Reviewerly/exHarmony huggingface dataset:
Semantic Image Completion and Enhancement Using GANs

Semantic inpainting or image completion alludes to the task of inferring arbitrary large missing regions in images based on image semantics. Since the prediction of image pixels requires an indication of high-level context, this makes it significantly tougher than image completion, which is often more concerned with correcting data corruption and removing entire objects from the input image. On the other hand, image enhancement attempts to eliminate unwanted noise and blur from the image along with sustaining most of the image details. Efficient image completion and enhancement model should be able to recover the corrupted and masked regions in images and then refine the image further to increase the quality of the output image. Generative Adversarial Networks (GAN) have turned out to be helpful in picture completion tasks. In this chapter, we will discuss the underlying GAN architecture and how they can be used for image completion tasks.
Integrating GEOBIA and multisource remote sensing images to lithological mapping: case study of Skhour Rehamna, Morocco

Satellite-borne remote sensing images are considered as one of the most important data sources for lithological mapping due to their extensive geographical coverage at an efficient cost. Integration of optical along with microwave satellite datasets, are leading to increase the lithological mapping accuracy. In this study, Geographic Object-Based Image Analysis (GEOBIA) was applied, using data from the freely available European Space Agency (ESA) Sentinel 1 SAR data and Sentinel 2 optical imagery, that were fused With a digital elevation model (DEM) of 13m spatial resolution, generated from two single look complex (SLC) sentinel 1 (C band) interferometry, in conjunction with slope and two geomorphic indices, Terrain Ruggedness (TRI) and Terrain Position Index (TPI) for mapping lithology in the southern of the Palaeozoic massif of Skhour Rehamna in Morocco. The statistical results of the fusion of Sentinel 1 and Sentinel 2 datasets have shown the highest accuracies, showing an overall accuracy (OA) of 92.80% and a kappa coefficient of and 0.89 compared to the layer stack of Sentinel 2 image bands with the first three Minimum noise fraction (MNF) and the Principal components bands (PC1, PC2 and PC6) that showed an OA of 91.50% and a kappa coefficient of and 0.87. With the achievable results in this study, the technique is useful in discriminating general rock type that outcrop in semiarid regions.
Using Neural Networks to Predict the Normal Reactions of a Walking Robot

Purpose of reseach . This work is devoted to solving one of the problems associated with the control of walking robots based on their dynamic mathematical model − the presence in it of obvious mechanical bonds due to reactions of bonds with the supporting surface. To solve this problem, it is proposed to use a fully connected neural network to evaluate the forces of normal reactions between the surface and the feet of a bipedal walking machine during its implementation of one step. Methods. The paper considers two neural network architectures based on fully connected layers with ReLU activation functions. The architecture of the neural network includes five fully connected layers (input, output and three hidden), and an alternative architecture includes a thinning layer after each fully connected layer. The input data for the network are the state of the robot and the required control actions, and the output is the predicted reaction forces. The training sample is generated by modeling a complete dynamic model of the robot. The network is built and trained using machine learning libraries Keras and TensorFlow. Results. The generation of training sample for neural network is described here, and it is carried out the training of two architectures of neural networks. Based on the simulation data, it was established that both trained neural networks are able to accurately predict the values of normal reactions using the values of generalized coordinates and velocities, as well as control actions as input, however, a static prediction error is observed. Conclusion . The results obtained within the framework of the article can be further used to control the movement of bipedal walking machines on various types of surfaces.
Road Traffic Sign Recognition and Vehicle Accident Avoidance System

Traffic sign recognition and vehicle accident avoidance system gets a of interest late by huge scale organizations, e.g., Apple, Google and Volkswagen and so on driven by the market requirements for smart applications, e.g. Automatic Driving and Driver Assistance Systems , Mobile Eye, Mobile Mapping and many more.In this paper, traffic sign recognition and vehicle accident avoidance system is utilized to keep up traffic and maintain a strategic distance from vehicle, caution the occupied drivers, and avoid activities that can lead a vehicle. An on-going programmed sign recognition and detection can support the driver with safety. System propose automated real time system which will capture the traffic sign and show it at driver dashboard with front obstacle exact distance on screen. The PiCam is associated with Raspberry Pi and it is utilized to capture pictures .Screen is utilized to show the system output e.g. appearing of traffic sign and separation of vehicle. This framework is configuration to maintain a strategic distance from vehicle happening on street.
Exploiting Performance Estimates for Augmenting Recommendation Ensembles

Ensembling multiple recommender systems via stacking has shown to be effective at improving collaborative recommendation. Recent work extends stacking to use additional user performance predictors (e.g., the total number of ratings made by the user) to help determine how much each base recommender should contribute to the ensemble. Nonetheless, despite the cost of handcrafting discriminative predictors, which typically requires deep knowledge of the strengths and weaknesses of each recommender in the ensemble, only minor improvements have been observed. To overcome this limitation, instead of engineering complex features to predict the performance of different recommenders for a given user, we propose to directly estimate these performances by leveraging the user’s own historical ratings. Experiments on real-world datasets from multiple domains demonstrate that using performance estimates as additional features can significantly improve the accuracy of state-of-the-art ensemblers, achieving [email protected] improvements by an average of 23% over not using them.
The Study of Blockchain Technology in Engineering Development: The Case of the Clutch Lever Assembly

The Engineering Development Process as proposed by Eppinger and others is well established. The framework comprises of various stages, which upon realization ensures the customers' needs. In this paper, the development of the Automobile Clutch Lever Assembly is taken up. It is a small but critical assembly used in two wheelers. It functions in a demanding environment, and must be highly reliable. Hence, proper controls during the manufacturing process are critical. The Engineering Development of this assembly requires many manufacturing and quality qualifications and the use of methods such as Failure Modes and Effects Analysis (FMEAs) and Quality Control Charts in order to meet the very stiff requirements. As is the case, such an approach requires the creation and storage of many development related documents and data. The traditional approach to data storage was availability of all relevant information with the Development Engineer on a standalone Personal Computer. A Blockchain approach is proposed to manage and store this data. The document structure is proposed to meet various requirements by the industry stakeholders who will use this data. Finally, the implications of using this approach are explored, along with the Analysis of using the Blockchain Approach.
TF-IDF Character N-grams versus Word Embedding-based Models for Fine-grained Event Classification: A Preliminary Study

Automating the detection of event mentions in online texts and their classification vis-a-vis domain-specific event type taxonomies has been acknowledged by many organisations worldwide to be of paramount importance in order to facilitate the process of intelligence gathering. This paper reports on some preliminary experiments of comparing various linguistically-lightweight approaches for fine-grained event classification based on short text snippets reporting on events. In particular, we compare the performance of a TF-IDF-weighted character n-gram SVM-based model versus SVMs trained on various of-the-shelf pre-trained word embeddings (GloVe, BERT, FastText) as features. We exploit a relatively large event corpus consisting of circa 610K short text event descriptions classified using a 25-event categories that cover political violence and protest events. The best results, i.e., 83.5% macro and 92.4% micro F1 score, were obtained using the TF-IDF-weighted character n-gram model.
Energy Credits Auction Mechanism for Enhancing the Grid’s Upward Flexibility Using Datacenters

Since modern utility grids lack large-scale energy storage capabilities, their supply and demand levels must always be balanced to maintain a reliable operation. This requirement has traditionally been fulfilled through several existing techniques. However, recent interests in greening the utility grids require using more renewable energy sources, which makes the balancing task more challenging due to the unpredictability of such sources. This necessitates enhancing the grid's flexibility (energy balancing capabilities) to ensure reliable grid operation. In this work, we propose a new energy auctioning mechanism that enhances the grid's upward flexibility by using cloud datacenters as managed loads to quickly balance excess renewable energy. The auction process increases the energy consumption at a certain datacenter by incentivizing workload migrations to it in order to consume the excess energy. We present the formulation of our system model, introduce the used auction mechanism and show through simulation that it achieves efficient grid balancing, generates revenue on the sale of the excess energy and guarantees positive utility for all auction participants.
Training Set Enlargement Using Binary Weighted Interpolation Maps for the Single Sample per Person Problem in Face Recognition

We propose a method of enlarging the training dataset for a single-sample-per-person (SSPP) face recognition problem. The appearance of the human face varies greatly, owing to various intrinsic and extrinsic factors. In order to build a face recognition system that can operate robustly in an uncontrolled, real environment, it is necessary for the algorithm to learn various images of the same person. However, owing to limitations in the collection of facial image data, only one sample can typically be obtained, causing difficulties in the performance and usability of the method. This paper proposes a method that analyzes the changes in pixels in face images associated with variations by extracting the binary weighted interpolation map (B-WIM) from neutral and variational images in the auxiliary set. Then, a new variational image for the query image is created by combining the given query (neutral) image and the variational image of the auxiliary set based on the B-WIM. As a result of performing facial recognition comparison experiments on SSPP training data for various facial-image databases, the proposed method shows superior performance compared with other methods.
A Hybrid Spherical Search and Moth-flame optimization Algorithm

Spherical search (SS) was recently proposed for solving the bound-constrained non-linear global optimization problems, and it shows quite competitive performance results with respect to other peer algorithms. However, it still suffers from the slow convergence speed and can't jump out the local minima once it is trapped. In this paper, we innovatively propose a hybrid spherical search and moth-flame optimization algorithm, namely SSMFO, aiming to make a better balance between exploration and exploitation. It is realized by enabling both algorithms to perform the search in a co-evolutionary manner. Experimental results based on 30 benchmark functions of IEEE CEC2017 demonstrate that the proposed algorithm outperforms other state-of-the-art algorithms in terms of solution quality and convergence speed.
Dynamic Complex Protein Detection using Binary Harris Hawks Optimization

Abstract Identifying protein complexes brings new insights in the field of understanding of cellular life and the mechanisms underlying complex diseases. In order to solve the challenge of protein complex prediction, a large number of tools have been developed to create an efficient approach. Most existing methods do not take changing protein-protein interactions with time into consideration. To address these challenges, we propose a new binary technique of Harris Hawks Optimizer (BHHO) to improve the accuracy of protein complex detection. According to the core-attachment structure, BHHO is used to discover the potential cores of protein complexes by simulating the process of cooperative behavior in chasing by Harris’ hawks in nature. We design a new fitness function to detect protein complexes with various densities and modularities. The experimental results show that BHHO achieves an ideal performance PIN in term of the F1 and outperforms of the classical algorithms in yeast in term Jaccard measure.
A Study on Public Acceptability of Traditional Chinese Medicine Meridian Instruments

In view of TCM Meridians' philosophy, this research advocates that human meridians and organs are the unit of the body; on the basis of the integration analysis from application and combination of Bio Electricity and Conscious Energy, as well as the energy adjustment to promote physical and mental balance apart from creative thinking, this model of integration methodology is constructed with an eye to promoting the balanced and sound development on civil personalities. Furthermore, it is the application of this project that clarify the theory foundation from TCM Meridians, inclusive of adopting civil back and forth situation analysis made by the energy adjustment, utilizing the energetic therapy through A.I. medical equipment, detecting the distribution of meridian energy in human bodies, comprehending if the subject has a well physical condition, and then fixing the way how to let individual body's meridian energy return to normal range after applying to the energy adjustment; on the top of that, this research will present the concept and model to conduct analyses by means of quantified data. Consequently, the operating process from statistical analysis out of each different variable is to reach for the correlation between the Chi-square value and the variable, what is more significant, to clarify public acceptability of TCM Meridian instruments.
Research on the direct teaching method of robot based on admittance control

Based on admittance control, a new control method for robot direct teaching was established. Firstly, the admittance control model was constructed and the inertia coefficient and damping coefficient in the controller were analyzed. The basic law of the effect of parameters on motion control is obtained. The fuzzy variable parameter configuration of the damping coefficient in the admittance controller was realized by integrating the cosine similarity of spraying task, the teaching guidance force and robot running speed parameters. Finally, the effectiveness of the control method was verified by experiments.
Blockchain for Secure Internet of Things

The most revolutionized technology, which changed the whole digital currency space by introducing platforms such as bitcoin is blockchain. It is a distributed ledger which has the capability of maintaining a log of all the transactions occurring in the network. Recently, IoT is captivating a significant research interest. However, it can be observed that in areas such as building a distributed, secure and without third party environment for IoT, blockchain can be a beneficial tool. This paper aims to bulid a extensive view of different benchmark contributions in this direction. Initially the basics of blockchain and its application toward auditability, security, and characteristics of decentralization is discussed. Further, current challenges of centralized IoT models and their solution using blockchains for achieving decentalized and secure medium for the IoT are introduced.
Application of Passive DNS in Cyber Security

As a distributed database that maps domain names and IP addresses to each other, DNS enables users to access the Internet more conveniently without having to remember IP addresses that can be directly read by the machine. DNS is the basic framework of the Internet, therefore DNS is also vulnerable to be taken advantage of by adversaries. However, DNS data can also be a tool to detect and retrace attacks. To be specific, some network security problems can be solved by obtaining passive DNS data. This paper discusses the collection and use of passive DNS data, and make a summary of current research.
47‐2: Solid State Projection Display Based on Angular Color Projection and MicroLED

A novel projection light engine structure based on Angular Color Projection (ACP) technology and MicroLED is proposed. This system uses three monochrome RGB MicroLED panels or sub panels to display information in spatially separated color zones. Patented projection optics based on ACP is utilized to merge the RGB zones into a full color picture. Preliminary design and evaluation show that compact, high‐resolution, low‐cost and highly efficient micro projectors are achievable by eliminating the transferring of mass amounts of color pixels needed in a conventional MicroLED display. All optical components can be glued together by automation onto the MicroLED face plate. Therefore, a Full Solid State Projection Display without discrete optical components is achievable. This technology enables a projection light engine with similar size of a CMOS camera used in a modern smart phone. It could greatly increase the applications of projection and Near to Eye (NTE) displays in smart phones and AR/VR glasses.
Investigating Agile Adoption in Saudi Arabian Mobile Application Development

Abstract Mobile app development has been considered as one of the fastest growing segments of the software industry both worldwide and in Saudi Arabia. Due to their pervasiveness, mobile applications call for consideration of complex and rapidly changing requirements given the diversity of their environments. Therefore, agile is considered the most suitable methodology for developing mobile apps. However, little research has investigated agile adoption in mobile app development in the real context. Therefore, the purpose of this PhD is to investigate the factors that have a significant impact on agile adoption in mobile app development by small and medium-size software organisations in Saudi Arabia. The expected key contribution of this research will be a deep insight into agile adoption in mobile app development, and the design and development of tools and techniques that may support agile adoption within Saudi context.
Cross-distribution Feedback in Software Ecosystems

Despite the proliferation of software ecosystems (SECOs), growing a sustainable and healthy SECO remains a significant challenge. One approach to mitigate this challenge is the utilization of a mechanism that collects feedback from distributors (distros) and end-users of the SECO releases. This presentation aims at investigating the effectiveness of the feedback mechanism implemented by OpenStack to address the needs of end-users and distros. I mined the OpenStack repositories and mapped 20 distros' bug-related activities. Results suggest that OpenStack releases are actively maintained for 18 months before reaching end-of-life (EOL), which makes coordination with distros difficult because distros usually provide services to their end-users for a period between 36 - 60 months before reaching EOL. Also, bugs are fixed faster by the distros (7 - 76 days) than the OpenStack community (average of 4 months). However, only 22% of the bugs addressed by OpenStack distros are pushed back upstream.
Discussion on Teaching Reform of Computer Education Major Based on Information Key Competency

In 2016, "Students Development Key Competency of China" was officially published. China places great emphasis on establishing students thinking mode in digital environment, who can use information technology to solve problems and have the sense of social responsibility. In 2017, the Ministry of Education published a revised version of "High School Information Technology Curriculum Standard", which clarifies the information key competency, reconstructs the content and module of the high school information technology curriculum. In order to enable high school students to develop information key competency, information technology teachers' key competency should be improved at first. This promotion needs to be accompanied by a systematic reform of pre-service cultivation. It is urgent to strengthen the training of information key competency of students majoring in computer education, who will be the information technology teachers in the future. This paper focuses on the information key competency of students majoring in computer education and analyse the present problems in the existing talent cultivation. Furthermore, we put forward the systematic reform measures for computer education major.
Image data augmentation method based on maximum activation point guided erasure

Deep neural networks usually contain tens to hundreds of millions of orders of learning parameters that provide the necessary representation to solve various visual tasks. But with the increase of the representational ability, the possibility of over-fitting also increase, which bring about poor generalization. In this paper, we propose MA (Maximum Activation point processing) algorithm, a new image data augmentation method which is designed to improve the generalization ability of the model and reduce the risk of overfitting. During the training process, the most discriminative part of the input image is searched for, and the model is driven to search for the supplementary information of the most important feature information by erasing the maximum attention image block. During this process, training images with different occlusion levels are generated as new inputs to the network and the model continues to be trained. The image erasure method based on the maximum activation point guidance only needs to modify the input image, which can effectively improve the robustness of the model to occluded image recognition, and can be integrated with various network structures. The effectiveness of our method is verified on the Cifar10, Cifar100 and Fashion-MNIST datasets.
Zero-Injection Meets Deep Learning: Boosting the Accuracy of Collaborative Filtering in Top-N Recommendation

Zero-Injection has been known to be very effective in alleviating the data sparsity problem in collaborative filtering (CF), owing to its idea of finding and exploiting uninteresting items as users' negative preferences. However, this idea has been only applied to the linear CF models such as SVD and SVD++, where the linear interactions among users and items may have a limitation in fully exploiting the additional negative preferences from uninteresting items. To overcome this limitation, we explore CF based on deep learning models which are highly flexible and thus expected to fully enjoy the benefits from uninteresting items. Empirically, our proposed models equipped with Zero-Injection achieve great improvements of recommendation accuracy under various situations such as basic top-N recommendation, long-tail item recommendation, and recommendation to cold-start users.