Miracle-dz/UniFAR-FTUs huggingface dataset:
AGD: A Learning-based Optimization Framework for EDA and its Application to Gate Sizing

In electronic design automation (EDA), most simulation models are not differentiable, and many design choices are discrete. As a result, greedy optimization methods based on numerical gradients have been used widely, although it suffers from suboptimal solutions. On the other hand, analytic methods may offer better solutions, but at the cost of enormous research efforts. Reinforcement learning (RL) has been leveraged to tackle this problem owing to its generality; however, RL also suffers from notorious sample inefficiency, which is exaggerated in EDA because data sampling in EDA is very expensive due to slow simulations. This paper proposes an alternative to RL for EDA, namely analytic gradient descent (AGD). Our method calculates analytic gradients of a design objective with respect to continuous and discrete design choices through a neural network learned by a simulation model. Then it performs a gradient descent procedure optimizing the design objective directly. We demonstrate AGD on the well-known gate sizing problem and show that our method can be very close to an industry-leading commercial tool in terms of design quality of result (QoR), while it only takes several person-months in comparison to dedicated efforts of human engineering over decades to develop. In addition, we also show that AGD can generalize to unseen circuits, with less training specific in a small amount of execution time.
A hybrid approach of knowledge-driven and data-driven reasoning for activity recognition in smart homes

Accurate activity recognition plays a major role in smart homes to provide assistance and support for users, especially elderly and cognitively impaired people. To realize this task, knowledge-driven approaches are one of the emerging research areas that have shown interesting advantages and features. However, several limitations have been associated with these approaches. The produced models are usually incomplete to capture all types of human activities. This resulted in the limited ability to accurately infer users’ activities. This paper presents an alternative approach by combining knowledge-driven with data-driven reasoning to allow activity models to evolve and adapt automatically based on users’ particularities. Firstly, a knowledge-driven reasoning is presented for inferring an initial activity model. The model is then trained using data-driven techniques to produce a dynamic activity model that learns users’ varying action. This approach has been evaluated using a publicly available dataset and the experimental results show the learned activity model yields significantly higher recognition rates compared to the initial activity model.
Cheating immune multi-secret sharing without predefined order of secrets

ABSTRACT A -threshold secret sharing scheme divides a secret into pieces and distributes them to players privately, in such a way that any or more players who collect their pieces can reconstruct the secret correctly, while any players know no information about the secret. To improve efficiency, a multi-secret sharing scheme is proposed in which multiple secrets are shared at one time. In this paper, we propose a new multi-secret sharing scheme based on hash function. We combine the mathematical ‘shift values’ proposed by He and Dawson in 1994 with asymmetric bivariate polynomials to construct pseudo shares. Our scheme has the following three extended properties: cheaters are detected thanks to the hash function, each share is used many times in secret reconstruction, and the order of recovery of secrets is not predefined. Furthermore, a secure channel between dealer and players is unnecessary, unlike some existing multi-secret sharing schemes.
Smart Tourism Technology Framework: A Gateway to Enhancing Tourists’ Experiences and Psychological Behaviour

Malaysian tourism industry has suffered losses of over RM100 billion due to the COVID-19 outbreak, as China and Singapore cancelled their tours to Malaysia. To counter this, the National Tourism Policy (2020-2030) must focus more on domestic travel and digitalisation to strengthen sustainable tourism and drive economic growth. The aim of the study is to investigate the determinants that contribute to smart tourism technologies and propose a framework that can create new revenue streams. Personalisation and digital experience should be prioritised to attract tourists and achieve Malaysia’s goal as a top Mind Ecotourism Destination of the World. The research employs a 400 self-administered survey for data collection from generations X and Y who are travellers by applying purposive sampling and PLS-SEM methods. Malacca, Negeri Sembilan, Perak and Penang are the focus group areas. The study is crucial to meet Budget 2022, KEGA 15, and SDGs 8, 12, 14, and 15, which aim to revitalise the tourism sector and improve economic growth. Tourists employ technology for attaining information on the infrastructure of the desired target destination.
Evaluating the Visitor Satisfaction on Cultural Heritage Digital Museum in Google Arts and Culture: A Study of Puppet Museum Jakarta

This study aims to investigate what factors play an important role in increasing digital museum visitor satisfaction. The research question that this study seeks to address is, to what degree do specific factors exert an influence on visitors' satisfaction during their visits to digital museums? The current study focused on Puppet Museum Jakarta that recently collaborated with Google Arts and Culture in providing digital museums that can be visited using Virtual Reality (VR). The survey questionnaire was distributed to 160 participants who were involved in this study. The result of this study found that there is a positive connection between the quality of information and the level of satisfaction of heritage digital museum visitors. Also, the result revealed that there is a positive relationship between the system's quality and the level of visitors' satisfaction. Furthermore, the study also revealed that the customers' perceived usefulness and perceived usability positively and significantly influence their level of satisfaction in visiting digital museums. The outcome of the current study is expected to make contributions to literature in digital museum and cultural heritage tourism.
Predicting Ice Hockey Results Using Machine Learning Techniques

Sport activities such as ice hockey experiences significant growth, however it is followed by lacking sports analytical techniques that enhances the success rate of ice hockey team. This study aims to analyze and compare the accuracy of various machine learning techniques in predicting ice hockey results, ultimately applying Logistic Regression to achieve higher accuracy and enhance the predictive capabilities in the sport. This study explores the prediction of field hockey scores using a number of machine learning models (ML), which were trained on data from the National Hockey League (NHL) seasons 2015-2021. The findings are presented in the form of confusion matrices, which contain number values to summarise accurate and inaccurate predictions according to class. To train the model, all of its features were carefully looked at and the most useful ones were drawn. It is shown that match-specific data, such as the features of home and away matches, is helpful to the model. The analysis result shows that the improved accuracy in predicting the outcome of ice hockey games, whereby the logistic regression model has the highest accuracy at 77.82% and therefore recommended for use in future analysis.
Adaptive Sparse Linear Prediction in Fixed-Filter ANC Headphone Applications for Multi-Speaker Speech Reduction

In some cases, speech can be a disturbing source of ambient noise. Active noise control (ANC) systems have difficulties in dealing with speech due to its non-stationary nature and constraints in the ANC system, which require the optimal filters to be non-causal. The non-causality is due to the delay incurred by, e.g., digital processing or acoustic propagation paths. We propose a fixed-filter feedforward ANC system, HOSpLP-ANC, which aims at attenuating voiced speech in, e.g., office environments. It comprises an adaptive high-order sparse linear predictor (HOSpLP) based on the improved proportionate normalised least mean square algorithm to predict speech ahead in time, thus overcoming such delay. Notably, HOSpLP provides high prediction performance of voiced speech by modelling the joint short- and long-term linear prediction scheme, but without using pitch estimation. This can be of particular significance in the case of the complicated multi-pitch estimation scenario. The results show that HOSpLP-ANC outperforms conventional adaptive feedforward ANC for delays in the order of milliseconds in both single- and multi-speaker environments.
Anti-Müllerian Hormone ( AMH ) and fertility management in agricultural species 1 2

Anti-Müllerian Hormone (AMH) and fertility management in agricultural species 1 2 F. Mossa, F. Jimenez-Krassel, D. Scheetz, M. Weber-Nielsen, A.C. O. Evans, J.J. 3 Ireland 4 Department of Veterinary Medicine, University of Sassari, via Vienna 2, 07100 Sassari, 5 Italy; Molecular Reproductive Endocrinology Laboratory, Department of Animal Science, 6 Michigan State University, East Lansing, MI, 48824-1225, USA. School of Agriculture and 7 Food Science, University College Dublin, Belfield, Dublin 4, Ireland 8 9 Corresponding author: Dr. Francesca Mossa, email: fmossa@uniss.it 10 11 Short title: AMH and reproductive longevity in ruminants 12 13 Funding and acknowledgements: The work by JJI was supported by National Research 14 Initiative Competitive Grant no. 2007-35203-18178 and Animal and Food Research 15 Institute Competitive Grant no. 2013-67015-20962 from the USDA National Institute of 16 Food and Agriculture, and funds from the MichiganAgBioResearch. We also thank Green 17 Meadows Farms, Inc for use of cattle. The work by FM was supported by the Irish 18 Department of Agriculture, Food and the Marine (RSF-06-328) and Science Foundation 19 Ireland (07/SRC/B1156) and is now financed by the Italian Ministry of University and 20 Research (Rita Levi Montalcini Grant 2010). 21 22
Sharing Policies in Multiuser Privacy Scenarios: Incorporating Context, Preferences, and Arguments in Decision Making

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Research Status and Technological Prospects of Real-Time Path Planning for Tug Collision Avoidance

In recent years, with the intelligent upgrade of ships and ports, there is increasing attention to the intelligence of tugs. As auxiliary vessels, tugs are responsible for assisting all kinds of ships in berthing, shifting, escorting, and transporting pilots in ports. They are also responsible for social responsibilities such as emergency rescue and firefighting in the port waters, playing a significant role in ensuring port safety. However, the complex working environment, high labor intensity of crew, and high safety risks of tugs make safety collision avoidance and energy consumption issues of paramount importance. Collision avoidance research between vessels has been concentrated on ocean-going ships, and research on tugs has been minimal. Due to the unique characteristics of tugs and their working scenarios, existing collision avoidance theories are impractical. To achieve tug path planning, the development strategy that meets the special characteristics of tugs, improves efficiency, and reduces energy consumption is proposed by analyzing the tug’s uniqueness and working scenarios. The difficult issues of tug path planning are analyzed, and key technologies for achieving tug path planning are demonstrated. It has far-reaching significance for realizing the intelligent upgrading of tugs, constructing smart ports.
HAWEN: Hardware Accelerator for Thread Wake-Ups in Linux Event Notification

The performance of multi-threaded applications relies on efficient inter-process communication. One common practice is putting a thread asleep while waiting for a certain condition. Exemplary Linux kernel mechanisms that use this practice include futex, sockets, epoll, eventfd and pipe. Once the condition is met, i.e., the associated event has occurred, the waiting thread is notified. Optimizations for event notification mechanisms in Linux mostly target the thread which receives events. Contrarily, we identified high potential in relieving the event-generating thread and propose HAWEN, a hardware accelerator for thread wake-up support. HAWEN has been integrated into Linux event notification in a minimally intrusive manner. Gem5-based multi-core architecture simulations revealed up to 80% faster thread wake-up times and a 53% shorter event-generating syscall.
Diabetes Detection Using Gait Analysis and Machine Learning

Diabetes mellitus (DM) is a medical condition affecting multiple organs and systems due to high blood glucose levels in the body. Diabetic neuropathy (DN) is a common consequence of DM, which causes sensitivity loss in the nerves of muscles. Another complication of DM is diabetic foot ulcer (DFU), which indicates the worsening of DN. Conventionally, diabetes is detected using invasive methods. However, invasive methods are not convenient and can cause pain to patients. Thus, an alternative method to detect diabetes is to use gait analysis. Gait sensors used in gait analysis can be categorised into kinematic and kinetic sensors. Kinematic sensors include accelerometers, shoe-type IMUs, ear-worn inertial sensors and motion capture systems, whereas kinetic sensors include force-measuring shoes, pressure sensors, EMG sensors and force plates. Although gait sensors provide many gait features such as acceleration, plantar pressure, EMG and ground force reaction, manual gait data analysis can be challenging. Therefore, machine learning (ML) and deep learning (DL) algorithms are used to analyse gait data. Most papers reviewed have used ML models in detecting diabetes using gait data. However, DL models are not commonly applied in diabetes detection. Thus, future studies should explore and implement DL models to evaluate their performance in diabetes detection.
A Deep Learning-Based Framework for End-To-End Identification of IMO Numbers on Ships

In the ship traffic management, the ship identity cannot be identified due to the ineffective working of the automatic ship identification system (AIS) equipment will seriously affect the navigation supervision. At the present stage, for the ship identification problem, the identification is mainly carried out by recognising the ship name, but the ship name does not have regularity, while the IMO number has uniqueness, which can effectively improve the accuracy of ship identification. Therefore, an end-to-end identification framework of ship IMO number based on deep learning is proposed, which takes into account the low positioning accuracy of small-sized licence plates of ships, low end-to-end identification rate and ship identity verification and other issues. In the localization phase, based on the YOLO7 framework and replacing its backbone network with ConvNeXt-Tiny, the IMO number is feature extracted and combined with the coordinate attention (CA) mechanism to fuse the direction and position information of the IMO number in the channel attention to enhance the generalization ability of the network for small target feature extraction in order to improve the localization accuracy of the IMO number; in the end-to-end recognition phase, the In the end-to-end recognition stage, perspective transformation and horizontal projection segmentation are used, followed by character recognition of IMO numbers based on CRNN to improve the recognition accuracy of ship IMO numbers; in the ship identity verification stage, IMO identification code rules are used to screen the ship identity. The experimental results show that the two-stage approach combining the two models can achieve an accuracy rate of 95.24% and an identification rate of 89.97% for ship IMO numbers, which fully proves the effectiveness of the framework and its application value in marine port management.
Factors Influencing Customers Loyalty Towards Online Food Delivery Applications in Klang Valley, Malaysia

The impact of consumers’ loyalty to food delivery applications is examined in this paper using a technological acceptance model. Data from 106 Malaysian via convenience sampling are obtained through an online social media platform. Five-point scales with closed-ended questions are used in the survey to gauge respondents’ responses. The method of data analysis employs descriptive analysis and structural equation modelling (Partial Least Squares). The convergent validity and discriminant validity were assessed via measurement model are deemed satisfactory. According to the results, system design is positively correlated with perceived usefulness and perceived ease of use. Technology acceptance is also found to be positively influenced by perceived usefulness and perceived ease of use. Additionally, acceptance of technology is positively correlated to customer loyalty. The research’s conclusions have given food and beverage businesses, as well as meal delivery services, vital information that will assist them in redesigning customer-focused business strategies.
Optimizing the Performance of NDP Operations by Retrieving File Semantics in Storage

In-storage Near-Data Processing (NDP) architectures can reduce data movement between the host and the storage device by offloading computing tasks to the storage. This encourages many studies on building NDP applications, such as recommendation systems and databases, on computational SSDs. However, in the data path of existing NDP architectures, an NDP application has to find out the address of the requested file data by calling the I/O stacks of the kernel on the host, which incurs large overhead for transferring data between the host and the computational SSD. In this paper, we present File Semantics Retriever (FSR) to optimize the data path of NDP architectures by locating and fetching the requested file data directly in the computational SSD. The key idea is to recognize the file system layout and the metadata structures in the storage with the collaboration of a user-space library and a handler in the firmware of the computational SSD. We implement a prototype of FSR and evaluate it on the Cosmos plus OpenSSD, a widely-used computational SSD platform. The experimental results show that FSR outperforms existing NDP architectures in both benchmarks and real-world NDP applications.
Improving Standard-Cell Design Flow using Factored Form Optimization

Factored form is a powerful multi-level representation of a Boolean function that readily translates into an implementation of the function in CMOS technology. In particular, the number of literals in a factored form correlates strongly with the number of transistors in the CMOS implementation. This paper develops novel methods for optimizing factored forms while working on the efficient and-inverter graph (AIG) representation of combinational logic. This is in contrast to the traditional logic synthesis based on logic networks, and other AIG-based methods that minimize the AIG nodes count. Experiments show that applying these methods helps to reduce the area after technology mapping by an additional 2.8% on average, compared to a high-effort area-oriented baseline. It is expected that deploying these methods as part of an industrial standard-cell design flow will reduce design costs and power consumption. Additionally, this work enables efficient transistor-level logic synthesis of large designs with various applications in design automation.
Early Child Care and Adolescent Functioning at the End of High School : Results From the NICHD Study of Early Child Care and Youth Development

Relations between early child care and adolescent functioning at the end of high school (EOHS; M age = 18.3 years) were examined in a prospective longitudinal study of 1,214 children. Controlling for extensive measures of family background, early child care was associated with academic standing and behavioral adjustment at the EOHS. More experience in center-type care was linked
Slim-Tasnet: A Slimmable Neural Network for Speech Separation

Existing speech separation methods utilize deep neural networks with a fixed computation graph in the training and inference phases. During inference, these networks cannot be easily re-configured to adapt to varying resource constraints. To address this challenge, we present Slim-TasNet, a slimmable neural network for speech separation, which is capable of achieving dynamic inference by utilizing different subsets of its computation graph. Specifically, network slimming is achieved by adjusting the width of the hidden layers at runtime based on an input utilization factor. Experimental evaluation shows that Slim-TasNet can be executed at different widths on the fly while maintaining similar performance to the corresponding static networks trained from scratch. We also compare the performance of Slim-TasNet with several network pruning mechanisms and demonstrate the effectiveness of the proposed method.
Architecture for cellular enabled integrated communication and sensing services

There is growing interest in the integrated sensing and communication (ISAC) to extend the 5G+/6G network capabilities by introducing sensing capability. While the solutions for mono-static or bi-static ISAC have shown feasibility and benefits based on existing 5G physical layer design, whether and how to coordinate multiple ISAC devices to better exert networking performance are rarely discussed. 3rd Partnership Project (3GPP) has initiated the ISAC use cases study, and the follow-up studies for network architecture could be anticipated. In this article, we focus on gNB-based sensing mode and propose ISAC functional framework with given of highlevel service procedures to enable cellular based ISAC services. In the proposed ISAC framework, three types of network functions for sensing service as Sensing Function (SF), lightweight-Edge Sensing Function (ESF) and full-version-ESF are designed with interaction with network nodes to fulfill the latency requirements of ISAC use cases. Finally, with simulation evaluations and hardware testbed results, we further verify the performance benefit and feasibility to enable ISAC in 5G for the gNB-based sensing mode with new design on SF and related signaling protocols.
Mobile Social Networks : Design Requirements , Architecture and State of the Art

Mobile social networks are new infrastructures for people to share content, communicate and interact anywhere, anytime. It brings together social computing and mobile networking techniques, and thus yields a plethora of research problems in community detection, information dissemination, privacy preservation and so forth. In this paper, we summarize the characteristics of MSNs, and outline several requirements that should be taken into consideration during the design of MSNs. A functional reference architecture of MSNs is designed, and its building blocks are described. We classify the literatures falling under MSNs according to the building blocks of the architecture, and give a detailed survey for each. At last, a conclusion on the future development of MSNs is presented.