Reviewerly/exHarmony huggingface dataset:
Speaker Identification: Variations of a Human voice

Communication is an essential part of human life. We humans use speech (words) to convey information to each other. Vocal tract characteristics in a human voice help identify a speaker. However, human speech signals are language independent and information is speaker dependent. With advanced technology, humans can use her/his voice as biometric authentication, which is unique for individuals. One can use their voice in different applications such as, adding an extra lawyer for security in online banking etc. The main aim of the paper is to identify the uniqueness of a voice, which should be independent of a speakers' language.
sCOs: Semi-Supervised Co-Selection by a Similarity Preserving Approach

In this paper, we focus on co-selection of instances and features in the semi-supervised learning scenario. In this context, co-selection becomes a more challenging problem as data contain labeled and unlabeled examples sampled from the same population. To carry out such semi-supervised co-selection, we propose a unified framework, called sCOs, which efficiently integrates labeled and unlabeled parts into the co-selection process. The framework is based on introducing both a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">sparse regularization term</i> and a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">similarity preserving approach</i> . It evaluates the usefulness of features and instances in order to select the most relevant ones, simultaneously. We propose two efficient algorithms that work for both convex and nonconvex functions. To the best of our knowledge, this paper offers, for the first time ever, a study utilizing nonconvex penalties for the co-selection of semi-supervised learning tasks. Experimental results on some known benchmark datasets are provided for validating sCOs and comparing it with some representative methods in the state-of-the art.
DRACO: Co-Optimizing Hardware Utilization, and Performance of DNNs on Systolic Accelerator

The number of processing elements (PEs) in a fixed-sized systolic accelerator is well matched for large and compute-bound DNNs; whereas, memory-bound DNNs suffer from PE underutilization and fail to achieve peak performance and energy efficiency. To mitigate this, specialized dataflow and/or micro-architectural techniques have been proposed. However, due to the longer development cycle and the rapid pace of evolution in the deep learning fields, these hardware-based solutions can be obsolete and ineffective in dealing with PE underutilization for state-of-the-art DNNs. In this work, we address the challenge of PE underutilization at the algorithm front and propose data reuse aware co-optimization (DRACO). This improves the PE utilization of memory-bound DNNs without any additional need for dataflow/micro-architecture modifications. Furthermore, unlike the previous co-optimization methods, DRACO not only maximizes performance and energy efficiency but also improves the predictive performance of DNNs. To the best of our knowledge, DRACO is the first work that resolves the resource underutilization challenge at the algorithm level and demonstrates a trade-off between computational efficiency, PE utilization, and predictive performance of DNN. Compared to the state-of-the-art row stationary dataflow, DRACO achieves 41.8% and 42.6% improvement in average PE utilization and inference latency (respectively) with negligible loss in predictive performance in MobileNetV1 on a 64×64 systolic array. DRACO provides seminal insights for utilization-aware DNN design methodologies that can fully leverage the computation power of systolic array-based hardware accelerators.
Smart Farming Using Internet of Thing(IoT) in Agriculture by Tangible Progarmming for Children

The internet of thing(IoT) applied in many applications such as smart industry, smart city, smart life, and Intelligent Agriculture normally the system design by expert. Which system design and programming maybe difficult for children or novices as they cannot learning and program it. This research present our vision tangible programming for children developmenting of internet of thing(IoT) in agriculture. Using tangible programming without program by computer or tablet. Which this encourages children to learn and apply concept for smart farm systems such as monitor temperature and humidity , on web online temperature, on web online humidity. We found that the children could understand idea smart farming using internet of thing(IoT) in agriculture and algorithm of programming.
An ontology-based framework for a Less Invasive Domestic Management System (LIDoMS)

Research in the fields of the Smart Home and Ambient Assisted Living has increased in the last decade. While some solutions are available to general public, there are still some concerns related to the design of smart solutions and their acceptance, especially when it comes to residents' monitoring and privacy. In this context, this paper introduces an ontology-based smart home framework, LIDoMS, aimed at focusing on inhabitants needs by providing a representation of their health conditions, while offering them customized services. End-users can interact with the system thanks to an adaptive and ubiquitous graphical interface. In this work, the ontological framework underlying LIDoMS is described; the system exploits the knowledge regarding the status of appliances and residents' location within the smart home to infer the activity they are involved in, and to provide customized adaptation of indoor comfort metrics, thus enabling a less invasive monitoring of the inhabitants. Two scenarios describe how different residents can interact with LIDoMS to personalize comfort metrics - pivotal for their health condition. Finally, a framework for the validation of LIDoMS is presented.
Comparison of Image Denoising using Traditional Filter and Deep Learning Methods

Image technology often uses in our everyday life. It is useful for many applications such as telecommunication system, automation system in self-driving vehicles, surveillance system and medical research area. However, the image can be interrupted by noise from the environment or electric signal that distorts the detail of the image. In recent years, there had been many image denoising algorithms. Generally, they can be divided into 2 types: traditional filter and deep learning method. This research presents a comparison of the image denoising algorithm using peak signal-to-noise ratio (PSNR) between traditional and deep learning methods on Gaussian noise and Salt and pepper noise condition. Moreover, this experiment also compared the PSNR value of deep learning between noise image to noise image (N2N) learning scheme and noise image to clean image (N2C) learning scheme. According to the results, deep learning method has PSNR value higher than traditional method and N2C learning scheme has PSNR value higher than N2N learning scheme.
Hand gesture recognition and voice controlled robot

This work addresses the robot controlled by hand gesture and voice control. In the technology era, the space between the physical and the digital world is brought closer by the introduction of gesture concept. For all the dangerous tasks we prefer technology rather than people. Even though these robots are being controlled manually in the early stages, these can now be controlled via voice and gestures. This technology of gesture and voice recognition can be defined by the interaction between the computer and the body language of human beings. This constructs the communication link between technology and mankind. The target of this work is to upgrade the complete security to the robot and to simplify the controlling mechanism.
A Comprehensive Overview of Fog Data Processing and Analytics for Healthcare 4.0

In recent technological era, the healthcare industry has been gaining momentum toward service-oriented facilities to the customers. The primary aim of the Healthcare 4.0 is to provide healthcare services anytime and anywhere. This is possible, as Healthcare 4.0 targets integration with current technologies like Internet of Things (IoT), Cloud Computing, Big Data, and Machine Learning. However, the integration of Healthcare 4.0, with IoT and cloud computing through Internet has several challenges in handling real-time applications such as access latency, cost, and lack of service availability. On the other hand, the fog computing (FC) is able to overcome these challenges using fog devices, that are capable of optimizing the delay in information gathering and processing. The primary advantage of the fog computing system is the geographical location of fog devices within proximity of patient and IoT healthcare systems. This enables fog computing system to perform computation, storage and networking services with lesser delay and jitter. However, the major issue in fog computing is to handle the voluminous healthcare data generate from different IoT healthcare edge systems. Hence, this chapter targets to present on various fog-based data processing and data analysis (FDPA) mechanisms in fog computing solutions toward achieving the objectives of Healthcare 4.0. This chapter is divided into five major sections namely architecture of fog data processing and analytics, applications of FDPA, data processing algorithms in fog computing and data compression mechanisms and data analysis mechanisms in Fog computing toward Healthcare 4.0. The fog data architecture discusses various layers namely sensing layer, fog gateway layer, fog-based data processing and data analysis layer, cloud layer, and service layer. Here, the process of sensing of healthcare data, maintenance of data, and various methods to analyze healthcare data are discussed. Further, the healthcare data gathered from sensor devices are raw and redundant in data, hence they are needed for various data processing algorithms for fog-based healthcare systems. Hence, various data processing techniques such as Dynamic Time Warping, Clinical Speech Processing are discussed. Next, in fog computing, the data compression techniques are required for optimizing the bandwidth consumption and energy efficiency are presented. Next, data analytics mechanism such as real-time decisive analysis, real-time control and context analysis, and real-time data analysis are presented.
Intelligent Control of the Steering for a Powered Wheelchair Using a Microcomputer

The research presented in this paper describes a new architecture for controlling powered wheelchairs. A Raspberry Pi microcomputer is considered to assist in controlling direction. A Raspberry Pi is introduced between user input switches and powered wheelchair motors to create an intelligent Human Machine Interface (HCI). An electronic circuit is designed that consists of an ultrasonic sensor array and a set of control relays. The sensors delivered information about obstructions in the surrounding environment of the wheelchair. Python programming language was used to create a program that digitized the user switches output and assessed information provided by the ultrasonic sensor array. The program was installed on a Raspberry Pi and the Raspberry Pi controlled power delivered to the motors. Tests were conducted and results showed that the new system successfully assisted a wheelchair user in avoiding obstacles. The new architecture can be used to intelligently interface any input device or sensor system to powered wheelchair.
Dual-Way Gradient Sparsification for Asynchronous Distributed Deep Learning

Distributed parallel training using computing clusters is desirable for large scale deep neural networks. One of the key challenges in distributed training is the communication cost for exchanging information, such as stochastic gradients, among training nodes. Recently, gradient sparsification techniques have been proposed to reduce the amount of data exchanged and thus alleviate the network overhead. However, most existing gradient sparsification approaches consider only synchronous parallelism and cannot be applied in asynchronous distributed training.
Factors that describe the use of digital devices in Latin American universities

Mobile digital devices are at the same time a tool for social interaction, an individual learning resource and can be a valuable contribution in the context of higher education to develop and promote new teaching and learning models. Recent studies show that both the more traditional pedagogical models of face-to-face teaching and distance teaching mediated by Virtual Learning Environments (VLE) can be enhanced by the use of these devices on and off campus. Likewise, the current context of Higher Education urges university institutions to promote a series of generic and specific competencies, where the use of these devices in a personal, academic and professional way acquires an outstanding value in the European Higher Education Area (EHEA), and represents an enrichment of university educational practice. This paper presents a study of the didactic and social use made by Hispanic American university students in 10 universities in several areas in order to establish common and divergent patterns of use so that useful conclusions can be extrapolated to improve the educational context of Higher Education in the Hispanic world.
Mapping Internal Control of Data Security Issues of BYOD Program in Indonesian Banking Sector

Nowadays Bring Your Own Devices (BYOD) has become a trend in the development of Information Technology (IT). BYOD has been used in Indonesian banking sector, in 2016 a Bank in Indonesia has been put BYOD program in their IT transformation strategic. But BYOD program also pose a threats and attacks on company data. Organizations also difficult to ensure BYOD program is complied with the enterprise security policies. Moreover, Banks in Indonesia must protect their customer data to comply with Otoritas Jasa Keuangan (OJK) regulations number POJK 1/POJK.07/2013. So that in order to reduce security incidents and ensure compliance with internal security policies, it is necessary to know which security controls are needed for the implementation of BYOD in Indonesian Banks. This paper proposed security controls needed called internal control to anticipate data security issues related to BYOD programs in the Indonesian Banking sector. Proposed internal controls are validated by using validity and reliability tests to ensure the internal control is applicable for Indonesian Banking sector. The results of this study are 20 valid internal controls for Indonesian Bank to anticipate BYODs data security issues such as malware threats, phising and social engineering, BYOD direct attacks, spoofing or data intersections, BYOD device loss, and policy violations by user.
Optimal Mechanisms Under Maximal Leakage

Side channels represent a broad class of security vulnerabilities in practical systems. Because completely eliminating side channels often leads to prohibitively high overhead, there is a need for principled techniques that trade off cost and leakage. Maximal leakage (MaxL) has been introduced as an operationally-interpretable leakage metric well-suited to side channels. We study the optimal trade-off between MaxL and expected costs. We demonstrate that for an important class of cost functions, optimal protection can be achieved using a combination of at most two deterministic schemes. We discuss the implications of this result for practical implementation and provide a fast heuristic algorithm for finding the best deterministic mechanism, which has a bounded suboptimality guarantee.
Understanding memes on Chinese social media

Abstract Memes as online graphic semiotic resources have developed into a globalized genre and a cultural form. The vernacularization of this global cultural form on Chinese social media is Biaoqing (literally, ‘facial expression’). Biaoqing is a phenomenon and a genre engendered by the development of information technology and growing accessibility to the internet. The most prominent features of Biaoqing on Chinese social media (cute, mischievous, decadent, dirty, violent) are spawned by and therefore reflect the structure of society. The ludic nature of Biaoqing enables them to serve as resources for new forms of communication, potential of reshaping existing social norms, the landscape of online culture, and culture and society at large. The results of this contribution constitute an invitation for a reimagination of the role of graphic semiotic signs and digital infrastructures in society, and a rethinking of theories for sociolinguistic research in a digital era.
Embedded System for Precision Positioning, Detection, and Avoidance (PODA) for Small UAS

This article described the implementation and development of positioning, detection, and avoid (PODA), an embedded electronic platform, installed on-board a UAV, designed for precision positioning. The experimental campaigns showed that the data fusion LIDAR/ADXL345 could assist a UAV during navigation, as well as in critical conditions, such as low visibility (i.e., fog, high concentration of smoke in case of fire). Further work will be related to PODA performance enhancement, in particular: Accurate accelerometer calibration with a least square method; Obstacle detection; - Additional sensors installed, such as inertial measurement units and time-of-flight sensors; Multisensor data fusion techniques and fault detection analysis; and Experimental sessions with faster data acquisition rates (i.e., sampling at 50 Hz) and simulation of different landing conditions (with and without obstacles in landing area).
Enhanced Unsupervised Change Detector for Industrial Surveillance Systems

Detecting an intruder that is trespassing a prohibited area is a critical task of intelligent surveillance systems. This task requires a change detector to segment an intruder (foreground object) from the background. The task suffers the inherent drawbacks of change detectors due to the dual-camera sensor (color/IR), illumination changes, night time, static, and camouflaged foreground objects. This article proposes an enhanced unsupervised change detector to compensate for the aforementioned challenges for industrial sterile zone monitoring. The camera switch detection based on skewness patterns detects a switch between the dual-camera sensors (color/IR). The optimal color space selection based on the mean squared error will select tolerant color space (RGB/YCbCr) to illumination changes for modeling the background. In addition, the IR camera frames are contrast-enhanced to tackle the camouflaged intruders during the night. The incoming frames are split into respective channels before modeling the background. The background is modeled by Gaussian mixture models. The adaptive background model update scheme is proposed to tackle the various challenges posed by environment and object such as a static foreground object. The comparison is performed on three databases with top-ranked unsupervised change detection algorithms.
Bi-Directional Timing-Power Optimisation on Heterogeneous Multi-Core Architectures

Optimisation of timing performance and power consumption on heterogeneous multi-core architectures is gaining increasing attention. Systems and devices may have varying demands on timing and power, which motivates more flexible optimisation. Along this line, we consider a heterogeneous computing architecture with multiple cores, where each core runs a mixed stream of general and dedicated tasks with a certain scheduling strategy. Employing the queuing model, we first propose a load balancing algorithm, which minimises the average response time of the general tasks whilst guaranteeing the timing requirements of the dedicated tasks. Built upon the above, we propose a bi-directional optimisation algorithm that is able to improve the timing performance under the constraint of power consumption, and reduces the power consumption for the given timing requirement. Extensive numerical experiments illustrate the significance of the proposed algorithms. Implementation on a real platform validates the consistency between the theoretical analysis and the practical results.
Sustainable Building Materials

Abstract This research is conducted to inform that a sustainable material is easy to be made and can be useful for the community. This material is also environmentally friendly. Observation, precedent, and literature study were used as the research method. The results indicate that the use of sustainable materials can be used optimally such as bamboo, recycled plastic waste, and others. As a conclusion, we can find out how important sustainable materials are and the benefit of a community self-provided material.
Adaptive Segmentation and Sequence Learning of human activities from skeleton data

Discovering underlying patterns for predicting future actions from spatio-temporal human activity information is a fundamental component of research related to the development of expert systems in human activity recognition and assistive robotics. Current research focuses on classification or learning representations of activities for various applications. However, not much attention is given to the pattern discovery of activities which have a major role in the prediction of unseen actions. This paper proposes a novel Adaptive Segmentation and Sequence Learning (ASSL) framework which aims at segmenting unlabelled observations of human activities from extracted 3D joint information. Learning from these obtained segments provides information about the underlying patterns of activity sequences needed in predicting subsequent actions. In the proposed method, the temporal accumulated motion energy of body parts in an activity is utilised in the segmentation process to obtain key actions from unlabelled activity sequences since body parts show changes in acceleration and deceleration during an activity. Based on the segments obtained, the temporal sequence of transitions across activity segments are learned by employing a Long Short-Term Memory Recurrent Neural Network. This ASSL technique has been evaluated using both an experimental human activity dataset and a public activity dataset, and achieved a better performance when compared with other techniques including an Auto-regressive Integrated Moving Average, Support Vector Regression and Gaussian Mixture Regression Models in learning to predict patterns of activity sequences.