Reviewerly/exHarmony huggingface dataset:
A geometric and fractional entropy-based method for family photo classification

Due to the power and impact of social media, unsolved practical issues such as human trafficking, kinship recognition, and clustering family photos from large collections have recently received special attention from researchers. In this paper, we present a new idea for family and non-family photo classification. Unlike existing methods that explore face recognition and biometric features, the proposed method explores the strengths of facial geometric features and texture given by a new fractional-entropy approach for classification. The geometric features include spatial and angle information of facial key points, which give spatial and directional coherence. The texture features extract regular patterns in images. The proposed method then combines the above properties in a new way for classifying family and non-family photos with the help of Convolutional Neural Networks (CNNs). Experimental results on our own as well as benchmark datasets show that the proposed approach outperforms the state-of-the-art methods in terms of classification rate.
Word Sense Disambiguation in Bengali language using unsupervised methodology with modifications

In this work, Word Sense Disambiguation (WSD) in Bengali language is implemented using unsupervised methodology. In the first phase of this experiment, sentence clustering is performed using Maximum Entropy method and the clusters are labelled with their innate senses by manual intervention, as these sense-tagged clusters could be used as sense inventories for further experiment. In the next phase, when a test data comes to be disambiguated, the Cosine Similarity Measure is used to find the closeness of that test data with the initially sense-tagged clusters. The minimum distance of that test data from a particular sense-tagged cluster assigns the same sense to the test data as that of the cluster it is assigned with. This strategy is considered as the baseline strategy, which produces 35% accurate result in WSD task. Next, two extensions are adopted over this baseline strategy: (a) Principal Component Analysis (PCA) over the feature vector, which produces 52% accuracy in WSD task and (b) Context Expansion of the sentences using Bengali WordNet coupled with PCA, which produces 61% accuracy in WSD task. The data sets that are used in this work are obtained from the Bengali corpus, developed under the Technology Development for the Indian Languages (TDIL) project of the Government of India, and the lexical knowledge base (i.e., the Bengali WordNet) used in the work is developed at the Indian Statistical Institute, Kolkata, under the Indradhanush Project of the DeitY, Government of India. The challenges and the pitfalls of this work are also described in detail in the pre-conclusion section.
TV-MV Analytics: A visual analytics framework to explore time-varying multivariate data

We present an integrated interactive framework for the visual analysis of time-varying multivariate data sets. As part of our research, we performed in-depth studies concerning the applicability of visualization techniques to obtain valuable insights. We consolidated the considered analysis and visualization methods in one framework, called TV-MV Analytics. TV-MV Analytics effectively combines visualization and data mining algorithms providing the following capabilities: (1) visual exploration of multivariate data at different temporal scales, and (2) a hierarchical small multiples visualization combined with interactive clustering and multidimensional projection to detect temporal relationships in the data. We demonstrate the value of our framework for specific scenarios, by studying three use cases that were validated and discussed with domain experts.
Facilitating the First Move

Actualizing positive social encounters remains both a key ends and means in many activities to foster a sense of community. Initiating encounters between strangers typically requires facilitative activities or artefacts, such as icebreakers or tickets-to-talk. However, there is little understanding of which designs are effective and why, and the broad design space remains largely underexplored. We address this challenge by presenting five candidates for inspirational design patterns on signaling social intentions and identifying impediments that deter commencement of encounters. The principles result from an extensive review of design cases and public art installations. Through focus groups and expert interviews, we assessed the perceived applicability and social acceptance of the proposed patterns. Three new design principles relating to the risks of initiating an encounter emerged through analyzing participant responses. These articulations of possible approaches and pitfalls for increasing conviviality may broaden the repertoire of, and support discussion between designers and others concerned with collocated social interaction.
Estimation Parameter of Generalized Poisson Regression Model Using Generalized Method of Moments and Its Application

Abstract Poisson Regression is a standard model for data counts that can be used to determine the relationship between the response variable and predictors variables. Equidispersion is assumptions that must be met in Poisson Regression. Equidispersion is a condition that the variance of the response variable is equal with the average of the response variable. In real cases, these assumptions are often not meet. In real cases, there are overdispersion and underdispersion cases. Generalized Poisson Regression (GPR) is one method that can handle cases of overdispersion and underdispersion. The GPR model is used to estimate regression parameters. Many articles proposed to use only Maximum Likelihood Estimation (MLE) to estimate the parameters of GPR. This article will develop the parameter estimation method of the GPR model, which is using the Generalized Method of Moments (GMM). GPR model is applied in the case of diarrhea in infants in Pasuruan Regency, East Java. The best model is chosen by the value of AICc. The smaller the value of AICc, the better the model. The best model is a model that includes exclusive breastfeeding, complete basic immunization, and healthy living behavior in the model.
Design of a Voice Control 6DoF Grasping Robotic arm Based on Ultrasonic Sensor, Computer Vision and Alexa Voice Assistance

The article presents a study to design a 6-degree of freedom robotic arm which can pick up objects in random positions on a 2D surface based on Arduino microcontroller, ultrasonic sensors and picamera. The robotic arm is able to recognise objects based on computer vision algorithm for shape detection. The ultrasonic sensor measures the distance between the objects and the robotic arm, and the position of the objects in the real world will be detected by its mass centre in the image to improve the accuracy of the pick-up movement. Arduino microcontroller will calculate the rotation angles for the joints of the robotic arm by using inverse kinematics algorithms. The movement of the robotic arm also can be controlled by an Amazon Alexa voice assistance device. The experiment of applying the artificial neural network to control the robotic arm pick-up movement is achieved. The artificial neural network can manipulate the position of the robotic arm to pick up objects after training using the values which are calculated by inverse kinematics equations. The Raspberry Pi is used for processing the computer vision data, and voice commands from Alexa Voice Service based on cloud service.
Alternating-time temporal logic ATL with finitely bounded semantics

We study a variant ATLFB of the alternating-time temporal logic ATL with a non-standard, 'finitely bounded' semantics (FBS). FBS was originally defined as a game-theoretic semantics where players must commit to time limits when attempting to verify eventuality (respectively, to falsify safety) formulae. It turns out that FBS has a natural corresponding compositional semantics that essentially evaluates formulae only on finite initial segments of paths and imposes uniform bounds on all plays for the fulfilment of eventualities. The resulting version ATLFB differs in some essential features from the standard ATL, as it no longer has the finite model property, though the two logics are equivalent on finite models. We develop two tableau systems for ATLFB. The first one deals with infinite sets of formulae and may run in a transfinite sequence of steps, whereas the second one deals only with finite sets of formulae in an extended language allowing explicit symbolic indication of time limits in formulae. We prove soundness and completeness of the infinitary tableau system and prove that it is equivalent to the finitary one. We also show that the finitary tableau system provides an exponential-time decision procedure for the satisfiability problem of ATLFB and thus establishes its EXPTIME-completeness. Furthermore, we present an infinitary axiomatization for ATLFB and prove its soundness and completeness.
Multimedia Learning Module Development based on SIGIL Software in Physics Learning

Abstract Technology is important in learning, especially in physics learning. This paper aims to determine the feasibility of the Multimedia Learning Module. Multimedia Learning Module in compliance with ADDIE development model. Helped by SIGIL software that can convert files into epub that can be used as an ebook, so that it can be compatible with smartphone educators and students and equipped with animations and analyzing videos that can help in understanding the concepts of physics, so they can solve problems in physics learning.
Forecasting popularity of news article by title analyzing with BN-LSTM network

In recent years, predicting the popularity of articles in the news has become a more urgent task for authors, online resources and advertisers. In the order of this task, we propose a new method based on the Online Deep Neural network with Bottleneck compression, what predicts the article popularity with only its headline. The proposed methodology evaluated on the Chinese and Russian language-based datasets with over than 800 000 samples in total. We describe the challenges and solutions related to the popularity prediction and the headline analysis. We show that the provided method can reach acceptable results even with different languages, news source popularity dynamics.
Blockchain Explorer based on RPC-based Monitoring System

Blockchain technology provides the advantage of maintaining a network without third party intervention, providing transparency because all participants have distributed ledgers with the same data. Using these characteristics, blockchain technology is being used in various fields, but the anonymity of blockchain can be exploited for illegal trades in the Darknet market. Therefore, a monitoring system for transactions occurring on the blockchain is needed. This paper proposes a monitoring system and an explorer to show the results collected from the system.
A Collaborative Platform to Collect Data for Developing Machine Translation Systems

The emergence of neural machine translation techniques has opened up a new era for developing translation systems. However, it requires a very large amount of parallel corpus, which is scarce for many under-resourced languages, e.g., Bangla. In order to develop a corpus, currently, there is a lack of publicly available collaborative system. In this paper, we report an online collaborative system for the development of the parallel corpus. The system is developed for supporting any language, however, we only evaluated for developing Bangla–English parallel corpus. In a task completion evaluation experiment, the system outperforms the widely used offline system, i.e., OmegaT.
Expert-Guided Entity Extraction using Expressive Rules

Knowledge Graph Construction (KGC) is an important problem that has many domain-specific applications, including semantic search and predictive analytics. As sophisticated KGC algorithms continue to be proposed, an important, neglected use case is to empower domain experts who do not have much technical background to construct high-fidelity, interpretable knowledge graphs. Such domain experts are a valuable source of input because of their (both formal and learned) knowledge of the domain. In this demonstration paper, we present a system that allows domain experts to construct knowledge graphs by writing sophisticated rule-based entity extractors with minimal training, using a GUI-based editor that offers a range of complex facilities.
Weather forecasting using parallel and distributed analytics approaches on big data clouds

In cloud environment, big data analytics is a very innovative idea; in this paper we have design architecture for parallel and distributed analysis of big data in cloud environment. We can use this architecture for every field in which we have a problem with data analytics. We are going to use this for meteorological data because meteorological department have a huge data which is related to weather. Weather forecasting refers to predicting future weather conditions on the basis of available data. These weather conditions can be utilized to prepare ourselves for future as well as alerts about disaster therefore saves the valuable human life. Keeping these things in mind we design system architecture for weather forecasting. First we use Hadoop to extract big data from NCDC through HDFS. Secondly we process this data through Map Reduce. Finally we get output which includes max temperature, minimum temperature, humidity, rainfall on any future date using past few years data. Analyzing such huge volume of data i.e big data and predicting future temperature brings immense importance to our work.
Sound simulation-based design optimization of brass wind instruments

A method for optimizing the inner shape of brass instruments using sound simulations is presented. This study considers different objective functions and constraints (representative of both the intonation and the spectrum of the instrument) for a relatively large number of design variables. A complete physics-based model, taking into account the instrument and the musician's embouchure, is used to simulate steady regimes of sounds by means of the harmonic balance technique, the instrument being represented by its input impedance. The design optimization variables are related to the geometrical dimensions of the resonator. The embouchure's parameters are varied during the optimization procedure to obtain an average behavior of the instrument. The objective and constraint functions of the optimization problem are evaluated using the physics-based simulation model, which is computationally expensive. Moreover, the gradients of the objective and constraint functions can be discontinuous, unavailable, or hard to approximate reliably. Therefore, a surrogate-assisted derivative-free optimization strategy using the mesh adaptive direct search algorithm was employed. One example of a B♭ trumpet's bore is used to demonstrate the effectiveness of the design optimization approach: the obtained results improve previously reported objective function values significantly.
Proposal of a Technological Ergonomic Model for People with Disabilities in the Public Transport System in Guayaquil

Transportation is associated with environmental problems, economic losses, population health, and social inequities. This last problem, in particular, a negative impact on generates the urban public transportation of people with disabilities, which entails a methodology of interpersonal, social, technological and institutional challenge without neglecting the characteristics of design for disabilities, as well as sensory ones. The objective of this article is a design proposal for the public transport system of the Guayaquil city, in the field of artificial intelligence, the human factor and ergonomic design for the disabled with an inclusive model according to the World Health Organization and the World Bank. The needs of people with limited capacities in urban public transport are explored for recurring services and destinations. Through the experiences lived in the metrobus public transport service, based on two methodologies; “Intelligent urban public transport dedicated to accessibility for people with disabilities” and “The effect of transport accessibility on the social inclusion of wheelchair users: An analysis of mixed methods”. While modeling the behavior of people with disabilities is a complex task, it has a remarkable social and economic impact. Therefore, in this article, neutrosophic cognitive maps are explored to represent the behavior and functioning of such complex systems. This soft computing technique to model how Allows us make decisions based on travelers their knowledge of different modes of transport properties at different levels of abstraction. The results of the study will help transport are decision-makers to better understand the needs of people with disabilities and, as a result, help them to update their policy. At the end of the article, we discuss how the design proposal can be articulated metrobus within the urban transport system and the accessibility of the use of ICT to people with disabilities.
Three-Mode Fuzzy Co-clustering and Collaborative Framework

Collaborative utilization of multiple cooccurrence information is expected a powerful tool for knowledge discovery in many real applications. This chapter presents a brief review on three-mode fuzzy co-clustering, which reveals the intrinsic co-cluster structures from three-mode cooccurrence information. Additionally, a secure process of collaborative analysis among different organizations is also considered with illustrative examples.
Feeding Convolutional Neural Network by hand-crafted features based on Enhanced Neighbor-Center Different Image for color texture classification

Texture analysis has many important applications, including material recognition, face recognition, object detection, image segmentation. Local feature descriptors were the principle approach for texture analysis in the past. Recently, Convolutional Neural Network (CNN) has provided more promising results for texture recognition and other related computer vision tasks. Standard CNN takes labeled RGB images as input. However, other encoded images were used as extra input to CNN, which have been shown to improve the performance. We propose to feed CNN with the new encoded image. The experimental results on four benchmark color texture database show the efficiency of our proposed approach. The source code of our algorithm and all simulations used for this paper are publicly available at: https://sites.google.com/view/vinhsiam/codes.
To Share or Not to Share? How Exploitation of Context Data Can Improve In-Network QoE Monitoring of Encrypted YouTube Streams

With the widespread use of encryption in Over the Top (OTT) traffic, Internet Service Providers (ISPs) for the most part lack insights into application performance, as well as into Quality of Experience (QoE) perceived by end users. Addressing challenges related to encryption, ISPs are looking into machine learning (ML) based solutions that can detect application performance solely from statistical properties of the traffic. On the other hand, OTT service providers are not willing to share service performance and content-related information with ISPs. While related work on OTT-ISP collaboration scenarios has addressed architectural aspects, business models, and to a certain extent incentives for sharing data, the focus of this paper is on the exchanged data itself. We investigate to what extent the performance of in-network ML-based QoE estimation models for HTTP adaptive video streaming could be improved with the availability of certain context data provided by OTT providers. We motivate OTT-ISP collaboration through more accurate in-network QoE monitoring and potential improvement of user experience, which is of interest to both sides.
CRAM: a Container Resource Allocation Mechanism for Big Data Streaming Applications

Containerization provides a lightweight alternative to the use of virtual machines for potentially reducing service cost and improving cloud resource utilization. A key challenge is how to allocate container resources to multiple competing streaming applications with varying QoS demands running on a heterogeneous cluster of hosts. In this paper, we focus on workload distribution for optimal resource allocation to meet the real-time demands of competing containerized big data streaming applications. We propose a container resource allocation mechanism (CRAM) based on game theory and formulate the problem as an n-player non-cooperative game among a set of heterogeneous containerized streaming applications. From our analysis, we obtain the optimal Nash Equilibrium state where no player can further improve its performance without impairing others. Experimental results demonstrate the effectiveness of our approach, which attempts to equally satisfy each containerized streaming application's request as compared to existing techniques that may treat some applications unfairly.
Microfluidic-based immunohistochemistry for breast cancer diagnosis: a comparative clinical study

Breast cancer is a highly heterogeneous disease. The efficacy of tailored therapeutic strategies relies on the precise detection of diagnostic biomarkers by immunohistochemistry (IHC). Therefore, considering the increasing incidence of breast cancer cases, a concomitantly time-efficient and accurate diagnosis is clinically highly relevant. Microfluidics is a promising innovative technology in the field of tissue diagnostic, enabling for rapid, reliable, and automated immunostaining. We previously reported the microfluidic-based HER2 (human epidermal growth factor receptor 2) detection in breast carcinomas to greatly correlate with the HER2 gene amplification level. Here, we aimed to develop a panel of microfluidic-based IHC protocols for prognostic and therapeutic markers routinely assessed for breast cancer diagnosis, namely HER2, estrogen/progesterone receptor (ER/PR), and Ki67 proliferation factor. The microfluidic IHC protocol for each marker was optimized to reach high staining quality comparable to the standard procedure, while concomitantly shortening the staining time to 16 min-excluding deparaffinization and antigen retrieval step-with a turnaround time reduction up to 7 folds. Comparison of the diagnostic score on 50 formaldehyde-fixed paraffin-embedded breast tumor resections by microfluidic versus standard staining showed high concordance (overall agreement: HER2 94%, ER 95.9%, PR 93.6%, Ki67 93.7%) and strong correlation (ρ coefficient: ER 0.89, PR 0.88, Ki67 0.87; p < 0.0001) for all the analyzed markers. Importantly, HER2 genetic reflex test for all discordant cases confirmed the scores obtained by the microfluidic technique. Overall, the microfluidic-based IHC represents a clinically validated equivalent approach to the standard chromogenic staining for rapid, accurate, and automated breast cancer diagnosis.