Reviewerly/exHarmony huggingface dataset:
Reconstructing Detailed Human Body Using a Viewpoint Auto-Changing RGB-D Sensor for Rescue Operation

In the aftermath of a disaster, such as an earthquake or the collapse of dilapidated buildings, it is necessary to enter the ruins to rescue the wounded, which is often accompanied by the casualties of rescuers. We propose a wounded body reconstruction system for rescue operations in disaster zones to replace humans with robots for rescue in hazardous environments. However, many limitations such as self-scanning constraints, reliable human semantic parsing, accurate surface reconstruction, and plausible motion tracking make this work even more challenging. We employ a parametric model to break the self-scanning constraints and obtain human semantic parsing fundamentally. To enable the parametric model to present human detail, we improved the human parametric model by defining a detail layer applying to the template as an additional offset. We propose automatically changing the viewpoint according to the human pose with the help of a robotic arm to obtain a dense and complete observation and achieve a high precision surface reconstruction. To meet the requirements of the rescue operation, we propose a new pipeline to track the wounded motion accurately. The experiments demonstrate that our method can accurately recover the surface of the human body and achieve accurate motion tracking free from the constraints of self-scanning and preserving reliable human semantic parsing.
Post-Quantum Cheating Detectable Private Information Retrieval

Private Information Retrieval (PIR) allows a user to privately retrieve any item from a database such that the server(s) holding the database cannot learn any information about the user’s choice. Most existing PIR protocols focus on minimizing the communication cost for retrieving one bit from the database, in an honest-but-curious server model. Dishonest servers were studied in an ad-hoc fashion including the robust PIR and verifiable PIR for cheater identification, where the former further guarantees error correction but only works when the number of dishonest servers are bounded and the latter works for any number of dishonest servers but has to rely on the intractability assumption of certain computational hard problems and a tag published by the honest data owner. We initiate a systematic study of the fundamental problem of cheating detection for PIR (cd-PIR). We first show a theoretic result that rules out the possibility of information-theoretically secure cd-PIR against arbitrary number of cheaters (even allowing the data owner to publish a tag and lifting cheater identification), which justifies our study of computational cd-PIR. On the positive side, we show that computational cd-PIR against arbitrary number of cheaters can be achieved much more efficiently than all previous constructions and with weaker cryptography hardness assumptions. In particular, we obtain efficient cheating detection for PIR with more than one server that resists quantum algorithm for the first time.
Synchronization procedure for data collection in offline-online sessions

This article proposes a system for data replication and synchronization in mobile devices which is managed offline, allowing data collection in remote locations or deprived of internet connection. In this process there were shortcomings in the convergence and stability of the data, for which a synchronization procedure (web services) is used to assist it. As a result it was obtained that the synchronization between a database hosted in the cloud, a database hosted locally on the mobile device, the compatibility between different programming languages such as Django of Python as server, the deployment of Web Services and C# as client in the consumption of synchronization services is a success, carrying out a synchronization where the integrity of the data is not lost, enabling the connection of the devices in offline mode, performing the corresponding activities, to the time of having an internet connection to upload the data and keep them synchronized.
AI Based Multi Label Data Classification of Social Media

We have unlabeled data in different ways- news article, and countless other types of documenting text. Large volumes of data are produced from many online sources such as emails, www, organization’s electronic health records, and databases. These data must be classified to avoid information loss and to boost data discovery and retrieval more quickly. This research is about classifying tweets, the New York Times, or other social media. It classifies the data into predefined generic classes, such as “business,” “sad,” “style,” “advertising,” “events,” “news,” etc., using author information and some features. The approach is to reduce the noise and identify tweets or any data classes as it is evident that an article can fall in more than one category.
Secure Multiparty Computation with Free Branching

We study secure multi-party computation (MPC) protocols for branching circuits that contain multiple sub-circuits (i.e., branches) and the output of the circuit is that of single “active” branch. Crucially, the identity of the active branch must remain hidden from the protocol participants. While such circuits can be securely computed by evaluating each branch and then multiplexing the output, such an approach incurs a communication cost linear in the size of the entire circuit. To alleviate this, a series of recent works have investigated the problem of reducing the communication cost of branching executions inside MPC (without relying on fully homomorphic encryption). Most notably, the stacked garbling paradigm [Heath and Kolesnikov, CRYPTO’20] yields garbled circuits for branching circuits whose size only depends on the size of the largest branch. Presently, however, it is not known how to obtain similar communication improvements for secure computation involving more than two parties. In this work, we provide a generic framework for branching multi-party computation that supports any number of parties. The communication complexity of our scheme is proportional to the size of the largest branch and the computation is linear in the size of the entire circuit. We provide an implementation and benchmarks to demonstrate practicality of our approach.
Spectral introductions of typical minerals during the periods of skarn formation and evolution at Zhuxi tungsten polymetallic deposit in Jiangxi Province

朱溪是近年来在江西塔前-赋春成矿带发现的一个世界级超大型钨铜矿床。本文采用短波红外+热红外光谱技术对矿区13个钻孔进行了光谱测量，结合岩石-矿物地球化学分析，探讨了朱溪矿床矽卡岩中典型蚀变矿物的形成与演化过程，厘定了矽卡岩形成不同阶段矿物组合的光谱特征，构建了朱溪矿床的短波红外+热红外光谱勘查模型。研究发现:（1）区内不同矿物组合形成了明显的蚀变分带，由内向外依次为绢云母（富Si）+长石（岩体顶层蚀变，多期流体叠加综合作用）→外矽卡岩:钙铝榴石+透辉石+（绢云母）→透辉石+蛇纹石+绿泥（帘）石+滑石→绢云母（富Al）+绿泥（帘）石（基底不整合面蚀变）；（2）Al-OH波长的移动可指示成矿流体压力、温度及pH值的变化；（3）研究区透辉石的形成、演化与矿体之间关系密切，虽然富矿体赋存于矽卡岩形成早期的透辉石-石榴子石蚀变带，但大量矿体则赋存于矽卡岩退蚀变阶段的蛇纹石-绿泥石蚀变带；（4）矿体的形成与流体的混合作用关系极大，伊利石光谱吸收特征能够指示外部冷水（大气降水或地下水）的灌入轨迹。;The Zhuxi tungsten polymetallic giant deposit is located in the Taqian- Fuchun metallogenic belt, Jiangxi Province. By measuring 13 drill cores with SWIR and TIR spectroscopy combined with geochemical analysis, we discussed the formation and evolution process of typical skarn minerals in the Zhuxi deposit, summarized their spectral characteristics, and constructed a spectral exploration model. It was found that:(1) different mineral assemblages form obvious alteration zones, i.e., sericite+feldspar (the top layer of the rock mass, caused by multistage fluid superposition probably) → grossular+diopside+(sericite) → diopside+serpentine+chlorite (epidote+talc) → sericite+chlorite (epidote) (alteration on the basement); (2) the shift of the Al-OH wavelength 2200nm can indicate the changes in pressure, temperature, and pH value of the ore-forming fluid; (3) diopside formation and evolution have a close relationship with ore bodies. Bonanza occurs in the grossular+diopside alteration zone, and a large number of ore bodies occur in the serpentine+chlorite alteration zone. It evidenced the importance of the fluid mixing and cooling for mineralization. The ore body emerged in the early stage of skarn formation, but the real enrichment stages occurred in the retrograde alteration stage; (4) the mixing fluid plays a key role in forming the deposit, and the illite spectrum can indicate the trace of the external cold water (atmospheric precipitation or groundwater).
Towards Cross Domain Transfer Learning for Underwater Correspondence Search

Underwater images are challenging for correspondence search algorithms, which are traditionally designed based on images captured in air and under uniform illumination. In water however, medium interactions have a much higher impact on the light propagation. Absorption and scattering cause wavelength- and distance-dependent color distortion, blurring and contrast reductions. For deeper or turbid waters, artificial illumination is required that usually moves rigidly with the camera and thus increases the appearance differences of the same seafloor spot in different images. Correspondence search, e.g. using image features, is however a core task in underwater visual navigation employed in seafloor surveys and is also required for 3D reconstruction, image retrieval and object detection. For underwater images, it has to be robust against the challenging imaging conditions to avoid decreased accuracy or even failure of computer vision algorithms. However, explicitly taking underwater nuisances into account during the feature extraction and matching process is challenging. On the other hand, learned feature extraction models achieved high performance in many in-air problems in recent years. Hence we investigate, how such a learned robust feature model, D2Net, can be applied to the underwater environment and particularly look into the issue of cross domain transfer learning as a strategy to deal with the lack of annotated underwater training data.
Poster: PhisherCop - An Automated Tool Using ML Classifiers for Phishing Detection

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Deep Learning Based Facial Mask Detection Using Mobilenetv2

The Covid-19 pandemic has had a profound effect on our daily lives. One of the most effective ways to protect ourselves from this virus is to wear face masks. This research paper introduces face mask detection that authorities can use to reduce and prevent COVID-19. The face mask recognition process in this research paper is done with a deep learning algorithm and image processing done using MobileNetV2. Steps to build the model are data collection, pre-processing, data classification, model training and model testing. The authors came up with this approach due to the recent Covid-19 situations for following specific guidelines and the uprising trend of Artificial Intelligence and Machine Learning and its real-world practices. This system has been made to detect more than one person whether they are wearing masks or not. This system also gives us the Covid cases-related worldwide updates as per our chosen country and type of cases like total cases, total deaths etc. Such systems are already available, but the efficiency of the available mask detection systems was not achieved thoroughly. This newly developed system proposes to take a step further, which recognizes more than one person at a time and increases the accuracy level to a much greater extent.
PLM and PDM

The objective of this chapter is to provide an introduction to Product Data Management (PDM) systems in the PLM environment. This chapter will help readers working in PLM Initiatives understand the importance of PDM systems and participate in activities related to PDM systems. This chapter also aims to give students who are studying PLM a basic understanding of the components of PDM systems.
An Automatized Identity and Access Management System for IoT Combining Self-Sovereign Identity and Smart Contracts

Nowadays, open standards for self-sovereign identity and access management enable portable solutions that are following the requirements of IoT systems. This paper proposes a blockchain-based identity and access management system for IoT – specifically smart vehicles- as an exemplar use-case, showing two interoperable blockchains, Ethereum and Hyperledger Indy, and a self-sovereign identity model.
Research and Implementation of Buzzword Detection Technology Based on the Dynamic Circulation Corpus

Buzzword detection technology is of great significance for reflecting the language life situation, describing the actual state of language usage, and developing language resources. At present, the “Top Ten Annual Buzzwords in Chinese Media” released every year are extracted based on a human-computer cooperation method, which consists of buzzword detection technology and selection by human experts. But the existing buzzword detection technology has the following shortcomings: coarse statistical granularity, slow query, large amount of candidate buzzwords and a high proportion of “non-word” character sequences. In view of those shortcomings, this paper proposes a data storage solution using the InfluxDB time series database based on the daily cycle, two buzzword detection models comprising of logistic curve fitting and abnormal rising segment identification, and utilizing the phrase mining technology for quality scoring and filtering of buzzword candidates. The comparison experiments show that the technology proposed in this paper can achieve better results, significantly reduce the subsequent manual workload, provide more statistical information on buzzword candidates and a more efficient data query method.
Technological Attributes that Predict Tourists’ Intention to Visit Destination, Recommend and Destination Image: Empirical Evidence from the Malaga Chatbot

Chatbots are a technology that is causing disruption in the tourism industry. Despite its implementation in destinations and services, there are no tourism studies that examine the informativeness, accessibility, interactivity, and empathy in behavioral intentions. Statistical tools such as exploratory factor analysis and the hierarchical regression method were used in the data analysis. The results suggest that the attributes of informativeness were the most significant predictor of the behavioral intentions, followed by perceived empathy.
Solar Radiation Prediction Using Artificial Neural Network: A Comprehensive Review

Solar energy has a great potential along with wind energy to fulfill the energy demands of any nation. In the last few decades, considerable research interest has been shown to harness solar energy. This paper is prepared to consider the role of artificial neural network (ANN) in predicting the solar radiation value of any location. The prime objective of this paper is to review the most recent study on ANN-based techniques for finding out the best available methods in the literature for the prediction of solar radiation in comparison with the conventional methods. A comprehensive discussion has been presented to find out some of the research gaps in this domain.
Forensic Analysis of the Snapchat iOS App with Spectacles-Synced Artifacts

The Spectacles wearable smart glasses device from Snapchat records snaps and videos for the Snapchat service. A Spectacles device can sync data with a paired smartphone and upload recorded content to a user's online account. However, extracting and analyzing data from a Snapchat app is challenging due to the disappearing nature of the media. Very few commercial tools are available to obtain data from Snapchat apps. This chapter focuses on the extraction and analysis of artifacts from Snapchat and, specifically, Spectacles devices paired with Apple iPhones. A methodology is presented for forensically imaging Apple iPhones before and after critical points in the Spectacles and Snapchat pairing and syncing processes. The forensic images are examined to reveal the effects of each step of the pairing process. Several photos, videos, thumbnails and metadata files originating from Spectacles devices were obtained and tied to specific times, devices and locations. The research provides interesting insights into evidence collection from Spectacles devices paired with Apple iPhones.
Partially Supervised Image Captioning Model for Urban Road Views

Automatically generating a characteristic language portrayal of an image has pulled in interests in light of its significance in practical applications and on the grounds that it associates two significant artificial intelligence fields: natural language processing and computer vision. This paper proposes a partially supervised model for generating image descriptions based on instance segmentation labels. Instance segmentation, a combined approach of object detection and semantic segmentation is used for generating instance level labels which is then used for generating natural language descriptions for the image. The instance segmentation model uses MRCNN framework with feature pyramid networks and region proposal networks for object detection, and fully convolution layer for semantic segmentation. Information obtained from different local region proposals are used to generate region wise captions. Important aspects of the caption include distance, color and region calculations based on the results obtained from the instance segmentation layers. This paper uses instance segmentation layer information such as ROIs, class labels, probability scores and segmentation values for generating effective captions for the image. The proposed model is evaluated on Cityscape dataset where the primary objective is to provide semantic scene understanding based on the instances available in urban areas.
A Highly Secured Image Encryption Scheme using Quantum Walk and Chaos

The use of multimedia data sharing has drastically increased in the past few decades due to the revolutionary improvements in communication technologies such as the 4th generation (4G) and 5th generation (5G) etc. Researchers have proposed many image encryption algorithms based on the classical random walk and chaos theory for sharing an image in a secure way. Instead of the classical random walk, this paper proposes the quantum walk to achieve high image security. Classical random walk exhibits randomness due to the stochastic transitions between states, on the other hand, the quantum walk is more random and achieve randomness due to the superposition, and the interference of the wave functions. The proposed image encryption scheme is evaluated using extensive security metrics such as correlation coefficient, entropy, histogram, time complexity, number of pixels change rate and unified average intensity etc. All experimental results validate the proposed scheme, and it is concluded that the proposed scheme is highly secured, lightweight and computationally efficient. In the proposed scheme, the values of the correlation coefficient, entropy, mean square error (MSE), number of pixels change rate (NPCR), unified average change intensity (UACI) and contrast are 0.0069, 7.9970, 40.39, 99.60%, 33.47 and 10.4542 respectively.
A Didactic Tool for Digital Forensics

Several tools exist for performing digital forensics investigations on evidence data. As the vast variety of options available provides a wide span of choices to select from, this variation itself contributes to the complexity of learning and navigating these tools. To facilitate user’s learning efforts, we present a didactic tool that can be used to explore different digital forensics tools for investigating various evidence files in different OS platforms. We use synthetically generated data in the form of a made up scenario that offers safe, realistic, yet reliable data analysis. The digital forensics tools we use are Autopsy, WinHex, ProDiscover, and StegHide; and we demonstrate the execution of these tools in two different OS platforms as Windows and Mac. Our tool is promising to offer explanation and deep insight into commonly available digital forensics tools, and is offered to serve digital forensics students and/or professionals.
Effects of Incentivized Fake Reviews on E-commerce Markets

This study analyzes difficulties posed by dealers operating in e-commerce who intervene in reviews and undermine credibility by offering money to purchasers who post highly positive reviews of their products: the incentivized fake review problem. Moreover, we study its effects on e-commerce markets. Offering biased incentives to receive more favorable reviews undermines the review mechanism validity. In e-commerce market transactions, reviews by buyers more strongly affect sales of products than in other markets. Because a loss of trust in reviews reduces the transaction volume, this incentivized fake review problem might reduce the profits of e-commerce operators and of dealers. No report of the relevant literature has described a theoretical test of these problem-related hypotheses, this study explores the subject. First, we developed a model in which buyers obtain information about sellers' products through user-generated reviews. Sellers can distort the reviews indirectly by providing incentives for highly rated reviews. Next, based on this model, we derived a Nash equilibrium incentive amount by taking a game theoretical approach to the situation of reading about incentive amounts offered by the seller. Finally, we analyzed transaction situations and the seller's gain in the equilibrium. The results revealed many points to be consistent with findings from earlier studies and with actual conditions prevailing in e-commerce markets. However, this research is limited to the proposal and analysis of a theoretical model. Therefore, future studies must be undertaken using economic experiments to verify the consistency of a model using actual transaction data and to verify the attitudes of buyers when purchasing.
An Ant System Algorithm Based on Dynamic Pheromone Evaporation Rate for Solving 0/1 Knapsack Problem

Chauhan, Ruchi Sharma, Nirmala Sharma, HarishIn this research paper, a meta-heuristic search technique of ant system algorithm based on dynamic pheromone evaporation rate (ASA-DPER) is introduced for solving 0/1 knapsack problem (0/1 KP). In ASA-DPER algorithm, the pheromone evaporation rate is dependent on the per-iteration knapsack profit produced by the algorithm. If the present-iteration knapsack profit is higher than the previous-iteration knapsack profit, the pheromone evaporation rate is “ER 1,” and if the present-iteration knapsack profit is equal to the previous-iteration knapsack profit, the pheromone evaporation rate is “ER 2.” The value of ER 1 is always greater than the value of ER 2. To validate efficiency of ASA-DPER algorithm, experiments are performed on thirty small-scale 0/1 KP instances, and results prove that the ASA-DPER improves search quality and produces feasible result converging iteration faster, with respect to the base meta-heuristic ant system algorithm based on static pheromone evaporation rate (ASA-SPER).