Reviewerly/exHarmony huggingface dataset:
Selective ensemble-based online adaptive deep neural networks for streaming data with concept drift

Concept drift is an important issue in the field of streaming data mining. However, how to maintain real-time model convergence in a dynamic environment is an important and difficult problem. In addition, the current methods have limited ability to deal with the problem of streaming data classification for complex nonlinear problems. To solve these problems, a selective ensemble-based online adaptive deep neural network (SEOA) is proposed to address concept drift. First, the adaptive depth unit is constructed by combining shallow features with deep features and adaptively controls the information flow in the neural network according to changes in streaming data at adjacent moments, which improves the convergence of the online deep learning model. Then, the adaptive depth units of different layers are regarded as base classifiers for ensemble and weighted dynamically according to the loss of each classifier. In addition, a dynamic selection of base classifiers is adopted according to the fluctuation of the streaming data to achieve a balance between stability and adaptability. The experimental results show that the SEOA can effectively contend with different types of concept drift and has good robustness and generalization.
Designing secure and lightweight user access to drone for smart city surveillance

The Internet of drones (IoD) is a very useful application of the Internet of things (IoT) and it can help the daily life comfort through various functions including the smart city surveillance. The IoD can enhance the comfort to reach inaccessible and hard to access sites and can save lot of effort, time and cost. However, in addition to traditional threats, the IoD may suffer from new threats and requires customized methods to combat the security weaknesses. Very recently, Wazid et al. proposed a security solution for securing IoD application scenario and claimed its security. However, in this paper we show that their scheme cannot resist stolen verifier and traceability attacks. Moreover, an attacker with access to the verifier, can impersonate any user, drone or server of the system. An enhanced scheme is then proposed to cope with these weaknesses. The security claims of proposed scheme are endorsed by formal and informal security analysis. Moreover, the performance and security comparisons show that proposed scheme completes a cycle of authentication with a slight increase in computation time, but it offers all the required security features as compared with the scheme of Wazid et al.
SFANet: A Spectrum-Aware Feature Augmentation Network for Visible-Infrared Person Reidentification

Visible-Infrared person reidentification (VI-ReID) is a challenging matching problem due to large modality variations between visible and infrared images. Existing approaches usually bridge the modality gap with only feature-level constraints, ignoring pixel-level variations. Some methods employ a generative adversarial network (GAN) to generate style-consistent images, but it destroys the structure information and incurs a considerable level of noise. In this article, we explicitly consider these challenges and formulate a novel spectrum-aware feature augmentation network named SFANet for cross-modality matching problem. Specifically, we put forward to employ grayscale-spectrum images to fully replace RGB images for feature learning. Learning with the grayscale-spectrum images, our model can apparently reduce modality discrepancy and detect inner structure relations across the different modalities, making it robust to color variations. At feature level, we improve the conventional two-stream network by balancing the number of specific and sharable convolutional blocks, which preserve the spatial structure information of features. Additionally, a bidirectional tri-constrained top-push ranking loss (BTTR) is embedded in the proposed network to improve the discriminability, which efficiently further boosts the matching accuracy. Meanwhile, we further introduce an effective dual-linear with batch normalization identification (ID) embedding method to model the identity-specific information and assist BTTR loss in magnitude stabilizing. On SYSU-MM01 and RegDB datasets, we conducted extensively experiments to demonstrate that our proposed framework contributes indispensably and achieves a very competitive VI-ReID performance.
Malicious URL Detection using Logistic Regression

One of the major challenges faced by the Internet in the present day is to deal with achieving web security from ever-rising diverse types of threats. Machine learning algorithms offer promising techniques to detect malicious websites performing unethical anonymous activities on the Internet. Attackers have been found to continuously evolve with updated techniques to attack web users using malicious Uniform Resource Locators (URLs). The main objective of such attacks is to gain financial benefits through acquiring personal information. In the present research, a machine learning (ML)-based approach is proposed to identify malicious users from URL data. An ML model is implemented using Logistic Regression to detect malicious URLs. The data set used in the study is collected from well-known sources like PhishTank, Kaggle.com, and Github.com. Our novel framework is further evaluated against traditional malicious URL models and our results highlight positive steps forward of the proposed approach.
Performance of reservoir computing in a random network of single-walled carbon nanotubes complexed with polyoxometalate

Abstract Molecular neuromorphic devices are composed of a random and extremely dense network of single-walled carbon nanotubes (SWNTs) complexed with polyoxometalate (POM). Such devices are expected to have the rudimentary ability of reservoir computing (RC), which utilizes signal response dynamics and a certain degree of network complexity. In this study, we performed RC using multiple signals collected from a SWNT/POM random network. The signals showed a nonlinear response with wide diversity originating from the network complexity. The performance of RC was evaluated for various tasks such as waveform reconstruction, a nonlinear autoregressive model, and memory capacity. The obtained results indicated its high capability as a nonlinear dynamical system, capable of information processing incorporated into edge computing in future technologies.
The impact of AI-enabled service attributes on service hospitableness: the role of employee physical and psychological workload

Purpose Hospitableness lies in the center of hospitality services. With the infusion of artificial intelligence (AI) technology in the hospitality industry, managers are concerned about how AI influences service hospitableness. Previous research has examined the consequences of AI technology based on customers’ assessment while ignoring the key players in service hospitableness – frontline employees (FLEs). This study aims to reveal how AI technology empowers FLEs physically, mentally and emotionally, facilitating hospitableness provision. Design/methodology/approach As the starting point, the instrument for AI-enabled service attributes was designed based on previous literature, hotel FLE interviews, expert panel and a pilot survey, and then validated using survey data. After that, a paired supervisor-employee sample was recruited in 15 hotels, and 342 valid questionnaires covering the constructs were obtained. Findings Factor analyses and measurement model evaluation suggest that the four factors, including anthropomorphic, entertainment, functional and information attributes, explain the construct of AI-enabled service attributes well, with high reliability and validity. Additionally, anthropomorphic, functional and information attributes of AI technology have been found to enable FLEs physically, mentally and emotionally, which further lead to increased service hospitableness. The entertainment attributes do not significantly reduce physical and mental fatigue but lead to positive emotions of FLEs significantly. Additionally, psychological job demand moderates the effects of AI-enabled service attributes on physical fatigue. Practical implications Practical implications can be made for AI technology application and hospitableness provision, in terms of AI technology analysis, job design and employee workload management. Originality/value This research contributes to understanding AI-enabled service attributes and their consequences, extends the conservation of resources theory to AI application context and promotes the research on service hospitableness.
Self-Supervision-Augmented Deep Autoencoder for Unsupervised Visual Anomaly Detection

Deep autoencoder (AE) has demonstrated promising performances in visual anomaly detection (VAD). Learning normal patterns on normal data, deep AE is expected to yield larger reconstruction errors for anomalous samples, which is utilized as the criterion for detecting anomalies. However, this hypothesis cannot be always tenable since the deep AE usually captures the low-level shared features between normal and abnormal data, which leads to similar reconstruction errors for them. To tackle this problem, we propose a self-supervised representation-augmented deep AE for unsupervised VAD, which can enlarge the gap of anomaly scores between normal and abnormal samples by introducing autoencoding transformation (AT). Essentially, AT is introduced to facilitate AE to learn the high-level visual semantic features of normal images by introducing a self-supervision task (transformation reconstruction). In particular, our model inputs the original and transformed images into the encoder for obtaining latent representations; afterward, they are fed to the decoder for reconstructing both the original image and applied transformation. In this way, our model can utilize both image and transformation reconstruction errors to detect anomaly. Extensive experiments indicate that the proposed method outperforms other state-of-the-art methods, which demonstrates the validity and advancement of our model.
A Comprehensive Study of Mobile Robot: History, Developments, Applications, and Future Research Perspectives

Intelligent mobile robots that can move independently were laid out in the real world around 100 years ago during the second world war after advancements in computer science. Since then, mobile robot research has transformed robotics and information engineering. For example, robots were crucial in military applications, especially in teleoperations, when they emerged during the second world war era. Furthermore, after the implementation of artificial intelligence (AI) in robotics, they became autonomous or more intelligent. Currently, mobile robots have been implemented in many applications like defense, security, freight, pattern recognition, medical treatment, mail delivery, infrastructure inspection and developments, passenger travel, and many more because they are more intelligent nowadays with artificial intelligence technology. To study the developments of mobile robots, we have studied an extensive literature survey of the last 50 years. In this article, we discuss a complete century of mobile robotics research, major sensors used in robotics, some major applications of mobile robots, and their impact on our lives and in applied engineering.
Service robots in full- and limited-service restaurants: Extending technology acceptance model

As the role of service robots has become increasingly important in service encounters, existing literature has widely adopted the technology acceptance model to understand customers' acceptance of robotic services. However, it remains unclear how customers' responses to service robots can vary in different service contexts. This research seeks to address this issue by experimentally examining customer-robot encounters in two different types of service contexts, full and limited services. The results of our multi-group SEM analysis show that customers who perceive having quality interaction with a service robot are more likely to perceive the robot as useful, form positive attitudes toward using the service robot, and experience rapport with the service robot in a full-service context than a limited-service context. Our findings contribute theoretically to the literature on robotic services and the technology acceptance model and provide implications for incorporating service robots into the design of full- and limited-service contexts.
GitHub Copilot AI pair programmer: Asset or Liability?

Automatic program synthesis is a long-lasting dream in software engineering. Recently, a promising Deep Learning (DL) based solution, called Copilot, has been proposed by OpenAI and Microsoft as an industrial product. Although some studies evaluate the correctness of Copilot solutions and report its issues, more empirical evaluations are necessary to understand how developers can benefit from it effectively. In this paper, we study the capabilities of Copilot in two different programming tasks: (i) generating (and reproducing) correct and efficient solutions for fundamental algorithmic problems, and (ii) comparing Copilot’s proposed solutions with those of human programmers on a set of programming tasks. For the former, we assess the performance and functionality of Copilot in solving selected fundamental problems in computer science, like sorting and implementing data structures. In the latter, a dataset of programming problems with human-provided solutions is used. The results show that Copilot is capable of providing solutions for almost all fundamental algorithmic problems, however, some solutions are buggy and non-reproducible. Moreover, Copilot has some difficulties in combining multiple methods to generate a solution. Comparing Copilot to humans, our results show that the correct ratio of humans’ solutions is greater than Copilot’s suggestions, while the buggy solutions generated by Copilot require less effort to be repaired. Based on our findings, if Copilot is used by expert developers in software projects, it can become an asset since its suggestions could be comparable to humans’ contributions in terms of quality. However, Copilot can become a liability if it is used by novice developers who may fail to filter its buggy or non-optimal solutions due to a lack of expertise.
Detection of Promoted Social Media Campaigns

Information spreading on social media contributes to the formation of collective opinions. Millions of social media users are exposed every day to popular memes — some generated organically by grassroots activity, others sustained by advertising, information campaigns or more or less transparent coordinated efforts. While most information campaigns are benign, some may have nefarious purposes, including terrorist propaganda, political astroturf, and financial market manipulation. This poses a crucial technological challenge with deep social implications: can we detect whether the spreading of a viral meme is being sustained by a promotional campaign? Here we study trending memes that attract attention either organically, or by means of advertisement. We designed a machine learning framework capable to detect promoted campaigns and separate them from organic ones in their early stages. Using a dataset of millions of posts associated with trending Twitter hashtags, we prove that remarkably accurate early detection is possible, achieving 95% AUC score. Feature selection analysis reveals that network diffusion patterns and content cues are powerful early detection signals.
On Trees, Chains and Fast Transactions in the Blockchain

A fundamental open problem in the area of blockchain protocols is whether the Bitcoin protocol is the only solution for building a secure transaction ledger. A recently proposed and widely considered alternative is the GHOST protocol which, notably, was proposed to be at the core of Ethereum as well as other recent proposals for improved Bitcoin-like systems. The GHOST variant is touted as offering superior performance compared to Bitcoin (potentially offering block production speed up by a factor of more than 40) without a security loss. Motivated by this, in this work, we study from a provable security point of view the GHOST protocol. We introduce a new formal framework for the analysis of blockchain protocols that relies on trees (rather than chains) and we showcase the power of the framework by providing a unified description of the GHOST and Bitcoin protocols, the former of which we extract and formally describe. We then prove that GHOST implements a “robust transaction ledger” (i.e., possesses liveness and persistence) and hence it is a provably secure alternative to Bitcoin; moreover, our bound for the liveness parameter is superior to that proven for the bitcoin backbone in line with the original expectation for GHOST. Our proof follows a novel methodology for establishing that GHOST is a robust transaction ledger compared to previous works, which may be of independent interest and can be applicable to other blockchain variants.
Progress on Artificial Neural Networks for Big Data Analytics: A Survey

Approximately 2.5 quintillion bytes of data are emitted on a daily basis, and this has brought the world into the era of “big data.” Artificial neural networks (ANNs) are known for their effectiveness and efficiency for small datasets, and this era of big data has posed a challenge to the big data analytics using ANN. Recently, much research effort has been devoted to the application of the ANN in big data analytics and is still ongoing, although it is in it is early stages. The purpose of this paper is to summarize recent progress, challenges, and opportunities for future research. This paper presents a concise view of the state of the art, challenges, and future research opportunities regarding the applications of the ANN in big data analytics and reveals that progress has been made in this area. Our review points out the limitations of the previous approaches, the challenges in the ANN approaches in terms of their applications in big data analytics, and several ANN architecture that have not yet been explored in big data analytics and opportunities for future research. We believe that this paper can serve as a yardstick for future progress on the applications of the ANN in big data analytics as well as a starting point for new researchers with an interest in the exploration of the ANN in big data analytics.
The future persona: a futures method to let your scenarios come to life

Purpose The purpose of this paper is to formally introduce the future persona, a futures method to let scenarios come to life. A future persona is a scenario-specific fictional individual living in the future scenario (s)he is meant to depict. The paper provides a formal, systematic and clear step-by-step guide on how to create engaging and effective future personas after a scenario planning exercise. Design/methodology/approach After having introduced the future persona method, tracing it back to the customer persona method in user centered design (UCD) and differentiating it from previous uses of futures characters in the futures studies literature and in other domains, an example of the creation process of four future personas based on four scenario archetypes of the futures of work is provided, illustrated with pictures and discussed. Findings Future personas, with their narratives and graphical illustrations, are found to be particularly useful to convey scenarios to a target audience. Practical implications Futures personas can be used in a scenario planning exercise to increase the clarity of scenarios in the mind of scenario planners and to let scenarios be known inside an organization. Originality/value Future personas can substantially enrich scenarios, increasing their liveliness, playfulness and empathy.
Self-Supervised Neural Machine Translation

We present a simple new method where an emergent NMT system is used for simultaneously selecting training data and learning internal NMT representations.This is done in a self-supervised way without parallel data, in such a way that both tasks enhance each other during training.The method is language independent, introduces no additional hyper-parameters, and achieves BLEU scores of 29.21 (en2f r) and 27.36 (f r2en) on new-stest2014 using English and French Wikipedia data for training.
Virtual reality games on accommodation and convergence

Increasing popularity of virtual reality (VR) gaming is causing increased concern, as prolonged use induces visual adaptation effects which disturbs normal vision. Effects of VR gaming on accommodation and convergence of young adults by measuring accommodative response and phoria before and after experiencing virtual reality were measured. An increase in accommodative response and a decrease in convergence was observed after immersion in VR games. It was found that visual symptoms were apparent among the subjects post VR exposure.
Contextual Sentiment Based Recommender System to Provide Recommendation in the Electronic Products Domain

The rush to purchase the latest products sometimes prevents people from thinking things through completely.Consequently, recommender services are increasingly emerging.By looking at industry trends, interviewing dozens of leading industry stakeholders, and using publicly available information, it is important to filter out the most relevant information for consumer electronics before purchasing their items.This paper presents an electronic product recommender system based on contextual information from sentiment analysis.The recommendation algorithms mostly rely on users' rating to make prediction of items.Such ratings are usually insufficient and very limited.We present a contextual information sentiment based model for recommender system by making use of user comments and preferences to provide a recommendation.The purpose of this approach is to avoid term ambiguity which is so called domain sensitivity problem in recommendation.The proposed contextual information sentiment-based model illustrates better performance by using results of RMSE and MAE measurements as compared to the conventional collaborative filtering approach in electronic product recommendation.
Distribution-Independent PAC Learning of Halfspaces with Massart Noise

We study the problem of {\em distribution-independent} PAC learning of halfspaces in the presence of Massart noise. Specifically, we are given a set of labeled examples $(\bx, y)$ drawn from a distribution $\D$ on $\R^{d+1}$ such that the marginal distribution on the unlabeled points $\bx$ is arbitrary and the labels $y$ are generated by an unknown halfspace corrupted with Massart noise at noise rate $\eta<1/2$. The goal is to find a hypothesis $h$ that minimizes the misclassification error $\pr_{(\bx, y) \sim \D} \left[ h(\bx) \neq y \right]$. We give a $\poly\left(d, 1/\eps\right)$ time algorithm for this problem with misclassification error $\eta+\eps$. We also provide evidence that improving on the error guarantee of our algorithm might be computationally hard. Prior to our work, no efficient weak (distribution-independent) learner was known in this model, even for the class of disjunctions. The existence of such an algorithm for halfspaces (or even disjunctions) has been posed as an open question in various works, starting with Sloan (1988), Cohen (1997), and was most recently highlighted in Avrim Blum's FOCS 2003 tutorial.