time, without being prompted by the user. However, achieving this is not straightforward, since there is the danger it could interrupt what the user is doing too much, resulting in it being distracting or even annoying. Furthermore, it could unwittingly, reveal sensitive/private information to third parties. In this report, we discuss the challenges of developing proactively initiated interactions, and suggest a framework for when it is appropriate for the device to intervene. To validate our design assumptions, we describe firstly, how we built a functioning prototype and secondly, a user study that was conducted to assess users' reactions and reflections when in the presence of a proactive voice assistant. This pre-print summarises the state, ideas and progress towards a proactive device as of autumn 2018.
Fraudster Mobile Apps Detector in Google Playstore

The identification of the fraudulent app in Google playstore is determined by users only by executing apps and by analyzing permission. An app which has higher rating, ranking and good reviews in Google playstore may attract more users to download and can also be ranked higher in the leader board. The rating, ranking and reviews are not always real to believe that an app is a legitimate one. Some fraudulent developers boost their apps dishonestly in Google playstore. Hence in this paper, we propose a method to detect the fraud app in Google playstore by aggregating the three evidences in the playstore such as ranking, reviews and rating of of the particular app. Thus by aggregating entire activities of leading apps, it can achieve accuracy in classifying standard datasets of fraudulent and legitimate apps.
Motion Planning for Multiple Unit-Ball Robots in $$\mathbb {R}^{{\varvec{d}}}$$

We present a decoupled algorithm for motion planning for a collection of unit-balls moving among polyhedral obstacles in \(\mathbb {R}^d\), for any \(d \geqslant 2\). We assume that the robots have revolving areas in the vicinity of their start and target positions: Revolving areas are regions where robots can maneuver in order to give way to another moving robot. Given that this assumption is fulfilled, the algorithm is complete, namely it is guaranteed to find a solution or report that none exists. A key goal in our design is to make the revolving areas as economical as possible and in particular to allow different revolving areas to overlap. This makes the analysis rather involved but in return makes the algorithm conceptually fairly simple. We show that for the case of m unit-discs moving among polygonal obstacles of total complexity n in \(\mathbb {R}^2\), the algorithm can be executed in \(O(n^2m + m(m+n)\log (m+n))\) time. We implemented the algorithm for this case and tested it on several scenarios, for which we show experimental results for up to 1000 robots. Finally, we address the problem of choosing the order of execution of the paths in decoupled algorithms that locally solve interferences and show that finding the optimal order of execution is NP-hard. This motivated us to develop a heuristic for choosing the order; we describe the heuristic and demonstrate its effectiveness in certain scenarios.
A conversation with Akhil Mathur

Ubiquity's senior editor Dr. Bushra Anjum sits with Akhil Mathur, a principal research scientist at Nokia Bell Labs in Cambridge (United Kingdom). Both discuss improving the robustness of AI models deployed on mobile and wearable devices to minimize the accuracy degradation as these devices are used in new domains.
Agnostic tests can control the type I and type II errors simultaneously

Despite its common practice, statistical hypothesis testing presents challenges in interpretation.For instance, in the standard frequentist framework there is no control of the type II error.As a result, the non-rejection of the null hypothesis (H 0 ) cannot reasonably be interpreted as its acceptance.We propose that this dilemma can be overcome by using agnostic hypothesis tests, since they can control the type I and II errors simultaneously.In order to make this idea operational, we show how to obtain agnostic hypothesis in typical models.For instance, we show how to build (unbiased) uniformly most powerful agnostic tests and how to obtain agnostic tests from standard p-values.Also, we present conditions such that the above tests can be made logically coherent.Finally, we present examples of consistent agnostic hypothesis tests.
The Importance of Teaching Activity in the Process of Cognitive Processing

Abstract Most young people have to adapt to society. More and more students have gaps in the communicative act, no real conditions about material or spiritual are known. More serious is that it is a very high percentage while it is dedicated to new technologies, while allocated it is being recreated in nature being increasingly reduced, reaching the final young people having problems adapting the place where they are in society. Building a connection between education and the environment, strengthening the connections between people and the influence of nature can have the physical health of the person but also mental health. Awareness of climate change for the last period and the desire for an opera can use phenomena that can be protected from the natural desert, produced more and more often.
The Associations among Human Resource Management (HRM) Practices, Total Quality Management (TQM) Practices and Competitive Advantages

The current research aimed to investigate the relationship between human resource management (HRM) practices, total quality management (TQM) practices and competitive advantages in Telecommunication organizations in Jordan. A self-administrated questionnaire was designed and distributed over a sample of telecommunication employees of 400 employees. The percentage of filled questionnaires is 80% (320 questionnaires). The research hypotheses were tested by using multiple regressions. The study concluded that there is a relationship between human resource management practices and total quality management practices. It was also concluded that there is a relationship between total quality management practices and competitive advantage. Finally, there is a relationship between human resource management practices and competitive advantage. A number of recommendations emerged in light of the findings, is that the telecommunication organization are advised to focus more on the human resource management activities and total quality management activities that improve the utilization of competitive advantages.
Exponential Automatic Amortized Resource Analysis

Automatic amortized resource analysis (AARA) is a type-based technique for inferring concrete (non-asymptotic) bounds on a program's resource usage. Existing work on AARA has focused on bounds that are polynomial in the sizes of the inputs. This paper presents and extension of AARA to exponential bounds that preserves the benefits of the technique, such as compositionality and efficient type inference based on linear constraint solving. A key idea is the use of the Stirling numbers of the second kind as the basis of potential functions, which play the same role as the binomial coefficients in polynomial AARA. To formalize the similarities with the existing analyses, the paper presents a general methodology for AARA that is instantiated to the polynomial version, the exponential version, and a combined system with potential functions that are formed by products of Stirling numbers and binomial coefficients. The soundness of exponential AARA is proved with respect to an operational cost semantics and the analysis of representative example programs demonstrates the effectiveness of the new analysis.
Enhancing Imbalanced Dataset by Utilizing (K-NN Based SMOTE_3D Algorithm)

To address this problem of imbalanced datasets, most of the researchers use Synthetic Minority Oversampling Technique (SMOTE) [3,4].Yet this algorithm suffers from drawbacks, like, while generating synthetic examples, SMOTE does not take into consideration neighboring examples from other classes.
Security Mechanisms in NoSQL DBMS’s: A Technical Review

The exponential growth of data in organizations, together with the success reported by large companies such as Facebook, Google or Twitter, has promoted to the migration of a large number of applications to the use of NoSQL databases, due to their scalability and performance to Big Data solutions. This phenomenon evidence the need to analyze these new databases from the information security point of view. This article presents a technical review of the controls that could be implemented to preserve security data in eight of the most used NoSQL Database Management Systems named MongoDB, Cassandra, Hbase, CouchDB, BigTable, DinamoDb, Neo4j, and GraphBD. Analysis is performed from the security mechanisms point of view, i.e, authentication, authorization, and encryption (in transit and at rest).
Modeling N-th Order Derivative Creation Based on Content Attractiveness and Time-Dependent Popularity

For amateur creators, it has been becoming popular to create new content based on existing original work: such new content is called derivative work. We know that derivative creation is popular, but why are individual derivative works created? Although there are several factors that inspire the creation of derivative works, such factors cannot usually be observed on the Web. In this paper, we propose a model for inferring latent factors from sequences of derivative work posting events. We assume a sequence to be a stochastic process incorporating the following three factors: (1) the original work's attractiveness, (2) the original work's popularity, and (3) the derivative work's popularity. To characterize content popularity, we use content ranking data and incorporate rank-biased popularity based on the creators' browsing behaviors. Our main contributions are three-fold. First, to the best of our knowledge, this is the first study modeling derivative creation activity. Second, by using real-world datasets of music-related derivative work creation, we conducted quantitative experiments and showed the effectiveness of adopting all three factors to model derivative creation activity and considering creators' browsing behaviors in terms of the negative logarithm of the likelihood for test data. Third, we carried out qualitative experiments and showed that our model is useful in analyzing following aspects: (1) derivative creation activity in terms of category characteristics, (2) temporal development of factors that trigger derivative work posting events, (3) creator characteristics, (4) N-th order derivative creation process, and (5) original work ranking.
A Hidden Markov Model for Forecasting Rainfall Data Availability at the Weather Station in West Sumatra

Indonesia is a maritime continent in Southeast Asian, laying between Indian Ocean and Pacific Ocean. This position intensely affects the level of rainfall in Indonesia, especially West Sumatra. The availability of rainfall data can form a Markov chain which its state is not able to be observed directly (hidden), is called the Hidden Markov Model (HMM). The purposes of this research are to predict the hidden state of the availability of rainfall data using decoding problems and to find the best state sequence (optimal) by using Viterbi Algorithm, and also to predict probability for the availability of rainfall data in the future by using the Baum Welch Algorithm in the Hidden Markov Model. This research uses secondary data with a period of one day from the availability of rainfall data at the Minangkabau Meteorological Station, Padang Pariaman Climatology Station, and Silaing Bawah Geophysics Station from January 2018 to July 2019. The results of the prediction show that the Hidden Markov Model can be used to predict the probability of rainfall data availability. The results for the availability of the highest rainfall data for one day ahead is at the Padang Pariaman Climatology Station, with a probability of 0.36, followed by Minangkabau Meteorological Station is 0.35, and Silaing Bawah Geophysics station is 0.29. The result has shown for the next one day period the probability of rainfall data available from the three stations will be available following the Viterbi algorithm.
Research identify the group for lead – zinc ore type to prepare exploration in Ban Lim area, Caobang province, Vietnam

Ban Lim area, Caobang province has proposed high potential of lead-zinc resources, which have occurred in different geological formations. In this paper, based on the collecting, synthetics and processing geological data, especially applied mathematical methods to recognize studied objects of exploration process and quantitative description. The results show that the lead-zinc orebodies in Ban Lim area are mainly occurred in lens-shaped and distributed in layered surfaces of dolomitized limestone of Coc Xo formation. The average lead-zinc content of the orebodies is in a range from 3.27% to 8.33%, its coefficient of variation ( V c ) is in a range from 13.71% (evenly) to 137.92% (very unevenly). On the whole, the lead-zinc contents of the orebodies in Ban Lim area mainly comply with the standard normal distribution. The average thicknesses of the orebodies are in a range from 0.92 m to 6.48 m, its coefficient of variation ( V m ) is in the range from 8.7% (stable) to 132.95% (very unstable). Quantitative calculation results have shown that Ban Lim lead-zinc deposit belongs to group III of deposits. For the exploration of this type of minerals, it is recommended to use linear grid pattern. Appropriate exploration grid pattern for category 122 reserve is (60-80)×(30-40) m.
Use of machine translation technology for understanding scientific and technical texts

The article deals with machine translation technology, which is being discussed by several sciences such as Computer Science, Programming, Linguistics recently. The purpose of this study is to investigate how the technology of machine translation can be used by scholars, scientists, users in order to understand scientific and technical texts. The principal results are the comparison of different systems (traditional, statistical, hybrid and neural) of machine translation, assessment their advantages and disadvantages, limitations, drawbacks, and errors; experimental analysis the quality of the scientific and technical text made by machine translation systems Google Translate and Pragma Online. Common and different in both translation systems have been proved experimentally. The obtained results were processed and analyzed. The methods of analysis, synthesis, comparison and classification were used as well as the method of mathematical statistics Student’s criterion in the experimental research. The major conclusions are we definitely have all the grounds for advising Google Translate to use for general understanding scientific and technical texts.
Characterization of Power Usage and Performance in Data-Intensive Applications Using MapReduce over MPI

This paper presents a quantitative evaluation of the power usage over time in data-intensive applications that use MapReduce over MPI. We leverage the PAPI powercap tool to identify ideal conditions for execution of our mini-applications in terms of (1) dataset characteristics (e.g., unique words in datasets); (2) system characteristics (e.g., KNL and KNM); and (3) implementation of the MapReduce programming model (e.g., impact of various optimizations). Results illustrate the high power utilization and runtime costs of data management on HPC architectures.
Efficient strategies for hierarchical text classification: External knowledge and auxiliary tasks

In hierarchical text classification, we perform a sequence of inference steps to predict the category of a document from top to bottom of a given class taxonomy. Most of the studies have focused on developing novels neural network architectures to deal with the hierarchical structure, but we prefer to look for efficient ways to strengthen a baseline model. We first define the task as a sequence-to-sequence problem. Afterwards, we propose an auxiliary synthetic task of bottom-up-classification. Then, from external dictionaries, we retrieve textual definitions for the classes of all the hierarchy's layers, and map them into the word vector space. We use the class-definition embeddings as an additional input to condition the prediction of the next layer and in an adapted beam search. Whereas the modified search did not provide large gains, the combination of the auxiliary task and the additional input of class-definitions significantly enhance the classification accuracy. With our efficient approaches, we outperform previous studies, using a drastically reduced number of parameters, in two well-known English datasets.
Lexical Semantic Recognition

Segmentation and (segment) labeling are generally treated separately in lexical semantics, raising issues due to their close inter-dependence and necessitating joint annotation. We therefore investigate the lexical semantic recognition task of multiword expression segmentation and supersense disambiguation, unifying several previously-disparate styles of lexical semantic annotation. We evaluate a neural CRF model along all annotation axes available in version 4.3 of the STREUSLE corpus: lexical unit segmentation (multiword expressions), word-level syntactic tags, and supersense classes for noun, verb, and preposition/possessive units. As the label set generalizes that of previous tasks (DiMSUM, PARSEME), we additionally evaluate how well the model generalizes to those test sets, with encouraging results. By establishing baseline models and evaluation metrics, we pave the way for comprehensive and accurate modeling of lexical semantics.