Reviewerly/exHarmony huggingface dataset:
Designing Experiments to Compare Web Page Segmenters

Web pages are mainly designed to be visualized, and users with no visual limitations can easily recognize the role of the parts and functional elements. On the other hand, computer programs can hardly identify the semantic of blocks and, therefore, tasks such as indexing the pages have, necessarily, limitations. Tools that can automatically identify the role can be useful to accomplish such a task. Web Page Segmenters are tools that, using visual and other clues in the page, can extract the functions of blocks. We suggest that a study to compare segmenters could be of interest to the community. In this paper, we highlight the research questions that the study should answer, suggest a possible methodology, and indicate the steps to create it.
Ensemble System for Identification of Cited Text Spans: Based on Two Steps of Feature Selection

CL-SciSumm Shared Task proposed a novel approach which is to generate scientific summary based on cited text spans (CTS) in target paper. This mechanism requires identifying CTS from reference paper according to citation sentence (citance) firstly. Therefore, CTS identification has then arisen the attention of many scholars since identified sentences will finally be aggregated for summary generation. Prior studies viewed this task as a text classification problem and feature selection is one key step for modeling the linkage between CTS and citance. Since most studies have paved the work by building features arbitrarily and applying them directly to model training. There is a lack of investigation to evaluate the effectiveness of features. Performance variation caused by different classifiers are barely taken into consideration as well. To further improve the performance of CTS identification, this paper builds an ensemble system based on two steps of feature selection. In the first step, we construct a set of features and do correlation analysis to select those which are higher-correlated with CTS. The second step is responsible for assigning several basic classifiers (SVM, Decision Tree and Logistic Regression) with their best performing feature sets. Experimental results demonstrate that our proposed systems can surpass the previous best performing one.
Introduction to Video Compression

With the sharing of video content over an Internet or communication channel, the compression of it is necessary. This chapter presents an overview of video compression, various types of video compression standards, and video quality matrices.
Research on Autonomous Detection Sonar System of UUV Flank Array

The flank array sonar system of unmanned underwater vehicle (UUV) has been developed in this paper. The sonar system has the characteristics of low power consumption, small size, modularity and networking. The signal detection algorithm can suppress the interference of the own vehicle and has good detection performance for weak signals. The autonomous detection and automatic tracking algorithm can complete the automatic tracking of multiple targets, and the tracking result is sent to the UUV control decision system through the network. The result of lake trial data processing show that the weak signal detection and autonomous detection algorithm have good performance.
Tumor Growth Prediction Using Convolutional Networks

Prognostic tumor growthComputer-Aided Diagnosis (CAD) modeling via volumetric medical imaging observations is a challenging yet important problem in precision and predictive medicine. It can potentially imply and lead to better outcomes of tumor treatment management and surgical planning. Traditionally, this problem is tackled through mathematical modeling. Recent advances of convolutional neural networksConvolutional Neural Networks (CNNs) (ConvNets) have demonstrated higher accuracy and efficiency than conventional mathematical models can be achieved in predicting tumor growth. This indicates that deep learning based data-driven techniques may have great potentials on addressing such problem. In this chapter, we first introduce a statistical group learning approach to predict the pattern of tumor growth that incorporates both the population trend and personalized dataPersonalized data, where deep ConvNet is used to model the voxel-wise spatiotemporal tumor progression. We then present a two-stream ConvNets which directly model and learn the two fundamental processes of tumor growth, i.e., cell invasion and mass effect, and predict the subsequent involvement regions of a tumor. Experiments on a longitudinalLongitudinal pancreatic tumor data set show that both approaches substantially outperform a state-of-the-art mathematical model-basedModel-based approach in both accuracy and efficiency.
Geochemical Methods to Assess Agriculture Sustainability

Facing global changes and the challenge of food security, scientists are being questioned by decision-makers and stakeholders on the sustainability of agrosystems.The main difficulty in dealing with this question is to obtain enough data over long periods of time.Monitoring slow drifts and weak noises is needed to forecast tipping points that can jeopardize the present steady state.High-resolution datations by radiocarbon coupled with detailed palynological determinations in sediments, historical archives on yields and crop quality, and high-frequency field in situ measurements give information on climatic changes from multi-secular to seasonal and hourly time scales.In the long term, climatic forcing dominates agriculture performance, at that time only organic agriculture, with oscillations between prosperity and misery driven by climate and intermediated by civilization flourishing and collapsing; in the medium term, in modern agriculture, irrigation provides a provisional buffering effect on yield and crop quality despite present warming; in the short term, either under non-fertilized forested ecosystem or intensive rice cropping, the same patterns are evidenced and point to the importance of soil microflora shifting from aerobiosis to anaerobiosis.In all cases, geochemistry offers appropriate tools to decipher the climate-soil-agriculture complex interplay.
Deep Learning of Visual and Textual Data for Region Detection Applied to Item Coding

In this work, we propose a deep learning approach that combines visual appearance and text information in a Convolutional Neural Network (CNN), with the aim of detecting regions of different textual categories. We define a novel visual representation of the semantic meaning of text that allows a seamless integration in a standard CNN architecture. This representation, referred to as text-map, is integrated with the actual image to provide a much richer input to the network. Text-maps are colored with different intensities depending on the relevance of the words recognized over the image. More specifically, these words are previously extracted using Optical Character Recognition (OCR) and they are colored according to the probability of belonging to a textual category of interest. In this sense, the presented solution is especially relevant in the context of item coding for supermarket products, where different types of textual categories must be identified (e.g., ingredients or nutritional facts). We evaluated our approach in the proprietary item coding dataset of Nielsen Brandbank, which is composed of more than 10,000 images for train and 2,000 images for test. The reported results demonstrate that our method focused on visual and textual data outperforms state-of-the-art algorithms only based on appearance, such as standard Faster R-CNN. These improvements are exhibited in precision and recall, which are enhanced in 42 and 33 points respectively.
Generating the Super-resolution Image for the Video from the In-vehicle Data Recorder

The goal of this research is to generate high-resolution images in ROI by the low-resolution video from the In-Vehicle Device Recorder (IVDR). First, we decide the search region and trajectory of the feature through the construction of the camera model and analysis the imaging geometry and characteristics in the moving camera. Next, we consider the image perturbation and blurring caused by camera movement and try to reduce the impact on image quality. Using the projection geometry can track the trajectory of the feature points movement in the video. An accurately point position estimation in different resolutions requirement is made by the phenomenon that the image will be enlarged over time. We use the patching method to create the high-resolution image in ROI. The proposed rebuilding method for super-resolution imaging is based on motion characteristics of spatial domain features. This method can avoid the problem that generates ring noise using the traditional frequency method. It also has the advantage of simple computing.
Exploiting Near-Memory Processing Architectures for Bayesian Neural Networks Acceleration

Bayesian inference is an effective approach to capture the model uncertainty as well as tackle the over-fitting problem in deep neural networks. Recently Bayesian neural networks (BNNs) are becoming more and more popular and have succeeded in many recognition tasks. However, the BNNs inference procedure requires numerous memory access operations due to the resulted sampling networks. In this paper, a near memory architecture is proposed for accelerating BNN inference by introducing additional memory units near the processing units. The near memory architecture could cache the frequently accessed data to reduce the data movement efficiently. Minimizing the expensive data movements between memory units and computation units contributes to cutting down the latency and energy consumption. Comparing with the traditional approach, the simulation results show that the proposed architecture reduces the energy consumption by 9% and achieves a 1:6 speedup at the cost of 4% area overhead.
Low-Light Face Image Enhancement Based on Dynamic Face Part Selection

A common challenge faced by face recognition community is struggling to circumvent face images that are acquired under low-light situation. The present work aims to couple the power of the popular CLAHE algorithm for face preprocessing with a Fuzzy inference system in such a way to correct the annoyance of non-uniform illumination of face images in a targeted and a precise manner. Due to the particularity of the low-light illumination problem. Firstly, the input face image is divided into two equal sub-regions. Subsequently, the degree of brightness in each sub-region and in the whole face is used for dynamic decision of whether to normalize. In the case where only one region of the face undertakes the CLAHE-Fuzzy approach is applied. Thus, the left and right face regions are grouped back followed by further processing like a blur removal and contrast enhancement (smoothing). Visual results showed that more facial features appeared in comparison with other approaches for enhancement. Besides, we quantitatively validate the accuracy of the developed Partial Fuzzy Enhancement Approach (PFEA) with four different metrics. The effectiveness of PFEA technique has been demonstrated by presenting extensive experimental results using Extended Yale-B, CMU-PIE, Mobio, and CAS-PEAL databases.
Memory-to-Sequence learning with LSTM joint decoding for task-oriented dialogue systems

Developing a dialogue system that accomplishes specific tasks through natural conversations with humans is challenging. Currently, although the task-oriented dialogue system constructed by the traditional pipeline model or sequence-to-sequence model has achieved great success, it is still unable to efficiently utilize the external knowledge base to generate high quality dialogue responses. This paper proposes a Memory-to-Sequence framework that uses Memory Neural Network (MemNN) and Long Short Term Memory (LSTM) joint decoding, which can better capture the dependence between system response and knowledge base. Experiments on In-Car Assistant Dataset show that our model significantly outperforms the baseline model and attains the state-of-the-art performance on the two subtasks of the dataset.
Adjustable Location Privacy-Preserving Nearest Neighbor Query Method

Location-based services facilitate the daily life of the people, nevertheless, they also bring about the problem of privacy preserving. Privacy preserving methods without anonymity server, for example, Coprivacy, attract increasing concerning from researchers for their simple and reliable structure and the avoidance of high cost of communication and computing resulting from the using of cloaking area. The drawbacks of Coprivacy are the high cost of communication and computing and the uncontrollability during query period. A feedback based incremental nearest neighbor query method (FINN) is propose to solve the problem. The user sends feedback to the server according to the query, and the server chooses POIs to send to the user according to the feedback. Theoretical analysis and experimental results show that FINN can improve the performance of the system significantly while ensures user's anonymous requirements.
A Novel Survey on Blockchain for Internet of Things

Internet of things (IoT) is ready to change human life and release tremendous financial benefits. It may be that lack of information security and the belief of the current IoT are actually restricting its selection. Blockchain changes in an appropriated and secure record holds reliable records of information in various areas and possibly resolves information security concerns in the IoT system. This chapter presents a thorough review on the existing blockchain progress with an accent on IoT applications. The authors first give an overview of blockchain architecture including blockchain technologies and key characteristics of blockchain. The authors then discuss the blockchain for the internet of things including blockchain for IoT: technologies. Furthermore, they list some challenges and problems that will hinder blockchain development and summarize some existing approaches for solving these problems. Some possible future directions are also discussed. Future research bearings are ordered for a viable mix of blockchains in the IoT system.
Simulation-based investigation on optical 3D surface measurement with composite spectral patterns

This paper presents the design and simulation of a single-shot optical 3D sensor based on multispectral pattern projection and a stereo-vision setup of two multispectral snapshot cameras. The performances of various combinations of available multispectral cameras, spatial light patterns, and 3D reconstruction algorithms as well as the geometric arrangements of the stereo-vision camera setup are simulated and analyzed. This simulation-based investigation delivers two optimized combinations of sensor components in terms of hardware and algorithm as orientations for practical sensor development in future, and an appropriate arrangement of the stereo-vision setup is determined. Moreover, the influences of sensor noise on 3D reconstruction are also estimated.
Teaching Introductory Formal Methods and Discrete Mathematics to Software Engineers: Reflections on a Modelling-Focussed Approach

Much has been written about the challenges of teaching discrete mathematics and formal methods. In this paper we discuss the experiences of delivering a course that serves as an introduction to both. The one-week intensive course, Software Engineering Mathematics, is delivered as part of the University of Oxford’s Software Engineering Programme to groups of professional software and security engineers studying for master’s degrees on a part-time basis. We describe how a change in the course’s emphasis—involving a shift towards a focus on modelling-based group exercises—has given rise to some pleasing results.
Human Information Processing (HIP)

This chapter explains the elements of human interactive proof. As a starting point, it uses a set of protocols which are able to authenticate humans compared to a computer. Furthermore, it defines all necessary properties that the human interaction proof should satisfy. Also, it determines a minimal human success rate of passing the given set of protocols. At the end, it proposes some applications where it can be used.
Cross-Project Estimation of Software Development Effort Using In-House Sources and Data Mining Methods - an Experiment

Application of data mining methods is well suited for problems of effort estimation in the field of software engineering. During development process, data mining can provide software engineers with valuable inputs that support decision makings. This way it is possible to overcome the problems caused by erroneous estimates made by the experts. Use of in-house data sources is encouraged because predictive models built on top of them typically provide better estimates compared to the models generated by using data from other sources. Model predictions can either confirm experts statements or propose an alternative solution. This way software development process can become more efficient. In this empirical investigation data from five different software projects originating from the same environment were used to conduct a formal experiment. The experiment uses cross-project estimation in which data sets from other projects are used to build predictive models for the project being estimated. Predictive models implement advanced learners that in general provided more accurate predictions, thus reducing the estimation error. The evaluation of results obtained during data mining process uses established criteria. The organization of the research carried out, distinctive model and structuring of the data together with obtained results encourage the application of similar models in practice.
Memory Management in Resource-Bounded Agents

In artificial intelligence, multi agent systems constitute an interesting typology of society modeling, and have in this regard vast fields of application, which extend to the human sciences. Logic is often used to model such kind of systems as it is easier to verify the explainability and validation, so for this reason we have tried to manage agents' memory extending a previous work by inserting the concept of time.
Smart Glasses in Galleries Libraries Archives Museums

We examine ways to engage visitors to art galleries in Canada and review less successful methods. We specifically explore the possibility of smart wearable devices, such as smart glasses, to improve visitors' experience and increase the number of visitors in galleries, archives and museums.
Modeling Multimodal-Multiuser Interactions in Declarative Multimedia Languages

Recent advances in hardware and software technologies have given rise to a new class of human-computer interfaces that both explores multiple modalities and allows for multiple collaborating users. When compared to the development of traditional single-user WIMP (windows, icons, menus, pointer)-based applications, however, applications supporting the seamless integration of multimodal-multiuser interactions bring new specification and runtime requirements. With the aim of assisting the specification of multimedia applications that integrate multimodal-multiuser interactions, this paper: (1) proposes the MMAM (Multimodal-Multiuser Authoring Model); (2) presents three different instantiations of it (in NCL, HTML, and a block-based syntax); and (3) evaluates the proposed model through a task-based user study. MMAM enables programmers to design and ponder different solutions for applications with multimodal-multiuser requirements. The proposed instantiations served as proofs of concept about the feasibility of our model implementation and provided the basis for practical experimentation, while the performed user study focused on capturing evidence of both the user understanding and the user acceptance of the proposed model. We asked developers to perform tasks using MMAM and then answer a TAM (Technology Acceptance Model)-based questionnaire focused on both the model and its instances. As results, the study indicates that the participants easily understood the model (most of them performed the required tasks with minor or no errors) and found it both useful and easy to use. 94.47% of the participants gave positive answers to the block-based representation TAM questions, whereas 75.17% gave positive answers to the instances-related questions.
Vagueness and Imprecise Credence

In this paper I investigate an alternative to imprecise probabilism. Imprecise probabilism is a popular revision of orthodox Bayesianism: while the orthodox Bayesian claims that a rational agent’s belief-state can be represented by a single credence function, the imprecise probabilist claims instead that a rational agent’s belief-state can be represented by a set of such functions. The alternative that I put forward in this paper is to claim that the expression ‘credence’ is vague, and then apply the theory of supervaluationism to sentences containing this expression. This gives us a viable alternative to imprecise probabilism, and I end by comparing the two accounts. I show that supervaluationism has a simpler way of handling sentences relating the belief-states of two different people, or of the same person at two different times; that both accounts may have the resources to develop plausible decision theories; and finally that the supervaluationist can accommodate higher-order vagueness in a way that is not available to the imprecise probabilist.
On the choice of high-dimensional regression parameters in Gaussian random tomography

The stochastic Radon transform was introduced by Panaretos in order to estimate a biophysical particle, modelled as a three-dimensional probability density subject to random and unknown rotations before being projected onto the imaging plane. Assuming that the particle has a Gaussian mixture distribution, the question arises as to how to recover the modes of the mixture, and the mixing weights. Panaretos and Konis proposed to do this in a sequential manner, using high-dimensional regression. Their approach has two drawbacks: first, the mixing weight estimates often have incorrect rank; second, much information inherent in the coefficients from the penalized regression approach (Lasso, or more generally elastic net) is lost. We propose a procedure, based on the asymptotic precision matrix, that exploits the information from the high-dimensional regression more efficiently, and yields in particular a method for choosing the parameter that balances the Lasso with ridge regression.
Classical Algorithms for Reasoning and Explanation in Description Logics

Description Logics (DLs) are a family of languages designed to represent conceptual knowledge in a formal way as a set of ontological axioms. DLs provide a formal foundation of the ontology language OWL, which is a W3C standardized language to represent information in Web applications. The main computational problem in DLs is finding relevant consequences of the information stored in ontologies, e.g., to answer user queries. Unlike related techniques based on keyword search or machine learning, the notion of a consequence is well-defined using a formal logic-based semantics. This course provides an in-depth description and analysis of the main reasoning and explanation methods for ontologies: tableau procedures and axiom pinpointing algorithms.