Reviewerly/exHarmony huggingface dataset:
Transparency of Land Administration and the Role of Blockchain Technology, a Four-Dimensional Framework Analysis from the Ghanaian Land Perspective

Existing studies on blockchain within land administration have focused mainly on replacing or complementing the technology for land registration and titling. This study explores the potential of using blockchain technology to enhance the transparency of all land administration processes using an integrative review methodology coupled with a framework analysis. This study draws on the Ghanaian land administration perspective to make this insightful. It appears possible to apply a permissionless public blockchain across all land administration processes. This integrates all departments, processes, and stakeholders of land administration to enhance openness, improve availability and accessibility to information, and foster participation for transparency simultaneously. This can change the transparency variation in land administration to be more equal and homogenous regardless of land type. This, however, depends on the standardization of processes across the divisions, as well as negotiation and consensus amongst all stakeholders, especially with chiefs. Limitations include: limited storage and scalability, as well as huge electricity consumption for operation. This study’s policy implications are a review of all paper-based land transactions, a comprehensive digitization of land administration processes, public–private partnership on blockchain-based land administration, and professionals and stakeholder education on the technology.
Heuristic-Based Address Clustering in Bitcoin

With the emergence of decentralized cryptocurrencies such as Bitcoin, it has become very difficult for law enforcement to detect suspicious activities, identify users and obtain transaction records for criminals who utilize the pseudoanonymity provided by the cryptocurrency system.Address clustering aims to break such pseudoanonymity by linking addresses that are controlled by the same user based on the information available from the blockchain, such as transaction graphs.There are already two widely used heuristics for Bitcoin address clustering.One is based on the multiple input addresses of transactions.The other is based on one-time change addresses.By reconsidering the one-time change address-based heuristic from the perspective of address reuse, we propose a new heuristic that detects one-time change addresses by eliminating addresses that are reused later as non-change addresses.As a result, this heuristic works for transactions whose one-time change addresses cannot be identified by the previous two heuristics.The experimental results for different scales of Bitcoin transaction data show that the proposed heuristic has a 0.33% mean contribution to the ratio of address reduction in addition to the contribution of the multiple input addresses and one-time change address heuristics.
Hierarchical Deep CNN Feature Set-Based Representation Learning for Robust Cross-Resolution Face Recognition

Cross-resolution face recognition (CRFR), which is important in intelligent surveillance and biometric forensics, refers to the problem of matching a low-resolution (LR) probe face image against high-resolution (HR) gallery face images. Existing shallow learning-based and deep learning-based methods focus on mapping the HR-LR face pairs into a joint feature space where the resolution discrepancy is mitigated. However, little works consider how to extract and utilize the intermediate discriminative features from the noisy LR query faces to further mitigate the resolution discrepancy due to the resolution limitations. In this study, we desire to fully exploit the multi-level deep convolutional neural network (CNN) feature set for robust CRFR. In particular, our contributions are threefold. (i) To learn more robust and discriminative features, we desire to adaptively fuse the contextual features from different layers. (ii) To fully exploit these contextual features, we design a feature set-based representation learning (FSRL) scheme to collaboratively represent the hierarchical features for more accurate recognition. Moreover, FSRL utilizes the primitive form of feature maps to keep the latent structural information, especially in noisy cases. (iii) To further promote the recognition performance, we desire to fuse the hierarchical recognition outputs from different stages. Meanwhile, the discriminability from different scales can also be fully integrated. By exploiting these advantages, the efficiency of the proposed method can be delivered. Experimental results on several face datasets have verified the superiority of the presented algorithm to the other competitive CRFR approaches.
Machine Learning in Fighting Pandemics: A COVID-19 Case Study

In today's digitised world, machine learning (ML) has been playing a very important role in identifying patterns from the ever-growing amount of data made available by the devices and sensors used in the day-to-day activities. Applications of ML have enriched many fields directly connected to our daily lives including education, finance, governance, healthcare, security and surveillance, etc. Its applications can also be extended in facilitating the management of pandemics, especially when the world is experiencing an unprecedented pandemic caused by the novel coronavirus disease (COVID-19). This chapter aims to provide an account of how ML can be utilised in fighting pandemics in general, with a focus on the COVID-19.
Adversarial Robustness Against the Union of Multiple Perturbation Models

Owing to the susceptibility of deep learning systems to adversarial attacks, there has been a great deal of work in developing (both empirically and certifiably) robust classifiers. While most work has defended against a single type of attack, recent work has looked at defending against multiple perturbation models using simple aggregations of multiple attacks. However, these methods can be difficult to tune, and can easily result in imbalanced degrees of robustness to individual perturbation models, resulting in a sub-optimal worst-case loss over the union. In this work, we develop a natural generalization of the standard PGD-based procedure to incorporate multiple perturbation models into a single attack, by taking the worst-case over all steepest descent directions. This approach has the advantage of directly converging upon a trade-off between different perturbation models which minimizes the worst-case performance over the union. With this approach, we are able to train standard architectures which are simultaneously robust against $\ell_\infty$, $\ell_2$, and $\ell_1$ attacks, outperforming past approaches on the MNIST and CIFAR10 datasets and achieving adversarial accuracy of 47.0% against the union of ($\ell_\infty$, $\ell_2$, $\ell_1$) perturbations with radius = (0.03, 0.5, 12) on the latter, improving upon previous approaches which achieve 40.6% accuracy.
Improved Whale Optimization Algorithm for Solving Constrained Optimization Problems

In view of the shortcomings of the whale optimization algorithm (WOA), such as slow convergence speed, low accuracy, and easy to fall into local optimum, an improved whale optimization algorithm (IWOA) is proposed. First, the standard WOA is improved from the three aspects of initial population, convergence factor, and mutation operation. At the same time, Gaussian mutation is introduced. Then the nonfixed penalty function method is used to transform the constrained problem into an unconstrained problem. Finally, 13 benchmark problems were used to test the feasibility and effectiveness of the proposed method. Numerical results show that the proposed IWOA has obvious advantages such as stronger global search ability, better stability, faster convergence speed, and higher convergence accuracy; it can be used to effectively solve complex constrained optimization problems.
The Impact of Executives’ IT Expertise on Reported Data Security Breaches

Data security breaches (DSBs) are increasing investor and regulator pressure on firms to improve their IT governance (ITG) in an effort to mitigate the related risk. We argue that DSB risk cannot be mitigated by one executive alone, but, rather, is a shared leadership responsibility of the top management team (TMT) (i.e., Chief Executive Officer [CEO], Chief Financial Officer [CFO], and Chief Information Officer [CIO]). Our results suggest that IT-savvy CEOs see technologies related to mitigating DSBs as a top-three most important type of digital methodology for their firm. Similarly, the results related to CFOs with IT expertise single out the critical investment in controls designed to prevent DSBs. Our strong findings for CIOs on the TMT add to the related guidance from COBIT 5 for information security and consistently suggest that they are the key executive for securing IT systems. Finally, our granular explanation of each executive’s DSB-related responsibility could potentially provide firms the start of a governance-led roadmap for compliance to the Securities and Exchange Commission’s and Justice Department’s cyber regulations.
Harmonizing safety and security risk analysis and prevention in cyber-physical systems

Cyber-physical system (CPS) has been widely adopted in modern industrial productions. Safety and security (S&S) play an important role in CPS, which assists the reliability of the system. Traditionally, safety and security risks were managed independently. As the development of cyber technology, S&S issues become complex and could affect each other in multiple ways. There is a strong need to develop a systematic method to manage safety and security risks simultaneously. In this work, a systematic method to integrately analyze S&S risks is proposed. Firstly, attack route models (ARM) as the root cause of typical cyber-threats are summarized from the literature together with their corresponding consequences in CPS. Secondly, in addition to commonly adopted physical safety prevention route (PSPR), cyber security prevention route (CSPR) based on ARM is developed to investigate the safety hazards and security threats. Then, safety critical variable analysis (SCVA) is proposed to quantify the S&S risk. Finally, SCVA, CSPR and PSPR are integrated via the bowtie method. The key contribution of the work is the method which simultaneously consider safety and security risk for CPS. In parallel. SCVA represents the working status of CPS devices, which would be useful to quantitatively determine the severity of consequence and further level of risk.
Software Defect Prediction for Healthcare Big Data: An Empirical Evaluation of Machine Learning Techniques

Software defect prediction (SDP) in the initial period of the software development life cycle (SDLC) remains a critical and important assignment. SDP is essentially studied during few last decades as it leads to assure the quality of software systems. The quick forecast of defective or imperfect artifacts in software development may serve the development team to use the existing assets competently and more effectively to provide extraordinary software products in the given or narrow time. Previously, several canvassers have industrialized models for defect prediction utilizing machine learning (ML) and statistical techniques. ML methods are considered as an operative and operational approach to pinpoint the defective modules, in which moving parts through mining concealed patterns amid software metrics (attributes). ML techniques are also utilized by several researchers on healthcare datasets. This study utilizes different ML techniques software defect prediction using seven broadly used datasets. The ML techniques include the multilayer perceptron (MLP), support vector machine (SVM), decision tree (J48), radial basis function (RBF), random forest (RF), hidden Markov model (HMM), credal decision tree (CDT), K-nearest neighbor (KNN), average one dependency estimator (A1DE), and Naïve Bayes (NB). The performance of each technique is evaluated using different measures, for instance, relative absolute error (RAE), mean absolute error (MAE), root mean squared error (RMSE), root relative squared error (RRSE), recall, and accuracy. The inclusive outcome shows the best performance of RF with 88.32% average accuracy and 2.96 rank value, second-best performance is achieved by SVM with 87.99% average accuracy and 3.83 rank values. Moreover, CDT also shows 87.88% average accuracy and 3.62 rank values, placed on the third position. The comprehensive outcomes of research can be utilized as a reference point for new research in the SDP domain, and therefore, any assertion concerning the enhancement in prediction over any new technique or model can be benchmarked and proved.
Automated evaluation of psychotherapy skills using speech and language technologies

With the growing prevalence of psychological interventions, it is vital to have measures which rate the effectiveness of psychological care to assist in training, supervision, and quality assurance of services. Traditionally, quality assessment is addressed by human raters who evaluate recorded sessions along specific dimensions, often codified through constructs relevant to the approach and domain. This is, however, a cost-prohibitive and time-consuming method that leads to poor feasibility and limited use in real-world settings. To facilitate this process, we have developed an automated competency rating tool able to process the raw recorded audio of a session, analyzing who spoke when, what they said, and how the health professional used language to provide therapy. Focusing on a use case of a specific type of psychotherapy called "motivational interviewing", our system gives comprehensive feedback to the therapist, including information about the dynamics of the session (e.g., therapist's vs. client's talking time), low-level psychological language descriptors (e.g., type of questions asked), as well as other high-level behavioral constructs (e.g., the extent to which the therapist understands the clients' perspective). We describe our platform and its performance using a dataset of more than 5000 recordings drawn from its deployment in a real-world clinical setting used to assist training of new therapists. Widespread use of automated psychotherapy rating tools may augment experts' capabilities by providing an avenue for more effective training and skill improvement, eventually leading to more positive clinical outcomes.
Variational Autoencoder for Image-Based Augmentation of Eye-Tracking Data

Over the past decade, deep learning has achieved unprecedented successes in a diversity of application domains, given large-scale datasets. However, particular domains, such as healthcare, inherently suffer from data paucity and imbalance. Moreover, datasets could be largely inaccessible due to privacy concerns, or lack of data-sharing incentives. Such challenges have attached significance to the application of generative modeling and data augmentation in that domain. In this context, this study explores a machine learning-based approach for generating synthetic eye-tracking data. We explore a novel application of variational autoencoders (VAEs) in this regard. More specifically, a VAE model is trained to generate an image-based representation of the eye-tracking output, so-called scanpaths. Overall, our results validate that the VAE model could generate a plausible output from a limited dataset. Finally, it is empirically demonstrated that such approach could be employed as a mechanism for data augmentation to improve the performance in classification tasks.
Adversarial Learning for Zero-Shot Stance Detection on Social Media

Stance detection on social media can help to identify and understand slanted news or commentary in everyday life.In this work, we propose a new model for zero-shot stance detection on Twitter that uses adversarial learning to generalize across topics.Our model achieves state-of-the-art performance on a number of unseen test topics with minimal computational costs.In addition, we extend zero-shot stance detection to new topics, highlighting future directions for zero-shot transfer.
A 5-D Multi-Stable Hyperchaotic Two-Disk Dynamo System With No Equilibrium Point: Circuit Design, FPGA Realization and Applications to TRNGs and Image Encryption

In this work, we devise a new 5-D hyperchaotic dynamo system by adding two feedback controllers to the Rikitake 2-disk dynamo system (1958). We show that the new 5-D hyperchaotic system does not possess any equilibrium point and deduce that the new 5-D system has a hidden hyperchaotic attractor. Using Multisim, we develop an electronic circuit design of the new 5-D hyperchaotic dynamo system for practical applications. We also exhibit the implementation of the new 5-D hyperchaotic dynamo system by using a field-programmable gate array (FPGA), which requires adders, subtractors and multipliers. The hardware resources are given for the application of three numerical methods, all of them providing results in good agreement with MATLAB simulations. As an application, we devise a dual core high speed hybrid true random number generator (TRNG) using Ring and Heun algorithm based on the new 5-D hyperchaotic oscillator on FPGA. Based on the hyperchaotic features of the proposed 5-D hyperchaotic dynamo system, we suggest a new encryption approach for colour images. Simulation outcomes of the presented encryption approach confirm that our chaotic system has good cryptographic properties and its usability in different cryptographic purposes.
MRN: A Locally and Globally Mention-Based Reasoning Network for Document-Level Relation Extraction

Document-level relation extraction aims to detect the relations within one document, which is challenging since it requires complex reasoning using mentions, entities, local and global contexts.Few previous studies have distinguished local and global reasoning explicitly, which may be problematic because they play different roles in intra-and inter-sentence relations.Moreover, the interactions between local and global contexts should be considered since they could help relation reasoning based on our observation.In this paper, we propose a novel mention-based reasoning (MRN) module based on explicitly and collaboratively local and global reasoning.Based on MRN, we design a co-predictor module to predict entity relations based on local and global entity and relation representations jointly.We evaluate our MRN model on three widelyused benchmark datasets, namely DocRED, CDR, and GDA.Experimental results show that our model outperforms previous state-ofthe-art models by a large margin.
MulDA: A Multilingual Data Augmentation Framework for Low-Resource Cross-Lingual NER

Named Entity Recognition (NER) for lowresource languages is a both practical and challenging research problem.This paper addresses zero-shot transfer for cross-lingual NER, especially when the amount of sourcelanguage training data is also limited.The paper first proposes a simple but effective labeled sequence translation method to translate source-language training data to target languages and avoids problems such as word order change and entity span determination.With the source-language data as well as the translated data, a generation-based multilingual data augmentation method is introduced to further increase diversity by generating synthetic labeled data in multiple languages.These augmented data enable the language model based NER models to generalize better with both the language-specific features from the target-language synthetic data and the language-independent features from multilingual synthetic data.An extensive set of experiments were conducted to demonstrate encouraging cross-lingual transfer performance of the new research on a wide variety of target languages. 1
Societal Biases in Language Generation: Progress and Challenges

Technology for language generation has advanced rapidly, spurred by advancements in pre-training large models on massive amounts of data and the need for intelligent agents to communicate in a natural manner.While techniques can effectively generate fluent text, they can also produce undesirable societal biases that can have a disproportionately negative impact on marginalized populations.Language generation presents unique challenges for biases in terms of direct user interaction and the structure of decoding techniques.To better understand these challenges, we present a survey on societal biases in language generation, focusing on how data and techniques contribute to biases and progress towards reducing biases.Motivated by a lack of studies on biases from decoding techniques, we also conduct experiments to quantify the effects of these techniques.By further discussing general trends and open challenges, we call to attention promising directions for research and the importance of fairness and inclusivity considerations for language generation applications.
Few-Shot Lifelong Learning

Many real-world classification problems often have classes with very few labeled training samples. Moreover, all possible classes may not be initially available for training, and may be given incrementally. Deep learning models need to deal with this two-fold problem in order to perform well in real-life situations. In this paper, we propose a novel Few-Shot Lifelong Learning (FSLL) method that enables deep learning models to perform lifelong/continual learning on few-shot data. Our method selects very few parameters from the model for training every new set of classes instead of training the full model. This helps in preventing overfitting. We choose the few parameters from the model in such a way that only the currently unimportant parameters get selected. By keeping the important parameters in the model intact, our approach minimizes catastrophic forgetting. Furthermore, we minimize the cosine similarity between the new and the old class prototypes in order to maximize their separation, thereby improving the classification performance. We also show that integrating our method with self-supervision improves the model performance significantly. We experimentally show that our method significantly outperforms existing methods on the miniImageNet, CIFAR-100, and CUB-200 datasets. Specifically, we outperform the state-of-the-art method by an absolute margin of 19.27% for the CUB dataset.
Blind Travel Prediction Based on Obstacle Avoidance in Indoor Scene

Blind people have intelligent tools to rely on for travel with the development of navigation technology. The GPS navigation, blind track, etc., are tools that blind people often use when traveling outdoors. However, indoor navigation tools and technology for blind people are lacking. We propose an obstacle avoidance algorithm and a spatial-temporal model of trajectory prediction for the indoor travel task of the blind. The focus of this work is that it enables the blind to accurately avoid obstacles and achieve high accuracy trajectory prediction aiming at the unique movement characteristics of the blind. We set up a variety of baselines to conduct an experimental evaluation on a dataset of blind trajectories in a multistorey shopping mall. The experimental results show the advantages of the data model and predictive model of this work.