Reviewerly/exHarmony huggingface dataset:
Hausa Intelligence Chatbot System

Chatbot is an AI based application that stimulates human conversations. In this research, we proposed a noble approach to the design of Chatbot that interacts with people in Hausa Language, which is the second most famous languages in Africa. Numerous chatbots have been created and continue to be created. To the best of our knowledge, there is very limited or no Chatbot that target Hausa Language, hence the catalyst of this research. The proposed Chatbot is capable of communicating with users in Hausa Language, tell some funny stories and above all has the ability of learning from users. It was designed as a web-based application that can feature in any platform with internet access and a web browser, where PHP was used as programming Language in conjunction with MySQL database. The interface was designed using HTML and CSS with JavaScript for interactivity. Developing this Chatbot system would bring the Hausa language into the Artificial intelligence stage and it would serve as a reference material for future research and development. The result shows that the system is capable of communicating in simple Hausa words and will be able to learn simple words in Hausa. Developing system whose ability is to learn, adapt and perform autonomously can be a major field of competition between technology inventors and Hausa-based language processing over the next few years.
Photograph enhancement via imitation-to-innovation training scheme

Post processing is usually necessary for photographs captured in undesired situations to improve their visual quality. As photo retouching requires operators to receive professional training, automatic photograph enhancing system would benefit amateur photographers and also save experts from tedious retouching tasks. In this paper, we present a deep learning framework based on imitation-to-innovation training scheme. Our method integrates the bilateral grid data structure and Generative Adversarial Network (GAN) to achieve high time-efficiency and appealing retouching output. Besides, we present bilateral loss function to maintain piecewise smoothness. The experimental results and user study demonstrate that our method produces improved results than other state-of-the-art methods.
Generalized linear mixed hidden <scp>semi‐Markov</scp> models in longitudinal settings: A Bayesian approach

Hidden Markov and semi‐Markov models (H(S)MMs) constitute useful tools for modeling observations subject to certain dependency structures. The hidden states render these models very flexible and allow them to capture many different types of latent patterns and dynamics present in the data. This has led to the increased popularity of these models, which have been applied to a variety of problems in various domains and settings, including longitudinal data. In many longitudinal studies, the response variable is categorical or count‐type. Generalized linear mixed models (GLMMs) can be used to analyze a wide range of variables, including categorical and count. The present study proposes a model that combines HSMMs with GLMMs, leading to generalized linear mixed hidden semi‐Markov models (GLM‐HSMMs). These models can account for time‐varying unobserved heterogeneity and handle different response types. Parameter estimation is achieved using a Monte Carlo Newton‐Raphson (MCNR)‐like algorithm. In our proposed model, the distribution of the random effects depends on hidden states. We illustrate the applicability of GLM‐HSMMs with an example in the field of occupational health, where the response variable consists of count values. Furthermore, we assess the performance of our MCNR‐like algorithm through a simulation study.
SafePay on Ethereum: A Framework For Detecting Unfair Payments in Smart Contracts

Smart contracts on the Ethereum blockchain are notoriously known as vulnerable to external attacks. Many of their issues led to a considerably large financial loss as they resulted from broken payments by digital assets, e.g., cryptocurrency. Existing research focused on specific patterns to find such problems, e.g., reentrancy bug, nondeterministic recipient etc., yet may lead to false alarms or miss important issues. To mitigate these limitations, we designed the SafePay analysis framework to find unfair payments in Ethereum smart contracts. Compared to existing analyzers, SafePay can detect potential blockchain transactions with feasible exploits thus effectively avoid false reports. Specifically, the detection is driven by a systematic search for violations on fair value exchange (FVE), i.e., a new security invariant introduced in SafePay to indicate that each party “fairly” pays to others. The preliminary evaluation validated the efficacy of SafePay by reporting previously unknown issues and decreasing the number of false alarms.
Integrating Cellphone-based Hardware Wallet with Visional Certificate Verification System

With the rapid growth of blockchain, more and more people possess cryptocurrencies or use decentralized applications (Dapp). A wallet in blockchain not only stores your assets but also represents your online identity. Nowadays, document notary using blockchain is getting mature to prevent the problem of the counterfeit certificate. In this paper, we integrate a cellphone-based wallet with a novel visional certificate verification system. By the trusted execution environment (TEE) protection, a user can isolate his/her private key without the leakage attack. Two-factor authentication is required to generate a signature. Different from existing platforms, our system adopts IPFS, a P2P network for storing and sharing data, for storing the certificate image, which offers the function of visional verification for better persuasiveness. Furthermore, the unique time-limited verification can restrict the accessing period of the verifier for better privacy protection. As a result, our verification system provides several promising features to enhance security and privacy strength.
Recognition of Communication Relationship Based on the Spectrum Monitoring Data by Improved VGGNET

The communication relationship can reflect the hidden information of the communication network, which is of great significance for discovering important nodes in the network. To overcome the difficulty of manually extracting expert features, this paper uses deep learning methods to study the communication relationship recognition. First, use the deep learning model to classify the spectrum data directly, and model the communication relationship as a classification problem with time feature data for processing. It is found that the neural network model is easy to fall into a local minimum; in order to limit the impact of the local minimum problem on recognition In this paper, combining the rules of frequency hopping communication to process the data, make the neural network take as few tasks as possible, and then propose the second design scheme, the communication time series classification scheme, and the final recognition rate reaches 97% on the test set. This article uses long and short memory networks and convolutional neural networks to conduct experiments. Among them, the improved VGG network structure has the best recognition rate in communication problems. The factors that affect the recognition rate of neural networks in the identification of communication relationships are discussed in depth, and suggestions on how to adjust these factors are given based on theory and experiment.
Smart Voltage Monitoring: Centralised and Blockchain-based Decentralised Approach

Voltage controls the majority of the processes around us, starting from lighting an incandescent lamp to running huge machines in industries. Therefore, voltage monitoring becomes essential, which demands efficient measurement and storage of voltage data. However, there is hardly any system till date that fulfils both the goals of voltage monitoring and voltage data storage. To achieve this goal, we propose the application of the Internet of Things along with the server-based framework and Distributed Ledger Technology to build systems for smart voltage monitoring. Two models - a centralised model and a decentralised model have been presented and analysed thoroughly in this paper. The centralised model is built on client-server architecture, whereas the decentralised model is based on a peer-to-peer architecture. Blockchain and InterPlanetary File System have been used for the implementation of the decentralised system. Potential improvements to make these systems robust have also been discussed. The methods proposed in this paper for voltage monitoring are novel; ensure efficient data storage and can be used for IoT data storage of any form.
High‐Sensitivity Profilometry for Measurement of Multilayer Structure with Low‐Coherence Composite Interferometer

Abstract High‐sensitivity profilometry provides valuable tools for measurement of the surface structure of a device. On the other hand, tomographic techniques such as ultrasound imaging or optical coherence tomography can be used to measure the inner structure of a device with micrometer resolution. However, the multilayer structure of a device such as a display panel cannot be measured with nanometer sensitivity using conventional profilometry and tomographic techniques. For this purpose, a new type of profilometry based on the technique of low‐coherence composite interferometer is proposed and demonstrated. With the phase compensation mechanism of the technique, phase fluctuation resulted from the instability of scanning component and environment can be significantly reduced and a high axial sensitivity can be achieved. The technique is demonstrated by measuring nine interfaces of a display panel with a displacement sensitivity of 11.73 nm without requiring any preknowledge on the structure and has the potential to become a new technique for inspecting the multilayer structure of a packaged device in industry.
Semantic Engineering Platform

To solve the problem of sparse semantic representation in semantic processing, this paper designs and develops a semantic engineering platform, which involves the accumulation of knowledge and common sense in the whole process of resolving primary mathematics application problems. Firstly, this paper analyzes the steps and technology of resolving primary mathematics application problems; secondly, it discusses various examples, knowledge and common sense types accumulated by the semantic engineering platform, as well as the internal management technologies of the semantic engineering platform; thirdly, an example is used to explain the steps and results of simulated running of humanoid resolving; finally, the paper summarizes the full paper and puts forward the future work directions.
Keypoint-Based Gaze Tracking

Effective assisted living environments must be able to perform inferences on how their occupants interact with their environment. Gaze direction provides strong indications of how people interact with their surroundings. In this paper, we propose a gaze tracking method that uses a neural network regressor to estimate gazes from keypoints and integrates them over time using a moving average mechanism. Our gaze regression model uses confidence gated units to handle cases of keypoint occlusion and estimate its own prediction uncertainty. Our temporal approach for gaze tracking incorporates these prediction uncertainties as weights in the moving average scheme. Experimental results on a dataset collected in an assisted living facility demonstrate that our gaze regression network performs on par with a complex, dataset-specific baseline, while its uncertainty predictions are highly correlated with the actual angular error of corresponding estimations. Finally, experiments on videos sequences show that our temporal approach generates more accurate and stable gaze predictions.
Research on Power Harmonic Noise Suppression Method Based on Genetic Algorithm for Urban TEM Data

Abstract In recent years, ground accidents are constantly occurring in the process of urban underground space development. TEM provides effective technical support for the rational development and safe operation of urban underground space as a flexible and low-cost geophysical method. However, the interference of human electromagnetic noise is serious in the urban environment, especially the power frequency harmonic noise has wide frequency band distribution, as it is difficult to suppress it quickly and effectively, resulting in low accuracy of the extracted secondary field signal and large inversion error. Aiming at the problem of power frequency harmonic noise interference, this paper proposes a noise suppression method based on genetic algorithm. Through the optimization algorithm, the feature matching segment in the reference noise is quickly searched, and it is self-adaptive offset with the power frequency harmonic noise in the signal. The simulation results show that the method can obtain higher signal-to-noise ratio when collecting less sample data. Furthermore, it is proved that this method of this paper has the characteristics of fast noise suppression and no signal loss.
Maximum Margin Separations in Finite Closure Systems

Monotone linkage functions provide a measure for proximities between elements and subsets of a ground set. Combining this notion with Vapnik's idea of support vector machines, we extend the concepts of maximal closed set and half-space separation in finite closure systems to those with maximum margin. In particular, we define the notion of margin for finite closure systems by means of monotone linkage functions and give a greedy algorithm computing a maximum margin closed set separation for two sets efficiently. The output closed sets are maximum margin half-spaces, i.e., form a partitioning of the ground set if the closure system is Kakutani. We have empirically evaluated our approach on different synthetic datasets. In addition to binary classification of finite subsets of the Euclidean space, we considered also the problem of vertex classification in graphs. Our experimental results provide clear evidence that maximal closed set separation with maximum margin results in a much better predictive performance than that with arbitrary maximal closed sets.
Reconstruction of a fluttering flag from a single image

Reconstructing a 3 D object from a single image is a challenging task because determining useful geometric structure information from a single image is difficult. In this paper, we propose a novel method to extract the 3 D mesh of a flag from a single image and drive the flag model to flutter with virtual wind. A deep convolutional neural fields model is first used to generate a depth map of a single image. Based on the Alpha Shape, a coarse 2 D mesh of flag is reconstructed by sampling at different depth regions. Then, we optimize the mesh to generate a mesh with depth based on Restricted Frontal-Delaunay. We transform the Delaunay mesh with depth into a simple spring model and use a velocity-based solver to calculate the moving position of the virtual flag model. The experiments demonstrate that the proposed method can construct a realistic fluttering flag video from a single image.
Structure Similarity for Image Quality Assessment from Isotropic to Anisotropic

As a pioneer work in the field of Image Quality Assessment (IQA), Structure Similarity (SSIM) has been widely used to evaluate the output quality of imaging systems and algorithms. However, the classical implementation of SSIM is inherent of isotropic nature and thus fail to model the anisotropic characteristic of Human Vision System (HVS). To alleviate this problem and realize as fully as possible the potential of SSIM, an anisotropic implementation is put forward in this letter in which a kernel adaptive to image local structures is integrated in SSIM computation. Extensive Numerical experiments reveal that this seemingly tiny modification (from isotropic to anisotropic) leads to significant performance improvement, and the anisotropic implementation of SSIM performs comparably with state-of-the-art on several benchmark datasets.
Trust-driven Distributed Self-collaborative Security Architecture of IoT Based on Blockchain and Smart Contracts

As Internet of Things (IoT) technology is growing fast, it is clearly forseen that the number of IoT devices and the scale of connections will be further expanded. IoT is capable of taking advantage of the existing network infrastructure effectively, so as to achieve data sharing between devices. However, the large scale and complexity of the network structure will bring potential security risks to IoT system. The traditional access control model is more complex and centralized. This paper aims to build a trust-driven distributed self-collaborative security architecture of IoT based on blockchain and smart contracts, which could overcome the single-point failure problem of centralized entities. Implementation of the security architecture shows that it still possesses characteristics including scalability, lightweight, and fine granularity, while the secure access control preserves the privacy of IoT devices.
Application of Supervised Machine Learning Based on Gaussian Process Regression for Extrapolative Cell Availability Evaluation in Cellular Communication Systems

Supervised machine learning models and their algorithms play major roles in extrapolative data analysis and getting valuable information of the data. One emerging robust supervised machine learning based model is the Gaussian process regression (GPR) model. A vital strength of the GPR model is its capacity to adaptively model multipath linear/nonlinear functional approximation, dimension reduction and classification problems. Nevertheless, there exist a plethora of approximation (prediction) methods and hyperparameters that systematically impact GPR kernel function for effective modeling and learning capacity. Some of the key approximation methods includes the Exact Gaussian process regression, Fully independent conditional, Subset of data points approximation, and Subset of regressors. Nonetheless, the problem of knowing how to identify or select the best from these approximation methods during data training and learning with GPR model so as to avoid the predictive variance problem is a challenge. In this contribution, we propose a stepwise selection algorithm to tackle the challenge. GPR modelling with stepwise selection algorithm has been tested for extrapolative regression analysis of live cell availability data obtained from an operational telecom service provider. The GPR extrapolative based evaluation results show that the proposed approach is not only tractable, but also yield low errors in terms of accurate service availability quantification and estimation. Also, in terms of mean absolute error, the regression results of the proposed GPR extrapolative based evaluation combined with stepwise kernels selection algorithm were also far better compared with the ones obtained using support vector regression.
Laser2Vec: Similarity-based Retrieval for Robotic Perception Data

As mobile robot capabilities improve and deployment times increase, tools to analyze the growing volume of data are becoming necessary. Current state-of-the-art logging, playback, and exploration systems are insufficient for practitioners seeking to discover systemic points of failure in robotic systems. This paper presents a suite of algorithms for similarity-based queries of robotic perception data and implements a system for storing 2D LiDAR data from many deployments cheaply and evaluating top-k queries for complete or partial scans efficiently. We generate compressed representations of laser scans via a convolutional variational autoencoder and store them in a database, where a light-weight dense network for distance function approximation is run at query time. Our query evaluator leverages the local continuity of the embedding space to generate evaluation orders that, in expectation, dominate full linear scans of the database. The accuracy, robustness, scalability, and efficiency of our system is tested on real-world data gathered from dozens of deployments and synthetic data generated by corrupting real data. We find our system accurately and efficiently identifies similar scans across a number of episodes where the robot encountered the same location, or similar indoor structures or objects.
Woodcutters 55 Years Later: A New Look at an Old Discovery

Abstract The Woodcutters Zn-Pb-Ag deposit in the Rum Jungle district of the Pine Creek orogen in northern Australia was discovered in 1964 and produced 4.6 Mt of ore grading 12.3% Zn, 5.6% Pb, and 83 g/t Ag between 1985 and 1999. Woodcutters, together with several other polymetallic, uranium, and phosphate deposits, is within a Paleoproterozoic sequence of fluviatile and shallow marine sediments deposited in a deepening basin between ~2100 and 2025 Ma around the margins of an Archean granitic and gneissic dome. These sediments were overlain by turbidites and volcaniclastic rocks until the basin was inverted and the sediments and mineral deposits were deformed and metamorphosed at 1860 Ma. Whereas the polymetallic and uranium bodies at Rum Jungle are considered to be syngenetic or syndiagenetic, sulfides in the Woodcutters orebody replace dolomitic horizons in an otherwise carbonaceous unit. This suggests that Woodcutters is similar to Mississippi Valley-type mineralization and rules out affinities with younger sedimentary exhalative-style deposits elsewhere in the Pine Creek orogen. A model is proposed whereby metals were eroded from Archean basement rocks into Paleoproterozoic sandstone aquifers following the Great Oxidation Event, which also liberated sulfur by oxidation of pyrite. Evaporative conditions, as suggested by the widespread occurrence of dolomite and magnesite, may have increased the chloride content of seawater and enhanced its capacity to transport metals. Subsequently, deeply circulating seawater leached metals from the aquifers and ascended up a deep, basin-penetrating fault until it intersected carbonaceous sediments. In this environment, Zn and Pb sulfides were deposited under reducing conditions, while sulfur may have been provided by H2S from organic material. The Woodcutters and other deposits at Rum Jungle show how metals formerly locked up in Archean cratons were delivered by erosion under an oxygenated atmosphere to Paleoproterozoic shorelines, where they were further mobilized and concentrated by a variety of processes.
Multispectral Image Denoising With Kriging Interpolation Based Wiener Filter

Recently, the application of interpolation to image denoising has shown remarkable denoising results. The multispectral images are mostly corrupted by noise during data acquisition. Therefore, the resulting image is of poor quality. Hence, to improve the image quality, a Kriging Interpolation based Wiener Filter (KIWF) is being introduced in the paper. Wiener filter takes advantage of the kriging interpolation algorithm to obtain the best possible estimate which is obtained by denoising the image. Global patch clustering using Gaussian mixture model is used to separate the noise pixels from clear pixels. The Obtained weight values are applied by estimating the semi variance between the clear patches and noisy patches. Interpolation always helps to suppress very high noise density and achieves good noise reduction. At the end, filter performance is tested and a comparative analysis takes place with the existing denoising techniques to show effectiveness of the filter.
High Availability Services of Client in Large-scale Cluster System

Abstract Aiming at the disadvantage of high availability based on node redundant, a high availability scheme based on storage devices redundant was researched and designed in large-scale cluster system with network storage devices by working on system kernel. The advantages of low-cost, low-complexity of maintenance and high-utilization of resources were received. Besides, an asynchronous prototype system of high availability based on log was implemented. Test proved that anticipated goals in system performance, data consistency and availability were achieved.