Reviewerly/exHarmony huggingface dataset:
Keeping Connected When the Mobile Social Network Goes Offline

WiFi Direct is an embedded technology in a vast majority of smartphone devices running the Android operating system. As a result, it represents a promising technology that can be exploited in re-establishing connectivity among user devices in case of cellular network outages. A technique that smart devices can use to restore connectivity in situations where they are unable to connect to a cellular tower or access point, but close enough to support device-to-device communication is presented. The proposed technique envisions a combination of security layers that ensure authentication, confidentiality, and integrity of communications among end users. Each device is issued a certificate by a central authentication entity at sign up and when it is unable to connect to the server component, it will attempt to form a group with nearby devices in the same situation over WiFi Direct. Once a WiFi Direct group has been formed, the group owner will temporarily assume the role of the server, and each group member and the group owner will verify each others identity and connect using mutual Transport Layer Security (mTLS), facilitating secure communication. The approach is validated through the implementation of a mobile social application involving several mobile devices, and overheads due to the additional security features are investigated.
Standard Bayesian Network Models for Spatial Time Series Prediction

Bayesian networks (BNs) are one of the key computational models in traditional AI and machine learning paradigm. These are also considered to belong to the probabilistic reasoning family of computational intelligence that forms the soft part of modern AI. In this chapter, we provide a preliminary idea on standard/classical Bayesian network, followed by its parameter learning and inference generation mechanism. We also cover the basic concepts of various categories of Bayesian networks , including dynamic Bayesian network, fuzzy Bayesian network, spatial Bayesian network, semantic Bayesian network etc. Further, we discuss on the potentials of BN in modeling the inter-variable dependencies while analyzing spatio-temporal data.
Optimization of the Numeric and Categorical Attribute Weights in KAMILA Mixed Data Clustering Algorithm

The mixed data clustering algorithms have been timidly emerging since the end of the last century. One of the last algorithms proposed for this data-type has been KAMILA (KAy-means for MIxed LArge data) algorithm. While the KAMILA has outperformed the previous mixed data algorithms results, it has some gaps. Among them is the definition of numerical and categorical variable weights, which is a user-defined parameter or, by default, equal to one for all features. Hence, we propose an optimization algorithm called Biased Random-Key Genetic Algorithm for Features Weighting (BRKGAFW) to accomplish the weighting of the numerical and categorical variables in the KAMILA algorithm. The experiment relied on six real-world mixed data sets and two baseline algorithms to perform the comparison, which are the KAMILA with default weight definition, and the KAMILA with weight definition done by the traditional genetic algorithm. The results have revealed the proposed algorithm overperformed the baseline algorithms results in all data sets.
Study on degraded image sharpening method

In industrial practice, images are often collected for study. In bad weather, the images collected may be degraded to different extents. Therefore, it is necessary to study how to sharpen these degraded images. Presently, domestic and foreign scholars mostly realise sharpening of degraded images by improving and optimising the existing classic algorithms based on image enhancement techniques and image restoration techniques. Meanwhile, subjective and objective evaluation methods for image sharpening effects have also been proposed. The article presents an image enhancement algorithm with adjustable threshold and tuning parameter and verifies this algorithm through experiment by subjective and objective evaluation methods, proposing a new solution for degraded image sharpening.
Hierarchical interpolation point anonymity for trajectory privacy protection

The traditional trajectory privacy protection algorithm approaches the task as a single-layer problem. Taking a perspective in harmony with an approach more characteristic of human thinking, in which complex problems are solved hierarchically, we propose a two-level hierarchical granularity model f or this problem. The first level of the proposed model is a coarse-grained layer, in which the original dataset is divided into groups. The second level is a fine-grained layer, where problems are solved in each group instead of on the original dataset, which reduces complexity and computation while improving efficiency. On the basis of this hierarchical model, we propose the interpolation trajectory-anonymous privacy protection algorithm with temporal and spatial granularity constraints. In addition, we propose interpolation-based modified Hausdorff distance on adjacent segment (IMHD_AS), which provides a smaller clustering area and better data utility than the traditional Euclidean distance, as the trajectory similarity criterion for clustering within each group. Further, we theoretically prove that the proposed algorithm outperforms the traditional algorithm in terms of data distortion and anonymity cost and verify its efficacy experimentally. Compared with the classic anonymity algorithm, the maximum information loss and the anonymity cost are reduced by up to 21.04% and 28.32%, respectively.
Wireless Speakers using WiFi and IoT

With the current technological advancements of the new age, connectivity is a major topic. Ranging from phones to other gadgets, wireless connectivity has become an essential component in this new era. Wireless audio systems are a useful tool but come with certain drawbacks. Most wireless speakers or headsets have connectivity via Bluetooth which enables only one device at a time and has a limited range. We have generated an idea to attach multiple types of devices that support audio output. We achieve this goal with the help of WiFi (Wireless Fidelity) system which covers a longer range and can handle data transfers in multiple devices without any delay. Further we have enhanced our system by adding IoT (Internet of things) which eliminates the range factor completely and also increases the speed.
Informatics Education in School: A Multi-Year Large-Scale Study on Female Participation and Teachers’ Beliefs

This paper describes the outcomes of a multi-year large-scale study on Informatics education in school, involving an average of 3,600 teachers per school year of all school levels. The study has been conducted in Italy, where - generally speaking - there is no compulsory informatics education in school. Teachers have voluntarily enrolled in the “Programma il Futuro” project, running since 2014, and have taught short introductory courses in Informatics. Answering - anonymously - to monitoring questionnaires, they have indicated whether girls or boys were more interested in Informatics activities and whether girls or boys were more effective. Answers show that the difference between the number of teachers thinking boys are more interested (or more effective) and the number of those judging girls more interested (or more effective) has constantly decreased over school years during the project. This variation in teachers’ beliefs over school years - that we attribute to their involvement in project activities - is important, since teachers’ beliefs are known to influence students’ motivations, hence their future choices. Our opinion is reinforced by the results of a differential analysis, in each school year, between teachers repeating activities and those executing them for the first time. Moreover, the analysis of disaggregated data shows that the difference between boys and girls relative to interest or effectiveness increases going up in school level. Our results provide an empirical support to the belief that it is important to start Informatics education early in school, before gender stereotypes consolidate.
Challenges in science technology engineering and math (STEM) learning in elementary schools based on literacy of social science

Learning based on Science Technology Engineering and Mathematics (STEM) strongly supports the current era of educational development. One of the components that support the success of STEM learning is the ability of students literacy in the field of science. This study aims to reveal the proficiency profile of science literacy that supports the achievement of the STEM. The method used is qualitative participatory where the participant is the students of grade 6 primary school in Bandung as much as 40 students. The instruments used are scientific literacy and triangulation literature by using technical interviews. The results of the study showed that: (1) science literacy skills supporting STEM varied by sex an average grade of achievement in the previous class, (2) the majority of the students still had low STEM literacy skills, and (3) students rely on compulsory textbooks as a source of scientific literacy as well as STEM. This study concludes that the skills of science and STEM literacy in basic school students in Bandung are still low. It is necessary to study and apply the appropriate STEM learning strategies based on authentic children's data and social literacy.
Beyond the Limits: SHA-3 in Just 49 Slices

The National Institute of Standards and Technology (NIST) chose in 2012 the winner of the third competition to decide a new hashing standard, establishing the new Secure Hash Algorithm-3 (SHA-3). Multiple FPGA implementations were published during the standardization process to compare performances, but also after the official announcement, improving and optimizing the state of the art further. In this work, we present new implementations to improve several different aspects from previous works. We introduce a new row-oriented architecture, never used before in the "race" for the most lightweight SHA-3 FPGA implementation. Featuring this new row-based processing, we present two architectures with different area-frequency trade-offs?. We use FPGA resources never considered before in the literature, and discuss the limitations they present. In addition to this, we present a pipelined implementation aiming for the best compromise between area and throughput. As a result, our lightweight implementation uses 44% fewer resources than the current state of the art. Moreover, our efficiency-tailored implementation is 70% more efficient than the previous best work on the Virtex-6 platform. In essence, in this work, we present the smallest and the most efficient SHA-3 FPGA tailored implementations to date.
Deep Learning in Speaker Recognition

It is supposed in Speaker Recognition (SR) that everyone has a unique voice which could be used as an identity rather than or in addition to other identities such as fingerprint, face, or iris. Even though steps have been taken long ago to apply neural networks in SR, recent advances in computing hardware, new deep learning (DL) architectures and training methods, and access to a large amount of training data have inspired the research community to make use of DL as in a large variety of other signal processing applications. In this chapter, the traditional principle techniques in SR are first briefly reviewed and the potential signal processing aspects of these techniques which can be improved by DL are addressed. Then the recent most successful DL architectures used in SR are introduced and some illustrative experiments from the authors are included.
Study Cases on Fast Compression Distance Based Data Visualization

In this paper, we develop a visualization tool to enhance the understanding of up to big data sets. Compared to classic data models which rely on the computing of the features (color, texture, etc.), this tool is fully feature free, as it processes directly on the data file. The Fast Compression Distance (FCD) and t-distributed Stochastic Neighbor Embedding (t-SNE) have been applied to visualize a large TerraSAR-X dataset which are annotated with up to three layers of hierarchical semantic labels, and a Sentinel-1 dataset with 10 annotated classes, in VV and VH polarization modes. We analyze the visualization results in manifold space, and try to understand and interpret them with the available semantic labels. The visualization interpretation is based on a vega-style interactive tool, which allows user zoom in, zoom out for processing large amount of data points.
Optimization design of automatic filing system of financial management information under the background of information technology development

The collection stability of traditional systems is low, it is not easy to delete interference data, the access speed is slow, and the error cannot be effectively detected. For this reason, this paper designs a new automatic filing optimization system of financial management information under the ba ckground of information technology development. From four aspects of data dynamic collection, data de-duplication, data access control and RAID error detection scheme improvement, the traditional system is optimized. The financial management information is collected through a data collection method that ensures high efficiency and high stability. In the process of accessing, the semantic ontology is formed, and the access group in the time domain is classified by the support vector machine method to improve the access speed. The repetitive data model is established, and the deduplication processing is realized by the fractional Fourier transform technique combined with the post-processing result of fourth-order cumulant. The limited set of HDD error detection is reset to help with long-term filing of RAID system data. The result is high filing speed and good filing effect, and the conclusion that the design system is highly practical is obtained.
Image Enhancement of Shadow Region Based on Polarization Imaging

Object of shadow area suffers from low contrast and noise, causing difficulties for object detection and recognition. However, conventional image enhancement methods perform inefficiently in severer shadow conditions, where the contrast enhancement is not obvious, or the noise amplification is huge. In fact, Polarization information can be preserved even in very heavy shadows. Combining the polarization information with the intensity and chromaticity information of the original image can overcome the above problems. This paper proposes a method of object enhancement in shadow based on polarization imaging. Firstly, this paper puts forward degree of polarization (DOP) Invariance Prior and DOP-Chromatic Consistency Prior. Then, based on these two priors, a feedback image enhancement method based on polarization imaging is proposed. Experiments show that the proposed method is superior to the other conventional image enhancement methods.
A Feature-based Video Transmission Framework for Visual IoT in Fog Computing Systems

The rapid development of the internet of things (IoT)promotes research in smart city and Fog computing. The vast volume of real-time visual data produced from the tremendous end devices in IoT is a big challenge for the network to transmit and for the data center to store. The typical case is the huge volume of visual data produced by the surveillance cameras in a smart city. In this paper, we consider the problem of how to allocate the calculation ability of the Fog node to handle the surveillance data to obtain low delay meanwhile maintain the video quality. To solve this challenge, we attempt to reduce the tremendous video data using deep learning models in the computational Fog node and optimize the transmission function for high efficiency. To reduce data, we extract video feature and keep salient zones with high resolution meanwhile leave the unavoidable distortion in less important areas. To obtain the least transmission delay under the dynamic bandwidth in Fog computing, we model the transmission delay function and solve it by Lagrangian dual decomposition. We make experiments on public dataset Cityscapes and 4G/LTE Bandwidth Log to evaluate our method. The experiment results show that our feature-based image processing method obtains around 68.7% higher average SSIM (structural similarity index)than the traditional HEVC in the salient zones, and our solution reduces the system delay by 71.02 % comparing with the plain transmission method. It proves our solution reduces the video transmission latency meanwhile keeps the SSIM of salient areas in the video.
Classification Performance Evaluation of Deep Learning Architectures for Complex Object Based Facility Recognition

Nuclear proliferation monitoring using remote sensing imagery is an essential area of research from a national security perspective. Studies have shown that traditional per-pixel and small patch based classification schemes prove to be insufficient in detecting complex facilities such as coal, gas and nuclear. In this paper, we evaluate the state of the art deep learning architectures for detecting the aforementioned complex facilities. We use the principles of transfer learning to fine-tune the state of the art deep learning architectures using models pre-trained on 1000 classes of Imagenet data. We evaluated the classification performance of these models on a benchmark imagery database consisting of two hundred complex facilities collected over the United States.
POSTER: Space and Time Optimal DNN Primitive Selection with Integer Linear Programming

Convolutional neural networks (CNNs) are used in many applications, from industrial robotics to biometric identification on mobile devices. But they can be too resource-hungry for mobile and embedded devices with tightly constrained memory and energy budgets. We propose an ahead-of-time primitive selection for CNNs, based on integer linear programming (ILP). Under a tight memory budget, our ILP solver selects the optimal primitive for each layer such that the entire network is optimized for execution time subject to a memory budget, or vice versa. Our method yields significant speedup and memory reduction compared to existing methods.
Optimum Parallelism in Spark Framework on Hadoop YARN for Maximum Cluster Resource Utilization

Spark is widely used as a distributed computing framework for in-memory parallel processing. It implements distributed computing by splitting the jobs into tasks and deploying them on executors on the nodes of a cluster. Executors are JVMs with dedicated allocation of CPU cores and memory. The number of tasks depends on the partitions of input data. Depending on the number of CPU cores allocated to executors, one or more cores get allocated to one task. Tasks run as independent threads on executors hosted on JVMs dedicated exclusively to the executor. One or more executors are deployed on the nodes of the cluster depending on the resource availability. The performance advantage provided by distributed computing on Spark framework depends on the level of parallelism configured at 3 levels, namely node level, executor level, and task level. The parallelism at each of these levels should be configured to fully utilize the available computing resources. This paper recommends optimum parallelism configuration for Apache Spark framework deployed on Hadoop YARN cluster. The recommendations are based on the results of the experiments conducted to evaluate the dependency of parallelism at each of these levels on the performance of Spark applications. For the purpose of the evaluation, a CPU-intensive job and an I/O-intensive job are used. The performance is measured by varying the parallelism at each of the 3 levels. The results presented in this paper help Spark users in selecting optimum parallelism at each of these levels for achieving maximum performance for Spark jobs by maximum resource utilization.
An Internal Folded Hardware-Efficient Architecture for Lifting-Based Multi-Level 2-D 9/7 DWT

In this paper, a novel internal folded hardware-efficient architecture of multi-level 2-D 9/7 discrete wavelet transform (DWT) is proposed. For multi-level DWT, the unfolded structure is more extensively used compared with the folded structure, because of its low memory consumption and low time delay. However, a set of input data valid every few clock cycles caused the mismatch between clock and data in the unfolded structure. The mismatch usually needs to be solved by multi-clock or complex data adjustment, which increases the consumption of hardware resources and the complexity of the overall system. To solve the above problem of the unfolded structure, we adjust the data input timing by using a single clock domain and folding the DWT architecture of different levels in varying degrees, according to their own clock-to-data ratios. For an image of size of N × N pixels and 3-level DWT, the proposed architecture requires only 6N words temporal memory. For 3-level DWT with an image of size 512 × 512 pixels, the hardware estimation and comparison of the existing architectures show that, the hardware estimation result shows at least 30.6% area-delay-product (ADP) decrease, and at least 22.4% transistor-delay-product (TDP) decrease for S = 8, and 25.77% transistor-delay-product (TDP) decrease for S = 16.
A Comprehensive Review Towards Appropriate Feature Selection for Moving Object Detection Using Aerial Images

Efficient feature extraction for moving object using aerial images is still an unsolved issue in computer vision, image processing and pattern recognition research domains. Aerial types of images contain various environmental constraints due to capture frames from various altitudes level, i.e. illumination, shadows, occlusion. For this reason, appropriate feature selection for those types of images needs more attention by the researchers to improve detection rate with fast and computationally less complex features extraction method. This research performed comprehensive review with critical analysis for using various features with various methods for moving object detection using aerial images. In this context, three aspects for critical analysis of using various features are identified followed by challenges of using various features. After that, existing methods with advantages and barriers are comprehensively described with various constraints claimed by the previous research. Next, justification for the need of new feature selection is elaborated for optimum detection performance. Later, adequate validation matrics are illustrated to evaluate various features based moving object detection using aerial images performed in the previous research. The overall review performed in this paper have been comprehensively studied and expected to contribute significantly in computer vision, image processing pattern recognition research field.
Digital application in teaching musical traditional instrument for children

Abstract The lack of learning media in the art of music in elementary schools is a barrier for teachers to teach the art of music. When learning music, students are often confused because they do not know the shape and sound of the instrument. This paper aims to analyze the use of Saron Digitals as one of the media solutions for learning music that can be used by teachers to be taught in elementary schools. This research method is carried out through the following stages: (1) introducing the Saron Digital application; (2) introducing the Saron Digital application feature; (3) use of Saron Digital applications; (4) study Saron using the Saron Digital application; (5) evaluating the learning process. The research subjects were grade 3 students in one of the elementary schools. The research subjects were 15 students. The results showed that using students from the Saron Digital application that did not know became aware of the shape and sound of Saron musical instruments. It can be concluded that technology can facilitate learning, teachers do not need to worry about the lack of learning media for the art of music.