Reviewerly/exHarmony huggingface dataset:
Content Compression Coding for Federated Learning

As machine learning requires distributed framework, Federated Learning(FL) is proposed to address the distributed computations in machine learning, especially for privacy protection. In FL, an important problem is to reduce the transmission quantity between the terminals and the centralized server. In this paper the content compression coding for FL is proposed, which can efficiently reduce the required transmission bandwidth. The correlation among the parameters of the distributed ANN models is first verified based on the entropy analysis and simulation results validate the propsed content compression coding using the WinRAR compression. Our work demonstrates that the proposed coding can compress the parameters in the distributed ANN with acceptable learning performance.
Nobrainer: An Example-Driven Framework for C/C++ Code Transformations

Refactoring is a standard part of any modern development cycle. It helps to reduce technical debt and keep software projects healthy. However, in many cases refactoring requires that transformations are applied globally across multiple files. Applying them manually involves large amounts of monotonous work. Nevertheless, automatic tools are severely underused because users find them unreliable, difficult to adopt, and not customizable enough. This paper presents a new code transformation framework. It delivers an intuitive way to specify the expected outcome of a transformation applied within the whole project. The user provides simple C/C++ code snippets that serve as examples of what the code should look like before and after the transformation. Due to the absence of any additional abstractions (such as domain-specific languages), we believe this approach flattens the learning curve, making adoption easier. Besides using the source code of the provided snippets, the framework also operates at the AST level. This gives it a deeper understanding of the program, which allows it to validate the correctness of the transformation and match the exact cases required by the user.
Application to train short-term memory according to the CHC intelligence model

This paper deals with a proposal of an application designed to train short-term memory according to the Cattell-Horn-Carroll (CHC) model of intelligence. This application is one of the parts in the project called „Intelligent neuro-rehabilitation system for patients with acquired brain damage in early stages of treatment”, whose aim is to develop a smart neuro-rehabilitation system to increase the effectivity of the acute phase of neuro-rehabilitation of patients with brain damage. Following the project’s requirements, the application is prepared as a web application built on the .NET technology.
A Novel Spatial and Temporal Context-Aware Approach for Drone-Based Video Object Detection

Nowadays, with the advent of Unmanned Aerial Vehicles (UAV), drones equipped with cameras have been fast deployed to a wide range of applications. Consequently, automatic and effective object detection plays an important role in understanding and analysis of visual data collected from the drones, which could be further applied to civilian and military fields. However, various challenges still exist in object detection of drone-based videos, such as defocus, motion blur, occlusion and various variations (e.g., illumination, view and size), leaving too weak visual clues for successful detections. In this paper, we propose a novel approach for object detection in drone-based videos, which includes the multi-model fusion detection, an efficient tracker and a new evaluation method for confidence of the track, and the false positive analysis with scene-level context information and inferences. The experimental results on VisDrone2018-VID [44] dataset demonstrate the effectiveness of the proposed approach.
An Approach to Design and Develop UX/UI for Smartphone Applications of Minority Ethnic Group

In a developing country like Bangladesh, minority ethnic groups or tribal people are less privileged to use the benefit of ICT interventions. The smartphone applications hardly persuade the tribal communities. Even though smart cell phones are cheaply available to these people, there is hardly any significant impact of mobile applications. We have studied the usage of smartphone and its applications by the people of various tribal communities. Especially, the UX/UI of the two popular smartphone applications (bKash and Bikroy.com) has been evaluated according to the usage of tribal people. Interestingly, we have found that the culture, language, and customs of the ethnic minority groups make issues to have a successful interaction with those two applications. The local software industries never consider them as a stakeholder of the generic software during the developments. In this paper, we not only have addressed the challenges and issues regarding UX/UI of smart applications for tribal people but also a suitable solution has been recommended in terms of design and user experience of tribal people. The findings of this paper can help in developing mobile applications and services which will be beneficent to the tribal and ethnic people.
A Compact Hardware Implementation for the SCA-resistant PRESENT Cipher

The side-channel attack (SCA) poses a great threat to the information security of the Internet of things (IoT). However, the excessive resource cost caused by the corresponding countermeasure will hinder the extensive application of SCA-defendable technology in resource-limited devices. In this paper, a compact hardware structure of PRESENT cipher combining Threshold Implementation (TI) technique with multiple-bit serial architecture is presented. The 16-bit serial PRESENT is implemented mainly by reducing the width of datapath and building a direction-alterable register array. The implementation of TI-based substitution box is optimized by reusing common circuit, which makes slices utilization on Xilinx Kintex-7 FPGA reduced by 14.0% for 16-bit serial PRESENT with TI. Compared to the typical implementation of TI-based PRESENT, the proposed work saves resource consumption by 59.1%, which is available to resource-constrained scenarios.
Real-Time Experimentation of Deep Learning-based RF Signal Classifier on FPGA

We demonstrate the embedded implementation of a deep learning-based RF classifier that is implemented on the Field Programmable Gate Array (FPGA) of the software-defined radio (SDR) platform, DeepRadio <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">TM</sup> . Supported by low-power embedded computing, the received signals are classified to different modulation types in real time. The deep neural network that is used for the RF signal classifier runs directly on the FPGA fabric of DeepRadio <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">TM</sup> . In the demonstration setup, a USRP radio transmits signals with different modulation types and DeepRadio <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">TM</sup> classifies each received signal (I/Q samples) to its modulation type. Classification results are shown on the mobile app of a smartphone that is connected to DeepRadio <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">TM</sup> . Demonstration results show that the classifier achieves high accuracy (close to software implementation) at low latency (microseconds per sample) and low energy consumption (microJoules per sample).
Proposal of Home Context Recognition Method Using Feature Values of Cognitive API

The emerging deep learning technology is a promising means for context recognition with multimedia data. We are interested in using the deep learning with images for context recognition in smart homes. In the home context recognition, the room layout, the environment, and the contexts to be recognized are different from one household to another. Therefore, a unique recognition model is required for every different household. For this, if we take a naive approach that uses the deep learning directly, a huge amount of labeled images are required, which is practically impossible for general households. The goal of this research is to develop an image-based context recognition method that is affordable at home. In the proposed method, we exploit a cognitive API which performs general image recognition, and retrieve the information within the image as text. By using the text as features, we classify the context with ordinal supervised machine learning. Compared with the expensive approach with deep learning, the proposed method uses generic image recognition of the cognitive API, and light-weight machine learning. As a result, the context recognition customized for every household can be achieved with much less effort.
Secure Textual Data Deduplication Scheme Based on Data Encoding and Compression

As the need for storage has grown exponentially in recent years, cloud storage has been providing a solution to this need by providing users expanded capacity and access. Providing adequate security and privacy, and lowering storage costs are some of the key challenges facing this solution. A common practice used by cloud service providers (CSPs)-data deduplication - identifies identical copies of users' data, and removing all, but one copy to lower required storage overhead. However, this can result in serious privacy concerns. In this paper, we formulate a new secure deduplication scheme for textual data. Our proposed method uses data encoding and compression techniques that not only result in reduce storage space required, but also in saving in required transmission bandwidth. The security of the data against the semi-honest CSP and malicious users is ensured by using Burrows Wheel Transform encoding scheme. The encoded data is further compressed to gain effective savings in terms of storage and reduced size of the data. Data encoding and data compression techniques are combined together to realize secure and efficient data deduplication. Through our scheme, the CSP will not only achieve huge storage space savings through data compression and data deduplication, but can also provide the users a satisfactory level of security for their data in the cloud.
Social Norms of Touch

This chapter discusses social norms with attention to their significance for researching and designing digital touch communication in a global world, notably gendered and cultural touch norms. It explores how social and cultural norms shape the ways that people (and machines) touch. Touch norms are shaped, regulated and enforced through social, economic, familial and legal mechanisms, they organise our experiences and expectations. Understanding of the touch norms that people, including digital touch researchers and designers, bring to their interactions with others provides a route into understanding the sociality that shapes digital touch. We discuss the significance of these given the expectations of the user, their touch repertoires, and the social cultural role that norms play in the take up and use of mediated digital touch communication devices and systems and environments. The chapter concludes that reflexive engagement with touch norms can provide insights and inspiration for thinking about, researching and designing digital touch communication, and help to address how cultural and gendered norms of touch might be engaged with, to constrain and re-produce or open-up the meaning potentials of digital touch.
Face tracking with a probabilistic Viola and Jones face detector

This work proposes a new method for face and mouth tracking using video cameras. This method has been proposed to be used in intelligent spaces as an initial stage to provide information to higher level applications. The method incorporates the classifiers of the Viola and Jones detection method, modified to provide a probabilistic output previously proposed by the authors, useful for tracking with particle filter. The method combines classifiers trained to detect specific poses (frontal and side face views), building an independent likelihood model of pose changes. We also propose to combine the model in several cameras to allow the mouth tracking in a three-dimensional space. The system has been evaluated on the AV16.3 database sequences showing good results in both precision and recall when using a single camera (in a bidimensional space), and an error below 3cm when using three cameras (in a three-dimensional space).
Digitizing Human Work Places in Manufacturing Through Augmented and Mixed Reality

It is no longer obvious that the share of knowledge work in production environments is increasing. However, this is not the only reason why more attention needs to be paid to the human factor in production. It is therefore increasingly important to provide workers in a production environment with the best possible support for their knowledge tasks by using modern information and communication technologies. In particular, the technical innovations in terms of augmented and mixed reality offer a great potential to be implemented in relevant applications in connection with the ongoing digitization or digital transformation of production. Against this background, this paper shows how current knowledge-based work processes in production environments can be best supported by augmented and mixed reality technologies. For this purpose, the paper reviews the state of the art of augmented and mixed reality technologies and then outlines in two concrete industrial use cases from the manufacturing domain how human work can be digitally augmented to facilitate knowledge-intensive production tasks.
Problems and Methods for Integrating Heterogeneous Data (on Example, Metallurgical Production)

In this article we discuss the features of heterogeneous data of a metallurgical enterprise, which is part of our continuous work on the development of a data warehouse. We analyze the possibility of applying integration methods to solve the problem of integrating heterogeneous data at a metallurgical enterprise.
Voice authentication by text dependent single utterance for in-car environment

Individual authentication using speech is called speaker verification. Speaker verification, which can be implemented in portable devices, is used in many scenarios. This study focuses on speaker verification while driving. Noise and long-term variability of feature are problems associated with speaker verification while driving. Considering the characteristics of noise in a moving car, spectral subtraction and cutting low frequency are implemented in noise reduction phase. We describe adaption of templates to noisy environment. We also evaluate long-term variability of feature by using speech that is recorded after approximately 10 months from the first enrollment.False reject rate(FRR) decreased to 66.6 % on average when implementing the noise reduction phase.In addition, FRR is improved by noise reduction phase through an update template using accepted speech.With respect to long-term variability, the FRR did not change after 10 months. This result indicates that GMM Posteriorgram is valid for inter-speaker variability. For future study, we need to consider template text to reduce the variation in GMM.
ADVISOR: An Adjustable Framework for Test Oracle Automation of Visual Output Systems

Test oracles differentiate between the correct and incorrect system behavior. Automation of test oracles for visual output systems mainly involves image comparison, where a snapshot of the output is compared with respect to a reference image. Hereby, the captured snapshot can be subject to variations such as scaling and shifting. These variations lead to incorrect evaluations. Existing approaches employ computer vision techniques to address a specific set of variations. In this article, we introduce ADVISOR, an adjustable framework for test oracle automation of visual output systems. It allows the use of a flexible combination and configuration of computer vision techniques. We evaluated a set of valid configurations with a benchmark dataset collected during the tests of commercial digital TV systems. Some of these configurations achieved up to 3% better overall accuracy with respect to state-of-the-art tools. Further, we observed that there is no configuration that reaches the best accuracy for all types of image variations. We also empirically investigated the impact of significant parameters. One of them is a threshold regarding image matching score that determines the final verdict. This parameter is automatically tuned by offline training. We evaluated runtime performance as well. Results showed that differences among the ADVISOR configurations and state-of-the-art tools are in the order of seconds per image comparison.
Training Wasserstein GANs for Estimating Depth Maps

Depth maps depict pixel-wise depth association with a 2D digital image. Point clouds generation and 3D surface reconstruction can be conducted by processing a depth map. Estimating a corresponding depth map from a given input image is an important and difficult task in the computer vision field. Fortunately, with the advent of artificial intelligence, and especially deep learning based techniques new approaches for difficult tasks have been developed. One of the attractive structures is named as Generative Adversarial Network (GAN). However, training a GAN has been reported to be problematic in terms of optimization leading to some convergence issues. Vanishing or exploding gradients and mode collapses are some examples of these issues. Lately, several alternative optimization functions and distance measures have been investigated in order to handle these difficulties. Among these approaches, Wasserstein-1 distance and Wasserstein GAN (WGAN) offers a promising alternative. In this study, Wasserstein functions and its variants are investigated for the depth map estimation task from a given 2D face image. Different network structures are trained and compared in order to assess the effectiveness and stability. Quantitative analysis is conducted by calculating two separate error metrics between the network outputs and ground-truth values.
Novel seed selection techniques for MR brain image segmentation using graph cut

Brain tumour segmentation is a challenging task because of high diversity of the tumour for different patients and the ambiguity in its location. The high accuracy of extracting the tumour region from the normal brain solely depends on the seed selection. This prior knowledge of the seed points is also required for the initialisation of the graph cut segmentation. In this paper, two techniques (Centroid-Based Seed Selection [CBSS] and k-Mean Seed Selection [KMSS]) for the seed selection and segmentation of the tumour region using the graph-cut technique are proposed. The intensity distribution in the MRI brain images is utilised for the calculation of these points. In (CBSS), the symmetrical intensity distribution on both half of the brain is exploited. While in (KMSS) different groups of the similar intensity distribution are formed. The seed values obtained initialise the Graph-cut method to perform the segmentation. The performance parameters for the proposed methods are evaluated using MR brain images from standard and real time dataset. The mean Dice Sensitivity Coefficient (DSC) and mean Jaccard Index (JI) values are evaluated for indicating effectiveness and accuracy of the proposed work.
A Virtual Spring Damper Method for Formation Control of the Multi Omni-directional Robots in Cooperative Transportation

This research reports the formation control method of multi holonomic robots for cooperative transportation. The proposed method is based on the concept of a virtual structure. There are virtual springs and dampers link between the virtual leader (VL) and follower robots. With the virtual spring and damper forces, a group of robots can follow the VL while keeping the distance from the VL and other follower robots to avoid a collision among them. The algorithm required a predefined formation shape and size depend on a transport object. The proposed method was developed based on the Robot Operating System (ROS) and evaluated in the simulation software Gazebo. Two experiments were conducted by using the 3D model of our Mecanum wheel robot platform. In the first experiment, the four follower robots can aggregate to the position of the VL to form a diamond shape formation. In the second experiment, a group of follower robots with an object on their bodies can maintain a formation while following the VL along a predefined trajectory. The simulation results confirm that the method proposed in this paper is feasible for the cooperative transportation task.
Blockchain Decision Matrix for IoT Systems

Blockchain technologies are explored as an overlay to existing IoT systems. Building such systems require design considerations regarding the type of technology and its suitability based on the number & frequency of transactions, data volume, privacy requirements, etc. Most of the existing work towards blockchain-IoT integration lacks systematic approach. This work proposes a systematic approach to apply blockchain technologies in IoT systems. The goal of this study is to show how to apply blockchain technologies in the IoT systems by better understanding the system and use-case requirements. To fully realize the challenges, we investigated several IoT systems. We introduce the initial concept and design of a decision matrix or analysis framework. This matrix is intended to help the interested stakeholders and system architects in developing blockchain applications for IoT systems and prototyping a fit-for-purpose technology stack with exchangeable components.
A Study on Customer Satisfaction Analysis of Public Institutions using Social Textmining

In this paper, the changes due to the 4 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">th</sup> Industrial Revolution will be more important in institutional and policy innovations than technological innovations. It is a little bit different from existing changes, economic growth and productivity increase because these innovations are becoming the core. When the importance of institutional and policy innovation increases, the role of public institutions will become more important. In such situation that the role of the public institution becomes the core and be more important, the effort is required to provide more effective and better service as well as information to customers by understanding how much the customers are satisfied with the service and information provided by each public institution. This study aims to suggest a new method for analyzing customer satisfaction through social text mining - a method that can be used together with supplementing the concern of customer satisfaction survey of public institutions which is currently being surveyed with questionnaire. By executing a kind of text mining emotional analysis of social text data left on SNS and homepage by people who used the services of each institution, it analyzes whether customers think of each institution is overall positive or negative.
Enhancing privacy in smart energy systems

Abstract The mission to move from fossil to renewable energy sources is accompanied and enabled by the digitalization of our energy systems. With the introduction of information and communication technologies, the widespread integration of distributed, renewable sources, even in the distribution grid, are enabled. New use cases such as fast EV charging, local energy communities and dynamic energy tariffs are also enabled. However, this move toward digitalization also increases the exposure of the energy systems for cybercrime and raises concerns regarding the privacy of personal data. In this article, we address the issue of privacy in smart energy systems and give an overview of current methods to enhance privacy.