Reviewerly/exHarmony huggingface dataset:
A differential privacy-based privacy-preserving data publishing algorithm for transit smart card data

This manuscript is focused on transit smart card data and finds that the release of such trajectory information after simple anonymization creates high concern about breaching privacy. Trajectory data is large-scale, high-dimensional, and sparse in nature and, thus, requires an efficient privacy-preserving data publishing (PPDP) algorithm with high data utility. This paper describes the investigation of the publication of non-interactive sanitized trajectory data under a Differential Privacy (DP) definition. To this end, a new prefix tree structure, an incremental privacy budget allocation model, and a spatial-temporal dimensionality reduction model are proposed to enhance the sanitized data utility as well as to improve runtime efficiency. The developed algorithm is implemented and applied to real-life metro smart card data from Shenzhen, China, which includes a total of 2.8 million individual travelers and over 220 million records. Numerical analysis finds that, compared with previous work, the proposed model outputs sanitized dataset with higher utilities, and the algorithm is more efficient and scalable.
Blockchain: Can It Be Trusted?

Blockchain technology claims to provide unparalleled security and data privacy, yet some vulnerabilities have recently been identified. In this article, we showcase the key advantages of blockchain technology and look at recent security breaches, critically analyzing its benefits while highlighting some resulting financial mishaps.
Central bank digital currencies: Potential risks and benefits

A growing activity has recently been watched in the sphere of central bank digital currencies (CBDC) creation, dictated by the desire of monetary authorities to increase the efficiency of payment systems and create an alternative to stablecoin projects like Libra. The paper discusses various types of CBCDs. Special attention is paid to potential risks and benefits associated with the emission of CBDCs, as well as their consequences for the banking sector and monetary policy. The first important issue associated with CBDC emission is the potential reduction of the role of the traditional banking system. The second issue is the change in the functioning of the monetary transmission channels as a result of the CBDCs emission. The third problem arises from the fact that in the event of a crisis some economic agents will prefer to transfer their funds from commercial banks to the CBDCs because they are less risky. This situation is expected to lead to instability of balances on deposit accounts in commercial banks. The problems listed are closely interrelated, and the significance and balance of risks and benefits associated with CBDCs emission are not completely obvious. At the same time CBDCs have a potential to become a new effective tool of monetary authorities in case of their proper design.
Research on Website Phishing Detection Based on LSTM RNN

In order to effectively detect phishing attacks, this paper designed a new detection system for phishing websites using LSTM Recurrent Neural Networks (RNN). LSTM has the advantage of capturing data timing and long-term dependencies. LSTM has strong learning ability, can automatically learn data characterization without manual extraction of complex features, and has strong potential in the face of complex high-dimensional massive data. Experimental results show that this model approach the accuracy of 99.1%, is higher than that of other neural network algorithms.
A Transformer-based Embedding Model for Personalized Product Search

Product search is an important way for people to browse and purchase items on E-commerce platforms. While customers tend to make choices based on their personal tastes and preferences, analysis of commercial product search logs has shown that personalization does not always improve product search quality. Most existing product search techniques, however, conduct undifferentiated personalization across search sessions. They either use a fixed coefficient to control the influence of personalization or let personalization take effect all the time with an attention mechanism. The only notable exception is the recently proposed zero-attention model (ZAM) that can adaptively adjust the effect of personalization by allowing the query to attend to a zero vector. Nonetheless, in ZAM, personalization can act at most as equally important as the query and the representations of items are static across the collection regardless of the items co-occurring in the user's historical purchases. Aware of these limitations, we propose a transformer-based embedding model (TEM) for personalized product search, which could dynamically control the influence of personalization by encoding the sequence of query and user's purchase history with a transformer architecture. Personalization could have a dominant impact when necessary and interactions between items can be taken into consideration when computing attention weights. Experimental results show that TEM outperforms state-of-the-art personalization product retrieval models significantly.
A Risk Analysis Framework for Social Engineering Attack Based on User Profiling

Social engineering attacks are becoming serious threats to cloud service. Social engineering attackers could get Cloud service custom privacy information or attack virtual machine images directly. Existing security analysis instruments are difficult to quantify the social engineering attack risk, resulting in invalid defense guidance for social engineering attacks. In this article, a risk analysis framework for social engineering attack is proposed based on user profiling. The framework provides a pathway to quantitatively calculate the possibility of being compromised by social engineering attack and potential loss, so as to effectively complement current security assessment instruments. The frequency of related operations is used to profile and group users for respective risk calculation, and other features such as security awareness and capability of protection mechanism are also considered. Finally, examples are given to illustrate how to use the framework in actual scenario and apply it to security assessment.
ProtoSpray: Combining 3D Printing and Spraying to Create Interactive Displays with Arbitrary Shapes

ProtoSpray is a fabrication method that combines 3D printing and spray coating, to create interactive displays of arbitrary shapes. Our approach makes novel use of 3D printed conductive channels to create base electrodes on 3D shapes. This is then combined with spraying active materials to produce illumination. We demonstrate the feasibility and benefits of this combined approach in 6 evaluations exploring different shaped topologies. We analyze factors such as spray orientations, surface topologies and printer resolutions, to discuss how spray nozzles can be integrated into traditional 3D printers. We present a series of ProtoSprayed objects demonstrating how our technique goes beyond existing fabrication techniques by allowing creation of displays on objects with curvatures as complex as a Mobius strip. Our work provides a platform to empower makers to use displays as a fabrication material.
Enhancing Intrinsic Adversarial Robustness via Feature Pyramid Decoder

Whereas adversarial training is employed as the main defence strategy against specific adversarial samples, it has limited generalization capability and incurs excessive time complexity. In this paper, we propose an attack-agnostic defence framework to enhance the intrinsic robustness of neural networks, without jeopardizing the ability of generalizing clean samples. Our Feature Pyramid Decoder (FPD) framework applies to all block-based convolutional neural networks (CNNs). It implants denoising and image restoration modules into a targeted CNN, and it also constraints the Lipschitz constant of the classification layer. Moreover, we propose a two-phase strategy to train the FPD-enhanced CNN, utilizing ε-neighbourhood noisy images with multi-task and self-supervised learning. Evaluated against a variety of white-box and black-box attacks, we demonstrate that FPD-enhanced CNNs gain sufficient robustness against general adversarial samples on MNIST, SVHN and CALTECH. In addition, if we further conduct adversarial training, the FPD-enhanced CNNs perform better than their non-enhanced versions.
Joint Training of Variational Auto-Encoder and Latent Energy-Based Model

This paper proposes a joint training method to learn both the variational auto-encoder (VAE) and the latent energy-based model (EBM). The joint training of VAE and latent EBM are based on an objective function that consists of three Kullback-Leibler divergences between three joint distributions on the latent vector and the image, and the objective function is of an elegant symmetric and anti-symmetric form of divergence triangle that seamlessly integrates variational and adversarial learning. In this joint training scheme, the latent EBM serves as a critic of the generator model, while the generator model and the inference model in VAE serve as the approximate synthesis sampler and inference sampler of the latent EBM. Our experiments show that the joint training greatly improves the synthesis quality of the VAE. It also enables learning of an energy function that is capable of detecting out of sample examples for anomaly detection.
Blockchain for Mobile Edge Computing: Consensus Mechanisms and Scalability

Mobile edge computing (MEC) and next-generation mobile networks are set to disrupt the way intelligent and autonomous systems are interconnected. This will have an effect on a wide range of domains, from the Internet of Things to autonomous mobile robots. The integration of such a variety of MEC services in an inherently distributed architecture requires a robust system for managing hardware resources, balancing the network load and securing the distributed applications. Blockchain technology has emerged a solution for managing MEC services, with consensus protocols and data integrity checks that enable transparent and efficient distributed decision-making. In addition to transparency, the benefits from a security point of view are evident. Nonetheless, blockchain technology faces significant challenges in terms of scalability. In this chapter, we review existing consensus protocols and scalability techniques in both well-established and next-generation blockchain architectures. From this, we evaluate the most suitable solutions for managing MEC services and discuss the benefits and drawbacks of the available alternatives.
Credit Card Fraud Detection using Artificial Neural Network and BackPropagation

In the current situation everyone uses plastic money ie., credit cards and so the demand for credit cards in payments significantly increases and also fraud. To prevent this, a model is created, using ANN (Artificial Neural Network) algorithm and Backpropagation. It behaves like a human brain and when compared to other algorithms it gives accurate as well as fast results. First, the data of the customer's credit card is collected which has many attributes including name, time, last transaction, transaction history, etc. Then eighty percent of data is trained from the dataset with the result and the remaining twenty percent of data will be divided into test and validate data which is processed without result. Hence, it gives a better result than other algorithms. This will be very useful in the future as our model gives real-time detection and predicted results of customer transactions also.
A secure spectrum auction scheme without the trusted party based on the smart contract

With the development of communication 5G networks and technologies, spectrum resources are increasingly scarce. The scarcity of the spectrum resource makes market-driven spectrum auction become an important means of spectrum allocation, and due to the complexity of the network environment, the security of spectrum auctions can not be ignored. Most existing secure spectrum auction schemes introduce a semi-honest agent to complete spectrum auction. However, the hypothetical semi-honest model does not guarantee the security of spectrum auction in the actual application scenario, which may lead to potential security threats: the agent may reveal the privacy of bidders, agent or auctioneer may collude with the bidder to manipulate the spectrum auction, and so on. In this paper, a secure spectrum auction scheme without a trusted party is proposed based on the smart contract technology, and the smart contract written into the blockchain replaces the traditional semi-honest agent to cooperate with the auctioneer server to complete the auction. In order to ensure the security of our scheme, a secure spectrum auction protocol is designed, in which the Software Guard Extensions (SGX) technology and Paillier cryptosystem are used to protect the privacy of bidders. Public verification is provided in our protocol by using extensive Pedersen commitment, which prevents the auctioneer server and the bidder from colluding with each other and verifies group bid sum values. Finally, the security analysis is given to propose several types of attacks that can be defended. Besides, theoretical analysis and simulation experiments of our protocol are also provided.
A Blockchain-Based Secure Inter-Hospital EMR Sharing System

In recent years, blockchain-related technologies and applications have gradually emerged. Blockchain technology is essentially a decentralized database maintained by the collective, and it is now widely applied in various fields. At the same time, with the growth of medical technology, medical information is becoming increasingly important in terms of patient identity background, medical payment records, and medical history. Medical information can be the most private information about a person, but due to issues such as operation errors within the network or a hacking attack by a malicious person, there have been major leaks of sensitive personal information in the past. In any case, this has become an issue worth studying to ensure the privacy of patients and protect these medical materials. On the other hand, under the current medical system, the patient’s EMR (electronic medical record) cannot be searched across the hospital. When the patient attends the hospital for treatment, repeated examinations will occur, resulting in a waste of medical resources. Therefore, we propose a blockchain-based secure inter-hospital EMR sharing system in this article. Through the programmatic authorization mechanism by smart contracts, the security of EMR is guaranteed. In addition to the essential mutual authentication, the proposed scheme also provides and guarantees data integrity, nonrepudiation, user untraceability, forward and backward secrecy, and resistance to replay attack.
Human activity detection using machine learning methods from wearable sensors

Purpose The paper aims to develop a novel method for the classification of different physical activities of a human being, using fabric sensors. This method focuses mainly on classifying the physical activity between normal action and violent attack on a victim and verifies its validity. Design/methodology/approach The system is realized as a protective jacket that can be worn by the subject. Stretch sensors, pressure sensors and a 9 degree of freedom accelerometer are strategically woven on the jacket. The jacket has an internal bus system made of conductive fabric that connects the sensors to the Flora chip, which acts as the data acquisition unit for the data generated. Different activities such as still, standing up, walking, twist-jump-turn, dancing and violent action are performed. The jacket in this study is worn by a healthy subject. The main phases which describe the activity recognition method undertaken in this study are the placement of sensors, pre-processing of data and deploying machine learning models for classification. Findings The effectiveness of the method was validated in a controlled environment. Certain challenges are also faced in building the experimental setup for the collection of data from the hardware. The most tedious challenge is to collect the data without noise and error, created by voltage fluctuations when stretched. The results show that the support vector machine classifier can classify different activities and is able to differentiate normal action and violent attacks with an accuracy of 98.8%, which is superior to other methods and algorithms. Practical implications This study leads to an understanding of human physical movement under violent activity. The results show that data compared with normal physical motion, which includes even a form of dance is quite different from the data collected during violent physical motion. This jacket construction with woven sensors can capture every dimension of the physical motion adding features to the data on which the machine learning model will be built. Originality/value Unlike other studies, where sensors are placed on isolated parts of the body, in this study, the fabric sensors are woven into the fabric itself to collect the data and to achieve maximum accuracy instead of using isolated wearable sensors. This method, together with a fabric pressure and stretch sensors, can provide key data and accurate feedback information when the victim is being attacked or is in a normal state of action.
Personalized recommendation framework design for online tourism: know you better than yourself

Purpose This study aims to create an idea and a framework to enhance customer stickiness and improve transformation efficiency flow of tourism products from online to offline platforms through the application of personalized recommendation technology. Design/methodology/approach Studies on an overview of progress in current personalized recommendation research, business scenario analysis of online tourism and some possible logical limitations discussion are required for improvement. This study clarifies concepts including online tourism user behavior and generated data, user preference themes and spaces, user models and image and user-product (two-dimensional matrix, etc.). The author then creates a user portrait based on behavior data convergence to locate the user's role from both horizontal and vertical dimensions and also clear the logical levels and associations among them, verifying the similarity in measurement and calculation and optimizing the implementation of the personalized recommendation program under online tourism business scenarios. Findings By providing a framework design about personalized recommendations of online tourism including a flow from data collection to a personalized recommendation algorithm selection, logical analysis is established while the corresponding personalization algorithm is improved. Originality/value This study show a logical shift of personalized recommendations in online tourism management from focusing on the simple collection of travel information and the logical speculation of tourism products to focusing on the individual behavior of potential travelers.
Vision-based neural predictive tracking control for multi-manipulator systems with parametric uncertainty

To deal with the coordination problem for multi-manipulator trajectory tracking systems with parametric uncertainties, this paper proposes a two-layer control scheme incorporating a model predictive strategy and an extended state observer. In the kinematic layer, the visual information is implemented and a visual servoing error model is derived by the image-based visual servoing strategy. A recurrent neural network model predictive control approach is proposed to obtain velocities which are regarded as the reference signals for the dynamic layer. For dynamics, a linear time-varying dynamic model of the multi-manipulator system coupled with the object is established, where the parametric uncertainty is recognized as an added disturbance. An extended state observer is sequentially designed to estimate the disturbance by using pole placement method. The input-to-state practical stability of the system is further analyzed with a bounded disturbance. Finally, simulations and comparison are given to verify the effectiveness and robustness of the proposed algorithm.
JDI-T: Jointly Trained Duration Informed Transformer for Text-To-Speech without Explicit Alignment

We propose Jointly trained Duration Informed Transformer (JDI-T), a feed-forward Transformer with a duration predictor jointly trained without explicit alignments in order to generate an acoustic feature sequence from an input text. In this work, inspired by the recent success of the duration informed networks such as FastSpeech and DurIAN, we further simplify its sequential, two-stage training pipeline to a single-stage training. Specifically, we extract the phoneme duration from the autoregressive Transformer on the fly during the joint training instead of pretraining the autoregressive model and using it as a phoneme duration extractor. To our best knowledge, it is the first implementation to jointly train the feed-forward Transformer without relying on a pre-trained phoneme duration extractor in a single training pipeline. We evaluate the effectiveness of the proposed model on the publicly available Korean Single speaker Speech (KSS) dataset compared to the baseline text-to-speech (TTS) models trained by ESPnet-TTS.
Accurate Detection of Wake Word Start and End Using a CNN

Small footprint embedded devices require keyword spotters (KWS) with small model size and detection latency for enabling voice assistants. Such a keyword is often referred to as \textit{wake word} as it is used to wake up voice assistant enabled devices. Together with wake word detection, accurate estimation of wake word endpoints (start and end) is an important task of KWS. In this paper, we propose two new methods for detecting the endpoints of wake words in neural KWS that use single-stage word-level neural networks. Our results show that the new techniques give superior accuracy for detecting wake words' endpoints of up to 50 msec standard error versus human annotations, on par with the conventional Acoustic Model plus HMM forced alignment. To our knowledge, this is the first study of wake word endpoints detection methods for single-stage neural KWS.
A multimodal LIBRAS-UFOP Brazilian sign language dataset of minimal pairs using a microsoft Kinect sensor

Sign language recognition has made significant advances in recent years. Many researchers show interest in encouraging the development of different applications to simplify the daily life of deaf people and to integrate them into the hearing society. The use of the Kinect sensor (developed by Microsoft) for sign language recognition is steadily increasing. However, there are limited publicly available RGB-D and skeleton joint datasets that provide complete information for dynamic signs captured by Kinect sensor, most of them lack effective and accurate labeling or stored in a single data format. Given the limitations of existing datasets, this article presents a challenging public dataset, named LIBRAS-UFOP. The dataset is based on the concept of minimal pairs, which follows specific categorization criteria; the signs are labeled correctly, and validated by an expert in sign language; the dataset presents complete RGB-D and skeleton data. The dataset consists of 56 different signs with high similarity grouped into four categories. Besides, a baseline method is presented that consists of the generation of dynamic images from each multimodal data, which are the input to two flow stream CNN architectures. Finally, we propose an experimental protocol to conduct evaluations on the proposed dataset. Due to the high similarity between signs, the experimental results using a baseline method reports a recognition rate of 74.25% on the proposed dataset. This result highlights how challenging this dataset is for sign language recognition and let room for future research works to improve the recognition rate.