Reviewerly/exHarmony huggingface dataset:
Audio Cells

Rich and elaborate communication will play an essential part in the success of social robotics. The role of non-verbal sound as a communication channel has received relatively little attention in human-robot interaction research so far. Audio Cells is a prototyping environment for spatial sonic interaction design. It is part of my ongoing work on the sonification of artificial bodies. Through a combination of modular loudspeakers and sensors, it allows the exploration of a range of spatial interactive audio effects. By bringing methods from the fields of sound design and spatial audio into the context of human-robot interaction, my research aims to enrich and refine the ways robotic agents and responsive structures communicate with humans.
Intelligent agent for formal modelling of temporal multi-agent systems

Abstract Software systems are becoming complex and dynamic with the passage of time, and to provide better fault tolerance and resource management they need to have the ability of self-adaptation. Multi-agent systems paradigm is an active area of research for modeling real-time systems. In this research, we have proposed a new agent named SA-ARTIS-agent, which is designed to work in hard real-time temporal constraints with the ability of self-adaptation. This agent can be used for the formal modeling of any self-adaptive real-time multi-agent system. Our agent integrates the MAPE-K feedback loop with ARTIS agent for the provision of self-adaptation. For an unambiguous description, we formally specify our SA-ARTIS-agent using Time-Communicating Object-Z (TCOZ) language. The objective of this research is to provide an intelligent agent with self-adaptive abilities for the execution of tasks with temporal constraints. Previous works in this domain have used Z language which is not expressive to model the distributed communication process of agents. The novelty of our work is that we specified the non-terminating behavior of agents using active class concept of TCOZ and expressed the distributed communication among agents. For communication between active entities, channel communication mechanism of TCOZ is utilized. We demonstrate the effectiveness of the proposed agent using a real-time case study of traffic monitoring system.
Incremental Learning for Metric-Based Meta-Learners

Majority of the modern meta-learning methods for few-shot classification tasks operate in two phases: a meta-training phase where the meta-learner learns a generic representation by solving multiple few-shot tasks sampled from a large dataset and a testing phase, where the meta-learner leverages its learnt internal representation for a specific few-shot task involving classes which were not seen during the meta-training phase. To the best of our knowledge, all such meta-learning methods use a single base dataset for meta-training to sample tasks from and do not adapt the algorithm after meta-training. This strategy may not scale to real-world use-cases where the meta-learner does not potentially have access to the full meta-training dataset from the very beginning and we need to update the meta-learner in an incremental fashion when additional training data becomes available. Through our experimental setup, we develop a notion of incremental learning during the meta-training phase of meta-learning and propose a method which can be used with multiple existing metric-based meta-learning algorithms. Experimental results on benchmark dataset show that our approach performs favorably at test time as compared to training a model with the full meta-training set and incurs negligible amount of catastrophic forgetting
Evasion Attacks On Svm Classifier

Support Vector Machine (SVM) is one of the main classification techniques used in many security-related applications like malware detection, spam filtering, etc. To incorporate SVM into real-world security applications they must be able to cope up with the attack patterns that will lead to misclassifications. In this system, the vulnerability of SVM to evasion attacks are measured. A simple but effective approach is presented that can be exploited to systematically assess the security of widely-used classification algorithms against evasion attacks. To identify the vulnerabilities some transformations are applied to the testing set of handwritten digit images. The obtained result is plotted as a confusion matrix that allows the visualization of the performance of the algorithm against evasion attack. The work demonstrates the correctness and performance of existing adversarial systems. This work also compares the performance level of feature descriptors like Speeded Up Robust Features (SURF) and Histogram of Oriented Gradients (HOG) and their level of vulnerability to the evasion attacks are also measured. It can be inferred from our system that, even though both HOG and SURF are vulnerable to evasion attacks, those images that are extracted using SURF is less vulnerable compared to those images extracted using HOG features.
Principal component analysis for Hilbertian functional data

In this paper we extend the functional principal component analysis for real-valued random functions to the case of Hilbert-space-valued functional random objects. For this, we introduce an autocovariance operator acting on the space of real-valued functions. We establish an eigendecomposition of the autocovariance operator and a Karuhnen-Loeve expansion. We propose the estimators of the eigenfunctions and the functional principal component scores, and investigate the rates of convergence of the estimators to their targets. We detail the implementation of the methodology for the cases of compositional vectors and density functions, and illustrate the method by analyzing time-varying population composition data. We also discuss an extension of the methodology to multivariate cases and develop the corresponding theory.
Virtual Construction: Interactive Tools for Collaboration in Virtual Reality

Virtual technologies and game engines provide new possibilities for collaborative virtual design within digital building models. The current paper describes an approach, in which computer-aided design (CAD) models of buildings are transferred into a game engine based environment, where they can be reviewed and further designed collaboratively. Following a user-centered design (UCD) process based on interviews and iterative interactions with designers and architects, the prototype of Virtual Construction─a game engine based platform for collaborative virtual design meetings─was designed and implemented using Unreal Engine 4. The interactive tools developed can be used both in full immersive virtual reality and using traditional devices (e.g. laptop or desktop computers). Based on identified user needs, interaction techniques were implemented for moving, rotating, and aligning objects, adding and resizing shapes and objects, as well as moving and measuring distances in the three-dimensional (3D) building model. In addition, the communication techniques implemented based on user needs included synchronous features such as voice communication, text chat, pointing, and drawing, and asynchronous features such as leaving messages and feedback augmented with screenshots to exact virtual locations. Other implemented scenarios included different lighting scenarios, an evacuation scenario and crowdsourced voting between different designs.
Medical Diagnosis Cloud Model for Detection of Kidney Diseases

the paper discusses detection of Chronic Kidney Disease (CKD) and Renal Cell Cancer (RCC). Detection is done by analyzing ultrasound images of patients. For CKD feature extraction, we apply contour detection on kidney's region of interest (ROI) and in RCC detection, a group of features (feature vector) is used for feature extraction. Later the data is deployed on cloud to ease users. This study will help patients, health organizations, doctor's etc. to directly communicate through internet and access medical repositories on cloud systems. This study will also provide an outline for improving healthcare data for effective storage to provide remote access.
Survivalence of Rouge Wild Animals Using Image Processing and IOT

Human and Wild creature strife is consistently expanding issue in the regions of Forest zone and farming field which results in huge loss of assets and may put human life at risk. So this zone must be checked to keep the creatures off or to prevent any undesirable interruption. In our proposed undertaking, we have structured a framework which identifies the movement of creatures by utilizing PIR sensors, then camera will take picture of interruption and the Picture is sent to the Picture handling sensors where it is characterized as a creature or a human using content based image classification. In case of bogus alert no advances are prepared in order to solve the issue. We have come up with a new method in which we can revert the animal by using IOT and Image Processing technique. In our proposed method we have used sensor which will identify the moments and the image taken will be compared to identify the animals and at the same time message to be sent to the farmers.
Security Lock with Effective Verification Traits

To manage and handle the issues of physical security in the modern world, there is a dire need for a multilevel security system to ensure the safety of precious belongings that could be money, military equipment or medical life-saving drugs. Security locker solution is proposed which is a multiple layer security system consisting of various levels of authentication. In most cases, only relevant persons should have access to their precious belongings. The unlocking of the box is only possible when all of the security levels are successfully cleared. The five levels of security include entering of password on interactive GUI, thumbprint, facial recognition, speech pattern recognition, and vein pattern recognition. This project is unique and effective in a sense that it incorporates five levels of security in a single prototype with the use of cost-effective equipment. Assessing our security system, it is seen that security is increased many a fold as it is near to impossible to breach all these five levels of security. The Raspberry Pi microcomputers, handling all the traits efficiently and smartly makes it easy for performing all the verification tasks. The traits used involves checking, training and verifying processes with application of machine learning operations.
Automatic Landmark Placement for Large 3D Facial Image Dataset

Facial landmark placement is a key step in many biomedical and biometrics applications. This paper presents a computational method that efficiently performs automatic 3D facial landmark placement based on training images containing manually placed anthropological facial landmarks. After 3D face registration by an iterative closest point (ICP) technique, a visual analytics approach is taken to generate local geometric patterns for individual landmark points. These individualized local geometric patterns are derived interactively by a user's initial visual pattern detection. They are used to guide the refinement process for landmark points projected from a template face to achieve accurate landmark placement. Compared to traditional methods, this technique is simple, robust, and does not require a large number of training samples (e.g. in machine learning based methods) or complex 3D image analysis procedures. This technique and the associated software tool are being used in a 3D biometrics project that aims to identify links between human facial phenotypes and their genetic association.
The red thread of information

Purpose In The Invisible Substrate of Information Science, a landmark article about the discipline of information science, Marcia J. Bates wrote that “…we are always looking for the red thread of information in the social texture of people's lives” (1999a, p. 1048). To sharpen our understanding of information science and to elaborate Bates' idea, the work at hand answers the question: Just what does the red thread of information entail? Design/methodology/approach Through a close reading of Bates' oeuvre and by applying concepts from the reference literature of information science, nine composite entities that qualify as the red thread of information are identified, elaborated, and related to existing concepts in the information science literature. In the spirit of a scientist–poet (White, 1999), several playful metaphors related to the color red are employed. Findings Bates' red thread of information entails: terms, genres, literatures, classification systems, scholarly communication, information retrieval, information experience, information institutions, and information policy. This same constellation of phenomena can be found in resonant visions of information science, namely, domain analysis (Hjørland, 2002), ethnography of infrastructure (Star, 1999), and social epistemology (Shera, 1968). Research limitations/implications With the vital vermilion filament in clear view, newcomers can more easily engage the material, conceptual, and social machinery of information science, and specialists are reminded of what constitutes information science as a whole. Future researchers and scientist–poets may wish to supplement the nine composite entities with additional, emergent information phenomena. Originality/value Though the explication of information science that follows is relatively orthodox and time-bound, the paper offers an imaginative, accessible, yet technically precise way of understanding the field.
A New Vector Representation of Short Texts for Classification

Short and sparse characteristics and synonyms and homonyms are main obstacles for short-text classification. In recent years, research on short-text classification has focused on expanding short texts but has barely guaranteed the validity of expanded words. This study proposes a new method to weaken these effects without external knowledge. The proposed method analyses short texts by using the topic model based on Latent Dirichlet Allocation (LDA), represents each short text by using a vector space model and presents a new method to adjust the vector of short texts. In the experiments, two open short-text data sets composed of google news and web search snippets are utilised to evaluate the classification performance and prove the effectiveness of our method.
Knowledge Graph of University Campus Issues and Application of Completion Methods

Contemporary societies face many urban issues. To address these issues, governments, corporations and individuals should disclose and share their related statistical and sensory data. However, existing published data appear in various formats and contain defects. Therefore, few problems have been solved using these data. In this research, we sought to address this problem, by considering a university campus as a microcosm of society, designed data integration schema, and consolidated data into a knowledge graph. We then, applied and modified existing completion methods. In particular, regarding the bicycle environment, we trained our knowledge graph and evaluated it with the conventional method and our proposed derivative method, respectively. Using approximately 650 parking data with various dates and times, our method correctly estimated 54.5 more bicycles than the conventional method by comparing each time's mean absolute error.
Statistical properties and nonlocal correlation between a two qubits and optical field in the even deformed binomial distribution

In this article we estimate the nonlocal correlation between a two qubits and OF (optical field) initially started in the even deformed binomial distribution (EDBS). Explicit forms of the EDBS as the initial states of the RF are presented. We investigate the dynamical and statistical properties of the linear entropy as a quantifier of the correlation and purity of the proposed system. Also, the correlation between the two qubits is quantified by the negativity. Some statistical estimations of the RF are investigated by of Mandel parameter and compared by the two types of correlation The relation between the qubit inversion, two qubits-RF nonlocal correlation, negativity and Mandel parameter is obtained. The effective of Stark shift on the dynamical operators for the RF distribution is examined. The results are shown the a high amount of field-qubit nonlocal is reached for a high value of deformation parameter. Also, the statistical properties of the field is Poissonian or sub-Poissonian photon distribution.
Reordering Genomic Sequences for Enhanced Classification via Compression Analytics

The full implications of sharing genomic information are still largely unknown. Understanding what attributes can be inferred from available information is therefore a critical part of genomic privacy and security. We show that compression analytics are successful at classifying, or inferring, unknown attributes of genomic sequences without the need for a predefined feature set and with very little training data. Compression analytics perform best when predictable elements within a sequence are local; however, long range dependencies are ubiquitous in the human genome. We therefore consider a variety of schemes to reorder genomic sequences so as to localize predictable elements and improve the performance of compression analytics. Compression analytics on both native and reordered sequences are shown to outperform more traditional, feature-based machine learning approaches.
Video Human Behavior Recognition Based on ISA Deep Network Model

Vision-based behavior recognition is the analysis and recognition of human behavior in video. It has been widely used in many aspects such as multimedia information retrieval, behavior monitoring, and robot perception. This paper uses the Independent Subspace Analysis (ISA) deep network model feature extraction method, which is based on the ISA model and neural network theory, and combines data preprocessing methods, [Formula: see text]-means clustering methods, and Support Vector Machine (SVM) classifiers to achieve video classification and identification of human behavior. The ISA-based deep network model feature extraction method is an unsupervised learning method that can obtain behavior characteristics with good invariance and characterization capabilities in video human behavior. The experiment was conducted on the basis of the Hollywood2 human behavior data set. This experiment was compared with other commonly used human behavior feature extraction and recognition methods. The experimental results validated the effectiveness and advantages of this method in the classification and recognition of human behavior.
Combining Text and Image data for Product Recommendability Modeling

Digital advertising aims to display relevant product based advertisements which matches user's intent. User's intent is usually captured via various metrics like clicks and sale. Research in the recent past have demonstrated that image of the product plays an important role in order to achieve the outcome of interest. However, product advertisements with nonrecommendable images, i.e., product depicting adult, racial or political content often leads to sub-optimal performances, along with bad reputation for the partner and the advertiser. To overcome this challenge, we propose a model for product recommendability, i.e., to identify whether the product is recommendable or not. Our proposed approach is a hybrid model build using image and text data. We demonstrate the superior performance of our proposed approach on a dataset, obtained from a major e-commerce advertiser.
Transitioning to Teaching Android With Kotlin and Jetpack Components

Android tends to be the platform of choice in the education community due to its openness and the use of Java. Any major changes to the way Android apps are built could be helpful to both teachers and students. As of last year, Android officially supports Kotlin -- a modern Java interoperable language that offers null safety, reduced verbosity and numerous other modern programming language features. Furthermore, the current Android developer tool-set has been expanded to include a set of new libraries, tools, and architectural guidances called Android Jetpack. The new components simplify the process of implementing complex mobile app tasks such as lifecycle handling, persistence and background operations. In this poster, we share our findings in updating and teaching a CS3 introductory mobile software development course that was originally taught in Java.
Community, Art and the Vernacular in Technological Ecosystems

Community, craft, and the vernacular in artificially intelligent systems take the position that everyone participating in society is an expert in our experiences within the community infrastructures, which inform the makeup of robotic entities. Though we may not be familiar with the jargon used in specialized professional contexts, we share the vernacular of who we are as people and communities and the intimate sense that we are being learned. We understand that our data and collaboration is valuable, and our ability to successfully cooperate with the robotic systems proliferating around is well served by the creation of qualitatively informed systems that understand and perhaps even share the aims and values of the humans they work with. Using her art practice, which interrogates a humanoid robot and seeks to create culturally specific voice interactive entities as a case in point, Dinkins examines how interactions between humans and robots are reshaping human-robot and human-human relationships and interactions. She ponders these ideas through the lens of race, gender, and aging. She argues communities on the margins of tech production, code, and the institutions creating the future must work to upend, circumvent, or reinvent the algorithmic systems increasingly controlling the world, including robotics, that maintain us.
Adaptive Volumetric Video Streaming Platform

This paper proposes an adaptive volumetric video streaming platform including real-time tracking, adaptive source view selection, DASH streaming, GPU accelerated view synthesis, HEVC decoding and view-dependent rendering. Volumetric use case supports 3 degree of freedom immersive experience with additional limited translational movement. The proposed adaptive streaming platform is built on advanced view synthesis technologies and DASH-based adaptive streaming to enhance the immersive experience while reducing the overall transmission bandwidth. The GPU acceleration further enables real-time view synthesis, HEVC decoding and view rendering. The simulation results verify that the proposed adaptive solution can utilize the bandwidth more efficiently. As a result, a more consistent viewing experience with high quality volumetric video content and low interactive latency can be delivered to the user. The platform has been demonstrated in the 2019 Mobile World Congress and 2nd VR ecosystems and standards workshop in April 2019.
Geometry and Topology of Deep Neural Networks' Decision Boundaries.

Geometry and topology of decision regions are closely related with classification performance and robustness against adversarial attacks. In this paper, we use differential geometry and topology to explore theoretically the geometrical and topological properties of decision regions produced by deep neural networks (DNNs). The goals are to obtain some geometrical and topological properties of decision regions for given DNN models, and provide some principled guidances to designing and regularizing DNNs. At first, we give the curvatures of decision boundaries in terms of network weights. Based on the rotation index theorem and Gauss-Bonnet-Chern theorem, we then propose methods to identify the closeness and connectivity of given decision boundaries, and obtain the Euler characteristics of closed ones, all without the need to solve decision boundaries explicitly. Finally, we give necessary conditions on network architectures in order to produce closed decision boundaries, and sufficient conditions on network weights for producing zero curvature (flat or developable) decision boundaries.