bluuebunny/crossref_metadata_2025 huggingface dataset:
HEAD: High-Speed Approximate HEterogeneous ADder for Error-Resilient Applications
Authors: Guturu Sahith , Anil Kumar Uppugunduru , Verma Shruti , Ahmed Syed Ershad
Published: July 2024

Media processing applications can tolerate error up to a certain limit due to the perceptual limitations of the human eye. Since adders are ubiquitous in power-hungry media processing applications, they can be approximated without much compromise in quality. An adder with better error characteristics without significant compromise in area and power is required to meet the future demands of ever-increasing computational needs. In order to achieve the same, a novel segmentation technique has been proposed. By varying the number of bits in each segment, three different designs are proposed, each with its advantage. Each proposed adder is segmented into three portions, namely Most Significant Portion (MSP), Intermediate Significant Portion (ISP) and Least Significant Portion (LSP). The sub-adder’s sum in each portion is computed concurrently to reduce the critical path delay.

The proposed heterogeneous adder (HEAD) designs achieve better accuracy than the state-of-the-art designs. Synthesis results show that HEAD consumes less area up to 41.8% and consumes less power ranging from 0.8% to 51%, than existing adders. Exhaustive simulations are carried out on benchmarking image sharpening application to prove that the proposed adders obtain a better quality-effort tradeoff than the existing designs.
Efficient Hardware Architecture Design of Radix-22 Fast Fourier Transform Using Coordinate Rotation Digital Computer
Authors: Das Kaushik , Pradhan Sambhu Nath , Bhattacharjee Abhishek
Published: July 2024

The fast Fourier transform (FFT) is a widely used algorithm for computing the discrete Fourier transform (DFT) in real-time signal processing. Achieving high performance with minimal resource usage is crucial for any real-time application. This paper introduces an architecture based on coordinate rotation digital computer (CORDIC) for implementing a radix-[Formula: see text] FFT. Traditional FFT implementations require complex twiddle factor multiplications, which is not cost-effective in terms of hardware utilization. To address this issue, we have proposed a radix-8 CORDIC-based approach integrated with FFT computation. The proposed radix-8 CORDIC requires fewer stages than the conventional radix-2 CORDIC, enhancing computational efficiency and reducing hardware utilization. The feedforward radix-[Formula: see text] FFT architecture is presented where the butterflies of the FFT are computed with the help of radix-8 CORDIC. A total of three FFT architectures are developed supporting 16-, 1024- and 2048-point FFTs. The proposed FFT architectures are developed using Verilog-HDL code and implemented on a Virtex-7 field-programmable gate array (FPGA). The FPGA-based radix-[Formula: see text] FFT achieves a clock frequency of 323.4[Formula: see text]MHz and utilizes minimal FPGA resources. Processing of 2048 data samples takes only 4.06[Formula: see text][Formula: see text]S by the proposed FPGA-based design. Additionally, two ASICs have been designed: the 1024-point FFT ASIC occupies an area of 1.079[Formula: see text]mm2, and the 2048-point FFT ASIC occupies an area of 1.544 mm2. The proposed FFT ASICs have better-normalized area per FFT and energy per FFT than the state-of-the-art designs.
Music Emotion Intensity Estimation Using Transfer Ordinal Label Learning Under Heterogeneous Scenes
Authors: Liu Yiying
Published: July 2024

Music emotion recognition plays an important role in many applications such as music material library construction and music recommendation system. The current music emotion recognition mainly focuses on discrete emotions or continuous emotions under single scene. However, on the one hand, intensity is one of important aspects of emotion, which can be represented as emotion rank or ordinal class. On the other hand, there may be not enough music emotion data in training set, which needs to transfer music emotion recognition model learnt from the music data in a source domain. The distribution of existing music data in target domain may differ from target music dataset. In order to overcome these two issues, this paper proposes to utilize transfer ordinal label learning (TOLL) to estimate music emotion. Compared with the previous works, TOLL-based music emotion intensity estimation implements music intensity estimation through transferring the knowledge in the existing source domain to unknown target domain. The experiments on several datasets show that TOLL can achieve promising results for emotion intensity estimation in single scene or across different scenes.
Path Planning for Mobile Robots Using Transfer Reinforcement Learning
Authors: Zheng Xinwang , Zheng Wenjie , Du Yong , Li Tiejun , Yuan Zhansheng
Published: July 2024

The path planning of mobile robots helps robots to perceive environment using the information obtained from sensors and plan a route to reach the target. With the increasing difficulty of task, the environment the mobile robots face becomes more and more complex. Traditional path planning methods can no longer meet the requirements of mobile robot navigation in complex environment. Deep reinforcement learning (DRL) is introduced into robot navigation However, it may be time-consuming to train DRL model when the environment is very complex and the existing environment may differ from the unknown environment. In order to handle the robot navigation in heterogeneous environment, this paper utilizes deep transfer reinforcement learning (DTRL) for mobile robot path planning. Compared with DRL, DTRL does not require the distribution of the existing environment is the same as that of the unknown environment. Additionally, DTRL can transfer the knowledge of existing model to new scenario to reduce the training time. The simulations show that DTRL can reach higher success rate than DRL for heterogeneous environment robot navigation. By using local policy, it costs less time to train DTRL than DRL for a complex environment and DTRL can consume less navigation time.
A REVIEW ON THE MECHANICAL AND TRIBOLOGICAL PROPERTIES OF THERMALLY SPRAYED HYDROXYAPATITE COATINGS FOR BIOMEDICAL IMPLANT MATERIALS
Authors: BAFILA GAURAV , MEHTA AMRINDER , VASUDEV HITESH
Published: July 2024

In recent years, many different biomedical implants have been created for prolonged usage within the human body. The number of these implants has been steadily expanding. It is a common practice to use metallic bioimplants as substitutes when attempting to replace human body components in an anatomically acceptable manner. But the metallic bioimplant was mostly made biocompatible by putting a biocompatible coating on its surface and then using the right surface modification techniques. This was the primary method by which the biocompatibility of the bioimplant was achieved. Thermal spraying is a method that is frequently employed in surface modification treatments to modify metallic bioimplants. These methods can be found in the medical field. This is due to the adaptability of the thermal spraying method. Because of their capacity to respond and adapt to physiochemical circumstances, coatings that are based on hydroxyapatite (HAp) are the most popular type to be used in thermal spraying. This is due to the fact that HAp is a naturally occurring mineral. On the other hand, the majority of the HAp coatings are unsuccessful as a result of the coating’s weak mechanical qualities and insufficient adhesive strength. Supplying the coating in question with reinforcement in the form of hard particles and doing so in the appropriate proportions makes it feasible to alter the qualities of HAp-based coatings to fulfill a variety of requirements. This can be accomplished by providing the coating with reinforcement. The purpose of this inquiry was to deconstruct and analyze a variety of thermally sprayed (TS) coatings. These coatings were used to adhere HAp-based coatings on bioimplants. In addition, a detailed discussion is offered on how the reinforcements have an effect on the mechanical and biocompatible qualities of the coatings. In addition, we have discussed the difficulties associated with TS HAp coatings and their potential uses in the manufacturing of 3D biomedical implants through the utilization of cold spray (CS).
EFFECT OF SILVER DOPING ON IRON OXIDE ELECTRICAL PROPERTIES
Authors: SARITAŞ SEVDA
Published: July 2024

Iron silver oxide (Agx[Formula: see text]O3; [Formula: see text]) structure for photoelectrochemical (PEC) water splitting was grown by RF–DC co-sputtering of an iron–silver target in argon/oxygen plasma mixtures at 300°C, followed by thermal annealing at 560°C. The chemical composition and structure of the deposited film can be tuned by controlling the metal doping and the annealing temperature. The thermal treatment extensively improves the PEC water-splitting performances of the films. XRD patterns of AgxFe[Formula: see text]O3 shown in the figure can be indexed to the hexagonal structure of silver oxide and the rhombohedral structure of hematite and new X-ray Diffraction (XRD) peaks of AgxFe[Formula: see text]O3 structure. The annealing and doping process seems to cause a serious change in the crystal structure of the thin film, it showed a serious change in its electrical properties. The electrical parameters of resistance for thin films were measured using the Hall measurement method at room temperature. With Hall measurements, the n-type carrier concentration of the Fe2O3 structure was calculated, and it was observed that its resistance was 1[Formula: see text]M[Formula: see text]. Likewise, it was observed that AgxFe[Formula: see text]O3 exhibited an n-type carrier concentration, and its resistance was calculated to be 0.5[Formula: see text]M[Formula: see text]. The conductivity change is based on silver doping. The doping process seems to cause a change in the bandgap of the thin film, it showed a change in its optical properties. The film’s optical absorption was measured by UV–Vis photo spectroscopy. Subsequently, the structural and topographic properties of AgxFe[Formula: see text]O3 structures were investigated by high-precision characterization devices.
EXPERIMENTAL ANALYSIS AND OPTIMIZATION OF PLASMA SPRAY PARAMETERS ON POROSITY AND CORROSION WEAR OF Cr3C2/8YSZ COMPOSITE-COATED AUSTENITIC STAINLESS STEEL
Authors: MAHARAJAN S. , MICHAEL THOMAS REX F. , RAVINDRAN D. , VEERA AJAY C.
Published: July 2024

Plasma spraying is a prospective method frequently employed because of its increased effectiveness in ceramic coatings. Optimizing process parameters is necessary to maximize the coating performance. This study employed response surface methodology (RSM) to investigate the impact of process variables, including current, powder feed rate, and standoff distance, on the porosity and corrosive wear loss of Cr3C2[Formula: see text][Formula: see text][Formula: see text]8YSZ composite coating. Experiments were done using the central composite design method, and quadratic regression models were created for the responses based on the completed trials. All parameters are observed to be most significant as the obtained p-value is under the threshold of 0.05 as per the analysis of variance (ANOVA) calculations. The optimal plasma spray process parameters were determined to be 500 A of current, a powder feed rate of 46[Formula: see text]gm/min, and a standoff distance of 3 inches for the expected lowest corrosive wear loss of 0.000025[Formula: see text]mm/year and a reduced area percentage porosity value of 0.93.
DEVELOPING HIGH-PERFORMANCE LOW-TEMPERATURE CO2 GAS SENSORS BASED ON NANOSTRUCTURED CO3O4 THIN FILMS: A SOL–GEL APPROACH AND THE ROLE OF ANNEALING
Authors: JOSHI GAYATRI , PUROHIT L. P. , SUHAS  , CHAUDHARY MONIKA , PAL PANKAJ K. , DEHGHANI MOHAMMAD HADI
Published: July 2024

In this study, we synthesized Co3O4 thin films using a sol–gel spin coating method and investigated the effect of heat treatment on their carbon dioxide (CO2) gas sensing properties. To optimize their crystallinity and enhance their CO2 sensitivity, the thin films were annealed at temperatures ranging from 400∘C to 550∘C. We characterized the morphological, structural, electrical, and optical properties of the Co3O4 thin films to gain insights into their behavior in the low operating temperature range (OTR). Our X-ray diffraction (XRD) analysis revealed that all the thin films exhibited a cubic structure, with improved crystallinity observed at higher annealing temperatures. We observed a decrease in band edges in the optical transmittance spectra of the thin films as the annealing temperature increased, indicating a redshift in the absorption edge. Notably, the highest electrical conductivity was observed for the sample annealed at 550∘C, suggesting enhanced crystallinity and improved CO2 gas sensitivity. These findings provide valuable insights into the optimization of Co3O4 thin films for CO2 gas sensing applications.
INFLUENCE OF CuO NANOPARTICLES ON THE STRUCTURAL PROPERTIES, SURFACE CHARACTERISTICS, AND OPTICAL BEHAVIOR OF POLY(4-CHLOROANILINE) POLYMERIC FILMS
Authors: ALOTAIBI B. M. , AL-YOUSEF HAIFA A. , ATTA A. , ABDELTWAB E.
Published: July 2024

In this study, the composite P(4ClAni)/CuO, which consists of Poly(4-Chloroaniline) P(4ClAni) and copper oxide (CuO) nanoparticles, was successfully synthesized utilizing a chemically oxidative polymerization approach to be applied in optoelectronics. Both FTIR and EDX analyses showed that CuO has been successfully integrated into the P(4ClAni) matrix. The SEM micrographs reveal uniform loading and distribution of CuO throughout the P(4ClAni) polymeric chains. The UV–Vis absorbance, the Urbach energy, the band edge and a number of carbon clusters were determined. The Tauc relationship was used to determine the band gaps, which revealed a decrease as the CuO concentration increased. The band gap drops from 3.84[Formula: see text]eV for P(4ClAni) to 3.09, 2.85, and 2.64[Formula: see text]eV, correspondingly for P(4ClAni)/CuO-1, P(4ClAni)/CuO-2, and P(4ClAni)/CuO-3. While, the Urbach tail is increased from 1.66[Formula: see text]eV for P(4ClAni) correspondingly to 1.81, 1.85, and 1.93[Formula: see text]eV. The results show composites made of P(4ClAni)/CuO have better optical characteristics than pure polymer P(4ClAni), suggesting that they can use these composites in optoelectronics devices.
Revisiting the Negative Profitability Effect on Capital Structure: Pecking-Order or Trade-Off Hypothesis
Authors: Huang I.-Hsiang , Chien Andy , Lee Liang-Chien , Wu I.-Hui
Published: July 2024

This paper investigates the empirical relationship between profitability and leverage ratios using a sample of Taiwan-listed firms. Like the U.S. evidence, we do find such a negative profitability-leverage relationship for the full sample. Although the profitability effect is still negative for passive firms that do not issue any security, it is positive for firms that issue both equity and debt but do not invest in fixed assets. Furthermore, while the dual issuers without investment adjust their leverages toward their targets the fastest, passive firms do so the slowest. Overall, our results on the profitability effect on leverage are consistent with the dynamic trade-off hypothesis that accounts for costly adjustments.
Implications of Pension and Other Post-Retirement Benefits (OPRB) Recognition Biases on Leverage Ratio Components
Authors: Hertz Sarah , Rozen Hannah , Fried Abraham N.
Published: July 2024

The leverage ratio is a frequently used measure of firm risk, utilized by firms, analysts, and investors. However, pension and other post-retirement benefits (OPRB) are largely ignored in determining a firm’s total liabilities. Similarly, pension and OPRB assets are not accurately incorporated into a firm’s total assets listed on the balance sheets. This is largely due to intricacies in pension fund accounting under Statement of Financial Accounting Standards (SFAS) No. 87 that can cause the true pension and OPRB liabilities and assets to be masked from the financial statements. SFAS No. 158 attempted to solve this problem, but while it has certainly mitigated it, it has not succeeded in mandating that the true asset and liability numbers are incorporated into balance sheet numbers. Furthermore, the rise in life expectancy has also increased the costs and presents a burden on firms’ activities (Taussig, RD (2024). Pension expenses, risk, and implications for stock returns. Finance Research Letters, 61, 105016). In this paper, we find that the measurement error in calculating leverage ratios may be significant if the leverage ratio does not accurately reflect the off-balance sheet items. We propose two new, innovative methods to correct this bias by adjusting “Total Assets” and “Total Liabilities” to properly incorporate pension and OPRB data. Our methods are more accurate since they encompass previously ignored footnote data. The implications are far-reaching, necessitating a closer look at previous research that uses leverage ratios as a factor.
Finite Element Analysis of Smart Magnetoelectric Cobalt Ferrite-Barium Titanate Core-Shell Nanocomposites at Inducing Localized Hyperthermia
Authors: Hossain Shadeeb , Guo Ruyan , Bhalla Amar
Published: July 2024

A feasibility study on cobalt ferrite-barium titanate (CFO-BTO) core-shell nanocomposite as a potential candidate to induce localized hyperthermia is performed through finite element analysis (FEA). The applied field in the radio frequency range (RF) to the simulated nanosized magneto-electric (ME) CFO-BTO core-shell nanocomposites is translated to heat energy due to (i) eddy current loss (ii) hysteresis loss and (iii) relaxation loss. A quantitative estimation of the hysteresis loss and eddy current loss is evaluated. The required temperature for hyperthermia is approximately 315[Formula: see text]K and the Pennes bioheat equation is used to study the rate of change in the temperature of the surrounding dielectric tissue. The FEA is used to study the temperature gradient as a function of time for four cases: (i) no nanoparticles (ii) using CFO-BTO to generate hyperthermia (iii) using cobalt ferrite (CFO) to generate hyperthermia (iv) increasing the concentration of CFO-BTO to generate hyperthermia. It was evident that the rate of temperature increase is higher for CFO-BTO compared to CFO. This can be attributed to the acoustic translation of energy between the magnetostrictive and piezoelectric shells. The results also indicate that increasing the concentration of CFO-BTO allows a faster rate of applied field energy translation to internal energy due to close interparticle interaction.
CREATIVITY IN R&D LABORATORIES: THE ROLE OF TRANSFORMATIONAL LEADERSHIP AND PSYCHOLOGICAL EMPOWERMENT
Authors: PEERZADAH SABZAR AHMAD
Published: July 2024

The evidence of the effect of leadership on creativity of research and development (R&D) employees has been both limited and debated. Some research indicates that leadership is unnecessary in an R&D setting, whereas other studies argue that it remains essential even in such environments. Thus, this study seeks to examine the relationship between transformational leadership and creativity of scientists, as well as the mediating role of psychological empowerment between transformational leadership and scientists’ creativity. The research is based on a sample of 357 scientists employed in various public sector R&D laboratories in India. Structural equation modelling techniques, utilising SmartPLS software, were employed to evaluate the relationships among the different constructs. The results indicate that both transformational leadership and psychological empowerment have a significant positive effect on scientists’ creativity. Scientists’ psychological empowerment has a significant positive impact on their creativity. Furthermore, psychological empowerment was found to mediate the relationship between transformational leadership and scientists’ creativity. This study provides valuable insights for public sector R&D leadership, highlights the role of psychological empowerment, and discusses implications for fostering creativity among R&D scientists.
Sums of singular series along arithmetic progressions and with smooth weights
Authors: Kuperberg Vivian
Published: July 2024

Sums of the singular series constants that appear in the Hardy–Littlewood k-tuples conjectures have long been studied in connection to the distribution of primes. We study constrained sums of singular series, where the sum is taken over sets whose elements are specified modulo r or weighted by smooth functions. We show that the value of the sum is governed by incidences modulo r of elements of the set in the case of arithmetic progressions and by pairings of the smooth functions in the case of weights. These sums shed light on sums of singular series in other formats.
Kepler sets of second-order linear recurrence sequences over Qp
Authors: Kumar Rishi
Published: July 2024

Let [Formula: see text] be a second-order linear recurrence sequence with constant coefficients over the field of p-adic numbers [Formula: see text]. We study the set of limit points of the sequence of consecutive ratios [Formula: see text] in [Formula: see text].
Almost prime triples and Chen’s theorem
Authors: Zhu Li
Published: July 2024

In this paper, we show that there are infinitely many primes p such that [Formula: see text] has at most two prime factors and [Formula: see text] has at most 11 prime factors. This result constitutes an improvement on that of Cai.
On integers of the form p + 2a2 + 2b2
Authors: Xu Ji-Zhen , Chen Yong-Gao
Published: July 2024

In this paper, it is proved that there is a positive proportion of positive odd integers which can be uniquely represented as [Formula: see text], where p is a prime and [Formula: see text] are two positive integers with [Formula: see text].
A smooth version of Landau’s explicit formula
Authors: Balanzario Eugenio P. , Cárdenas-Romero Daniel E. , Chacón-Serna Richar
Published: July 2024

In this paper, we present a smooth version of Landau’s explicit formula for the von Mangoldt arithmetical function. By assuming the validity of the Riemann hypothesis, we show that in order to determine whether a natural number [Formula: see text] is a prime number, it is sufficient to know the location of a number of nontrivial zeros of the Riemann zeta function of order [Formula: see text]. Next we use Heisenberg’s inequality to support the conjecture that this number of zeros cannot be essentially diminished.
Congruences for partial sums of the generating series for 3k k
Authors: Mattarei Sandro , Tauraso Roberto
Published: July 2024

We produce congruences modulo a prime [Formula: see text] for sums [Formula: see text] over ranges [Formula: see text] and [Formula: see text], where q is a power of p. Here x equals either [Formula: see text], or [Formula: see text], where c and s are indeterminates. In the former case, we deal more generally with shifted binomial coefficients [Formula: see text]. Our method derives such congruences directly from closed forms for the corresponding series.
Singmaster-type results for Stirling numbers and some related diophantine equations
Authors: Bazsó András , Mező István , Pintér Ákos , Tengely Szabolcs
Published: July 2024

Motivated by the work of David Singmaster, we study the number of times an integer can appear among the Stirling numbers of both kinds. We provide an upper bound for the occurrences of all the positive integers, and present certain questions for further study. Some numerical results and conjectures concerning the related diohantine equations are collected.
Statistical Evaluation of Cancer Drugs by QSPR Modeling
Authors: Zaman Shahid
Published: July 2024

The prospect of discovering a cure for cancer has been present for the past two to three decades. Annually, this illness affects approximately 10 million individuals worldwide. Anticancer medications are pivotal in treating cancer and other malignant diseases. In this paper, the physical properties and chemical reactions associated with the anticancer medications of various topological indices (TIs) have been established. Additionally, we have discussed the degree-based TIs and their Quantitative Structure-Property Relationship (QSPR) analysis. In mathematical chemistry, molecular descriptors are essential, particularly for researchers investigating Quantitative Structure-Activity Relationship (QSAR) and QSPR models. The goal of the QSPR study is to establish a mathematical relationship between the properties under investigation (such as boiling point and flash point) and various descriptors related to the molecular structure of the drugs. Furthermore, we show the correlation with the physicochemical properties of anticancer medications.
Human-Inspired Meta-Reinforcement Learning Using Bayesian Knowledge and Enhanced Deep Q-Network
Authors: Ho Joshua , Wang Chien-Min , King Chung-Ta , You Yi-Hsin , Feng Chi-Wei
Published: July 2024

Over the last decades, there has been growing interest in research in multiple and interdisciplinary fields of human-AI computing. In particular, approaches integrating human’s perspective and design with reinforcement learning (RL) have received more attention. However, the current research on RL may need to consider its enhancement from human-inspired approaches further. In this work, we focus on enabling a meta-reinforcement learning (meta-RL) agent to achieve adaptation and generalization, according to modeling Markov Decision Processes (MDP) using Bayesian knowledge and analysis. By introducing a novel framework called human-inspired meta-RL (HMRL), we incorporate the agent performing resilient actions to leverage the dynamic dense reward based on the knowledge and prediction of a Bayesian analysis. The proposed framework can make the agent learn generalization and prevent the agent from failing catastrophically. The experimental results show that our approach helps the agent reduce computational costs with learning adaptation. In addition to the system design, we have also extended further algorithmic improvement based on learning within a deep Q-network (DQN) implementations for more complicated future tasks, which compared replay buffers to possibly enhance the optimization process. Finally, we conclude and anticipate that integrating human-inspired meta-RL can enable learning more formulations relating to robustness and scalability, leading to promising directions and more complex AI goals in the future.
Forecasting Stock Market Performance: An Ensemble Learning-Based Approach
Authors: Ramaraju Venkat , Rao Jayanth , Smith James , Bansal Ajay
Published: July 2024

There is tremendous value in the ability to predict stock market trends and outcomes. The public sentiment surrounding a stock is unquestionably a vital factor contributing to the rise or fall of a stock price. This paper aims to detail how data from public sentiment can be integrated into traditional stock analyses and how these analyses can then be used to make predictions of stock price trends. Headlines from seven news publications and conversations from Yahoo! Finance’s conversations forum were processed by the Valence Aware Dictionary and sEntiment Reasoner (VADER) natural language processing package to determine numerical polarities that represent a positive, negative, or neutral public sentiment around a stock ticker. The resulting polarities were paired with popular stock-table metrics to create a dataset for a Logistic Regression machine learning model. The model was trained on approximately 4400 major stocks to determine a binary “Buy” (1) or “Not Buy” (0) recommendation for each stock. We present approaches for a variety of machine learning models – Logistic Regression, Random Forest Classifiers, and Extreme Gradient Boosting models and do a statistical comparison on the balanced accuracies of each machine learning model. This paper aims to detail our understanding of how one can leverage the public sentiment of a stock to gauge its future value.
AI-Based Cropping of Sport Videos Using SmartCrop
Authors: Dorcheh Sayed Mohammad Majidi , Sarkhoosh Mehdi Houshmand , Midoglu Cise , Sabet Saeed S. , Kupka Tomas , Riegler Michael A. , Johansen Dag , Halvorsen Pål
Published: July 2024

In the rapidly evolving landscape of digital platforms, the need for optimizing media representations to cater to various aspect ratios is palpable. In this paper, we pioneer an approach that utilizes object detection, scene detection, outlier detection, and interpolation for smart cropping. Using soccer as a case study, our primary goal is to capture the frame salience using object (player and ball) detection and tracking using AI models. To improve the object detection and tracking, we rely on scene understanding and explore various outlier detection and interpolation techniques. Our pipeline, called SmartCrop, is efficient, and supports various configurations for object tracking, interpolation, and outlier detection to find the best point-of-interest to be used as the cropping center of the video frame. An objective evaluation of the performance of individual pipeline components has validated our proposed architecture. Moreover, a crowdsourced subjective user study, assessing the alternative approaches for cropping from 16:9 to 1:1 and 9:16 aspect ratios, confirms that our proposed approach increases the end-user quality of experience.
Optimizing Mobile Reading Experience: Adaptive Font Size Adjustment in a Context-Aware Application
Authors: Mallas Andreas , Papadatou Hara , Xenos Michalis
Published: July 2024

The reading experience on a mobile device can be influenced by many external elements that affect the viewing conditions. The user holding position, operating style, and the light conditions of the surroundings can all play a crucial factor in the legibility of the text. Furthermore, the user’s eye conditions may be an extra factor to consider when determining how to improve the reading experience. This study presents a context-aware application that adjusts the text font size based on the distance between the user and the device. The users select their preferred font size at certain distances in the calibration phase, accounting for individual preferences or their eye conditions, and the application uses the device’s front camera to calculate the distance in real time and adjust the font size accordingly. We present implementation details and challenges faced during development and how they were addressed. A two-part experiment ([Formula: see text] = 34) revealed that the application scored an excellent usability score while no differences were found between users with and without eye conditions, showing that both user groups find it equally usable. Additionally, initial results suggest that the visual landmarks used showed a promising start at addressing issues related to our approach.
Carrying Capacity of Water Environment and the Associated Optimization of Socioeconomic Structure of Yangzong Lake Basin, China
Authors: Wang Wanbin , Liu Yan , Jia Haifeng , Guan Tangzhen , Zhang Xingzi , Liu Yuexiong , Li Sen
Published: July 2024

Yangzong Lake is the third largest deep-water plateau lake in Yunnan, China. Rapid socioeconomic development of the basin has resulted in pollution of water environment. This study aimed to identify the carrying capacity of water environment and to conduct the associated optimization of socioeconomic development. The results of this study showed that: (1) The carrying capacities of tributary rivers were small in relation to that of the lake. The maximum allowable annual loads of chemical oxygen demand (COD), ammonia nitrogen (NH3-N), and total phosphorus (TP) into the main rivers of the basin were 449.37, 24.73, and 5.38 tons, respectively. (2) Under the planning scenario, COD and NH3-N loads remain the primary constraints on economic and social development in the watershed. (3) In further optimizing the structure of the plantation industry, focusing on strengthening the collection and treatment capacity of rural domestic sewage, the watershed can achieve high-quality development of the tourism industry. The tourism output value in 2025 and 2035 can carry up to 20.28 billion CNY (growth rate: around 15.44%) and 76.80 billion CNY (growth rate: around 14.64%), respectively. This study can provide basic support for the future economic and social development of the watershed for decision-makers.
Study on Cavitation Noise Monitoring and Characteristics Based on Venturi Bench Test
Authors: Wang Xiaoyu , Sun Qiang , Wang Tian , Ma Junqi , Lv Wanyu
Published: July 2024

Cavitation phenomena are accompanied by harsh cavitation noise. Studying cavitation noise is an effective method to monitor cavitation conditions. In order to study the characteristics of cavitation noise, a Venturi test bench experiment platform is built, the cavitation flow field and cavitation noise of Venturi test bench are simulated. The frequency domain analysis of cavitation noise is carried out, and the relationship between cavitation region, flow rate and sound pressure level of cavitation noise is verified by comparison between simulation and experiment. Because the cavitation noise is easily polluted by ambient noise, wavelet transform is used to analyze the cavitation noise and background noise, and the characteristics of cavitation noise are studied by comparing the difference between them.
Crossing Borders Between Languages: The Great Transition of Hindi Literary Translation with Special Reference to Geetanjali Shree’s Tomb of Sand
Authors: LEE DONGWON
Published: July 2024

The surging number of Hindi speakers in recent decades raises an intriguing question: does the expanding influence of Hindi across India lead to the commensurate pan-Indianization or globalization of its literature? Despite its widespread use within the country, Hindi literature often remains overshadowed by Indian English literature (IEL), particularly on the global stage. It appears to be somewhat confined within regional boundaries. However, a transformative event occurred in 2022 when Geetanjali Shree, a Delhi-based Hindi novelist, clinched the prestigious International Booker Prize for her work, Tomb of Sand, originally titled Ret Samadhi in Hindi. The prize was equally shared between Geetanjali Shree, the writer, and Daisy Rockwell, the translator. With this award, Tomb of Sand has emerged as a symbol of Hindi literary translation, heralding a significant transition in the Hindi literary translation scene. This paper aims to explore the challenges and prospects of Hindi literary translation in the era of the great transition, focusing specifically on the translation methods and strategies employed in Tomb of Sand. Through this analysis, I intend to contribute to the ongoing scholarly discourse concerning the promotion and appreciation of Hindi literature in international literary circles.
Great Transition in India’s Regional Cooperation: Beyond the Neighboring Countries
Authors: KIM MISU
Published: July 2024

India’s regional cooperation in neighboring South Asia and the broader Indo-Pacific region is undergoing a transformative evolution driven by geopolitical, economic, and strategic dynamics. From the stagnation of traditional regional cooperation frameworks like SAARC to the emergence of sub-regional initiatives and strategic partnerships, the region is witnessing a concerted effort to foster peace, prosperity, and security. India, as a key player in the region, has been actively engaging in various multilateral platforms such as BIMSTEC, SCO, and IORA to advance its strategic objectives while addressing shared concerns like terrorism and energy security. Initiatives like the BBIN Motor Vehicles Agreement and SASEC demonstrate India’s commitment to deepening sub-regional integration and unlocking economic potential. However, challenges such as geopolitical rivalries, exclusionary practices, and resource constraints continue to hinder the realization of the full potential of regional cooperation initiatives. Policy recommendations include strengthening sub-regional mechanisms and enhancing connectivity and trade facilitation. By embracing these recommendations, India’s regional cooperation in neighboring South Asia and the broader Indo-Pacific region can work toward building a more prosperous, stable, and interconnected future.
EXAMINING CRITICAL FACTORS INFLUENCING GENERATION Z’S ACCEPTANCE OF MOBILE PAYMENT SYSTEMS IN BANGLADESH: A UTAUT MODEL ANALYSIS
Authors: SALAM MUHAMMAD ABDUS , RAYUN S. M. NAFIZ , LEONG VAI SHIEM
Published: July 2024

Mobile payment systems have become an integral part of the digital ecosystem, simplifying financial transactions among individuals, businesses, and even the government in some cases (C2G, G2C) using handheld mobile gadgets. The current academic literature concentrates on the acceptance of mobile payment systems (MPS), including generational diversity, emphasizing the necessity for more concentration on the factors behind MPS adoption trends based on specific economic aspects concerning marketers’, marketing professionals', and practitioners' interests. Therefore, this study synthesizes the existing insights to address the current knowledge gap regarding MPS adoption among Generation Z citizens in developing economies, particularly focusing on Bangladesh. However, grounded in the UTAUT model, this study utilizes structural equation modeling (SEM). IBM SPSS 27 and SmartPLS 3.2.9 have been used for data analysis. The results of this study reveal that Gen Z citizens have positive perspectives toward MPS, and the important factors affecting their acceptance are effort expectancy, enjoyment, performance expectancy, and trust. Finally, the study offers significant insights for further studies in terms of empirical and theoretical contributions.
Natural Is the Best: Model-Agnostic Code Simplification for Pre-trained Large Language Models
Authors: Wang Yan , Li Xiaoning , Nguyen Tien N. , Wang Shaohua , Ni Chao , Ding Ling
Published: July 2024

Pre-trained Large Language Models (LLM) have achieved remarkable successes in several domains. However, code-oriented LLMs are often heavy in computational complexity, and quadratically with the length of the input code sequence. Toward simplifying the input program of an LLM, the state-of-the-art approach has the strategies to filter the input code tokens based on the attention scores given by the LLM. The decision to simplify the input program should not rely on the attention patterns of an LLM, as these patterns are influenced by both the model architecture and the pre-training dataset. Since the model and dataset are part of the solution domain, not the problem domain where the input program belongs, the outcome may differ when the model is pre-trained on a different dataset. We propose SlimCode, a model-agnostic code simplification solution for LLMs that depends on the nature of input code tokens. As an empirical study on the LLMs including CodeBERT, CodeT5, and GPT-4 for two main tasks: code search and summarization, we reported that 1) the removal ratio of code has a linear-like relation with the saving ratio on training time, 2) the impact of categorized tokens on code simplification can vary significantly, 3) the impact of categorized tokens on code simplification is task-specific but model-agnostic, and 4) the above findings hold for the paradigm–prompt engineering and interactive in-context learning. The empirical results showed that SlimCode can improve the state-of-the-art technique by 9.46% and 5.15% in terms of MRR and BLEU score on code search and summarization, respectively. More importantly, SlimCode is 133 times faster than the state-of-the-art approach. Additionally, SlimCode can reduce the cost of invoking GPT-4 by up to 24% per API query, while still producing comparable results to those with the original code. With this result, we call for a new direction on code-based, model-agnostic code simplification solutions to further empower LLMs.
Go Static: Contextualized Logging Statement Generation
Authors: Li Yichen , Huo Yintong , Zhong Renyi , Jiang Zhihan , Liu Jinyang , Huang Junjie , Gu Jiazhen , He Pinjia , Lyu Michael R.
Published: July 2024

Logging practices have been extensively investigated to assist developers in writing appropriate logging statements for documenting software behaviors. Although numerous automatic logging approaches have been proposed, their performance remains unsatisfactory due to the constraint of the single-method input, without informative programming context outside the method. Specifically, we identify three inherent limitations with single-method context: limited static scope of logging statements, inconsistent logging styles, and missing type information of logging variables.

To tackle these limitations, we propose SCLogger, the first contextualized logging statement generation approach with inter-method static contexts.First, SCLogger extracts inter-method contexts with static analysis to construct the contextualized prompt for language models to generate a tentative logging statement. The contextualized prompt consists of an extended static scope and sampled similar methods, ordered by the chain-of-thought (COT) strategy. Second, SCLogger refines the access of logging variables by formulating a new refinement prompt for language models, which incorporates detailed type information of variables in the tentative logging statement.

The evaluation results show that SCLogger surpasses the state-of-the-art approach by 8.7% in logging position accuracy, 32.1% in level accuracy, 19.6% in variable precision, and 138.4% in text BLEU-4 score. Furthermore, SCLogger consistently boosts the performance of logging statement generation across a range of large language models, thereby showcasing the generalizability of this approach.
RavenBuild: Context, Relevance, and Dependency Aware Build Outcome Prediction
Authors: Sun Gengyi , Habchi Sarra , McIntosh Shane
Published: July 2024

Continuous Integration (CI) is a common practice adopted by modern software organizations. It plays an especially important role for large corporations like Ubisoft, where thousands of build jobs are submitted daily. Indeed, the cadence of development progress is constrained by the pace at which CI services process build jobs. To provide faster CI feedback, recent work explores how build outcomes can be anticipated. Although early results show plenty of promise, the distinct characteristics of Project X—a AAA video game project at Ubisoft, present new challenges for build outcome prediction. In the Project X setting, changes that do not modify source code also incur build failures. Moreover, we find that the code changes that have an impact that crosses the source-data boundary are more prone to build failures than code changes that do not impact data files. Since such changes are not fully characterized by the existing set of build outcome prediction features, state-of-the art models tend to underperform.

Therefore, to accommodate the data context into build outcome prediction, we propose RavenBuild, a novel approach that leverages context, relevance, and dependency-aware features. We apply the state of-the-art BuildFast model and RavenBuild to Project X, and observe that RavenBuild improves the F1 score of the failing class by 50%, the recall of the failing class by 105%, and AUC by 11%. To ease adoption in settings with heterogeneous project sets, we also provide a simplified alternative RavenBuild-CR, which excludes dependency-aware features. We apply RavenBuild-CR on 22 open-source projects and Project X, and observe across-the-board improvements as well. On the other hand, we find that a naïve Parrot approach, which simply echoes the previous build outcome as its prediction, is surprisingly competitive with BuildFast and RavenBuild. Though Parrot fails to predict when the build outcome differs from their immediate predecessor, Parrot serves well as a tendency indicator of the sequences in build outcome datasets. Therefore, future studies should also consider comparing to the Parrot approach as a baseline when evaluating build outcome prediction models.
Towards Efficient Verification of Constant-Time Cryptographic Implementations
Authors: Cai Luwei , Song Fu , Chen Taolue
Published: July 2024

Timing side-channel attacks exploit secret-dependent execution time to fully or partially recover secrets of cryptographic implementations, posing a severe threat to software security. Constant-time programming discipline is an effective software-based countermeasure against timing side-channel attacks, but developing constant-time implementations turns out to be challenging and error-prone. Current verification approaches/tools suffer from scalability and precision issues when applied to production software in practice. In this paper, we put forward practical verification approaches based on a novel synergy of taint analysis and safety verification of self-composed programs. Specifically, we first use an IFDS-based lightweight taint analysis to prove that a large number of potential (timing) side-channel sources do not actually leak secrets. We then resort to a precise taint analysis and a safety verification approach to determine whether the remaining potential side-channel sources can actually leak secrets. These include novel constructions of taint-directed semi-cross-product of the original program and its Boolean abstraction, and a taint-directed self-composition

of the program. Our approach is implemented as a cross-platform and fully automated tool CT-Prover. The experiments confirm its efficiency and effectiveness in verifying real-world benchmarks from modern cryptographic and SSL/TLS libraries. In particular, CT-Prover identify new, confirmed vulnerabilities of open-source SSL libraries (e.g., Mbed SSL, BearSSL) and significantly outperforms the state-of-the-art tools.
Generative AI for Pull Request Descriptions: Adoption, Impact, and Developer Interventions
Authors: Xiao Tao , Hata Hideaki , Treude Christoph , Matsumoto Kenichi
Published: July 2024

GitHub's Copilot for Pull Requests (PRs) is a promising service aiming to automate various developer tasks related to PRs, such as generating summaries of changes or providing complete walkthroughs with links to the relevant code. As this innovative technology gains traction in the Open Source Software (OSS) community, it is crucial to examine its early adoption and its impact on the development process. Additionally, it offers a unique opportunity to observe how developers respond when they disagree with the generated content. In our study, we employ a mixed-methods approach, blending quantitative analysis with qualitative insights, to examine 18,256 PRs in which parts of the descriptions were crafted by generative AI. Our findings indicate that: (1) Copilot for PRs, though in its infancy, is seeing a marked uptick in adoption. (2) PRs enhanced by Copilot for PRs require less review time and have a higher likelihood of being merged. (3) Developers using Copilot for PRs often complement the automated descriptions with their manual input. These results offer valuable insights into the growing integration of generative AI in software development.