Miracle-dz/UniFAR-FTUs huggingface dataset:
State Representation Learning for Task and Motion Planning in Robot Manipulation

Employing the knowledge representation model designed by human experts has long been the dominant methodology in task and motion planning (TAMP). However, this type of method is time-consuming and suffers from the domain-dependence problem. In this paper, we focus on TAMP of robot arm manipulation based on state representation learning. We present a state representation learning method and a joint learning strategy for both the state representation model and the environment model, enabling the robot to learn the environment model autonomously, thereby mitigating the issue of domain-dependence. To improve planning efficiency and task success rate, we also incorporate a search pruning strategy based on value function learning and a re-planning method based on Model Predictive Control (MPC). The proposed method is evaluated in the simulation and real-robot experiments and shown to be effective compared to current TAMP systems.
User-Centric Security and Privacy Threats in Connected Vehicles: A Threat Modeling Analysis Using STRIDE and LINDDUN

The increasing equipment of cars with smart systems and their networking with other devices is leading to a growing network of connected vehicles. Connected cars are Internet of Things (IoT) devices that communicate bidirectionally with other systems, enabling internet access and data exchange. Artificial Intelligence (AI) offers benefits such as autonomous driving, driver assistance programs, and monitoring. The increasing connectivity of cars also brings new risks to users' privacy. Our study focuses on privacy threats in connected cars from a user perspective. Our study provides a comprehensive threat model analysis based on a combination of STRIDE and LINDDUN. We analyze the various threats and vulnerabilities that arise from connecting cars to the internet and other devices, including Vehicle-to-Vehicle (V2V), Vehicle-to-Vloud (V2C), and Vehicle-to-Device (V2D). We conduct our study based on a theoretical model of a modern-day connected vehicle of another study. Our study shows that several types of threats can negatively impact the privacy of connected car users. This encapsulates the potential risks, such as the inadvertent disclosure of personal data due to the vehicle's interconnectedness with other devices, including smartphones, and the subsequent susceptibility to unauthorized access, while also highlighting the need for robust security measures indicated by our comprehensive threat modeling, to safeguard against a wide array of identified cybersecurity threats.
Low-Delay Proactive Mechanisms for Resilient Communication

Millimeter-wave (mmWave) networks form a core part of a variety of civilian and military applications, particularly delay-sensitive applications by offering high-speed communications. For the promise of these applications, a well-known challenge is that mmWave links are highly sensitive to blockage and communication can get disrupted, especially in military communications. In this paper, we propose and evaluate low-complexity proactive transmission mechanisms that are resilient to network disruptions. Our work leverages the multipath environment and the existence of accurate models that estimate the link blockage probabilities in mmWave networks. We propose to deploy multilevel codes across paths while suitably balancing the average information rate with a graceful performance degradation. We define the rate region to operate along with an optimization formulation to select a high-performing set of rates for the source sequences. Our evaluations show that our proposed coding schemes achieve a graceful performance degradation compared to alternative schemes (such as erasure correcting codes), while significantly reducing the code complexity.
Deep neural network for lateral control of self-driving cars in urban environment

The exponential growth of the automotive industry clearly indicates that self-driving cars are the future of transportation. However, their biggest challenge lies in lateral control, particularly in urban bottlenecking environments, where disturbances and obstacles are abundant. In these situations, the ego vehicle has to follow its own trajectory while rapidly correcting deviation errors without colliding with other nearby vehicles. Various research efforts have focused on developing lateral control approaches, but these methods remain limited in terms of response speed and control accuracy. This paper presents a control strategy using a deep neural network (DNN) controller to effectively keep the car on the centerline of its trajectory and adapt to disturbances arising from deviations or trajectory curvature. The controller focuses on minimizing deviation errors. The Matlab/Simulink software is used for designing and training the DNN. Finally, simulation results confirm that the suggested controller has several advantages in terms of precision, with lateral deviation remaining below 0.65 meters, and rapidity, with a response time of 0.7 seconds, compared to traditional controllers in solving lateral control.
The Lexicon and Morphosyntax of Child Spanish as Predictors of Inhibition

This study investigates the relationships between lexical development and inhibition, as well as morphosyntax and inhibition, in typically developing monolingual Spanish-speaking children. Recent studies of the relationship between lexical development and inhibition suggest that, as the size of the lexicon increases, so does inhibitory ability. However, the relationship between grammar and inhibition seems more controversial. The work distinguishing the relationships between inhibition and lexicon vs. grammar have been carried out in English, which has relatively impoverished inflectional morphology. Because the relationships considered in the literature are hypothetically not language-particular to English, but rather claims about cognition in general, we would expect to find that they also hold in other languages, including languages with richer morphology, such as Spanish. These considerations led us to ask the following: are expressive and receptive measures of the lexicon and morphosyntax predictive of typically developing monolingual child Spanish-speakers’ inhibitory ability? A sample of 82 monolingual, typically developing Spanish-speaking children in Mexico City were tested with 5 lexical measures, 4 morphosyntax measures, and the Flanker Task measure of inhibition. Results showed that all lexical and morphosyntactic variables correlated significantly with Flanker (p < 0.01), except for Number of Different Words (NDW), calculated on the spontaneous production sample. Therefore, inhibition is predicted by lexical development in child Spanish. Additionally, an ever-increasing set of competitor morphological forms requires an ever-increasing inhibitory ability as well.
A Simple Microwave Imaging System for Food Product Inspection through a Symmetry-Based Microwave Imaging Approach

In the food industry, there is a growing demand for cost-effective methods for the inline inspection of food items able to non-invasively detect small foreign bodies that may have contaminated the product during the production process. Microwave imaging may be a valid alternative to the existing technologies, thanks to its inherently low-cost and its capability of sensing low-density contaminants. In this paper, a simple microwave imaging system specifically designed to enable the inspection of a large variety of food products is presented. The system consists of two circularly loaded antipodal Vivaldi antennas with a very large operative band, from 1 to 15 GHz, thus allowing a suitable spatial resolution for different food products, from mostly fatty to high water-content foods. The antennas are arranged in such a way as to collect a signal that can be used to exploit a recently proposed real-time microwave imaging strategy, leveraging the inherent symmetries that usually characterize food items. The system is experimentally characterized, and the achieved results compare favorably with the design specifications and numerical simulations. Relying on these positive results, the first experimental proof of the effectiveness of the entire system is presented confirming its efficacy.
An Analysis of Accelerator Data-Transfer Modes in NoC-Based SoC Architectures

Data movement is a key factor impacting the performance of hardware accelerators. In a complex SoC architecture, multiple accelerators compete for accessing the resources of on-chip communication and off-chip memory interfaces. For a program that invokes many accelerators, orchestrating the data movement is critically important to avoid degrading the speedup that each standalone accelerator can achieve. We present a comparative analysis of the two main data-transfer modes among accelerators: memory-based and point-to-point (p2p) communication. We describe their implementation on FPGA for both single-thread and multi-thread software programs. We analyze the implications on programmability, performance, and energy efficiency by using a variety of synthetic benchmarks to evaluate the data-transfer modes in different scenarios and by accelerating two real-world image processing applications: Nightvision and Wide-Area Motion Imagery (WAMI). We demonstrate that for various configurations of a tile-based many-accelerator SoC, p2p outperforms memory-based communication.
A New Recursive Approach to Sparse Representation

This work presents a new approach to online sparse representation. Online sparse representation concerns the estimation of non-redundant structures using data that are sequentially and continuously collected. A novel sparse regularized recursive least squares algorithm, named SP-R-RLS, is proposed. SP-R-RLS combines a reweighting technique to approximate the L0 norm (a measure of sparsity) with a smooth approximation to address lack of differentiability. When compared to state-of-the-art algorithms, it is shown that SP-R-RLS has better performance in terms of sparsity of the estimated system and accuracy of the estimate.
An Efficient Accelerator on FPGA for Large Convolution and Correlation using Winograd

FPGAs have become an attractive option for deep learning algorithms acceleration due to their reconfigurability and abundant logic resources. However, facing large kernel convolution and feature correlation, FPGA-based accelerators are often limited by on-chip DSP resources, making it difficult to cope with the exponentially increased computational load. To break through the limitation, we design the Winograd Accelerator Cores (WACs) to achieve efficient computation of convolution and correlation at the same time. To meet the bandwidth requirements of WACs with less FPGA resources, we propose an on-chip buffer system based on Line-Buffer and sliding registers. To maximize the usage of FPGA resources for computing while ensuring the scalability of accelerators on different FPGA devices, we propose a dataflow-driven multi-core accelerator architecture based on WACs. Finally, we implemented the accelerator using Xilinx Vivado HLS and deployed it on FPGA devices for performance evaluation to demonstrate the efficiency. On Xilinx xczu9eg, the actual performance can reach 1078.73GOPS, and the DSP utilization radio reaches 4.21. Its performance is more than 37.83% higher than that of Angel-Eye, and the DSP utilization ratio is 158.40% and 12.60% higher than that of Angel-Eye and Extended-DPU, respectively.
Knowledge Distillation for Traversable Region Detection of LiDAR Scan in Off-Road Environments

In this study, we propose a knowledge distillation (KD) method for segmenting off-road environment range images. Unlike urban environments, off-road terrains are irregular and pose a higher risk to hardware. Therefore, off-road self-driving systems are required to be computationally efficient. We used LiDAR point cloud range images to address this challenge. The three-dimensional (3D) point cloud data, which are rich in detail, require substantial computational resources. To mitigate this problem, we employ a projection method to convert the image into a two-dimensional (2D) image format using depth information. Our soft label-based knowledge distillation (SLKD) effectively transfers knowledge from a large teacher network to a lightweight student network. We evaluated SLKD using the RELLIS-3D off-road environment dataset, measuring the performance with respect to the mean intersection of union (mIoU) and GPU floating point operations per second (GFLOPS). The experimental results demonstrate that SLKD achieves a favorable trade-off between mIoU and GFLOPS when comparing teacher and student networks. This approach shows promise for enabling efficient off-road autonomous systems with reduced computational costs.
A Portable and Disposable Electrochemical Sensor Utilizing Laser-Scribed Graphene for Rapid SARS-CoV-2 Detection

The COVID-19 pandemic caused by the virus SARS-CoV-2 was the greatest global threat to human health in the last three years. The most widely used methodologies for the diagnosis of COVID-19 are quantitative reverse transcription polymerase chain reaction (RT-qPCR) and rapid antigen tests (RATs). PCR is time-consuming and requires specialized instrumentation operated by skilled personnel. In contrast, RATs can be used in-home or at point-of-care but are less sensitive, leading to a higher rate of false negative results. In this work, we describe the development of a disposable, electrochemical, and laser-scribed graphene-based biosensor strips for COVID-19 detection that exploits a split-ester bond ligase system (termed ‘EsterLigase’) for immobilization of a virus-specific nanobody to maintain the out-of-plane orientation of the probe to ensure the efficacy of the probe-target recognition process. An anti-spike VHH E nanobody, genetically fused with the EsterLigase domain, was used as the specific probe for the spike receptor-binding domain (SP-RBD) protein as the target. The recognition between the two was measured by the change in the charge transfer resistance determined by fitting the electrochemical impedance spectroscopy (EIS) spectra. The developed LSG-based biosensor achieved a linear detection range for the SP-RBD from 150 pM to 15 nM with a sensitivity of 0.0866 [log(M)]−1 and a limit of detection (LOD) of 7.68 pM.
Estimation of Source Parameters by the Inversion of Near Source Strong Motion Wave Forms

A method has been developed allowing the direct inversion of near source · strong motion records to obtain an estimate of the seismic source from . a moment tensor. The inversion is implemented in the time domain, and the least-square method is used to estimate the source mechanism: Significant verification using synthetic seismograms including artificial noise was done. The inversion of synthesized data suggests that with this method it may be possible to retrieve the source information of local earthquakes using only a few high-quality, three-component seismograms. This method has been ap­ plied to the study of the source parameters of the ML= 5.4 event of May 28, 1992 in southeastern Taiwan using only the strong motion data recorded at Chengkung. The retrieved-source mechanism is in agreement with the distri­ bution of the few observed first P motion of short-period network data and is similar to the CMT solution. The advantage of this method is its capability for fast determination of the source mechanisms for locally felt events using strong motion data from the currently distributed seismic stations.
Competence in Sports Pharmacy among Pharmacy Students in Norway

Pharmacists are competent to promote the proper use of medicines. According to the International Pharmaceutical Federation, pharmacists must develop competence in sports pharmacy and the contents of the World Anti-Doping Agency code. This explorative study aimed to identify the status of sports pharmacy in pharmacy education in Norway and competence in sports pharmacy among Norwegian pharmacy students. The study curricula of pharmacy education were examined for the content of sports pharmacy. An online questionnaire was also developed and distributed among pharmacy students. The anonymous survey collected demographic information and data on competence in sports pharmacy. Data from 122 participants were analyzed. Only 22.5% of pharmacy students had acquired a form of training in sports pharmacy and 91.7% wished to gain higher competence. In total, 40.2% of respondents were uncomfortable in advising athletes and trainers on medication use in sports. Study year was found to correlate with competence level with a significant difference between the 3rd year (bachelor) and 5th year (master) students. Age, institution, and number of years engaged in sports were not associated with competence level. The inclusion of sports pharmacy in pharmacy programs holds practical relevance for enhancing competency levels. This implementation can be realized through the integration of sports pharmacy modules and/or the incorporation of research-based activities.
Personalized E-commerce based recommendation systems using deep-learning techniques

As technology is surpassing each day, with the variation of personalized drifts relevant to the explicit behavior of users using the internet. Recommendation systems use predictive mechanisms like predicting a rating that a customer could give on a specific item. This establishes a ranked list of items according to the preferences each user makes concerning exhibiting personalized recommendations. The existing recommendation techniques are efficient in systematically creating recommendation techniques. This approach encounters many challenges such as determining the accuracy, scalability, and data sparsity. Recently deep learning attains significant research to enhance the performance to improvise feature specification in learning the efficiency of retrieving the necessary information as well as a recommendation system approach. Here, we provide a thorough review of the deep-learning mechanism focused on the learning-rates-based prediction approach modeled to articulate the widespread summary for the state-of-art techniques. The novel techniques ensure the incorporation of innovative perspectives to pertain to the unique and exciting growth in this field.
Near-Ballistic Uni-Traveling-Carrier Photodiodes with Undercut Collector for Enhancements in THz Output Power and Responsivity

We demonstrate novel structures of ultra-fast photodiodes to further enlarge their active area and saturation power at sub-THz regime. Higher responsivity (0.11 vs. 0.1A/W) and output power (0.4 vs. -2.4 dBm@165GHz) as compared to those of references with miniaturized diameters (3μm) has been demonstrated.
A Triplet Network Fusing Optical and SAR Images for Colored Steel Building Extraction

The identification of colored steel buildings in images is crucial for managing the construction sector, environmental protection, and sustainable urban development. Current deep learning methods for optical remote sensing images often encounter challenges such as confusion between the roof color or shape of regular buildings and colored steel structures. Additionally, common semantic segmentation networks exhibit poor generalization and inadequate boundary regularization when extracting colored steel buildings. To overcome these limitations, we utilized the metal detection and differentiation capabilities inherent in synthetic aperture radar (SAR) data to develop a network that integrates optical and SAR data. This network, employing a triple-input structure, effectively captures the unique features of colored steel buildings. We designed a multimodal hybrid attention module in the network that discerns the varying importance of each data source depending on the context. Additionally, a boundary refinement (BR) module was introduced to extract the boundaries of the colored steel buildings in a more regular manner, and a deep supervision strategy was implemented to improve the performance of the network in the colored steel building extraction task. A BR module and deep supervision strategy were also implemented to sharpen the extraction of building boundaries, thereby enhancing the network’s accuracy and adaptability. The results indicate that, compared to mainstream semantic segmentation, this method effectively enhances the precision of colored steel building detection, achieving an accuracy rate of 83.19%. This improvement marks a significant advancement in monitoring illegal constructions and supporting the sustainable development of the Beijing–Tianjin–Hebei metropolitan region.
Mobile Test Script Generation from Natural Language Descriptions

Mobile applications are increasingly integral to our daily lives. Currently, the correctness of GUI functions of mobile application is mainly ensured by executing manually written test scripts. However, manually writing these test scripts is not only time-consuming but also costly. Moreover, test scripts are highly vulnerable to application modifications and prone to corruption. In this paper, we propose a novel approach for writing test scripts that enables testers to directly express test intents in natural language within the script. Additionally, we present a new test script generation tool that transforms these test intents into their corresponding test events. Our proposed tool, named GenDroid, employs pre-trained models in conjunction with random forest to facilitate the conversion of test intents into the respective test scripts. To further alleviate the workload of testers and enable them to focus on composing critical test intents, we leverage the application’s UI transfer graph to facilitate the automated generation of other test events, such as jump actions, throughout the generation process. Our results indicate an intent coverage of 88.1%, a notable 20.68% improvement compared to the similar-purpose tool, seq2act.
Smartphone-Based Pedestrian Inertial Tracking: Dataset, Model, and Deployment

Inertial measurement units (IMUs) are widely adopted for pedestrian tracking with high-frequent, scale-consistent, and environment-independent ego-motion measurements. However, such inertial readings in smartphones are usually plagued by heavy noises, causing unexpected tracking errors and impeding the deployment at large-scale. Currently, many research works explore motion perception and position estimation with deep neural networks (DNNs), but training a general model requires sufficient and widespread data covering most scenarios. In this article, we present a smartphone inertial measurement dataset (SIMD) with more than 4500 walking trajectories, which takes about 190 h with a total walking distance of more than $700\,\text {km}$ . It covers four cities, 12 indoor and outdoor scenarios, seven phone attitudes, and more than 150 volunteers with their smartphones. To locate pedestrians indoors, we propose a general inertial tracking framework to train with our dataset and infer user’s trajectory. Furthermore, we explore the potential deployment of model customization on individual smartphones. Extensive experiments have shown our effectiveness on pedestrian tracking and navigation, compared with the state-of-the-art.