Reviewerly/exHarmony huggingface dataset:
Estimation of Appropriate Investment in Higher Education based on Higher Education Indicators

The purpose of this study is to estimate the appropriate amount of investment in higher education using major higher education indicators. Existing studies on the size of investment in higher education have limitations in that they do not take into account the actual environmental variables of higher education by only considering individual programs at a micro level or estimating only GDP. Accordingly, this study evaluated the impact of calculation-participation-financial-environmental variables based on OECD's major higher education indicators, and estimated the optimal size of domestic higher education investment by applying the expansion of existing calculation formula. As a result of the study, it was found that the appropriate investment in higher education calculated when only GDP was taken into account still needs to be increased compared to the actual size, and it is far below the OECD average. However, when considering the change in the school-age population, the appropriate size was found to be more similar to the actual support size. Since then, when the major factors of higher education are taken into account, it found that a higher level of appropriate investment in higher education than the conventional method is required. Although it is still below the OECD average, it is higher than the case where only GDP is taken into account. Based on this, various implications for estimating the optimal investment size linked to the higher education index will be discussed.
Grayscale Image Colorization Using Deep Learning: A Case Study

Colorization is a process of adding colors to a black and white image. There exists vast amounts of illustrations and photographs that lack color information such as modern medical imaging to antique photography. Visual appeal and expressiveness of these black and white images can be improved by adding colors. Image colorization is a challenging topic of current researches in Computer Visions. Significant user interaction is required in the form of placing numerous color scribbles, looking at related images etc for coloring the image in traditional or manual coloring of image. This is difficult because of the complex shades of colors present in the image. Automating the image colorization methods has various benefits in different field such as medical, art, entertainment etc. Applying machine learning techniques to this process reduces the human efforts substantially. Image colorization is the process of taking an input grayscale image and then producing an output colorized image. In this proposed chapter a technique of colorization of grayscale image using deep learning is discussed.
Sample-Efficient Iterative Lower Bound Optimization of Deep Reactive Policies for Planning in Continuous MDPs

Recent advances in deep learning have enabled optimization of deep reactive policies (DRPs) for continuous MDP planning by encoding a parametric policy as a deep neural network and exploiting automatic differentiation in an end-to-end model-based gradient descent framework. This approach has proven effective for optimizing DRPs in nonlinear continuous MDPs, but it requires a large number of sampled trajectories to learn effectively and can suffer from high variance in solution quality. In this work, we revisit the overall model-based DRP objective and instead take a minorization-maximization perspective to iteratively optimize the DRP w.r.t. a locally tight lower-bounded objective. This novel formulation of DRP learning as iterative lower bound optimization (ILBO) is particularly appealing because (i) each step is structurally easier to optimize than the overall objective, (ii) it guarantees a monotonically improving objective under certain theoretical conditions, and (iii) it reuses samples between iterations thus lowering sample complexity. Empirical evaluation confirms that ILBO is significantly more sample-efficient than the state-of-the-art DRP planner and consistently produces better solution quality with lower variance. We additionally demonstrate that ILBO generalizes well to new problem instances (i.e., different initial states) without requiring retraining.
TRF: Learning Kernels with Tuned Random Features

Random Fourier features (RFF) are a popular set of tools for constructing low-dimensional approximations of translation-invariant kernels, allowing kernel methods to be scaled to big data. Apart from their computational advantages, by working in the spectral domain random Fourier features expose the translation invariant kernel as a density function that may, in principle, be manipulated directly to tune the kernel. In this paper we propose selecting the density function from a reproducing kernel Hilbert space to allow us to search the space of all translation-invariant kernels. Our approach, which we call tuned random features (TRF), achieves this by approximating the density function as the RKHS-norm regularised least-squares best fit to an unknown ``true'' optimal density function, resulting in a RFF formulation where kernel selection is reduced to regularised risk minimisation with a novel regulariser. We derive bounds on the Rademacher complexity for our method showing that our random features approximation method converges to optimal kernel selection in the large N,D limit. Finally, we prove experimental results for a variety of real-world learning problems, demonstrating the performance of our approach compared to comparable methods.
Lower Bounds on Intermediate Results in Bottom-Up Knowledge Compilation

Bottom-up knowledge compilation is a paradigm for generating representations of functions by iteratively conjoining constraints using a so-called apply function. When the input is not efficiently compilable into a language - generally a class of circuits - because optimal compiled representations are provably large, the problem is not the compilation algorithm as much as the choice of a language too restrictive for the input. In contrast, in this paper, we look at CNF formulas for which very small circuits exists and look at the efficiency of their bottom-up compilation in one of the most general languages, namely that of structured decomposable negation normal forms (str-DNNF). We prove that, while the inputs have constant size representations as str-DNNF, any bottom-up compilation in the general setting where conjunction and structure modification are allowed takes exponential time and space, since large intermediate results have to be produced. This unconditionally proves that the inefficiency of bottom-up compilation resides in the bottom-up paradigm itself.
Pronoun Replacement Approach for Enhancing Arabic Text Summarization

The world witnessed massive and unprecedented growth in the amount of information available online. Identifying and consuming needed content has been difficult and time-consuming. Therefore, automatic text summarization to obtain accurate and relevant collections of the vast amount of available information is essential. Methodologies for automatic summarization of Arabic text remain immature, due to the complexity inherent in the Arabic language in terms of structure and morphology. The summarization process is based on the frequency which the trainer used to fetch important sentences to compute the keywords weightage that aims to construct the summary. This study proposes replacing pronouns with proper nouns before applying the Arabic text summarizer. This enhances the quality of the summarization process and helps reader better understand the summary.
Non-Linear Mapping for Image Enhancement

The existing low-light image enhancement methods may cause under enhancement, unbalanced brightness and blurriness. To address these shortcomings, we proposed the non-linear mapping method based on the Retinex theory (NMMR). We use an improved traditional gamma function to estimate the reflectance, and we proposed the maximum brightness channel to estimate the illumination. The main steps of this approach can be described as follows: First, we convert the image from the RGB (red, green, blue) color space to the HSV (hue, saturation, value) color space and the V channel is processed to estimate the reflectance and illumination. Then, we use a piecewise function to stretch the gray level dynamic range to achieve contrast enhancement. Finally, we use the fast pixelwise method to correct the color saturation and convert the image to the RGB color space. The experimental results show that the proposed method has lower computational complexity, the enhanced image has better objective and subjective evaluation results than other state-of-the-art methods.
First-Order Convex Fitting and Its Application to Economics and Optimization

This paper studies a function fitting problem which we coin first-order convex fitting (FCF): given any two vector sequences x1, ..., xT and p1, ..., pT, when is it possible to efficiently construct a convex function f(x) that ``fits'' the two sequences in the first-order sense, i.e, the (sub)gradient of f(xi) equals precisely pi, for all i = 1, ..., T? Despite a basic question of convex analysis, FCF has surprisingly been overlooked in the past literature. With an efficient constructive proof, we provide a clean answer to this question: FCF is possible if and only if the two sequences are permutation stable: p1 * x1 + ... + pT * xT is greater than or equal to p1 * x’1 + ... + pT * x’T where x’1, ..., x’T is any permutation of x1, ..., xT. We demonstrate the usefulness of FCF in two applications. First, we study how it can be used as an empirical risk minimization procedure to learn the original convex function. We provide efficient PAC-learnability bounds for special classes of convex functions learned via FCF, and demonstrate its application to multiple economic problems where only function gradients (as opposed to function values) can be observed. Second, we empirically show how it can be used as a surrogate to significantly accelerate the minimization of the original convex function.
Intelligent Tamper Detection Systems using Machine Learning

Existing anti-tamper designs protect against limited forms of attacks and have deterministic tamper responses, which can undermine the availability of systems. Advancements in physical inspection techniques have enabled stealthier attacks. Therefore, there is a pressing need for more intelligent defenses that ensure a longer operational time while keeping up with the expected increase in the capabilities of adversaries. This study proposes to enhance existing physical protection methods by developing an intelligent anti-tamper using machine learning algorithms. It uses an analytic system capable of detecting and classifying multiple types of behaviors (e.g., normal operation conditions, known attack vectors, and anomalous behavior). A prototype of the proposed system has been implemented and its functionality has been successfully verified for two types of normal operating conditions and further four forms of physical attacks. In addition, a systematic threat modelling analysis and security validation was carried out, which indicated the proposed solution provides better protection against including information leakage, loss of data, and disruption of operation.
A Survey on Fraudulent Transaction Detection using Random Forest

In the evolution of the electronic money system, frequent transaction fraud has been a shadow behind the prosperity. It not only endangers the property security of users, but also hinders the development of digital finance in the world. With the development of data mining and machine learning, some mature technologies are gradually applied to the detection of transaction fraud. This paper proposes a transaction fraud detection model based on random forest. The experimental results of IEEE CIS fraud dataset show that the method of this model is better than the benchmark model, such as logistic regression, support vector machine. Finally, the accuracy of our model reached 97.4%, and the AUC ROC score was 92.7%. The random forest classifier is composed of a group of decision trees. Each tree is generated by independent sampling random vectors, and each tree votes to find the most popular category to classify the input. Random forest has both sample randomness and characteristic randomness, and its generalization performance is superior. At the same time, random forest has good processing ability for high-dimensional data sets, which is very suitable for IEEE CIS data sets. It can process a large number of inputs and determine the most important characteristics. Therefore, further feature mining is carried out on the data extracted by RFECV.
DeepCARSKit: A Demo and User Guide

Context-aware recommender systems were proposed and built to adapt the recommendations to different context situations. With the development of deep learning based recommendation techniques, the neural network models have also been utilized to improve the quality of the context-aware recommendations. Recently, we released the DeepCARSKit library which is an open-source and deep learning based library for context-aware recommendations. It provides a unified platform for implementing and evaluating context-aware recommendation models based on neural networks. This paper provides a short summary of the DeepCARSKit library, and delivers a user guide to help better use and evaluate the library.
Modeling of Chaotic Processes by Means of Antisymmetric Neural ODEs

The main goal of this work is to construct an algorithm for modeling chaotic processes using special neural ODEs with antisymmetric matrices (antisymmetric neural ODEs) and power activation functions (PAFs). The central part of this algorithm is to design a neural ODEs architecture that would guarantee the generation of a stable limit cycle for a known time series. Then, one neuron is added to each equation of the created system until the approximating properties of this system satisfy the well-known Kolmogorov theorem on the approximation of a continuous function of many variables. In addition, as a result of such an addition of neurons, the cascade of bifurcations that allows generating a chaotic attractor from stable limit cycles is launched. We also consider the possibility of generating a homoclinic orbit whose bifurcations lead to the appearance of a chaotic attractor of another type. In conclusion, the conditions under which the found attractor adequately simulates the chaotic process are discussed. Examples are given.
A Hierarchical Processing and Completion Mechanism of Foreground Information for Person Re-Identification

Person re-identification (Re-ID) arises in many applications such as video surveillance and intelligent security. Background clutter and distribution drift are two issues that cross-domain person Re-ID faces. In this research, we propose that the background clutter problem be solved by combining semantic segmentation technology with human attribute identification technology. To overcome the distribution drift problem, we propose employing MMD as a metric for distribution differences and processing methods based on feature properties. The results of the experiments reveal that our strategy yielded the best results.
HAAPIE 2022: 7th International Workshop on Human Aspects in Adaptive and Personalized Interactive Environments

Nowadays, the profound digital transformation has upgraded the role of the computational system into an intelligent multidimensional communication medium that creates new opportunities, competencies, models and processes. The need for human-centered adaptation and personalization is even more recognizable since it can offer hybrid solutions that could adequately support the rising multi-purpose goals, needs, requirements, activities and interactions of users. The HAAPIE workshop1 embraces the essence of the “human-machine co-existence” and brings together researchers and practitioners from different disciplines to present and discuss a wide spectrum of related challenges, approaches and solutions. In this respect, the seventh edition of HAAPIE includes 5 long papers and 2 short papers.
Query Facet Mapping and its Applications in Streaming Services

In an instant search setting such as Netflix Search where results are returned in response to every keystroke, determining how a partial query maps onto broad classes of relevant entities orfacets --- such as videos, talent, and genres --- can facilitate a better understanding of the underlying objective of that query. Such a query-to-facet mapping system has a multitude of applications. It can help improve the quality of search results, drive meaningful result organization, and can be leveraged to establish trust by being transparent with Netflix members when they search for an entity that is not available on the service. By anticipating the relevant facets with each keystroke entry, the system can also better guide the experience within a search session. When aggregated across queries, the facets can reveal interesting patterns of member interest. A key challenge for building such a system is to judiciously balance lexical similarity with behavioral relevance. In this paper, we present a high level overview of a Query Facet Mapping system that we have developed at Netflix, describe its main components, provide evaluation results with real-world data, and outline several potential applications.
Vocal Airmail Service for Visually Impaired through Multimedia Approach

With the advent of technologies in computer, many technological solutions have been implemented for visually impaired so that they can utilize them, and get benefited by them. Considering it as a key idea we have built a desktop application that will help blind people to send and read emails as ordinary people do. In this research paper we describe the VMAIL system architecture for Python platform that can be used by a Blind Person to access e-mails easily. The application uses ‘text to speech’ and voice recognizer to facilitate sending, reading, emails using a computer. Internet has become one of the basic amenities for day-to-day living. Every human being is widely accessing the knowledge and information through internet. However, blind people face difficulties in accessing these text materials, also in using any service provided through internet. The advancement in computer based accessible systems has opened up many avenues for the visually impaired across the globe in a wide way. Audio feedback based virtual environment like, the screen readers have helped Blind people to access internet applications immensely. We describe the Voicemail system architecture that can be used by a Blind person to access e-Mails easily and efficiently
Rethinking the Value of Gazetteer in Chinese Named Entity Recognition

Gazetteer is widely used in Chinese named entity recognition (NER) to enhance span boundary detection and type classification. However, to further understand the generalizability and effectiveness of gazetteers, the NLP community still lacks a systematic analysis of the gazetteer-enhanced NER model. In this paper, we first re-examine the effectiveness of several common practices of the gazetteer-enhanced NER models and carry out a series of detailed analyses to evaluate the relationship between the model performance and the gazetteer characteristics, which can guide us to build a more suitable gazetteer. The findings of this paper are as follows: (1) the gazetteer has a positive impact on the NER model in most situations. (2) the performance of the NER model greatly benefits from the high-quality pre-trained lexeme embeddings. (3) a good gazetteer should cover more entities that can be matched in both the training set and testing set.
Precise Recognition Method of Local Fuzzy Features in Sprint Video Images

In the acceleration phase, sprinters need to have enough strength to support to better complete technical movements. In order to improve the recognition effect of local features in sprint video images, this paper analyzes the sprint process based on the accurate recognition method of local fuzzy features in video images. Moreover, this paper discusses the camera calibration method, and improves the calibration method according to the camera placement and the problems and requirements in field use to obtain an engineered fast calibration method. According to the simulation research, the accurate identification method of local fuzzy features in sprint video images proposed in this paper can play a certain role in sprint video image recognition.
CAPTCHA-based Login Scheme in Security Education

In this paper a novel password-reminder scheme will be presented, which can facilitate security education processes. The goal was to create a CAPTCHA-like image with hidden meaning, that only user would be able to decode. With selected visual pattern, being presented each time during logging in, the user is asked to associate a password consisting of two or more words and a personal number.
Experimentally testing the interpretation of multiple sluicing and multiple questions in Hungarian

Abstract Hungarian multiple sluicing has been claimed to only be allowed in contexts that set up a pair-list, but not a single-pair reading. This has been taken as evidence that multiple sluicing is derived from multiple wh-fronting questions, which only license pair-list, but not single-pair answers. Providing novel experimental evidence, we show that all three relevant constructions in Hungarian – multiple sluicing, single wh-fronting questions, and multiple wh-fronting questions – in fact pattern alike in their answerhood conditions: both pair-list and single-pair readings are allowed, with a modest preference for single-pair readings. Based on our results, we thus argue that answerhood conditions are not sufficient to determine the source of multiple sluicing.
Tingkat Pencemaran Lindi Pada Air Tanah Dangkal Di Sekitar TPA Kebon Kongok Menggunakan Parameter Fisika dan Kimia

Research has been conducted on the level of pollution caused by Leachate in shallow groundwater around Kebon Kongok landfill, Gerung, West Lombok. This research was conducted considering that water has many benefits in daily life, so it is important to know the quality of the water consumed. The research used experimental methods by analyzing physical parameters (Temperature, conductivity, pH and TDS) and chemical parameters in the form of heavy metal content (Pb, Fe, Cu and Mn). The results of physical parameter tests mostly show that the quality of shallow groundwater is still within the safe quality standard, there is 1 shallow groundwater point that is polluted due to the close proximity to the leachate. Chemical parameters indicate that the content of heavy metals is mostly well below the quality standard, except Cu metal is detected at 0.029 mg / l and Mn metal 1,457 mg / l at a distance of 50 m.KEYWORDS : Leachate; Shallow Water; Physics Parameters; Chemical Parameters