Reviewerly/exHarmony huggingface dataset:
Chatbot Interaction with Artificial Intelligence: human data augmentation with T5 and language transformer ensemble for text classification

Abstract In this work we present the Chatbot Interaction with Artificial Intelligence (CI-AI) framework as an approach to the training of a transformer based chatbot-like architecture for task classification with a focus on natural human interaction with a machine as opposed to interfaces, code, or formal commands. The intelligent system augments human-sourced data via artificial paraphrasing in order to generate a large set of training data for further classical, attention, and language transformation-based learning approaches for Natural Language Processing (NLP). Human beings are asked to paraphrase commands and questions for task identification for further execution of algorithms as skills. The commands and questions are split into training and validation sets. A total of 483 responses were recorded. Secondly, the training set is paraphrased by the T5 model in order to augment it with further data. Seven state-of-the-art transformer-based text classification algorithms (BERT, DistilBERT, RoBERTa, DistilRoBERTa, XLM, XLM-RoBERTa, and XLNet) are benchmarked for both sets after fine-tuning on the training data for two epochs. We find that all models are improved when training data is augmented by the T5 model, with an average increase of classification accuracy by 4.01%. The best result was the RoBERTa model trained on T5 augmented data which achieved 98.96% classification accuracy. Finally, we found that an ensemble of the five best-performing transformer models via Logistic Regression of output label predictions led to an accuracy of 99.59% on the dataset of human responses. A highly-performing model allows the intelligent system to interpret human commands at the social-interaction level through a chatbot-like interface (e.g. “Robot, can we have a conversation?”) and allows for better accessibility to AI by non-technical users.
Heuristic Quantum Optimization for 6G Wireless Communications

6G technologies such as massive MIMO, dense cells, innovative air interface multiplexing techniques, and ultra-high frequencies stand to significantly benefit from significantly increased amounts of computation at the base station. This position article surveys recent work the authors have undertaken to realize this vision on today's noisy intermediate scale quantum devices, illustrating possible system architectures to leverage the power of quantum devices for wireless networks. We sketch the state of the art in quantum processing devices, offering insights into their current and future evolution, and update our recent experimental results with the most recent such devices, to give the reader a sense of the trajectory of performance improvements over the past 12-24 months.
Exploring syntactic and semantic features for authorship attribution

Authorship attribution is to extract features to identify authors of anonymous documents. Many previous works on authorship attribution focus on statistical style features (e.g., sentence/word length), content features (e.g., frequent words, n-grams). Modeling these features by regression or some transparent machine learning methods gives a portrait of the authors’ writing style. But these methods do not capture the syntactic (e.g., dependency relationship) or semantic (e.g., topics) information. In recent years, some researchers model syntactic trees or latent semantic information by neural networks. However, few works take them together. In this paper, we propose a novel Multi-Channel Self-Attention Network (MCSAN) incorporating both the inter-channel and inter-positional interaction to extract n-grams of the characters, words, parts of speech (POS), phrase structures, dependency relationships, and topics from multiple dimensions (style, content, syntactic and semantic features) to distinguish different authors. And then we incorporate these extracted features with logistic regression (LR) to do experiments, and the experimental results manifest that our extracted features are effective. Our methods improve 2.1% and 3.0% on CCAT10 and CCAT50, respectively, comparing with state-of-the-art models.
Improving Object Detection by Label Assignment Distillation

Label assignment in object detection aims to assign targets, foreground or background, to sampled regions in an image. Unlike labeling for image classification, this problem is not well defined due to the object’s bounding box. In this paper, we investigate the problem from a perspective of distillation, hence we call Label Assignment Distillation (LAD). Our initial motivation is very simple, we use a teacher network to generate labels for the student. This can be achieved in two ways: either using the teacher’s prediction as the direct targets (soft label), or through the hard labels dynamically assigned by the teacher (LAD). Our experiments reveal that: (i) LAD is more effective than soft-label, but they are complementary. (ii) Using LAD, a smaller teacher can also improve a larger student significantly, while soft-label can’t. We then introduce Co-learning LAD, in which two networks simultaneously learn from scratch and the role of teacher and student are dynamically interchanged. Using PAA-ResNet50 as a teacher, our LAD techniques can improve detectors PAA-ResNet101 and PAA-ResNeXt101 to 46AP and 47.5AP on the COCO test-dev set. With a stronger teacher PAA-SwinB, we improve the students PAA-ResNet50 to 43.7AP by only 1× schedule training and standard setting, and PAA-ResNet101 to 47.9AP, significantly surpassing the current methods. Our source code is released at https://git.io/JrDZo.
A neighbor level set framework minimized with the split Bregman method for medical image segmentation

• We propose a model based on the neighbor region, the level set framework and the split Bregman method for medical image segmentation. • Numerous experiments and comparisons have proved that our model is very competitive in terms of accuracy, effectiveness and speed. • We extend the proposed model to the multi-phase segmentation model and the 3D segmentation model and have achieved good results. Medical image segmentation has a huge challenge due to intensity inhomogeneity and the similarity of the background and the object. To meet this challenge, we propose an improved active contour model, in which we combine the level set method and the split Bregman method, and provide the two-phase formulation, the multi-phase formulation and 3D formulation. In this paper, the proposed model is presented in a level set framework by including the neighbor region information for segmenting medical images in which the energy functional contains the data fitting term and the length term. The neighbor region and the local intensity variances in the data fitting term are designed to optimize the minimization process. To minimize the energy functional then we apply the split Bregman method which contributes to get faster convergence. Besides, we extend our model to the multi-phase segmentation model and the 3D segmentation model for cardiac MR images, which have all achieved good results. Experimental results show that the new model not only has strong robustness to other cardiac tissue effects and image intensity inhomogeneity, but it also can much better conduce to the extraction of effective tissues. As we expected, our model has higher segmentation accuracy and efficiency for medical image segmentation.
A memristive RLC oscillator dynamics applied to image encryption

One of the major challenges in chaos dynamics is the design of simple and realisable electronic circuits capable of complex dynamic behaviours. Our contribution studies the dynamics and implementation of a simple chaotic memristive RLC oscillator circuit of jerky type as well as its application in designing image cryptosystem. The proposed circuit consists of an operational amplifier and an RLC network, comprising a resistor, an inductor, and two capacitors. Considering the crucial role of symmetry in physical, chemical, biological, and mechanical systems, a voltage control memristor emulator is employed to induce symmetry in the proposed circuit. For the dynamic analysis, fourth-order differential equation with exponential nonlinearities is used. Furthermore, standard analysis tools such as Lyapunov spectrum, bifurcation analysis, and phase space trajectories are employed. The phase space is magnetized using four symmetric attractors coexisting for the same set of parameters and different initial conditions indicate the symmetricity of the proposed system. Meanwhile, asymmetric attractor is capable of merging into one symmetric attractor through an attractor-merging bifurcation. Extensive experimental validations of these properties via PSPICE simulations and by using real electronic components are reported. Finally, the chaotic sequences emanating from the proposed oscillator are exploited to design an encryption algorithm that is subsequently validated via simulations and standard image security analysis with prominent outcomes in terms of very low encryption time (0.0746s for 128 × 128 colour image), mean entropy of 7.999 and mean NPCR of 99.62% just to name a few.
Make the Rocket Intelligent at IoT Edge: Stepwise GAN for Anomaly Detection of LRE With Multisource Fusion

Anomaly detection (AD) for liquid rocket engine (LRE) is essential to improve the reliability and safety of space launch missions. However, it is difficult for existing methods to implement fast and precise detection with single-source information and absent anomalous samples. The Internet of Things (IoT) enables intelligent LRE AD via edge computing using multisource data. This article proposed an edge-based detection algorithm named stepwise generative adversarial network (StepGAN) for AD of LRE with multisource fusion at the Edge of IoT. StepGAN incorporates deep autoencoder and relativistic generative adversarial network. Through multistage stepwise training with unlabeled normal data, an encoder-generator-discriminator network is obtained to identify LRE anomalies at the edge. In addition, by fusing multisource information at feature level and aggregating neighboring information at decision level during real-time detection, the performance of StepGAN is further improved. To demonstrate its ability for IoT edge-based AD, the proposed method is compared with typical methods, using real static ignition data of LRE as cloud and edge data, and achieves the best result under multiple evaluation indexes. To evaluate the proposed fusing approach, a channel-varying case is carried out and extensive experimental results indicate its effectiveness.
Quantum scrambling with classical shadows

Quantum dynamics is of fundamental interest and has implications in quantum information processing. The four-point out-of-time-ordered correlator (OTOC) is traditionally used to quantify quantum information scrambling under many-body dynamics. Due to the OTOC's unusual time ordering, its measurement is challenging. We propose higher-point OTOCs to reveal early-time scrambling behavior, and present protocols to measure any higher-point OTOC using the shadow estimation method. The protocols circumvent the need for time-reversal evolution and ancillary control. They can be implemented in near-term quantum devices with single-qubit readout.
Few-shot website fingerprinting attack

Website fingerprinting (WF) attack stands opposite against privacy protection in using the Internet, even when the content details are encrypted, such as Tor networks. Whilst existing difficulty in the preparation of many training samples, we study a more realistic problem — few-shot website fingerprinting attack where only a few training samples per website are available. We introduce a novel Transfer Learning Fingerprinting Attack (TLFA) that can transfer knowledge from the labeled training data of websites disjoint and independent to the target websites. Specifically, TLFA trains a stronger embedding model with the training data collected from non-target websites, which is then leveraged in a task-agnostic manner with a task-specific classifier model fine-tuned on a small set of labeled training data from target websites. We conduct expensive experiments to validate the superiority of our TLFA over the state-of-the-art methods in both closed-world and open-world attacking scenarios, at the absence and presence of strong defense.
Detecting Node.js prototype pollution vulnerabilities via object lookup analysis

Prototype pollution is a type of vulnerability specific to prototype-based languages, such as JavaScript, which allows an adversary to pollute a base object’s property, leading to a further consequence such as Denial of Service (DoS), arbitrary code execution, and session fixation. On one hand, the only prior work in detecting prototype pollution adopts dynamic analysis to fuzz package inputs, which inevitably has code coverage issues in triggering some deeply embedded vulnerabilities. On the other hand, it is challenging to apply state-of-the-art static analysis in detecting prototype pollution because of the involvement of prototype chains and fine-grained object relations including built-in ones.
Energy-Aware Blockchain and Federated Learning-Supported Vehicular Networks

The aerial capabilities and flexibility in movement of Unmanned Aerial Vehicles (UAVs) has enabled them to adaptively provide both traditional and more contemporary services. In this article, we introduce a solution that integrates the capabilities of both UAVs and Unmanned Ground Vehicles (UGVs) to provide both intelligent connectivity and services to both aerial and ground connected devices. A cooperative solution is adopted that considers nodes’ power and movement constraints. The UAV and UGV cooperative process ensures continuous power availability to UAVs to support seamless and continuous service availability to end-devices. A Federated Learning (FL) approach is adopted at the edge to ensure accurate and up-to-date service provisioning in accordance with the surrounding environment and network constraints. Moreover, Blockchain technology is used to decentralize the provisioning and control aspects, and ensure authenticity and integrity. Extensive simulations are conducted to test the soundness and applicability of the proposed solution. Results show significant improvement in terms of connectivity, service availability, and UAV energy enhancements when compared to traditional mobile and vehicular communication techniques.
Benchmarking blockchain-based gene-drug interaction data sharing methods: A case study from the iDASH 2019 secure genome analysis competition blockchain track

Blockchain distributed ledger technology is just starting to be adopted in genomics and healthcare applications. Despite its increased prevalence in biomedical research applications, skepticism regarding the practicality of blockchain technology for real-world problems is still strong and there are few implementations beyond proof-of-concept. We focus on benchmarking blockchain strategies applied to distributed methods for sharing records of gene-drug interactions. We expect this type of sharing will expedite personalized medicine.We generated gene-drug interaction test datasets using the Clinical Pharmacogenetics Implementation Consortium (CPIC) resource. We developed three blockchain-based methods to share patient records on gene-drug interactions: Query Index, Index Everything, and Dual-Scenario Indexing.We achieved a runtime of about 60 s for importing 4,000 gene-drug interaction records from four sites, and about 0.5 s for a data retrieval query. Our results demonstrated that it is feasible to leverage blockchain as a new platform to share data among institutions.We show the benchmarking results of novel blockchain-based methods for institutions to share patient outcomes related to gene-drug interactions. Our findings support blockchain utilization in healthcare, genomic and biomedical applications. The source code is publicly available at https://github.com/tsungtingkuo/genedrug.
Blockchain Implementation for Privacy preserving and securing the Healthcare data

The contemporary medical system has a significant inconsistency between privacy issues and health requirements. A Blockchain-based technique to hide medical information and keep it safe is proposed in this study. This will ensure the data is accessible and reliable to study reviews and patients. There is no permission given to uncover patients' private information without providing any explanations to the patients. This proposal would execute distributed consensus between patients and research institutions while they function using the PBFT algorithm. The paper will discuss the security and privacy concerns of the proposed scheme. The proposed work is less expensive than other current ideas in terms of time involved in its implementation, with 150 blocks in almost 60 minutes.
Research Data Management (RDM) and the Evolving Identity of Academic Libraries and Librarians: A Literature Review

Academic libraries and their staff are increasingly involved in the Research Data Management (RDM) practices and processes in their universities. This article explores the impact that such initiatives have on the image and identity of academic libraries. This paper proposes that involvement in and leadership of RDM university practices has the potential to re-shape the library's role, image, and identity within the university, and going forward, to contribute to the library's continuing relevance to research communities. It also points to the need to develop librarians' skills and competencies in RDM, and reflects on the dynamics associated with collaboration and competition in RDM. The article concludes with an agenda for future research.
Opportunities and limitations of public blockchain-based supply chain traceability

Purpose Blockchain technology is provoking significant disruptions, thereby affecting supply chain management. This study endeavoured to advance research regarding blockchain-based supply chain traceability by identifying the opportunities and limitations that accompany the adoption of public blockchains. Therefore, the purpose of the study is to contribute to contemporary supply chain research by an assessment of blockchain technology and its linkages to traceability. Design/methodology/approach This paper is conceptual. The authors summarised the relevant literature on the concepts of supply chain traceability, conceptualised key elements exclusive to the public blockchain and highlighted opportunities and limitations in implementing traceability using blockchains. Findings Incompatibilities were identified between general traceability and the public blockchain. However, when embracing the blockchain's privacy model, the blockchains can support information exchange in supply chains where vulnerability towards third parties, the confidentiality of information, or the privacy of participants are concerns. Furthermore, the public blockchain can support areas of supply chains where institutional interest is lacking. Originality/value This is one of the first papers in an international supply chain management journal to critically analyse the intersection of specific blockchain characteristics and supply chain traceability requirements. The authors thereby add to the discussion of designs for a disintermediated, peer-to-peer models and guide researchers and practitioners alike in exploring the application of disruptive change from blockchain technologies. By setting focus on the privacy model, the paper identifies the potential application and future research approaches to exploit the elementary strength of the blockchain.
A longitudinal analysis of bloated Java dependencies

We study the evolution and impact of bloated dependencies in a single software ecosystem: Java/Maven. Bloated dependencies are third-party libraries that are packaged in the application binary but are not needed to run the application. We analyze the history of 435 Java projects. This historical data includes 48,469 distinct dependencies, which we study across a total of 31,515 versions of Maven dependency trees. Bloated dependencies steadily increase over time, and 89.2% of the direct dependencies that are bloated remain bloated in all subsequent versions of the studied projects. This empirical evidence suggests that developers can safely remove a bloated dependency. We further report novel insights regarding the unnecessary maintenance efforts induced by bloat. We find that 22% of dependency updates performed by developers are made on bloated dependencies, and that Dependabot suggests a similar ratio of updates on bloated dependencies.
An attempt to evaluate STEAM project-based instruction from a school mathematics perspective

Official documents in several educational systems reflect the importance of integrating Science, Technology, Engineering, Arts, and Mathematics (STEAM) and consider project-based learning (PBL) as a way of integrating such disciplines in the classroom. Although STEAM-PBL has been characterized and evaluated in different ways, its impact on school mathematics teaching remains unclear. Mathematics is recognized as the fundamental basis of other disciplines; however, many students still perceive it as a difficult subject and abandon it. To analyze STEAM-PBL classroom implementation from a school mathematics standpoint, we examined 41 classroom experiences from 11 Spanish secondary education teachers (five in-field mathematics teachers), who participated in a STEAM training program for more than 4 years. To frame this study, Thibaut et al.'s (J STEM Educ 3(1):02, 2018) and Schoenfeld's (Educ Res 43(8):404-412, 2014) characterizations of well-designed and implemented projects, respectively, were employed. The results showed that in-field mathematics teachers avoided transdisciplinary projects in which school mathematics is difficult to address, while out-of-field teachers tended to overlook the mathematics in interdisciplinary projects. Unlike out-of-field teachers, mathematics teachers often eluded design-based learning processes for deeply exploiting school mathematics. The latter teachers promoted high cognitive demands and positive perceptions about mathematics in projects where formative environments were generated through discussion and a meaningful feedback loop.
Divide well to merge better: A novel clustering algorithm

In this paper, a novel non-parametric clustering algorithm which is based on the concept of divide-and-merge is proposed. The proposed algorithm is based on two primary phases, after data cleaning: (i) the Division phase and (ii) the Merging phase. In the initial phase of division, the data is divided into an optimized number of small sub-clusters utilizing all the dimensions of the data. In the second phase of merging, the small sub-clusters obtained as a result of division are merged according to an advanced statistical metric to form the actual clusters in the data. The proposed algorithm has the following merits: (i) ability to discover both convex and non-convex shaped clusters, (ii) ability to discover clusters different in densities, (iii) ability to detect and remove outliers/noise in the data (iv) easily tunable or fixed hyperparameters (v) and its usability for high dimensional data. The proposed algorithm is extensively tested on 20 benchmark datasets including both, the synthetic and the real datasets and is found better/competing to the existing state-of-the-art parametric and non-parametric clustering algorithms.