Search

Automated debugging techniques, including fault localization and program repair, have been studied for over a decade. However, the only existing connection between fault localization and program repair is that fault localization computes the potential buggy elements for program repair to patch. Recently, a pioneering work, ProFL, explored the idea of unified debugging to unify fault localization and program repair in the other direction for the first time to boost both areas. In this way, ProFL also extends the application scope of automated repair to all possible bugs (not only the small ratio of bugs that repair systems can automatically fix). However, ProFL only considers one program repair system, and it is not clear how other repair systems contribute to unified debugging. In this work, we perform an extensive study of the unified debugging approach on 16 state-of-the-art program repair systems for the first time. Our initial experimental results on the Defects4J benchmark reveal various practical guidelines for unified debugging, such as (1) nearly all 16 studied repair systems positively contribute to unified debugging despite their varying repair capabilities, (2) repair systems targeting multi-edit patches can bring extraneous noise, (3) repair systems with more executed/plausible patches tend to perform better, (4) unified debugging effectiveness does not rely on the availability of correct patches, and (5) we propose a new technique, UniDebug++, which localizes over 20% more bugs within Top-1 than state-of-the-art technique ProFL. Furthermore, we extend the above experiments to make the following additional contributions: we (6) further perform an extensive study on 76.3% additional bugs and confirm that UniDebug++ again outperforms ProFL by localizing 185 (out of 395) bugs within Top-1, (7) investigate the impact of 33 SBFL formulae and observe UniDebug++ consistently improving upon all formulae, (8) demonstrate that UniDebug++ can substantially boost state-of-the-art learning-based method-level fault localization techniques, (9) extend unified debugging to the statement level for first time and observe that UniDebug++ localizes 78 (out of 395) bugs within Top-1 and outperforms state-of-the-art learning-based fault localization techniques by 30%, and finally (10) propose a new technique, UniDebug+*, based on detailed patch statistics, to further improve upon UniDebug++.

In this article, we study the problem of guaranteed display ads (GDAs) allocation, which requires proactively allocate display ads to different impressions to fulfill their impression demands indicated in the contracts. Existing methods for this problem either assume the impressions that are static or solely consider a specific ad's benefits. Thus, it is hard to generalize to the industrial production scenario where the impressions are dynamical and large-scale, and the overall allocation optimality of all the considered GDAs is required. To bridge this gap, we formulate this problem as a sequential decision-making problem in the scope of multiagent reinforcement learning (MARL), by assigning an allocation agent to each ad and coordinating all the agents for allocating GDAs. The inputs are the states (e.g., the demands of the ad and the remaining time steps for displaying the ads) of each ad and the impressions at different time steps, and the outputs are the display ratios of each ad for each impression. Specifically, we propose a novel hierarchical MARL (HMARL) method that creates hierarchies over the agent policies to handle a large number of ads and the dynamics of impressions. HMARL contains: 1) a manager policy to navigate the agent to choose an appropriate subpolicy and 2) a set of subpolicies that let the agents perform diverse conditioning on their states. Extensive experiments on three real-world data sets from the Tencent advertising platform with tens of millions of records demonstrate significant improvements of HMARL over state-of-the-art approaches.

The person re-identification (ReID) method in a single-domain achieves appealing performance, but its reliance on label information greatly limits its extensibility. Therefore, the unsupervised cross-domain ReID method has received extensive attention. Its purpose is to optimize the model by using the labelled source domain and the unlabelled target domain and finally make the model well generalized in the target domain. We propose an unsupervised cross-domain ReID method based on median stable clustering (MSC) and global distance classification (GDC). Specifically, the measurement method used by MSC comprehensively considers the similarity between clusters, the number of samples in a cluster, and the combined similarity within a cluster. Different from the method based on triple loss, GDC can separate the distance distribution of positive and negative sample pairs in a global scope. In addition, considering that model performance is very sensitive to probability parameters when source domain memory is reconsolidated, we designed a dynamic memory reconsolidation (DMR) method to reduce the influence of parameters on performance. Extensive experiments on large-scale datasets (Market-1501, DukeMTMC-reID and MSMT17) demonstrate the superior performance of MSC-GDC over the state-of-the-art methods.

Speculative execution attacks leverage the speculative and out-of-order execution features in modern computer processors to access secret data or execute code that should not be executed. Secret information can then be leaked through a covert channel. While software patches can be installed for mitigation on existing hardware, these solutions can incur big performance overhead. Hardware mitigation is being studied extensively by the computer architecture community. It has the benefit of preserving software compatibility and the potential for much smaller performance overhead than software solutions. This paper presents a systematization of the hardware defenses against speculative execution attacks that have been proposed. We show that speculative execution attacks consist of 6 critical attack steps. We propose defense strategies, each of which prevents a critical attack step from happening, thus preventing the attack from succeeding. We then summarize 20 hardware defenses and overhead-reducing features that have been proposed. We show that each defense proposed can be classified under one of our defense strategies, which also explains why it can thwart the attack from succeeding. We discuss the scope of the defenses, their performance overhead, and the security-performance trade-offs that can be made., Accepted to 2021 International Symposium on Secure and Private Execution Environment Design (SEED)

This paper studies the delay bounds problem of second-order multi-agent systems with input and communication delays under the directed graph. Firstly, the systems with input delay only are studied and the delay margin of input delay is given. Then the critical value of communication delay is considered for the fixed input delay within the proposed scope. Then, the input delay and communication delay are analyzed simultaneously from geometric perspective. The explicit geometric description of the consensus crossing curves is obtained. Then the consensus regions can be completely determined.

Scheduling the technical sessions of scientific events is an arduous task commonly faced by many organizers worldwide. Due the particularities of each conference, there is no consensus regarding the problem definition, and researchers have tackled each specific case individually. Despite their distinct characteristics, one often expects the sessions to be composed of presentations of similar scope. This natural assumption led us to define a basic yet sufficiently general version of the problem that aims at maximizing the benefit of clustering papers with common topics in the same session, while leaving the particularities of the event to be addressed by means of side constraints. Three mathematical formulations based on integer linear programming are proposed for the problem, which in turn is shown to be NP-hard. The first model consists of a compact formulation, whereas the second and third models serve as underlying formulations for branch-and-cut (BC) and branch-cut-and-price (BCP) algorithms, respectively. Computational experiments were carried out on real-life and artificial instances derived from two conferences, considering several scenarios. While the compact formulation and the BC procedure could solve instances with moderate size, the BCP approach managed to produce tight bounds for instances with up to 163 presentations.

In this article, we investigate a novel reinforcement-learning (RL)-based scheme to address the optimal leader-follower consensus issue for constrained-input continuous-time multiagent systems. First, as for input-constrained problems, the fundamental smooth assumption is not satisfied for the value function. To deal with this problem, we employ the method of vanishing viscosity solutions to relax this smooth assumption to a continuity assumption for the value function, which broadens the scope of RL applications. Second, the control cost functions take a more general form which guarantees the continuity of the optimal control policy instead of the specific integrand form used in previous input-constrained RL-based schemes. Based on these results, we introduce a novel identifier-critic-actor structure to extend the conventional critic-actor RL framework into a distributed model-free one, where the learning of the identifier, critic, and actor is online and simultaneous. We provide the simulation examples to validate the effectiveness of the proposed scheme.

In the field of computer vision, fine-grained visual categorization has attracted a lot of attention and made great progress due to convolutional neural networks and a large number of publicly available datasets. With next-generation sensing technology, RGB-D cameras can provide high-quality synchronized RGB and depth images for solving many computer vision problems. Although RGB-D cameras have been used in the context of multi-view object category detection and scene understanding, they have not been widely used in fine-grained classification. In this paper, we introduce a multiview RGB-D dataset RGBD-FG for fine-grained categorization. Currently, the dataset contains 93,051 RGB-D images covering 19 super-categories and 50 sub-categories of common vegetables and fruit, and is organized in a hierarchical manner. We provide extensive experimental results to establish state-of-the-art benchmarks for our dataset, illustrating its diversity and scope for improvement through future work. We also propose a novel modality-specific multimodal network called FS-Multimodal network, which can solve two limitations of multimodal networks trained based on fine-tuning techniques: over-fitting and lack of effective depth-specific features. We hope that our study lays the foundations for fine-grained categorization of RGB-D data.

Developers work on a broad variety of tasks during their workdays and constantly switch between them. While these task switches can be beneficial, they can also incur a high cognitive burden on developers, since they have to continuously remember and rebuild task context - the artifacts and applications relevant to the task. Researchers have therefore proposed to capture task context more explicitly and use it to provide better task support, such as task switch reduction or task resumption support. Yet, these approaches generally require the developer to manually identify task switches. Automatic approaches for predicting task switches have so far been limited in their accuracy, scope, evaluation, and the time discrepancy between predicted and actual task switches. In our work, we examine the use of automatically collected computer interaction data for detecting developers' task switches as well as task types. In two field studies - a 4h observational study and a multi-day study with experience sampling - we collected data from a total of 25 professional developers. Our study results show that we are able to use temporal and semantic features from developers' computer interaction data to detect task switches and types in the field with high accuracy of 84% and 61% respectively, and within a short time window of less than 1.6 minutes on average from the actual task switch. We discuss our findings and their practical value for a wide range of applications in real work settings.

Optimal operation of an actual mine main fan switchover process relies heavily on a good measurement of underground airflow quantity (UAQ). However, real-time measuring the UAQ is difficult using conventional measurement techniques. In this article, a novel randomized learning model, named compact incremental random weight network (CIRWN), is proposed to estimate the UAQ. Since the hidden parameters of the original IRWN are generated in a fixed scope with a random manner, which is prone to create redundant hidden nodes, a CIRWN with new inequality constraints is proposed. The inequality constraints have several attractive properties, including dynamically guiding the generation of hidden parameters, effectively enhancing the convergence rate, and successfully establishing a universal approximator. Experiments using four benchmark datasets and an industrial dataset show that the established model possesses a more compact network structure and better modeling accuracy as well as faster convergence rate compared with other methods.

People often communicate with auto-answering tools such as conversational agents due to their 24/7 availability and unbiased responses. However, chatbots are normally designed for specific purposes and areas of experience and cannot answer questions outside their scope. Chatbots employ Natural Language Understanding (NLU) to infer their responses. There is a need for a chatbot that can learn from inquiries and expand its area of experience with time. This chatbot must be able to build profiles representing intended topics in a similar way to the human brain for fast retrieval. This study proposes a methodology to enhance a chatbot's brain functionality by clustering available knowledge bases on sets of related themes and building representative profiles. We used a COVID-19 information dataset to evaluate the proposed methodology. The pandemic has been accompanied by an “infodemic” of fake news. The chatbot was evaluated by a medical doctor and a public trial of 308 real users. Evaluations were obtained and statistically analyzed to measure effectiveness, efficiency, and satisfaction as described by the ISO9214 standard. The proposed COVID-19 chatbot system relieves doctors from answering questions. Chatbots provide an example of the use of technology to handle an infodemic.

A phoneme is the smallest perceptually distinct sound unit that can be distinguished among words in a particular language. Every language has its own set of phonemes, and all possible words can be considered as ordered sequences of phonemes.The total number of phonemes contained in a language is always very few in comparison to the size of the vocabulary supported by the language. These facts have made phoneme recognition an attractive proposition in the entire journey of the Automatic Speech Processing (ASP) till date. As a result, the classification and recognition of phonemes are considered as the primary tasks of automatic speech recognition (ASR) systems irrespective of application domain. The dynamic nature of phonemes and several sources of their variability create lots of barriers in accurate identification of phonemes from an acoustic signal. The contribution of Machine Learning (ML) based techniques in overcoming these obstructions in automatic phoneme recognition (APR) is remarkable. Nowadays with lot of data availability, ML based ASR is preferred because of its simplicity over acoustic-phonetic based methods. The ML based techniques do not follow the conventional method based on identification of acoustic properties. Rather, ML techniques build their own trained model (algorithm) using readily available data. They do so by finding out the hidden patterns in speech signals, and acquire predictive intelligence through learning. Therefore, ML techniques can be said to provide a more generalized model for phoneme classification. In this paper, we present a comprehensive survey of ML tools to build phoneme recognizers. We also highlight some applications of speech (especially phoneme) recognition which illustrate the current scope as well as future prospects of APR.

Remote sensing image retrieval (RSIR) has broad application prospects, but related challenges still exist. One of the most important challenges is how to obtain discriminative features. In recent years, although the powerful feature learning ability of convolutional neural networks (CNNs) has significantly improved RSIR, their performance can be restricted by the complexity of remote sensing (RS) images, such as small objects, varying scales, and wide scope. To address these problems, we propose a novel wide-context attention network (W-CAN). It leverages two attention modules to adaptively learn local features correlated in the spatial and channel dimensions, respectively, which can obtain discriminative features with extensive context information. During training, a hybrid loss is introduced to enhance the intraclass compactness and interclass separability of the features. Moreover, we add a branch to learn binary descriptors and realize the end-to-end descriptor aggregation. Experiments on four RS benchmark data sets demonstrate that the proposed method can outperform some state-of-the-art RSIR methods.

Klasifikasi teks saat ini telah menjadi sebuah bidang yang banyak diteliti, khususnya terkait Natural Language Processing (NLP). Terdapat banyak metode yang dapat dimanfaatkan untuk melakukan klasifikasi teks, salah satunya adalah metode deep learning. RNN, CNN, dan LSTM merupakan beberapa metode deep learning yang umum digunakan untuk mengklasifikasikan teks. Makalah ini bertujuan menganalisis penerapan kombinasi dua buah metode deep learning, yaitu CNN dan LSTM (C-LSTM). Kombinasi kedua metode tersebut dimanfaatkan untuk melakukan klasifikasi teks berita bahasa Indonesia. Data yang digunakan adalah teks berita bahasa Indonesia yang dikumpulkan dari portal-portal berita berbahasa Indonesia. Data yang dikumpulkan dikelompokkan menjadi tiga kategori berita berdasarkan lingkupnya, yaitu “Nasional”, “Internasional”, dan “Regional”. Dalam makalah ini dilakukan eksperimen pada tiga buah variabel penelitian, yaitu jumlah dokumen, ukuran batch, dan nilai learning rate dari C-LSTM yang dibangun. Hasil eksperimen menunjukkan bahwa nilai F1-score yang diperoleh dari hasil klasifikasi menggunakan metode C-LSTM adalah sebesar 93,27%. Nilai F1-score yang dihasilkan oleh metode C-LSTM lebih besar dibandingkan dengan CNN, dengan nilai 89,85%, dan LSTM, dengan nilai 90,87%. Dengan demikian, dapat disimpulkan bahwa kombinasi dua metode deep learning, yaitu CNN dan LSTM (C-LSTM),memiliki kinerja yang lebih baik dibandingkan dengan CNN dan LSTM.

Linguistic studies in language teaching do not get a dominant place, because the relationship between the two is not direct. Even if we look further, we find that what is taught can be said directly proportional to the meaning of the language itself, which is nothing but the study of linguistics as described by linguists. In the development that occurred, various theories were used in studying the phenomenon of language, one of which was the implicature theory. This theory requires that language be seen from its effects, so that words are arranged and spoken with the aim of giving implications for listeners. More than that, balagah in Arabic studies is wider in scope, because it requires context in speaking, who is the other person, to the choice of words and sentence phrases. In this case, balagah is a good internal approach in compiling, to developing teaching materials in teaching Arabic. With a strict approach, it is hoped that the language taught is actually used and adapted to the situation of students and teachers as part of the community members who use the language and who carry out the learning and teaching process.

We show how to recover a discrete twist over an ample Hausdorff groupoid from a pair consisting of an algebra and what we call a quasi-Cartan subalgebra. We identify precisely which twists arise in this way (namely, those that satisfy the local bisection hypothesis), and we prove that the assignment of twisted Steinberg algebras to such twists and our construction of a twist from a quasi-Cartan pair are mutually inverse. We identify the algebraic pairs that correspond to effective groupoids and to principal groupoids. We also indicate the scope of our results by identifying large classes of twists for which the local bisection hypothesis holds automatically., 47 pages. This version matches the version in International Mathematics Research Notices (IMRN)

A novel functional regression model was introduced in this research in which, the predictor is a curve linked to a scalar fuzzy response variable. An absolute error-based penalized method with SCAD loss function was also proposed to evaluate the unknown components of the model. For this purpose, a concept of fuzzy-valued function was developed and discussed. Then, a fuzzy large number notion was proposed to estimate the fuzzy-valued function. The performance of the proposed method was examined by some common goodness-of-fit criteria. The efficiency of the proposed method was then evaluated through two numerical examples; a simulation study and an applied example in the scope of watershed management. The proposed method was also compared with several common fuzzy regression models in cases where the functional data were converted to scalar ones.

PLAZA is a platform for comparative, evolutionary, and functional plant genomics. It makes a broad set of genomes, data types and analysis tools available to researchers through a user-friendly website, an API, and bulk downloads. In this latest release of the PLAZA platform, we are integrating a record number of 134 high-quality plant genomes, split up over two instances: PLAZA Dicots 5.0 and PLAZA Monocots 5.0. This number of genomes corresponds with a massive expansion in the number of available species when compared to PLAZA 4.0, which offered access to 71 species, a 89% overall increase. The PLAZA 5.0 release contains information for 5 882 730 genes, and offers pre-computed gene families and phylogenetic trees for 5 274 684 protein-coding genes. This latest release also comes with a set of new and updated features: a new BED import functionality for the workbench, improved interactive visualizations for functional enrichments and genome-wide mapping of gene sets, and a fully redesigned and extended API. Taken together, this new version offers extended support for plant biologists working on different families within the green plant lineage and provides an efficient and versatile toolbox for plant genomics. All PLAZA releases are accessible from the portal website: https://bioinformatics.psb.ugent.be/plaza/.

Training data in a specific domain are often insufficient in the area of text sentiment classifications. Cross-domain sentiment classification (CDSC) is usually utilized to extend the application scope of transfer learning in text-based social media and effectively solve the problem of insufficient data marking in specific domains. Hence, this paper aims to propose a CDSC method via parameter transferring and attention sharing mechanism (PTASM), and the presented architecture includes the source domain network (SDN) and the target domain network (TDN). First, hierarchical attentional network with pre-training language model on training data, such as global vectors for word representation and bidirectional encoder representations from transformers (BERT), are constructed. The word and sentence levels of parameter transferring mechanisms are introduced in the model transfer. Then, parameter transfer and fine-tuning techniques are adopted to transfer network parameters from SDN to TDN. Moreover, sentiment attention can serve as a bridge for sentiment transfer across different domains. Finally, word and sentence level attention mechanisms are introduced, and sentiment attention is shared from the two levels across domains. Extensive experiments show that the PTASM-BERT method achieves state-of-the-art results on Amazon review cross-domain datasets.

The purpose of this study was to analyze the scope related to the subject of problem-solving skills based on multiple representation in 2016 – 2020 with 20 documents through bibliometric analysis. The research method used was a literature study through all the articles analyzed in this study. The articles were taken from the Scopus database with sampling in 2003 – 2020, resulting in 29 scientific work data exported in *.ris (RIS) and *CVS formats. Then, those data were processed using VOSviewer and Microsoft Excel. The results of publications in the last five years have increased. Indonesia is the dominant country in publicizing papers about this topic. Institutions from Germany managed to publish most of the documents about multi representation. Meanwhile, Poland is the origin country of the authors with most publications. The visualization of research trends on multi representation resulted in four main clusters: (1) multi representation related to students, representation, and learning processes (2) multi representation as a class (3) multi representation related to the problem (4) multi representation as a model and process. Meanwhile, Indonesian researchers are very active in contributing to this topic, in line with the number of publications by country, namely Indonesia.

One vision for program synthesis, and specifically for programming by example (PBE), is an interactive programmer's assistant, integrated into the development environment. To make program synthesis practical for interactive use, prior work on Small-Step Live PBE has proposed to limit the scope of synthesis to small code snippets, and enable the users to provide local specifications for those snippets. This paradigm, however, does not work well in the presence of loops. We present LooPy, a synthesizer integrated into a live programming environment, which extends Small-Step Live PBE to work inside loops and scales it up to synthesize larger code snippets, while remaining fast enough for interactive use. To allow users to effectively provide examples at various loop iterations, even when the loop body is incomplete, LooPy makes use of live execution , a technique that leverages the programmer as an oracle to step over incomplete parts of the loop. To enable synthesis of loop bodies at interactive speeds, LooPy introduces Intermediate State Graph , a new data structure, which compactly represents a large space of code snippets composed of multiple assignment statements and conditionals. We evaluate LooPy empirically using benchmarks from competitive programming and previous synthesizers, and show that it can solve a wide variety of synthesis tasks at interactive speeds. We also perform a small qualitative user study which shows that LooPy's block-level specifications are easy for programmers to provide.

Smart contracts are programs that execute in blockchains such as Ethereum to manipulate digital assets. Since bugs in smart contracts may lead to substantial financial losses, there is considerable interest in formally proving their correctness. However, the specification and verification of smart contracts faces challenges that rarely arise in other application domains. Smart contracts frequently interact with unverified, potentially adversarial outside code, which substantially weakens the assumptions that formal analyses can (soundly) make. Moreover, the core functionality of smart contracts is to manipulate and transfer resources; describing this functionality concisely requires dedicated specification support. Current reasoning techniques do not fully address these challenges, being restricted in their scope or expressiveness (in particular, in the presence of re-entrant calls), and offering limited means of expressing the resource transfers a contract performs. In this paper, we present a novel specification methodology tailored to the domain of smart contracts. Our specifications and associated reasoning technique are the first to enable: (1) sound and precise reasoning in the presence of unverified code and arbitrary re-entrancy, (2) modular reasoning about collaborating smart contracts, and (3) domain-specific specifications for resources and resource transfers, expressing a contract's behaviour in intuitive and concise ways and excluding typical errors by default. We have implemented our approach in 2vyper, an SMT-based automated verification tool for Ethereum smart contracts written in Vyper, and demonstrated its effectiveness for verifying strong correctness guarantees for real-world contracts., Proceedings of the ACM on Programming Languages, 5 (OOPSLA), ISSN:2475-1421

This paper presents forensic analysis of anti-forensic file-wiping tools on the Windows platform. The goal is to identify and extract the evidence of the tools used to wipe files and the files wiped by them on the Windows operating system. To achieve this goal, we analyzed the changes made by these tools to metadata structures of Windows file systems during file wiping. We also analyzed Registry keys and .lnk files to collect the evidence. Our experiments used four file-wiping tools (SecureDelete v1.0, Secure Eraser v5.2, PC Shredder v1.1, and Blank and Secure v5.88) to wipe files on three Windows file systems (FAT32, exFAT and NTFS). The results suggest that FAT32 and exFAT file system directory structures and $MFT entries of NTFS file system can confirm the use of wiping tools, identify these tools and provide the remnants of the wiped files. Also, $LogFile and $UsnJrnl files of NTFS file system, and Windows Registry keys provide detailed evidence of wiping tools used and the files wiped by them. We also found that the contents of resident and non-resident alternate data streams, $LogFile and $UsnJrnl files, and Windows Registry keys are not wiped by these tools. Finally, this study makes many recommendations, highlights the limitations of the work and points out the future scope.

The article considers the notional nature and peculiarities of genus-species relations between the phenomena of opposition and oppositivity ; it also deals with the lexical-and-grammatica ways of expressing the latter in an English literary text. The study establishes that, besides the logical component, an opposition also expresses a subjective one, manifesting a communicants unique view on the communicative situation and thus displaying semantics of oppositivity. Unlike spatial, logical and grammatical oppositions, generally characterized by a symmetrical structure, the subjective (oppositive) opposition is asymmetrical. It is revealed that due to the structural asymmetry this kind of opposition is marked by greater semantic potential compared to logical and grammatical oppositions. It is proved that the complication of subjective oppositions with antonymous pairs, lexical and syntactical repetitions, antithetic units, oxymorons etc. extends the scope of meanings implied by them; and the use of subjective (oppositive) oppositions in a literary text themselves becomes an effective tool of contrast-building both on the lexical-grammatical and textual levels.

The subject of the article is a research into the influence exerted by the publisher on the way and scope of rendering third culture elements in the translated text. The concept of the foreign in translation, idea of third culture elements and the issue of patronage in the literary system will be discussed in turn. The research material is a story in English titled “The Room in Le Dragon Volant” from a collection In a Glass Darkly, written by the 19th century Irish writer, Sheridan Le Fanu and its translation into Polish by the author of the article, published under the title "Pokoj w gospodzie Le Dragon Volant", in a collection W ciemnym zwierciadle. The research will involve an analysis of translation of selected examples of third culture elements, a term of address and two proper nouns in French and the discussion of recommendations from the editor regarding their transposition in the Polish language. The result of negotiations between the editor and the translator and the consequences of the adopted solution for the achieved level of foreignness in the target text will be presented. Finally, conclusions regarding the influence exerted by the publisher on the way of translation of third culture elements and the role of translator in the process of their rendering in the target language will be formulated.

Natural language, being unstructured, makes the tedious task for building a model to parse it into a query language successfully. An incorrect query for a particular question would lead to an absurd answer. As seen in many semantic parsing approaches, the inaccurate answering of complex questions increases significantly. This leads to many novel effective strategies that can apply to semantic parsing approaches to accurately parse complex natural language questions and generate an appropriate answer over a large knowledge graph. The defense system in a particular country is one of the most crucial components, and the lack of a proper defensive domain interface makes it a significant motivation. Hence, a knowledge graph has been constructed to collect and enquire about all information in one place. In this paper, KG4ASTRA has been designed with a Missile Knowledge Graph consisting of 177 entities linked using 400 relationships. A query-answering model then utilizes this manually created Missile Knowledge Graph, which generates tabular or graph representation for the natural language question. Neo4j platform has been used to prepare the knowledge graph, and Cypher queries are used to execute queries. The modeled knowledge has been evaluated by executing natural language queries on KG4ASTRA query-answering model and compared the search results with other existing knowledge graphs. As of our best knowledge, neither the knowledge graph nor the question-answering model has been designed for Indian Missiles. As a future scope, the proposed knowledge graph will be connected with the existing knowledge graph and extended to automatically extract domain-specific entities.

The peculiarities of adaptation of anglicisms in the Chinese language are considered. It is shown that the penetration of English linguistic units into the Chinese language is a modern trend, especially in the field of computer technology. It is noted that the morphemes of borrowed words, penetrating into the Chinese language, take part in word formation in the role of classifiers, becoming a constant component of a complex word, which ensures the productivity of a certain model in the Chinese language. Particular attention is paid to the analysis of the semantic scope of the term hacker. It has been established that the word hacker has developed different meanings over the past decades. It is pointed out that as the role of the hacker in life changed, the word acquired new interpretations, from neutral ‘specialist’ to negative ‘miscreant’. It is shown that the most active way of borrowing anglicisms is transliteration: the word hacker functions in the Chinese language in the form of two hieroglyphs 黑客, where the second component is included in the “X + 客” model, according to which other neologisms are created. It was revealed that the model is the most productive for the formation of words with the meaning of a person engaged in a certain activity, which is largely due to the active use of the word hacker in the Chinese language.

We enumerate the 15768 15768 perfect groups of order up to 2 ⋅ 10 6 2\cdot 10^6 , up to isomorphism, thus also completing the missing cases reported by Holt and Plesken [Perfect groups, The Clarendon Press, Oxford University Press, New York, 1989]. The work supplements the by now well-understood computer classifications of solvable groups, illustrating scope and feasibility of the enumeration process for nonsolvable groups.

Keywords: video game, intellectual property law, computer program, software, digitalinformation, electronic information, multimedia work, audiovisual work The article is devoted to thestudy of the legal understanding of video games as an object of intellectual propertyrights. The author concludes that video games constitute digital information, whichgreatly facilitates their development, however, complicates the protection of intellectualproperty rights. There is a contradictory connection between the concepts of«electronic (digital) information», «audiovisual work», «computer program» and «literarywork», which establishes some uncertainty in the protection of video games,namely, what should be understood by these concepts in terms of law.The author found that the use of the latest technologies to improve the visual componentbrings the video game even closer to the game audiovisual object, which increasesthe complexity of the process of protection of intellectual property rights. However, thecurrent situation with the use of the term «computer program» in the context of protectionof intellectual property rights to video games does not cover all features of the latter,covering only the main program among the software recording and operation of thegame, such as audiovisual content. Therefore, there is a need to use the generalized conceptof «software» as opposed to the narrower concept of «computer program». In thiscase, in the absence of at least one of the characteristics of the multimedia work, the computerprogram should be referred to the scope of legal regulation of software.The author proposed to understand the video game as object of intellectual propertylaw in the following manner: multimedia work expressed in the form of digital(electronic) information, the principle of which is determined by the algorithms of thesoftware provided for installation in the memory of computer devices.In general, the digital nature of video games presupposes a revision of existing approachesto the protection of intellectual property rights, which may be aimed atweakening the regulatory function of the law where it is possible to regulate the relationshipby more flexible means.

In the regression analysis, Takagi-Sugeno fuzzy model gives a way of exploiting fuzzy logic to tackle nonlinear issues. However, the general Takagi-Sugeno fuzzy model encounters challenges when facing second order regression problems because of its insufficient fitting ability. In this study, the second order Takagi-Sugeno fuzzy model called TS2 fuzzy model is proposed to extend the application scope of the original model. Moreover, domain adaptation in transfer learning is applied to the proposed model by using space transformation. It aims to further reduce the model’s cumulative error. The experimental results indicate that the proposed model has a better performance with not much extra processing time when dealing with second order nonlinear regression tasks.

El presente trabajo de investigación se planteó como objetivo proponer un programa de estrategias de lectura para mejorar la comprensión de textos en estudiantes del 4° de primaria del colegio 15028 Bellavista – 2020. Investigación que se desarrolló bajo un enfoque cuantitativo de tipo aplicada con un diseño no experimental y alcance transversal, se manejó una muestra poblacional de 60 estudiantes, los datos fueron obtenidos por medio de la técnica de la encuesta usando un cuestionario para la variable estrategia de lectura y con una prueba objetiva para la variable comprensión de textos, el constructo de los mimos fueron revisados según el juicio de expertos en el área quienes dieron su aprobaron en su diseño y aplicación para después hacer la prueba de fiabilidad del Alfa de Cronbach con un coeficiente de 0,871 para la variable estrategias de lectura y un KR-20 de 0,864 para la variable comprensión de textos; en consecuencia, los instrumentos aplicados son altamente fiables. El resultado de la investigación indica que los estudiantes presentan dificultades al momento de efectuar su lectura ocasionando que los textos leídos no tengan significancia para ellos por lo cual es imperioso el desarrollo de un programa de estrategias de lectura.

This paper deals with the construction of digital lexicons within the scope of Natural Language Processing. Data Structures called Hash Tables have demonstrated to generate good results for Natural Language Interface for Databases and have data dispersion, response speed and programming simplicity as main features. The storage of the desired information is done by associating a key through the hashing functions that is responsible for distributing the information in this table. The objective of this paper is to present the tool called Visual TaHs that uses a sparse table to a real lexicon (Lexicon of Herbs), improving performance results of several implemented hash functions. Such structure has achieved satisfactory results in terms of speed and storage when compared to conventional databases and can work in various media, such as desktop, Web and mobile.

Rough set is a very powerful invention to the whole world for dealing with uncertain, incomplete and imprecise problems. Also soft set theory and neutrosophic set theory are advance mathematical tools to handle these uncertain, incomplete, inconsistent information in a better way. The purpose of this article is to expand the scope of rough set, soft set and neutrosophic set theory. We have introduced the concept of neutrosophic soft set with roughness without using full soft set. Some definitions, properties and examples have been established on neutrosophic soft rough set. Moreover, dispensable and equalities are written on roughness with neutrosophic soft set.

The use of IoT-based Emotion Recognition (ER) systems is in increasing demand in many domains such as active and assisted living (AAL), health care and industry. Combining the emotion and the context in a unified system could enhance the human support scope, but it is currently a challenging task due to the lack of a common interface that is capable to provide such a combination. In this sense, we aim at providing a novel approach based on a modeling language that can be used even by care-givers or non-experts to model human emotion w.r.t. context for human support services. The proposed modeling approach is based on Domain-Specific Modeling Language (DSML) which helps to integrate different IoT data sources in AAL environment. Consequently, it provides a conceptual support level related to the current emotional states of the observed subject. For the evaluation, we show the evaluation of the well-validated System Usability Score (SUS) to prove that the proposed modeling language achieves high performance in terms of usability and learn-ability metrics. Furthermore, we evaluate the performance at runtime of the model instantiation by measuring the execution time using well-known IoT services.

The paper states that the current conditions in which the education system is located, and the rapid development of IT require constant improvement of methodological materials, taking into account the full capacity of the current software. Examples of programs necessary for preparation of methodological materials for high-quality classes are given. The purpose of the paper was to identify the Wolfram Mathematica didactic potential when conducting classes in the disciplines of the geometric and graphic profile at a technical high educational institution. In this paper has been performed analysis of literature sources both domestic and foreign ones on the Wolfram Mathematica system application in science and teaching of various disciplines. It has been shown that the program use scope is very wide, in fact, it is comprehensive and requires additional and in-depth study. Examples of Wolfram Mathematica using in mathematics, physics, chemistry, geometry, robotics, virology, and the humanities are given. In the paper have been provided examples for pedagogical design of simulation models for an electronic course on descriptive geometry in the Moodle system. An example of code written in the Wolfram Mathematica is provided. Interactive models developed during the design are presented, which allow the user to change the constructed curves and surfaces’ parameters. Have been defined some functional capabilities of the system, and has been revealed the Wolfram Mathematica didactic potential for teaching geometric and graphic disciplines. Have been considered other authors’ similar models, which can be used in the educational process to increase the clarity of the material presented in the classroom. In conclusion it is pointed out that interactive visualization in the "Descriptive Geometry" discipline, together with classical working practices, significantly enriches the content of geometric education.

An algorithm of a current protection measuring device based on a four-dimensional representation of a signal is proposed for cases in which the input signal can be represented by the sum of complex amplitudes with complex frequencies. In comparison with the existing iterative algorithms, the proposed algorithm has a wider scope of application and can be used to analyze the results of simplified deterministic mathematical and physical models of power systems.

To recommend relevant merchandises for seasonal retail events, we rely on item retrieval from marketplace inventory. With feedback to expand query scope, we discuss keyword expansion candidate selection using word embedding similarity, and an enhanced tf-idf formula for expanded words in search ranking., The work is oral presented on the SIGIR Symposium on IR in Practice (SIRIP) 2018

As AI technology advances and its use increases, efforts to incorporate machine learning for malware detection are increasing. However, for malware learning, a standardized data set is required. Because malware is unstructured data, it cannot be directly learned. In order to solve this problem, many studies have attempted to convert unstructured data into structured data. In this study, the features and limitations of each were analyzed by investigating and analyzing the method of converting unstructured data proposed in each study into structured data. As a result, most of the data conversion techniques suggest conversion mechanisms, but the scope of each technique has not been determined. The resulting data set is not suitable for use as training data because it has infinite properties.

3D Virtual Tour Rumah Gadang Istana Pagaruyung merupakan aplikasi virtual tour berbasis desktop yang bertujuan untuk mengedukasi dan memudahkan masyarakat dalam mengenal Rumah Gadang Istana Pagaruyung, selain itu pengguna juga dapat menjelajahi, menikmati keindahan arsitektur dan memperoleh informasi yang bermanfaat di bagian-bagian tertentu secara interaktif kemanapun selagi dalam ruang lingkup Rumah Gadang Istana Pagaruyung. Aplikasi ini dibangung menggunakan game engine Unreal Engine 4 dan beberapa software pendukung seperti Blender, Materialize dll. Metode penelitiannya adalah penelitian kualitatif dimana strategis yang digunakan adalah Design dan Creation. Penelitian ini menggunakan metode pengumpulan data observasi melalui media-media yang ada di internet. Informasi yang disajikan sebatas pengetahuan umum. Pengujian akan dilakukan dengan menggunakan PC atau Laptop. Aplikasi dapat berjalan pada desktop yang mempunyai memori VRAM minimal 1GB, total memori VGA 2GB, RAM 2GB dan prosesor minimal Intel Core Duo

The Translator and the Translation From the Perspective of the Translation Standard (PN-EN 15038 and PN-EN ISO 17100) This article aims to look at the translator and the translation from the perspective of the translation standard, understood as a standardization document. Therefore, it is a look from the perspective of the translation market, which sees translation in terms of an economic phenomenon. The article discusses the assumptions of standard, its objective and subjective scope of application, requirements for translators and the course of the translation process based on the international standard ISO 17100 and its predecessor, the European Standard 15038.

The Transportation Services Index (TSI) lags two months from its release date due to source data availability, and it is desirable to publish a preliminary TSI that is advanced two months ahead We model and forecast TSI with a co-integrated Vector Autoregression, also considering two explanatory series that do not have publication delay Thus we are able to produce forecasts and nowcasts of the index, and we demonstrate that – during normal economic conditions – out-of-sample performance is within the scope expected by the forecast confidence intervals We also examine the performance of the models at the onset of the COVID-19 pandemic, and the large forecast errors at this regime change are beyond the bounds indicated by our model The practical ramifications of this methodology is discussed [ABSTRACT FROM AUTHOR] Copyright of Statistical Journal of the IAOS is the property of IOS Press and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission However, users may print, download, or email articles for individual use This abstract may be abridged No warranty is given about the accuracy of the copy Users should refer to the original published version of the material for the full abstract (Copyright applies to all Abstracts )

Arbitrary projection is a well-known AI calculation, which can be executed by neural organizations and prepared in an exceptionally productive way. Notwithstanding, the quantity of highlights should be huge enough when applied to a fairly enormous scope informational collection, which brings about moderate speed in testing technique and more extra room under certain conditions. Besides, a portion of the highlights are repetitive and even uproarious since they are arbitrarily created, so the presentation might be influenced by these highlights. In the proposed framework is to actualize Adaptive Regularization Parameter Selection. It will regularize the highlights which are significant and select a most appropriate component for an explicit issue. The primary objective is to diminish the computational expense of both characterization and relapse task by utilizing Randomization Algorithm. It gives the most ideal outcome to the high-dimensional improvement. It utilizes Multilayer neural organization for performing direct and non-straight capacity .

WaveNet is introduced for waveform generation. It produces high quality text-to-speech synthesis, music generation, and voice conversion. However, it generally requires a large amount of training data, that limits its scope of applications, e.g. in voice conversion. In this paper, we propose a factorized WaveNet for limited data tasks. Specifically, we apply singular value decomposition (SVD) on the dilated convolution layers of WaveNet to reduce the number of parameters. By doing so, we reduce the data requirement for WaveNet training, while maintaining similar network performance. We use voice conversion as a case study to validate the proposed idea. Two sets of experiments are conducted, where WaveNet is used as a vocoder and an integrated converter–vocoder respectively. Experiments on CMU-ARCTIC and CSTR-VCTK corpora show that factorized WaveNet consistently outperforms its original WaveNet counterpart when using the same amount of training data. We also apply SVD similarly to real-time neural vocoder Parallel WaveGAN for voice conversion, and observe similar improvement.

This study aims to determine the descriptive analysis of each variable: e-commerce technology, independent variables; customer experience, the dependent variable; and e-commerce platform; the intervening variables are then analyzed how the direct or indirect influence of the independent variables, e-commerce technology, on the dependent variable, consumer experience, when using the e-commerce platform as an intervening variable. The quantitative method is used in this study. Based on the results of factor analysis and Structural Equation Modeling (SEM) data processing, using SPSS 23 and SMART-PLS software, the results are: 1) e-commerce technology variables indirectly have a positive and significant effect on customer experience when accessing the e-commerce platform, 2) the e-commerce technology variable has a positive and significant direct effect on customer experience. The area in this study is still limited because it merely covers Bandung Raya and is not specific for the assessment of the e-commerce platform used. For future research, the scope of research can be expanded, and the e-commerce platform used can be more specific to certain platforms so that more detailed results can be obtained

Background/Objectives: Every day millions of people visit search engines like Quora, reedit, stack overflow, etc., the demand for new intelligent techniques is growing, to help individuals find better solutions. Methods: In our proposed system, the Quora datasets were filtered using SQLite which takes one-quarter of the time taken to pre-process the same dataset using existing approaches like python functions. We used machine learning techniques namely the Random Forest, Logistic Regression, Linear SVM (Support Vector Machine) and XGBoost to analyze and identify the most suitable model. Findings: The error log loss functions (0.887, 0.521, 0.654 and 0.357) of the above machine learning techniques were analyzed and compared respectively. The performance of XGBoost is the best among the other models, hence XGBoost is the most efficient model. Conclusion/Future Scope: It is concluded that XGBoost has outperformed other machine learning techniques discussed in the study. It is also found that pre-processing using SQLite has improved the response time. In the future, we would like to apply a similar approach for various other search engines that are available like reedit, stack overflow, etc. and one could ensemble the best models of each type (linear, tree-based, and neural network). Keywords Machine Learning, Question Pair Similarity, XGBoost, Linear SVM, Logistic Regression, Random Forest

Precisely timed execution of resource constrained bare-metal applications is difficult, because the embedded software developer usually has to implement and check the timeliness of the executed application through manual interaction with timers or counters. In the scope of this work, we propose a combined timing specification and concept for time annotation and control blocks in C++. Our proposed blocks can be used to measure and profile software block execution time. Furthermore, it can be used to control and enforce the software time behavior at runtime. After the application of these time blocks, a trace-based verification against the block-based timing specification can be performed to obtain evidence on the correct implementation and usage of the time blocks on the target platform. We have implemented our time block concept in a C++ library and tested it on an ARM Cortex A9 bare-metal platform. The combined usage of timing specification and our time block library has been successfully evaluated on a critical flight-control software for a multi-rotor system.

This paper describes a set of five focus enclitics in Bodo, a Tibeto-Burman language spoken in Assam, India. The focus enclitics are phonologically bound morphemes which are attached to a phrase-level constituent of a sentence, such as the verb, its arguments, or its adjuncts. They all trigger existential presuppositions and express various kinds of semantic and pragmatic relations between the asserted proposition and presuppositions, such as inclusion, exclusion, contrast, concession, and correction. The description of these relations as well as other more context and construction specific functions encoded by the focus enclitics constitutes the core of this paper. This paper also examines the distribution of the focus enclitics and discusses their associated scope and interpretations. Finally, this paper investigates co-occurrence, co-occurrence restrictions, and interpretations of sequences of the enclitics.

We conducted two lexical decision experiments and one replication study to examine the scope of transposed-phoneme effects when the transposition involves non-adjacent phonemes. The critical stimuli were non-words derived from a real word (the base-word) either by transposing two phonemes or by substituting the same phonemes with different phonemes. In Experiment 1, the transposed phonemes belonged either to the same syllable (e.g. /bis.tɔk/ for the French base-word /bis.kɔt/) or to a different syllable (e.g. /ʃo.lo.ka/ for the French base-word /ʃo.ko.la/) and were located either at the beginning of the speech signal (e.g. /sib.kɔt/ for /bis.kɔt/; /ko.ʃo.la/ for /ʃo.ko.la/) or at the end (e.g. /bis.tɔk/ for /bis.kɔt/; /ʃo.lo.ka/ for /ʃo.ko.la/). Experiment 2 compared within-syllable and between-syllable transpositions derived from the same set of bi-syllabic base-words (e.g., /sib.kɔt/, /bik.sɔt/, /bis.tɔk/ for the base-word /biskɔt/). In both experiments, we found clear transposed-phoneme effects with longer "no" decisions for transposed-phoneme non-words compared with the matched substituted-phoneme non-words. The effect was of similar magnitude when the transposed phonemes occurred in the same syllable and across different syllables. Also, for both the within- and between-syllable transpositions, the size of the transposed-phoneme effect did not vary as a function of the position of the transposition. Overall, our results suggest that phonemes can migrate across their respective positions not only within a syllable, but also across syllables. More importantly, they also suggest that position-independent phonemes exert a continuous influence during the entire processing of the auditory stimulus to the extent that there is sufficient time for this influence to manifest itself.

The detection of abnormal events in surveillance footage remains a challenge and has been the scope of various research works. Having observed that the state-of-the-art performance is still unsatisfactory, this paper provides a novel solution to the problem, with four-fold contributions: 1) upon the work of Sultani et al., we introduce one iterative learning framework composed of two experts working in the weak and self-supervised paradigms and providing additional amounts of learning data to each other, where the novel instances at each iteration are filtered by a Bayesian framework that supports the iterative data augmentation task; 2) we describe a novel term that is added to the baseline loss to spread the scores in the unit interval, which is crucial for the performance of the iterative framework; 3) we propose a Random Forest ensemble that fuses at the score level the top performing methods and reduces the EER values about 20% over the state-of-the-art; and 4) we announce the availability of the ”UBI-Fights” dataset, fully annotated at the frame level, that can be freely used by the research community. The code, details of the experimental protocols and the dataset are publicly available at http://github.com/DegardinBruno/ .

Binary code similarityapproaches compare two or more pieces of binary code to identify their similarities and differences. The ability to compare binary code enables many real-world applications on scenarios where source code may not be available such as patch analysis, bug search, and malware detection and analysis. Over the past 22 years numerous binary code similarity approaches have been proposed, but the research area has not yet been systematically analyzed. This article presents the first survey of binary code similarity. It analyzes 70 binary code similarity approaches, which are systematized on four aspects: (1) the applications they enable, (2) their approach characteristics, (3) how the approaches are implemented, and (4) the benchmarks and methodologies used to evaluate them. In addition, the survey discusses the scope and origins of the area, its evolution over the past two decades, and the challenges that lie ahead.