Miracle-dz/UniFAR-FTUs huggingface dataset:
Does Scarcity Add Value in Influencing Consumers in the Try-Before-You-Buy Model?

ABSTRACT The number of shopping options for consumers in the e-commerce retail environment continues to expand. One such option allows consumers to try products at home before committing to purchase, referred to as the Try-Before-You-Buy (TBYB) model. This study examines the role of scarcity in affecting consumers’ product acceptance intention. Our results reveal that product scarcity increases both consumers’ perceived utilitarian value and hedonic value, which enhance their product acceptance intention in the TBYB model. Additionally, we find that price scarcity also stimulates consumers’ product acceptance intention, but channel scarcity does not. Furthermore, we find that a high hassle cost does not necessarily increase consumers’ passive acceptance intention of products in the TBYB model. Our findings provide implications for TBYB retailers to enhance consumers’ intention to accept products through advertising the scarcity of their products and price, improving both the utilitarian values and hedonic values of their product offerings, but not setting obstacles for consumers to return their products. This study helps companies understand consumers’ perception in the TBYB model, which is critical to improving overall performance. This study also contributes to uniqueness theory by revealing that the effect of scarcity on consumers’ perceived uniqueness is not uniform. Scarcity in product acquisition and scarcity in payment have a higher impact on consumers’ perceived uniqueness than the scarcity of process in terms of the channel in which consumers use to acquire the products.
TOFU - Toggle Count Analysis made simple

Protection against physical attacks is a major requirement for cryptographic implementations running on devices which are accessible to an attacker. Side-channel attacks are the most common types of physical attacks, the most frequent side-channel is the device’s power consumption. In this work we propose a novel open-source tool called TOFU which synthesizes VCD simulation traces into power traces, with adjustable leakage models. Additionally, we propose a workflow which is only based on open-source tools. The functionality of TOFU and the proposed workflow was verified by a CPA of a AES hardware implementation. We also provide numbers for the required running time of TOFU for a trace synthesis with respect to the according VCD file size. Furthermore, we provide TOFU’s source code.
Research on a Security Model of Data in Computer Supported Collaborative Design Integrated with PDM System

A new security model of product data in computer supported collaborative design integrated with the product data management (PDM) is presented. The security of system is realized by several methods like user login, user and access control management, collaborative collision, logs audit, and encryption/decryption of product data. The research results show that the model can be effectively applied in the environment of computer supported collaborative design integrated with PDM and solve the problem of data security.
Anomaly Detection and Fault Diagnostics for Underwater Gliders Using Deep Learning

Near Real-Time (NRT) anomaly detection and fault diagnostics for underwater gliders are challenging because satellite connections with limited bandwidth allow only decimated data to be sent back from the remote vehicle, whilst on-board systems are constrained by power and computational limits. Currently, anomaly detection and fault diagnostics for such vehicles require intensive monitoring from the operating pilots, which prohibits large scale deployments with multiple vehicles. This paper presents a system with NRT anomaly detection and fault diagnostics for multi-vehicle underwater glider fleets based on Bidirectional Generative Adversarial Networks with assistive hints. The unsupervised anomaly detection system is applied to assist in annotating deployment datasets to train a supervised fault diagnostics model. The results suggest that the anomaly detection system has successfully detected different types of anomalies, validated against model-based and rule-based approaches. In addition, the supervised fault diagnostics system has achieved high fault diagnostics accuracy on the test dataset.
A Renwable Electricity-Hydrogen-Integrated Hybrid DC Traction Power System

The coexistence of electricity-driven train and hydrogen-driven train is becoming an important trend for future metro transportation. This article presents a novel electricity-hydrogen-integrated hybrid DC traction power system (HDCTPS) to integrate renewable microgrid, hydrogen subsystem and DC traction power system (DCTPS). A triple active bridge converter-based metro energy router is developed to integrate efficiently those three subsystems in HDCTPS. Then, the operation and control framework of HDCTPS is established, where the four operation modes are developed according to the power characteristic of renewable energy and regenerative braking operation of DCTPS. Power management and control strategy of HDCTPS are developed to perform smooth transition between different modes. Simulation results are given to validate the proposed HDCTPS and control framework. The proposed HDCTPS, as a novel operation strategy for future metro system, is able to utilize efficiently renewable energies and regenerative braking energy to achieve energy saving.
Review on the EU Research Projects of Integration of Renewable Energy Sources and Distributed Generation

Facing the serious energy situation, Europe Union pays more and more attention to the researches on the integration of renewable energy sources and distributed generation, and supports a series of relevant research projects under The 5th, 6th and 7th Framework Programme. This paper introduces the overall objectives of the projects, focusing on the work packages and results of the main projects such as DISPOWER, MICROGRIDS, MORE MICROGRIDS, EU-DEEP, FENIX. The concept of SmartGrids, as the trend of future energy network of Europe, is also introduced. Finally, the enlightenment of what these researches bring to us is summarized as follows: to change the idea from centralization control to distribution control; to attach importance to the application of electric and electronic technologies and information communication technology; and to think much of reliability, security and power quality that come from distributed energy resources (DER) with a high penetration.
Transcriptional regulation of the mra region in Escherichia coli

Cell division in Escherichia coli is a complex process requiring strict regulation to maintain both spatial and temporal control of the septation event. The mra region in E. coli is a large cluster of 16 contiguous genes, many of which are involved in peptidoglycan biosynthesis and cell division. A single promoter, designated Pmra, was previously identified in the fruR-yabB intergenic region upstream of the mra region. Pmra was reported to be essential for expression of genes up to ftsW in the centre of this gene cluster. This study investigates potential mechanisms of transcriptional regulation at Pmra, in addition to the regulation of two newly identified promoters (Pmra2 and P,nra3) also located in thefruR-yabB intergenic region. Slow growing cells are smaller than fast growing cells of the same strain and therefore require greater expression of cell division proteins to form the septal machinery. We therefore hypothesised that transcription of the mra region may be regulated by growth rate. Using lacZ reporter strains we determined that transcription from the Pmra promoters was inversely related to growth rate. We then identified mechanisms of transcriptional regulation of the Pmra promoters, mediated by factors such as FIS and ppGpp, which may contribute to the inverse growth rate control. Promoters Pmral and P,flra3 were the major contributors to the transcription of early genes in the mra region. Transcripts originating from these promoters were detected by RT-PCR to span at least as far as ftsL. Pmra3 activity was repressed by many factors, in particular by the presence of the entire fruR-yabB intergenic region, this indicated that transcriptional regulators interact with the DNA upstream of the promoter. The 50S subunit ribosomal protein L3 was shown to bind to the DNA upstream of Pmra3. We hypothesise that L3 may represent a transcriptional regulator of the Pmra promoters that is directly linked to the growth rate of the cell. Although Pmra3 was highly repressed under the experimental conditions, it was not possible to delete it from the chromosome, indicating that it makes an important contribution to transcription of the mra region, which may be essential for cell viability.
Boundary-Source Coherent Beamforming Using Metasurface

A method of boundary-source coherent beamforming (BSCB) using metasurface is proposed. Unlike conventional methods where a boundary surface is designed to simple primary sources such as plane-wave or spherical wave, the proposed method extends the design method to arbitrary primary sources for a shaped secondary scattering beam. For proof of concept, two reflective metasurfaces are designed, one to a realistic Vivaldi antenna of non-spherical wavefront and the other to an ideal point source. By comparison, the proposed BSCB method shows advantages in side-lobe level, Axial-Ratio (AR) beamwidth and beam pointing. The proposed method can be used for reflective beamforming in satellite communication, reconfigurable intelligent surface and so on.
Camera Attitude Estimation by Neural Network Using Classification Network Method Instead of Numerical Regression

In this paper, we propose a method for estimating the camera pose using a classification neural network based on the idea of OCR. Some terrestrial robots can exhibit high maneuverability by freely tilting their upper bodies. When estimating the posture of such robots, posture estimation using IMU and gyroscopic sensors as in the case of drones is affected by the noise generated by the unevenness of the ground, making posture estimation difficult. Pose estimation using deep learning from camera images is one solution to these problems, and various studies have been conducted in the past. However, the accuracy of pose estimation using only inference by deep learning with camera images is extremely poor and is not practical. In order to solve this problem, this paper proposes a classification neural network based on the idea of OCR, which can ensure high inference accuracy in the pose estimation task.
Modelling and Analysis for Interactive Crossing Decision of Pedestrian at Non-signalized Intersection

With the development of autonomous driving technology, verifying the system by a simulator is an essential process, and pedestrians’ behavior is a particularly important factor in the process. To express the pedestrians’ behavior, this paper proposes the model of pedestrians’ crossing decisions for a non-signalized intersection, where pedestrian fatalities are the most frequent environment in Japan. This crossing decision, whether to go through a crosswalk or to wait for a vehicle, is an interactive factor that is influenced by other traffic participants. For this reason, we developed a multi-player interactive simulator, in which vehicles and pedestrians can be controlled by the humans. This simulator makes it easy to measure the crossing decision data that takes into account the differences in individual characteristics. Besides, the decision model was constructed for this simulation data, and multi-class logistic regression was used as the model to express human decision ambiguity. The individual decision characteristics were analyzed by the logistic regression model in two- and three-person interactive situations. Finally, to evaluate the performance and decision characteristics of the model, simulation experiments were conducted for 15 groups.
A Training Method For VideoPose3D with Ideology of Action Recognition

Action recognition and pose estimation from videos are closely related to understand human motions, but more literature focuses on how to solve pose estimation tasks alone from action recognition. This research shows a faster and more flexible training method for VideoPose3D which is based on action recognition. This model is fed with the same type of action as the type that will be estimated, and different types of actions can be trained separately. Evidence has shown that, for common pose-estimation tasks, this model requires a relatively small amount of data to carry out similar results with the original research, and for action-oriented tasks, it outperforms the original research by 4.5% with a limited receptive field size and training epoch on Velocity Error of MPJPE. This model can handle both action-oriented and common pose-estimation problems.
Vector-Approximate-Message-Passing-Based Channel Estimation for MIMO-OFDM Underwater Acoustic Communications

Accurate channel estimation is critical to the performance of orthogonal frequency-division multiplexing (OFDM) underwater acoustic (UWA) communications, especially under multiple-input multiple-output (MIMO) scenarios. In this paper, we explore Vector Approximate Message Passing (VAMP) coupled with Expected Maximum (EM) to obtain channel estimation (CE) for MIMO OFDM UWA communications. The EM-VAMP-CE scheme is developed by employing a Bernoulli-Gaussian (BG) prior distribution for the channel impulse response, and hyperparameters of the BG prior distribution are learned via the EM algorithm. Performance of the EM-VAMP-CE is evaluated through both synthesized data and real data collected in two at-sea UWA communication experiments. It is shown the EM-VAMP-CE achieves better performance-complexity tradeoff compared with existing channel estimation methods.
Thyristor based bridge-type fault current limiter for fault current limiting capability enhancement

This study deals with a developed thyristor based bridge-type fault current limiter (TBFCL), in which the main fault current limiting function is relegated to a limiting circuit controlled by a silicon controlled rectifier (SCR). Since SCR is a reliable power electronic switch and can be found in high ratings, the proposed TBFCL can be utilised in higher ratings in comparison with other proposed BFCLs. The TBFCL limits fault current in two steps; (i) by a fully controlled DC reactor during transient fault interval (ii) by a SCR based current limiting circuit during steady state fault interval. The TBFCL has a bridge-type DC reactor controlled by an isolated gate bipolar transistor and a current limiting circuit in parallel with the bridge controlled by a thyristor. Upon fault occurrence, the DC reactor limits increasing rate of the fault current, while the generated over voltage activates auto-triggering control circuit of the SCRs. Therefore, the fault current is limited through the SCR current limiting path and the DC reactor path is interrupted. Analysis of the proposed TBFCL is presented in detail. Simulation and experimental results verify the performance of the TBFCL.
Twilight: A Differentially Private Payment Channel Network

Payment channel networks (PCNs) provide a faster and cheaper alternative to transactions recorded on the blockchain. Clients can trustlessly establish payment channels with re-lays by locking coins and then send signed payments that shift coin balances over the network’s channels. Although payments are never published, anyone can track a client’s payment by monitoring changes in coin balances over the network’s channels [22, 30]. We present Twilight, the ﬁrst PCN that provides a rigorous differential privacy guarantee to its users. Relays in Twilight run a noisy payment processing mechanism that hides the payments they carry. This mechanism increases the relay’s cost, so Twilight combats selﬁsh relays that wish to avoid it using a trusted execution environment (TEE) that ensures they follow its protocol. The TEE does not store the channel’s state, which minimizes the trusted computing base. Crucially, Twilight ensures that even if a relay breaks the TEE’s security, it cannot break the integrity of the PCN. We analyze Twilight in terms of privacy and cost and study the trade-off between them. We implement Twilight using Intel’s SGX framework and evaluate its performance using relays deployed on two continents. We show that a route consisting of 4 relays handles 820 payments/sec.
E-Tenon: An Efficient Privacy-Preserving Secure Open Data Sharing Scheme for EHR System

The transition from paper-based information to Electronic-Health-Records (EHRs) has driven various advancements in the modern healthcare-industry. In many cases, patients need to share their EHR with healthcare professionals. Given the sensitive and security-critical nature of EHRs, it is essential to consider the security and privacy issues of storing and sharing EHR. However, existing security solutions excessively encrypt the whole database, thus requiring the entire database to be decrypted for each access request, which is a time-consuming process. On the other hand, the use of EHR for medical research (e.g., development of precision-medicine, diagnostics-techniques), as well as optimisation of practices in healthcare organisations, requires the EHR to be analysed, and for that, they should be easily accessible without compromising the privacy of the patient. In this paper, we propose an efficient technique called E-Tenon that not only securely keeps all EHR publicly accessible but also provides the desirable security features. To the best of our knowledge, this is the first work in which an Open Database is used for protecting EHR. The proposed E-Tenon empowers patients to securely share their EHR under multi-level, fine-grained access policies defined by themselves. Analyses show that our system outperforms existing solutions in terms of computational-complexity.
e-Nsino: Um Sistema de Auxílio a Produção e Gestão de Documentos Didáticos

This paper describes a system to provide support for E-Learning content production. Basically, LMS systems provide functionalities to manage student records, to facilitate communication between students and between students and teacher, to control accesses and produce statistics, schedules, eva-luation, and an open platform to help teachers make lecture content available online. However they do not dictate what kind of technology or format should be used to prepare those contents. Although this issue can be seen as an advantage in certain contexts it leads to a format anarchy and makes support for content production impossible. Here is where e-Nsino comes into the scene.
A Novel Program Suspend Scheme for Improving the Reliability of 3D NAND Flash Memory

Experimental results indicate that the conventional program suspend scheme in 3D NAND flash memory chip can generate unexpected additional read fail bits and reduce the reliability of 3D NAND flash memory. These extra read fail bits are observed when the program suspend command is issued during the program stage, and particularly, they become more obvious as the delay time between program suspend operation and other following operations exceeds tens of milliseconds. By analyzing the waveform of conventional program suspend scheme, it is suggested that the unexpected extra read fail bits are caused by the different occupancy of grain boundary traps (GBTs) in the polycrystalline silicon (poly-Si) channel during the idle time after the program suspend operation. Accordingly, a novel program suspend scheme is proposed by adding a “stabilizing” pulse immediately after the program stage. Silicon experimental data show that the proposed scheme can effectively limit the read fail bit count (FBC) to a normal range, thus improving the reliability of 3D NAND flash memory significantly.
Event-Triggered PI Control of Buck Converter in Cyber-Physical Framework

This paper presents the voltage control of a DC-DC buck converter connected in a cyber-physical space using an event-triggered PI controller. The communication between the cyber layer and the plant is invoked whenever a threshold condition is violated. The Lyapunov stability of the closed-loop system is established along with a Zeno free behaviour. Finally, the proposed control scheme is applied to a practical system to verify its effectiveness.
NTRU-v-um: Secure Fully Homomorphic Encryption from NTRU with Small Modulus

NTRUEncrypt is one of the first lattice-based encryption schemes. Furthermore, the earliest fully homomorphic encryption (FHE) schemes rely on the NTRU problem. Currently, NTRU is one of the leading candidates in the NIST post-quantum standardization competition. What makes NTRU appealing is the age of the cryptosystem and relatively good performance. Unfortunately, FHE based on NTRU became impractical due to efficient attacks on NTRU instantiations with "overstretched'' modulus. In particular, currently, NTRU-based FHE schemes to support a reasonable circuit depth require instantiating NTRU with a very large modulus. Breaking the NTRU problem for such large moduli turns out to be easy. Due to these attacks, any serious work on practical NTRU-based FHE essentially stopped. In this paper, we reactivate research on practical FHE that can be based on NTRU. We design an efficient bootstrapping scheme in which the noise growth is small enough to keep the modulus to dimension ratio relatively small, thus avoiding the negative consequences of "overstretching'' the modulus. Our bootstrapping algorithm is an accumulator-type bootstrapping scheme analogous to AP/FHEW/TFHE. Finally, we show that we can use the bootstrapping procedure to compute any function over \mathbbZ _t. Consequently, we obtain one of the fastest FHE bootstrapping schemes able to compute any function over elements of a finite field alongside reducing the error.
Fake Content Identification Using Pre-Trained Glove-Embedding

Fake news is misleading content presented as real news. It is intentionally written to deceive the reader. Fake news is created to fulfill personal motives including political motives. Fake content creates a large amount of revenue for malicious entities. It is required to reduce the propagation of fake news to reduce its severe impact. In existing studies, the useful approach employs lexico-syntactic features for fake news detection. Existing methods used a small training dataset. To solve this problem, this study applied pre-trained word embedding on the Gossip-cop dataset. Glove embedding is combined with deep learning classifiers for final prediction. Glove embedding generated vector representation of words based on their co-occurrence with other words. It transforms the words into high dimensional space. Dataset is collected by scrapping Gossipcop fact-checking websites. The total number of fake news samples is 4,947 while the total true news is 16,694. The proposed model improves the accuracy of the dataset in comparison to existing state of art methods.
QTMM2012c+: A Queryable Empirically-grounded Resource of Dialogue with Argumentation

This paper introduces QTMM2012c+, a resource which links relations between propositions (inference, conflict and rephrase) to dialogue act sequences. QTMM2012c+ builds on the MM2012c annotated corpus of BBC Moral Maze debates, extending it with new annotations – for speaker roles (chair, panellists and witnesses), speaker stances (neutral, pro and con) and locution chronological ordering – and making the information available in a queryable format. We show how the new resource allows for: i) automatic extraction of empirically-grounded dialogue rules which describe choice and frequency of dialogue acts with specific argumentative functions given the dialogue history, and ii) extraction of generation templates that reflect naturally-occurring argumentative locutions in empirically-grounded dialogue. QTMM2012c+ facilitates automatic analysis of argument transitions between speakers, extending previous manual analysis of the MM2012c corpus, enabling empirical tests of theories of argumentative dialogue.