Reviewerly/exHarmony huggingface dataset:
Cognitive and Computational Aspects of Intercultural Communication in Human-Computer Interaction

Human-computer interaction is the foundation of basically all fundamental communication practices which are underway in today’s global world of interconnectedness. The areas included are mostly business, corporate and managerial communication, social media communication patterns and practices, various kinds of intercultural communication, etc. Human-computer interaction is also massively implemented in modern educational strategies, such as eLearning, blended learning, hybrid learning and mLeaning. These two areas, i.e. business and learning processes, are heavily influenced by current trends in ICT and all aspects are influenced by new communication paradigms. The new communication paradigms are both cognitive and computational. Analog communication has been vastly abandoned and substituted by modern communication tools, means and processes. These cognitive and computational aspects must be taken into consideration so that we can optimize our communication patterns. The paper highlights the importance of intercultural aspects in human-computer interaction and communication. The research shows that the awareness of this topic is still not sufficient and tries to highlight its importance. The paper also provides pragmatic solutions for global digital communication patterns and strategies which could be implemented in the data transfer through visualization in websites. This way of optimizing is necessary for any human-computer interaction so that it will remain competitive and sustainable in the global world of interconnectedness.
Study on the Development of Ruichang Bamboo Weaving Patterns Based on Computer Graphics and Machine Learning

Ruichang bamboo weaving in Jiangxi Province, known as one of China’s significant intangible cultural heritages (ICH), faces multiple problems such as old-fashioned and monotonous patterns due to a lack of inheritors, uncreative design and low efficiency. This study applies machine learning and computer graphics to the generation of new Ruichang bamboo weaving patterns, or more specifically, it generates new patterns by establishing the Generative Adversarial Networks (GAN) algorithm model, inputting reasonable parameters and applying Python and Tensorflow frameworks. This study develops and diversifies bamboo weaving patterns through machine deep learning, providing new approaches and ideas for the ICH protection.
Usability Evaluation Towards a Cultural Perspective: A Systematic Literature Review

Usability heuristics participate in the design and evaluation of websites. Based on traditional heuristics, cultural-oriented heuristics are used to determine if a website reaches an adequate level of usability, focusing on the target audience of similar cultural characteristics. However, to know what heuristics are use to design or evaluate a website, it is necessary to know which ones are focused on the cultural environment and the type of website of interest. Through a systematic review, common procedures for establishing cultural-oriented heuristics based on traditional heuristics have been identified. In addition, the main types of website to which these heuristics are applied for usability evaluation have been also identified. This paper shows the results of the systematic review.
String indexing for top-k close consecutive occurrences

The classic string indexing problem is to preprocess a string S into a compact data structure that supports efficient subsequent pattern matching queries, that is, given a pattern string P , report all occurrences of P within S. In this paper, we study a basic and natural extension of string indexing called the string indexing for top-k close consecutive occurrences problem (Sitcco).Here, a consecutive occurrence is a pair (i, j), i < j, such that P occurs at positions i and j in S and there is no occurrence of P between i and j, and their distance is defined as ji.Given a pattern P and a parameter k, the goal is to report the top-k consecutive occurrences of P in S of minimal distance.The challenge is to compactly represent S while supporting queries in time close to the length of P and k.We give three time-space trade-offs for the problem.Let n be the length of S, m the length of P , and ∈ (0, 1].Our first result achieves O (n log n) space and optimal query time of O (m + k).Our second and third results achieve linear space and query times either O (m + k 1+ ) or O (m + k log 1+ n).Along the way, we develop several techniques of independent interest, including a new translation of the problem into a line segment intersection problem and a new recursive clustering technique for trees.
Video Object Segmentation using Optical Flow and Recurrent Neural Networks

This paper implements the task of semi-supervised Video Object Segmentation (VOS), i.e., the separation of an object from the background in a video, given the mask of the first frame. To accomplish this task, modern Machine learning techniques have been used, such as, Convolution Neural Networks (CNNs) and Convolutional Recurrent Neural Networks (CRNNs). The motion of objects between consecutive frames of the sequence, caused by the relative movement between the object and camera is very important information in this task and bears the name Optical Flow (OF). OF was used in the paper to improve the algorithm performance. Experiments, carried out on the Densely Annotated VIdeo Segmentation (DAVIS) database, show competitive results as other algorithms from this field.
An innovative Method of Accessing Digital Video Archives through Video Indexing

Digital videos are stored in a digital video archive as a collection of records which is kept for future reference. Now, metadata is used for accessing the video files which is stored along with video files separately to enable the users to access these video resources, identifying and managing them efficiently. Each Video document is described by a video metadata about the physical and formal attributes. To access the video files precisely based on a specific topic, the video metadata is not sufficiently able to describe the content of a video document. In this research paper, an innovative method is suggested to access digital video archives for a specific topic of interest using dense captioning videos and indexing. In this proposed method, each video is represented as a script using the video storytelling technique. This script is used for generating an index which is for searching for a particular topic in the video archive. A master index table is used for storing all video indexes in which each keyword points to video file and significant time slots. When the end-user searches for a keyword in a master video index table, he/she will get the details of the video file and time slots which is relevant to the keyword he/she searches.
On the Effect of Walsh/Fourier transform in surrogate-assisted genetic algorithms

There have been many investigations on constructing efficient surrogate models to reduce the computation time of the black-box optimization problems, which require extensive computation. Here, we transformed the basis of each training data in the continuous domain and discrete domain, and constructed a surrogate model using the transformed solution. In the continuous domain, discrete Fourier transform was used; in the discrete domain, the discrete Walsh transform was used. Various benchmarks problems were experimented with, and the practical evidence for the accuracy of the proposed model was provided. In particular, it was confirmed that when the fitness function of the genetic algorithm was substituted with the proposed model, the genetic algorithm can find a better solution.
A Novel Proof-of-concept Framework for the Exploitation of ConvNets on Whole Slide Images

Traditionally, the analysis of histological samples is visually performed by a pathologist, who inspects under the microscope the tissue samples, looking for malignancies and anomalies. This visual assessment is both time consuming and highly unreliable due to the subjectivity of the evaluation. Hence, there are growing efforts towards the automatisation of such analysis, oriented to the development of computer-aided diagnostic tools, with a ever-growing role of techniques based on deep learning. In this work, we analyze some of the issues commonly associated with providing deep learning based techniques to medical professionals. We thus introduce a tool, aimed at both researchers and medical professionals, which simplifies and accelerates the training and exploitation of such models. The outcome of the tool is an attention map representing cancer probability distribution on top of the Whole Slide Image, driving the pathologist through a faster and more accurate diagnostic procedure.
Identification of Target Speech Utterances from Real Public Conversation

We are developing a conversation support system that can estimate the smooth progress of human-to-human conversation. When the system senses there has been little progress in the conversation, it attempts to provide a topic to lead a smoother discussion and good atmosphere. The conversation atmosphere is estimated using the fundamental frequency (F0) and sound power (SP). In its practical use, the following problems occur: In this paper, we propose an identification method for target speech utterances from ambient speech noises or laughter utterances using the standard deviation value of SP and Mel-Frequency Cepstral Coefficients (MFCC).
Production Implementation of Recurrent Neural Networks in Adaptive Instructional Systems

This paper reviews current research on deep knowledge tracing (DKT) and discusses the benefits of using DKT in adaptive instructional systems (AIS). Namely, DKT allows for accurate measurement of ability levels across a set of attributes in a content domain and this information can be leveraged to deliver personalized content to the learner. DKT uses a recurrent neural network with long short-term memory units (RNN-LSTM), which is difficult to interpret, although provides higher prediction accuracy than Bayesian knowledge tracing (BKT) or item response theory (IRT) measurement approaches. This makes DKT ideal for learner-focused or formative assessment systems, in which the measurement of attribute proficiencies and the delivery of relevant content to promote learning is valued above the understanding of the measurement process itself. The paper focuses on practical considerations for preparing and deploying an RNN-LSTM in a production system. Namely, data demands for training the network are explored through an analysis on real data from an adaptive tutoring program, and novel methods for training the network when no data are available and for measuring learning trajectories are proposed. Finally, strategies around monitoring production prediction services are discussed, as well as tips for approaching latency, stability, and security issues in production environments. These discussions are meant to provide a researcher or data scientist with enough information to effectively collaborate with technical teams on the production implementation of RNNs, with the goal of making cutting-edge advances in DKT available to real learners.
An integrated facies, diagenesis and geochemical analysis along with sequence stratigraphy of the Lower Triassic Aghe-Darband basin (north-east Iran)

The Sefid Kuh Formation (Lower Triassic), in Aghe-Darband tectonic window (NE of Iran) reflects a complex setting as is deducible from intercalation of carbonates with volcanic deposits. A detailed sediment petrological, sequence stratigraphical and geochemical study of this formation was carried out to develop a comprehensive knowledge of the complexity in this formation. Field observations of four stratigraphic sections complemented by sediment petrographical analysis led to the identification of microfacies that are grouped into four facies associations. The predominance of tidal flat facies along with oolitic shoal facies and absence of any large barrier/reef features supports the existence of a homoclinal-ramp in an arc basin setting during Sefid Kuh Formation deposition. Calcite cementation and dolomitization are the main diagenetic processes that affected the original strata. Stratigraphic and field studies revealed four 3rd order depositional sequences in this formation which architecture as reflects tectonic control. The measured elemental concentrations (Mn, Sr, Na, Fe, Mg and Ca) along with petrographic studies (e.g. existence of fibrous isopachous cement) suggest aragonite as a precursor mineralogy of the Sefid Kuh carbonates that were deposited in a shallow warm-water subtropical environment. Detailed petrographic and geochemical analysis reflect marine, meteoric and burial diagenetic overprint. Variations in Sr/Ca ratio suggest that diagenetic alteration occurred in an open diagenetic system, with high rock-water interaction.
Time and Location Topic Model for analyzing Lihkg forum data

Open Source Intelligence (OSINT) is a choice for collecting information today for law enforcement to monitor illegal activities and allocate police resources effectively. However, massive amounts of public information cannot be analyzed by humans alone and so automatic pre-processing must be performed in advance. In traditional text analysis, the common word segmentation tools do not match the needs in special fields and special words (such as proper nouns, dialects, acronyms, metaphors, and so on). In the context of the Chinese language, we consider the problem of automatically determining the time and location of major public gatherings and demonstrations using public available information. As experimental scenario, we use the Lihkg online forum from August 1st to October 10th, 2019 as a corpus, and propose a topic vectorization method based on character embedding and Chinese word segmentation, using MLP (multi-layer perceptron) neural network as a location topic model. The result proves that the method and the model can correctly identify the time and the location of discussed activities by learning the existing location corpus.
TRUE – Transparency of Recommended User Experiences

One of the fundamental goals of user recommendations is to drive higher user engagement with applications and ecosystems. However, there is a lack of clarity for the user on how end user data is used to create these recommendations. This paper explores a framework that explains and gives control to a user, on how their usage data was utilized to generate recommendations. It will shed light on how a system recommendation could be more transparent from the user’s perspective. The framework aims to expose the relationship between recommendations and contextual user activity, thereby leading to transparency. This framework would also allow users to finetune how usage data from their activity, can be better utilized to generate recommendations. The adoption of this framework could lead to higher user engagement with the application and a better user experience for the user.
Predicting Customers’ Next-to-Be Purchased Products

Maximizing customer satisfaction and increasing sales and profit is the first and the most crucial goal of the retail sector in an increasingly competitive environment and rapidly evolving technology. We aim to improve customer experience and to customize their journey by creating personalized offers and baskets, allowing them to use their time more effectively and making them feel as we know them. Using machine learning, we create a customer-specific product (SKU) list by estimating which of the previously purchased products would be re-purchased within a specific timeframe by considering the customers’ previous purchase patterns. Our machine learning model uses customer information, product information, and customer-product information derived from the loyalty card program, sales data and promotion data. Currently, we use the outcome of the model to create promotional offers, both at the product level and the category level. Recent results show that the utilization rates of individual promotions at the category level increased by more than 5%. To the best of our knowledge, previous studies show that the models are mostly trained infrequently with sampled data. In this study, we use our entire data to train our model weekly to capture any changes to the customers’ behavior.
Nature at Your Service - Nature Inspired Representations Combined with Eye-gaze Features to Infer User Attention and Provide Contextualized Support

Internet of Things (IoT) enables the creation of sensing and computing machines to enhance the level of continuous adaptation and support provided by intelligent systems to humans. Nevertheless, these systems still depend on human intervention, for example, in maintenance and (re)configuration tasks. To this measure, the development of an Adaptive Instructional System (AIS) in the context of IoT allows for the creation of new, improved learning and training environments. One can test new approaches to improve the training and perception efficiency of humans. Examples are the use of virtual and augmented reality, the inclusion of nature inspired metaphors based on biophilic design and calm computing principles and the design of technology that aims at changing the users’ behaviour through persuasion and social influence. In this work, we specifically propose a nature inspired visual representation concept, BioIoT, to communicate sensor information. Our results show that this new representation contributes to the users’ well-being and performance while remaining as easy to understand as traditional data representations (based on an experiment with twelve participants over two weeks). We present a use case under which we apply the BioIoT concept. It serves the purpose of demonstrating the BioIoT benefits in a AR setting, when applied in households and workplaces scenarios. Furthermore, by leveraging our previous experience in the development of adaptive and supportive systems based on eye-tracking, we discuss the application of this new sensing technology to the support of users in machine intervention by using the user attention, i.e., eye-gaze, on different machine parts as a way to infer the user’s needs and adapt the system accordingly. In this way, a new level of continuous support can be provided to the users depending on their skill level and individual needs in the form of contextualized instructions and action recommendations based on user attention.
Implications of Cognitive Theories for Optimizing Higher Education Learning

Cognitive theories have the potential to provide suggestions to design more effective learning environments for higher education. The goal of this chapter is to review cognitive theories and principles based on empirical findings and suggest implications for practice. Working memory theory, distributed cognition theory, dual-process theory, modulatory emotional consolidation theory, mental model theory, metacognitive theory, transfer appropriate processing principle, generation effect, testing effect, and spacing effect are presented in the current study. Based on these theoretical frameworks, novel recommendations for educational practice are suggested.
An Improved Harmony Search Algorithm with Segmented Search

Abstract Harmony search (HS) algorithm has received widely attention in various fields because of simple operation and outstanding performance. However, there exists some bottlenecks which hinders the further development of HS algorithm, such as easy trapping into local optimum and slow convergence velocity.This paper proposes an improvement HS algorithm by using segmented search strategy (SHS) to overcome these drawbacks. Comparative experiments reveal the proposed SHS algorithm has perfect performance, especially in high dimensions.
A Transformational Modified Markov Process for Chord-Based Algorithmic Composition

The goal of this research is to maximize chord-based composition possibilities given a relatively small amount of information. A transformational approach, based in group theory, was chosen, focusing on chord intervals as the components of a modified Markov process. The Markov process was modified to balance between average harmony, representing familiarity, and entropy, representing novelty. Uniform triadic transformations are suggested as a further extension of the transformational approach, improving the quality of tonality. The composition algorithms are demonstrated given a short chord progression and also given a larger database of albums by the Beatles. Results demonstrate capabilities and limitations of the algorithms.
An efficient <scp>ID</scp>‐based cryptographic technique using <scp>IFP</scp> and <scp>GDLP</scp>

Abstract Implementing an improved ID‐based cryptographic mechanism is the main objective of the proposed work. In this article, an ID‐based cryptographic (IBC) technique using generalized discrete logarithm problem (GDLP) and the integer factorization problem (IFP) presented by Meshram et al have improved. Meshram et al have given IBC technique without using the bilinear pair and also, reveal that their technique can attain data protection and security objectives. Besides, their technique will deter the adversary from eavesdropping the encrypted information or the secret key of the user. However, it has found that their system carries a deadlock problem. Encryption process, as expected by the user, is not guaranteed to be secure. It is because the user may require private information about key authentication center (KAC), which has kept secret from users. Pang et al have proposed an improved technique that overcomes the deadlock problem. It has found that Pang et al have not discussed the analysis and proofs of security. In this article, generalized discrete logarithms in multiplicative group over finite fields and IFP have used to improve the technique and also a key distribution system has discussed. It has analyzed that the proposed strategy is safer than the technique described by Pang et al. Also, it has found that the proposed technique addresses the deadlock problem.
InterCriteria Analysis Applied to Emerging Europe and Central Asia University Rankings

InterCriteria analysis (ICA) is a new tool for decision making. This method can help to make decision according to the similarities or dissimilarities between the criteria. In the current research work an investigation of the Emerging Europe and Central Asia (EECA) university rankings is discussed. The aim of the ICA application over the datasets from Emerging Europe and Central Asia (EECA) university rankings is to identify the behavior of the universities in different countries. The datasets are downloaded by QS World University Rankings website. The aim of ICA application is to discover possible dependencies, correlations or opposite behavior between the universities in different countries. The indicators' correctness is estimated by second ICA application. The resulting sets contain a segmentation of the universities activities according to the selected indicators. The investigations are made using ICrAData software.