Miracle-dz/UniFAR-FTUs huggingface dataset:
Verified Lustre Normalization with Node Subsampling

Dataflow languages allow the specification of reactive systems by mutually recursive stream equations, functions, and boolean activation conditions called clocks. Lustre and Scade are dataflow languages for programming embedded systems. Dataflow programs are compiled by a succession of passes. This article focuses on the normalization pass which rewrites programs into the simpler form required for code generation. Vélus is a compiler from a normalized form of Lustre to CompCert’s Clight language. Its specification in the Coq interactive theorem prover includes an end-to-end correctness proof that the values prescribed by the dataflow semantics of source programs are produced by executions of generated assembly code. We describe how to extend Vélus with a normalization pass and to allow subsampled node inputs and outputs. We propose semantic definitions for the unrestricted language, divide normalization into three steps to facilitate proofs, adapt the clock type system to handle richer node definitions, and extend the end-to-end correctness theorem to incorporate the new features. The proofs require reasoning about the relation between static clock annotations and the presence and absence of values in the dynamic semantics. The generalization of node inputs requires adding a compiler pass to ensure the initialization of variables passed in function calls.
Virtual Horse: an Anthropomorphic Notification Interface for Traffic Accident Reduction

With the development of Vehicle-to-Infrastructure (V2I) and Vehicle-to-Everything (V2X), the difficulty of predicting traffic accidents is decreasing. Vehicles can communicate with each other about their status and receive notifications from traffic infrastructures about the prediction movements of other objects on the road. The efficiency of showing warnings to human drivers will become the bottleneck of reducing traffic accidents. In this study, we propose Virtual Horse: an anthropomorphic notification interface using implicit information to implicate potential dangers to the driver. The virtual horse represents the car’s behaviors and indicates potential dangers via body language like a real horse. We expect this anthropomorphic interface can reduce drivers’ reaction time for preventing dangers. We developed a prototype and ran a pilot study in a web-based driving simulation. The results showed potential benefits of using the proposed interface as well as showing useful insights for our future development of a user study.
CICERO: A Domain-Specific Architecture for Efficient Regular Expression Matching

Regular Expression (RE) matching is a computational kernel used in several applications. Since RE complexity and data volumes are steadily increasing, hardware acceleration is gaining attention also for this problem. Existing approaches have limited flexibility as they require a different implementation for each RE. On the other hand, it is complex to map efficient RE representations like non-deterministic finite-state automata onto software-programmable engines or parallel architectures. In this work, we present CICERO , an end-to-end framework composed of a domain-specific architecture and a companion compilation framework for RE matching. Our solution is suitable for many applications, such as genomics/proteomics and natural language processing. CICERO aims at exploiting the intrinsic parallelism of non-deterministic representations of the REs. CICERO can trade-off accelerators’ efficiency and processors’ flexibility thanks to its programmable architecture and the compilation framework. We implemented CICERO prototypes on embedded FPGA achieving up to 28.6× and 20.8× more energy efficiency than embedded and mainstream processors, respectively. Since it is a programmable architecture, it can be implemented as a custom ASIC that is orders of magnitude more energy-efficient than mainstream processors.
Evaluation of Driver’s Sense of Control in Lane Change Maneuvers with a Cooperative Steering Control System

Balancing safety and comfort is an important requirement in highly automated driving. In order to achieve safe and comfortable transportation using automated driving technology, cooperative driving, in which the driver and the system work together on the common driving task, may be more effective than a system that completely replaces the driver. Therefore, in this research, we address a cooperative driving system that provides steering assistance and steer-angle regulation based on force-feedback and steer-by-wire systems. We evaluate the safety and comfort of the system through a simulated driving study under different levels of system intervention in lane change maneuvers. Experimental results show that steer-angle regulation without force feedback gives the highest sense of control (involvement in driving) to drivers, although the driving maneuver is overridden by the system to maintain safety in the current condition.
Obtaining High SPL Piezoelectric MEMS Speaker via a Rigid-Flexible Vibration Coupling Mechanism

Among the main problems affecting the sound pressure level (SPL) of piezoelectric microelectromechanical systems (MEMS) speakers is that the membrane vibration displacement of those with sealed membranes is small, while those with unsealed membranes suffer from acoustic loss. Presented here is a high-SPL piezoelectric MEMS speaker via a rigid-flexible coupling mechanism, which can maintain large vibration displacement of the unsealed membranes without acoustic loss. The ingenious deposition of Parylene C on the upper surface before etching the back cavity is the key process forming the rigid–flexible-coupling sealed vibration membrane. Compared with that of the proposed speaker without the flexible sealed Parylene C at 2 V, the SPL generated by that with the rigid–flexible-coupling sealed vibration membrane is 3.0–26.1% higher, the frequency range of the speaker with the sealed membrane is widened from 250 Hz–20 kHz to the human hearing frequency range of 20 Hz–20 kHz for SPLs exceeding 59 dB, and a 101.2-dB SPL is obtained at the resonance frequency of 6.7 kHz. This simple strategy represents a significant step toward practical applications of piezoelectric MEMS speakers, given the wide frequency band, high SPL, and low power consumption. [2021-0041]
A novel chromosome instance segmentation method based on geometry and deep learning

In medicine, any abnormalities in the number of chromosomes or the structure of chromosomes may cause the newborn baby to suffer from genetic diseases, such as Edward syndrome and so on. Chromosome karyotype analysis is the most important and common method for prenatal diagnosis to determine whether a newborn baby has chromosome defects refers to segment chromosome instances from stained cell images and arrange chromosome instances according to their categories. However, due to the non-rigid nature of chromosomes, chromosome instances may overlap and adhere to each other, which makes the task of segmenting chromosome instances time-consuming and error-prone. This paper proposes a novel chromosome instance segmentation method that includes three stages. First, we segment a given stained cell image into several segments using geometric connectivity. Second, a machine learning method is proposed to distinguish chromosome of individual instances and clusters. Finally, a deep learning-based method is applied to separate chromosome instances from clusters. It shows that the proposed method achieves 97.61% instance segmentation accuracy in a hold-out clinical dataset with 162 cell images consisting of 7,452 chromosome instances, which is a promising result in clinical application. The innovation of this work is to combine geometry and deep learning to handle tasks for different stages of chromosome instance segmentation issue. The benefit of this innovation is that it can obtain a much better performance than existing geometric-based methods with a small number of training samples. Meanwhile, the segmentation performance of the proposed method is superior to existing methods fully based on deep learning.
One New Super-Twisting Sliding Mode Direct Thrust Control for Linear Induction Machine Based on Linear Metro

In this article, a new supertwisting sliding mode direct thrust control (NSTSM-DTC) scheme is proposed to improve the dynamic response, robustness ability of the linear induction machine, and overall drive performance. First, a NSTSM algorithm is proposed and implemented in both electromagnetic thrust and primary flux-linkage control loops. The design aspects, implementation process, and stability analysis of the NSTSM based on the DTC method are fully investigated, which can effectively achieve faster dynamic response, smaller steady state speed tracking error, lower electromagnetic thrust ripple, and increase the system robustness against load variation in comparison to those of the conventional control methods. The proposed method is fully confirmed by the comprehensive simulations and experiments based on one prototype of 3 kW arc induction machine.
Review on the Architectures and Applications of Deep Learning in Agriculture

This article is a brief review of the architectures of the state-of-the-art deep learning networks, including Deep Belief Networks (DBNs), AutoEncoders (AEs), Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) and their applications in agriculture. The paper is organized as follows. First, a brief introduction to some basic concepts is conducted to clarify the widely spoken terms. Then, main deep learning architectures are introduced, and characteristics of them are compared briefly. Thirdly, by surveying related literatures in the applications of the above architectures in agriculture, the typical and proper applications of each structure are summarized and analyzed. Last but not least, the challenges and outlook of deep learning networks are discussed.
FUNC-ESIM: A Dual Pairwise Attention Network for Cross-version Binary Function Matching

Binary function matching compares two pieces of binary functions to identify their similarities, which has wide applications in the field of malware origin tracing, vulnerability searching, binary level plagiarism detection, etc. Up-to-date methods commonly independently map each function to an embedding and rarely consider fine-grained pairwise semantic similarity, which influences the accuracy of matching. Moreover, few methods are available to detect similarities between versions spanning a long period for cross-version vulnerability detection or patch positioning. To solve these issues, we propose a novel binary function matching method, which takes a pair of binary functions as input, and then computes a similarity score jointly on the pair through a specifical dual pairwise cross-attention network. Specially, we apply our method to detecting similarities between cross-version binaries. The experimental analysis demonstrates that FUNC-ESIM achieves promising results on the cross-version binary matching task, where the average recall@1 reaches 85.98%.
MPC-based control strategy of a neuro-inspired quadruped robot

This paper proposes the application of the Model Predictive Control (MPC) strategy to the locomotion of a quadrupedal robot endowed with a Central Pattern Generator (CPG) neural locomotion architecture. The neural structure is adaptive based on the proprioceptive information and the exteroceptive signals acquired through ground contact sensors. The MPC generates the high-level descending commands used by the CPG, controlling the robot navigation. Using its capability to provide optimized output guaranteeing, at the same time, the state and input constraints of the system, the MPC allows a robust heading control of the robot suitably interacting with the neural locomotion paradigm. The obtained results are analyzed and compared with those obtained in the same robotic architecture using a standard PID controller.
Blockage Avoidance Based Sensor Data Dissemination in Multi-Hop MmWave Vehicular Networks

MmWave communication has emerged as a promising technology to fulfill requirements of gigabit-level data rates, so as to support the exchange of large amounts of sensor data in vehicle-to-vehicle (V2V) communications. However, inter-vehicle line-of-sight (LOS) blockages may significantly deteriorate the link quality and lead to data packet loss. Addationally, it is difficult to coordinate directional transmissions to broadcast massive sensor data to nearby vehicles timely for enhanced environmental awareness. To overcome these challenges, this article proposes a blockage avoidance based multi-hop sensor data dissemination (BAMD) scheme in a centralized manner. First, we propose a mmWave LOS link availability identification method based on the geometric relation among vehicles. Second, the problem of sensor data dissemination among all vehicles is formulated aiming at minimizing the overall transmission delay. To find an approximate solution, a novel transmission utility function based multi-hop data dissemination algorithm is designed jointly considering the distance between vehicles, packet queue, the number of LOS neighbors and concurrent transmissions. By theoretical analysis and extensive simulation, it is indicated that the proposed scheme can achieve a reliable and fast sensor data dissemination. In high-density case, BAMD can improve reliability by 20$\%$ and reduce transmission delay by 82$\%$ compared with baselines.
Investigation of Trap-Induced Performance Degradation and Restriction on Higher Ferroelectric Thickness in Negative Capacitance FDSOI FET

Bias Temperature Instability (BTI) has always been a critical reliability issue in a field effect transistor (FET). In a negative capacitance (NC) FET, a study of BTI with considering the traps at different interfaces is needed to investigate the device performance, which is not yet explored. In this work, for the first time, we have addressed the individual and the combined effect of bulk traps and the interface traps introduced at different interfaces in an NC fully depleted silicon on insulator (FDSOI) FET. We found: 1) interface Si–SiO2 and SiO2–HfO2 or bulk HfO2 traps nullify the threshold voltage variation and 2) the presence of traps changes the ferroelectric (FE) polarization and in turn vertical field distribution which leads to an increased OFF current ( ${I}_{ \mathrm{\scriptscriptstyle OFF}}$ ). Further, it causes the early end of lifetime (EOL) due to bulk HfO2, and HfO2–SiO2 traps for NC FDSOI. These results have been extracted by using a well-calibrated TCAD framework. Since the current amplification and the subthreshold reduction in NC transistors can be achieved by increasing the FE thickness; but in our work, we found that increasing the thickness is not appropriate as the higher FE thickness predominantly degrades the NC device performance, and a thickness optimization is highly required from the reliability perspective.
Dual Convolutional Neural Network for Lung Nodule Classification

In some computer vision tasks, in addition to images, we also have a graph indicating the relationship of the images. In this case, we can use Graph Convolutional Network (GCN) to get extra information from the graph. GCN is a widely-used deep neural network with graph convolutional layers extracting high-level feature maps from the graph. In this paper, we design a Dual Convolution Neural Network (Dual-CNN) with a CNN solving the images and a GCN solving the graph. We take the lung nodule classification task as an example to introduce our model. In this task, we have features including a CT image and several categorical attributes for each nodule, and the purpose is to classify if the nodule is malignant. Conventional classifiers take these features as the input and then give the prediction, yet we additionally construct a graph based on attributes and perform graph convolution on it. We also design experiments to demonstrate the effectiveness of our Dual-CNN models. Codes are available on https://github.com/temp2244/lung_nodules_classification.
Pest Detection in Crop Images Based on OTSU Algorithm and Deep Convolutional Neural Network

With the development of computer image detection technology in agriculture, the accurate detection of crop pests under complex background has become an important issue in agriculture. Due to various forms and complex environments, some pest images cannot be accurately detected by existing detection algorithms. In order to improve the detection accuracy, a deep convolutional neural network based on feature fusion is proposed. This algorithm is based on Mask R-CNN network and OSTU, introduces automatic threshold segmentation algorithm. In the feature extraction stage, an improved threshold segmentation algorithm is introduced, and then the feature data generated by segmentation is used to replace the original feature maps. The experiment on crop pest detection shows that this detection algorithm proposed in this paper can effectively detect crop pests and achieve the effect of identification and instance segmentation.
A Contextual Attention Network for Multimodal Emotion Recognition in Conversation

Emotion recognition in conversation (ERC) is a challenging task due to the complexity of emotions and dynamics in dialogues. Current studies for emotion recognition mostly focus on the modeling of a single utterance in dialogue, which neglects self and inter-speaker influence. This paper presents a contextual attention neural network based on the multimodal framework that leverages the conversational information from both target and the other speaker for utterance-level emotion detection. Specifically, we utilize recurrent neural networks based on contextual attention for modeling the transaction and dependence between speakers. Further, the feature fusion is proposed to unite the important modal information extracted from multiple modalities, including audio, text and video, hence providing more useful and comprehensive knowledge for emotion recognition. The proposed approach shows its superiority in extracting contexts for self and inter-speaker influence and synthesizing them as global features that are beneficial to detect individual emotion state. Experiment result on the IEMOCAP corpus reports an accuracy of 64.6%, demonstrating the superiority of the proposed method in emotion recognition comparing to the state-of-the-arts.
Binaural Auralization of Microphone Array Room Impulse Responses Using Causal Wiener Filtering

Binaural room auralization involves Binaural Room Impulse Responses (BRIRs). Dynamic binaural synthesis (i.e., head-tracked presentation) requires BRIRs for multiple head poses. Artificial heads can be used to measure BRIRs, but BRIR modeling from microphone array room impulse responses (RIRs) is becoming popular since personalized BRIRs can be obtained for any head pose with low extra effort. We present a novel framework for estimating a binaural signal from microphone array signals, using causal Wiener filtering and polynomial matrix formalism. The formulation places no explicit constraints on the geometry of the microphone array and enables directional weighting of the estimation error. A microphone noise model is used for regularization and to balance filter performance and noise gain. A complete procedure for BRIR modeling from microphone array RIRs is also presented, employing the proposed Wiener filtering framework. An application example illustrates the modeling procedure using a 19-channel spherical microphone array. Direct and reflected sound segments are modeled separately. The modeled BRIRs are compared to measured BRIRs and are shown to be waveform-accurate up to at least 1.5 kHz. At higher frequencies, correct statistical properties of diffuse sound field components are aimed for. A listening test indicates small perceptual differences to measured BRIRs. The presented method facilitates fast BRIR data set acquisition for use in dynamic binaural synthesis and is a viable alternative to Ambisonics-based binaural room auralization.
A Multiple-Vehicle Decision-Making Model for Left-Turn Behavior of AVs at Urban Intersections based on conflict resolution

In the complex and dynamic urban road environment, conflicts in time and space between different traffic participants are inevitable. In this paper, a model to predict the trajectories of oncoming straight vehicles is proposed based on the Gaussian Process Regression (GPR) model with the consideration of analysis of left-turn potential conflict behaviors. Then, a conflict resolution method for left-turn behavior of autonomous vehicles at urban intersections and a driving action selection method considering multiple factors, such as safety, efficiency, comfort and altruism, are proposed. Experiments show that the model can guide the unmanned vehicles to complete the task with a success rate of more than 90%. The research results can provide decision-making support for autonomous vehicles passing through urban intersections safely.
Modeling Edge Features with Deep Bayesian Graph Networks

We propose an extension of the Contextual Graph Markov Model, a deep and probabilistic machine learning model for graphs, to model the distribution of edge features. Our approach is architectural, as we introduce an additional Bayesian network mapping edge features into discrete states to be used by the original model. In doing so, we are also able to build richer graph representations even in the absence of edge features, which is confirmed by the performance improvements on standard graph classification benchmarks. Moreover, we successfully test our proposal in a graph regression scenario where edge features are of fundamental importance, and we show that the learned edge representation provides substantial performance improvements against the original model on three link prediction tasks. By keeping the computational complexity linear in the number of edges, the proposed model is amenable to large-scale graph processing.
Research on the Dynamic Multisocial Networks Influence Maximization Problem Based on Common Users

The influence maximization problem of a single social network is to find a set of $k$ seed nodes $S$ so that the spread of information from the seed set to the single network has the largest influence. This problem has attracted the attention of many researchers worldwide. In recent years, with the rapid development of the internet and the popularity of social networks, a variety of social platforms have appeared, allowing people to have multiple social accounts simultaneously; that is, one person will participate in multiple social networks and spread information on the various social platforms simultaneously. Consequently, the problem of influence maximization has been extended from a single social network to multiple social networks. However, many studies are based on static networks, and the critical challenge is that social networks usually have dynamic characteristics. At present, there is almost no research on dynamic multiple social networks. Therefore, based on common users, this paper establishes a dynamic multisocial network communication model to study the dynamic multisocial network influence maximization problem (DMNIMP). In this model, multiple dynamic networks are merged into a dynamic network, in which the self-propagating edges of common users are added to the snapshots of each frame of the integrated network. Experimental analysis shows that the proposed model can not only accurately and vividly represent dynamic characteristics but also reflect the mutual influence of common users on multiple social networks. If common users are chosen as the nodes with greater influence in each network, the communication range of the integrated network is obviously larger than that of a single network, and the interaction of dynamic multisocial networks is more obvious.