Reviewerly/exHarmony huggingface dataset:
i-Parser: Interactive Parser Development Kit for Natural Language Processing

This demonstration paper presents i-Parser, a novel development kit that produces high-performance semantic parsers. i-Parser converts training graphs into sequences written in a context-free language, then our proposed model learns to generate the sequences. With interactive configuration and visualization, users can easily build their own parsers. Benchmark results of i-Parser showed high performances of various parsing tasks in natural language processing.
Makerspaces

The idea of makerspace in a library is one that has re-emphasized the importance of library spaces in today’s digitally controlled society. Makerspace as a concept promotes creative learning, critical thinking, communication, collaboration and problem solving skills among people and also inspires them to shift from being passive consumers to active creators. The library makerspace provide space for people of all ages to build prototypes, explore new ideas and build something together, thus, enhancing human capacity building among library users. Consequently, this paper theoretically examined makerspace as a library tool for capacity building in developing countries. The concept of makerspace, makerspace tools, and the best practices for effective makerspace were discussed. The paper also explored makerspace as a library tool for capacity building as well as the challenges faced in creating library makerspace. The paper concluded that library makerspace provide a safe place for people to feel comfortable creating and using powerful tools without feeling pressured.
Real-time Web Search Framework for Performing Efficient Retrieval of Data

With the rapidly growing amount of information on the internet, real-time system is one of the key strategies to cope with the information overload and to help users in finding highly relevant information. Real-time events and domain-specific information are important knowledge base references on the Web that frequently accessed by millions of users. Real-time system is a vital to product and a technique must resolve the context of challenges to be more reliable, e.g. short data life-cycles, heterogeneous user interests, strict time constraints, and context-dependent article relevance. Since real-time data have only a short time to live, real-time models have to be continuously adapted, ensuring that real-time data are always up-to-date. The focal point of this manuscript is for designing a real-time web search approach that aggregates several web search algorithms at query time to tune search results for relevancy. We learn a context-aware delegation algorithm that allows choosing the best real-time algorithms for each query request. The evaluation showed that the proposed approach outperforms the traditional models, in which it allows us to adapt the specific properties of the considered real-time resources. In the experiments, we found that it is highly relevant for most recently searched queries, consistent in its performance, and resilient to the drawbacks faced by other algorithms
REAL-TIME DEPTH MAP ESTIMATION FROM INFRARED STEREO IMAGES OF RGB-D CAMERAS

Abstract. RGB-D cameras are novel sensing systems that can rapidly provide accurate depth information for 3D perception, among which the type based on active stereo vision has been widely used. However, there are some problems exiting in use, such as the short measurement range and incomplete depth maps. This paper presents a robust and efficient matching algorithm based on semi-global matching to obtain more complete and accurate depth maps in real time. Considering characteristics of captured infrared speckle images, the Gaussian filter is performed firstly to restrain noise and enhance the relativity. It also adopts the idea of block matching for reliability, and a dynamic threshold selection of the block size is used to adapt to various situation. Moreover, several optimizations are applied to improve precision and reduce error. Through experiments on the Intel Realsense R200, the excellent capability of our proposed method is verified.
Semi-Discrete Social Recommendation (Student Abstract)

Combining matrix factorization (MF) with network embedding (NE) has been a promising solution to social recommender systems. However, such a scheme suffers from the online predictive efficiency issue due to the ever-growing users and items. In this paper, we propose a novel hashing-based social recommendation model, called semi-discrete socially embedded matrix factorization (S2MF), which leverages the dual advantages of social information for recommendation effectiveness and hashing trick for online predictive efficiency. Experimental results demonstrate the advantages of S2MF over state-of-the-art discrete recommendation models and its real-valued competitors.
Developing Construct 2 Android-Based Education Math Game to Improve the ICT Literacy on Number Patterns Subject

This research aimed to describe the development process and results of the Construct 2 Android-based education math game on number pattern subjects to increase student's ICT literacy. This research employed the Research and Development method with a 4-D development model. The development model consisted of 4 stages: the Define stage, the Design stage, the Development stage, and the dissemination stage. Also, the researchers developed the Android-based application based on ICT literacy indicators. The application must be valid, practical, and effective. The results showed a correlation coefficient value of 0.87 and included in the valid category with very high interpretation. The level of practicality of the developed product was 83.96% based on a users' responses questionnaire. The effectiveness was determined based on the percentage of students' learning outcomes which was 83.33%. The increase in students' ICT literacy was determined based on the ICT literacy questionnaire and learning outcome tests' average N-Gain score. The average N-Gain value obtained was 0.75, with an average value for the ICT literacy questionnaire of 0.89 and the average value for the learning outcome test of 0.61. Based on these three criteria, it can be concluded that the Construct 2 android-based educational math game on the number pattern material can improve students' ICT literacy.
Hybrid Evaluation for Distributed Iterative Matrix Computation

Distributed matrix computation is common in large-scale data processing and machine learning applications. Existing systems that support distributed matrix computation already explore incremental evaluation for iterative-convergent algorithms. However, they are oblivious to the fact that non-zero increments are scattered in different blocks in a distributed environment. Additionally, we observe that incremental evaluation does not always outperform full evaluation. To address these issues, we propose matrix reorganization to optimize the physical layout upon the state-of-art optimized partition schemes, and thereby accelerate the incremental evaluation. More importantly, we propose a hybrid evaluation to efficiently interleave full and incremental evaluation during the iterative process. In particular, it employs a cost model to compare the overhead costs of two types of evaluations and a selective comparison mechanism to reduce the overhead incurred by comparison itself. To demonstrate the efficiency of our techniques, we implement HyMAC, a hybrid matrix computation system based on SystemML. Our experiments show that HyMAC reduces execution time on large datasets by 23% on average in comparison to the state-of-art optimization technique and consequently outperforms SystemML, ScaLAPACK, and SciDB by an order of magnitude.
Print on Air: Result Analysis on Implementation of a Cloud-based Printing Order Management System

Within the span of an undergrad, printing reports is vital whether it is in scholarly assignments, investigate or notes. Be that as it may, the current printing offices are imperfect and time devouring. To overcome these issues, we effectively created a proposed framework to progress the productivity of current printing administrations. This work expounds on the plan and improvement of the printing benefit utilizing android and google Firebase. Amid Covid-19 Widespread, Offline Print shops are encountering major plunges in foot activity. However, this doesn't mean closing shops completely and believe that the circumstances will be the conclusion. Vendors are utilizing the online space to get orders, amid the lockdown. There are numerous existing frameworks for this issue, but there are a few escape clauses in each framework, so there's a requirement for a framework that gives the client to put orders from anyplace any time utilizing the portable application with the assistance of cloud computing. Before setting an order, the purchaser must calculate of fairs, envisioned time of transport must be calculated.
A deep look into the dagum family of isotropic covariance functions

Abstract The Dagum family of isotropic covariance functions has two parameters that allow for decoupling of the fractal dimension and the Hurst effect for Gaussian random fields that are stationary and isotropic over Euclidean spaces. Sufficient conditions that allow for positive definiteness in $\mathbb{R}^d$ of the Dagum family have been proposed on the basis of the fact that the Dagum family allows for complete monotonicity under some parameter restrictions. The spectral properties of the Dagum family have been inspected to a very limited extent only, and this paper gives insight into this direction. Specifically, we study finite and asymptotic properties of the isotropic spectral density (intended as the Hankel transform) of the Dagum model. Also, we establish some closed-form expressions for the Dagum spectral density in terms of the Fox–Wright functions. Finally, we provide asymptotic properties for such a class of spectral densities.
Proactive personality, voice behavior, and the roles of team social exchange

Purpose: This research examined the links of proactive personality, team social exchange comprising leader-member exchange (LMX) and team-member exchange (TMX), and two types of voice behavior comprising team-voice behavior (TVB) and organizational-voice behavior (OVB). Under self-determination theory, the current study proposes the mediating effects of team social exchange on proactive personality and voice behavior. Design/methodology/approach: This study was based on online questionnaire surveys from 238 Indonesian employees. A structural equation modeling (SEM) analysis with the bootstrapping technique was used to examine the hypothesized relationships. Findings: The current study found 1) the significant influences of proactive personality on both LMX and TMX; 2) the significant influences of proactive personality on TVB and OVB; 3) the influences of LMX and TMX on both TVB and OVB; and 4) the partial mediating effects of LMX and TMX. Research limitation/implications: A single-rater method in data collection may occur in some extents of common method variance (CMV). The findings contribute to the empirical evidence of the pivotal role of team social exchange on the relationships between proactive personality and two types of voice behavior. Practical implications: Since voice behavior may have positive impacts on the organization’s performance and survival, organizations may wish to include proactive attributes in the HRM process (e.g., selection, promotion) and may also want to place more attention on efforts directed at improving team social exchange relationships. Also, organizational practitioners are advised to promote team social exchange as an additional factor impacting voice behavior. In addition, the proposal of two types of voice behavior may offer a wider scope of this construct. Originality/value: This study proposes two types of voice behavior, namely team-voice behavior (TVB) and organizational-voice behavior (OVB) which extend the scope of this construct.
Query complexity of least absolute deviation regression via robust uniform convergence

Consider a regression problem where the learner is given a large collection of $d$-dimensional data points, but can only query a small subset of the real-valued labels. How many queries are needed to obtain a $1+\epsilon$ relative error approximation of the optimum? While this problem has been extensively studied for least squares regression, little is known for other losses. An important example is least absolute deviation regression ($\ell_1$ regression) which enjoys superior robustness to outliers compared to least squares. We develop a new framework for analyzing importance sampling methods in regression problems, which enables us to show that the query complexity of least absolute deviation regression is $\Theta(d/\epsilon^2)$ up to logarithmic factors. We further extend our techniques to show the first bounds on the query complexity for any $\ell_p$ loss with $p\in(1,2)$. As a key novelty in our analysis, we introduce the notion of robust uniform convergence, which is a new approximation guarantee for the empirical loss. While it is inspired by uniform convergence in statistical learning, our approach additionally incorporates a correction term to avoid unnecessary variance due to outliers. This can be viewed as a new connection between statistical learning theory and variance reduction techniques in stochastic optimization, which should be of independent interest.
Quantum-Resistant Network for Classical Client Compatibility

Quantum computing is no longer a thing of the future. Shor’s algorithm proved that a quantum computer couldtraverse key of factoring problems in polynomial time. Because the time-complexity of the exhaustive keysearch for quantum computing has not reliably exceeded the reasonable expiry of crypto key validity, it is believedthat current cryptography systems built on top of computational security are not quantum-safe. Quantumkey distribution fundamentally solves the problem of eavesdropping; nevertheless, it requires quantumpreparatory work and quantum-network infrastructure, and these remain unrealistic with classical computers.In transitioning to a mature quantum world, developing a quantum-resistant mechanism becomes a stringentproblem. In this research, we innovatively tackled this challenge using a non-computational difficulty schemewith zero-knowledge proof in order to achieve repellency against quantum computing cryptanalysis attacks foruniversal classical clients.
Odlaw: A Tool for Retroactive GDPR Compliance

In this demo, we present ODLAW, a new tool for retroactive compliance with privacy laws like the European Union's General Data Protection Regulation (GDPR). The GDPR enumerates the explicit rights of individuals regarding the use of their personal data, and regulators can impose strict penalties for organizations that fail to comply. While others have advocated for a completely new class of systems to address these regulations, ODLAW takes a different approach by achieving GDPR compliance while allowing an organization to keep its existing data management infrastructure intact. Using a variety of realistic datasets, the demo will show the specific ways that ODLAW can help with GDPR compliance, as well as highlight some of the key challenges that arise in real-world settings.
Precision and accuracy of modal analysis methods for clastic deposits and rocks: A statistical and numerical modeling approach

Quantifying the proportions of certain components in rocks and deposits (modal analysis or componentry) is important in earth sciences. Relevant methods for cross-sections (two- dimensional exposures) of clastic rocks include point counts or line counts. The accuracy of these methods has been supposed to be good in the literature but not necessarily verified empirically. Natural materials are inappropriate for assessing accuracy because the true proportions of each component are unknown. The precision of modal analysis methods has traditionally been evaluated from statistical models (primarily the normal approximation to the binomial distribution) but again rarely verified in practice because it is also extremely difficult to obtain different slices through the same material at outcrop scale. Here we create a set of numerical models of red and blue spheres with different proportions and sizes and cut 60 slices through the models, on which we perform point counts and line counts. We show that both of these methods are indeed able to retrieve the correct volumetric proportions of components, on average, when enough fragments are counted or intersected. As already known, precision is controlled by component abundance and the number of points counted or clasts intersected. However, we show that other important factors include differences between slices, which are relevant for our unequal-size models, and the proportion of voids, matrix, and/or cement in the rock. We present empirical precision charts for clast counts and line counts based on our models and make recommendations for future field studies.
Zera-Shot Sentiment Analysis for Code-Mixed Data

Code-mixing is the practice of alternating between two or more languages. A major part of sentiment analysis research has been monolingual and they perform poorly on the code-mixed text. We introduce methods that use multilingual and cross-lingual embeddings to transfer knowledge from monolingual text to code-mixed text for code-mixed sentiment analysis. Our methods handle code-mixed text through zero-shot learning and beat state-of-the-art English-Spanish code-mixed sentiment analysis by an absolute 3% F1-score. We are able to achieve 0.58 F1-score (without a parallel corpus) and 0.62 F1-score (with the parallel corpus) on the same benchmark in a zero-shot way as compared to 0.68 F1-score in supervised settings. Our code is publicly available on github.com/sedflix/unsacmt.
Prediksi Kelulusan Mahasiswa Stikom Tunas Bangsa Prodi Sistem Informasi Dengan Menggunakan Algoritma C4.5

Graduation is an important element in an accreditation assessment process of an institution or university. It is important to find out information about the predictions of student graduation in the Information Systems Study Program at STIKOM Tunas Bangsa Pematangsiantar, so that students who cannot graduate on time can be identified earlier. The application of data mining can be used to predict student graduation. Method that often used to predict student graduation is classification method. This research using C4.5 Algorithm. Data that used as training data are from alumni of Information Systems Study Program at STIKOM Tunas Bangsa and the final level student data of Information Systems Study Program at STIKOM Tunas Bangsa used as testing data. Through this research, it is expected that the results can provide information on predictions of student graduation on time and as a suggestion for the university in making good decisions for improvement in the future.
LenAtten: An Effective Length Controlling Unit For Text Summarization

Fixed length summarization aims at generating summaries with a preset number of words or characters.Most recent researches incorporate length information with word embeddings as the input to the recurrent decoding unit, causing a compromise between length controllability and summary quality.In this work, we present an effective length controlling unit Length Attention (LenAtten) to break this trade-off.Experimental results show that LenAtten not only brings improvements in length controllability and ROGUE scores but also has great generalization ability.In the task of generating a summary with the target length, our model is 732 times better than the bestperforming length controllable summarizer in length controllability on the CNN/Daily Mail dataset.
Documents of Representative Authority Bodies as a Source on the History of Urbanonyms in Volgograd: A Source Studies Aspect: The 1950s–70s

The article is devoted to identification and analysis of documents of the executive committee of the Volgograd City Council of Workers’ Deputies containing decisions on naming and renaming of streets and squares of the city. As they reflect the historical process, the urbanonyms have great research potential. A comprehensive study of urbanonyms as a historical source involves disclosure of factors motivating to name and rename urban sites and consideration of decision-making process, which in its turn involves a wide range of documentary material. The source base of the study is minutes of meetings and decisions (with appendices) of the Executive Committee of the Volgograd City Council of Workers’ Deputies. In the course of the study, a representative sample collection has been formed, the object of which is the Battle of Stalingrad. Memory of the heroism of the defenders of Stalingrad is the brightest component of the social environment of modern Volgograd. The organizational and administrative documentation has allowed the authors to reconstruct the process of naming and renaming of streets and squares in Volgograd, to determine the mechanism of names selection, to point out specific features of the City Executive Committee’s work on this issue. Documents of Volgograd representative authority contain diverse historical information about the city toponymy: on causes and initiators of naming and renaming of streets and squares; on persons chosen for perpetuation in street names; on financial and technical issues of decisions implementation; on procedure for informing the executive authority bodies of names chosen for urban objects. The sources reveal the difficulties of this process, which are usually subjective in nature. Urbanonyms reflect substantive aspects of the history of city development (historical and military events), they take note of heroic guard units, role of outstanding personalities of the region and the country, population, administrative units, twin cities. In this regard, the attitude of representative authority bodies to preservation of historical memory in form of urbanonyms is important. Historical memory not only is one of the main channels for transmitting experience and information about the past, but also an important component of a person’s self-identification. The authors note that decision-making procedure for naming of streets and squares has changed under modern conditions; it now involves publicity, comprehensive discussion, and collegiality, thus eliminating errors in preparation of documents and implementation of decisions.
THE INFLUENCE OF NATIVE ENGLISH LECTURER IN IMPROVING STUDENTS MOTIVATION IN COMMUNICATION SKILL

The development of information and technology has influenced many aspects of life including education. a lot of people start from students in the university and worker already speaking English fluently, but still there are some problem specifically for the students to speak English, university that have English English language programt is looking to find the solution. By hiring native English teacher or leacturer to improve their student’s motivation in English communication skill, the question is can the native English teacher gives the influence to the student. This research made to analyze how well the native English teacher can give the influence to the students especially in Darma Persada University for the students that are majoring in English Literature and in English Department. This research showed whether hired Native English teacher is a good idea or it isn’t. In this study, researcher used aquantitative approach to analyze the influence of native English lecturer in improving students motivation communication skill.. This research was conducted using the experimental method using pre-test and post-test control design.