Reviewerly/exHarmony huggingface dataset:
Application of maximum entropy for mineral prospectivity mapping in heavily vegetated areas of Greater Kurile Chain with Landsat 8 data

Exploration for strategic mineral resources, like precious metals, in remote areas of the Greater Kurile Chain is challenging. Climate and weather conditions, dissected relief, soil and forest cover in this region impede acquisition of geological data. Mineral exploration requires development of fast and cost-effective methods that allow processing of insufficient and fragmented data like spaceborne images. Consideration of specific defoliation technique of directed principal components (DPC) and mineral mapping as well as application of assessment technique of Maximum Entropy (MaxEnt, also Logistic Regression, logit) led us to use Landsat 8 OLI for modeling of mineralization distribution in the Kunashir and Iturup Islands. DPC analysis is an image enhancement technique that processes two band ratio images. One of these images contains information of the component of interest (e.g., hydrothermal alteration), the other image contains information related to the spectrally interfering component (e.g., vegetation). Computed DPCs with loadings of opposite signs on either of input images, highlight unique contributions of each band ratio. Therefore, DPC analysis allows to decrease influence of noise related to vegetation. MaxEnt is a general-purpose technique of spatial prognosis based on incomplete data; its key feature is to assess the degree of target presence or absence by approximating the probability distribution of maximum entropy or nearest to uniform distribution based on a set of constraints taken from the incomplete data. Mapping based on DPC analysis in this work aimed to outline hydrothermal alteration associated with known mineralization. Division of the study area into zones according to their mineral prospectivity was then achieved with configured, trained and validated MaxEnt model using the datasets pertaining to a part of the Kunashir Island, which is better studied and explored. The trained predictive model was then applied to the less explored Iturup Island. As a result, the predictive model rendered a mineral prospectivity map, which correctly outlines the locations of the known mineralization. The application of the specific computation techniques to spectral data processing allows assessment of the contribution of Landsat 8 band ratios into the directed principal components and their implementation for cost effective prospectivity mapping including known and probable locations of unknown Au–Ag-bearing zones.
The Decentralized Identity and Its Application for Industrial Internet

The decentralized identity is a new type of basic network resource that integrates traditional Internet protocols, blockchain and cryptography technologies. It is a key breakthrough to realize information protection and autonomy especially for industrial applications. Meanwhile, the Industrial Internet is the product of the integration of a new generation of network information technology and manufacturing and is an important infrastructure for realizing the development of digital, networked, and intelligent industries. Therefore, by closely following the development status of decentralized identity both domestic and abroad, and analyzing its application cases in the Industrial Internet, this article comes up with ideas for the follow-up research of decentralized identity and helps industrial digitization transformation.
Research on Fast Pre-Processing Method of Tunnel Point Cloud Data in Complex Environment

Abstract In recent years, the application and research of three-dimensional laser scanning technology in tunnel engineering has continuously emerged, while the shortcomings still exists. This paper conducts research on quick preprocessing method of tunnel point cloud data based on three-dimensional laser scanning. For streamlining the tunnel point cloud model data, comparison analysis discusses are applied to both the downsampling algorithm and the point cloud denoising method respectively. Simulation experiments are introduced to compare the effects of different downsampling algorithms applied to the tunnel point cloud model, analyze the sampling efficiency and performance of each algorithm. Combining with statistical denoising and radius denoising algorithms, a distance denoising based on an iterative filtering model method is proposed. The result shows that this method is suitable for the tunnel point cloud model during the construction period with complex environment, and can effectively eliminate most of the point cloud noise.
PokeRRT: Poking as a Skill and Failure Recovery Tactic for Planar Non-Prehensile Manipulation

In this work, we introduce <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">PokeRRT</i> , a novel motion planning algorithm that demonstrates poking as an effective non-prehensile manipulation skill to enable fast manipulation of objects and increase the size of a robot’s reachable workspace. We showcase poking as a failure recovery tactic used synergistically with pick-and-place for resiliency in cases where pick-and-place initially fails or is unachievable. Our experiments demonstrate the efficiency of the proposed framework in planning object trajectories using poking manipulation in uncluttered and cluttered environments. In addition to quantitatively and qualitatively demonstrating the adaptability of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">PokeRRT</i> to different scenarios in both simulation and real-world settings, our results show the advantages of poking over pushing and grasping in terms of success rate and task time.
Research of Intelligent Video Surveillance System based on Artificial Neural Network

Abstract In order to improve the video monitoring capabilities, this paper designs an intelligent video surveillance system. Firstly, it analyzes the current problems of video surveillance, and then proposes intelligent requirement in three aspects. In the training process, the model is obtained by searching, gray conversion, and training. Finally, the correctness of this system is verified by the actual application in real station.
Run-time adaptation of stream processing spanning the cloud and the edge

Applications that process streams of events generated by sensors are important in a wide range of domains. It is now common to distribute stream processing across edge devices and the cloud. This exploits processing power near the sensors, reducing the load on the cloud and often the required network bandwidth. In this paper we focus on one challenge in distributed stream processing: automatically adapting the partitioning of the processing between the edge and the cloud without a loss of service. An example is when the event arrival rate increases and the edge processor can no longer meet performance requirements. Re-partitioning without loss of service involves moving computations between the edge and the cloud while events are still being processed. In this paper we describe StrIoT - a stream processing system that supports automatic re-partitioning. It is based on a set of functional stream operators, and the paper describes how the run-time system can automatically adapt applications that use them. A key feature is support for the fission and fusion of operators during adaptations. Performance evaluation shows that StrIoT can move parts of a stream processing application between the cloud and edge with only a low, temporary impact on performance.
A quantification mechanism for assessing adherence to information security governance guidelines

Purpose Boards of Directors and other organisational leaders make decisions about the information security governance systems to implement in their companies. The increasing number of cyber-breaches targeting businesses makes this activity inescapable. Recently, researchers have published comprehensive lists of recommended cyber measures, specifically to inform organisational boards. However, the young cybersecurity industry has still to confirm and refine these guidelines. As a starting point, it would be helpful for organisational leaders to know what other organisations are doing in terms of using these guidelines. In an ideal world, bespoke surveys would be developed to gauge adherence to guidelines, but this is not always feasible. What we often do have is data from existing cybersecurity surveys. The authors argue that such data could be repurposed to quantify adherence to existing information security guidelines, and this paper aims to propose, and test, an original methodology to do so. Design/methodology/approach The authors propose a quantification mechanism to measure the degree of adherence to a set of published information security governance recommendations and guidelines targeted at organisational leaders. The authors test their quantification mechanism using a data set collected in a survey of 156 Italian companies on information security and privacy. Findings The evaluation of the proposed mechanism appears to align with findings in the literature, indicating the validity of the present approach. An analysis of how different industries rank in terms of their adherence to the selected set of recommendations and guidelines confirms the usability of our repurposed data set to measure adherence. Originality/value To the best of the authors’ knowledge, a quantification mechanism as the one proposed in this study has never been proposed, and tested, in the literature. It suggests a way to repurpose survey data to determine the extent to which companies are implementing measures recommended by published cybersecurity guidelines. This way, the proposed mechanism responds to increasing calls for the adoption of research practices that minimise waste of resources and enhance research sustainability.
A Characterization of Maximally Entangled Two-Qubit States

As already known by Rana's result, all eigenvalues of any partial-transposed bipartite state fall within the closed interval [-12,1]. In this note, we study a family of bipartite quantum states where the minimal eigenvalues of partial-transposed states are -12. For a two-qubit system, we find that the minimal eigenvalue of its partial-transposed state is -12 if and only if such a two-qubit state is maximally entangled. However this result does not hold in general for a two-qudit system when the dimensions of the underlying space are larger than two.
How Do Successful and Failed Projects Differ? A Socio-Technical Analysis

Software development is at the intersection of the social realm , involving people who develop the software, and the technical realm , involving artifacts (code, docs, etc.) that are being produced. It has been shown that a socio-technical perspective provides rich information about the state of a software project. In particular, we are interested in socio-technical factors that are associated with project success . For this purpose, we frame the task as a network classification problem. We show how a set of heterogeneous networks composed of social and technical entities can be jointly embedded in a single vector space enabling mathematically sound comparisons between distinct software projects. Our approach is specifically designed using intuitive metrics stemming from network analysis and statistics to ease the interpretation of results in the context of software engineering wisdom. Based on a selection of 32 open source projects, we perform an empirical study to validate our approach considering three prediction scenarios to test the classification model’s ability generalizing to (1) randomly held-out project snapshots, (2) future project states, and (3) entirely new projects. Our results provide evidence that a socio-technical perspective is superior to a pure social or technical perspective when it comes to early indicators of future project success. To our surprise, the methodology proposed here even shows evidence of being able to generalize to entirely novel (project hold-out set) software projects reaching predication accuracies of 80%, which is a further testament to the efficacy of our approach and beyond what has been possible so far. In addition, we identify key features that are strongly associated with project success. Our results indicate that even relatively simple socio-technical networks capture highly relevant and interpretable information about the early indicators of future project success.
A Unified Framework for Automobile Instrument Detection System

As a critical interface between humans and automobiles, the precision of automobile instruments is directly related to safety. During production testing, the check for automobile instruments is essential. Considering that a manufacturing line is necessary to detect various types of instruments frequently, this article proposes a unified and generalized detection framework for pointer meters. The proposed framework innovatively gives the corresponding solutions in view of the existing problems for each subtask. First, a pointer-free template is established by pixelwise background modeling. Then, the two-stage similarity measurement based on fuzzy theory and image reconstruction is designed to enhance images and emphasize pointers, boosting the generalization of the detection framework. For the pointer positioning, the edges of pointers are fit by the set-to-set distances introducing the directionality and intensity of pointers to improve the robustness. For the scale marks’ positioning, the spatial periodicity of scale marks is creatively introduced to address the challenges caused by noises and the connected scale marks. Finally, the indicator value (the value that the pointer points to) is calculated according to the obtained positions of the pointer and scale marks. Experimental results show that the proposed detection framework can reliably detect various types of pointer meters, even under complex illumination conditions.
Pseudo-conventional Approach for Cyberbullying and Hate-speech detection

Cyberbullying is an upsetting web misconduct with alarming results. It shows up in various structures, and in a large portion of the informal communities, it could be found in various textual or image formats. Detection of such episodes requires shrewd frameworks. In this paper we have compared various approaches that were and are still being used to detect cyberbullying. An approach with multiple ML Algorithms and DL model has been proposed and tested by us for the cause.
CryptoMaze: Privacy-Preserving Splitting of Off-Chain Payments

Payment protocols developed to realize off-chain transactions in Payment channel network (PCN) assumes the underlying routing algorithm transfers the payment via a single path. However, a path may not have sufficient capacity to route a transaction. It is inevitable to split the payment across multiple paths. If we run independent instances of the protocol on each path, the execution may fail in some of the paths, leading to partial transfer of funds. A payer has to reattempt the entire process for the residual amount. We propose a secure and privacy-preserving payment protocol, CryptoMaze. Instead of independent paths, the funds are transferred from sender to receiver across several payment channels responsible for routing, in a breadth-first fashion. Payments are resolved faster at reduced setup cost, compared to existing state-of-the-art. Correlation among the partial payments is captured, guaranteeing atomicity. Further, two party ECDSA signature is used for establishing scriptless locks among parties involved in the payment. It reduces space overhead by leveraging on core Bitcoin scripts. We provide a formal model in the Universal Composability framework and state the privacy goals achieved by CryptoMaze. We compare the performance of our protocol with the existing single path based payment protocol, Multi-hop HTLC, applied iteratively on one path at a time on several instances. It is observed that CryptoMaze requires less communication overhead and low execution time, demonstrating efficiency and scalability.
Communication-Loss Trade-Off in Federated Learning: A Distributed Client Selection Algorithm

Mass data generation occurring in the Internet-of-Things (IoT) requires processing to extract meaningful information. Deep learning is commonly used to perform such processing. However, due to the sensitive nature of these data, it is important to consider data privacy. As such, federated learning (FL) has been proposed to address this issue. FL pushes training to the client devices and tasks a central server with aggregating collected model weights to update a global model. However, the transmission of these model weights can be costly, gradually. The trade-off between communicating model weights for aggregation and the loss provided by the global model remains an open problem. In this work, we cast this trade-off problem of client selection in FL as an optimization problem. We then design a Distributed Client Selection (DCS) algorithm that allows client devices to decide to participate in aggregation in hopes of minimizing overall communication cost — while maintaining low loss. We evaluate the performance of our proposed client selection algorithm against standard FL and a state-of-the-art client selection algorithm, called Power-of-Choice (PoC), using CIFAR-10, FMNIST, and MNIST datasets. Our experimental results confirm that our DCS algorithm is able to closely match the loss provided by the standard FL and PoC, while on average reducing the overall communication cost by nearly 32.67% and 44.71% in comparison to standard FL and PoC, respectively.
A Code-Diverse Kannada-English Dataset For NLP Based Sentiment Analysis Applications

Due to expanded praxis of social media, there is an elevated interest in the Natural Language Processing (NLP) of textual substance. Code swapping is a ubiquitous paradox in multilingual nation and the social communication shows mixing of a low resourced language with a highly resourced language mostly written in non-native script in the same text. It is essential to refine the code swapped text to support distinctive NLP tasks such as Machine Translation, Automated Conversational Systems and Sentiment Analysis (SA). The preeminent objective of SA is to identify and analyze the attitude, opinion, emotion or the sentiment in the dataset. Though there are multiple systems skilled on mono-dialectal dataset, all of them break down when it comes for code-diverse data because of the heightened intricacy of blending at various standards of text. Nonetheless, there exist a smaller number of assets for modelling such definitive code-mixed data and the Machine Learning or the Deep Learning algorithms enforcing supervised learning approach yield the better results compared to the unsupervised learning. Such datasets are available for Hindi-English, Tamil-English, Malayalam-English, Bengali-English, German-English, Spanish-English, Japanese-English, Arabic-English etc. Though our research is concentrated towards NLP for emotion and sentiment detection of Kannada, a vibrant south Indian language, to start with, we build the first ever platinum standard corpus for NLP applications of code-diverse text in Kannada-English, as there is no such resource in our native language. The performance analysis of our dataset through Krippendorff&#x2019;s Alpha value of 0.89 indicates that it is a benchmark in development of Automatic Sentiment Analysis system for Kannada.
Together Alone

We present an online narrative using video conferencing that is designed to facilitate collaborative storytelling using physical objects. Although research into the use of video conferencing platforms for storytelling and games is limited, we suggest that there is much to learn from a design space that includes interpretations of popular game formats like escape rooms, role-playing games, and board games. Drawing on examples from online and offline contexts, we focus on the tangible and embodied characteristics of these forms of play to identify narrative design opportunities for video conferencing platforms. We contribute the documentation of our research through design process, resulting in three preliminary design recommendations to help facilitate tangible and embodied remote storytelling.
Classification of Stress in Office Work Activities Using Extreme Learning Machine Algorithm and One-way ANOVA F-Test Feature Selection

Stress is a condition when humans feel psychological pressure consciously or unconsciously comes from within themselves and the environment. Based on the survey, more than 50&#x0025; of workers experienced stress at work. Neglecting stressful conditions repeatedly can worsen the performance and health of workers. Identifying stress condition manually is not effective and may take some time. There is a need to build a system to diagnose stress quickly and accurately. Machine learning classification is one of the solutions to problems that are applied to intelligent systems. One of the machine learning methods that can be used is the Extreme Learning Machine (ELM) algorithm. One-way ANOVA F-test is used as a method of feature selection to require a quick decision so the reduction in features is expected to accelerate the results of the classification. The dataset used is the Heart Rate Variability totaling 5000 samples with 35 features and 3 classes. Based on experiments, the conventional ELM algorithm produces an accuracy of 0.878 while the combination of the ELM algorithm and the One-way ANOVA F-test produces an accuracy of 0.91 with 33 selected features. Thus, the effect of using the feature selection method can increase accuracy and reduce computational time, and the addition of hidden neurons results in a significant increase in accuracy and computational time.
Secure key issuing scheme in ID-based cryptography with revocable ID

ABSTRACTABSTRACTRecently security plays a crucial part throughout the communication framework. In the most recent decade, organizations have endeavored to move from a paper-concentrated environment to a sans paper environment where data security is one of the essential issues, and any weakness in this respect can have crushing impacts. Security implies the assurance of the data from any unauthorized access or manipulation through spying or scientific or probabilistic calculations and different strategies. For the security of information, researchers are doing more work on ID-based Cryptography (IDC). However, it has two limitations: one suffers from an inherent key escrow problem, and the second is how to revoke the misbehavior/compromised user. Tseng and Tsai have proposed a novel Revocable ID-Based Encryption (RIBE). Yet, they have not considered the key escrow problem. This paper proposed an approach in which secure key issuance is done only for non-revocable ID. This will reduce the key escrow problem as multiple entities rather than one Private Key Generator (PKG) issue the key. It will reduce the possibility of compromised PKG as the private key is cooperatively issued by PKG and multiple Key Privacy Authorities (KPAs).KEYWORDS: Securitypaper-concentratedID-based cryptographyRIBEkey escrowPKGmultiple KPAs Disclosure statementNo potential conflict of interest was reported by the author(s).