Reviewerly/exHarmony huggingface dataset:
Shared-ACS Binary Code Pair Search Exploiting Coupled Symparity

Binary code negation and reversal are known to leave the autocorrelation sequence unchanged. However, for some code lengths, there exist code pairs for which the mechanism behind shared autocorrelation is not so simple; these are called shared-autocorrelation (or shared-ACS) binary code pairs. They are useful for, among other things, constructing binary and quad-phase Golay pairs of longer lengths. We define a symparity vector for any given binary code. For every shared-ACS binary code pairs up to length 39, the two code have the same symparity, or coupled symparity. Conditions are identified for which shared-ACS binary code pairs have coupled symparity. The coupled-symparity condition is used to develop an algorithm to find shared-ACS binary code pairs, with reduced complexity relative to more brute-force exhaustive searches. Although the algorithm assumes extra structure in the shared-ACS code pairs, it rediscovered all the sets of binary shared-ACS code pairs for lengths up to 20. It is then run for lengths 40 to 44, returning all the shared-ACS binary code pairs for each length. In particular, the algorithm results establish that there exist shared-ACS code pairs for the prime lengths 41 and 43.
A Combinatorial Optimized Knapsack Linear Space for Information Retrieval

Key information extraction can reduce the dimensional effects while evaluating the correct preferences of users during semantic data analysis.Currently, the classifiers are used to maximize the performance of web-page recommendation in terms of precision and satisfaction.The recent method disambiguates contextual sentiment using conceptual prediction with robustness, however the conceptual prediction method is not able to yield the optimal solution.Context-dependent terms are primarily evaluated by constructing linear space of context features, presuming that if the terms come together in certain consumerrelated reviews, they are semantically reliant.Moreover, the more frequently they coexist, the greater the semantic dependency is.However, the influence of the terms that coexist with each other can be part of the frequency of the terms of their semantic dependence, as they are non-integrative and their individual meaning cannot be derived.In this work, we consider the strength of a term and the influence of a term as a combinatorial optimization, called Combinatorial Optimized Linear Space Knapsack for Information Retrieval (COLSK-IR).The COLSK-IR is considered as a knapsack problem with the total weight being the "term influence" or "influence of term" and the total value being the "term frequency" or "frequency of term" for semantic data analysis.The method, by which the term influence and the term frequency are considered to identify the optimal solutions, is called combinatorial optimizations.Thus, we choose the knapsack for performing an integer programming problem and perform multiple experiments using the linear space through combinatorial optimization to identify the possible optimum solutions.It is evident from our experimental results that the COLSK-IR provides better results than previous methods to detect strongly dependent snippets with minimum ambiguity that are related to inter-sentential context during semantic data analysis.
Solving Problems or Enabling Problem-Solving? from Purity in Empirical Software Engineering to Effective Co-production (Invited Keynote)

Studying and collaborating with any software-intensive organization demands for excellence in empirical software engineering research. The ever-growing complexity and context-dependency of software products, however, demands for more pragmatic and solution-focused research. This is a great opportunity but it also conflicts with the traditional quest for "purity" in research and a very narrow focus of the work. In this short positioning, we elaborate on challenges which emerge from academia-industry collaborations and discuss touch upon pragmatic ways of approaching them along the co-production model which emerged from SERL Sweden.
Patterns of coining and constructions

Abstract We develop the notion of pattern of coining found in some complete-inheritance models of Construction Grammar ( Fillmore 1997 ; Kay 2013 ), which are processes used to coin new units based on analogy with an existing one. Unlike constructions, they cannot be considered systematically productive in synchrony. After providing measurement methods, we assess the productivity of three patterns (‧ whelm , ‧ licious and ‧ holic ). To do so, we carried out a statistical analysis using two web corpora. Unlike Kay, we show that the difference between constructions and patterns of coining is not so clear-cut, since patterns of coining may undergo constructionalization, and that qualitative aspects should be taken into account along with quantitative data when trying to assess the status of a word-formation pattern.
APPLICATION OF THE COMPETENCE APPROACH IN EDUCATION – METHODS, APPROACHES, ORGANIZATIONAL FORMS, METHODOLOGICAL SOLUTIONS

The application of the competence approach in the education field is a complex process that requires the creation and testing in practice a number of methodological solutions. The change from a knowledgereproducing learning process to competence-oriented one must be realized at all levels of the educational structure. Even in their preparation, future teachers must get acquainted with the modern achievements of pedagogy and be ready to apply in practice a variety of organizational forms other than classroom-lesson. The article presents methods, approaches, forms that facilitate the application of the competence approach in the education of children and students of preschool and primary school age.
Knowledge Management in a Globalized World and the Coronavirus Age

Research background: Although the world is still in a coronavirus crisis, it also sees hope in vaccines being developed, government measures taken to revive the economy and borders reopening. At the same time, the globalized world has learned a lot. Knowledge management and knowledge sharing in a globalized world now have even higher mission than before, since work regimes have completely changed and the necessary requirements for the protection of employees´ health and safety are being adopted. Purpose of the article: Knowledge management is a multidisciplinary activity; its individual disciplines will be described, characterized and researched. Research surveys will map the activities of knowledge management in new work regimes with the protection of employee health and safety in order to optimize knowledge management of businesses in a globalised world with existing threats. Methods: After the questionnaire survey is completed, the respondents’ answers will be encoded in Microsoft Excel. The encoded data will be exported to the SPSS statistical software. The objective of the statistical evaluation is to extract relationships and linkages that could contribute to optimising the knowledge management of enterprises in a globalised world. To detect basic relationships and linkages, Pearson’s chi-square test of independence will be used at the level of descriptive statistics, using contingency tables. Findings &amp; Value added: Based on the conducted research, it diagnoses existing knowledge management barriers and knowledge sharing in globalized enterprises.
Comprehensive Analysis of Multimodal Recommender Systems

Recommender systems are destined to solve the immense issue of analyzing information overload and supporting customers in decision making with more relevant and personalized information. Most of recommender systems only consider the feedback provided by the customers or content of items. They do not consider the different modes of available information. Smartphones, smart devices, web 2.0, etc., enable users to generate different multimedia content which may help to learn about user’s preferences. Multimodal information can be analyzed to learn users’ preference dynamics and generate more accurate personalized information by considering different modes of available information simultaneously. In recent years, multimodal recommender systems have been developed by using multimodal information of users and items. In this paper, a comprehensive analysis of multimodal recommender systems is provided. Our paper focuses on various aspects such as modality, applications and techniques for multimodal recommender systems. The positive and negative aspects of using multimodality are also discussed in recommender systems.
Multi-strategy Sentiment Analysis of Banking Reviews Based on Semantic Fuzziness

Right now, characterize every one of those perspectives as a component of item and present a multi-dimensional opinion investigation approach for banking audits. Specifically, we utilize an opinion dictionary extending instrument to expel the word equivocalness among various measurements and propose a calculation for feeling examination on banking surveys dependent on rules and a dimensional conclusion vocabulary. Make word net lexicon: In this kind of record, every single positive word is worked out independently and every negative word is worked out at one spot. Extraction of dataset: First dataset of openly accessible financial audits was downloaded from the Web, and afterward the entry extraction system distinguishes significant segments of the content which is generally illustrative of the substance of the record. All the more explicitly, this progression includes distinguishing and extricating those particular financial highlights and the conclusions on them.
Ranking Cryptocurrencies by Brand Importance: A Social Media Analysis in ENEAGRID

This paper presents a preliminary analysis of cryptocurrency brands on Twitter, carried out through the Semantic Brand Score Brand Intelligence App (a tool hosted in the ENEAGRID digital infrastructure). The aim is to rank five digital coins (i.e. Bitcoin, Ethereum, Zcash, Monero, Litecoin). Web crawling, data storage and brand scoring activities require computational power. The ENEAGRID infrastructure faces this challenge in terms of computational costs, with its computing core represented by the HPC CRESCO clusters. In our methodology, we run periodic sessions of web crawling to create a database of tweets (concerning digital coins), then we use the Semantic Brand Score to evaluate cryptocurrencies relevance and study their brand image. This case study is a first step towards collaborating with experts and research communities in financial domain and opening access to ENEA Virtual Labs.
Face Clustering Based on Fusion of Face Tracking and Optimization

Face clustering algorithms play an important role in face recognition and data analysis. Since the slow speed and high complexity face clustering algorithm of large-scale data in the face recognition system, we propose a face clustering algorithm combining face tracking and optimization. According to the face image and coordinates obtained by the face detection algorithm, we create face tracks. Then using the face quality assessment algorithm to select the face image with better facial posture and finer image from one of those face tracks and as the representative picture of the face track. Combining the similarity matrix of the face representative image and constraint matrix between face tracks, we can use unsupervised clustering algorithm to cluster representative images of those faces and get better clustering results. Experimental results show that our method can quickly and effectively cluster face images compared with other algorithms.
A Pixel Count Approach for Lossy Image Compression

Digital image storage and transmission plays a very important role in today’s modern world, as most of the data transfer involves images. Hence, digital image compression is of great importance. The compression leads to either lossy or lossless type of images. Here, we discuss about a unique approach for lossy image compression, which involves a threshold. The number of pixels whose sum is lesser than a threshold is counted, and this count is saved in a file instead of actual pixel intensity values. A difference between the threshold and the computed sum is also stored. Later, reconstruction is done by reading the count and difference values. Average is calculated, and the count number of pixels is replaced with this value. We find that we can achieve better quality at lower PSNR values with this approach as compared to JPEG algorithm.
An Eco-Logic of Change: the need for an Affective Re-valuation of Urban Space

This paper attempts to make a contribution to the growing awareness put forward in this special issue that we need to cultivate new ways of bringing the lived, felt qualities and atmosphere of urban spaces into city planning.The paper proposes thinking with the entanglements, atmospheric politics and qualities of this critical zone and its throwntogetherness of space through an exploration of how affective data might become instrumental in revaluing urban space attuned to an ecologic of change.In the paper, we will present a conceptual foundation for the work in relation to an ongoing collaboration with Byhaven pa Sundholm (the City Garden in Sundholm). We will focus on the production of different forms of affective data and how it might influence urban planning and technology design, and reflect on the methodological implications this has for our respective fields of inquiry; evental urbanism and affective interaction design.
Effective, Explainable and Ethical: AI for Law Enforcement and Community Safety

We describe the Artificial Intelligence for Law Enforcement and Community Safety (AiLECS) research laboratory, a collaboration between the Australian Federal Police and Monash University. The laboratory was initially motivated by work towards countering online child exploitation material. It now offers a platform for further research and development in AI that will benefit policing and mitigating threats to community wellbeing more broadly. We outline the work the laboratory has undertaken, results to date, and discuss our agenda for scaling up its work into the future.
Analysis of Relationship Between Learning Outcomes and Student’s Exam Results Using Association Rule Mining and Fuzzy Inference Rules

The academic success of students largely depends on the correct selection and evaluation of learning outcomes. The most effective tool for measuring student knowledge is exams. In this context, the scores of the students in the exams are the important indicators of their learning achievements. In this study the relationship between learning outcomes and exam results of students is analyzed. The research was carried out based on mathematics exam scores of students studying in the 6th grade of secondary school and the data related to the learning outcomes of this course. To reveal this relationship, an approach based on fuzzy logic and data mining methods is considered. The weights of the learning outcomes in the questions were analyzed, then the relationships between the outcomes and scores were determined using the Apriori algorithm. Then fuzzy inference rules were created based on the most frequent items determined as the results of the Apriori algorithm.
Brain Image Parcellation Using Fully Convolutional Network with Adaptively Selected Features from Brain Atlases

Brain image parcellation is an important data processing step in neuroscience. Since multi-atlas based parcellation (MAP) uses prior information from brain atlases (i.e., manually labeled brain regions), it can provide accurate brain parcellation and has been widely adopted. Recently, some deep learning based brain image parcellation (DLP) methods using fully convolutional network (FCN) have been proposed. Compared with MAP, DLP has high computational efficiency, making it more applicable in practice. However, existing DLP methods either neglect or partially utilize brain atlases, making it difficult to get comparable parcellation accuracy as MAP. In this paper, we propose a new DLP method which is able to use brain atlases in an effective way. The network is based on FCN and non-local block based channel attention module (NL module). The input of our network is the target brain image to be parcellated as well as available brain atlases, and the parcellation result is produced through the FCN guided by the features of brain atlases selected by NL modules at different scales. In the experiments using two public MR brain image datasets (LPBA40 and NIREP-NA0), our method outperforms MAP and the state-of-the-art DLP methods due to the effective usage of brain atlases.
Fuzzy Clique Set Determination Method as an Example of Fuzzy Temporal Graph Invariant

The paper investigates the problem of the invariant determination in graphs that have fuzzy-estimated parameters and temporal characteristics. The basic idea is to find fuzzy clique set in such graphs. Fuzzy temporal graph capture the notion of temporal characteristics and uncertainty while processing some operations on it. In this paper the idea of temporality in such graph models is treated in the way that adjacency of the vertices may change over time periods. Cliques normally refer to subgraphs in a graph such that vertices in each subgraph are pairwise adjacent. Adjacency may be uncertain due to some features of the network and may vary in time. In the problem of maximum clique determination the idea is to search the clique with most vertices within a graph. So the fuzzy adjacency here can be interpreted in terms of immediate likelihood of vertex to capture or to share whatever is flowing though the graph network. The idea of maximum clique subset for the fuzzy graph with fuzzy-estimated characteristics is presented in this paper. A method for determination of all maximum clique sets is proposed, as well as a fuzzy clique set is determined. The illustrative examples are given as well.
Multiple Leader-Follower Coordination for Cooperative Missions

In this work we present an algorithm for coordination in cooperative multi-vehicle missions. As it will be shown throughout the paper, our solution guarantees satisfaction of inter-vehicle safety constraints with minimal variations to the vehicles' nominal trajectories. Moreover, it relies on a leader-follower structure, and models a realistic communication network. Finally, theoretical guarantees, as well as numerical results to demonstrate the efficacy of the proposed solution are provided.
Improving 3D Object Detection via Joint Attribute-oriented 3D Loss

3D object detection has become a hot topic in intelligent vehicle applications in recent years. Generally, deep learning has been the primary framework used in 3D object detection, and regression of the object location and classification of the objectness are the two indispensable components. In the process of training, the ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sub> (n=1,2) and the focal loss are considered as the frequent solutions to minimize the regression and classification loss, respectively. However, there are two problems to be solved in the existing methods. For regression component, there is a gap between evaluation metrics, e.g., 3D Intersection over Union (IoU), and the traditional regression loss. As for the classification component, confidence score exists ambiguous due to the binary label assignment of target. To solve these problems, we propose a loss by jointing 3D IoU and other geometric attributes (named as jointed attribute-oriented 3D loss), which can be directly used in optimizing the regression component. In addition, the jointed attribute-oriented 3D loss can assign a soft label for supervising the training of the classification. By incorporating the proposed loss function into several state-of-the-art 3D object detection methods, the significant performance improvement has been achieved on the KITTI benchmark.
A model for learning structured representations of similarity and relative magnitude from experience

How a system represents information tightly constrains the kinds of problems it can solve. Humans routinely solve problems that appear to require abstract representations of stimulus properties and relations. How we acquire such representations has central importance in an account of human cognition. We briefly describe a theory of how a system can learn invariant responses to instances of similarity and relative magnitude, and how structured, relational representations can be learned from initially unstructured inputs. Two operations, comparing distributed representations and learning from the concomitant network dynamics in time, underpin the ability to learn these representations and to respond to invariance in the environment. Comparing analog representations of absolute magnitude produces invariant signals that carry information about similarity and relative magnitude. We describe how a system can then use this information to bootstrap learning structured (i.e., symbolic) concepts of relative magnitude from experience without assuming such representations a priori.
Related Article Extraction Using Learning to Rank

Related articles of a news article help readers to access other beneficial information efficiently. Thus, it is an important task for newspaper companies to link appropriate related articles to a news article on news websites. However, it costs to select related articles from enormous news articles that have been published in the past. To solve the issue, we introduce “learning to rank” methods to the related article extraction task to support business operation. We propose some ranking methods and report the accuracy of each method with three evaluation metrics. We also show some input-output examples by the method that scores the highest accuracy in our experiments.
Few-Shot Domain Adaptation For Many Class Classification Using Commercial Products

In order to train a classifier for self checkout system in convenience store at low cost, a few-shot domain adaptation problem has to be solved. Since the system treats a classifier for large number of classes, conventional method of few-shot domain adaptation should be extended for many classes. This paper propose to exploit meta-class information by executing the adaptation on the normal-class level and the meta-class level simultaneously. The proposed method are shown to be effective for improving adaptation accuracy of a classifier for many classes. The results of our ablation study implies that i) the meta-class should be decided by using k-means clustering method rather than clustering manually, and that ii) the ratio between the number of normal-class and the number of meta-class should be fixed.