Reviewerly/exHarmony huggingface dataset:
Software Bug Prediction Employing Feature Selection and Deep Learning

It was proven that the cost of fixing errors escalates as a project moves through its life cycle in an exponential fashion. Identifying buggy classes, as soon as they are committed to the Version Control System, would have a significant impact on reducing such cost. Mining in software repositories is a growing research area, where innovative techniques and models are designed to analyze software repositories data and uncover useful information that can help in software bug prediction. Previous studies showed that Deep Learning has achieved remarkable results in many fields and it keeps evolving. In this paper, experiments are carried out to study the effect of feature selection on the performance of bug prediction models and to check if better results can be obtained by using the promising Deep Learning techniques. Results show that applying feature selection, using a simple filter approach, such as selecting the highly ranked 9 and 5 features out of the 17 features, did not enhance the performance measures in most cases. On the other hand, results show that Deep Learning model (DL) achieves higher performance measures than the selected set of base classifiers for small and balanced datasets.
Integration of urban-rural planning and human geography for online education under the impact of COVID-19

At present, the normal teaching is seriously disturbed by the epidemic situation. In order to ensure the smooth progress of teaching, online teaching is a necessary way. Based on the current situation, this paper discusses the integration of urban and rural planning and human geography teaching in university courses. Based on the full analysis of the advantages of the original curriculum, this paper adjusts the curriculum of urban and rural planning and human geography. The new system focuses on the characteristics of “broad foundation and clear main line”, and follows the three main lines of “art, design and geography” to optimize the design. A questionnaire survey was conducted among 200 online students in our school and the data were processed by multidimensional BP neural network analysis. The experimental results show that the method proposed in this paper can improve the efficiency of online teaching.
Supervised <i>t</i>-Distributed Stochastic Neighbor Embedding for Data Visualization and Classification

We propose a novel supervised dimension reduction method, called supervised t-distributed stochastic neighbor embedding (St-SNE), which achieves dimension reduction by preserving the similarities of data points in both feature and outcome spaces. The proposed method can be used for both prediction and visualization tasks, with the ability to handle high-dimensional data. We show through a variety of datasets that when compared with a comprehensive list of existing methods, St-SNE has superior prediction performance in the ultra-high dimensional setting where the number of features p exceeds the sample size n, and has competitive performance in the p ≤ n setting. We also show that St-SNE is a competitive visualization tool that is capable of capturing within cluster variations. In addition, we propose a penalized Kullback-Leibler divergence criterion to automatically select the reduced dimension size k for St-SNE.
Inverse Covariance Intersection Fusion of Multiple Estimates

Linear fusion of estimates is a basic tool for combining probabilistic data. If the correlation of estimation errors is unknown, the fusion performance is evaluated with respect to the worst case. Inverse Covariance Intersection fusion is a rule for combining two estimates with partially known crosscorrelation matrix. This paper generalises the rule to fusing multiple estimates. First, the generalised assumption and the essential theory are presented. A suboptimal solution with a simple parametrisation is derived next and it is shown to be better than the solution for unknown correlation. Finally, a recursive fusion of multiple estimates is designed.
Explainable Intelligent Environments

The main focus of an Intelligent environment, as with other applications of Artificial Intelligence, is generally on the provision of good decisions towards the management of the environment or the support of human decision-making processes. The quality of the system is often measured in terms of accuracy or other performance metrics, calculated on labeled data. Other equally important aspects are usually disregarded, such as the ability to produce an intelligible explanation for the user of the environment. That is, asides from proposing an action, prediction, or decision, the system should also propose an explanation that would allow the user to understand the rationale behind the output. This is becoming increasingly important in a time in which algorithms gain increasing importance in our lives and start to take decisions that significantly impact them. So much so that the EU recently regulated on the issue of a “right to explanation”. In this paper we propose a Human-centric intelligent environment that takes into consideration the domain of the problem and the mental model of the Human expert, to provide intelligible explanations that can improve the efficiency and quality of the decision-making processes .
MULTAV: A Multi-chain Token Backed Voting Framework for Decentralized Blockchain Governance

Governance is a critical component in cryptocurrency systems for their sustainable development and evolution. In particular, on-chain governance has attracted a lot of attention in cryptocurrency communities after the hard forks of Bitcoin and Ethereum. The on-chain governance mechanisms offered by the existing cryptocurrencies have been implemented on their own blockchains. This approach, while working well for the established cryptocurrencies, raises certain security concerns for newly launched cryptocurrency projects with small market capitalization and initial circulating supply. To mitigate potential attacks against on-chain governance, we present a multi-chain token backed voting framework named MULTAV in this contribution. The MULTAV framework is able to enhance security of the current on-chain governance practices by enabling token holders to vote on multiple established cryptocurrency systems. The instantiation of the MULTAV framework for electing block producers on the IoTeX network demonstrates its feasibility and effectiveness in practice.
Human Activity Recognition: From Sensors to Applications

Human activity recognition (HAR) being a dynamic research topic in recent decades due to its high demand in countless applications, for instance, in healthcare, gaming, security and surveillance, and sports. Despite the amount of work contributed by the researcher to this well-researched field, there are still many challenging aspects and open issues that should be addressed in future works. In this paper, the current state-of-the-art in HAR from three holistic aspects is surveyed: sensors, models, and open challenges. First, we summarize the existing sensory systems, including sensor-based, vision-based sensors, and multimodal solutions. Next, the recent advances in HAR algorithms - from hierarchical fusion methods to handcrafted features to deep features, traditional machine learning algorithms to deep learning techniques - are discussed. Finally, the principal issues and challenges that should be addressed in future research are discussed.
Who Is the Next “Wolf of Wall Street”? Detection of Financial Intermediary Misconduct

Financial intermediaries are essential for investors’ participation in financial markets. Because of their position within the financial system, intermediaries who commit misconduct not only harm investors but also undermine trust in the financial system, which ultimately has a significant negative impact on the economy as a whole. Building upon information manipulation theory and warranting theory and making use of self-disclosed data with different levels of external verification, we propose different classifiers to automatically detect financial intermediary misconduct. In particular, we focus on self-disclosed information by financial intermediaries on the business network LinkedIn. We match user profiles with regulator-disclosed information and use these data for classifier training and evaluation. We find that self-disclosed information provides valuable input for detecting financial intermediary misconduct. In terms of external verification, our classifiers achieve the best predictive performance when also taking regulator-confirmed information into account. These results are supported by an economic evaluation. Our findings are highly relevant for both investors and regulators seeking to identify financial intermediary misconduct and thus contribute to the societal challenge of building and ensuring trust in the financial system.
End-to-End Amdo-Tibetan Speech Recognition Based on Knowledge Transfer

The end-to-end speech recognition technology solves the problem that each component is independent and models cannot be jointly optimized in the traditional speech recognition model. It incorporates such components as the acoustic model, language model, and decoding unit of the hybrid model into a single neural network, that can avoid the inherent defects of multiple modules and greatly reduces the complexity of the speech recognition model. In this research, an Amdo-Tibetan speech recognition system is constructed based on Listen, Attend and Spell (LAS) model by the end-to-end speech recognition technology. It can realize the direct conversion from Amdo-Tibetan speech sequence to the corresponding character sequence and greatly reduces the difficulty of building the Amdo-Tibetan speech recognition model. To further improve the performance of the proposed system, the following improvements have been made: firstly, the Multi-Head Attention mechanism is introduced to improve the alignment accuracy between state vectors of decoder and encoder; secondly, the label smoothing technique is adopted to solve the problem of over-fitting; thirdly, an N-gram language model is combined with the LAS model to increase the accuracy of speech recognition and the maximum mutual information (MMI) criterion is employed for discriminative training; and finally, transfer learning is utilized to overcome the problem of insufficient training data. Experimental results show that the proposed model can significantly enhance the performance of Amdo-Tibetan speech recognition.
Unique Faces Recognition in Videos

This paper tackles face recognition in videos employing metric learning methods and similarity ranking models. The paper compares the use of the Siamese network with contrastive loss and Triplet Network with triplet loss implementing the following architectures: Google/Inception architecture, 3D Convolutional Network (C3D), and a 2-D Long short-term memory (LSTM) Recurrent Neural Network. We make use of still images and sequences from videos for training the networks and compare the performances implementing the above architectures. The dataset used was the YouTube Face Database designed for investigating the problem of face recognition in videos. The contribution of this paper is two-fold: to begin, the experiments have established 3-D Convolutional networks and 2-D LSTMs with the contrastive loss on image sequences do not outperform Google/Inception architecture with contrastive loss in top n rank face retrievals with still images. However, the 3-D Convolution networks and 2-D LSTM with triplet Loss outperform the Google/Inception with triplet loss in top n rank face retrievals on the dataset; second, a Support Vector Machine (SVM) was used in conjunction with the CNNs' learned feature representations for facial identification. The results show that feature representation learned with triplet loss is significantly better for n-shot facial identification compared to contrastive loss. The most useful feature representations for facial identification are from the 2-D LSTM with triplet loss. The experiments show that learning spatio-temporal features from video sequences is beneficial for facial recognition in videos.
Spectral and spatial isolation of single tungsten diselenide quantum emitters using hexagonal boron nitride wrinkles

Monolayer WSe2 hosts bright single-photon emitters. Because of its compliance, monolayer WSe2 conforms to patterned substrates without breaking, thus creating the potential for large local strain, which is one activation mechanism of its intrinsic quantum emitters. Here, we report an approach to creating spatially isolated quantum emitters from WSe2 monolayers that display clean spectra with little detrimental background signal. We show that a bilayer of hexagonal boron nitride and WSe2 placed on a nanostructured substrate can be used to create and shape wrinkles that communicate local strain to WSe2, thus creating quantum emitters that are isolated from substrate features. We compare quantum emitters created directly on top of substrate features with quantum emitters formed along wrinkles and find that the spectra of the latter consist of mainly a single peak and a low background fluorescence. We also discuss possible approaches to controlling the emitter position along hexagonal boron nitride wrinkles.
Complex nurse care activity recognition using statistical features

Human activity recognition has important applications in healthcare, human-computer interactions and other arenas. The direct interaction between the nurse and patient can play a pivotal role in healthcare. Recognizing various activities of nurses can improve healthcare in many ways. However, it is a very daunting task due to the complexities of the activities. "The 2nd Nurse Care Activity Recognition Challenge Using Lab and Field Data'' provides sensor-based accelerometer data to predict 12 activities conducted by the nurses in both the lab and real-life settings. The main difficulty of this dataset is to process the raw data because of a high imbalance among different classes. Besides, all activities have not been performed by all subjects. Our team, 'Team Apophis' has processed the data by filtering noise, applying windowing technique on time and frequency domain to extract various features from lab and field data distinctly. After merging lab and field data, the 10-fold cross-validation technique has been applied to find out the model of best performance. We have obtained a promising accuracy of 65% with an F1 score of 40% on this challenging dataset by using the Random Forest classifier.
Rule-enhanced Noisy Knowledge Graph Embedding via Low-quality Error Detection

Knowledge graphs (KGs) have been widely applied in many fields such as recommendation systems and knowledge reasoning. Embedding KGs into a continuous vector space has quickly gained significant attention. However, most traditional KG embedding models assume that all the facts in the existing KGs are completely correct, ignoring that KG construction usually involves automatic mechanisms. These automatic construction processes inevitably generate a lot of noises and conflicts, including low-quality errors (e.g., entity type errors). Moreover, these low-quality noises could greatly influence the quality of rule extraction, which may reduce the efficiency of Rule-Guided Embedding model (RUGE). To address this problem, an efficient method to eliminate those entity type errors in triples is proposed and applied to RUGE. Experimental results demonstrate that the filtering of low-quality noises can greatly improve the accuracy of knowledge representation learning as well as the quality of rules, further illustrating the effectiveness of our method.
Mediating Data Trustworthiness by Using Trusted Hardware between IoT Devices and Blockchain

In recent years, with the progress of data analysis methods utilizing artificial intelligence (AI) technology, concepts of smart cities collecting data from IoT devices and creating values by analyzing it have been proposed. However, making sure that the data is not tampered with is of the utmost importance. One way to do this is to utilize blockchain technology to record and trace the history of the data. Park and Kim proposed ensuring the trustworthiness of the data by utilizing an IoT device with a trusted execution environment (TEE). Also, Guan et al. proposed authenticating an IoT device and mediating data using a TEE. For the authentication, they use the physically unclonable function of the IoT device. Usually, IoT devices suffer from the lack of resources necessary for creating transactions for the blockchain ledger. In this paper, we present a secure protocol in which a TEE acts as a proxy to the IoT devices and creates the necessary transactions for the blockchain. We use an authenticated encryption method on the data transmission between the IoT device and TEE to authenticate the device and ensure the integrity and confidentiality of the data generated by the IoT devices.
Inheritance software metrics on smart contracts

Blockchain systems have gained substantial traction recently, partly due to the potential of decentralized immutable mediation of economic activities. Ethereum is a prominent example that has the provision for executing stateful computing scripts known as Smart Contracts. These smart contracts resemble traditional programs, but with immutability being the core differentiating factor. Given their immutability and potential high monetary value, it becomes imperative to develop high-quality smart contracts. Software metrics have traditionally been an essential tool in determining programming quality. Given the similarity between smart contracts (written in Solidity for Ethereum) and object-oriented (OO) programming, OO metrics would appear applicable. In this paper, we empirically evaluate inheritance-based metrics as applied to smart contracts. We adopt this focus because, traditionally, inheritance has been linked to a more complex codebase which we posit is not the case with Solidity based smart contracts. In this work, we evaluate the hypothesis that, due to the differences in the context of smart contracts and OO programs, it may not be appropriate to use the same interpretation of inheritance based metrics for assessment.
An Object Model for Seafloor Observatory Sensor Control in the East China Sea

Seafloor observatories enable long-term, real-time, and continuous observation that marks a new way for oceanographic measurements. In terms of seafloor observatory research, sensor control is a key issue for the stable and effective operations of functional observatories. In this paper, an object model is proposed to standardize seafloor observatory sensor control and data acquisition. The object model is conceptionally designed as a set of sensor resource objects, based on the attributes and operations of which a client–server sensor control architecture is enabled for bidirectional information flow of control commands and observation data. The object model-based architecture is implemented with a prototype control system for plug-and-play enablement. The prototype system was put into a series of tests before applied to the East China Sea Experimental Seafloor Observatory, performing consistently with all the project requirements. Given the successful experiment, the object model design and prototype implementation are feasible to resolve seafloor observatory sensor control and beneficial for ocean observatory sciences.
On Tensor Distances for Self Organizing Maps: Clustering Cognitive Tasks

Self organizing maps (SOMs) are neural networks designed to be in an unsupervised way to create connections, learned through a modified Hebbian rule, between a high- (the input vector space) and a low-dimensional space (the cognitive map) based solely on distances in the input vector space. Moreover, the cognitive map is segmentwise continuous and preserves many of the major topological features of the latter. Therefore, neurons, trained using a Hebbian learning rule, can approximate the shape of any arbitrary manifold provided there are enough neurons to accomplish this. Moreover, the cognitive map can be readily used for clustering and visualization. Because of the above properties, SOMs are often used in big data pipelines. This conference paper focuses on a multilinear distance metric for the input vector space which adds flexibility in two ways. First, clustering can be extended to higher order data such as images, graphs, matrices, and time series. Second, the resulting clusters are unions of arbitrary shapes instead of fixed ones such as rectangles in case of $$\ell _1$$ norm or circles in case of $$\ell _2$$ norm. As a concrete example, the proposed distance metric is applied to an anonymized and open under the Creative Commons license cognitive multimodal dataset of fMRI images taken during three distinct cognitive tasks. Keeping the latter as ground truth, a subset of these images is clustered with SOMs of various configurations. The results are evaluated using the corresponding confusion matrices, topological error rates, activation set change rates, and intra-cluster distance variations.
A Novel Multi-Chaos Based Compressive Sensing Encryption Technique

Compressive sensing is a compression technique that can be effectively utilised in multimedia encryption. This paper proposes a new compressive sensing image encryption scheme using the Secure Hash Algorithm (SHA-512), Discrete Cosine Transform (DCT), orthogonal matrix and discrete Chirikov map-based random permutation. DCT is applied on a plaintext image and a block of DCT coefficients is multiplied with an orthogonal matrix. Inverse DCT and scaling are performed to restrict the values between 0 and 255. Furthermore, values are shuffled using Chirikov-based pseudo-random permutation. A strong trade-off exists between DCT block size and computational efficiency. The quality and Signal to Noise Ratio (SNR) of the decrypted image decreases when the size of the DCT matrix is reduced, increasing the speed of the encryption algorithm. An extensive security analyses of the proposed scheme are performed, which establishes the robustness, computational efficiency and security of the technique against cryptographic attacks.
The influence of transformational leadership on subordinate creative behaviour development process

This study utilized a multilevel sample of 543 employees and 106 managers and structural equation modelling (SEM) to test the proposed hypotheses. The results showed the mediating role of the group-level work environment in the relationships between transformational leadership and individual-level social capital and human capital. Moreover, social capital and knowledge transfer mediate the relationship between human capital and creative behaviour. Theoretical and managerial implications are discussed.
Smartphone location identification and transport mode recognition using an ensemble of generative adversarial networks

We present a generative adversarial network (GAN) approach to recognising modes of transportation from smartphone motion sensor data, as part of our contribution to the Sussex-Huawei Locomotion-Transportation (SHL) recognition challenge 2020 as team noname. Our approach identifies the location where the smartphone of the test dataset is carried on the body through heuristics, after which a location-specific model is trained based on the available published data at this location. Performance on the validation data is 0.95, which we expect to be very similar on the test set, if our estimation of the location of the phone on the test set is correct. We are highly confident in this location estimation. If however it were wrong, an accuracy as low as 30% could be expected.