Reviewerly/exHarmony huggingface dataset:
Explainable Authorship Verification in Social Media via Attention-based Similarity Learning

Authorship verification is the task of analyzing the linguistic patterns of two or more texts to determine whether they were written by the same author or not. The analysis is traditionally performed by experts who consider linguistic features, which include spelling mistakes, grammatical inconsistencies, and stylistics for example. Machine learning algorithms, on the other hand, can be trained to accomplish the same, but have traditionally relied on so-called stylometric features. The disadvantage of such features is that their reliability is greatly diminished for short and topically varied social media texts. In this interdisciplinary work, we propose a substantial extension of a recently published hierarchical Siamese neural network approach, with which it is feasible to learn neural features and to visualize the decision-making process. For this purpose, a new large-scale corpus of short Amazon reviews for text comparison research is compiled and we show that the Siamese network topologies outperform state-of-the-art approaches that were built up on stylometric features. Our linguistic analysis of the internal attention weights of the network shows that the proposed method is indeed able to latch on to some traditional linguistic categories.
Integrating Part of Speech Guidance for Image Captioning

To generate an image caption, firstly, the content of the image should be fully understood; and then the semantic information contained in the image should be described using a phrase or statement that conforms to certain grammatical rules. Thus, it requires techniques from both computer vision and natural language processing to connect the two different media forms together, which is highly challenging. To adaptively adjust the effect of visual information and language information on the captioning process, in this paper, the part of speech information is proposed to novelly integrate with image captioning models based on the encoder-decoder framework. First, a part of speech prediction network is proposed to analyze and model the part of speech sequences for the words in natural language sentences; then, different mechanisms are proposed to integrate the part of speech guidance information with merge-based and inject-based image captioning models, respectively; finally, according to the integrated frameworks, a multi-task learning paradigm is proposed to facilitate model training. Experiments are conducted on two widely used image captioning datasets, Flickr30 k and COCO, and the results have validated that the image captions generated by the proposed method contain more accurate visual information and comply with language habits and grammar rules better.
Markerless cooperative augmented reality-based smart manufacturing double-check system: Case of safe PCBA inspection following automatic optical inspection

Augmented reality (AR) is a key technology anchored towards realizing Industry 4.0 smart manufacturing aims. In manufacturing inspection, AR has previously been employed to support operators in assessing thickness of manufactured parts, ship building, aircraft subassemblies and printed circuit board assemblies (PCBAs) with or without external markers in standalone head-mounted (HMD) or handheld systems. These AR systems often use optical see-through (OST) or video see-through (VST) technologies. Currently, cyber physical integration of processes in an industrial system synergizes production through increased efficiency and improved quality while facilitating customization. An operator manually double-checking/inspecting a product that has previously been automatically inspected can better rely on this existing defect location information once it is contextually and spatially overlaid within their field of view to intuitively and efficiently execute the task. Additional interactive information provided can quickly reorient a user, provide necessary contextual reference, and monitor progress of the task while alerting them of their safety. In this study, adopting an automatic optical inspection (AOI) wirelessly aided HMD-OST AR-based manual inspection system requiring no external markers for contextual registration is a promising direction towards smart and safe PCBA manufacturing in line with Industry 4.0. Animated rectangular bracket of high color contrast is employed to localize the spawned AOI defect point, an arrow overlaid with distance text provides user guidance, a contextual resizable image facilitates user double-checking and a progress bar tracks the inspection progress while monitoring tracking state. We evaluate the developed system's robustness, effectiveness of display technology used, suitability of contextual against static display modes, and display technology influence on various inspection attributes in a user study. Results demonstrate the system to be robust, OST-HMD outperforms handheld VST devices, contextual display mode to be significantly preferred to static mode, and display technology employed has no significant influence on the inspection attributes. Finally, registration precision results demonstrate usability of the system while superposition of distance to orientation information raises the inspection rate of PCBAs.
An innovative flower pollination algorithm for continuous optimization problem

The flower pollination algorithm (FPA) is a relatively new swarm optimization algorithm that inspired by the pollination phenomenon of natural phanerogam. Since its proposed, it has received widespread attention and been applied in various engineering fields. However, the FPA still has certain drawbacks, such as inadequate optimization precision and poor convergence. In this paper, an innovative flower pollination algorithm based on cloud mutation is proposed (CMFPA), which adds information of all dimensions in the global optimization stage and uses the designed cloud mutation method to redistribute the population center. To verify the performance of the CMFPA in solving continuous optimization problems, we test twenty-four well-known functions, composition functions of CEC2013 and all benchmark functions of CEC2017. The results demonstrate that the CMFPA has better performance compared with other state-of-the-art algorithms. In addition, the CMFPA is implemented for five constrained optimization problems in practical engineering, and the performance is compared with state-of-the-art algorithms to further prove the effectiveness and efficiency of the CMFPA.
Chaos based multiple order optical transform for 2D image encryption

In this paper, a robust yet simple and efficient method is proposed for a 2D image encryption. The purpose of this work is to plug the gap related to blind decryption and lack of randomness in optical transform domain which limits its application in encryption of data. The novelty of this work is in the method of generating multiple transform orders. Two piecewise linear chaotic maps (PWLCM) are used to generate multiple transform orders along both the dimensions. A fractional Fourier transform is obtained with the generated multiple order vectors. The transform domain data is scrambled with yet another integrated chaotic map to further enhance the security. In the proposed work, the chaotic maps are selected, keeping in view, limitations related to 1D chaotic maps. The PWLCM and an integrated chaotic map are robust due to absence of periodic windows that exist in 1D chaotic maps. A reality preserving algorithm is used to overcome the limitation of dealing with complex transform coefficients for storage and transmission purposes thereby making it suitable for fast and real time processing. The results and experimental analysis prove that proposed scheme is highly random in encrypted domain and it possesses excellent sensitivity. The entropy in encrypted domain is considerably higher than other similar state-of-art schemes. The scheme is robust against classical attacks, noise attack, differential attacks and entropy attack. The present work is confined to 2D images, however the proposed scheme can be further extended to multispectral images.
Robust Wireless Fingerprinting via Complex-Valued Neural Networks

A "wireless fingerprint" which exploits hardware imperfections unique to each device is a potentially powerful tool for wireless security. Such a fingerprint should be able to distinguish between devices sending the same message, and should be robust against standard spoofing techniques. Since the information in wireless signals resides in complex baseband, in this paper, we explore the use of neural networks with complex- valued weights to learn fingerprints using supervised learning. We demonstrate that, while there are potential benefits to using sections of the signal beyond just the preamble to learn fingerprints, the network cheats when it can, using information such as transmitter ID (which can be easily spoofed) to artificially inflate performance. We also show that noise augmentation by inserting additional white Gaussian noise can lead to significant performance gains, which indicates that this counter-intuitive strategy helps in learning more robust fingerprints. We provide results for two different wireless protocols, WiFi and ADS-B, demonstrating the effectiveness of the proposed method.
Measurement of Users’ Well-Being Through Domotic Sensors and Machine Learning Algorithms

This paper proposes a specific domotic sensor network to measure the well-being of elderly people in private home environments through Machine Learning (ML) algorithms trained with daily surveys. The tests have been conducted in 5 apartments lived by 8 older people where the non-obtrusive sensor network is installed. Two ML algorithms are compared, Random Forest (RF) and Regression Tree (RT), such that to verify whether the users' well-being is encoded in behavioural patterns obtained from the domotic data. These data are used to measure users' well-being and compared with three reference indices obtained through a daily survey: a physical (Phy), a mental (Mind) and a general health index (Avg). The extracted indices from the daily survey are used to train ML algorithms in the estimation of user's well-being for users that live alone (single-resident) or with others (multi-resident). Single-house and multi-house procedures are tested, both to extract a user-specific behaviour, and assess whether the model is able to generalise across different users and environments. Results show that the RF algorithm provides better performance than the RT algorithm in predicting the level of well-being with a Mean Absolute Error in the multi-house procedure of 32%, 13% and 17% for the Avg, Mind and Phy indices, respectively.
An Ensemble Learner-Based Bagging Model Using Past Output Data for Photovoltaic Forecasting

As the world is aware, the trend of generating energy sources has been changing from conventional fossil fuels to sustainable energy. In order to reduce greenhouse gas emissions, the ratio of renewable energy sources should be increased, and solar and wind power, typically, are driving this energy change. However, renewable energy sources highly depend on weather conditions and have intermittent generation characteristics, thus embedding uncertainty and variability. As a result, it can cause variability and uncertainty in the power system, and accurate prediction of renewable energy output is essential to address this. To solve this issue, much research has studied prediction models, and machine learning is one of the typical methods. In this paper, we used a bagging model to predict solar energy output. Bagging generally uses a decision tree as a base learner. However, to improve forecasting accuracy, we proposed a bagging model using an ensemble model as a base learner and adding past output data as new features. We set base learners as ensemble models, such as random forest, XGBoost, and LightGBMs. Also, we used past output data as new features. Results showed that the ensemble learner-based bagging model using past data features performed more accurately than the bagging model using a single model learner with default features.
Improving software bug-specific named entity recognition with deep neural network

There is a large volume of bug data in the bug repository, which contains rich bug information. Existing studies on bug data mining mainly rely on using information retrieval (IR) technology to search relevant historical bug reports. These studies basically treat a bug report as a closed unit, ignoring the semantic and structural information within it. Named-entity recognition (NER) is an important task of information extraction (IE) technology. Based on NER, fine-grained factual information could be comprehensively extracted to further form structured data, which provides a new way to improve the accessibility of bug information. However, bug NER is different from general NER tasks. Bug reports are free-form text, which include a mixed language environment studded with code, abbreviations and software-specific vocabularies. In this paper, we propose a deep neural network approach for bug-specific entity recognition called DBNER using bidirectional long short-term memory (LSTM) with Conditional Random Fields decoding model (CRF). DBNER extracts multiple features from the massive bug data and uses attention mechanism to improve the consistency of entity tags in the bug reports. Experiment results show that the F1-score reaches an average of 91.19%. In addition, in cross-project experiments, the DBNER’s F1-score reaches an average of 84%.
A novel random forest approach for imbalance problem in crime linkage

Crime linkage is a challenging task in crime analysis, which is to find serial crimes committed by the same offenders. It can be regarded as a binary classification task detecting serial case pairs. However, most case pairs in the real world are nonserial, so there is a serious class imbalance in the crime linkage. In this paper, we propose a novel random forest based on the information granule. The approach does not resample the minority class or the majority class but concentrates on indistinguishable case pairs at the classification boundary. The information granule is used to identify case pairs that are difficult to distinguish in the dataset and constructs a nearly balanced dataset in the uncertainty region to deal with the imbalanced problem. In the proposed approach, random trees come from the original dataset and the above mentioned nearly balanced dataset. A real-world robbery dataset and some public imbalanced datasets are employed to measure the performance of the approach. The results show that the proposed approach is effective in dealing with class imbalances, and it can be extended to combine with other methods solving class imbalances.
Simulation and modeling of an improved multi-verse optimization algorithm for QoS-aware web service composition with service level agreements in the cloud environments

Cloud computing is a new trend in information technology that provides resources and shares services by web services over the internet for users. Many web services have similar functionality with different quality of service (QoS) in the cloud. Thus, an appropriate service composition method is needed to assign an optimal composition of services to the users. Furthermore, service level agreement (SLA) which is the level of service expected from the service provider should be satisfied by the service composition method that less considered in previous studies. Introducing an appropriate algorithm for web service composition that could achieve high QoS while satisfying SLA constrains is one of the main issues in the cloud computing area. In this study, an improved multi-verse optimization algorithm for web service composition that is called IMVO algorithm is proposed to improve QoS while satisfying SLA. The simulation results show increasing of normalized QoS up to 57% in comparison with the other approaches, especially for service composition problems with SLA.
Design and Field Implementation of Blockchain Based Renewable Energy Trading in Residential Communities

This paper proposes a peer to peer (P2P), blockchain based energy trading market platform for residential communities with the objective of reducing overall community peak demand and household electricity bills. Smart homes within the community place energy bids for its available distributed energy resources (DERs) for each discrete trading period during a day, and a double auction mechanism is used to clear the market and compute the market clearing price (MCP). The marketplace is implemented on a permissioned blockchain infrastructure, where bids are stored to the immutable ledger and smart contracts are used to implement the MCP calculation and award service contracts to all winning bids. Utilizing the blockchain obviates the need for a trusted, centralized auctioneer, and eliminates vulnerability to a single point of failure. Simulation results show that the platform enables a community peak demand reduction of 46%, as well as a weekly savings of 6%. The platform is also tested at a real-world Canadian microgrid using the Hyperledger Fabric blockchain framework, to show the end to end connectivity of smart home DERs to the platform.
A Bayesian Filter for Multi-View 3D Multi-Object Tracking With Occlusion Handling

This paper proposes an online multi-camera multi-object tracker that only requires monocular detector training, independent of the multi-camera configurations, allowing seamless extension/deletion of cameras without retraining effort. The proposed algorithm has a linear complexity in the total number of detections across the cameras, and hence scales gracefully with the number of cameras. It operates in the 3D world frame, and provides 3D trajectory estimates of the objects. The key innovation is a high fidelity yet tractable 3D occlusion model, amenable to optimal Bayesian multi-view multi-object filtering, which seamlessly integrates, into a single Bayesian recursion, the sub-tasks of track management, state estimation, clutter rejection, and occlusion/misdetection handling. The proposed algorithm is evaluated on the latest WILDTRACKS dataset, and demonstrated to work in very crowded scenes on a new dataset.
An examination of the effect of recent phishing encounters on phishing susceptibility

This paper examines online users' perceived susceptibility to phishing attacks. We posit that an individual's phishing susceptibility may be shaped by recent phishing encounters and, more importantly, that the effect of new experience on susceptibility will be heterogeneous among users. To facilitate our investigation, we focus on both the process and outcome of phishing detection. Survey data from college students confirms that one's susceptibility is affected by detection process difficulty and detection outcome failures in the recent phishing encounter. Results also reveal the importance of personal attributes, such as past success in phishing detection and phishing desensitization, in regulating the effects of a recent phishing encounter. Finally, results show the relationship between detection process difficulty and outcome failures, in addition to confirming antecedents to the two detection components. Our research generates new knowledge that contributes to the phishing literature and it also sheds new insights that inform practitioners, although the use of college students limits the generalizability of the current findings.
Real-Time and Embedded Deep Learning on FPGA for RF Signal Classification

We designed and implemented a deep learning based RF signal classifier on the Field Programmable Gate Array (FPGA) of an embedded software-defined radio platform, DeepRadio™, that classifies the signals received through the RF front end to different modulation types in real time and with low power. This classifier implementation successfully captures complex characteristics of wireless signals to serve critical applications in wireless security and communications systems such as identifying spoofing signals in signal authentication systems, detecting target emitters and jammers in electronic warfare (EW) applications, discriminating primary and secondary users in cognitive radio networks, interference hunting, and adaptive modulation. Empowered by low-power and low-latency embedded computing, the deep neural network runs directly on the FPGA fabric of DeepRadio™, while maintaining classifier accuracy close to the software performance. We evaluated the performance when another SDR (USRP) transmits signals with different modulation types at different power levels and DeepRadio™receives the signals and classifies them in real time on its FPGA. A smartphone with a mobile app is connected to DeepRadio™to initiate the experiment and visualize the classification results. With real radio transmissions over the air, we show that the classifier implemented on DeepRadio™achieves high accuracy with low latency (microsecond per sample) and low energy consumption (microJoule per sample), and this performance is not matched by other embedded platforms such as embedded graphics processing unit (GPU).
Generating Representative Headlines for News Stories

Millions of news articles are published online every day, which can be overwhelming for readers to follow. Grouping articles that are reporting the same event into news stories is a common way of assisting readers in their news consumption. However, it remains a challenging research problem to efficiently and effectively generate a representative headline for each story. Automatic summarization of a document set has been studied for decades, while few studies have focused on generating representative headlines for a set of articles. Unlike summaries, which aim to capture most information with least redundancy, headlines aim to capture information jointly shared by the story articles in short length and exclude information specific to each individual article.
Valve: Securing Function Workflows on Serverless Computing Platforms

Serverless Computing has quickly emerged as a dominant cloud computing paradigm, allowing developers to rapidly prototype event-driven applications using a composition of small functions that each perform a single logical task. However, many such application workflows are based in part on publicly-available functions developed by third-parties, creating the potential for functions to behave in unexpected, or even malicious, ways. At present, developers are not in total control of where and how their data is flowing, creating significant security and privacy risks in growth markets that have embraced serverless (e.g., IoT).
Cloudy transaction costs: a dive into cloud computing economics

Abstract Looking merely from the neoclassical perspective, cloud computing is price effective. However, according to institutional and transaction cost economics, cloud customers should estimate other costs beyond the price. Such costs may not be known to cloud customers, leading to unmet expectations and implementation challenges. The aim of this paper is to study transaction costs of cloud computing from the customer perspective to make the cloud journey less cloudy, i.e. more informed and well planned. This paper applies transaction cost theory to cloud computing through a 360-degree industry analysis. Expert interviews with vendor, customer and consultancy sides were conducted to understand costs associated with cloud computing. Findings were validated through a case study. Findings of this research indicate that cloud has high ‘asset specificity’ due to change management costs, meta services costs and business process reengineering costs. Cloud also has a considerable level of ‘uncertainty’ asking for managing contracts, investing in cloud-specific monitoring solutions and consciously reviewing of the legal compliance. Finally, cloud has high ‘transaction frequency’, which compensates for the needed investments triggered by ‘uncertainty’ and ‘asset specificity’.