Reviewerly/exHarmony huggingface dataset:
Autonomous Generation of Robust and Focused Explanations for Robot Policies

Transparency of robot behaviors increases efficiency and quality of interactions with humans. To increase transparency of robot policies, we propose a method for generating robust and focused explanations that express why a robot chose a particular action. The proposed method examines the policy based on the state space in which an action was chosen and describes it in natural language. The method can generate focused explanations by leaving out irrelevant state dimensions, and avoid explanations that are sensitive to small perturbations or have ambiguous natural language concepts. Furthermore, the method is agnostic to the policy representation and only requires the policy to be evaluated at different samples of the state space. We conducted a user study with 18 participants to investigate the usability of the proposed method compared to a comprehensive method that generates explanations using all dimensions. We observed how focused explanations helped the subjects more reliably detect the irrelevant dimensions of the explained system and how preferences regarding explanation styles and their expected characteristics greatly differ among the participants.
A Color Image Representation Approach for Content-Based Image Retrieval

In this paper, a color image representation approach is proposed for color image retrieval. First, an input color image is represented by a number representative images (called color degree images) consisting of chromatic (including red, green, blue, cyan, magenta, and yellow) and achromatic (including black and white) degree images. For each pixel, the degree to which its color value is similar to each representative color will be computed. For the local window centered at each detected key point, a vector representing the degree values, called local color degree (LCD) descriptor, will be computed to describe the local color property. Meanwhile, the scale-invariant feature transform (SIFT) descriptor is extracted to describe the local texture characteristics. Both LCD descriptor and SIFT descriptor are combined together to form the local descriptor. Vector of locally aggregated descriptors (VLAD) is then employed to aggregate all local descriptors to get a single VLAD vector for each input image. Experimental results on INRIA Holidays and Leeds Butterfly datasets have shown that the proposed descriptor yield higher retrieval accuracy.
Classification of user attitudes in Twitter -beginners guide to selected Machine Learning libraries

This paper presents an interesting use case for learning as well as teaching basics of Machine Learning systems. Starting from a brief historical outline of the ML, the authors propose and compare a set of popular ML libraries in an interesting exemplary implementation, to present their usability. The paper also describes text classification methods, the aim of which is to distinguish positive and negative labels of particular messages within the Twitter social network. The study is summarized by a brief comparison of the quality of the classification of the libraries and methods used, as an assessment of their suitability. Final thoughts on the importance of teaching ML are included.
Industrial Use Case - AR Guidance using Hololens for Assembly and Disassembly of a Modular Mold, with Live Streaming for Collaborative Support

For the development of industrial Augmented Reality (AR) applications beyond the scope of a marketing showcase for trade shows, the practical usability of researched technologies needs to be considered. In this paper we show the design process for a Microsoft HoloLens* application, with the goal of demonstrating the viability of AR-guidance in industrial business. The design is consistently focussing on the benefit of the application as well as usability in an industrial environment. This includes specifically aspects of image and object recognition, which are impaired in a dirty environment or with objects frequently subjected to wear and tear.
Ari: a P2P optimization for blockchain systems

Distributed ledgers based on blockchain, such as Bitcoin and Ethereum, are known and used worldwide now. But the existing distributed ledgers are too slow for commercial requirements when compared with centralized systems like Visa. To tackle this issue, many strategies have been put forward, including GHOST, Bitcoin-NG, Sharding, State channel, Plasma, etc. But few of them is focused on the P2P layer, while the P2P layer plays a crucial role as a distributed ledger needs to be updated via block propagation process. We propose an alternative protocol named Ari, which is a P2P optimization for blockchain systems. Ari protocol makes the block propagation graph an intercrossing net rather than a unidirectional tree, thus leading to a great reduction on latency as well as a great expansion on throughput, and the security as well as scalability is still guaranteed.
A Study on Service Identification Based on Server Name Indication Analysis

Identifying services constituting traffic from given IP network flows is essential to various applications, such as the management of quality of service (QoS) and the prevention of security issues. Typical methods for achieving this objective include identifications based on IP addresses and port numbers. However, such methods are not sufficiently accurate and require improvement. Deep Packet Inspection (DPI) is one of the most promising methods for improving the accuracy of identification. In addition, many current IP flows are encrypted using Transport Layer Security (TLS). Hence, it is necessary for identification methods to analyze flows encrypted by TLS. For that reason, a service identification method based on DPI and n-gram that focuses only on the non-encrypted parts in the TLS session establishment was proposed. However, there is room for improvement in identification accuracy because this method analyzes all the non-encrypted parts including Random Values without protocol analyses. In this paper, we propose a method for identifying the service from given IP flows based on analysis of Server Name Indication (SNI). The proposed method clusters flow according to the value of SNI and identify services from the occurrences of all clusters. Our evaluations, which involve identifications of services on Google and Yahoo sites, demonstrate that the proposed method can identify services more accurately than the existing method.
Indigenous HCl

The Workshop on Indigenous HCI aims to bring together researchers and practitioners (Indigenous and otherwise) who work with Indigenous people and communities on technology projects and human-computer interaction (HCI) research.
Conformal prediction based active learning by linear regression optimization

Conformal prediction uses the degree of strangeness (nonconformity) of data instances to determine the confidence values of new predictions. We propose a conformal prediction based active learning algorithm, referred to as CPAL-LR, to improve the performance of pattern classification algorithms. CPAL-LR uses a novel query function that determines the relevance of unlabeled instances through the solution of a constrained linear regression model, incorporating uncertainty, diversity, and representativeness in the optimization problem. Furthermore, we present a nonconformity measure that produces reliable confidence values. CPAL-LR is implemented in conjunction with support vector machines, sparse coding algorithms, and convolutional networks. Experiments conducted on face and object recognition databases demonstrate that CPAL-LR improves the classification performance of a variety classifiers, outperforming previously proposed active learning techniques, while producing reliable confidence values.
Non-reference image quality assessment based on deep clustering

Image quality assessment (IQA) is an indispensable technique in computer vision and pattern recognition Existing deep IQA methods have achieved remarkable performance. As far as we know, these deep learning-based IQA algorithms lack an adaptive features extraction mechanism toward input images with varying sizes and the stability in avoiding disturbance from data noises and model deviation. To solve these problems, we propose a non-reference IQA method by designing a novel unsupervised deep clustering framework, where a 13-layer network structure is proposed that upgrades the fully-connected layers to produce high-level features with adaptive sizes. Moreover, we add a contracted regular term with a contracted autoencoder into the clustering loss function to form a quality model reflecting the clustering structure. Compared to the other IQA algorithms, our model with simple structure exhibits more stable and robust performance by the initial configuration of network parameters during end-to-end training. The experimental results on the LIVE and CSIP databases have shown that our method not only performs better than the state-of-the-art IQA algorithms, but also has a simpler structure and better adaptability.
Soliloquising Social Robot in a Hotel Room

This study examines the possible services a social robot can provide in a hotel room. This area of research falls under the relatively unexplored domain of social robot services. This paper proposes and tests the proactive services rendered by a soliloquising social robot that has three core functions: responding to a customer's request, soliloquising about the hotel service, and proactively expressing goodwill (e.g. greetings and farewells), according to the context. We expect that, through its proactive interaction (in this case, soliloquising and expressing goodwill), a soliloquising social robot can become an essential room companion for customers. Rather than being a mere tool, the soliloquising robot can relieve a customer's feeling of loneliness and provide him or her some interpersonal warmth. However, the proactive social robot may appear to be intrusive. Thus, a field experiment was conducted at a hotel on the expectations and risks associated with using such a robot. The results reveal that although social robots can make potential room companions in hotels, it is necessary to resolve a few issues to enable them gain wider acceptance.
Enhanced Map Reduce Techniques for Big Data Analytics based on K-Means Clustering

The Clustering methods have been greatly adopted in various real world data analysis applications, such as customer behavior analysis, medical data analysis, digital forensics, etc. In existing system, MR-mafia subspace clustering algorithm becomes inefficient as well as ineffective because the data size are continuously increasing, and data blocks are overlaying. Big Data environment inherits several knowledge and we extracts the necessary knowledge and K-means clustering algorithm is being designed. This paper focused on K - mean clustering algorithm based on improved map reduce techniques. The algorithm takes advantages to avoid unnecessary input and output data and also used to optimize data storage and also to achieve the out sourcing of data privacy. We have using a medical datasets of this project, and Enhanced map reduce based K - means clustering algorithm have been proposed which work effectively done and that can be outsourced to cloud server.
Mining Logic Patterns from Visual Data

Logic reasoning, a fundamental ability of human intelligence, is an important topic in artificial intelligence. Often, these reasoning patterns need to beforehand be provided by some domain experts, which has resulted in an interesting research topic: can reasoning patterns be directly learned from given data in which meaning of any symbols is not known in advance? In order to study this issue, in this study, we design four visual logic data set (named Fashion-Logic) using the Fashion-MNIST data, in which first each image is embedded with n special figure symbols each of which is a picture from the Fashion-MNIST data set, then a relation (the relations: Bitwise And, Bitwise Or, Addition and Subtraction are used in this paper) is added to any two images to generate one output image. And these resulting images are called the logical images. Our aim is to obtain a learning model that can directly mine and reason the logic relation between two input images and one output image, without presetting any reasoning patterns (i.e. a learning model does not know the meaning of the figure symbols embedded in the images and the relation between the input images and the output image in advance). This novel task, mining the logic patterns from the Fashion-Logic data set, is called a fashion logic task in this paper. In this work, we test the performances of many typical neural network models on Fashion-Logic data set, and produce fruitful results which indicates that it is feasible to directly mine the logic reasoning patterns using a learning strategy from given data. Last, we test and find that the ability of models is very poor to mine the abstract logic patterns on the more complex data sets called Fashion-LogicV2.
Spherical Lanczos Interpolation in Planar Projection or Format Conversions of Panoramic Videos

Panoramic videos provide users with an amazing experience with immersive and interactive viewing, and are now gaining global popularity. Since panoramic videos are spherical in nature while effectively processed in planar projection formats in applications, it is crucial to implement sphere-to-plane projection, where a good interpolation algorithm is of great importance regarding generating high quality planar perspective videos. Moreover, different planar projection formats adapt to different applications according to their own characteristics. It is common to convert one projection format to another, where interpolation algorithms also make a difference on performance. In this paper, an interpolation algorithm named spherical Lanczos (SLAN) is proposed to achieve advanced performance in sphere-to-plane projection or planar projection format conversions of panoramic videos. In SLAN, we fully consider panoramic videos are naturally spherical. Reference pixels are selected carefully, and the weights are calculated correctly. Experimental results demonstrate that the SLAN interpolation algorithm is beneficial for the whole processing chain of panoramic videos both with lossless compression coding and with loss compression coding in terms of the increase in various end-to-end PSNR and the decrease in BD_rate. The cubic projection (CBP) and adjusted cubemap projection (ACP) are also evaluated to illustrate that the proposed algorithm is applicable for all kinds of planar projection format due to their inevitable deformation.
Clustering-Based Blockchain Technique for Securing Wireless Sensor Networks

Security is one of the major issues in WSNs. Due to the emergence of the WSNs, new requirements such as accessibility to services, data consistency, and network lucidity have come into picture. Although many protocols have been developed, they satisfy only a few requirements in managing security. This paper proposes a blockchain technology-based energy-efficient clustering approach for WSNs to provide security to the transaction records by including cryptographic techniques. It uses multiple collaborative base stations to reduce the network malfunctioning due to the compromised base stations. The proposed method achieves accessibility to services, data consistency, and network lucidity.
Generating adversarial examples with input significance indicator

Lots of adversarial attacks have been put forward to identify vulnerabilities in existing DNNs and improve their robustness. However, an indispensable benchmark, Jacobian-based Saliency Map Attack (JSMA), suffers from computational issues that prevent its application. In this paper, we propose to generate adversarial examples based on the input significance indicator, with two indicators -sensitivity and relevance- included to find the most sensitive or relevant element by backpropagating scores based on DNNs’ output. Our experiment results show that the sensitivity-based attack can alleviate the heavy computational problem of JSMA and still remain the effectiveness, while the relevance-based indicator can avoid the saturation problem of gradients with a slight sacrifice of runtime, thus reducing the magnitude of the perturbations with a higher rate of attack success.
Human-Centered Deep Learning Neural Network Trained Myoelectric Controller for a Powered Wheelchair

The powered wheelchair is one of the most widely used devices that assists users in activities of daily living. Although powered wheelchairs have improved the quality of life for most people, some users with certain disabilities, such as Spinal Cord Injury, may have difficulties with common types of controllers like joysticks. Because of this, other types such as head, mouth, vision, and speech controls have been employed, but each control type has its own limitations in terms of safety and accuracy. This research presents a human-centered approach that detects hand gestures using non-invasive surface electromyography (sEMG) signals from the human forearm by an artificial intelligence in the form of a Deep Learning Neural Network (DLNN). An Institutional Review Board application was approved to recruit participants without muscular disability between 18 to 50 years of age to evaluate the proposed controller mechanism. Each participant's sEMG signals were acquired at 200 Hz sampling frequency followed by DLNN training and validation. Methods such as an ultrasonic sensor to avoid large obstacles and a proportional-integral (PI) controller to produce smooth wheelchair motor movements were integrated with DLNN-based hand gesture recognition in this research to ensure the user's safety. The trained Bayesian Regularization DLNN had an average accuracy of 98.4% across all subjects and hidden layers. All subjects were able to successfully navigate the path using the proposed controller in an average of 4.85 minutes and touching an average of 2.3 obstacles out of 13. Being that so many people require the use of a wheelchair but oftentimes have difficulties controlling it, there is a clear need for the development of a safer and easier method of control and this research strives to fulfil that need.
Fast discrete factorization machine for personalized item recommendation

Personalized item recommendation has become an essential target of Web applications, but it suffers from the efficiency problem due to a large volume of data. In particular, feature-based factorization machine models are generally limited by the vast number of feature dimensions, leading to catastrophic computation time. In this paper, we propose a Fast Discrete Factorization Machine (FDFM) method to resolve these issues by applying the hash coding technologies to factorization machine models. Specifically, it discretizes the real-valued feature vectors in the parameter model during the process of learning personalized item rankings, whereby the overall computational time can be greatly reduced. Besides, we propose convergence update rules to optimize the quantization loss of the binarization problem, which can be used in personalized ranking scenarios efficiently. Based on the evaluation in two real-world datasets, our proposed approach consistently shows better performance than other baselines, especially when using shorter binary codes.
Learning from animals: How to Navigate Complex Terrains

We develop a method to learn a bio-inspired motion control policy using data collected from hawkmoths navigating in a virtual forest. A Markov Decision Process (MDP) framework is introduced to model the dynamics of moths and sparse logistic regression is used to learn control policy parameters from the data. The results show that moths do not favor detailed obstacle location information in navigation, but rely heavily on optical flow. Using the policy learned from the moth data as a starting point, we propose an actor-critic learning algorithm to refine policy parameters and obtain a policy that can be used by an autonomous aerial vehicle operating in a cluttered environment. Compared with the moths' policy, the policy we obtain integrates both obstacle location and optical flow. We compare the performance of these two policies in terms of their ability to navigate in artificial forest areas. While the optimized policy can adjust its parameters to outperform the moth's policy in each different terrain, the moth's policy exhibits a high level of robustness across terrains.
Application of STEM learning approach through simple technology to increase data literacy

Abstract This study aims to investigate the STEM approach’s effect on data literacy through simple technology on the theory of spring vibration. The research method used a quasi-experimental design with one group pretest posttest design. The study was conducted in class X MIA 2 MAN 1 Yogyakarta with 26 students. Data collection used pretest and posttest scores as measurement of data literacy. Based on data analysis results, this study obtained an enhancement in data literacy abilities. It can be seen in the N-gain test result. The results showed that 54% of students had data literacy in the high category, 42% in the medium category, and 4% in a low category. This study shows that there is an increase in student data literacy abilities after they are learning with the STEM approach through simple technology.
Research on motion planning for an indoor spray arm based on an improved potential field method

The target spraying effect of spray robots mainly depends on the control performance of the spraying arm during the processes of aiming and tracking. To further improve the robustness of the endpoint control and positioning accuracy of the spray arm, an improved potential field algorithm for the motion planning and control of the spray arm is proposed based on prophase research. The algorithm introduces a velocity potential field, visual field constraints and joint position limit constraints into the traditional artificial potential field method. The velocity potential field is used to ensure that the target state of the spraying arm is at the same velocity as the target crop (relative velocity) to achieve stable target tracking. The visual field constraints and joint position limit constraints are utilized to ensure the efficiency of the visual servo control and the movement of the spray arm. The algorithm can plan a feasible trajectory for the spraying arm in Cartesian space and image space, and use the speed controller to control the spraying arm movement along the trajectory for aiming and tracking. Simulation analysis shows that the algorithm can plan better motion trajectories than the servo controller based on image moments in previous studies. In addition, the experimental results show that the algorithm can effectively improve the robustness of targeting and tracking control for the spray robot.