Reviewerly/exHarmony huggingface dataset:
Unravelling of Convolutional Neural Networks through Bharatanatyam Mudra Classification with Limited Data

Non-verbal forms of communication are universal, being free of any language barrier and widely used in all art forms. For example, in Bharatanatyam, an ancient Indian dance form, artists use different hand gestures, body postures and facial expressions to convey the story line. As identification and classification of these complex and multivariant visual images are difficult, it is now being addressed with the help of advanced computer vision techniques and deep neural networks. This work deals with studies in automation of identification, classification and labelling of selected Bharatnatyam gestures, as part of our efforts to preserve this rich cultural heritage for future generations. The classification of the mudras against their true labels was carried out using different singular pre-trained / non-pre-trained as well as stacked ensemble convolutional neural architectures (CNNs). In all, twenty-seven classes of asamyukta hasta (single hand gestures) data were collected from Google, YouTube and few real time performances by artists. Since the background in many frames are highly diverse, the acquired data is real and dynamic, compared to images from closed laboratory settings. The cleansing of mislabeled data from the dataset was done through label transferring based on distance-based similarity metric using convolutional siamese neural network. The classification of mudras was done using different CNN architecture: i) singular models, ii) ensemble models, and iii) few specialized models. This study achieved an accuracy of >95%, both in single and double transfer learning models, as well as their stacked ensemble model. The results emphasize the crucial role of domain similarity of the pre-training / training datasets for improved classification accuracy and, also indicate that doubly pre-trained CNN model yield the highest accuracy.
One-way Transfer of Quantum States via Decoherence

In many quantum information processing applications, it is important to be able to transfer a quantum state from one location to another — even within a local device. Typical approaches to implement the quantum state transfer rely on unitary evolutions or measurement feedforward operations. However, these existing schemes require many accurate pulse operations and/or precise timing controls. Here, we propose a one-way transfer of the quantum state with near unit efficiency using dissipation from a tailored environment. After preparing an initial state, the transfer can be implemented without external time dependent operations. Moreover, our scheme is irreversible due to the non-unitary evolution, and so the transferred state remains in the same site once the system reaches the steady state. This is in stark contrast to the unitary state transfer where the quantum states continue to oscillate between different sites. Our novel quantum state transfer via the dissipation paves the way toward robust and practical quantum control.
Processing of Egocentric Camera Images from a Wearable Food Intake Sensor

The cameras in passive food intake sensors are worn on the body and thus are prone to motion blur that may reduce visibility of the food items in the image. Additionally, captured images may contain personal information, such as faces of other people, making it imperative to uphold the privacy of the wearer and the people appearing in the images. This paper proposes an automatic image sharpness estimation followed by face removal. A new metric for image sharpness was defined and a classifier for binary classification of sharp and blurred images was tested. Then, face detection was followed by face removal to enable privacy. The methods were developed and tested on two datasets consisting of a total of 1600 images each. The datasets were inclusive of the several lighting conditions and image capture situations, such as indoor/outdoor, low-light/normal lighting conditions, food/non-food images, and stationary/moving images. Validation showed that the misclassification rate for the binary classification of sharpness of 10% to 15%. The accuracy of human face detection was 86.67% with an F1 score of 0.81.
Design Model to Improve Task Scheduling in Cloud Computing Based on Particle Swarm Optimization

Cloud computing is one of the most modern innovations that are growing rapidly in all technical fields because of its advantages in securing multiple resources and high speed in performance. It provides users connected via the Internet with a variety of storage and computing supplies. Cloud computing is both a catalyst and an enabler for major technological developments such as mobile computing, big data, and machine learning. With all these features, task scheduling has become one of the most difficult challenges facing the cloud, It plays a key role in the effectiveness of all cloud computing tools. Scheduling tasks means allocating tasks to the suitable virtual machine so that the course of computation can be executed to satisfy QoS constraints defined by users such as time to finish and expense. Most current optimization algorithms focus on only one aspect. In this paper, Depending on the characteristics of the swarm algorithm we will develop a model to solve the dilemma of task scheduling as well in order to get the fastest time to transfer tasks and the fastest time for execution with the lowest possible expense. In addition to that, we will develop a multi-purpose algorithm based on the PSO method to provide an optimal solution for the proposed model. The experimental consequences of this paper demonstrate that the proposed manner is more efficacious in expediting tasks and reducing expenses.
A 2020 perspective on “Transformative value of the Internet of Things and pricing decisions”

The Internet of Things (IoT) has become increasingly influential, particularly because of the significant new developments in the technologies of big data, cloud computing, 5G, and artificial intelligence. In this paper, we briefly explain how these new developments in the IoT may create a new electronic commerce landscape and opportunities associated with it; these developments pose interesting questions for future research.
3D bare-hand interactions enabling ubiquitous interactions with smart objects

Abstract Ubiquitous augmented reality (UAR) implementation can benefit smart shop floor operations significantly. UAR from a user’s first-person view can support and provide the user with suitable and comprehensive information without him/her being distracted from ongoing tasks. A natural hand-based interaction interface, namely, a mobile bare-hand interface (MBHI), is proposed to assist a user in exploring and navigating a large amount of information for a task in the user’s first-person view. The integration of a smart shop floor and UAR-based MBHI is particularly challenging. A real shop floor environment is composed of challenging conditions for the implementation of UAR, e.g., messy backgrounds and significant changes in illumination conditions. Meanwhile, the MBHI is required to provide precise and quick responses to minimize the difficulty of a user’s task. In this study, a wearable UAR system integrated with an MBHI is proposed to augment the shop floor environment with smart information. A case study is implemented to demonstrate the practicality and effectiveness of the proposed UAR and MBHI system.
Towards Integrating Data-Driven Requirements Engineering into the Software Development Process: A Vision Paper

[Context and motivation] Modern software engineering processes have shifted from traditional upfront requirements engineering (RE) to a more continuous way of conducting RE, particularly including data-driven approaches. [Question/problem] However, current research on data-driven RE focuses more on leveraging certain techniques such as natural language processing or machine learning than on making the concept fit for facilitating its use in the entire software development process. [Principal ideas/results] In this paper, we propose a research agenda composed of six distinct research directions. These include a data-driven RE infrastructure, embracing data heterogeneity, context-aware adaptation, data analysis and decision support, privacy and confidentiality, and finally process integration. Each of these directions addresses challenges that impede the broader use of data-driven RE. [Contribution] For researchers, our research agenda provides topics relevant to investigate. For practitioners, overcoming the underlying challenges with the help of the proposed research will allow to adopt a data-driven RE approach and facilitate its seamless integration into modern software engineering. For users, the proposed research will enable the transparency, control, and security needed to trust software systems and software providers.
A Multi-Scale Feature Selection Method for Steganalytic Feature GFR

The Rich Model of the Gabor filter (referred to as the GFR steganalytic feature) can detect JPEG-adaptive steganography objects.However, feature dimensionality that is too high will lead to too much computation and will correspondingly reduce the detection efficiency.To reduce the dimensionality and the operating time of GFR steganalytic features and to improve the stego image detection accuracy, this paper proposes a multi-scale feature selection method for steganalytic feature GFR.First, we use the SNR criterion to measure the uselessness of each feature component and to provide a basis for the removal of useless steganalytic feature components.Second, we improve the Relief algorithm to measure the importance of feature components in detecting stego images, which provides a basis for the selection of important feature components.Then, we set the threshold value for deleting the useless feature components, and we select the important feature components as the final feature.Finally, we conduct experiments on feature selection for GFR with high-dimensional steganalytic features, and we compared the proposed method with the Fisher-based method, the PCA-based method, the SSFC method, and the Steganalysis-α method.The results show that the method proposed in this paper is effective and fast.
Design and Integration of a Distributed, Autonomous and Collaborative Multi-Robot System for Exploration in Unknown Environments

Multi-robot systems are preferred compared to single robot systems in complex missions such as, unknown and unstructured area exploration, due to scalability, robustness, efficiency and cost considerations. However, there are multiple practical challenges that need to be addressed to realize such systems. In this paper, we demonstrate a generic, low power, compute efficient hardware platform to accomplish complex tasks using a team of robots. This platform is used to demonstrate exploration mission in unknown environment using a team of four robots. Further, overall analysis and results for the demo application are presented and practical challenges in deploying such multi-agent systems are discussed.
Image Compression with Deeper Learned Transformer

Deep learning is known for its flexibility and infinite potential to approximate any function. Is it possible to approximate image compression using deep learning? The answer is yes. This article compares three major deep learning techniques used in image compression now and proposed an approach with deeper learned transformer and improved optimization goal, which achieved improved peak signal-to-noise ratio (PSNR) and multi-scale structural similarity (MS-SSIM) under very low bits per pixel (bpp). Experimental results show that the proposed approach outperformed BPG (RGB 4:4:4) in natural scene images compression, and is capable to handle arbitrary image shapes, which makes it applicable to practical image compression workloads.
The adoption of Facebook as a virtual class whiteboard: Promoting EFL students’ engagement in language tasks

TESOL JournalVolume 11, Issue 3 e00515 CLASSROOM EXPLORATIONS The adoption of Facebook as a virtual class whiteboard: Promoting EFL students' engagement in language tasks Mark B. Ulla, Mark B. Ulla orcid.org/0000-0003-1005-5120 Walailak UniversitySearch for more papers by this authorWilliam F. Perales, William F. Perales Walailak UniversitySearch for more papers by this author Mark B. Ulla, Mark B. Ulla orcid.org/0000-0003-1005-5120 Walailak UniversitySearch for more papers by this authorWilliam F. Perales, William F. Perales Walailak UniversitySearch for more papers by this author First published: 19 March 2020 https://doi.org/10.1002/tesj.515Citations: 3Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat Citing Literature Volume11, Issue3September 2020e00515 RelatedInformation
Interactive course for JavaScript in LMS Moodle

Nowadays the web pages are a necessity not just for business. Creating static web pages is nothing special, even the young pupils are capable to create static web pages through some WYSIWYG editors. Students from Slovak high schools focused on computer science are able to create static web pages in HTML and CSS. Some of them are also familiar with developing dynamic web pages through scripting language JavaScript. The contribution is focusing on creating teaching material in LMS Moodle for programming web technologies through JavaScript. The course is suitable for teaching with classic method (explanation and exercises) and also for teaching with innovative education method EduScrum.
Use of Hierarchical Dirichlet Processes to Integrate Dependent Observations From Multiple Disparate Sensors for Tracking

We consider the problem of tracking a target by integrating observations from multiple disparate sources in a multimodal sensing system. Based on the sensing modalities, these observations are associated with different measurement models. They are also statistically dependent if acquired synchronously while capturing the same scene. Although dependency among measurements is largely overlooked, improved performance can be achieved if this additional information is modeled and incorporated in the tracking formulation. This paper employs a hierarchical Dirichlet process mixture to model the data dependency and extract the time-varying cardinality of the measurements of each sensor. The hierarchical Dirichlet process framework provides a joint measurement density model that is integrated with Bayesian tracking methods to estimate the target state information.
Data Mining based Handling Missing Data

Today, a huge amount of data are generated in many applications than ever before. However, most of the application's data are affected by the issue of missing values. This issue has gained significant attention throughout the statistical research. Several obvious examples involve repositories related to the management of equipment, business applications, and surveys. One of the usual ways to handle this issue is to fill the value using imputation. Several imputation techniques have been proposed until now to handle the missing data. With the rapidly increasing size of the dataset, a modern imputation approach algorithm is required. In this paper, we provide an extensive overview of the current imputation methods, with a special focus on algorithms utilizing the local or global correlation available within the dataset. Furthermore, the paper shows how the prediction made can be validated and some possible future directions. This paper is expected to give the researchers a good grasp of the current trends in this domain and to enable them to create a more robust and efficient algorithm.
Higher-dimensional wavelets and the Douglas-Rachford algorithm

We recast the problem of multiresolution-based wavelet construction in one and higher dimensions as a feasibility problem with constraints which enforce desirable properties such as compact support, smoothness and orthogonality of integer shifts. By employing the Douglas-Rachford algorithm to solve this feasibility problem, we generate one-dimensional and non-separable two-dimensional multiresolution scaling functions and wavelets.
Exemplar-Supported Representation for Effective Class-Incremental Learning

Catastrophic forgetting is a key challenge for class-incremental learning with deep neural networks, where the performance decreases considerably while dealing with long sequences of new classes. To tackle this issue, in this paper, we propose a new exemplar-supported representation for incremental learning (ESRIL) approach that consists of three components. First, we use memory aware synapses (MAS) pre-trained on the ImageNet to retain the ability of robust representation learning and classification for old classes from the perspective of the model. Second, exemplar-based subspace clustering (ESC) is utilized to construct the exemplar set, which can keep the performance from various views of the data. Third, the nearest class multiple centroids (NCMC) is used as the classifier to save the training cost of the fully connected layer of MAS when the criterion is met. Intensive experiments and analyses are presented to show the influence of various backbone structures and the effectiveness of different components in our model. Experiments on several general-purpose and fine-grained image recognition datasets have fully demonstrated the efficacy of the proposed methodology.
Explaining Visual Models by Causal Attribution

Model explanations based on pure observational data cannot compute the effects of features reliably, due to their inability to estimate how each factor alteration could affect the rest. We argue that explanations should be based on the causal model of the data and the derived intervened causal models, that represent the data distribution subject to interventions. With these models, we can compute counterfactuals, new samples that will inform us how the model reacts to feature changes on our input. We propose a novel explanation methodology based on Causal Counterfactuals and identify the limitations of current Image Generative Models in their application to counterfactual creation.
ScriptNet: Neural Static Analysis for Malicious JavaScript Detection

Malicious scripts are an important computer infection threat vector for computer users. For internet-scale processing, static analysis offers substantial computing efficiencies. We propose the ScriptNet system for neural malicious JavaScript detection which is based on static analysis. We also propose a novel deep learning model, Pre-Informant Learning (PIL), which processes Javascript files as byte sequences. Lower layers capture the sequential nature of these byte sequences while higher layers classify the resulting embedding as malicious or benign. Unlike previously proposed solutions, our model variants are trained in an end-to-end fashion allowing discriminative training even for the sequential processing layers. Evaluating this model on a large corpus of 212,408 JavaScript files indicates that the best performing PIL model offers a 98.10% true positive rate (TPR) for the first 60K byte subsequences and 81.66% for the full-length files, at a false positive rate (FPR) of 0.50%. Both models significantly outperform several baseline models. The best performing PIL model can successfully detect 92.02% of unknown malware samples in a hindsight experiment where the true labels of the malicious JavaScript files were not known when the model was trained.
PACC: Large scale connected component computation on Hadoop and Spark

A connected component in a graph is a set of nodes linked to each other by paths. The problem of finding connected components has been applied to diverse graph analysis tasks such as graph partitioning, graph compression, and pattern recognition. Several distributed algorithms have been proposed to find connected components in enormous graphs. Ironically, the distributed algorithms do not scale enough due to unnecessary data IO & processing, massive intermediate data, numerous rounds of computations, and load balancing issues. In this paper, we propose a fast and scalable distributed algorithm PACC (Partition-Aware Connected Components) for connected component computation based on three key techniques: two-step processing of partitioning & computation, edge filtering, and sketching. PACC considerably shrinks the size of intermediate data, the size of input graph, and the number of rounds without suffering from load balancing issues. PACC performs 2.9 to 10.7 times faster on real-world graphs compared to the state-of-the-art MapReduce and Spark algorithms.
Multi-algorithmic Fusion for Reliable Age and Gender Estimation from Face Images

Automated estimation of demographic attributes, such as gender and age, became of great importance for many potential applications ranging from forensics to social media. Although previous works reported performances that closely match human level. These solutions lack of human intuition that allows human beings to state the confidences of their predictions. While the human intuition subconsciously considers surrounding conditions or the lack of experience in a certain task, current algorithmic solutions tend to mispredict with high confidence scores. In this work, we propose a multi-algorithmic fusion approach for age and gender estimation that is able to accurately state the model&#x0027;s prediction reliability. Our solution is based on stochastic forward passes through a dropout-reduced neural network ensemble. By utilizing multiple stochastic forward passes combined from the neural network ensemble, the centrality and dispersion of these predictions are used to derive a confidence statement about the prediction. Our experiments were conducted on the Adience benchmark. We showed that the proposed solution reached and exceeded state-of-the-art performance for the age and gender estimation tasks. Further, we demonstrated that the reliability statements of the predictions of our proposed solution capture challenging conditions and underrepresented training samples.
Causal Relation Classification using Convolutional Neural Networks and Grammar Tags

Identification of a cause-effect pair from nominal words is an important problem in knowledge reasoning of natural language texts. Most of the initial techniques for identifying cause effect relation are based on predefined linguistic and syntactic rules. Modern approaches uses Machine Learning techniques mainly Deep Neural Networks on top of sets of lingusitic rules and semantic knowledge to identify nominal word relations in a text. In this paper, a novel algorithm has been proposed using convolutional neural networks and grammar tags. The proposed approach has been evaluated using SemEval-2010 Task 8 dataset and shown to perform better than the other state-of-the-art algorithms.