Reviewerly/exHarmony huggingface dataset:
Security and provenance for Internet of Health Things: A systematic literature review

Abstract Internet of Health Things (IoHT) is an extension of the Internet of Things (IoT), which plays an important role in the observation, consultation, monitoring, and treatment process of remote exchange data processes in healthcare. The integration, computation, and interoperability are facilitated through various sensors, controllers, and actuators. Security is considered as one of the important factors for the communication of these devices to smoothly run the activities of healthcare. The current literature associated to the security of IoHT cover diverse aspects, though there is dire need of the knowledge, which can thoroughly show the review of current state‐of‐the‐art work in a systematic way. To overcome this limitation, the planned study offerings a systematic literature review (SLR) for the IoHT security and provenance. This review will support researchers to take benefit from the present literature and devise novel solutions considering the current study as evidence in IoHT. This paper focused on the security analysis and provenance for the IoHT and answering the defined questions in the current study.
XDA: Accurate, Robust Disassembly with Transfer Learning

Accurate and robust disassembly of stripped binaries is challenging.The root of the difficulty is that highlevel structures, such as instruction and function boundaries, are absent in stripped binaries and must be recovered based on incomplete information.Current disassembly approaches rely on heuristics or simple pattern matching to approximate the recovery, but these methods are often inaccurate and brittle, especially across different compiler optimizations.We present XDA, a transfer-learning-based disassembly framework that learns different contextual dependencies present in machine code and transfers this knowledge for accurate and robust disassembly.We design a self-supervised learning task motivated by masked Language Modeling to learn interactions among byte sequences in binaries.The outputs from this task are byte embeddings that encode sophisticated contextual dependencies between input binaries' byte tokens, which can then be finetuned for downstream disassembly tasks.We evaluate XDA's performance on two disassembly tasks, recovering function boundaries and assembly instructions, on a collection of 3,121 binaries taken from SPEC CPU2017, SPEC CPU2006, and the BAP corpus.The binaries are compiled by GCC, ICC, and MSVC on x86/x64 Windows and Linux platforms over 4 optimization levels.XDA achieves 99.0% and 99.7% F1 score at recovering function boundaries and instructions, respectively, surpassing the previous state-of-the-art on both tasks.It also maintains speed on par with the fastest MLbased approach and is up to 38× faster than hand-written disassemblers like IDA Pro.We release the code of XDA at https://github.com/CUMLSec/XDA.
Semantic Segmentation of Aerial Imagery via Split-Attention Networks with Disentangled Nonlocal and Edge Supervision

In this work, we propose a new deep convolution neural network (DCNN) architecture for semantic segmentation of aerial imagery. Taking advantage of recent research, we use split-attention networks (ResNeSt) as the backbone for high-quality feature expression. Additionally, a disentangled nonlocal (DNL) block is integrated into our pipeline to express the inter-pixel long-distance dependence and highlight the edge pixels simultaneously. Moreover, the depth-wise separable convolution and atrous spatial pyramid pooling (ASPP) modules are combined to extract and fuse multiscale contextual features. Finally, an auxiliary edge detection task is designed to provide edge constraints for semantic segmentation. Evaluation of algorithms is conducted on two benchmarks provided by the International Society for Photogrammetry and Remote Sensing (ISPRS). Extensive experiments demonstrate the effectiveness of each module of our architecture. Precision evaluation based on the Potsdam benchmark shows that the proposed DCNN achieves competitive performance over the state-of-the-art methods.
Face Detection and Recognition for Criminal Identification System

The process of identifying and spotting a criminal is slow and difficult. Criminals, these days are getting smarter by not leaving any form of biological evidence or fingerprint impressions on the crime scene. A quick and easy solution is using state-of-the-art face identification systems. With the advancement in security technology, CCTV cameras are being installed at most of the buildings and traffic lights for surveillance purposes. The video footage from the camera can be used to identify suspects, criminals, runaways, missing persons etc. This paper explores a way to develop a criminal identification system using ML and deep neural networks. The following method can be used as an elegant way to make law enforcement hassle-free.
Artificial Intelligence and Financial Technology FinTech: How AI Is Being Used Under the Pandemic in 2020

The chapter is about the development of Artificial Intelligence (AI) technology in finance, especially under the case of the COVID-19 pandemic in 2020. It does not only present the applications, but also the regulation of AI and Financial Technology FinTech. An innovation regulatory framework at the regulation level and compulsory restrictive guidance and supervision for AI-based technology to allow sustainable growth will promote the accelerated growth of AI in finance. The AI in the financial industry itself focuses on the main characteristics of “digitalization”, “onlineization”, “remoteization”, “visualization”, and “intelligence”, building a multi-functional, all-process end-to-end system based on data, enabling multi-user multi-terminal concurrent office, intelligently assisting in dealing with problems and giving solutions. The advent of AI and its ever-broader effects on other industries demands an assessment of its influence on achieving sustainable development goals.
Evaluation of Machine Learning Algorithms for the Detection of Fake Bank Currency

The one important asset of our country is Bank currency and to create discrepancies of money miscreants introduce the fake notes which resembles to original note in the financial market. During demonetization time it is seen that so much of fake currency is floating in market. In general by a human being it is very difficult to identify forged note from the genuine not instead of various parameters designed for identification as many features of forged note are similar to original one. To discriminate between fake bank currency and original note is a challenging task. So, there must be an automated system that will be available in banks or in ATM machines. To design such an automated system there is need to design an efficient algorithm which is able to predict weather the banknote is genuine or forged bank currency as fake notes are designed with high precision. In this paper six supervised machine learning algorithms are applied on dataset available on UCI machine learning repository for detection of Bank currency authentication. To implement this we have applied Support Vector machine, Random Forest, Logistic Regression, Naïve Bayes, Decision Tree, K- Nearest Neighbor by considering three train test ratio 80:20, 70:30 and 60:40 and measured their performance on the basis various quantitative analysis parameter like Precision, Accuracy, Recall, MCC, F1-Score and others. And some of SML algorithm are giving 100 % accuracy for particular train test ratio.
An Experience with Code-Size Optimization for Production iOS Mobile Applications

Modern mobile application binaries are bulky for many reasons: software and its dependencies, fast-paced addition of new features, high-level language constructs, and statically linked platform libraries. Reduced application size is critical not only for the end-user experience but also for vendor's download size limitations. Moreover, download size restrictions may impact revenues for critical businesses. In this paper, we highlight some of the key reasons of code-size bloat in iOS mobile applications, specifically apps written using a mix of Swift and Objective-C. Our observation reveals that machine code sequences systematically repeat throughout the app's binary. We highlight source-code patterns and high-level language constructs that lead to an increase in the code size. We propose whole-program, fine-grained machine-code outlining as an effective optimization to constrain the code-size growth. We evaluate the effectiveness of our new optimization pipeline on the UberRider iOS app used by millions of customers daily. Our optimizations reduce the code size by 23%. The impact of our optimizations on the code size grows in magnitude over time as the code evolves. For a set of performance spans defined by the app developers, the optimizations do not statistically regress production performance. We applied the same optimizations to Uber's UberDriver and UberEats apps and gained 17% and 19% size savings, respectively.
Hepatocellular Carcinoma Automatic Diagnosis within CEUS and B-Mode Ultrasound Images Using Advanced Machine Learning Methods

Hepatocellular Carcinoma (HCC) is the most common malignant liver tumor, being present in 70% of liver cancer cases. It usually evolves on the top of the cirrhotic parenchyma. The most reliable method for HCC diagnosis is the needle biopsy, which is an invasive, dangerous method. In our research, specific techniques for non-invasive, computerized HCC diagnosis are developed, by exploiting the information from ultrasound images. In this work, the possibility of performing the automatic diagnosis of HCC within B-mode ultrasound and Contrast-Enhanced Ultrasound (CEUS) images, using advanced machine learning methods based on Convolutional Neural Networks (CNN), was assessed. The recognition performance was evaluated separately on B-mode ultrasound images and on CEUS images, respectively, as well as on combined B-mode ultrasound and CEUS images. For this purpose, we considered the possibility of combining the input images directly, performing feature level fusion, then providing the resulted data at the entrances of representative CNN classifiers. In addition, several multimodal combined classifiers were experimented, resulted by the fusion, at classifier, respectively, at the decision levels of two different branches based on the same CNN architecture, as well as on different CNN architectures. Various combination methods, and also the dimensionality reduction method of Kernel Principal Component Analysis (KPCA), were involved in this process. These results were compared with those obtained on the same dataset, when employing advanced texture analysis techniques in conjunction with conventional classification methods and also with equivalent state-of-the-art approaches. An accuracy above 97% was achieved when our new methodology was applied.
Accurate Range Query With Privacy Preservation for Outsourced Location-Based Service in IoT

With the maturity of Internet-of-Things technology, location-based service (LBS) is developing rapidly in intelligent terminal devices, and it brings new vitality to the fields of logistics, transportation, product traceability and so on. The popularity of LBS produces a lot of spatial data, which inevitably brings burden to the storage and management of LBS provider (LBSP). With the help of cloud computing and cloud storage, outsourcing spatial data to cloud server has become a new trend. However, due to the cloud server is not trusted, data outsourcing will face the problems of data disclosure and query disclosure. Range query is a common query in LBS, considering the situation of data outsourcing, this article proposes an accurate range query (ARQ) scheme, which can realize efficient range query while preserving LBSP's data privacy and user's query privacy from being disclosed to the cloud server. The ARQ scheme is suitable for spatial data in any form without being limited to the case that the data points are only integers, which has a certain practical significance. In addition, by dividing the region into atomic regions, ARQ can realize sublinear search time and ensure dynamic update of spatial data. We proved the security of the proposed scheme through security analysis, and demonstrated the effectiveness of the scheme through experiments.
Candidate region aware nested named entity recognition

Named entity recognition (NER) is crucial in various natural language processing (NLP) tasks. However, the nested entities which are common in practical corpus are often ignored in most of current NER models. To extract the nested entities, two categories of models (i.e., feature-based and neural network-based approaches) are proposed. However, the feature-based models suffer from the complicated feature engineering and often heavily rely on the external resources. Discarding the heavy feature engineering, recent neural network-based methods which treat the nested NER as a classification task are designed but still suffer from the heavy class imbalance issue and the high computational cost. To solve these problems, we propose a neural multi-task model with two modules: Binary Sequence Labeling and Candidate Region Classification to extract the nested entities. Extensive experiments are conducted on the public datasets. Comparing with recent neural network-based approaches, our proposed model achieves the better performance and obtains the higher efficiency.
Towards Fighting Cybercrime: Malicious URL Attack Type Detection using Multiclass Classification

Malicious Uniform Resource Locators (URLs) re-main one of the most common threats to cybersecurity. They are commonly spread through phishing, malware and spam. One popular way to detect malicious URLs is through black-lists. Blacklists maintain records of previously known malicious URL reputations. These lists are however shortcoming when there is need to detect newly generated malicious URLs. For that reason, modern research has resorted to training machine learning algorithms to detect malicious URLs. In this paper, we contributed towards the detection of malicious URLs using URL based features in a multiclass classification setting. We focused on three popular URL attack types which are phishing, spam and malware. Our work can be used as a supplementary tool in new or existing anti-phishing, anti-spam and anti-malware detection platforms. We compared the performance of the following ensemble learners: Extreme Gradient Boosting (XGBoost), Adaptive Boosting (AdaBoost), Light Gradient Boosting (LightGBM) and Categorical Boosting (CatBoost). We evaluated the performance of some URL features that we referred to as our features. These included priority features like Kullback-Leibler Divergence (KL divergence), bag of words segmentation and other word-based features. Results showed that our features performed better when compared to experiments we conducted without our features. We trained these algorithms on 126 983 URLs from benchmark datasets and all four learners returned an overall accuracy above 0.95.
A survey on cloud-based video streaming services

Video streaming, in various forms of video on demand (VOD), live, and 360 degree streaming, has grown dramatically during the past few years. In comparison to traditional cable broadcasters whose contents can only be watched on TVs, video streaming is ubiquitous and viewers can flexibly watch the video contents on various devices, ranging from smartphones to laptops, and large TV screens. Such ubiquity and flexibility are enabled by interweaving multiple technologies, such as video compression, cloud computing, content delivery networks, and several other technologies. As video streaming gains more popularity and dominates the Internet traffic, it is essential to understand the way it operates and the interplay of different technologies involved in it. Accordingly, the first goal of this paper is to unveil sophisticated processes to deliver a raw captured video to viewers’ devices. In particular, we elaborate on the video encoding, transcoding, packaging, encryption, and delivery processes. We survey recent efforts in academia and industry to enhance these processes. As video streaming industry is increasingly becoming reliant on cloud computing, the second goal of this survey is to explore and survey the ways cloud services are utilized to enable video streaming services. The third goal of the study is to position the undertaken research works in cloud-based video streaming and identify challenges that need to be obviated in future to advance cloud-based video streaming industry to a more flexible and user-centric service.
Optimized Mobile Crane Path Planning in Discretized Polar Space

Improper planning and management of heavy lifts is a major cause of cost overruns, delays, and, more importantly, safety incidents in industrial megaprojects. Automated lift planning is widely acknowledged as an effective solution. This research presents an automated lift path planning system for mobile cranes leveraging space discretization and an obstacle-avoidance technique from robotics. The proposed method treats the lifted object as a three-degree-of-freedom convex traveling through the surrounding environment [a given two-dimensional (2D) elevation] with discretized rotational and translational motions in polar coordinates. It efficiently finds the best feasible pick location and optimized collision-free lift path in the polar coordinate system to the set location. This system is a state-of-the art advancement in crane path planning because it mimics the crane's intrinsic behavior and generates paths considering cost functions for safety, practicality, and economic objectives to enable its implementation in real practice settings. Illustrative examples are presented to verify the proposed approach and demonstrate its superiority over past similar path planning systems in terms of optimality and operational ease.
Semantic Perceptual Image Compression With a Laplacian Pyramid of Convolutional Networks

The existing image compression methods usually choose or optimize low-level representation manually. Actually, these methods struggle for the texture restoration at low bit rates. Recently, deep neural network (DNN)-based image compression methods have achieved impressive results. To achieve better perceptual quality, generative models are widely used, especially generative adversarial networks (GAN). However, training GAN is intractable, especially for high-resolution images, with the challenges of unconvincing reconstructions and unstable training. To overcome these problems, we propose a novel DNN-based image compression framework in this paper. The key point is decomposing an image into multi-scale sub-images using the proposed Laplacian pyramid based multi-scale networks. For each pyramid scale, we train a specific DNN to exploit the compressive representation. Meanwhile, each scale is optimized with different aspects, including pixel, semantics, distribution and entropy, for a good “rate-distortion-perception” trade-off. By independently optimizing each pyramid scale, we make each stage manageable and make each sub-image plausible. Experimental results demonstrate that our method achieves state-of-the-art performance, with advantages over existing methods in providing improved visual quality. Additionally, a better performance in the down-stream visual analysis tasks which are conducted on the reconstructed images, validates the excellent semantics-preserving ability of the proposed method.
ALADIN: All Layer Adaptive Instance Normalization for Fine-grained Style Similarity

We present ALADIN (All Layer AdaIN); a novel architecture for searching images based on the similarity of their artistic style. Representation learning is critical to visual search, where distance in the learned search embedding reflects image similarity. Learning an embedding that discriminates fine-grained variations in style is hard, due to the difficulty of defining and labelling style. ALADIN takes a weakly supervised approach to learning a representation for fine-grained style similarity of digital artworks, leveraging BAM-FG, a novel large-scale dataset of user generated content groupings gathered from the web. ALADIN sets a new state of the art accuracy for style-based visual search over both coarse labelled style data (BAM) and BAM-FG; a new 2.62 million image dataset of 310,000 fine-grained style groupings also contributed by this work.
Deterministic CNOT gate and complete Bell-state analyzer on quantum-dot-confined electron spins based on faithful quantum nondemolition parity detection

The quantum controlled-NOT (CNOT) gate is a prototypical two-qubit quantum logic gate that provides the basic controlled logic for a set of gates for universal quantum computation. It has been shown that parity checking devices can be used to construct CNOT gates, and the fidelity of a CNOT operation is highly constrained by the fidelity of parity detection with this strategy. In this paper, a scheme to implement a CNOT operation on two stationary electron spins confined in quantum dots (QDs) inside double-sided optical microcavities is presented, based on the faithful parity detection achieved by a heralded and robust two-electron-spin quantum nondemolition (QND) parity detector. The QND parity detector is considerably different from previous implementations and experimentally more realizable, and works in the heralded and repeat-until-success fashion with robust fidelity, which enables our CNOT gate to be implemented deterministically with unity fidelity. Moreover, based on the features of the QND parity detector, a complete Bell-state analysis on two QD-confined electron spins can be realized without wrong judgment or any destruction of the analyzed entangled state. The efficiency of parity detection is also discussed by considering currently achievable system parameters.
Swarm intelligence based robotic search in unknown maze-like environments

• A robotic search algorithm for search in complex unknown environments is presented. • Robots operate asynchronously and move continuously in both time and space. • The proposed decentralized algorithm needs no communication center. • The fitness function is based on minimal knowledge about the environment. This paper proposes a novel decentralize and asynchronous robotic search algorithm based on particle swarm optimization (PSO), which has focused on solving mazes and finding targets in unknown environments with minimal inter-swarm communication and without any synchronization or communication center. In the proposed method, robots are advanced particles of the PSO algorithm, enriched with a toolkit, including an angle of rotation to change the course when confronted with obstacles to avoid them ( AoR tool), and a memory to remember and reuse their best personal experiences to turn back from dead-ends ( Mem tool). This toolkit enables the swarm to avoid obstacles and solve mazes while moving toward the target. The performance of the proposed algorithm is tested in a specially designed framework. As a validation, the proposed algorithm is compared with some recently published methods, including Adaptive Robotic PSO (A-RPSO), Robotic Bat Algorithm (RBA), and Adaptive Robotic Bat Algorithm (ARBA), in simple search environments that they can solve. The results of this comparison show that the introduced search method has the highest success rate (100%) in environments of different sizes and reflects the nature of swarm intelligence better. The proposed method is also tested in various maze-like search environments. The results depict the algorithm’s high efficiency to solve mazes in varying complexity levels and locate the target in a reliable time. It is also shown that the performance of the proposed algorithm does not decrease and remains constant as the complexity of search environments increases.