Reviewerly/exHarmony huggingface dataset:
Blockchain based Producer-Consumer Model for Farmers

The agricultural activity is considered to be the largest trade, as it is feeding more people. Though it is playing a vital role in economic development, the farmers who are producing the crops are not reaping abundant benefits from it. Precision agriculture systems equipped with advanced technologies allow farmers to increase their productivity in ecological friendly manner, but with minimum profit. Our traditional producer-consumer model makes farmers to hinge on the interagent or middleman to sell their goods to consumers, as they have fewer restrictions in selling the goods directly to consumers. This paper proposes Blockchain based Producer- Consumer Model (BPCM) which allows farmer to sell their goods directly to the consumers by preventing the interagents, to gain profit over farmers using smart contracts. It also resists the Sybil attack, by blocking interagent with multiple counterfeit identities to trade with consumers thereby increasing profit of the farmers.
Clustering-based feature subset selection with analysis on the redundancy–complementarity dimension

In the era of big data, dimensionality reduction plays an extremely important role in many fields driven by machine learning and data mining techniques. The existing information-theoretic feature selection algorithms generally reduce the dimension by selecting the features with maximum class-relevance and minimum redundancy, while relatively overlook the complementary correlation among features and sometimes deal with it improperly. This paper proposes a novel feature subset selection algorithm called the Clustering-based Feature Selection with Redundancy–Complementarity Analysis (CFSRCA). The proposed algorithm can be mainly divided into two steps, namely, (a) selecting the candidate class-relevant features, and (b) selecting the representative features. In the latter step, the representative features are defined as the features with minimum redundancy and maximum complementarity, and a clustering method based on the minimum spanning tree (MST) is proposed to distinguish them effectively. To validate the effectiveness of CFSRCA, three comparative feature selection algorithms (ReliefF, CFS, and FOU) and four well-known classifiers (C4.5, SVM, kNN, and NBC) are used to conduct classification experiments on eight datasets. Experimental results verify the effectiveness of the proposed feature subset algorithm.
Bitcoin: A Solution for Payment Systems or a Solution in Search of a Problem?

In October 2008 a mysterious article was published under the pseudonym Satoshi Nakamoto: “Bitcoin: a peer-to-peer electronic cash system”. Bitcoin’s entry into operation some months later in early 2009 barely caused a ripple. Since then, the scheme has accumulated more than half a million blocks in its blockchain and they include more than 300 million transactions. In view of the media impact of Bitcoin, it is worth explaining in some detail how Bitcoin works and what its limitations are. This article reviews the aims and basic functioning of Bitcoin, analyses its strengths and weaknesses, and discusses its usefulness as an exchange mechanism.
Coded Computing and Cooperative Transmission for Wireless Distributed Matrix Multiplication

Consider a multi-cell mobile edge computing network, in which each user wishes to compute the product of a user-generated data matrix with a network-stored matrix. This is done through task offloading by means of input uploading, distributed computing at edge nodes (ENs), and output downloading. Task offloading may suffer long delay since servers at some ENs may be straggling due to random computation time, and wireless channels may experience severe fading and interference. This paper aims to investigate the interplay among upload, computation, and download latencies during the offloading process in the high signal-to-noise ratio regime from an information-theoretic perspective. A policy based on cascaded coded computing and on coordinated and cooperative interference management in uplink and downlink is proposed and proved to be approximately optimal for a sufficiently large upload time. By investing more time in uplink transmission, the policy creates data redundancy at the ENs, which can reduce the computation time, by enabling the use of coded computing, as well as the download time via transmitter cooperation. Moreover, the policy allows computation time to be traded for download time. Numerical examples demonstrate that the proposed policy can improve over existing schemes by significantly reducing the end-to-end execution time.
Decision Making with Machine Learning and ROC Curves

The receiver operating characteristic (ROC) curve is a representation of the statistical information from binary classification problems and is a key concept in machine learning and data science. We study the properties of ROC curves and their implications. The relation between ROC curves and fundamental properties of “fine” ROC curves are clarified. Our discussions are based on the randomization method. Our theoretical discussion is demonstrated using a large dataset of pregnancy outcomes and doctor diagnoses from pre-pregnancy checkups provided by the Chinese Ministry of Health and a large dataset of vehicle loan default information collected in India.
Navigation of Autonomous Light Vehicles Using an Optimal Trajectory Planning Algorithm

Autonomous navigation is a complex problem that involves different tasks, such as location of the mobile robot in the scenario, robotic mapping, generating the trajectory, navigating from the initial point to the target point, detecting objects it may encounter in its path, etc. This paper presents a new optimal trajectory planning algorithm that allows the assessment of the energy efficiency of autonomous light vehicles. To the best of our knowledge, this is the first time in the literature that this is carried out by minimizing the travel time while considering the vehicle’s dynamic behavior, its limitations, and with the capability of avoiding obstacles and constraining energy consumption. This enables the automotive industry to design environmentally sustainable strategies towards compliance with governmental greenhouse gas (GHG) emission regulations and for climate change mitigation and adaptation policies. The reduction in energy consumption also allows companies to stay competitive in the marketplace. The vehicle navigation control is efficiently implemented through a middleware of component-based software development (CBSD) based on a Robot Operating System (ROS) package. It boosts the reuse of software components and the development of systems from other existing systems. Therefore, it allows the avoidance of complex control software architectures to integrate the different hardware and software components. The global maps are created by scanning the environment with FARO 3D and 2D SICK laser sensors. The proposed algorithm presents a low computational cost and has been implemented as a new module of distributed architecture. It has been integrated into the ROS package to achieve real time autonomous navigation of the vehicle. The methodology has been successfully validated in real indoor experiments using a light vehicle under different scenarios entailing several obstacle locations and dynamic parameters.
Conflicting management of evidence combination from the point of improvement of basic probability assignment

An open issue of the Dempster combination rule is the conflicting management, which is very important in multisource data fusion, such as group decision making and target recognition. To address this issue, an improved method to generate basic probability assignment is presented. Then, a new combination method to assign the conflicting mass function without the normalization is proposed to handle a highly conflicting environment. Compared with other methods, this proposed method is convenient in computing and has better accuracy to predict potential possibilities especially when disposing of extreme status. Some numerical examples and real benchmark data collected in UCI database are illustrated to verify the validity and rationality of the proposed method.
Context-aware and dynamically adaptable activity recognition with smart watches: A case study on smoking

Motion sensors available on wearable devices make it possible to recognize various user activities. An accelerometer is mostly sufficient to detect simple activities, such as walking, but adding a gyroscope or sampling at a higher rate can increase the recognition rate of more complex activities, such as smoking while walking. However, using a high sampling rate, more than one sensor at a time, may cause higher and unnecessary resource consumption on these resource-limited devices. In this paper, we propose a context-aware activity recognition algorithm (Conawact), which dynamically activates different sensors, sampling rates and features according to the type of the activity. We evaluate the performance of Conawact and compare with using static and semi-dynamically adaptable parameters. Results show that Conawact achieves 6% better recognition rate, on average, and up to 20% for some complex activities, such as smoking in a group, and 22% less energy consumption compared to using static parameters.
Saliency aware image cropping with latent region pair

As one of the fundamental problems for image enhancing editing, image cropping seeks the best crop with high aesthetic quality and removes extraneous areas. Many recent deep learning methods have been proposed to address the problem, but they do not reveal the intrinsic mechanism of image cropping. In this paper, we explore the latent region pair and then fulfill its potential in our proposed deep learning methodology (SAIC-Net) with saliency map for automatic image cropping. For each image, a lightweight multi-scale feature extraction network is first adopted to produce deep and informative features. Then, the features of latent region pair (ROI and ROD) are aligned and refined by the proposed saliency-aware align operators and context channel attentions. Finally, hybrid loss composed by ranking loss and Huber loss is minimized when training our model. In our experiments, to reduce the searching space for candidate crops, we conduct a saliency-aware grid cropping candidates generation method to eliminate irrelevant crops. Afterwards, we provide a thorough ablation study to better figure out the superiority of each part in our method, and conduct user study against the state-of-the-art methods on a fraction of performance metrics. The quantitative and qualitative results on three benchmark datasets demonstrate the superiority of our SAIC-Net in the task of automatic cropping.
Why do Public Blockchains Need Formal and Effective Internal Governance Mechanisms?

With the birth and rise of cryptocurrencies following the success of Bitcoin and the popularity of “Initial Coin Offerings”, public awareness of blockchain technologies has substantially increased in recent years. Many blockchain advocates claim that these software artefacts enable radically new forms of decentralised governance by relying upon computational trust created via cryptographic proof, obviating the need for reliance on conventional trusted third-party intermediaries. But these claims rest on some key assumptions, which this paper subjects to critical examination. It asks: can existing mechanisms and procedures for collective decision-making of public blockchains (which we refer to as internal blockchain governance) live up to these ambitions? By drawing upon HLA Hart’s Concept of Law, together with literature from regulatory governance studies, we argue that unless public blockchain systems establish formal and effective internal governance, they are unlikely to be taken up at scale as a tool for social coordination, and are thus likely to remain, at best, a marginal technology.
Deep Learning for Medical Image Recognition: Open Issues and a Way to Forward

In the recent decade, deep learning has taken lead over available analysis techniques. Today’s deep learning is used in diversified sectors like health care, traffic management, agriculture, etc. The expectations of researchers or people concerned with deep learning are very high. On another side, healthcare sector is totally different from other industry. It required serious attention, care of people and services (regardless of cost) towards patients; here, issue is a matter of patient’s life. Also, this sector requires a high budget and many people to work in parallel, to provide efficient services to each patient. The interpretations of data in the medical field are vested in the hands of medical experts, and this proves to be quite restrained because of the intervening sophistication, wide ranging varieties spread across a number of compilers, etc. The great victory of deep learning in real-time applications has led to the creation of striking results with extreme accuracy and precision, hence paving way to the spotlight in futuristic health sector applications. In this paper, we discussed state-of-the-art deep learning architecture and its optimization used for medical image segmentation and classification. Also, this sector discusses several useful components like open issues, challenges deep learning-based methods for medical imaging and future research directions.
A Survey Comparing Specialized Hardware And Evolution In TPUs For Neural Networks

This survey paper is based on the evolution of TPUs from first generation TPUs to edge TPUs and their architectures.This paper compares CPUs, GPUs, FPGAs and TPUs, their hardware architectures, their similarities and differences will be discussed.Modern neural networks are immensely used these days but they require more time, computation and energy.Due to the greater demand and attractive options for architects to explore, companies are continuously working to reduce training and inference response time.Due to the demands and cost factors different kinds of ASICs (application specific integrated circuits) are developed and research is increased in this area.Many models of CPUs, GPUs and TPUs have been developed to support these networks and to improve training and inference phase.Intel developed CPUs for this purpose, NVIDIA developed GPUs and Google developed cloud TPUs.The hardware of CPUs and GPUs can be sold to businesses while Google offers TPU processing for everyone from the cloud.When the data is away from the computational source, it increases the overall cost and to reduce this cost companies implements memory management and caching techniques close to ALUs.
Multi-task face analyses through adversarial learning

The inherent relations among multiple face analysis tasks, such as landmark detection, head pose estimation, gender recognition and face attribute estimation are crucial to boost the performance of each task, but have not been thoroughly explored since typically these multiple face analysis tasks are handled as separate tasks. In this paper, we propose a novel deep multi-task adversarial learning method to localize facial landmark, estimate head pose and recognize gender jointly or estimate multiple face attributes simultaneously through exploring their dependencies from both image representation-level and label-level. Specifically, the proposed method consists of a deep recognition network R and a discriminator D. The deep recognition network is used to learn the shared middle-level image representation and conducts multiple face analysis tasks simultaneously. Through multi-task learning mechanism, the recognition network explores the dependencies among multiple face analysis tasks from image representation-level. The discriminator is introduced to enforce the distribution of the multiple face analysis tasks to converge to that inherent in the ground-truth labels. During training, the recognizer tries to confuse the discriminator, while the discriminator competes with the recognizer through distinguishing the predicted label combination from the ground-truth one. Though adversarial learning, we explore the dependencies among multiple face analysis tasks from label-level. Experimental results on benchmark databases demonstrate the effectiveness of the proposed method for multi-task face analyses.
Detection of Malaysian Traffic Signs via Modified YOLOv3 Algorithm

Traffic signs images when captured in real environment are small in size compared to other objects. Thus, making it difficult to be accurately detected, more so identified. Of recent, the convolutional neural network (CNN) has been making tremendous progress in object detection due to its high accuracy and fast execution. YOLO (You Only Look Once) is an object detection method, which uses the CNN core module and suited for detection of traffic sign in real environment. In this paper, we implemented the YOLOv3 framework for identification of Malaysian traffic sign in real environment. With the inclusion of the spatial pyramid pooling (SPP) in our YOLOv3 framework, it enables the identification of far and small traffic signs in real environment due to pooling of multi-sizes grids of SPP. We presented results of this implementation on our data set of 2000 images of Malaysian traffic sign and the results at 0.5% confidence level are in the range of 90% with a mean average precision of 82.5%, which is promising.
Systematic Review on Social Engineering: Hacking by Manipulating Humans

Despite the availability of advanced security software and hardware mechanisms available, still, there has been a breach in the defence system of an organization or individual.Social engineering mostly targets the weakest link in the security system i.e. "Humans" for gaining access to sensitive information by manipulating human psychology.Social engineering attacks are arduous to defend as such attacks are not easily detected by available security software or hardware.This article surveys recent studies on social engineering attacks with discussion on the social engineering phases and categorizing the various attacks into two groups.The main aim of this survey is to examine the various social engineering attacks on individuals and countermeasures against social engineering attacks are also discussed.
3D Hand Pose Estimation Based on Five-Layer Ensemble CNN

Estimating accurate 3D hand pose from a single RGB image is a highly challenging problem in pose estimation due to self-geometric ambiguities, self-occlusions, and the absence of depth information. To this end, a novel Five-Layer Ensemble CNN (5LENet) is proposed based on hierarchical thinking, which is designed to decompose the hand pose estimation task into five single-finger pose estimation sub-tasks. Then, the sub-task estimation results are fused to estimate full 3D hand pose. The hierarchical method is of great benefit to extract deeper and better finger feature information, which can effectively improve the estimation accuracy of 3D hand pose. In addition, we also build a hand model with the center of the palm (represented as Palm) connected to the middle finger according to the topological structure of hand, which can further boost the performance of 3D hand pose estimation. Additionally, extensive quantitative and qualitative results on two public datasets demonstrate the effectiveness of 5LENet, yielding new state-of-the-art 3D estimation accuracy, which is superior to most advanced estimation methods.
Lattice-based signcryption with equality test in standard model

A signcryption, which is an integration of a public key encryption and a digital signature, can provide confidentiality and authenticity simultaneously. Additionally, a signcryption associated with equality test allows a third party (e.g., a cloud server) to check whether or not two ciphertexts are encrypted from the same message without knowing the message. This application plays an important role especially in computing on encrypted data. In this paper, we propose the first lattice-based signcryption scheme equipped with a solution to testing the message equality in the standard model. The proposed signcryption scheme is proven to be secure against insider attacks under the learning with errors assumption and the intractability of the short integer solution problem. As a by-product, we also show that some existing lattice-based signcryptions either is insecure or does not work correctly.
Application Research of Ensemble Learning Frameworks

In the field of artificial intelligence, machine learning is a hot research direction. With the improvement of computing power, machine learning has been better developed in the era of big data. A single machine learning algorithm has some shortcomings. Multiple simple base learning methods are combined to form an ensemble learning through a certain strategy, and meanwhile the performance of the ensemble learning will be greatly improved. The ensemble learning framework includes three integration modes: boosting, bagging, and stacking. This article introduces the development process of ensemble learning and studies the principles of the three ensemble methods, summarizes the existing ensemble learning algorithms, summarizes the characteristics and applications of the ensemble algorithms, and finally discusses the improvement and future development of the algorithms.
Domain adversarial neural networks for domain generalization: when it works and how to improve

Abstract Theoretically, domain adaptation is a well-researched problem. Further, this theory has been well-used in practice. In particular, we note the bound on target error given by Ben-David et al. (Mach Learn 79(1–2):151–175, 2010) and the well-known domain-aligning algorithm based on this work using Domain Adversarial Neural Networks (DANN) presented by Ganin and Lempitsky (in International conference on machine learning, pp 1180–1189). Recently, multiple variants of DANN have been proposed for the related problem of domain generalization , but without much discussion of the original motivating bound. In this paper, we investigate the validity of DANN in domain generalization from this perspective. We investigate conditions under which application of DANN makes sense and further consider DANN as a dynamic process during training. Our investigation suggests that the application of DANN to domain generalization may not be as straightforward as it seems. To address this, we design an algorithmic extension to DANN in the domain generalization case. Our experimentation validates both theory and algorithm.
Aspect-Aware Response Generation for Multimodal Dialogue System

Multimodality in dialogue systems has opened up new frontiers for the creation of robust conversational agents. Any multimodal system aims at bridging the gap between language and vision by leveraging diverse and often complementary information from image, audio, and video, as well as text. For every task-oriented dialog system, different aspects of the product or service are crucial for satisfying the user’s demands. Based upon the aspect, the user decides upon selecting the product or service. The ability to generate responses with the specified aspects in a goal-oriented dialogue setup facilitates user satisfaction by fulfilling the user’s goals. Therefore, in our current work, we propose the task of aspect controlled response generation in a multimodal task-oriented dialog system. We employ a multimodal hierarchical memory network for generating responses that utilize information from both text and images. As there was no readily available data for building such multimodal systems, we create a Multi-Domain Multi-Modal Dialog (MDMMD++) dataset. The dataset comprises the conversations having both text and images belonging to the four different domains, such as hotels, restaurants, electronics, and furniture. Quantitative and qualitative analysis on the newly created MDMMD++ dataset shows that the proposed methodology outperforms the baseline models for the proposed task of aspect controlled response generation.