Reviewerly/exHarmony huggingface dataset:
Book detection systems

Protecting the collections may be seen as the highest priority in many libraries where they are the most expensive resource in terms of accumulated value and replacement cost. Many libraries have successfully installed one of the well established electronic book detection systems on the market and there can be little doubt that a well managed and properly maintained detection system effectively reduces the loss of books and other materials. In libraries with full circulating systems, readers with correctly issued books can move freely in and out of the library and only those in possession of an unissued volume will set off the alarm and require attention by a member of staff. The detection systems simply detect unissued books and normally respond by activating an alarm and temporarily locking a gate or door, depending on the configuration selected for the circumstances of the particular library.
On Detecting and Handling target occlusions in Correlation-filter-based 2D tracking

This paper focuses on the application of 2D visual object tracking in Unmanned Aerial Vehicles (UAV) for the coverage of live outdoor events, by filming moving targets (e.g., athletes, boats, cars etc.). In this application scenario, a 2D target tracker visually assists the UAV pilot (or cameraman) to maintain proper target framing, or it is employed for autonomous UAV operation. It should be expected that in such scenarios, the 2D tracker may fail due to target occlusions, illumination variations, fast 3D target motion, etc., thus, the 2D tracker should be able to recover from such situations. The proposed long-term 2D tracking algorithm solves exactly this problem, by detecting occlusions from the 2D tracker responses. Moreover, according to the immensity of the occlusion, the tracker may stop updating the tracker model or try to re-detect the target in a broader frame region. Experimental results indicate that our proposed tracking algorithm outperforms state-of-the art correlation filter trackers in UAV orientated visual tracking benchmarks, as well as in realistic UAV cinematography applications.
Two cross-validation techniques to comprehensively characterize global horizontal irradiation regression models: Single data-splitting is insufficient

Data-splitting is the most widely used method to cross-validate global horizontal irradiation regression models. An available dataset is split into two subsets, one to calibrate models and the other to validate them. This study investigated the sufficiency of this method within the ambit of two other cross-validation techniques—Monte Carlo cross-validation nested with double cross-validation and leave-one-year-out cross-validation. These techniques facilitated cross-validation in long and short term periods, respectively. They were applied to the De Souza and Hargreaves-Samani temperature-based regression models. Unlike data-splitting, the techniques promoted full characterization of the models by the averages and sensitivities (%) of their tuned parameters, the averages and spread of their predictive accuracies via root mean square errors, and their stability (Monte Carlo-determined). On a monthly average daily time scale, their fully characterized (less their average tuned parameters) Monte Carlo results were &amp;lt;6%, 0.56 ± 0.12 and 0.032 MJ m−2 day−1 for the De Souza model, and &amp;lt;1.5%, 0.94 ± 0.14 and 0.174 MJ m−2 day−1 for the Hargreaves-Samani model. Similarly, the leave-one-year-out results were &amp;lt;2% and 0.88 ± 0.28 MJ m−2 day−1 for the De Souza model and &amp;lt;1% and 1.31 ± 0.24 MJ m−2 day−1 for the Hargreaves-Samani model. The De Souza model performed better. We further demonstrated the erroneous assessments possible with models subjected to traditional data-splitting which proved inadequate. Consequently, we proposed an algorithm to implement our cross-validation techniques that reduces computational burden for multiple model evaluation. This was achieved by including a novel controlled data-splitting cross-validation subroutine.
Research on university scientific research patent management information system based on BS mode

The development of this system is based on ASP technology, and through the application of B/S structure and SQL Server database, it provides necessary scientific research data support for the sharing of scientific research information in colleges and universities, and basically realizes the manage ment of personnel files, personnel management and professional title. Management integration of scientific research projects and management of information systems. Develop a research goal of web-based network research management information system. Through this system, it can manage and standardize more scientific research projects. The system is a complete set of college plans, declarations, contracts, Research management information systems for results, patents, funds, personnel, equipment, etc. The system is based on the B/S mode, perfecting the output system of the report, and providing corresponding data support for the smooth development of scientific research projects, providing a solid material foundation and software hardware for the research and management of scientific research projects. Support, providing an electronic data platform for scientific research and academic exchanges, successfully achieved design tasks and goals.
Very High Resolution Image Semantic Segmentation with Contextualized Convolutional Neural Network Coupled with Higher Order Conditional Random Fields

Feature representation and context incorporation play a key role in image semantic segmentation. We propose a framework for very high resolution image semantic segmentation which is able to combine learned internal representation and context in a unified manner. The proposed method is based on contextualized convolutional neural networks and high order conditional random field to learn feature representation and encode context feature respectively. We first devise an architecture to obtain learned feature representation which helps to keep semantic consistency within classes by encoding semantic edge context into convolutional neural networks. Then, the learned features and superpixel based higher order potential are integrated into a conditional random field to infer semantic segmentation. Both pixel-level and region-level potential are considered in conditional random field to address high variation within classes. Experimental results on public available semantic segmentation contest dataset demonstrated the effectiveness of the proposed method.
‘A matter both of curioſity and uſefulneſs’: Compiling the Corpus of English Texts on Language

This paper describes the compilation of CETeL, the subcorpus on ‘Language and Linguistics’ in the Coruña Corpus of English Scientific Writing, and discusses the various challenges encountered during the process of selection and digitisation of material. CETeL includes forty-four samples of texts on Language, Languages, and Linguistics from the period 1700–1900, and on completion will contain around 400,000 words. The paper will examine the historical context of academic writing in that period and the way in which this context affects the process of compilation. Likewise, the criteria followed in the compilation of the Coruña Corpus will be discussed in order to show the extent to which these criteria have affected the compilation of CETeL, and how they contribute towards making the corpus representative of the disciplinary practices of the period. Finally, the corpus will also be described according to a series of parameters used to assure representativeness and balance, namely the date of publication of samples, their genre, and the sex and linguistic background of their authors.
Directedness and persistence in audio-visual interface for emergent users

"Emergent users" in the developing regions can interact better with a visually augmented audio interface (AV, the audio-visual) than an audio interface (A) like IVRs and a graphic user interface (G) on two grounds. First, audio prompts users to meet goals and thereby brings directedness to the interface. Second, visuals bring persistence by presenting options on the screen in parallel. To illustrate, 36 users performed information and transaction tasks on prototyped AV, A and G banking applications. Results: With AV, users exhibited better task completion than G and A. Though they were not as fast as G but they took less time than A. They rated AV higher on SUS scale than A, G.
Unified Image Aesthetic Prediction via Scanpath-Guided Feature Aggregation Network

The performance of automatic aesthetic prediction has achieved significant improvement by utilizing deep convolutional neural networks (CNNs). However, existing CNN methods can only achieve limited success because (1) most of the methods take one fixed-size patch as the training example, which loses the fine-grained details and the holistic layout information, and (2) most of the methods ignore the biologically cues such as the gaze shifting sequence in image aesthetic assessment. To address these challenges, we propose a scanpath-guided feature aggregation model for aesthetic prediction. In our model, human fixation map and the view scanpath are predicted by a multi-scale network. Then a sequence of regions are adaptively selected according to the scanpath. These attended regions are then progressively fed into the CNN and LSTM network to accumulate the information, yielding a compact image level representation. Extensive experiments on the large scale aesthetics assessment benchmark AVA and Photo.net data set thoroughly demonstrate the efficacy of our approach for unified aesthetic prediction tasks: (i) aesthetic quality classification; (ii) aesthetic score regression; and (iii) aesthetic score distribution prediction.
Analysis of Structural MRI Using Functional and Classification Approach in Multi-feature

Magnetic resonance imaging (MRI) is adjacent to nature and a multi-modality practice which provides the complementary in sequence about dissimilar aspects of diseases. As a competent image (CET) contrast enhancement tool, (AGC) adaptive gamma correction control which was relating with gamma parameter and (CDF) cumulative distribution function in the conventional method which is to engender the function of the pixel gray levels contained by an image. AGC should deals well within the most dimmed images, but fails for worldwide intense images and also the dimmed images within the local bright regions. Such two categories of images which are observed from MRIs, the brightness-distorted images are widespread in genuine scenario, such as to the improper exposure and to the white object regions. To attenuate such kind of deficiencies here we intend an improved the aspects by two methods which are (RIC) region by iteration method of convolution and (S-KC) segmentation by k-levels of clustering. In this proposed work the above given two methods are used by iteration method and the other segmentation in multiple levels of clustering which is to enhance the required response of the MRIs. Both the levels and methods are analyzed in a closed system which to eliminate the unwanted signals and to get the better performance in the MRIs imaging.
Beyond patent families

Beyond patent families, there are other closely interrelated patents and patent applications. Firms who wish to be active in a particular area of technology need to ensure that their products will not infringe existing patents or existing patent applications which might later be granted. This requires an adequate patent search to uncover all interrelated patents and applications. Frequently, interrelated patents and applications are from a single applicant, but often with multiple inventors, or sets of inventors, and concern similar, but not identical subject-matter. The challenge is to identify such relationships between patents and applications, as distinct from other relationships such as citations or patent families, which may include divisional applications. This paper presents simple-to-use but effective techniques which anyone can apply, using freely available data, to retrieve such related applications. The techniques are based on the applicant name, inventors and titles. Additional possibilities derive from the application number, priority dates and classes. These techniques are based on ones used at the European Patent Office to find similar applications at the pre-search stage, techniques which have proven their value in widespread systematic use for over twenty years using tools the Office has developed. Compared to existing techniques such as patent landscaping, the techniques proposed here are simpler to use, quicker and less expensive. Examples from various fields of technology will be presented using Espacenet.
Sequential Peak Detection for Flow Cytometry

Circulating tumor cells in blood are identified by means of sequential peak detection taking into account the memory and real time applicability constraints. Three different spatial domain algorithms: derivative approach, energy detector and baseline method are compared with three different peak detection algorithms based on machine learning: linear and nonlinear support vector machines and artificial neural networks. Performance of the peak detection algorithms are tested on both synthetic and real data. Experimental results indicate superiority of machine learning algorithms over the other three algorithms which are widely used in practice. Due to Gaussianity assumption in the signal model, a linear support vector machine is found to be as good as other machine learning schemes.
Visual predictive modeling of incomplete time series panel data

It is not easy to predict incomplete panel data whose overall trend is not complete. For example, physical growth data for age 7-18 has been collected every a few months for up to seven years; therefore, only short growth pattern pieces exist in the data. When using previous prediction techniques, it is challenging to create a growth prediction model that reflects individual growth patterns. Also, uncertainties in predicted results emerge since the overall trend of the data is unknown. In this work, we present a predictive analysis and modeling to forecast incomplete data over time. We extend the Bayesian network model, which is relative data-driven approaches that explore similar data and weave them to create approximate inference margins. Besides, we propose a visual analytics system that enables us to design various predictive models that reflect individual growth pattern. Our visual analytics system assists us to discover new growth patterns in the process of analyzing the accuracy of previously designed predictive models. Moreover, the system allows us to optimize predictive models to fit unusual growth patterns.
Gaussian source coding based on variance‐mismatched three‐level scalar quantisation using Q ‐function approximations

This study deals with three-level scalar quantisation of a Gaussian source, followed by Huffman encoder, especially suitable for signal compression purposes. The variance-matched and variance-mismatched versions of quantiser are considered. Simple approximate closed-form expressions for performance evaluation in terms of signal-to-quantisation-noise ratio and bit rate are derived using the corresponding Q-function approximations. The accuracy of the derived formulas is tested using the relative error as a measure of performance. It is shown that the derived formulas for variance-matched (for a reference variance) and variance-mismatched (in a wide range of input signal variances) provide higher accuracy in comparison to baselines using other available Q-function approximations or available approximate formulas for non-uniform scalar quantisation of Gaussian source.
Training Variational Autoencoders with Discrete Latent Variables Using Importance Sampling

The Variational Autoencoder (VAE) is a popular generative latent variable model that is often used for representation learning. Standard VAEs assume continuous-valued latent variables and are trained by maximization of the evidence lower bound (ELBO). Conventional methods obtain a differentiable estimate of the ELBO with reparametrized sampling and optimize it with Stochastic Gradient Descend (SGD). However, this is not possible if we want to train VAEs with discrete-valued latent variables, since reparametrized sampling is not possible. In this paper, we propose an easy method to train VAEs with binary or categorically valued latent representations. Therefore, we use a differentiable estimator for the ELBO which is based on importance sampling. In experiments, we verify the approach and train two different VAEs architectures with Bernoulli and categorically distributed latent representations on two different benchmark datasets.
Dynamic Adaptive Bit-Rate Selection Algorithm Based on DASH Technology

Aiming at the existing problems of the dynamic adaptive bit-rate selection algorithm, an improved dynamic adaptive bit-rate selection algorithm based on DASH technology is proposed. To solve the optimal allocation of resources in the process of streaming media transmission, the algorithm reduces the number of video re-buffering by dynamically adjusting the buffer’s key value, and improves the broadcasting quality of video by effectively reducing the startup time of video playback and switching frequency between videos with different quality. Simulation results show that the proposed algorithm can better adjust the playback bit-rate and increase the quality and stability of video playback under various bandwidth conditions. It can optimal configuration of DASH service and provide users with a good video playback experience.
Investigating the additive probability of repeated language production decisions

Abstract This paper introduces an experimental paradigm based on probabilistic evidence of the interaction between construction decisions in a parsed corpus. The approach is demonstrated using ICE-GB, a one million-word corpus of English. It finds an interaction between attributive adjective phrases in noun phrases with a noun head, such that the probability of adding adjective phrases falls successively. The same pattern is much weaker in adverbs preceding a verb phrase, implying this decline is not a universal phenomenon. Noun phrase postmodifying clauses exhibit a similar initial fall in the probability of successive clauses modifying the same NP head, and embedding clauses modifying new NP heads. Successive postmodification shows a secondary phenomenon of an increase in additive probability in longer sequences, apparently due to ‘templating’ effects. The author argues that these results can only be explained as cognitive and communicative natural phenomena acting on and within recursive grammar rules.
The implementation of peer teaching for motivating students to study at university (on the example of teaching a foreign language)

The article highlights a type of learning “Peer Teaching” as a tool for students’ motivation to study at universities. The peculiarities of using this type of collaborative learning as well as advantages and disadvantages of its implementation in the Ukrainian educational environment are revealed in the article. Peer Teaching along with formal traditional education and other approaches in informal education is defined as a strategic way of information transaction in the society in order to motivate students to self-development and self-improvement. Peer Teaching implies collaborative learning, namely a group work, without any interference of a teacher that is more common for cooperative learning. Among the principles of its realization in the educational process the following ones have been distinguished: integrity, complexity, availability, progression, equality, activity, priority of human rights and responsibility. The author recommends for more effective Peer Teaching to conduct workshops and trainings as well as other forms and teaching methods that will be more informal and will be able to create natural and comfortable environment as well as to have all the necessary equipment and trained tutors. It is offered in the article to divide Peer Teaching into learning within a group; learning at the faculty level; interdisciplinary learning both at the inter-faculty and inter-university levels. The results of the experiment have shown the necessity of the realization of Peer Teaching for the integration of the national system of education into the world educational and scientific environment as well as the implementation of the European experience of organization of the educational process at universities and for humanization and democratization of learning.
Design of Flexible Multiplier Using Wallace Tree Structure for ECC Processor Over Galosis Field

Security is a main parameter in Networks. To ensure the security in network various cryptography algorithms are implemented. Instead of using software based security, the hardware based security system increases security features. ECC processor is a hardware based security crypto processor works based on Elliptic curve cryptography, the key generation is very fast compare to AES, RSA. ECC processor has many modules such as arithmetic and logic units, Control unit etc.,. The Arithmetic unit Consist of Multiplier, Adder. Various optimization techniques and multiplier algorithm are implemented to increase the efficiency. In this paper we are proposed the novel architecture of flexible multiplier.
ALL VISION: A Novel Security Architecture and Alarm system Using Object Detection And Computer Vision Technology For Large Campuses

The primary objective of the ALL VISION is to reduce the cost and human effort required for the task of constantly monitoring the CCTV feed in any institutes or organization that has large campus. ALL VISION uses modern computer science innovations such as Object-Detection, Image Processing and Data mining to alert the nearby guards with the help of the alarm about present danger/threat or illegal activities prohibited by administration. Data storages are also maintained to keep logs of suspicious activities that might be needed to reference some day. It will serve all the organization with fully automated security environment. Situational detection works in regards to humans and their activities to provide notification and provide red screen in case of violence is detected
A VoLTE Covert Channel with Intelligent Confrontation

VoLTE (voice over LTE) is widely popular and the potential target for covert communications. The transmission of continuous and large amounts of data over VoLTE enables the construction of a covert channel. The existing covert channel construction scheme based on IPD (inter-packet delay) cannot be applied to VoLTE due to the particularity of the IPD of VoLTE traffics. In this paper, we propose a covert channel with intelligent confrontation for VoLTE traffic. It modulates messages in the relative relationship of voice and video packets based on learning of the characteristics of active attack traffic. The experimental results indicate that the proposed covert channel is undetectable and robust.
Improved Turning Angle Calculation for an Unmanned Ground Vehicle

This paper focuses on the calculation of turning angle for an unmanned ground vehicle equipped with ultrasonic sensors for static obstacle avoidance. An improved method for evaluating the turning angle is proposed based on the concept of forward safe path. The proposed algorithm can direct the unmanned ground vehicle to turn at a certain angle, thereby avoiding the obstacle with minimum number of iterations compared to the well developed algorithms available in the literature.