Reviewerly/exHarmony huggingface dataset:
New feature selection paradigm based on hyper-heuristic technique

Feature selection (FS) is a crucial step for effective data mining since it has largest effect on improving the performance of classifiers. This is achieved by removing the irrelevant features and using only the relevant features. Many metaheuristic approaches exist in the literature in attempt to address this problem. The performance of these approaches differ based on the settings of a number of factors including the use of chaotic maps, opposition-based learning (OBL) and the percentage of the population that OBL will be applied to, the metaheuristic (MH) algorithm adopted, the classifier utilized, and the threshold value used to convert real solutions to binary ones. However, it is not an easy task to identify the best settings for these different components in order to determine the relevant features for a specific dataset. Moreover, running extensive experiments to fine tune these settings for each and every dataset will consume considerable time. In order to mitigate this important issue, a hyper-heuristic based FS paradigm is proposed. In the proposed model, a two-stage approach is adopted to identify the best combination of these components. In the first stage, referred to as the training stage, the Differential Evolution (DE) algorithm is used as a controller for selecting the best combination of components to be used by the second stage. In the second stage, referred to as the testing stage, the received combination will be evaluated using a testing set. Empirical evaluation of the proposed framework is based on numerous experiments performed on the most popular 18 datasets from the UCI machine learning repository. Experimental results illustrates that the generated generic configuration provides a better performance than eight other metaheuristic algorithms over all performance measures when applied to the UCI dataset. Moreover, The overall paradigm ranks at number one when compared against state-of-the-art algorithms. Finally, the generic configuration provides a very competitive performance for high dimensional datasets.
A Kubernetes Autoscaler Based on Pod Replicas Prediction

Kubernetes, as the de facto standard for scheduling and scheduling containers, has been widely used in the industry. However, the existing scaling strategy of Kubernetes is only the response scaling of thresholds. To improve the efficiency of resource scaling, an active Kubernetes auto scaling device based on Pod replica prediction is proposed. The experiment shows that the autoscaler proposed in this paper has the advantage of faster response speed.
A deep learning approach for 2D ultrasound and 3D CT/MR image registration in liver tumor ablation

Liver tumor ablation is often guided by ultrasound (US). Due to poor image quality, intraoperative US is fused with preoperative computed tomography or magnetic tomography (CT/MR) images to provide visual guidance. As of today, the underlying 2D US to 3D CT/MR registration problem remains a very challenging task. We propose a novel pipeline to address this registration problem. Contrary to previous work, we do not formulate the problem as a regression task, which - for the given registration problem - achieves a low performance regarding accuracy and robustness due to the limited US soft-tissue contrast and the inter-patient variability on liver vessels. Instead, we first estimate the US probe angle roughly by using a classification network. Given this coarse initialization, we then improve the registration by formulation of the problem as a segmentation task, estimating the US plane in the 3D CT/MR through segmentation. We benchmark our approach on 1035 clinical images from 52 patients, yielding average registration errors of 11.6° and 4.7 mm, which outperforms the state of the art SVR method[1]. Our results show the efficiency of the proposed registration pipeline, which has potential to improve the robustness and accuracy of intraoperative patient registration.
Safe path planning for UAV urban operation under GNSS signal occlusion risk

This paper introduces a concept of safe path planning for UAV’s autonomous operation in an urban environment where GNSS-positioning may become unreliable or even unavailable. If the operation environment is a priori known and geo-localized, it is possible to predict a GNSS satellite constellation and hence to anticipate its signal occlusions at a given point and time. Motivated from this, our main idea is to utilize such sensor availability map in path planning task for ensuring UAV navigation safety. The proposed concept is implemented by a Partially Observable Markov Decision Process (POMDP) model. It incorporates a low-level navigation and guidance module for propagating the UAV state uncertainty in function of the probabilistic sensor availability. A new definition of cost function is introduced in this model such that the resulting optimal policy respects a user-defined safety requirement. A goal-oriented version of Monte-Carlo Tree Search algorithm, called POMCP-GO, is proposed for POMDP solving. The developed safe path planner is evaluated on two simple obstacle benchmark maps as well as on a real elevation map of San Diego downtown, along with GPS availability maps.
Aspect-Level Sentiment Analysis Based on Bidirectional-GRU in SIoT

A variety of independent research activities have recently been undertaken to explore the feasibility of incorporating social networking principles into the Internet of Things solutions. The resulting model, called the Social Internet of Things, has the potential to be more powerful and competitive in supporting new IoT applications and networking services. This paper's main contribution is in sentiment analysis, which aims to predict aspect sentiments to improve the making of automated decisions and communication between associates in the social internet of things. In recent years, to analyze sentiment polarity at a subtle level, sentiment classification has become a primetime attraction. Current approaches commonly use the Long-Short Term Memory network to figure aspects and contexts separately. Usually, they perform sentiment classification using simple attention mechanisms and avoiding the bilateral information between sentences and their corresponding aspects. Therefore, the results are not satisfactory. This manuscript intends to develop a new Bidirectional gated recurrent unit model by depending on natural language processing for fully-featured mining to perform the aspect-level sentiment classification task. Our proposed model uses the Bidirectional gated recurrent unit network to acquire the dependency-based semantic analysis of sentences and their corresponding terms compared to earlier work. At the same time, it proposes a method to learn the sentiment polarity of terms in sentences. To check out our model's achievements, we perform several experiments on datasets, namely, (LAPTOP, RESTUARANT, and TWITTER). Our experiment results demonstrate that our model has achieved compelling performance and efficiency improvements in aspect sentiment classification compared with several existing models.
A Hard Crystal - Implementing Dilithium on Reconfigurable Hardware

CRYSTALS-Dilithium as a lattice-based digital signature scheme has been selected as a finalist in the Post-Quantum Cryptography (PQC) standardization process of NIST. As part of this selection, a variety of software implementations have been evaluated regarding their performance and memory requirements for platforms like x86 or ARM Cortex-M4. In this work, we present a first set of Field-Programmable Gate Array (FPGA) implementations for the low-end Xilinx Artix-7 platform, evaluating the peculiarities of the scheme in hardware, reflecting all available round-3 parameter sets. As a key component in our analysis, we present results for a specifically adapted Number-Theoretic Transform (NTT) core for the Dilithium cryptosystem, optimizing this component for an optimal Look-Up Table (LUT) and Flip-Flop (FF) utilization by efficient use of special purpose Digital Signal Processors (DSPs). Presenting our results, we aim to shed further light on the performance of lattice-based cryptography in low-cost and high-throughput configurations and their respective potential use-cases in practice.
Collaborative Filtering Recommendation with Fluctuations of User’ Preference

Traditional recommendation systems (RSs) recommend users to personal items according to their pre-behaviors to infer users' personal preference. Although researchers have made some improvements, which take time factor into traditional recommendation systems, on catching changes of users' interest, it is difficult to always compute the users' preference fluctuations. Therefore, to solve the mentioned above problems, we add personal preference fluctuations into traditional collaborative filtering systems. In this paper, we will quantify user's preference change. Besides, it also adapts recommending in the opposite direction, that is to say, we will recommend items which dissimilar users have rated to the pointed user. The experiment of fluctuation method run on MovieLens 1M and MovieLens latest small, show that the method including changes of personal preference is better than other methods.
An Efficient Hardware Design for Accelerating Sparse CNNs With NAS-Based Models

Deep convolutional neural networks (CNNs) have achieved remarkable performance at the cost of huge computation.As the CNN models become more complex and deeper, compressing CNNs to sparse by pruning the redundant connection in the networks has emerged as an attractive approach to reduce the amount of computation and memory requirement.On the other hand, FPGAs have been demonstrated to be an effective hardware platform to accelerate CNN inference.However, most existing FPGA accelerators focus on dense CNN models, which are inefficient when executing sparse models as most of the arithmetic operations involve addition and multiplication with zero operands.In this work, we propose an accelerator with software-hardware co-design for sparse CNNs on FPGAs.To efficiently deal with the irregular connections in the sparse convolutional layers, we propose a weight-oriented dataflow that exploits element-matrix multiplication as the key operation.Each weight is processed individually, which yields low decoding overhead.Then, we design an FPGA accelerator that features a tile look-up table (TLUT) and a channel multiplexer (CMUX).The TLUT is designed to match the index between sparse weights and input pixels.Using TLUT, the runtime decoding overhead is mitigated by using an efficient indexing operation.Moreover, we propose a weight layout to enable efficient on-chip memory access without conflicts.To cooperate with the weight layout, a CMUX is inserted to locate the address.Finally, we build a neural architecture search (NAS) engine that leverages the reconfigurability of FPGAs to generate an efficient CNN model and choose the optimal hardware design parameters.The experiments demonstrate that our accelerator can achieve 223.4-309.0GOP/s for the modern CNNs on Xilinx ZCU102, which provides a 2.4×-12.9×speedup over previous dense CNN accelerators on FPGAs.Our FPGA-aware NAS approach shows 2× speedup over MobileNetV2 with 1.5% accuracy loss.
Classification of Customer Reviews Using Machine Learning Algorithms

The information resulting from the use of the organization's products and services is a valuable resource for business analytics. Therefore, it is necessary to have systems to analyze customer reviews. This article is about categorizing and predicting customer sentiments. In this article, a new framework for categorizing and predicting customer sentiments was proposed. The customer reviews were collected from an international hotel. In the next step, the customer reviews processed, and then entered into various machine learning algorithms. The algorithms used in this paper were support vector machine (SVM), artificial neural network (ANN), naive bayes (NB), decision tree (DT), C4.5 and k-nearest neighbor (K-NN). Among these algorithms, the DT provided better results. In addition, the most important factors influencing the great customer experience were extracted with the help of the DT. Finally, very interesting results were observed in terms of the effect of the number of features on the performance of machine learning algorithms.
Density decay graph-based density peak clustering

Abstract In 2014, Rodriguez and Laio proposed a famous clustering algorithm based on a fast search and find density peaks dubbed as DPC (Rodriguez and Laio, 2014). DPC has been widely used in many fields because of its simplicity and effectiveness. However, DPC has two obvious drawbacks. First, initial cluster centers must be manually selected from the decision graph, which is extremely difficult and even impossible in some cases. Second, the result of DPC is affected by the so called “chain reaction” problem. To overcome these two drawbacks, we propose a density decay graph-based density peak clustering (DGDPC) algorithm. The key to DGDPC is the concept of density decay graph, which is inspired by the decay phenomenon (a common phenomenon in nature). In our algorithm, we form initial clusters according to density decay graphs and then merge clusters based on a simple method. As a result, there is no need to manually select cluster centers and the chain reaction problem is alleviated. Although DGDPC introduces an additional parameter m, m is robust and easy to determine in advance. Experiments on 10 synthetic datasets and 10 real-world datasets show that our algorithm outperforms DPC, DGB (Wu and Wilamowski, 2016), K-means (MacQueen et al., 1967), DBSCAN (Ester et al., 1996), and Single-link (Murtagh and Contreras, 2012) in most cases.
Smart Contract enabled Online Examination System Based in Blockchain Network

In today's world, data is one of the most important assets than any other. Every user wants to secure their data from the outer world. Blockchain is the prominent technology that can provide the security and loyalty of data. Initially, blockchain has been used for the cryptocurrency and all the data were available on the public distributed ledger. But now a days, private blockchain is widely used within the organizations for data security. Blockchain engenders decentralized systems in which data can be send and receive securely and efficiently over the network. It means there is everything is hidden from the outer world; only authorized users have the authority to read and write the data on the network. World's topmost industries; like Walmart, IBM, Google, etc. are adopting the blockchain technology to build the Decentralized Applications (DApps). Decentralized Applications are the smart systems that are executed on a distributed computer network. Blockchain enables one of the most secure applications called Smart Contract. Smart Contracts are the computerized and secured distributed ledgers that enable secure, transparent, and tamper-proof transactions. Smart contracts create and verify the data with the help of hashing. It is a mathematical procedure that uses the most powerful algorithm cryptographic Hash Algorithm i.e. SHA-256. It engenders 256-bit signature for the input text. Ethereum Blockchain Platform is a widely used platform to build the DApps. This platform is a public network platform, which is open to all and anyone can participate in this network to send and receives the transactions. Blockchain Technology is enabled in every sector like marketing, business, education and supply-chain, etc. This paper carries out the study Ethereum Blockchain Platform in Education System. We have developed an application for the Online-Examination System using Blockchain Ethereum Platform with features of Smart Contracts that enables server runtime environment NodeJS and MongoDB database system. Blockchain-based system is truly more secure than all the Cloud-based systems. We have also analysed that how blockchain based online examination is more trustworthy as compare the other systems.
LightNE: A Lightweight Graph Processing System for Network Embedding

We propose LightNE, a cost-effective, scalable, and high quality network embedding system that scales to graphs with hundreds of billions of edges on a single machine. In contrast to the mainstream belief that distributed architecture and GPUs are needed for large-scale network embedding with good quality, we prove that we can achieve higher quality, better scalability, lower cost and faster runtime with shared-memory, CPU-only architecture. LightNE combines two theoretically grounded embedding methods NetSMF and ProNE. We introduce the following techniques to network embedding for the first time: (1) a newly proposed downsampling method to reduce the sample complexity of NetSMF while preserving its theoretical advantages; (2) a high-performance parallel graph processing stack GBBS to achieve high memory efficiency and scalability; (3) sparse parallel hash table to aggregate and maintain the matrix sparsifier in memory; and (4) Intel MKL for efficient randomized SVD and spectral propagation.
Steganography using Improved LSB Approach and Asymmetric Cryptography

Steganography deals with the craft of obscuring private data inside a spread media. In confidential data communication security is a vital issue. In this paper, we use a two-layer security. At first, data encryption is achieved by the method of RSA algorithm of asymmetric cryptography, and later the ciphered data is hidden into host image by an innovative embedding technique. To hide our ciphered data into host image, we modify the existing LSB technique and use a mapping function that ensures a secure and confidential image steganography resulting in a stego image. Here cryptography is blended with steganography and provides two level security in the confidential data transmission over the internet.
Bot Datasets on Twitter: Analysis and Challenges

The reach and influence of social networks over modern society and its functioning have created new challenges and opportunities to prevent the misuse or tampering of such powerful tools of social interaction. Twitter, a social networking service that specializes in online news and information exchange involving billions of users world-wide, has been infested by bots for several years. In this paper, we analyze both public and private databases from the literature of bot detection on Twitter. We summarize their advantages, disadvantages, and differences, recommending which is more suitable to work with depending on the necessities of the researcher. From this analysis, we present five distinct behaviors in automated accounts exhibited across all the bot datasets analyzed from these databases. We measure their level of presence in each dataset using a radar chart for visual comparison. Finally, we identify four challenges that researchers of bot detection on Twitter have to face when using these databases from the literature.
An Adaptive Model of Gaze-based Selection

Gaze-based selection has received significant academic attention over a number of years. While advances have been made, it is possible that further progress could be made if there were a deeper understanding of the adaptive nature of the mechanisms that guide eye movement and vision. Control of eye movement typically results in a sequence of movements (saccades) and fixations followed by a 'dwell' at a target and a selection. To shed light on how these sequences are planned, this paper presents a computational model of the control of eye movements in gaze-based selection. We formulate the model as an optimal sequential planning problem bounded by the limits of the human visual and motor systems and use reinforcement learning to approximate optimal solutions. The model accurately replicates earlier results on the effects of target size and distance and captures a number of other aspects of performance. The model can be used to predict number of fixations and duration required to make a gaze-based selection. The future development of the model is discussed.
Confidential computing for OpenPOWER

This paper presents Protected Execution Facility (PEF), a virtual machine-based Trusted Execution Environment (TEE) for confidential computing on Power ISA. PEF enables protected secure virtual machines (SVMs). Like other TEEs, PEF verifies the SVM prior to execution. PEF utilizes a Trusted Platform Module (TPM), secure boot, and trusted boot as well as newly introduced architectural changes for Power ISA systems. Exploiting these architectural changes requires new firmware, the Protected Execution Ultravisor. PEF is supported in the latest version of the POWER9 chip. PEF demonstrates that access control for isolation and cryptography for confidentiality is an effective approach to confidential computing. We particularly focus on how our design (i) balances between access control and cryptography, (ii) maximizes the use of existing security components, and (iii) simplifies the management of the SVM life cycle. Finally, we evaluate the performance of SVMs in comparison to normal virtual machines on OpenPOWER systems.
Exploring thermal images for object detection in underexposure regions for autonomous driving

Underexposure regions are vital in constructing a complete perception of the surrounding environment for safe autonomous driving. The availability of thermal cameras has provided an essential alternative to explore regions where other optical sensors lack in capturing interpretable signals. A thermal camera captures an image using the heat difference emitted by objects in the infrared spectrum, and object detection in thermal images becomes effective for autonomous driving in challenging conditions. Although object detection in the visible spectrum domain has matured, thermal object detection lacks effectiveness. A significant challenge is the scarcity of labeled data for the thermal domain, which is essential for SOTA artificial intelligence techniques. This work proposes a domain adaptation framework that employs a style transfer technique for transfer learning from visible spectrum images to thermal images. The framework uses a generative adversarial network (GAN) to transfer the low-level features from the visible spectrum domain to the thermal domain through style consistency. The efficacy of the proposed object detection method in thermal images is evident from the improved results when using styled images from publicly available thermal image datasets (FLIR ADAS and KAIST Multi-Spectral).
Large Message Homomorphic Secret Sharing from DCR and Applications

We present the first homomorphic secret sharing (HSS) construction that simultaneously (1) has negligible correctness error, (2) supports integers from an exponentially large range, and (3) relies on an assumption not known to imply FHE—specifically, the Decisional Composite Residuosity (DCR) assumption. This resolves an open question posed by Boyle, Gilboa, and Ishai (Crypto 2016). Homomorphic secret sharing is analogous to fully-homomorphic encryption, except the ciphertexts are shared across two non-colluding evaluators. Previous constructions of HSS either had non-negligible correctness error and polynomial-size plaintext space or were based on the stronger LWE assumption. We also present two applications of our technique: a two server ORAM with constant bandwidth overhead, and a rate-1 trapdoor hash function with negligible error rate.