Miracle-dz/UniFAR-FTUs huggingface dataset:
An Efficient Probabilistic Registration Based on Shape Descriptor for Heritage Field Inspection

Heritage documentation is implemented by digitally recording historical artifacts for the conservation and protection of these cultural heritage objects. As efficient spatial data acquisition tools, laser scanners have been widely used to collect highly accurate three-dimensional (3D) point clouds without damaging the original structure and the environment. To ensure the integrity and quality of the collected data, field inspection (i.e., on-spot checking the data quality) should be carried out to determine the need for additional measurements (i.e., extra laser scanning for areas with quality issues such as data missing and quality degradation). To facilitate inspection of all collected point clouds, especially checking the quality issues in overlaps between adjacent scans, all scans should be registered together. Thus, a point cloud registration method that is able to register scans fast and robustly is required. To fulfill the aim, this study proposes an efficient probabilistic registration for free-form cultural heritage objects by integrating the proposed principal direction descriptor and curve constraints. We developed a novel shape descriptor based on a local frame of principal directions. Within the frame, its density and distance feature images were generated to describe the shape of the local surface. We then embedded the descriptor into a probabilistic framework to reject ambiguous matches. Spatial curves were integrated as constraints to delimit the solution space. Finally, a multi-view registration was used to refine the position and orientation of each scan for the field inspection. Comprehensive experiments show that the proposed method was able to perform well in terms of rotation error, translation error, robustness, and runtime and outperformed some commonly used approaches.
Combating Fake News in "Low-Resource" Languages: Amharic Fake News Detection Accompanied by Resource Crafting

The need to fight the progressive negative impact of fake news is escalating, which is evident in the strive to do research and develop tools that could do this job. However, a lack of adequate datasets and good word embeddings have posed challenges to make detection methods sufficiently accurate. These resources are even totally missing for “low-resource” African languages, such as Amharic. Alleviating these critical problems should not be left for tomorrow. Deep learning methods and word embeddings contributed a lot in devising automatic fake news detection mechanisms. Several contributions are presented, including an Amharic fake news detection model, a general-purpose Amharic corpus (GPAC), a novel Amharic fake news detection dataset (ETH_FAKE), and Amharic fasttext word embedding (AMFTWE). Our Amharic fake news detection model, evaluated with the ETH_FAKE dataset and using the AMFTWE, performed very well.
Thermodynamics of Crystals and Melting

The Helmholtz free energy, A, of a rigid body is a function of temperature, and of the six homogeneous strain components. If the crystal is to be rigid, three inequalities must be satisfied for the derivatives of A with respect to the six strain components, for a regular (cubic) lattice. This enables one to limit the pressure‐temperature range for which the crystal is stable. The violation of the condition c44>0, that the crystal resist shearing, is interpreted as leading to melting. From a knowledge of the forces between the molecules the phase integral, and therefore the free energy, may be calculated as a function of T, V, and the six strain components. The numerical calculations are carried out for a body‐centered cubic lattice. The product of all the frequencies is calculated directly, so that the assumption that the Debye equation for the frequency distribution holds, is not necessary. The melting curve, pressure against temperature, is then determined.
A Cyber-Security Methodology for a Cyber-Physical Industrial Control System Testbed

Due to recent increase in deployment of Cyber-Physical Industrial Control Systems in different critical infrastructures, addressing cyber-security challenges of these systems is vital for assuring their reliability and secure operation in presence of malicious cyber attacks. Towards this end, developing a testbed to generate real-time data-sets for critical infrastructure that would be utilized for validation of real-time attack detection algorithms are indeed highly needed. This paper investigates and proposes the design and implementation of a cyber-physical industrial control system testbed where the Tennessee Eastman process is simulated in real-time on a PC and the closed-loop controllers are implemented on the Siemens PLCs. False data injection cyber attacks are injected to the developed testbed through the man-in-the-middle structure where the malicious hackers can in real-time modify the sensor measurements that are sent to the PLCs. Furthermore, various cyber attack detection algorithms are developed and implemented in real-time on the testbed and their performance and capabilities are compared and evaluated.
Estimation of Moisture Content in Thickened Tailings Dams: Machine Learning Techniques Applied to Remote Sensing Images

Chile is one of the major producers of copper in the world, and as such is responsible for 1.7 million tons of tailings per day. While the most commonly used deposit to store this type of mining waste is historically tailings sand dams, the mining industry has over the last two decades been inclined toward thickened tailings dams (TTD) because of their advantages in water resource recovery, lower environmental impact, and better physical and chemical stability over conventional deposits. Within the geotechnical area, one key requirement of TDD, is the need to monitor moisture content (w%) during operation, which is today mostly performed in situ – via conventional geotechnical or simple visual means by TTD operators – or off site, via remote sensing. In this work, an intelligent system is proposed that allows estimation of different classes of in-situ states and w% in TTD using Machine learning algorithms based on Artificial Neural Networks (ANN), Support Vector Machine (SVM) and Random Forest (RF). The results show an accuracy of between 94% and 97% in the classification task of the Dry, Semisolid, Plastic and Saturated classes, and between 0.356 and 0.378 of the MAE metric in the regression task, which is sufficient to estimate the w% with ML methods.
Identifying Fake News on Social Networks Based on Natural Language Processing: Trends and Challenges

The epidemic spread of fake news is a side effect of the expansion of social networks to circulate news, in contrast to traditional mass media such as newspapers, magazines, radio, and television. Human inefficiency to distinguish between true and false facts exposes fake news as a threat to logical truth, democracy, journalism, and credibility in government institutions. In this paper, we survey methods for preprocessing data in natural language, vectorization, dimensionality reduction, machine learning, and quality assessment of information retrieval. We also contextualize the identification of fake news, and we discuss research initiatives and opportunities.
Assessment of Twitter Data Clusters with Cosine-Based Validation Metrics Using Hybrid Topic Models

Text data clustering is performed for organizing the set of text documents into the desired number of coherent and meaningful sub-clusters. Modeling the text documents in terms of topics derivations is a vital task in text data clustering. Each tweet is considered as a text document, and various topic models perform modeling of tweets. In existing topic models, the clustering tendency of tweets is assessed initially based on Euclidean dissimilarity features. Cosine metric is more suitable for more informative assessment, especially of text clustering. Thus, this paper develops a novel cosine based external and interval validity assessment of cluster tendency for improving the computational efficiency of tweets data clustering. In the experimental, tweets data clustering results are evaluated using cluster validity indices measures. Experimentally proved that cosine based internal and external validity metrics outperforms the other using benchmarked and Twitter-based datasets.
Sketches for Blockchains

Blockchains suffer from a critical scalability problem where traditionally each network node maintains all network state, including records since the establishment of the blockchain. Sketches are popular hash-based data structures used to represent a large amount of data while supporting particular queries such as on set membership, cardinality estimation and identification of large elements. Often, to achieve time and memory savings, sketches allow potential inaccuracies in answers to the queries. The design of popular blockchain networks such as Bitcoin and Ethereum makes use of sketches for various tasks such as summarization of transaction blocks or declaring the interests of light nodes. Similarly, they seem natural to deal with the size of the state in blockchains. In this paper, we study existing and potential future applications of sketches in blockchains. We first summarize current blockchain use cases of sketches. Likewise, we explore how this coupling can be generalized to a wider range of sketches and additional functionalities. In particular, we explain how sketches can detect anomalies based on efficient an summary of the state or traffic.
User mobility into NOMA assisted communication: Analysis and a Reinforcement Learning with Neural Network based approach

This article proposes a performance analysis of a non-orthogonal multiple access (NOMA) transmission system in the presence of user mobility. The main objective is to illustrate how the users’ mobility can affect the system performance in terms of downlink aggregated throughput, downlink network fairness, and percentage of quality-of-service requirement guaranteed. The idea behind is to highlight the importance to take into account user mobility in designing power allocation policies for NOMA systems. It is shown how the communication technologies are mainly dependent from channel state information (CSI) which in turns depends on users’ mobility. In addition a reinforcement learning (RL) to tackle with user mobility is proposed. Performance investigations regarding the proposed framework have shown how the network performances in presence of users’ mobility can be improved, especially when a feed-forward neural network is used as CSI estimator. Received on 10 December 2020; accepted on 19 December 2020; published on 07 January 2021
Lockable Signatures for Blockchains: Scriptless Scripts for All Signatures

Payment Channel Networks (PCNs) have given a huge boost to the scalability of blockchain-based cryptocurrencies: Beyond improving the transaction rate, PCNs enabled cheap cross-currency payments and atomic swaps. However, current PCNs proposals either heavily rely on special scripting features of the underlying blockchain (e.g. Hash Time Lock Contracts) or are tailored to a handful of digital signature schemes, such as Schnorr or ECDSA signatures. This leaves us in an unsatisfactory situation where many currencies that are being actively developed and use different signature schemes cannot enjoy the benefits of a PCN.In this work, we investigate whether we can construct PCNs assuming the minimal ability of a blockchain to verify a digital signature, for any signature scheme. In answering this question in the affirmative, we introduce the notion of lockable signatures, which constitutes the cornerstone of our PCN protocols. Our approach is generic and the PCN protocol is compatible with any digital signature scheme, thus inheriting all favorable properties of the underlying scheme that are not offered by Schnorr/ECDSA (e.g. aggregatable signatures or post-quantum security). While the usage of generic cryptographic machinery makes our generic protocol impractical, we view it as an important feasibility result as it may serve as the basis for constructing optimized protocols for specific signature schemes. To substantiate this claim, we design a highly efficient PCN protocol for the special case of Boneh-Lynn-Shacham (BLS) signatures. BLS signatures enjoy many unique features that make it a viable candidate for a blockchain, e.g. short, unique, and aggregatable signatures. Yet, prior to our work, no PCN was known to be compatible with it (without requiring an advanced scripting language). The cost of our PCN is dominated by a handful of calls to the BLS algorithms. Our concrete evaluation of these basic operations shows that users with commodity hardware can process payments with minimal overhead.
Generation of atomic oxygen in the effluent of an atmospheric pressure plasma jet

The planar 13.56 MHz RF-excited low temperature atmospheric pressure plasma jet (APPJ) investigated in this study is operated with helium feed gas and a small molecular oxygen admixture. The effluent leaving the discharge through the jet's nozzle contains very few charged particles and a high reactive oxygen species' density. As its main reactive radical, essential for numerous applications, the ground state atomic oxygen density in the APPJ's effluent is measured spatially resolved with two-photon absorption laser induced fluorescence spectroscopy. The atomic oxygen density at the nozzle reaches a value of ∼1016 cm−3. Even at several centimetres distance still 1% of this initial atomic oxygen density can be detected. Optical emission spectroscopy (OES) reveals the presence of short living excited oxygen atoms up to 10 cm distance from the jet's nozzle. The measured high ground state atomic oxygen density and the unaccounted for presence of excited atomic oxygen require further investigations on a possible energy transfer from the APPJ's discharge region into the effluent: energetic vacuum ultraviolet radiation, measured by OES down to 110 nm, reaches far into the effluent where it is presumed to be responsible for the generation of atomic oxygen.4 This study forms part of: S Reuter 2007 Formation mechanisms of atomic oxygen in an atmospheric pressure plasma jet characterised by spectroscopic methods Dissertation Duisburg-Essen University and was presented at the 59th Gaseous Electronics Conference (GEC) (Columbus, OH).
An Examination of People's Privacy Concerns, Perceptions of Social Benefits, and Acceptance of COVID-19 Mitigation Measures That Harness Location Information: A Comparative Study of the U.S. and South Korea

This paper examines people&rsquo;s privacy concerns, perceptions of social benefits, and acceptance of various COVID-19 control measures that harness location information using data collected through an online survey in the U S and South Korea The results indicate that people have higher privacy concerns for methods that use more sensitive and private information The results also reveal that people&rsquo;s perceptions of social benefits are low when their privacy concerns are high, indicating a trade-off relationship between privacy concerns and perceived social benefits Moreover, the acceptance by South Koreans for most mitigation methods is significantly higher than that by people in the U S Lastly, the regression results indicate that South Koreans (compared to people in the U S ) and people with a stronger collectivist orientation tend to have higher acceptance for the control measures because they have lower privacy concerns and perceive greater social benefits for the measures These findings advance our understanding of the important role of geographic context and culture as well as people&rsquo;s experiences of the mitigation measures applied to control a previous pandemic
Two-Stage Inference for LncRNA-Disease Associations Based on Diverse Heterogeneous Information Sources

Long non-coding RNAs (lncRNAs) exert impacts on multiple fundamental and important biological processes. Many lncRNAs have been functionally associated with cancers. Utilizing experimental and bioinformatics approaches to identify and annotate lncRNAs with cancer-associated roles is laborious and time-consuming. Therefore, more and more researchers have focused on computational methods as an alternative candidate to find out unknown associations between lncRNAs and diseases, in particular, cancers. In this study, under the situation that there were few known lncRNA-disease associations out of huge unknown associations, we explored a novel two-stage prediction model (namely DRW-BNSP) for inferring lncRNA-disease associations: In the first stage, we designed a Dual Random Walk (DRW) model to obtain the primary prediction scores by walking on two combined similarity networks which were reconstructed; In the second stage, we used a Bipartite Network Space Projection (BNSP) model to make the primary prediction scores to be more accurate furtherly. Compared with other state-of-the-art methods in similar type, our DRW-BNSP could not only function on new lncRNAs and isolated diseases, but also achieve higher AUC value of 0.9344 and 0.9432 on the first dataset (namely Dataset1) and second dataset (namely Dataset2) built by us. Furthermore, case study further confirmed the predictive dependability of our DRW-BNSP for inferring potential lncRNA-disease associations.
Human-Centered AI to Support an Adaptive Management of Human-Machine Transitions with Vehicle Automation

This article is about the Human-Centered Design (HCD), development and evaluation of an Artificial Intelligence (AI) algorithm aiming to support an adaptive management of Human-Machine Transition (HMT) between car drivers and vehicle automation. The general principle of this algorithm is to monitor (1) the drivers’ behaviors and (2) the situational criticality to manage in real time the Human-Machine Interactions (HMI). This Human-Centered AI (HCAI) approach was designed from real drivers’ needs, difficulties and errors observed at the wheel of an instrumented car. Then, the HCAI algorithm was integrated into demonstrators of Advanced Driving Aid Systems (ADAS) implemented on a driving simulator (dedicated to highway driving or to urban intersection crossing). Finally, user tests were carried out to support their evaluation from the end-users point of view. Thirty participants were invited to practically experience these ADAS supported by the HCAI algorithm. To increase the scope of this evaluation, driving simulator experiments were implemented among three groups of 10 participants, corresponding to three highly contrasted profiles of end-users, having respectively a positive, neutral or reluctant attitude towards vehicle automation. After having introduced the research context and presented the HCAI algorithm designed to contextually manage HMT with vehicle automation, the main results collected among these three profiles of future potential end users are presented. In brief, main findings confirm the efficiency and the effectiveness of the HCAI algorithm, its benefits regarding drivers’ satisfaction, and the high levels of acceptance, perceived utility, usability and attractiveness of this new type of “adaptive vehicle automation”.
Facilitating Workers' Task Proficiency with Subtle Decay of Contextual AR-Based Assistance Derived from Unconscious Memory Structures

Contemporary assistance systems support a broad variety of tasks. When they provide information or instruction, the way they do it has an implicit and often not directly graspable impact on the user. System design often forces static roles onto the user, which can have negative side effects when system errors occur or unique and previously unknown situations need to be tackled. We propose an adjustable augmented reality-based assistance infrastructure that adapts to the user’s individual cognitive task proficiency and dynamically reduces its active intervention in a subtle, not consciously noticeable way over time to spare attentional resources and facilitate independent task execution. We also introduce multi-modal mechanisms to provide context-sensitive assistance and argue why system architectures that provide explainability of concealed automated processes can improve user trust and acceptance.
Gustav E. R. Schulze's pioneering work on Laves phases

Abstract There are several reasons to recollect the merits of Gustav Ernst Robert Schulze on the occasion of Fritz Laves' anniversary. Firstly, he had been a good friend of Laves and shared his interest in intermetallic compounds. Secondly, his work on Laves phases, though containing important early results, is less well known for some reasons. Thirdly, Schulze was the founder of a very efficient research centre on intermetallics in Dresden which is still active today. Finally, he introduced Metal Physics at the TU Dresden as a broad subject of bringing together structure and properties of the metallic state from a more fundamental point of view. Later on, this had a clustering effect for the development of materials research institutes in Dresden.
An Optimized Machine Learning Model for Stock Trend Anticipation

Security market is an economical-volatile in nature as it is driven by not only based on historical prices various unpredictable external factors like financial news, changes in socio-political issues and natural calamities happened in real world; hence its forecasting is a challenging task for traders. To gain profits and to overcome any crisis in financial market, it is essential to have a very accurate calculation of future trends by for the investors. The trend prediction results can be used as recommendations for investors as to whether they should buy or sell. Feature selection, dimensionality reduction and optimization techniques can be integrated with emerging advanced machine learning models, to get improvised prediction in terms of quality, performance, security and for effective assessment external factors role in stock market nonlinear signals. In this empirical research work, a set of hybrid models were built and their predictive abilities were compared to find consistent model. This work implies the base model, boosted model and deep learning model along with optimization techniques. From the experimental result, the optimized deep learning model, ABC-LSTM was turned out superior to all other considered financial models LSSVM, Gradient Boost, LSTM, ABC-LSSVM, ABC-Gradient Boost by showing best Mean Absolute Percentage Error (MAPE) value, which was low.
A New Efficient Identity-Based Encryption Without Pairing

So far, most of the Identity-Based Encryption (IBE) schemes have been realized by employing bilinear pairings, lattices, trapdoor discrete logarithm, or based on the quadratic residue problem. Among the IBE schemes, only pairing-based methods seem to be practical. Previously published non-pairing-based schemes are generally ineﬃcient in encryption, decryption, key generation, ciphertext size or key size. In this paper, we propose an IBE scheme based on a hybrid of Diﬃe-Hellman and RSA-like hardness assumption. The computational cost of the proposed scheme is lower than the previous schemes and the ciphertext size for an l -bit plaintext is only 2 l bits. The proposed scheme is similar to the well-known ElGamal encryption algorithm; therefore it might be used in applications such as oblivious computation.