Reviewerly/exHarmony huggingface dataset:
Double Shot: Preserve and Erase Based Class Attention Networks for Weakly Supervised Localization (Peca-Net)

Weakly supervised localization has attracted increasing attention since only image-wise labels are needed. One mainstream approach, CAM based top-down localization method, suffers from poor resolution and localizing only the most discriminative regions. Another kind, model agnostic perturbation based method, suffers from multiple iterations for each sample. In this paper, we introduce PECA-Net: Preserve and Erase Based Class Attention Networks, which adopts preserve and erase perturbed U-net as the basis, with class activation mechanism as attention to enhance localization capability. Class attention module strengthens informative features and achieves a basic localization. Preserve and erase perturbed U-net replaces the random and iterative extrinsic perturbation with meaningful erasing. In addition, this structure refines the preliminary localization. Since the target object is hit twice, therefore, entitled as double shot. Experiments validate that localization error of both CUB-200 and ILSVRC ImageNet dataset is the new state-of-the-art.
Nadine-Bot: An Open Domain Migrant Integration Administrative Agent

This paper proposes a conversational agent - chatbot framework that adopts a two-step text similarity approach in order to retrieve relevant answers from large knowledge bases. The proposed "NADINE-bot" is an online question-answer (QA) system able to respond to asylum seekers and other vulnerable migrants’ categories about EU related administrative questions, in their native language. The developed agent combines several state-of-the-art models in order to initially retrieve the most relevant document and then, by splitting it into paragraphs, it seeks for an answer in the most relevant paragraph. The NADINE-bot working knowledge source is a collection of administrative tasks, related frequently asked questions (FAQs) concerning EU countries of reception. Nonetheless, for testing purposes the methodology has been applied it to a vast collection of Wikipedia articles, displaying satisfactory results.
Utilization of Resnet in RGB-D Facial Recognition Problems

Resnet, from its emergence, has always been a state-of-the-art model for facial recognition problems. The 2019 Bench Council posted several challenges, including an International 3D Face Recognition Algorithm Challenge, which aims at soliciting new approaches to advance the state-of-the-art in face recognition. We focus on utilizing a 4-channeled Resnet on this new problem and achieve 90% validation set accuracy resulting in second prize on the Bench-19 International Artificial Intelligence System Challenges.
Hiding Secret Image in Absolute Moment Block Truncation Code by using a Block-Selection Scheme

Absolute moment block truncation coding(AMBTC) is a simple and efficient image compression method. While we try to hide secret data in the absolute moment block truncation code, the PSNR of the stego image is decreased significantly. This problem arises from the inherent characteristic of the AMBTC method. In the compressed code, the code 1 represents the high-mean value while the code 0 represents the low-mean value. When we switch a 1 to 0, a high-mean value in the decompressed image is replaced by a low-mean value. The high-mean value and the low-mean value are varying from block to block. Some switches may result in severe gray level changes in the decompressed image and thus greatly reduce the PSNR value. In this paper, we propose a novel method to hide a secret image in an absolute moment block truncation coded image. The differences of the high-mean and the low-mean in all coding blocks are calculated first. Then, the statistical distribution of the differences is analyzed. According to the size of the secret image, we truncate the difference value at a proper threshold. Finally, the secret image is hidden in the low-difference blocks of the AMBTC image. In order to get a better hiding capacity, the secret image is quantized into a binary dithering image in advance. In our experiment, a cover image of size 512×512 could be applied to hide a secret image of size 256×256 with a very slight degradation of PSNR.
A Heat-Recirculation-Aware VM Placement Strategy for Data Centers

Data centers consisted of a great number of IT devices (e.g., servers, switches and etc.) which generates a massive amount of heat emission. Due to the special arrangement of racks in the data center, heat-recirculation often occurs between nodes. It can cause a sharp rise in temperature of the equipment coupled with local hot spots in data centers. Existing VM placement strategies can minimize energy consumption of data centers by optimizing resource allocation in terms of multiple physical resources (e.g., memory, bandwidth, cpu and etc.). However, existing strategies ignore the role of heat-recirculation in the data center. To address this problem, in this study, we propose a heat-recirculation-aware VM placement strategy and design a Simulated Annealing Based Algorithm (SABA) to lower the energy consumption of data centers. Different from the existing SA algorithm, SABA optimize the distribution of the initial solution and the way of iteration. We quantitatively evaluate SABA’s performance in terms of algorithm efficiency, the activated servers and the energy saving against with XINT-GA algorithm (Thermal-aware task scheduling Strategy), FCFS (First-Come First-Served), and SA. Experimental results indicate that our heat-recirculation-aware VM placement strategy provides a powerful solution for improving energy efficiency of data centers.
THE TEACHING AND LEARNING OF WRITING IN INDONESIAN: A CASE STUDY OF A PRIMARY SCHOOL

This case study is about the teaching of writing in an Indonesian state primary school, that is, in Kupang, East Nusa Tenggara Province. It aims at finding out: 1) how writing in Indonesian is taught in the research school; 2) how competent the students are in writing in Indonesian as the results of their teachers’ teaching of writing in Indonesian; 3) proper teaching teachniques done in order to better the athmosphere of the teaching and learning of writing in the research school. The method used was a case study with the school as a research object and the teachers teaching Indonesian writing in the school and their students from grade I to grade VI learning to write in Indonesian as reseach subjects. This study was done from July to November 2017. The data collected through interviews, observations, and documentary studies were analyzed descriptively. It was found that the teaching and learning of writing in the school was traditionally done, that is, it was not to improve students’ writing in Indonesian, but to improve other skills like reading, speaking, and listening. Despite such a traditional method in the teaching and learning of writing in Indonesian, it was found that the students could write generally well in Indonesian. The teachers in the school believe that they need to do some changes in order to ensure that their students can write well in Indonesian like: “joining more courses/trainings on the teaching and learning of writing for publication;” continuing their formal study/degree; providing more teaching facilities so that it is much easier for them to teach writing, to revise, to edit, and to publish their students’ writings; and, working hand in hand with fellow teachers to improve their students’ writing competence.
Real-time mosaicking for infrared videos from an oblique sweeping camera

Image mosaicking is widely used in Geographic Information Systems (GISs) for large-scale ground surface analysis. However, most existing mosaicking methods can only be used in offline processing due to the enormous amounts of computation. In this paper, we propose a novel and practical algorithm for real-time infrared video mosaicking. To achieve this, a novel fast template matching algorithm based on Sum of Cosine Differences (SCD) is proposed to coarsely match the sequential images. The high speed of the proposed template matching algorithm is obtained by computing correlation with Fast Fourier Transform (FFT). We also propose a novel fast Least Squares Matching (LSM) algorithm for inter-frame fine registration, which can significantly reduce the computation without degrading the matching accuracy. In addition, the proposed fast LSM can effectively adapt for noise degradation and geometric distortion. Based on the proposed fast template matching algorithm and fine registration algorithm, we develop a practical real-time mosaicking approach which can produce seamless mosaic image highly efficiently. Experiments on synthetic and real-world datasets demonstrate that the proposed algorithm is not just computationally efficient but also robust against various noise distortions.
FPGA Design of Elliptic Curve Cryptosystem (ECC) for Isomorphic Transformation and EC ElGamal Encryption

Attacking or tampering with sensitive data continues to increase risks to economic processes or human activities. These risks are significant key factors to improve the development and implementation of security systems. Therefore, improving cryptography is essentially needed to enhance the security of critical data. For example, elliptic curve cryptography (ECC) over the Galois field GF(2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">163</sup> ) is one of the public-key (asymmetric) cryptographic techniques, in which demands mapping a message (163-bit) to a point in the prime subgroup of the elliptic curve. To the best of our knowledge, mapping methods are not yet available on Field-Programmable Gate Arrays (FPGAs). Also, asymmetric encryption schemes often do not consider encrypting/decrypting data packets because of their computation complexity and performance limitations. In this letter, we propose and develop a concurrent reconfigurable cryptosystem to encrypt and decrypt stream of data using ECC on FPGA. First, we present hardware design and implementation to map a plain message on the elliptic curve based on isomorphic transformation, then second, we architect the elliptic curve ElGamal public-key encryption method by using point addition and multiplication on Koblitz elliptic curve on FPGA. Our proposed cryptosystem is synthesized and implemented on Intel Cyclone 10 GX and Xilinx Kintex-7 FPGAs to evaluate throughput, and it achieves 25.73-57.1 Mbps.
Ego-Posture Estimation for a Pedestrian Using a Monocular Wearable Camera

In recent years, the increase of aging population in developed countries has become a significant social problem. There are not a few accidents caused by their low walking ability. The improvement of the walking ability for the elderly and post recovery patients is the main concern of this paper. For this purpose, it is necessary to analyze and estimate their walking posture. In this paper, we propose a posture analysis and estimation method and a gait index acquisition method using a monocular wearable camera. The idea of this paper is to employ an ego-camera mounted on a user. His/her walking posture is analyzed by the frontal view video provided from the camera. This idea intends that the proposed system will be used on a daily basis in the future by those who want to improve their walking posture to keep a healthy life.
Video summarisation with visual and semantic cues

Video summarisation greatly improves the efficiency of people browsing videos and saves storage space. A good video summary should satisfy human visual interestingness and preserve the theme of the original video at the semantic level. Unlike many existing methods that consider only visual features to generate video summaries, this study proposes a method that combines visual and semantic cues to extract important information for dynamic video summarisation. The authors propose visual-verbal saliency consistency to add semantic information and propose a novel attention motion, along with other visual features to fully represent visual interestingness. Based on the importance score of each frame calculated by combining these features, they select an optimal subset of segments to generate an important and interesting summary. They evaluate their method using the SumMe and TVSum datasets and experimental results show that their method generates high-quality video summaries.
GeoSharding—A Machine Learning-Based Sharding Protocol

Ruparel, Hardik Chiplunkar, Shreyashree Shah, Shalin Goradia, Madhav Shirole, MaheshSharding is one of the most prominent concepts which involves the division of the network into shards for concurrent processing of transactions. Different sharding protocols are being implemented in blockchains to enhance its scalability. The existing blockchain systems create shards using proof-of-work consensus protocol. This research aims at developing a machine learning-based sharding process that uses the nodes’ geographical locations—latitudes and longitudes. IP addresses of the nodes are mapped to geographical coordinates, and these coordinates are then divided into shards using a suitable clustering algorithm. The nodes in the shards are geographically closer, thereby reducing the propagation delay in the network during intra-shard communication. GeoSharding has been tested to be significantly faster as compared to PoW-based sharding. This optimizes the network sharding process, thus escalating the scalability to a new level.
Large utility scale PV power plant optimum site selection in Iran using GIS analysis

Iran is ranked 20 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">th</sup> in the world in terms of land area and also enjoys excellent solar irradiation throughout its land area. Although at first glance site selection for large utility scale PV power plant in Iran seems a trivial task only limited by closeness to electrical grid, roads, points of electrical consumption, site meteorological data, etc. further examination reveals that parameters such that slope of the site, direction of the slope, site altitude, large scale sand and dust storms, local sand and dust storms, etc. have a direct influence on the performance of the PV power plant as well as the CAPEX and OPEX of the power plant. This papers aims to emphasize the parameters that are usually ignored in the process of site selection of large utility scale PV power plants. For the purpose of this paper the capacity ≤ 100 MW (AC) in a single site is classified as small utility scale PV power plant and a PV power plant with capacity > 100 MW (AC) in a single site is classified as large utility scale PV power plant. Residential and commercial PV systems are not considered in the current paper irrespective of their installed capacities.
Outside-in Electromagnetic Tracking Method for Augmented and Virtual Reality 6-Degree of Freedom Head-Mounted Displays

Tracking of position and orientation in 3D space termed as 6-Degree of Freedom, is critical for head-mounted displays to experience immersive content in augmented reality and virtual reality applications. Today's outside-in tracking system commonly uses a light-based tracking method which is very accurate but has limitations of occlusion and line of sight dependency, causing loss of track and bad user experience. In this research work, we present a novel approach of an outside-in tracking system for augmented reality and virtual reality devices using electromagnetism which provides occlusion free and line of sight independent 6-degree of freedom tracking. The tracking method consists of a central coil wound in three orthogonal directions and a set of orthogonally wound receiver coil embedded in the head-mounted display. We propose a very novel closed-form and computationally in-expensive Beyond Line-of-Sight (PLoS) tracking approach to locate the position as well as the orientation angle of the head-mounted display (HMD) concerning the central magnetic coil. Our approach is independent of line-of-sight so the central coil unit can track the HMD in all directions with physical obstruction between the path of central unit and HMD. Our study demonstrates that this electromagnetism based approach can be used to track augmented reality and virtual reality HMD within the field range of the central magnetic coil. We have tested up to 100 cm (1 meter) range with an accuracy up to ± 1cm considering light-based outside-in tracking as ground truth.
Powerstrip

The proliferation of smart outlets and meters with submetering capabilities has led to an explosion in the availability of device-level energy data. The increasing volume of current and historical data presents a storage and distribution challenge, particularly for utilities and large-scale energy datasets. To address these challenges, we present Powerstrip, a fast, effective, and nearly-lossless compression algorithm for integer energy data. Powerstrip is optimized for device-level measurements and exploits common characteristics of real-world energy consumption to achieve typical compression rates over 90% on such data. We evaluate Powerstrip on real-world energy data and compare against multiple state-of-the-art compression algorithms. Our experiments show that when compared to the best reference algorithms, Powerstrip achieves the highest compression ratios (by up to 35%) as well as the fastest speeds (by up to 70%). We also present case studies demonstrating the potential of Powerstrip for large-scale energy data storage and distribution.
Superpixel Image Segmentation Based on Improved K-means

Abstract Superpixel algorithm is the pretreatment of the image processing steps, in the processing of computer vision, precision of the superpixel algorithm is very important. In order to improve the accuracy of superpixel segmentation, an improved k-means algorithm for superpixel segmentation is proposed. This algorithm uses the improved k-means algorithm to cluster the images. By changing the calculation method of distance, it improves the measurement of similarity, improves the accuracy of segmentation, and improves the quality of segmentation results. Experimental data show that this algorithm performs better than the traditional algorithm and improves the segmentation quality and visual effect.
Smart Contracts: Application Scenarios for Deductive Program Verification

Smart contracts are programs that run on a distributed ledger platform. They usually manage resources representing valuable assets. Moreover, their source code is visible to potential attackers, they are distributed, and bugs are hard to fix. Thus, they are susceptible to attacks exploiting programming errors. Their vulnerability makes a rigorous formal analysis of the functional correctness of smart contracts highly desirable. In this short paper, we show that the architecture of smart contract platforms offers a computation model for smart contracts that yields itself naturally to deductive program verification. We discuss different classes of correctness properties of distributed ledger applications, and show that design-by-contract verification tools are suitable to prove these properties. We present experiments where we apply the KeY verification tool to smart contracts in the Hyperledger Fabric framework which are implemented in Java and specified using the Java Modeling Language.
Sweeping for Leakage in Masked Circuit Layouts

Masking schemes are the most popular countermeasure against side-channel analysis. They theoretically decorrelate information leaked through inherent physical channels from the key-dependent intermediate values that occur during computation. Their provable security is devised under models that abstract complex physical phenomena of the underlying hardware. In this work, we investigate the impact of the physical layout to the side-channel security of masking schemes. For this we propose a model for co-simulation of the analog power distribution network with the digital logic core. Our study considers the drive of the power supply buffers, as well as parasitic resistors, inductors and capacitors. We quantify our findings using Test Vector Leakage Assessment by relative comparison to the parasitic-free model. Thus we provide a deeper insight into the potential layout sources of leakage and their magnitude.
Soft Biometrics and Its Implementation in Keystroke Dynamics

Abstract Biometrics is a unique art that exists within a person and allows it to be used to differentiate between one another. These biometrics can be divided into two categories namely behaviour and physical such as face, fingerprint, hand, voice and gait. There are previous studies which examine the personal’s characteristic or personality as gender, age, cultural, weight, height, colour of hair etc. This personal’s characteristic or personality also known as soft biometric. Several previous studies have shown that the use of soft biometric element (one or combination of elements) in the process of identifying individuals can improve the performance of individual recognition. This paper will elaborate and concludes past studies related to the use of soft biometric elements in KD. Several soft biometric elements applied in various methods of recognition on previous studies have been listed and the results of the studies are compared.
Chatbot for Efficient Utilization of College Laboratories

Chat-agents commonly known as chatbots have gained immense attention from multiple fields. It is the participants’ queries and the responses received. Here in this paper, we have discussed having a chatbot in educational institutions. When considering an institution regardless, whether it is a school or university it is consequential that the students are edified in a congruous environment. This generalizes that the infrastructure should fascinate every requisite as cardinal or required by the students or the faculty in that environment. The present-day process implies that all the work is done manually and is bound to wreak mistakes. To minimize the mistakes it is predictable to have a computer availed web-predicted system that will invigilate the infrastructure allotment considering these factors. The system will thereby contribute to reducing the manual efforts taken by the time-table coordinator and also the time consumed for the process. We aim at providing a system that will be effective for institutions, so that henceforth rather than doing the work manually, the system will be made use of.
Master-slave Blockchain Based Cross-domain Trust Access Mechanism for UPIOT

In order to solve the problem that the power terminal cross-domain authentication system is not compatible with the existing authentication system, this paper designs a cross-domain authentication model for power terminals based on master-slave Blockchain, including ubiquitous sensing module, cross-domain authentication module and grid application system module, original authentication system module. Secondly, based on the authentication model, the Blockchain-based registration process of the power terminal, the intra-domain authentication process based on the Blockchain, and the cross-domain authentication process based on the Blockchain are designed. Finally, from the two aspects of the registration process of the power terminal and the authentication process of the power terminal, it is verified that the authentication mechanism proposed in this paper can not only achieve cross-domain authentication of the power terminal, but also be compatible with the original identity authentication system.