Reviewerly/exHarmony huggingface dataset:
Using Grover's search quantum algorithm to solve Boolean satisfiability problems, part 2

column Share on Using Grover's search quantum algorithm to solve Boolean satisfiability problems, part 2 Authors: Diogo Fernandes School of Sciences of University of Porto, Portugal School of Sciences of University of Porto, PortugalView Profile , Carla Silva University of Porto, Portugal University of Porto, PortugalView Profile , Inês Dutra University of Porto, Portugal University of Porto, PortugalView Profile Authors Info & Claims XRDS: Crossroads, The ACM Magazine for StudentsVolume 26Issue 2Winter 2019 pp 68–71https://doi.org/10.1145/3368085Published:25 November 2019Publication History 0citation443DownloadsMetricsTotal Citations0Total Downloads443Last 12 Months63Last 6 weeks7 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Mechanical Parts Detection Algorithm Based on Enhanced Faster R-CNN

Industrial target detection has been a popular field of target detection, because it can improve the efficiency of industrial production intuitively. Many scholars at home and abroad have achieved many research results till now, but there are still some limitations in the traditional algorithms. Many algorithms can only adapt to the industrial manufacturing conditions partially, and cannot be applied well when changing to another industrial environment. Deep learning has exploded in popularity in the field of target detection during the last several years, but its application to industrial target detection is not mature. We postulate an enhanced Faster R-CNN-based industrial target localization algorithm for mechanical parts in this research. We constructed our own mechanical parts dataset by shooting sampling, and improved Faster R-CNN target detection algorithm in five aspects. The target detection model was trained by migration learning. The experimental findings showed that our industrial target identification method based on enhanced Faster R-CNN outperforms the classic VGG16 Faster R-CNN model and Resnet50 Faster R-CNN model in terms of target detection accuracy.
Motion Sickness

Although motion sickness is a widespread problem that seems to have been around forever, there is a tendency to play down its significance. There are those who suggest that it does not play a vital role in the community as a whole or in the military in particular. Others describe it as “wimpish” and not worthy of attention. These attitudes are quite wrong. Motion sickness, is a motion adaptation syndrome, that should be recognised for what it is, namely, a maladaptation to novel provocative motion environments. It is a normal protective mechanism that can be managed effectively if we make the effort to understand its various features. It is also important to realise that these efforts will pay significant dividends in terms of time and money. Those who are fortunate have an easy transition to various forms of travel, whereas others have some difficulty in adapting to the protective responses that are incurred. With understanding and help, these problems can be overcome. I propose to begin this review of motion sickness by examining the very basic aspects of this syndrome that can beset us when we venture to travel other than on our own two feet and, when exposed to “vehicular” motion, begin to adapt to this new world.
Unique Whale Optimization Algorithm for Harvesting and Clustering the Key Features

In many applications, the feature selection plays an important role as only when we can get the best feature, we can bring out accurate results. The features selected must represent the entire dataset. Here we have chosen the whale optimization algorithm for feature extraction. To the whale optimization algorithm, we have added the convergence function and the fitness function. The fitness function is used to check the accuracy of the algorithm. Here we also used the Minkowski distance between feature and the cluster centroid to group the common features together. Grouping of common features is useful in many applications like applying a common methodology to the similar feature, spam detection, email classification. This paper has proposed an algorithm which extracts the features by checking its accuracy with fitness function and clusters the common features using Minkowski distance and k clusters.
A privacy-preserving framework for smart contracts based on stochastic model checking

In the process of using smart contracts, users need to provide privacy data such as personal account information to the contract for transactions. However, as the number of users continues to increase, privacy data leakage has become more and more serious. Also, the current model checking methods do not involve verifying the privacy data in smart contracts. For these reasons, we introduce a privacy-preserving framework based on stochastic model checking called VeriPrivData. It formally expresses privacy data by data sensitivity. The smart contract is defined as the Commitments tuple with data sensitivity. Then the Commitments tuple is used to model as DTMC (Discrete Time Markov Chains). We extend PCTL (Probabilistic Computation Tree Logic) to ds-PCTL (Probabilistic Computation Tree Logic with data sensitivity) for describing the privacy requirements. Finally, we verify whether DTMC satisfies the ds-PCTL formula. In this paper, the VeriPrivData framework uses the stochastic model checking tool PRISM, and experiments are carried out. Experimental results show that this method can effectively avoid illegal disclosure of privacy data and enhance the protection of privacy data in the contract.
OUTSIDE: Multi-Scale Semantic Segmentation of Universal Outdoor Scenes

Semantic segmentation aims at providing a fine-grained image prediction by assigning each pixel to a specific semantic category. Convolutional neural networks offer significant benefits for solving this problem. However, the success of such networks is closely related to the availability of corresponding data sets. To facilitate semantic segmentation in a broader range of scenarios, such as augmented reality in outdoor environments or universal image-to-image translation, adequate training data sets are necessary. We present OUTSIDE15k, a large-scale data set for semantic segmentation of universal outdoor scenes. The data is labeled with 24 different semantic classes. The images contain multiple outdoor scenarios and cover a variety of different resolutions. Additionally, we present OUTSIDE-Net, an improved neural network architecture integrating multi-level pooling, feature fusion, and a spatial mask for semantic segmentation of universal outdoor scenes. It extracts spatial and semantic features from the input images to perform the segmentation. With the presented data set, we show the capability of our network which outperforms state-of-the-art approaches by achieving up to 91.5% pixel accuracy.
Openness and Community Geospatial Science for Monitoring SDGs – An Example From Tanzania

This chapter focuses on two aspects that enable the monitoring of the United Nations (UN) sustainable development goals (SDGs): openness and community geospatial science.
Personalized Learning and Online Instruction

This development of digital inclusion with personalized learning has had an impact on how courses are designed and delivered. To that end, a behavioral approach that combines digital with personalized learning is CAPSI (computer-aided personalized system of instruction). In CAPSI, students decide when and where to study course material and where and when to take a test on their learning. The changes occurring in higher education also need to incorporate the development of critical thinking skills. CAPSI is highly adaptable to developing critical or higher-level thinking based on Bloom's taxonomy; CAPSI's emphasis on written answers, providing feedback, and writing appeals leads to higher order thinking. To assess student satisfaction, questionnaires given at the end of a course show that many students find CAPSI to be beneficial to their learning. Also, due to its flexible design, CAPSI is highly modifiable and can be used in all courses in a variety of locations and with students at different educational levels.
Neural computing

Neural computing studies the nature and ability of nonprogramming, adaptive and brain-style information processing.
Track-before-detect Bernoulli filters for combining passive and active sensors

This paper is concerned with the implementation of track-before-detect (TkBD) algorithms for a range of single-target multi-sensor scenarios with only intermittently visible targets. Visible targets generate measurements from sensors characterised by data rate and clutter density. Bernoulli filters implementing multiple hypothesis tracking (MHT) strategies are deployed to infer both the target location and existence probability. Various Bernoulli filter configurations are compared, including integrated probabilistic data association filters (IPDAF) and integrated expected likelihood particle filters (IELPF) using both prior and Gaussian mixture proposal distributions for the latter. Performance is evaluated against the clutter density in scenarios featuring one low data rate active sensor or two sensors, complimenting the former with a high data rate passive sensor with opposing measurement resolutions. The performance measures used are the area under the receiver operating characteristic (ROC) curve, localisation root mean squared error (RMSE) compared with the posterior Cramér-Rao lower bound (PCRLB), and computation time. Simulation results show that Kalman filters provide an effective solution at low computational expense in less cluttered and comparatively easy scenarios, whereas particle filters implementing Gaussian mixture proposal distributions provide performance benefits relative to computational costs as scenarios become more cluttered and comparatively challenging.
Restoration of Blurred Images Corrupted by Impulse Noise via Median Filters and ℓ<sup>p</sup>-ℓ<sup>q</sup>Minimization

This paper proposes a two-phase fully automatic scheme for restoring images that have been corrupted by blur, impulse noise, and, possibly, Gaussian noise. In a first phase, a median filter is used to partially correct most of the pixels that are contaminated by impulse noise. Then the image is restored by solving the ℓ <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</sup> -ℓ <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">q</sup> minimization. This problem consists of a fidelity term, that is defined in terms of a p-norm, and a regularization term, that is defined in terms of a q-norm. We allow 0 < p, q ≤ 2. In particular, the p-norm is not a norm when 0 < p < 1; similarly for the q-norm. The relative influence of the fidelity and regularization terms is determined by a regularization parameter. We describe how this parameter can be chosen without user interaction. The numerical scheme described in this paper can restore images that have been contaminated by blur and with up to 70% of the pixels contaminated by salt-and-pepper noise.
Intelligently Identified Drug Delivery Car Based on Template Matching

In view of the need of non-contact drug delivery in medical applications, this paper took STM32CRT6 as the core master controller and collected images through the camera module. The template matching algorithm was adopted to identify the digital number images of the destination. Aided by photoelectric tracking and APP remote monitoring, an intelligent drug delivery car was designed and realized. The car has is equipped with multiple functions such as the automatic identification of destination numbers, the real-time tracking of drugs, automatic routing, customized routes, app page display and so on. The test results showed that: the tracking is stable and accurate in the daily light, and the digital recognition rate could reach 90% from multiple angles. It realizes the functions of the online real-time monitoring of the car and the non-contact access, so the method is of certain timeliness, reliability and security.
Case Studies

This chapter proposes the use of the Differential Evolution algorithm with fuzzy logic for parameter adaptation in the optimal design of fuzzy controllers for non-linear plants. The Differential Evolution algorithm is enhanced using Type-1 and Interval Type-2 fuzzy systems for achieving dynamic adaptation of the mutation parameter. In this chapter four control optimization problems in which the Differential Evolution algorithm optimizes the membership functions of the fuzzy controllers are presented. First, the experiments were performed with the original algorithm, second the experiments were performed with the Fuzzy Differential Evolution (in this case the mutation parameter is dynamic) and the last experiments were performed applying noise to the control plant by using Fuzzy Differential Evolution were performed. The characteristics of each of the control problems to be used for experimentation are listed below.
Software Development Methodology for Cloud Computing and Its Impact

This chapter emphasizes mainly on the software development methodology basically agile methods of software development in cloud computing platforms and its impact on software development processes. This chapter also covers the benefits of agile development methodology in cloud computing platform. Along with this all traditional software development phases are analyzed to discuss the differences between the traditional software development processes and software development in cloud computing environment. This chapter also includes a brief description of programming models such as MapReduce, BSPCloud, and Dryad etc. available in the literature to handle big data in SaaS cloud. Finally, we highlight the challenges and future scope of software development process in cloud computing environment.
Information Needs of Women Subsistence Farmers in the Dlangubo Village, South Africa

Abstract Subsistence farming is important for food security and thus breaking the poverty cycle. Notwithstanding that, in many developing countries including South Africa, the development of women in rural areas remains a major concern. The purpose of this study was to explore information needs of women subsistence farmers and thus contribute to the already existing body of knowledge. Qualitative data were collected using semi-structured interviews, participant observation and focus group discussions. Grounded theory helped in data analysis. The use of Wilson’s model and the farmer-to-farmer extension (FFE) model helped in understanding the information need monitoring chain of women subsistence farmers. Findings revealed that they had both expressed and unexpressed information needs. Expressed needs included information about the recycling of seeds, soil fertility management, controlling of pests and stray animals, drought management skills, how to transcend from subsistence to commercial farming, and how to build strong social structures. Women livestock keepers needed information regarding drought management, support for livestock feeds, primary healthcare services for their livestock and support services for their extension officer who was deployed to another area. Unexpressed needs included the need for adult education as most women subsistence farmers had low levels of information literacy skills. It transpired that the word of mouth was the main source of information. The other sources of information such as radios, television and cell phones were used at a very limited rate. Local libraries were not sufficiently reaching out to respond to the women information needs. It is recommended that local libraries become visible and engage in library-led projects that can help meet women’s information needs and thus improve their living standards.
Content-Based Retrieval in Digital Libraries

This chapter is concerned with finding images or videos from (possibly very large) collections of these. Each type of modality in multimedia information, e.g., text and image, provides its own type of semantic information to help in search for content. That is, text-based search is bolstered by information in images and videos, from low-level features to high-level semantic content. In this book, we focus only on techniques and systems that make use of image and video features themselves, without text, to retrieve images or videos from databases or from the web. Detail is provided on specific features useful to this purpose. Image search engines devised on these features are said to be content based: the search is guided by image similarity measures based on the statistical content of each image. At a higher semantic level, we go on to examine action recognition in video and quality-aware mobile visual search in some detail. At last, we present some recent works advocating the deep learning approach (e.g., convolutional neural network (CNN), Deep Incremental Hashing Network) to content-based retrieval.
Design and Simulation Analysis of Simultaneous Localization and Mapping System for Robot Using Multi Photoelectric Sensors

Robot technology integrates motion control, information fusion and wireless communication technology. Among them, simultaneous localization and mapping (SLAM) technology is the key to realize robot autonomous navigation. In this exploration, multi photoelectric sensors are used for information fusion to solve the nonlinear and uncertain problems of robot navigation system in obstacle avoidance. The acoustic optical sensor and photoelectric sensor are integrated to collect the information of the surrounding environment. The photoelectric encoder of the drive motor is used to control the direct current machine with fuzzy PID closed-loop control. The methods of extended Kalman filter based SLAM (EKF-SLAM) and particle filter based SLAM (PF-SLAM), which are commonly used in navigation problems, are studied and compared. The proposed SLAM system adopts ARM11 high performance processor structure of S3C6410. ARM processor is responsible for task management, input and output, acoustic optical sensor can provide distance and position information, and photoelectric sensor can provide two-dimensional plane information. Their information fusion helps to restore the three-dimensional information of the environment around the robot. In the simulation analysis, the robot moves in a circular way on the horizontal ground. SLAM algorithm based on the information fusion of multi photoelectric sensors can help the robot better perceive the surrounding environment in unknown environment, increase the movement distance and reduce the overall positioning error; the robot can avoid obstacles well in autonomous mode after hardware and algorithm debugging. In the remote control mode, the movement of the robot can be adjusted by controlling the direction.
CPS Attack Detection of Ships using Particle Filter

In this paper, a method based on particle filter (PF) is proposed to detect the attack on cyber-physical Systems (CPS) of ships. Considering that CPS usually has state delay and Markov property, this paper estimates the markov jumping parameters of the time-series amplified system by particle filter, and estimates the system state by using Kalman filter. The residual evaluation function is designed to detect whether the attack exists. The above method is verified by the real ship data, which proves that the method can be used in the actual CPS attack detection and shows a good detection effect.
Understanding, Idealization, and Explainable AI

Many AI systems that make important decisions are black boxes: the way they function is opaque even to their developers. This is due to their high complexity and to the fact that they are trained rather than programmed. Explainable AI (XAI) methods aim to provide explanations for black box systems. These post hoc XAI methods typically involve approximating the black box system with a distinct ``explanation'' algorithm. The explanation provided by an XAI method is composed of a different, more easily interpreted kind of algorithm. However, there is debate about what it means for an AI system to be interpretable, and for what it takes to give an adequate explanation. Computer scientists and philosophers have objected to post hoc XAI methods on the grounds that: (a) the explanation system functions in a very different way than the black box system to be explained; and (b) the explanation system is imperfectly faithful, sometimes making a different prediction than the black box system. I argue that we should understand interpretability and explainability in terms of understanding. I appeal to the epistemology of understanding for a better account of the goals XAI methods. Moreover, I suggest that XAI explanation models are akin to idealized scientific models. Idealized scientific models (e.g., the ideal gas law, or evolutionary game theory) deliberately employ false representations of their target phenomena. While there is disagreement over the import of these ``felicitous falsehoods'' for debates about epistemic value and factivity, the common view in epistemology is that idealized models can provide a high-degree of genuine understanding. If XAI methods are like idealized models, then neither (a) nor (b) offer reason to doubt that XAI methods can provide adequate understanding.
Imaging Informatics

The field of imaging informatics lies at the intersection of traditional medical imaging, medical physics, and computer science. Research in the field ranges from improving workflow, to enhancing imaging, to automation and clinical decision support, all for the enhancement of patient health outcomes. An introduction to the core concepts of imaging informatics will be presented in this chapter and include: data acquisition considerations when converting from analog to digital formats, quantization, lossless image compression, lossy image compression, standards for digital medical image communication and storage, health enterprise integration, quality assurance, teleradiology, artificial intelligence, traditional machine learning, and deep learning.
An ABI-based Classification Approach for Ethereum Smart Contracts

Due to the rapid increasing of smart contracts in Ethereum, retrieving the related smart contracts has become an important issue. A key step of recognizing the related smart contracts is to classify them. Nowadays most of the classification methods of smart contracts are based on source code or bytecode analysis. However, as the interface of a smart contract, ABI has key information about the smart contract's function and behavior. Aiming to find out the effect of ABI on the classification of smart contracts, we propose an automatic classification method that utilize popular text classification method on ABI granularity. Our experiments are based on basic machine learning algorithms - SVM, XGBoost, Stacking and Bagging, which take TF-IDF vectors of ABIs as input, and the results indicate that ABI-based smart contracts classification is an effective approach. Compared with source code-based classification method, the MCC score of our method increased by more than 0.1 on average.