Reviewerly/exHarmony huggingface dataset:
Improved Blockchain Commodity Traceability System Using Distributed Hash Table

The problem of supply chain management erodes trust of consumer and cause huge economic loss. It's essential to track and authenticate every step of supply chain to solve the problem of trust. The traditional centralized storage system has the problem of tamperability so that it can't ensure that data is reliable when consumers query them. Using blockchain technology to store data can solve part of trust problems of tracing system. It ensures that stored data is unalterable, that is, the original data cannot be wiped out. This paper designs a traceability system using blockchain technology. According to the characteristics of supply chain, the distributed hash table is used to improve the storage structure, reduce the load of network and nodes, and improve the storage utilization.
BCB-X3DH: a Blockchain Based Improved Version of the Extended Triple Diffie-Hellman Protocol

The Extended Triple Diffie-Hellman (X3DH) protocol has been used for years as the basis of secure communication establishment among parties (i.e, humans and devices) over the Internet. However, such a protocol has several limits. It is typically based on a single trust third-party server that represents a single point of failure (SPoF) being consequently exposed to well- known Distributed Denial of Service (DDOS) attacks. In order to address such a limit, several solutions have been proposed so far that are often cost expensive and difficult to be maintained. The objective of this paper is to propose a BlockChain-Based X3DH (BCB-X3DH) protocol that allows eliminating such a SPoF, also simplifying its maintenance. Specifically, it combines the well- known X3DH security mechanisms with the intrinsic features of data non-repudiation and immutability that are typical of Smart Contracts. Furthermore, different implementation approaches are discussed to suits both human-to-human and device-to-device scenarios. Experiments compared the performance of both X3DH and BCB-X3DH.
A Privacy-Preserving and Overhead-Free Protocol for Direct Donations to People Impacted by COVID-19 Lockdowns

COVID-19 caused large-scale, long-lasting lockdowns in many parts of the world, which resulted in many people in need for financial or other aids.Governments, charities, and communities rushed to provide help.To complement these efforts and to provide direct assistance to individuals who may not get sufficient and/or timely assistance otherwise, we designed a privacy-preserving and overhead-free protocol named "Fireside Help" for matching donors and people-in-need.This protocol aims to provide maximum privacy protection.Only the assigned volunteer verifiers can know about details of the applications from people-in-need and the donations from the donors that they verified.No one else, including the Fireside Help system itself, can gain any knowledge beyond digital checksums and information intentionally made public.This protocol uses proven, free, and widely available hash functions such as SHA-256, SHA-1, or MD-5 to ensure the integrity of the system without revealing private information.The protocol was designed initially to help students in Wuhan, China, the very first city to lock down.Later, it was revised to help anyone around the world who has been impacted by COVID-19.Several real-world transactions were completed in the system that demonstrated its utility and robustness.
Construction of intelligent query system for metro electromechanical equipment faults based on the knowledge graph

The intelligent inquiry system for metro electro-mechanical equipment faults based on the knowledge graph can effectively consolidate various semi-structured failure messages, and can provide users with quick, accurate and high-quality intelligent inquiry services such as equipment fault causes-researching and solutions-delivering, which could be really relevant to this research field and application areas. The recorded date which related to metro electromechanical equipment failures were in this research collected, consolidated and converted, so that these failures could be stored in our databases. In this context, various functions of the intelligent inquiry system have been implemented, including: natural language question analysis, language Cypher-based question and answer design, Naive Bayesian classification based on characteristic core words, and user interaction interface realization. The experimental results show that the system can effectively solve the problems related to fault handling in metro mechanical and electrical equipment, thus improving the efficiency of equipment fault maintenance.
Hash Learning With Variable Quantization for Large-Scale Retrieval

Approximate Nearest Neighbor(ANN) search is the core problem in many large-scale machine learning and computer vision applications such as multimodal retrieval. Hashing is becoming increasingly popular, since it can provide efficient similarity search and compact data representations suitable for handling such large-scale ANN search problems. Most hashing algorithms concentrate on learning more effective projection functions. However, the accuracy loss in the quantization step has been ignored and barely studied. In this paper, we analyse the importance of various projected dimensions, distribute them into several groups and quantize them with two types of values which can both better preserve the neighborhood structure among data. One is Variable Integer-based Quantization (VIQ) that quantizes each projected dimension with integer values. The other is Variable Codebook-based Quantization (VCQ) that quantizes each projected dimension with corresponding codebook values. We conduct experiments on five common public data sets containing up to one million vectors. The results show that the proposed VCQ and VIQ algorithms can both achieve much higher accuracy than state-of-the-art quantization methods. Furthermore, although VCQ performs better than VIQ, ANN search with VIQ provides much higher search efficiency.
Confidence Calibration on Multiclass Classification in Medical Imaging

Current deep learning methods developed to address classification problems related to medical imaging for disease detection and diagnosis are primarily based on binary labels and also with limited focus on confidence calibration. Confidence estimates are closely related to classification accuracy. While existing neural networks have the capability of extending binary labels to multiclass labels, the confidence calibration procedure is generally overlooked. To address the issue, we propose a method called knowledge discriminator risk network (KDR) and a confidence calibration voting algorithm (KDR-CCV) that together enhance classification accuracy, with an emphasis on confidence calibration. Comparative studies on multiclass classification based on the Breast Imaging Reporting and Data Systems (BI-RADS) assessment categories with a dataset containing only binary labels of ultrasound images are conducted. Experimental results show KDR-CCV achieves the overall best classification performance in comparison to other methods that conform to the BI-RADS criterion in addition to the effective improvement on classification accuracy. The proposed method incorporates BI-RADS assessment and artificial intelligence from an application-based broad practice, and can be extended to other medical imaging problems.
Semi-blind robust watermarking with dual complex tree wavelet based hybrid transform and SVD

This paper presents an intelligent scheme for robust digital watermarking of image. The inherent property of perfect reconstruction of dual complex tree wavelet transform and secret transform order in fractional Hartley transform is motivation to select these transforms. These transformation in 2D are used to generate a hybrid transform. The LL-sub band of this hybrid transform is further decomposed into SVD components for embedding watermark. Only LL-band of original image is required during watermark extraction and therefore is considered as semi- blind watermarking scheme. Experimental evaluation proves that the proposed scheme is robust against variety of attacks.
A novel privacy-preserving healthcare information sharing platform using blockchain

Secure sharing of medical data is essential for both patients and providers of various interests. Patients' main concern is to securely and confidentially have their information stored and shares their sensitive medical records among different providers. Due to the inability of querying hospital's or doctor's private databases, the need for an immutable and decentralized database emerges. Blockchain is considered to provide an important role in improving interoperability in health data systems due to its characteristics of sharing and distribution. Many blockchain-based models for sharing medical data are proposed in the literature. However, many of these models do not prioritize the patient's concerns or interests. In this paper, we propose a patient-driven medical data sharing system using blockchain technology. We show how our model provides both the properties for a patient-driven system and the main security features for secure sharing of healthcare information.
An Overview on Fine-grained Text Sentiment Analysis: Survey and Challenges

Abstract Among various natural language process tasks, sentiment analysis has always been a research hotspot. From the initial sentence-level and document-level coarse-grained sentiment analysis to recent fine-grained sentiment analysis on the aspect word level, researchers are committed to applying diverse methods to obtain better sentiment analysis results, ranging from lexicon-based, statistical machine learning methods to deep learning models. In the change of technology, several benchmark datasets that can be used for model performance comparison are gradually yielded. This article summarizes the current research status of aspect-level text sentiment analysis from multiple dimensions such as dataset, mainstream methods, and evaluation indicators, finally, it puts forward the challenges facing and potential research directions from a unique perspective.
An augmented crowd simulation system using automatic determination of navigable areas

Crowd simulations imitate the group dynamics of individuals in different environments. Applications in entertainment, security, and education require augmenting simulated crowds into videos of real people. In such cases, virtual agents should realistically interact with the environment and the people in the video. One component of this augmentation task is determining the navigable regions in the video. In this work, we utilize semantic segmentation and pedestrian detection to automatically locate and reconstruct the navigable regions of surveillance-like videos. We place the resulting flat mesh into our 3D crowd simulation environment to integrate virtual agents that navigate inside the video avoiding collision with real pedestrians and other virtual agents. We report the performance of our open-source system using real-life surveillance videos, based on the accuracy of the automatically determined navigable regions and camera configuration. We show that our system generates accurate navigable regions for realistic augmented crowd simulations.
MixBoost: Synthetic Oversampling using Boosted Mixup for Handling Extreme Imbalance

Training a classification model on a dataset where the instances of one class outnumber those of the other class is a challenging problem. Such imbalanced datasets are standard in real-world situations such as fraud detection, medical diagnosis, and customer churn prediction. We propose a data augmentation method, MixBoost, which intelligently selects (Boost) and then combines (Mix) instances from the majority and minority classes to generate synthetic hybrid instances that have elements of both classes. We evaluate MixBoost on 20 benchmark datasets and show that it outperforms existing approaches. We evaluate the impact of the different components of MixBoost using ablation studies.
Upper Bounding Graph Edit Distance Based on Rings and Machine Learning

The graph edit distance (GED) is a flexible distance measure which is widely used for inexact graph matching. Since its exact computation is [Formula: see text]-hard, heuristics are used in practice. A popular approach is to obtain upper bounds for GED via transformations to the linear sum assignment problem with error-correction (LSAPE). Typically, local structures and distances between them are employed for carrying out this transformation, but recently also machine learning techniques have been used. In this paper, we formally define a unifying framework LSAPE-GED for transformations from GED to LSAPE. We also introduce rings, a new kind of local structures designed for graphs where most information resides in the topology rather than in the node labels. Furthermore, we propose two new ring-based heuristics RING and RING-ML, which instantiate LSAPE-GED using the traditional and the machine learning-based approach for transforming GED to LSAPE, respectively. Extensive experiments show that using rings for upper bounding GED significantly improves the state of the art on datasets where most information resides in the graphs’ topologies. This closes the gap between fast but rather inaccurate LSAPE-based heuristics and more accurate but significantly slower GED algorithms based on local search.
A Study of Improved Global Path Planning Algorithm for Parking Robot Based on ROS

This paper proposes an improved global path planning algorithm to generate the optimal global path that satisfies the kinematic constraints of parking robots. The estimation function is improved through BP neural network, which improves the planning efficiency of finding the shortest path. Improve the drivability of the planned route by setting up the prohibited area and the route backtracking. A simulation platform is built based on ROS, and the path planning effect of the traditional A* algorithm is compared with the effect of the improved global path planning algorithm. The results show that the improved algorithm has a shorter path length and better drivability. The overall deviation of the simulated trajectory driving along this path is small. The improved algorithm is used to conduct multiple terminal path planning experiments. The results show that the total length of the path generated by the algorithm is close to the global optimum, the path is smooth and easy to track.
Realization of a Secure Visible Light Communication System via Chaos Synchronization

A novel technique for transmission of information through visible light communication (VLC) is developed in this study. A light-emitting diode is used as the light source at the transmitting side to send the encrypted information. At the receiving side, a light sensor, OPT-101, is used to receive the light signals that carry the encrypted information. The Arduino Due microcontroller board is used for digital signal processing at both the transmitting and receiving sides. Furthermore, to prevent the transmitted message from being intercepted, two chaotic systems, a master and a slave, with a synchronization controller are designed to obtain the transmitted audio signals. The design enables not only a VLC system with the light transmission path as a straight line (so that data cannot be stolen) but also the encryption of the audio signals with the chaotic system (Rössler system) to enhance data transmission security. The effectiveness of this system is then experimentally verified.
Web Page Classification based on Unsupervised Learning using MIME type Analysis

The properties of a web page have a strong impact on the experience of web users. In this work, a classification method based on unsupervised clustering is proposed to group web pages into classes based on download content that may affect the Quality of Experience (QoE) perceived by the user. Groups are defined based on Multipurpose Internet Mail Extensions (MIME) content breakdown and external subdomain connections, obtained with a desktop personal computer (PC) running WebPageTest tool. The dataset is generated with a PC as a terminal, emulating the first access to 500 popular websites. The collected data is divided into groups with a classical unsupervised learning algorithm, namely K-means clustering. Results show how web pages are classified in six groups and their cluster characteristics.
Instance-based learning using the half-space proximal graph

• We propose a parameter-free instance-based classifier , the HSP classifier. • Our accuracy outperforms kNN while keeping its simplicity and complexity. • We also present a probabilistic and asymptotic versions of the HSP improving both the time and the original proposal’s accuracy. • The same weigthing methods that improves kNN, also improves the HSP and its asymptotic variants The primary example of instance-based learning is the k -nearest neighbor rule (kNN), praised for its simplicity and the capacity to adapt to new unseen data and toss away old data. The main disadvantages often mentioned are the classification complexity , which is O ( n ) , and the estimation of the parameter k , the number of nearest neighbors to be used. The use of indexes at classification time lifts the former disadvantage, while there is no conclusive method for the latter. This paper presents a parameter-free instance-based learning algorithm using the Half-Space Proximal (HSP) graph. The HSP neighbors simultaneously possess proximity and variety concerning the center node. To classify a given query, we compute its HSP neighbors and apply a simple majority rule over them. In our experiments, the resulting classifier bettered K N N for any k in a battery of datasets. This improvement sticks even when applying weighted majority rules to both kNN and HSP classifiers. Surprisingly, when using a probabilistic index to approximate the HSP graph and consequently speeding-up the classification task , our method could improve its accuracy in stark contrast with the kNN classifier, which worsens with a probabilistic index.
IF-GAN: Generative Adversarial Network for Identity Preserving Facial Image Inpainting and Frontalization

Face recognition has seen rapid development and widespread usage in recent years. However, recognizing faces with occlusions or in large pose variations still poses great challenges for existing face recognition algorithms. While these two obstructions frequently occur in real-world applications simultaneously, many recent research efforts can only solve one of these two problems. To address this issue, in this paper, we propose an identity preserving two-stage generative adversarial network that can simultaneously complete the task of de-occlusion and face frontalization. Unlike previous works that can only inpaint synthetic rectangular occlusion which is unlikely to occur in real-life scenarios, we employ partial convolutions in our face inpainting stage to handle the realistic irregular occlusions. For face frontalization, we use a dualpathway structure that processes global facial shape and local facial fiducial region details separately, allowing the network to gain enough semantic information to faithfully reconstruct the frontal facial image. Both stages are supervised on both pixel and feature levels such that the network can produce photo-realistic yet identity preserving unoccluded frontal facial images. Qualitative and quantitative experiments demonstrate that the proposed approach is able to improve the performance of existing face recognition systems by restoring identity information contaminated by occlusions and pose variations.
Construction fuzzy logic with curve shoulder in inference system mamdani

Abstract Fuzzy Logic is a popular methodology and widely used at cybernetics in developing software programs. Fuzzy logic is a logic which the truth values of variables any real number between 0 and 1 both are inclusive with the truth value shown through the membership function (curve). It is used to deal with the concept of partial truth, where the truth value may range between completely true or completely false. In general, fuzzy logic provides an inference structure that capable to match human reasoning which called an inference system. Fuzzy inference system Mamdani is the process of decision making based on fuzzy sets, fuzzy rules and fuzzy logic with Mamdani method (min-max). On Mamdani method, the fuzzy sets are obtained by taking the minimum value of the rule, then use it to modify the fuzzy region and apply it to the output with the operator OR (union). This paper concerns fuzzy logic construction with curve shoulder in inference system Mamdani. The result shows five steps that used to construct inference system Mamdani which are fuzzification, operator fuzzy logic, implication, aggregation, and defuzzification.
On Collaborative Radio Frequency Observatory in India

In this paper, we describe a national level collaborative radio frequency observatory (CRFO) for maintaining a collection of RF measurement data from various Indian states. The online observatory will be primarily used for monitoring and analyzing the national spectrum usage as well as for the development of machine learning based radio signal classification and identification techniques for anti-jamming, interference management, and cognitive radio (CR) systems. Initial results on national spectrum usage analysis observed from different parts of India show that only a small percentage of GSM spectrum is utilized due to service migration of cellular users to 4G networks. It is also seen that the 4G bands are under-utilized during low traffic conditions. The results suggest that depending on the location, the under-utilized spectrum can be used for CR based services.
Remote Interviewer Training for COVID-19 Data Collection: Challenges and Lessons Learned From 3 Countries in Sub-Saharan Africa

<h3>ABSTRACT</h3> There is an urgent need for data to inform coronavirus disease (COVID-19) pandemic response efforts. At the same time, the pandemic has created challenges for data collection, one of which is interviewer training in the context of social distancing. In sub-Saharan Africa, in-person interviewer training and face-to-face data collection remain the norm, requiring researchers to think creatively about transitioning to remote settings to allow for safer data collection that respects government guidelines. Performance Monitoring for Action (PMA, formerly PMA2020) has collected both cross-sectional and longitudinal data on key reproductive health measures in Africa and Asia since 2013. Relying on partnerships with in-country research institutes and cadres of female interviewers recruited from sampled communities, the project was well-positioned to transition to collecting data on COVID-19 from the onset of the pandemic. This article presents PMA9s development of a remote training system for COVID-19 surveys in the Democratic Republic of the Congo, Kenya, and Nigeria, including challenges faced and lessons learned. We demonstrate that remote interviewer training can be a viable approach when data are critically needed and in-person learning is not possible. We also argue against systematic replacement of in-person trainings with remote learning, instead recommending consideration of local context and a project9s individual circumstances when contemplating a transition to remote interviewer training.