Miracle-dz/UniFAR-FTUs huggingface dataset:
Listening Effort in School-Aged Children With Normal Hearing Compared to Children With Limited Useable Hearing Unilaterally

Objectives: Children with limited hearing unilaterally might experience more listening effort than children with normal hearing, yet previous studies have not confirmed this. This study compared listening effort in school-aged children with normal hearing and limited hearing unilaterally using behavioral and subjective listening effort measures. Design: Two groups of school-aged children (aged 7-12 years) participated: 19 with limited hearing unilaterally and 18 with normal hearing bilaterally. Participants completed digit triplet recognition tasks in quiet and in noise (-12 dB signal-to-noise ratio) in three loudspeaker conditions: midline, direct, and indirect. Verbal response times during the recognition task were interpreted as behavioral listening effort. Subjective ratings of “task difficulty” and “hard to think” were interpreted as subjective listening effort. Participant age was included as a covariate in analysis of behavioral data. Results: Noise negatively affected digit triplet recognition for both groups in the midline loudspeaker, and for participants with limited hearing unilaterally in the direct and indirect conditions. Relative to their peers with normal hearing, children with limited hearing unilaterally exhibited significantly longer response times and higher ratings of effort only in the noisy indirect condition. Differences between groups were evident even when age differences were controlled for statistically. Conclusions: Using behavioral and subjective indices of listening effort, children with limited unilateral hearing demonstrated significantly more listening effort relative to their peers with normal hearing during the difficult indirect listening condition. Implications include classroom accommodations to limit indirect listening situations for children with limited useable hearing unilaterally and consideration of intervention options.
Sustainable Consumption Scales: Measuring Young Customers’ Sustainable Food Consumption Behaviour

: Changing the sustainable consumption patterns of food are in the focus of sustainable consumption goals. Changing the attitudes, purchasing intentions and purchasing habits are vital for making changes. Young customers have specific awareness, and consumption motivations, therefore specific measurement scales are needed for measuring their sustainable food consumption behaviour. The research objective was to collect and categorise the sustainable consumption measurement scales which are related to sustainable food consumption. The validated measurement scales for sustainable food consumption of young customers are specified to certain areas of food consumption and have cultural specifications. There seems to be a lack of validated measurement scales for eastern European and Hungarian customers. The findings of the research can serve as a basis for further scale development for Hungarian young customers.
The Mediating Effect of Self-control Between College Students' Social Exclusion and Fear of Missing Out

Objective: To examine the effects of college students' social exclusion on fear of missing out and the mediating role of self-control between college students' social exclusion and fear of missing out. Methods: A total of 407 university students (53.8%male,46.2% female; M age = 20.04, SD age = 1.56) were administered with the Social Exclusion Questionnaire, the Fear of Missing Out Scale and the Self-Control Scale(SCS). Results: (1) College students' social exclusion had a significant positive correlation with fear of missing out and a significant negative correlation with self-control. Self-control could negatively predict fear of missing out significantly;(2) Self-control mediated the relationship between college students' social exclusion and fear of missing out. Conclu-sion: College students' social exclusion positively predicted the level of fear of missing out, and self-control mediated the relationship between social exclusion and fear of missing out.
Music Transformer 기반 음악

: In this paper, we propose a new model for the conditional generation of music, considering key and rhythm, fundamental elements of music. MIDI sheet music is converted into a WAV format, which is then transformed into a Mel Spectrogram using the Short-Time Fourier Transform (STFT). Using this information, key and rhythm details are classified by passing through two Convolutional Neural Networks (CNNs), and this information is again fed into the Music Transformer. The key and rhythm details are combined by differentially multiplying the weights and the embedding vectors of the MIDI events. Several experiments are conducted, including a process for determining the optimal weights. This research represents a new effort to integrate essential elements into music generation and explains the detailed structure and operating principles of the model, verifying its effects and potentials through experiments. In this study, the accuracy for rhythm classification reached 94.7%, the accuracy for key classification reached 92.1%, and the Negative Likelihood based on the weights of the embedding vector resulted in 3.01.
Monetary Policy Options in the Context of Secular Stagnation and Financial Uncertainty in Nigeria

This paper examined the phenomenon of secular stagnation in the face of financial uncertainty, with a view to determining the most appropriate monetary policy instrument(s) that could be employed to quicken the pace of output gap recovery in Nigeria. A threshold regime-switching vector autoregressive (VAR) technique was applied on quarterly data for the 2000Q1-2018Q4 period. The response of the output gap to shocks to the various monetary policy variables and the relative contribution of each of the policy variables to output gap recovery, were obtained for financial uncertainty regime and the “normal times” regime. The findings revealed that exchange rate policies were the most effective in engendering output growth during the financial uncertainty regime. In contrast, interest rate policies were less effective in stimulating output growth under conditions of financial uncertainty compared with “normal times”. The paper highlights the need for further research on the relative effectiveness of conventional and unconventional monetary policy in boosting the recovery of output growth in Nigeria, given the notion that the use of unconventional monetary policy to stimulate output growth during a crisis could be more effective.
Disentangling Meaning and Style for Positive Text Reframing

Positive Text Reframing (PTR), as the new branch of Text Style Transfer (TST) task has attracted interest from researchers as it is crucial and extensively applicable in the NLP area. Due to the signiﬁcant generalization and representation capability of the Transformer based pre-trained language model (PLM), a beneﬁcial baseline can be easily obtained by just ﬁne-tuning the PLM on the annotated dataset directly. However, it is a challenging problem to transfer the sentiment attributes of the source sentence into a sentence that gives a positive perspective while preserving the original sense of the context. In this paper, we disentangle positive text reframing into aspects: a sentence meaning and style and learn a model for each aspect, i.e., paraphrase generation and sentiment transfer to boost the generation performance on a positive perspective. Experimental results on Positive Psychology Frames (PPF), show that our approach outperforms the baseline by seven evaluation metrics.
Journal of Network and Computer Applications

In recent years, the adoption of the service mesh as a dedicated infrastructure layer to support cloud-native systems has gained significant popularity. Service meshes involve the incorporation of proxies to handle communication between microservices, thereby speeding up the development and deployment of microservice applications. However, the use of service meshes also increases the request latency because they elongate the packet transmission between services. After investigating the transmission path of packets in a representative service mesh Istio, we observed that the service mesh dedicates approximately 25% of its time to packet transmission in the Linux kernel network stack. To shorten this process, we propose a non-intrusive solution that enables packets to bypass the kernel network stack through the implementation of socket redirection and tc (traffic control) redirection with eBPF (extended Berkeley Packet Filter). We also conduct comprehensive experiments on the widely-used Istio. The evaluation results show that our approach can significantly reduce the request latency by up to 21%. Furthermore, our approach decreases CPU usage by 1.73% and reduces memory consumption by approximately 0.98% when compared to the original service mesh implementation.
PEAR: a fast and accurate Illumina Paired-End reAd mergeR

Motivation: The Illumina paired-end sequencing technology can generate reads from both ends of target DNA fragments, which can subsequently be merged to increase the overall read length. There already exist tools for merging these paired-end reads when the target fragments are equally long. However, when fragment lengths vary and, in particular, when either the fragment size is shorter than a single-end read, or longer than twice the size of a single-end read, most state-of-the-art mergers fail to generate reliable results. Therefore, a robust tool is needed to merge paired-end reads that exhibit varying overlap lengths because of varying target fragment lengths. Results: We present the PEAR software for merging raw Illumina paired-end reads from target fragments of varying length. The program evaluates all possible paired-end read overlaps and does not require the target fragment size as input. It also implements a statistical test for minimizing false-positive results. Tests on simulated and empirical data show that PEAR consistently generates highly accurate merged paired-end reads. A highly optimized implementation allows for merging millions of paired-end reads within a few minutes on a standard desktop computer. On multi-core architectures, the parallel version of PEAR shows linear speedups compared with the sequential version of PEAR. Availability and implementation: PEAR is implemented in C and uses POSIX threads. It is freely available at http://www.exelixis-lab.org/web/software/pear. Contact: Tomas.Flouri@h-its.org
Genmapp 2: New Features and Resources for Pathway Analysis Repository Citation Genmapp 2: New Features and Resources for Pathway Analysis

Background: Microarray technologies have evolved rapidly, enabling biologists to quantify genome-wide levels of gene expression, alternative splicing, and sequence variations for a variety of species. Analyzing and displaying these data present a significant challenge. Pathway-based approaches for analyzing microarray data have proven useful for presenting data and for generating testable hypotheses.
NIGERIA’S ECONOMIC GROWTH AND DIVERSIFICATION: An Appraisal of the Economic Recovery and Growth Plan and Stakeholders' Perspectives

In the quest to enhance economic recovery and sustained growth economic diversification in Nigeria, the medium-term plan, the Economic Recovery and Growth Plan (ERGP), for the 2017 to 2020 period was launched. Using an analytical and survey research design methodology, this study is a mid-term assessment of the performance of the ERGP, covering the first two years of implementation in terms of its projections for key economic indicators as well as the perspectives of relevant stakeholders such as market women, traditional institutions, youths, and public servants, among others. First, the study evaluates the validity of the projections of the ERGP, benchmarked against actual official data over the relevant period; and second, assessed the performance of the Plan at the project and community level through a survey of stakeholder perspectives in Lagos State, largely regarded as the country’s commercial capital as well as a melting pot of all
Conditional CAPM and Cross sectional Returns - A Study of Indian Securities Market

Inability of Fama and Macbeth (1973) two-pass regression procedure to accommodate time varying moments is offered as one of the reasons for the poor cross sectional explanatory power and the anomalies found in Capital Asset Pricing Model (CAPM). We allow the first and second moments of the stock returns to change over time and test three variants of conditional CAPM for value weighted size-based portfolios constructed to test the validity of the same and to capture the 'size effect'. Given the non-normality and temporal dependence in the data, Hansen's Generalized Method of Moments (GMM) has been used for the robust estimation of the parameters. We find that small stock portfolios behave differently from the larger portfolios in terms of their betas and price of covariance risk. While the time variation in beta is significant for the larger stocks, it is not so in the case of smaller stocks. Also the price of covariance risk is found to be inversely related to the size of the portfolios. Given the smaller sample size and highly nonlinear restrictions of the models, size and statistical power of the models are presented.
CO2 Emissions and Health Expenditures: The Case of EU

: The link between greenhouse gases and climate change has caused the environment to become more important within the scope of national and global health policies. For this reason, the effects of deterioration in environmental quality on health have been intensively investigated in recent years. One of the important greenhouse gases that harm human health and environmental quality is CO2 emissions. In this context, the effect of carbon emissions on health expenditures in the period of 2001-2019 for 26 EU countries was examined by panel data analysis. In order to empirically test the subject mentioned above, the dependency of health expenditures; An econometric model has been established in which carbon emissions and economic growth are independent variables. In the findings, it was determined that carbon emission and economic growth variables had a positive effect on health expenditures. In other words, as carbon emissions increase, health expenditures increase, and as carbon emissions decrease, health expenditures decrease.
Solar Radiation Prediction in the UTEQ based on Machine Learning Models

This research explores the effectiveness of various Machine Learning (ML) models used to predicting solar radiation at the Central Campus of the State Technical University of Quevedo (UTEQ). The data was obtained from a pyranometer, strategically located in a high area of the campus. This instrument continuously recorded solar irradiance data since 2020, offering a comprehensive dataset encompassing various weather conditions and temporal variations. After a correlation analysis, temperature and the time of day were identified as the relevant meteorological variables that influenced the solar irradiance. Different machine learning algorithms such as Linear Regression, K-Nearest Neighbors, Decision Tree, and Gradient Boosting were compared using the evaluation metrics Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and the Coefficient of Determination ($R^2$). The study revealed that Gradient Boosting Regressor exhibited superior performance, closely followed by the Random Forest Regressor. These models effectively captured the non-linear patterns in solar radiation, as evidenced by their low MSE and high $R^2$ values. With the aim of assess the performance of our ML models, we developed a web-based tool for the Solar Radiation Forecasting in the UTEQ available at http://https://solarradiationforecastinguteq.streamlit. app/. The results obtained demonstrate the effectiveness of our ML models in solar radiation prediction and contribute a practical utility in real-time solar radiation forecasting, aiding in efficient solar energy management.
Quantum measure as a necessary ingredient in quantum gravity and modified gravities

We suggest commutation relations for a quantum measure. In one version of these relations, the right-hand side takes account of the presence of curvature of space; in the simplest case, this yields the action of general relativity. We consider the cases of the quantization of the measure on spaces of constant curvature and show that in this case the commutation relations for the quantum measure are analogues of commutation relations in loop quantum gravity. It is assumed that, in contrast to loop quantum gravity, a triangulation of space is a necessary trick for quantizing such a nonlocal quantity like a measure; in doing so, the space remains a smooth manifold. We consider the self-consistent problem of the interaction of the quantum measure and classical gravitation. It is shown that this inevitably leads to the appearance of modified gravities. Also, we consider the problem of defining the Euler-Lagrange equations for a matter field in the background of a space endowed with quantum measure.
Principled Gradient-based Markov Chain Monte Carlo for Text Generation

Recent papers have demonstrated the possibility of energy-based text generation by adapting gradient-based sampling algorithms, a paradigm of MCMC algorithms that promises fast convergence. However, as we show in this paper, previous attempts on this approach to text generation all fail to sample correctly from the target language model distributions. To address this limitation, we consider the problem of designing text samplers that are faithful, meaning that they have the target text distribution as its limiting distribution. We propose several faithful gradient-based sampling algorithms to sample from the target energy-based text distribution correctly, and study their theoretical properties. Through experiments on various forms of text generation, we demonstrate that faithful samplers are able to generate more fluent text while adhering to the control objectives better.
Integer-Based Pattern Synthesis for Asymmetric Multi-Reflection RIS

This study delves into the radiation pattern synthesis of reconfigurable intelligent surfaces (RIS) / reflection metasurfaces. Through superimposing multiple single-reflection profiles, which comprise the amplitude and/or phase settings of all constituent elements, a single incident wave can be effectively reflected in multiple asymmetric directions. However, some mismatch and interference between adjacent reflection beams may be caused by this superposition as well. Additionally, it is constrained by the inherent limitation that achieving linear and continuous amplitude adjustments and phase shifts in real-world designs is challenging. Consequently, the reconfigurable amplitude and phase must be approximated to discrete values, necessitating the arrangement of reflection profile before and after optimization based on integer. Therefore, in this paper, we adapt the traditional particle swarm optimization (PSO) algorithm to discretized integer-based PSO by proposing the concepts of 'discard rate' and 'knowledge. ' With the enhancement of the integer-based programming, the multiple asymmetric reflection pattern can be synthesized with suppressed sidelobe levels within limited iterations and time cost.
Can you See me? On the Visibility of NOPs against Android Malware Detectors

Android malware still represents the most significant threat to mobile systems. While Machine Learning systems are increasingly used to identify these threats, past studies have revealed that attackers can bypass these detection mechanisms by making subtle changes to Android applications, such as adding specific API calls. These modifications are often referred to as No OPerations (NOP), which ideally should not alter the semantics of the program. However, many NOPs can be spotted and eliminated by refining the app analysis process. This paper proposes a visibility metric that assesses the difficulty in spotting NOPs and similar non-operational codes. We tested our metric on a state-of-the-art, opcode-based deep learning system for Android malware detection. We implemented attacks on the feature and problem spaces and calculated their visibility according to our metric. The attained results show an intriguing trade-off between evasion efficacy and detectability: our metric can be valuable to ensure the real effectiveness of an adversarial attack, also serving as a useful aid to develop better defenses.
Interpretations of syntactic fragments of theories of fields

We set up general machinery to study interpretations of fragments of theories. We then apply this to existential fragments of theories of fields, and especially of henselian valued fields. As an application we prove many-one reductions between various existential theories of fields. In particular we exhibit several theories of fields many-one equivalent to the existential theory of $\mathbb{Q}$.
On the variability of squeezing in tunneling

The intensity of rock deformations and rock pressures in a tunnel section where there are squeezing conditions may vary over short distances. The variability of squeezing can be traced back to heterogeneities of the ground at different scales and with respect both to its mechanical and to its hydraulic characteristics. Often the cause of this phenomenon is an advance through a sequence of rock zones with different degrees of crushing or shearing. The results of numerical calculations indicate that even relatively thin competent rock interlayers may have a pronounced stabilizing effect. However, even in a macroscopically homogeneous rock mass, a large variation of deformations may be observed. This can be explained theoretically by the fact that the results of ground response analyses are highly sensitive to minor changes in rock properties.
AIJack: Security and Privacy Risk Simulator for Machine Learning

This paper introduces AIJack, an open-source library designed to assess security and privacy risks associated with the training and deployment of machine learning models. Amid the growing interest in big data and AI, advancements in machine learning research and business are accelerating. However, recent studies reveal potential threats, such as the theft of training data and the manipulation of models by malicious attackers. Therefore, a comprehensive understanding of machine learning's security and privacy vulnerabilities is crucial for the safe integration of machine learning into real-world products. AIJack aims to address this need by providing a library with various attack and defense methods through a unified API. The library is publicly available on GitHub (https://github.com/Koukyosyumei/AIJack).
Factoring sparse polynomials fast

Consider a sparse polynomial in several variables given explicitly as a sum of non-zero terms with coefficients in an effective field. In this paper, we present several algorithms for factoring such polynomials and related tasks (such as gcd computation, square-free factorization, content-free factorization, and root extraction). Our methods are all based on sparse interpolation, but follow two main lines of attack: iteration on the number of variables and more direct reductions to the univariate or bivariate case. We present detailed probabilistic complexity bounds in terms of the complexity of sparse interpolation and evaluation.
Ground‐Based LiDAR

This entry provides a brief overview of ground-based LiDAR. Two different working modes of ground-based LiDAR, stationary scanning and dynamic scanning, are discussed. System components and applications of ground-based LiDAR are presented. Emphasis is placed on mobile LiDAR for large-scale applications, such as road infrastructure mapping and monitoring as well as urban modeling. Keywords: ground-based LiDAR; mobile LiDAR; terrestrial laser scanning