Reviewerly/exHarmony huggingface dataset:
Graph of Primitives Matching Problem in the World Wide Web CBIR Searching Using Query by Approximate Shapes

The problem of matching two graphs has been considered by many researchers. Our research, the WWW CBIR searching using Query by Approximate Shapes is based on decomposing a query into a graph of primitives, which stores in nodes the type of a primitive with its attributes and in edges the mutual position relations of connected nodes. When the graphs of primitives are stored in a multimedia database used for World Wide Web CBIR searching, the methods of comparisons should be effective because of a very huge number of stored data. Finding such methods was a motivation for this research. In this initial research only the simplest methods are examined: NEH-based, random search-based and Greedy.
Additive decompositions of polynomials over unique factorization domains

Additive decompositions over finite fields were extensively studied by Brawely and Carlitz. In this paper, we study the additive decomposition of polynomials over unique factorization domains.
Modeling, ontology and wild thought: Toward an anthropology of the artificially intelligent

The question at the heart of this essay begins with a curious mid-twentieth-century co-occurrence in the Anglo-American world: on the one hand, of the invention and transformative spread of digital computing; on the other hand, the return from philosophical exile of “ontology” and its subsequent pluralization. What makes this co-occurrence interesting is, first, the machine’s requirement of a “model”—and so an ontology—of the domain to be affected, and second, the indeterminate nature of all digital models. Furthermore, as improvements in the technology have made the machine an ever more fit instrument of that “wily intelligence” (mẽtis) celebrated by Marcel Detienne and Jean-Pierre Vernant (1974), the laborious construction of models has become the agile practice of modeling—and so brought ever greater emphasis to local, experimental, shifting ontologies.
Socially-Aware Diagnosis for Constraint-Based Recommendation

Constraint-based group recommender systems support the identification of items that best match the individual preferences of all group members. In cases where the requirements of the group members are inconsistent with the underlying constraint set, the group needs to be supported such that an appropriate solution can be found. In this paper, we present a guided approach that determines socially-aware diagnoses based on different aggregation functions. We analyzed the prediction quality of different aggregation functions by using data collected in a user study. The results indicate that those diagnoses guided by the Least Misery aggregation function achieve a higher prediction quality compared to the Average Voting, Most Pleasure, and Majority Voting. Moreover, another major outcome of our work reveals that diagnoses based on aggregation functions outperform basic approaches such as Breadth First Search and Direct Diagnosis.
Preventing Information Leakage

Traders devote significant resources to producing private information. The value of such informational is eroded when it is leaked. We study the use of multiple brokers by institutions to help mitigate information leakage. We document that trades using multiple brokers better predict future returns, consistent with information possessed by investors driving the decision to use multiple brokers. We find that trades using multiple brokers have lower price impacts and implementation shortfalls, higher long-term profitability, and fewer follower trades than similar single broker trades. The results suggest that traders use multiple brokers to successfully reduce information leakage.
Collaborative HRI and Machine Learning for Constructing Personalised Physical Exercise Databases

Recent demographics indicate that we have a growing population of older adults with increasingly complex care-related needs, and a shrinking care workforce with limited resources to support them. As a result, there are a large number of research initiatives investigating the potential of intelligent robots in a domestic environment to augment the support care-givers can provide and improve older adults’ well-being, particularly by motivating them in staying fit and healthy through exercise. In this paper, we propose a robot-based coaching system which encourages collaboration with the user to collect person-specific exercise-related movement data. The aim is to personalise the experience of exercise sessions and provide directed feedback to the user to help improve their performance. The way each individual user is likely to perform specific movements will be based on their personal ability and range of motion, and it is important for a coaching system to recognise the movements and map the feedback to the user accordingly. We show how a machine learning technique, a Nearest Neighbour classifier enhanced with a confidence metric, is used to build a personalised database of 3D skeletal tracking data. This approach, combined with collaborative Human-Robot Interaction to collect the data, could be used for robust and adaptable exercise performance tracking by a collaborative robot coach, using the information to provide personalised feedback.
Extensible Attribute Similarity Data Mining for Categorical Data Streams in Web Usage Framework

Information mining is a forcefully idea in data recovery in view of various characteristics from various information sources. Emergence increase of web 2.0 oriented applications for best user encounters and availability without time and geographical limitations. For the researchers, Web usage logs are becoming a major role across the world, and user’s behavior and analysis of data are one of the different concepts for decision making in business intelligence. To employ customer-centric Web-oriented applications, we implement extensible Web usage mining framework (XWUMF), i.e., hybrid framework to handle data extraction based on user behavior with different attributes in data sets. Proposed hybrid approach is the combination of extensible and classification by pattern-based hierarchical clustering (ECPBHC). Extensible and classification by pattern-based hierarchical clustering (ECPBHC) is used to extract relational data from different Web-oriented categorical data sets based on user’s behavior and analysis with different attributes’ relations.
A Horizontal Patent Test Collection

We motivate the need for, and describe the contents of a novel patent research collection, publicly available and for free, covering multimodal and multilingual data from six patent authorities. The new patent test collection complements existing patent test collections, which are vertical (one domain or one authority over many years). Instead, the new collection is horizontal: it includes all technical domains from the major patenting authorities over the relatively short time span of two years. In addition to bringing together documents currently scattered across different test collections, the collection provides, for the first time, Korean documents, to complement those from Europe, US, Japan, and China. This new collection can be used on a variety of tasks beyond traditional information retrieval. We exemplify this with a task of high-relevance today: de-anonymisation.
Telugu Word Segmentation Using Fringe Maps

In this paper, we propose a word segmentation method that is based on fringe maps on Telugu script. Our objective is to create a data set of word images for enabling direct training for recognition on those. The standard methods employed for the task of word segmentation in Telugu OCR systems are projection profiles and run-length smearing. However those methods have their limitations. In this work a different application of fringe maps is shown for line segmentation into words. Fringes were previously applied successfully for carrying out classification and line segmentation. Telugu script, which has consonant modifiers that are usually placed below or below-right to the base consonants. This kind of orthographic property leads to characters that may touch each other. One way to deal with touched characters is to make use of segmentation free methods, which do not need prior segmentation of word images into characters or connected components. The novelty of our method is that we analyze fringe maps of document images to find an appropriate fringe value threshold and apply it for word segmentation of Telugu documents. Encouraging results are observed with our fringe value threshold based word segmentation. We observe that choosing higher threshold fringe values leads to under-segmentation of words, whereas lower values cause over-segmentation of words. Our word segmentation approach is successfully compared with the widely used projection profiles based word segmentation method.
Scientific Bibliographic Description of the Collections of Russian Literature Abroad in the Russian State Library (to the History of Problem)

Scientific bibliographic description of collections, which includes bibliographic description of the holding items, their systematization and scientific research, serves to disclose the library holdings. The purpose of the article is to study the process of scientific bibliographic description of the collections of Russian literature abroad in the Russian State Library (RSL) in its historical development. The author shows the evolution of this activity carried out by specialists of various departments of the RSL, and using reference to specific scientific articles on the theory of bibliography explains the importance of scientific bibliographic description of collections for creation of retrospective national bibliography. The author analyses and systematizes several dozen works related to the scientific bibliographic description of the Russian literature abroad in the RSL. This part of Rossika in the Library is mainly a single array. The work on its scientific bibliographic description is carried out in the traditional (printed) form, in modern (electronic) form, responding to contemporary trends, as well as in parallel in both forms. The article presents the consolidated list of local databases (DB) of Russian literature abroad, created in the RSL. Two main types of DB are distinguished: 1) Established on the basis of the collections of Russian literature abroad and intended mainly for their disclosure. The names of these resources fully reflect their essence: “Photographs in the Collection...”, “Publications with the owner’s marks from the Collections...”, “Displaced cultural values in the Collection...”; 2) Consolidated databases based on the collections of Russian literature abroad and a number of other sources. It is reflected and even underlined in the title, for example: “Consolidated catalogue of periodicals and continuing editions of Russian abroad in Moscow libraries”. The author collected and analysed scientific articles of the RSL researchers, revealing the history of these databases, the principles of their construction, the goals, objectives, opportunities and prospects. The article presents correlation between the database and traditional bibliographic indexes of Russian literature abroad in the RSL, as well as lists the cases when the DB is based on the index or, conversely, database serves as the basis for it. The author lists, systematizes and briefly describes traditional bibliographic indexes of the collections of Russian literature abroad created by the staff of the Russian State Library, as well as shows how to use technical possibilities in the indexes placed on electronic media.
Detecting unsatisfiable CSS rules in the presence of DTDs

Cascading Style Sheets (CSS) is a popular language for describing the styles of XML documents as well as HTML documents. For a DTD D and a list R of CSS rules, due to specificity R may contain “unsatisfiable” rules under D, e.g., rules that are not applied to any element of any document valid to D. In this paper, we consider the problem of detecting unsatisfiable CSS rules under DTDs. We focus on CSS fragments in which descendant, child, adjacent sibling, and general sibling combinators are allowed. We show that the problem is coNP-hard in most cases, even if only one of the four combinators is allowed. We also show that the problem is in coNP or PSPACE depending on restrictions on DTDs and CSS. Finally, we present two conditions under which the problem can be solved in polynomial time.
Stereo Matching Based on Guided Image Filtering with Permeability Filter

The formation of current stereo matching algorithms based on the guided image filtering starts from the minimization of an energy function. We extend this approach by introducing weights to the function such that contribution of different pixels can be assigned individually. This scheme is integrated with the so-called permeability filter for the weighting factors. As the whole picture is involved in the computation, no decision for the window size is required. Extensive experimental comparisons with several state-of-the-art methods using the Middlebury Stereo Evaluation version 3 datasets show that the proposed scheme has a great advantage in stereo matching.
A Computing Framework to Check Real-Time Requirements in Ambient Intelligent Systems

Ambient Intelligent (AmI) systems are related to environments that usually demand real-time characteristics in their context-aware applications. These systems can require a timing response according to specific needs dealing with context changes and user interactions. The current work presents a computing framework able to check and measure those times required by ubiquitous systems, running AmI processes on mobile or networked devices. The proposed framework focuses on checking real-time requirements of applications which can monitor and recognize human activities. This framework is then applied to two examples of well-known datasets related to Human Activity Recognition (HAR) and it supports the implementation of a mobile application which allows researchers to measure those latency times involved in HAR processes. The computation cost of training and prediction processes for these datasets is measured using such application and the obtained results highlight the critical effect of smartphone performance when processing sensor-based data and, specially, during the elaboration of online training models in this context.
A Study of Subminimal Logics of Negation and Their Modal Companions

We study propositional logical systems arising from the language of Johansson’s minimal logic and obtained by weakening the requirements for the negation operator. We present their semantics as a variant of neighbourhood semantics. We use duality and completeness results to show that there are uncountably many subminimal logics. We also give model-theoretic and algebraic definitions of filtration for minimal logic and show that they are dual to each other. These constructions ensure that the propositional minimal logic has the finite model property. Finally, we define and investigate bi-modal companions with non-normal modal operators for some relevant subminimal systems, and give infinite axiomatizations for these bi-modal companions.
Locomotion-in-Place and Teleport: Which Is the Best Technique to Be Used in Human Behavior Research Using Virtual Reality?

In human behavior studies in critical situations, such as emergency situations, it’s necessary for the participant to feel that is in danger and that behaves the same way as in the real world. For this behavior to be natural, the technique to control the avatar in the virtual environment should not distract users. A wrong choice of locomotion technique distracts users from their task and result in poor performance, frustration and cybersickness. In this context, the objective of this study is to know if a new technique (locomotion-in-place) is more efficient than the teleport, for human behavior studies. We evaluated collisions of the avatar with walls and objects in the virtual environment, the level of presence and cognitive maps. The results showed that Locomotion-in-place is a technique easier to control, regarding the number of collisions, but we didn’t find differences in presence and cognitive maps between the two locomotion techniques.
Learning Style of College General Mathematics Course According to Academic Achievement and Essential-elective Course

The purpose of this study is to analyze the relationship between learning style and academic achievement of essential mathematics course students and elective mathematic course students for effective college mathematics curriculum. By using Kolb Learning Style Inventory, we surveyed all 143 students in 4 classes(2 essential course, 2 elective course) classified A, B, C groups by academic achievement and analyzed significant consequence. The results of this study are as follows. First, the ranking of learning style of ‘Essential mathematics course’ is Accommodator, Diverger, Assimilator, Converger. Second, the ranking of learning style of ‘Elective mathematics course’ is Diverger, Assimilator, Accommodator, Converger. Third, the ranking of learning style of ‘A group’is Assimilator, Diverger, Accommodator, Converger. Fourth, the ranking of learning style of ‘B group’ is Diverger, Accommodator, Assimilator, Converger. Fifth, the ranking of learning style of ‘C group’ is Accommodator, Diverger, Assimilator, Converger. Based on this study, professors and educators should develop teaching method that fits each students groups’ learning style and properties of college mathematics curriculum.
Transition Towards Continuous Delivery in the Healthcare Domain

Continuous Delivery is meanwhile well-established in many parts of the software industry. In a transition towards continuous delivery in the healthcare domain, there are a number of additional challenges that should be addressed. We present how we have addressed some of these challenges and highlight some potential research topics that could be addressed in this space to make further progress in this important area. Although our focus is on the healthcare domain, the approach and the research topics are applicable also to a broad range of other application domains.
Evaluation of bottom-up saliency model using deep features pretrained by deep convolutional neural networks

We present extensive evaluations of deep features pretrained by state-of-the-art deep convolutional neural networks (DCNNs) for predictions of human fixations. The evaluations are conducted using a bottom-up saliency model, which utilizes deep features of DCNNs pretrained for object classification. Using various selections of deep feature maps, 35 implementations of the bottom-up saliency model are computed, evaluated, and compared over three publicly available datasets using four evaluation metrics. The experimental results demonstrate that the pretrained deep features are strong predictors of human fixations. The incorporation of multiscale deep feature maps benefits the saliency prediction. The depth of DCNNs has a negative effect on saliency prediction. Moreover, we also compare the performance of the proposed deep features-based bottom-up saliency model with the other eight bottom-up saliency models. The comparison results show that our saliency model can outperform other conventional bottom-up saliency models.
Incremental Causal Discovery and Visualization

Discovering causal relations from limited amounts of data can be useful for many applications. However, all causal discovery algorithms need huge amounts of data to estimate the underlying causal graph. To alleviate this gap, this paper proposes a novel visualization tool which incrementally discovers causal relations as more data becomes available. That is, we assume that stronger causal links will be detected quickly and weaker links revealed when enough data is available. In addition to causal links, the correlation between variables and the uncertainty of the strength of causal links are visualized in the same graph. The tool is illustrated on three example causal graphs, and results show that incremental discovery works and that the causal structure converges as more data becomes available.
Thinking Like Humans: A New Approach to Machine Translation

Existing machine translation approaches do not adequately mimic how humans do translation from one natural language to another. This paper presents a novel approach to machine translation that is inspired by how humans translate natural languages. We have exploited the theory of psycholinguistic sentence-parsing to develop a human-like machine translation system. This approach has been modeled as a multi-agent system, named EnSiMaS, which translates an English sentence into Sinhala sentence. The multi-agent system has been implemented through the MaSMT framework with two manager agents and over 100 agents which deliberate on different aspects of machine translation. These agents are clustered into eight-agent swarms to consider morphological, syntactic, and semantic concerns of the source and the target languages. The EnSiMaS system has been tested with the different types of sentences and successful results were obtained.
On Applying Metamorphic Testing: An Empirical Study on Academic Search Engines

Software testing can be a helpful practice to certify the quality of a product. However, there are programs which are hard, if not impossible, to determine the expected outputs. This problem is called the "oracle problem". Metamorphic testing (MT) is an approach that aims to alleviate this problem by setting a series of relations, called metamorphic relations (MRs). This paper applies MT through a user-oriented approach and the following MRs: MPublished, MPTitle, MPShuffleJD and Top1Absent as a strategy for evaluating, verifying, and validating four Academic Search Engines (ASEs): ACM, IEEE, ScienceDirect, and Springer. Therefore, we conducted an experimental study to analyze how MRs can contribute to verifying the correctness of the properties of ASEs. Results indicate that the ASEs have performed differently in their execution for each MR analyzed. This shows that the algorithms of the scientific search engines have different behaviors among the characteristics analyzed. These behaviors may not be properly characterized by a possible failure, but by a simple design choice. On the other hand, the approach is useful for search engine users that need to be sure that the ASEs behave as expected during a search, as the results of secondary studies strongly depend on the correct behavior of the scientific search engine used.
Binary Independence Language Model in a Relevance Feedback Environment

Model construction is a kind of knowledge engineering, and building retrieval models is critical to the success of search engines. This article proposes a new (retrieval) language model, called binary independence language model (BILM). It integrates two document-context based language models together into one by the log-odds ratio where these two are language models applied to describe document-contexts of query terms. One model is based on relevance information while the other is based on the non-relevance information. Each model incorporates link dependencies and multiple query term dependencies. The probabilities are interpolated between the relative frequency and the background probabilities. In a simulated relevance feedback environment of top 20 judged documents, our BILM performed statistically significantly better than the other highly effective retrieval models at 95% confidence level across four TREC collections using fixed parameter values for the mean average precision. For the less stable performance measure (i.e. precision at the top 10), no statistical significance is shown between the different models for the individual test collections although numerically our BILM is better than two other models with a confidence level of 95% based on a paired sign test across the test collections of both relevance feedback and retrospective experiments.