Miracle-dz/UniFAR-FTUs huggingface dataset:
Robust 3D Reconstruction Using HDR-Based SLAM

3D reconstruction is an important topic in the field of the emerging applications such as smart robotics, virtual reality (VR), augmented reality (AR), and autonomous driving. RGB-D simultaneous localization and mapping (SLAM) technique is widely used in the reconstruction process. However, low light and low textured environment often results in insufficient point features and fails the reconstruction. To address this problem, we propose a robust RGB-D SLAM system using high dynamic range (HDR) image information called HDR-based SLAM. The deep learning based HDR generation method is adopted to map a single low dynamic range (LDR) image into a radiance map which is normalized to exclude the influence of exposure time. We retrained the ORB descriptor patch to fit the normalized radiance maps in the feature matching step. The proposed method can improve the quantitative camera trajectory accuracy and qualitative result of geometry reconstruction. Experimental results show that the proposed method has better performance compared to that of the standard range imaging SLAM under challenging low light environment, which helps expand the applicability of 3D reconstruction system.
Deep Transfer Network With Multi-Kernel Dynamic Distribution Adaptation for Cross-Machine Fault Diagnosis

Recently, various deep learning models, which are mainly based on data-driven algorithms, have received more and more attention in the field of intelligent fault diagnosis and prognostics. However, there are two major assumptions accepted by default in the existing studies: 1) The training (source domain) and testing (target domain) data sets obey the same feature distribution; 2) Sufficient labeled data with fault information is available for model training. In real industrial scenarios, especially for different machines, these assumptions are mostly invalid, which makes it a huge challenge to build reliable diagnostic model. Motivated by transfer learning, we present a novel intelligent method named deep transfer network (DTN) with multi-kernel dynamic distribution adaptation (MDDA) to address the problem of cross-machine fault diagnosis. In the proposed approach, the DTN has wide first-layer convolutional kernel and several small convolutional layers, which is utilized to extract transferable features across different machines and suppress high frequency noise. Then, the MDDA method constructs a weighted mixed kernel function to map different transferable features to a unified feature space, and the relative importance of the marginal and conditional distributions are also evaluated dynamically. The proposed method is verified by three transfer learning tasks of bearings, in which the health states of wind turbine bearings in real scenario are identified by using diagnosis knowledge from two different bearings in laboratories. The results show that the proposed method can achieve higher diagnosis accuracy and better transfer performance even under different noisy environment conditions than many other state-of-the-art methods. The presented framework offers a promising approach for cross-machine fault diagnosis.
A Note on Advanced Encryption Standard with Galois/Counter Mode Algorithm Improvements and S-Box Customization

Advanced Encryption Standard used with Galois Counter Mode, mode of operation is one of the the most secure modes to use the AES. This paper represents an overview of the AES modes focusing the AES-GCM mode and its particularities. Moreover, after a detailed analysis of the possibility of enhancement for the encryption and authentication phase, a method of generating custom encryption schemes based on GF(2 8 ) irreducible polynomials diﬀerent from the standard polynomial used by the AES-GCM mode is provided. Besides the polynomial customization, the solution proposed in this paper oﬀers the possibility to determine, for each polynomial, the constants that can be used in order to keep all the security properties of the algorithm. Using this customization method, allows changing the encryption schemes over a period of time without interfering with the process, bringing a major improvement from the security point of view by avoiding pattern creation. Furthermore, this paper sets the grounds for implementing authentication enhancement using a similar method to determine the polynomials that can be used instead of the default authentication polynomial, without changing the algorithm strength at all.
Privacy Policy Analysis of Banks and Mobile Money Services in the Middle East

Privacy compliance of the Middle East’s financial sector has been relatively unexplored. This paper evaluates the privacy compliance and readability of privacy statements for top banks and mobile money services in the Middle East. Our analysis shows that, overall, Middle Eastern banks have better privacy policy availability and language distribution, and are more privacy compliant compared to mobile money services. However, both the banks and mobile money services need to improve (1) compliance with the principles of children/adolescent’s data protection, accountability and enforcement, and data minimization/retention, and (2) privacy statement texts to be comprehensible for a reader with ~8 years of education or less.
Relationships Among Grain Hardness, Pentosan Fractions, and End-Use Quality of Wheat

ABSTRACT Grain texture (hardness) in wheat (Triticum aestivum L.) is a major determinant of end-usage. Variation in grain texture can be conceptually assigned to the two major hardness classes that result from the action of one major gene (Hardness) or to as-yet undetermined factors contributing to residual variation within hardness classes. Identifying the physicochemical basis of both sources of texture variation could provide a means of better controlling or manipulating this quality trait. Pursuant to this objective, the role of pentosans was examined. Pentosan fractions (membrane-associated, total, and soluble) were isolated from 13 hard and 13 soft wheat samples and their flours. Among the hard wheat samples, pentosans had a minimal role in modifying grain hardness. However, among the soft wheat samples, pentosans appeared to have a significant hardness-modifying effect that carried over into end-use quality. Among the soft wheat samples, pentosan fractions, along with wheat protein, accounted for 5.
A Machine Learning Approach to Enhance the Performance of D2D-Enabled Clustered Networks

Clustering has been suggested as an effective technique to enhance the performance of multicasting networks. Typically, a cluster head is selected to broadcast the cached content to its cluster members utilizing Device-to-Device (D2D) communication. However, some users can attain better performance by being connected with the Evolved Node B (eNB) rather than being in the clusters. In this article, we apply machine learning algorithms, namely Support Vector Machine, Random Forest, and Deep Neural Network to identify the users that should be serviced by the eNB. We therefore propose a mixed-mode content distribution scheme where the cluster heads and eNB service the two segregated groups of users to improve the performance of existing clustering schemes. A D2D-enabled multicasting scenario has been set up to perform a comprehensive simulation study that demonstrates that by utilizing the mixed-mode scheme, the performance of individual users, as well as the whole network, improve significantly in terms of throughput, energy consumption, and fairness. This study also demonstrates the trade-off between eNB loading and performance improvement for various parameters.
A Note on IBE Performance of a Practical Application

—Every user can be identiﬁed online by a unique string used for email or nickname on some of the many platforms out there. IBE systems propose a simple cryptosystem in which the public key system can be omitted by using the unique string as public identiﬁcation. In this paper we present a minimal email application that uses Clifford Cocks’ proposed IBE scheme. We analyze the impact of using it inside our application and how it can be improved to better ﬁt the need of nowadays applications.
Banners: Binarized Neural Networks with Replicated Secret Sharing

Binarized Neural Networks (BNN) provide efficient implementations of Convolutional Neural Networks (CNN). This makes them particularly suitable to perform fast and memory-light inference of neural networks running on resource-constrained devices. Motivated by the growing interest in CNN-based biometric recognition on potentially insecure devices, or as part of strong multi-factor authentication for sensitive applications, the protection of BNN inference on edge devices is rendered imperative. We propose a new method to perform secure inference of BNN relying on secure multiparty computation. While preceding papers offered security in a semi-honest setting for BNN or malicious security for standard CNN, our work yields security with abort against one malicious adversary for BNN by leveraging on Replicated Secret Sharing (RSS) for an honest majority with three computing parties. Experimentally, we implement Banners on top of MP-SPDZ and compare it with prior work over binarized models trained for MNIST and CIFAR10 image classification datasets. Our results attest the efficiency of Banners as a privacy-preserving inference technique.
Secure Healthcare Data Aggregation and Transmission in IoT—A Survey

Internet of medical things (IoMT) is getting researchers’ attention due to its wide applicability in healthcare. Smart healthcare sensors and IoT enabled medical devices exchange data and collaborate with other smart devices without human interaction to securely transmit collected sensitive healthcare data towards the server nodes. Alongside data communications, security and privacy is also quite challenging to securely aggregate and transmit healthcare data towards Fog and cloud servers. We explored the existing surveys to identify a gap in literature that a survey of fog-assisted secure healthcare data collection schemes is yet contributed in literature. This paper presents a survey of different data collection and secure transmission schemes where Fog computing based architectures are considered. A taxonomy is presented to categorize the schemes. Fog assisted smart city, smart vehicle and smart grids are also considered that achieve secure, efficient and reliable data collection with low computational cost and compression ratio. We present a summary of these scheme along with analytical discussion. Finally, a number of open research challenges are identified. Moreover, the schemes are explored to identify the challenges that are addressed in each scheme.
Towards Collaborative Predictive Maintenance Leveraging Private Cross-Company Data

: The accuracy of a predictive maintenance model is largely determined by the available training data. This puts such machine learning systems out of reach for small and medium-sized production engineering companies, as they are often unable to provide training data in sufficient quality and quantity. Building a collaborative model by pooling training data across many companies would solve this issue, but this data cannot simply be consolidated in a central location while at the same time preserving data integrity and security. This paper enables a collaborative model for predictive maintenance on cross-company data without exposing participants’ business information by connecting two recent methodologiesȷ blockchain and federated learning.
Aspects Regarding Safety and Security in Hotels: Romanian Experience

This study investigates safety and security from the perspective of Romanian tourists by assessing the level of importance that tourists give to safety and security depending on the level of classification of the services they experienced and the generation to which they belong. We used a quantitative research method in the form of a questionnaire and analysed eight dimensions of safety and security: detectors, emergency preparedness, medical preparedness, staff security, guestroom security, pool and beach security, hotel access control, and cyber security. We identified the differences between tourists’ perception of safety and security depending on the level of classification of accommodation services and on generation.
Let's not forget about language proficiency and cultural variations while linking bilingualism to executive control

Valian (2014) argues that current approaches in the study of bilingualism's beneficial effects on executive functions and cognition are at best methodologically weak. There is no unanimity on what constitutes executive function and which tasks measure it. I agree with her view that executive function is not a single phenomenon but a collection of processes. Different tasks measure different aspects of them. Performance on different executive function tasks is related to the particular cognitive demands of the tasks as well as the participants’ profile. It does not follow necessarily that negotiating two languages should lead to an enhancement of ability on that task. Therefore, no single task can capture the cognitive benefits of bilingualism. Valian proposes that factors such as education, socio-economic status, and even extracurricular skills involving sports and exercise could boost executive functioning independent of bilingualism.
Data Protection Impact Assessment Case Study for a Research Project Using Artificial Intelligence on Patient Data

Advances in artificial intelligence, smart sensors, data mining, and other fields of ICT have resulted in a plethora of research projects aimed at harnessing these technologies, for example to generate new knowledge about diseases, to develop systems for better management of chronic diseases, and to assist the elderly with independent living. While the algorithms themselves can be developed using anonymized or synthetic data, conducting a pilot study is often one of the key components of a research project, and such studies unavoidably involve actual users with their personal data. Although one of the derogations stipulated in Article 89 of the GDPR is related to the data processed for scientific purposes, the GDPR still is applicable to that processing in a broader interpretation. The computer scientists and engineers working in research projects may not always be fully familiar with all the details of the GDPR, a close collaboration with a lawyer specialized in the European data protection legislation is highly beneficial for the success of a project. In this paper, we consider a hypothetical research project developed by an engineer dealing with sensitive personal data and a lawyer conducting Data Protection Impact Assessment to ensure legality and quality of the research project. Povzetek: Prispevek varstvo osebnih podatkov pri uporabi metod umetne inteligence za analizo pacientov.
Terahertz Plasmon-Induced Transparency Effect in Parallel Plate Waveguide

Recently reported metamaterial analogues of plasmon-induced transparency are very promising in designing novel optical components, such as slow-light devices and optical switches. However, most of the previous works mainly focus on manipulating the PIT effects using planar structures in the free space under normal incidence, where the resonances are mostly induced by the incident electric fields. Here, we present a PIT analogue in a waveguide system in the terahertz regime by inserting coupled split-ring resonator pairs into a tapered parallel plate waveguide. Different from the free-space cases, the incident magnetic fields can also play an important role here. By carefully arranging the relative orientations of the coupled split-ring resonators in each pair, giant modulation of the PIT effect is observed. This straightforward approach may enable novel devices for integrated terahertz devices.
Towards End-to-End Deep Learning-based Writer Identification

: Writer identification is an important task to gain knowledge about life in the past, which is commonly solved by paleographic experts. In this work, we investigate an automatic writer identification procedure based on deep learning. So far, the most approaches are based on two or more different pipeline steps and only few of them can be trained in an end-to-end manner. In this paper, we propose a fully end-to-end deep learning-based model, which consists of a U-Net for binarization, a ResNet-50 for feature extraction, and an optimized learnable residual encoding layer to obtain global descriptors. We evaluate the proposed end-to-end model on the ICDAR17 competition dataset on historical document writer identification (Historical-WI) dataset. Moreover, we investigate the performance of our optimized encoding layer on three texture datasets. While the optimized encoding layer does not work well in the task of writer identification, it provides better performance on the texture datasets. Furthermore, we show that a pre-trained U-Net can improve the performance for writer identification.
The Bluetooth CYBORG: Analysis of the Full Human-Machine Passkey Entry AKE Protocol

from a device, and adversarial ability to modify communications back from the user to the device (see Figure 1). We reference the Tap n’ Ghost attack as an illustrative example throughout, but it is not the only attack leveraging the UtD communication channel. Touchloggers [12], [16], the StrandHogg vulnerability [24], social engineering, and shoulder-surﬁng attacks also fall into this category. All such attack vectors are systematically accounted for in our CYBORG model. Bluetooth’s Passkey Entry [10] generates and shares a random value (a passkey ) via the user to effectively achieve Abstract —In this paper, we computationally analyze Passkey Entry in its entirety as a cryptographic authenticated key exchange (AKE) – including user-protocol interactions that are typically ignored as out-of-band. To achieve this, we model the user-to-device channels, as well as the typical device-to-device channel, and adversarial control scenarios in both cases. In particular, we separately capture adversarial control of device displays on the initiating and responding devices as well as adversarial control of user input mechanisms using what we call a CYBORG model. The CYBORG model enables realistic real-world security analysis in light of published attacks on user-mediated protocols such as Bluetooth that leverage malware and device displays. In light of this, we show that all versions of Passkey Entry fail to provide security in our model. Finally, we demonstrate how slight modiﬁcations to the protocol would allow it to achieve stronger security guarantees for all current variants of passkey generation, as well as a newly proposed twofold mode of generation we term Dual Passkey Entry . These proof-of-concept modiﬁcations point to improved design approaches for user-mediated protocols. Finally, this work points to categories of vulnerabilities, based on compromise type, that could be exploited in Bluetooth Passkey Entry.
Repetition Preferences in Two-Handed Balanced Signs: Vestigial Locomotor Central Pattern Generators Shape Sign Language Phonetics and Phonology

Language is produced by bodies that evolved to fulfill a variety of functions, most of them non-communicative. Vestigial influences of adaptation for quadrupedal locomotion are still affecting bimanual actions, and have consequences on manual communication systems such as sign languages of the deaf. We discuss how central pattern generators (CPGs), networks of nerve cells in the spinal cord that drive locomotion, influence bimanual actions with alternating movements to be produced with repeated motion. We demonstrate this influence with data from three unrelated sign languages, American Sign Language, British Sign Language, and Hong Kong Sign Language: in all three sign languages two-handed balanced signs produced with alternating movements have a tendency to be repeated, whereas other types of two-handed balanced signs show the opposite tendency for single movements. These tendencies cannot be fully explained by factors such as iconicity. We propose a motoric account for these results: as alternating bimanual movements are influenced by locomotor patterns, they favor repeated movements.
Redox Regulation of the Actin Cytoskeleton in Cell Migration and Adhesion: On the Way to a Spatiotemporal View

The actin cytoskeleton of eukaryotic cells is a dynamic, fibrous network that is regulated by the concerted action of actin-binding proteins (ABPs). In particular, rapid polarization of cells in response to internal and external stimuli is fundamental to cell migration and invasion. Various isoforms of ABPs in different tissues equip cells with variable degrees of migratory and adhesive capacities. In addition, regulation of ABPs by posttranslational modifications (PTM) is pivotal to the rapid responsiveness of cells. In this context, phosphorylation of ABPs and its functional consequences have been studied extensively. However, the study of reduction/oxidation (redox) modifications of oxidation-sensitive cysteine and methionine residues of actin, ABPs, adhesion molecules, and signaling proteins regulating actin cytoskeletal dynamics has only recently emerged as a field. The relevance of such protein oxidations to cellular physiology and pathophysiology has remained largely elusive. Importantly, studying protein oxidation spatiotemporally can provide novel insights into localized redox regulation of cellular functions. In this review, we focus on the redox regulation of the actin cytoskeleton, its challenges, and recently developed tools to study its physiological and pathophysiological consequences.
Exploring the Role of a Cytokinin-Activating Enzyme LONELY GUY in Unicellular Microalga Chlorella variabilis

LONELY GUY has been previously characterized in flowering plants to be involved in the direct activation of cytokinins. In this study, the function of the only LONELY GUY gene (CvarLOG1) from unicellular green microalga Chlorella variabilis NC64A has been investigated. CvarLOG1 expressed mainly in the lag and log phases of growth and was confirmed to be a cytokinin-activating enzyme. Overexpression of CvarLOG1 in Chlorella led to extended life in culture by almost 10–20 days, creating a “stay-green” phenotype. In the transformed alga, the cell cycle was lengthened due to delayed entry into the G2/M phase contrary to the known role of cytokinins in stimulating G2/M transition possibly due to excessive levels of this hormone. However, due to the sustained growth and delayed senescence, there was an increase in cell number by 11% and in biomass by 46% at the stationary phase, indicating a potential application for the biofuel industry. The total carbohydrate and lipid yield increased by approximately 30 and 20%, respectively. RNA-Seq-based transcriptomic analysis revealed that the genes associated with light and dark reactions of photosynthesis were upregulated, which may be the reason for the increased biomass. These data show that LOG plays an essential role during the cell cycle and in the functioning of the chloroplast and that the pathway leading to direct activation of cytokinins via LOG is functional in algae.