Reviewerly/exHarmony huggingface dataset:
Sign Language Recognition Using Microsoft Kinect

Sign language is a language used by people with hearing impairments to communicate using gestures. Hand motion is one of the strategies utilized in gesture-based communication. Through the years, many researchers have put forth their models for automatic sign language recognition. The use of an RGB camera for recognition was a development, some of the models required signers to wear cyber gloves, etc., for ease in recognition. Despite the fact that the utilization of gesture-based communication is mainstream among the hard of hearing individuals yet different groups do not attempt to learn it, this hinders communication and thus turns into a reason for the disconnection of certain groups in society. There is a necessity of a framework that can encourage a method for communication between these two groups. This paper provides a model that overcomes issues with using just an RGB camera. The framework makes use of Kinect, an RGB camera and a depth sensor. The model was developed using a convolutional neural network.
A General Framework for Mixed and Incomplete Data Clustering Based on Swarm Intelligence Algorithms

Swarm intelligence has appeared as an active field for solving numerous machine-learning tasks. In this paper, we address the problem of clustering data with missing values, where the patterns are described by mixed (or hybrid) features. We introduce a generic modification to three swarm intelligence algorithms (Artificial Bee Colony, Firefly Algorithm, and Novel Bat Algorithm). We experimentally obtain the adequate values of the parameters for these three modified algorithms, with the purpose of applying them in the clustering task. We also provide an unbiased comparison among several metaheuristics based clustering algorithms, concluding that the clusters obtained by our proposals are highly representative of the “natural structure” of data.
SSRGAN: A Generative Adversarial Network for Streaming Sequential Recommendation

Studying the sequential recommendation in streaming settings becomes meaningful because large volumes of user-item interactions are generated in a chronological order. Although a few streaming update strategies have been developed, they cannot be applied in sequential recommendation, because they can hardly capture the long-term user preference only by updating the model with random sampled new instances. Besides, some latent information is ignored because the existing streaming update strategies are designed for individual interactions, without considering the interaction subsequence. In this paper, we propose a Streaming Sequential Recommendation with Generative Adversarial Network (SSRGAN) to solve the streaming sequential recommendation problem. To maintain the long-term memory and keep sequential information, we use the reservoir-based streaming storage mechanism and exploit an active subsequence selection strategy to update model. Moreover, to improve the effectiveness and efficiency of online model training, we propose a novel negative sampling strategy based on GAN to generate the most informative negative samples and use Gumble-Softmax to overcome the gradient block problem. We conduct extensive experiments on two real-world datasets and the results shows the superiority of our approaches in streaming sequential recommendation.
Non-verbal expressivity in alexithymia: A study on emoji use in text messaging across varying levels of alexithymia

The present study examined whether emoji use would be quantitatively and/or qualitatively altered in alexithymia. 646 individuals (Mean Age = 23.11, SD = 7.03; 424 Female) with varying levels of alexithymia took part in a series of vignettes where they were positioned as senders of hypothetical text messages. Participants were tasked to opt in or out of tagging an emoji onto positive and negative text messages. Emoji use frequency was diminished at the high end of the alexithymic continuum, relative to the middle and low spectrums. However, emoji for positive and negative text messages were used with similar patterns of frequency across alexithymic groups of varying severity, suggesting that emoji use is quantitatively but not qualitatively altered in alexithymia.
Joint Intent Detection and Slot Filling via CNN-LSTM-CRF

Intent detection and slot filling are two main tasks in the domain of Spoken Language Understanding (SLU). The methods employed may treat the intent detection and slot filling as two independent tasks or use a joint model. Using a joint model takes into account the cross impact between the two tasks. In this article, we introduce CoBiC a new model combining CNN (Convolutional Neural Network), Bidirectional LSTM (Long Short-Term Memory) and CRF (Conditional Random Field) to extract the intents and the related slots. The same architecture of CoBiC can either be used as an independent model or joint model for intent detection and slot filling. Our method improves the state-of-the-art results on ATIS (Airline Travel Information Systems) benchmark. We also apply our model on a private dataset consisting of clients requests to a vocal assistant. The results demonstrate that CoBiC has strong generalization capability.
Martingale posterior distributions

Abstract The prior distribution is the usual starting point for Bayesian uncertainty. In this paper, we present a different perspective that focuses on missing observations as the source of statistical uncertainty, with the parameter of interest being known precisely given the entire population. We argue that the foundation of Bayesian inference is to assign a distribution on missing observations conditional on what has been observed. In the i.i.d. setting with an observed sample of size n, the Bayesian would thus assign a predictive distribution on the missing Yn+1:∞ conditional on Y1:n, which then induces a distribution on the parameter. We utilize Doob’s theorem, which relies on martingales, to show that choosing the Bayesian predictive distribution returns the conventional posterior as the distribution of the parameter. Taking this as our cue, we relax the predictive machine, avoiding the need for the predictive to be derived solely from the usual prior to posterior to predictive density formula. We introduce the martingale posterior distribution, which returns Bayesian uncertainty on any statistic via the direct specification of the joint predictive. To that end, we introduce new predictive methodologies for multivariate density estimation, regression and classification that build upon recent work on bivariate copulas.
Designing of Improved Monitoring System for Crack Detection on Railway Tracks

Railways provide people with many advantages such as easy access to locations, low cost fare travel, etc. This is one of the most widely used public transport mode, due to its various advantages. In recent years, there have been numerous incidents on Indian railways, such as cracks on railways and many other faults. Railways are therefore a risky option for travel. It has proven inappropriate for the control system currently in use in Indian railways. A robust monitoring system has been suggested and clarified in this paper to address the shortcomings of the existing rail surveillance system to detect cracks. This paper also presents the technique for resolving animal death and injury through automated monitoring and warning on railway tracks.
The Body and the Eye—The I and the Other: Critical Reflections on the Promise of Extended Empathy in Extended Reality Configurations

This contribution critically discusses the diction of VR/XR as the “ultimate empathy machine” by presenting different concepts of immersion and embodied interaction. In a movement from the eye to the body and from the body to the mind, it is argued that specific XR configurations promote paradigm shifts from ocular centrism and tactile visuality to physical embodiment and interaction. One crucial question hereby is whether a virtual body swap also implies a mindset swap, which would be an index for an enhanced stimulation of empathy. The argument is backed up by reflections on the potential of interventionist experience as a socio-cultural and socio-technological experiment. A case study of &quot;The Machine to be Another&quot;, an XR embodiment system, serves as an exemplary assemblage which aspires to translate current discourses in collective and individual psychology, neurosciences and STS into the field of artistic research and intervention. Revisiting concepts of illusionary embodiment and virtual body swaps as well as physical and virtual presence, the focus lies on different dimensions of empathy in XR-settings. It is argued that the concept of immersion should always be accompanied by critical disimmersion or estrangement that reminds user-interactors of the contingency of mediated emotions and affects and allows to bridge the gap between ‘primary empathy’ and ‘extended empathy’.
The Fractional Order Generalization of HP Memristor Based Chaotic Circuit with Dimensional Consistency

Formulae display:?Mathematical formulae have been encoded as MathML and are displayed in this HTML version using MathJax in order to improve their display. Uncheck the box to turn MathJax off. This feature requires Javascript. Click on a formula to zoom.
Land Resource Management Information Platform Based on Artificial Intelligence Technology

Cloud computing, big data and artificial intelligence have become the hot spots in the current information system construction, and the integration of artificial intelligence into the information management platform has become a new means. This paper mainly studies the land resource management information platform based on artificial intelligence technology. In this paper, the concept of artificial intelligence is first described. Secondly, combined with the key technologies and daily business requirements, the system requirements analysis was completed, and the specific functional requirements of the platform were deeply analyzed through the use case diagram. Thirdly, combined with the system design principles, the system logic structure and function module design are completed. Finally, combined with the system design and implementation, the system test is completed from the two aspects of system function test and performance test. The results of function test and performance test show that the land resource management information fully meets the expected design requirements and all the performance indicators meet the requirements.
Metric properties of Sierpiński triangle graphs

Sierpiński triangle graphs Ŝn have often been mistaken for Sierpiński graphs S3n. Whereas the latter's metric properties are by now well understood, the former graphs were mostly just considered as a pictorial representation of approximations to the Sierpiński triangle fractal. Therefore, we present here a new labeling for them which shows the relation, but also the differences to the more famous Sierpiński graphs proper. On the base of this labeling we describe an algorithm to obtain individual distances between vertices. This type of algorithm can then be extended to base-p Sierpiński triangle graphs Ŝpn which are related to the class of classical Sierpiński graphs Spn, p≥2. Some of the metric properties of Spn can now be investigated for Ŝpn as well; e.g., we characterize center and periphery of Ŝpn.
Decision Making Among Online Product in E-Commerce Websites

In the present era, customers are mainly engrossed in the product-based system. To make their exertion easy all pupils are trusting in internet marketing. By catching this public interest, all the product-based systems are playing enormous activities which may be legal or illegal. Due to this reason, decision making among products in e-commerce websites is making most ambiguity. Considering this perspective, this paper is providing an analysis of how to evaluate customer reviews. It deals with deciding how to manage the customer experience in marketing. This paper presents how to analyze online product reviews. The framework aims to distill large volumes of qualitative data into quantitative insights on product features so that designers can make more informed decisions. This paper set out to identify customer’s likes and dislikes found in reviews to guide product development.
Specifying Requirements for Data Collection and Analysis in Data-Driven RE. A Research Preview

[Context and motivation] According to Data-Driven Requirements Engineering (RE), explicit and implicit user feedback can be considered a relevant source of requirements, thus supporting requirements elicitation. [Question/problem] Less attention has been paid so far to the role of implicit feedback in RE tasks, such as requirements validation, and on how to specify what implicit feedback to collect and analyse. [Principal idea/results] We propose an approach that leverages on goal-oriented requirements modelling combined with Goal-Question-Metric. We explore the applicability of the approach on an industrial project in which a platform for online training has been adapted to realise a citizen information service that has been used by hundreds of people during the COVID-19 pandemic. [Contributions] Our contribution is twofold: (i) we present our approach towards a systematic definition of requirements for data collection and analysis, at support of software requirements validation and evolution; (ii) we discuss our ideas using concrete examples from an industrial case study and formulate a research question that will be addressed by conducting experiments as part of our research.
Data Quality Visual Analysis (DQVA) A tool to process and pinspot raw data irregularities

This project proposes a machine learning (ML) pipeline for inferring office employee's well-being, from heterogeneous sources of contextual data (cf. physiological, social and workplace environment), which brings several demanding issues. In this paper we focus specifically in raw data collection problems and pre-processing challenges. To start with, context data was collected in real environments, during weeks, in several office organizations and involving employees along theirs daily working routines. Moreover, data collection resort to a wide range of sources (e.g. sensors, questionnaires, apps, etc.) that were subject to potential interferences and noisy conditions. Given the influence of data quality in ML algorithms results and considering the number of instruments used, it was essential to implement a pre-processing stage to automate and improve the quality of collected data. Hence, the usefulness of the proposed DQVA tool, which computes several common statistical measures and provides also graphical and tabular visual insights about the data. For example, it allows to: i) compare data sources from different participants and organizations, on a per sensor/data source basis (through data tables, data distribution histograms, and visualizations); iii) check and pinspot the existence of outliers; iv) visually spot signal gaps; etc. Therefore, we argue that the proposed DQVA tool allows to evaluate, per sensor and per individual, raw data quality, on the integration stage of our classification pipeline. It proved to be an agile, useful and simple to re-use tool for detecting raw data irregularities, thus increasing data quality assurances for the next steps of our classification pipeline.
Bidirectional Generation of Object Images and Positions using Deep Generative Models for Service Robotics Applications

The introduction of systems and robots for automated services is important for reducing running costs and improving operational efficiency in the retail industry. To this aim, we develop a system that enables robot agents to display products in stores. The main problem in automating product display using common supervised methods with robot agents is the huge amount of data required to recognize product categories and arrangements in a variety of different store layouts. To solve this problem, we propose a crossmodal inference system based on joint multimodal variational autoencoder (JMVAE) that learns the relationship between object image information and location information observed on site by robot agents. In our experiments, we created a simulation environment replicating a convenience store that allows a robot agent to observe an object image and its 3D coordinate information, and confirmed whether JMVAE can learn and generate a shared representation of an object image and 3D coordinates in a bidirectional manner.
The MOORA method for selecting software App: price-quality ratio approach

Seeing the rapid progress of software app. in today's destructive era, the need for software app. is very reliable for industrial progress in the era of the 4.0 generation. Especially in object-oriented software app. The main objective of this research is to measure the technical capabilities of object-based software app. and to find out the process of selecting the best number of app.software in terms of the appropriate price. Many techniques can be developed in object-based software app. such as class implementation, Inheritance, Encapsulation, Polymorphic, Constructor, Accessor, Mutator, Visibility, Overwrite and Overload. This technique is an advantage of object-based software app. Taking advantage of these advantages causes difficulty in selecting and evaluating software. Indeed, it is very difficult to evaluate software products, because they are qualitative. In order for the assessment to be objective, it requires good method collaboration, thus an objective method is needed in the assessment of the selection of a number of program app. The test was carried out with the Multi Objective Optimization by Ratio Analysis (MOORA) method collaborated with the Price-Quality Ration approach. The results obtained are the selection of object-based software app. that can be done optimally and provide efficiency in the benefits and costs incurred.
Assessing Quality by Anti-pattern Detection in Web Services

Design pattern plays an important role in development of a good quality software system. Service oriented architecture forms the basis for designing web services. Web services must be carefully designed taking into consideration the quality of service parameters like delay, throughput, reliability and cost. In order to maintain QOS design patterns should be analyzed to detect antipatterns, which can hinder the quality of software system. An antipattern is something, which initially appears like a good indication but creates negative results .The analysis can be done on the basis of several metrics. The paper takes into consideration the web service description files collected from various reliable sources and analyze the same based on static metric content. The static metrics used in this research paper are standard CKJM metrics. The future scope of the paper will focus on detection of antipattern based on dynamic metrics e.g. response time.
DBL: Efficient Reachability Queries on Dynamic Graphs

Reachability query is a fundamental problem on graphs, which has been extensively studied in academia and industry. Since graphs are subject to frequent updates in many applications, it is essential to support efficient graph updates while offering good performance in reachability queries. Existing solutions compress the original graph with the Directed Acyclic Graph (DAG) and propose efficient query processing and index update techniques. However, they focus on optimizing the scenarios where the Strong Connected Components (SCC s) remain unchanged and have overlooked the prohibitively high cost of the DAG maintenance when SCC s are updated. In this paper, we propose DBL, an efficient DAG-free index to support the reachability query on dynamic graphs with insertion-only updates. DBL builds on two complementary indexes: Dynamic Landmark (DL) label and Bidirectional Leaf (BL) label. The former leverages landmark nodes to quickly determine reachable pairs whereas the latter prunes unreachable pairs by indexing the leaf nodes in the graph. We evaluate DBL against the state-of-the-art approaches on dynamic reachability index with extensive experiments on real-world datasets. The results have demonstrated that DBL achieves orders of magnitude speedup in terms of index update, while still producing competitive query efficiency.
Information hiding in images using Discrete Cosine Transform

Abstract In this era of development and increasing use of the Internet, user facing security problem during information sharing in which unauthorized receivers can gain access to the information transmitted. Data hiding during the transmission of information via the internet is one of the techniques used to keep the secrecy of the information to the unauthorized/ unintended persons. Steganography is a way of hiding secrets information by inserting it into an audio, video, image or text file without raising any suspicious to unintended persons. This paper presents the study which hides text messages into the image file by using Discrete Cosine Transform (DCT) steganography method. The method of this work by transforming the cover image and text message from spatial domain to frequency domain and use redundant bit of cover image to hide text message in such a way that unintended person will continue to see it as a normal image. The method continues to keep the quality of the cover image which transfers the information to reduce suspicion. Steganography is most used in military, law enforcement agency, corporate spies and in the medical area for hiding sensitive information.
Simulation Optimization Based Feature Selection, a Study on Data-Driven Optimization with Input Uncertainty

In machine learning, removing uninformative or redundant features from a dataset can significantly improve the construction, analysis, and interpretation of the prediction models, especially when the set of collected features is extensive. We approach this challenge with simulation optimization over a high dimensional binary space in place of the classic greedy search in forward or backward selection or regularization methods. We use genetic algorithms to generate scenarios, bootstrapping to estimate the contribution of the intrinsic and extrinsic noise and sampling strategies to expedite the procedure. By including the uncertainty from the input data in the measurement of the estimators' variability, the new framework obtains robustness and efficiency. Our results on a simulated dataset exhibit improvement over state-of-the-art accuracy, interpretability, and reliability. Our proposed framework provides insight for leveraging Monte Carlo methodology in probabilistic data-driven modeling and analysis.