Miracle-dz/UniFAR-FTUs huggingface dataset:
Long-Pulse Gastric Electrical Stimulation Repairs Interstitial Cells of Cajal and Smooth Muscle Cells in the Gastric Antrum of Diabetic Rats

Background/Aims The damage of interstitial cells of Cajal and smooth muscle cells has far-reaching implications in the pathogenesis of gastroparesis in diabetic patients. Gastric electrical stimulation (GES) is an efficient therapy for gastric motility disorders, but the mechanisms of GES require clarification. Methods Male rats were randomly divided into the control group, diabetic rat group (DM), diabetic rats with sham GES group (DM + SGES), and diabetic rats with different frequency GES group (DM + GES) (GES1: 5.5 cpm, 100 ms, 4 mA; GES2: 5.5 cpm, 300 ms, 4 mA; and GES3: 5.5 cpm, 550 ms, 2 mA). Gastric contractions were explored using the organ bath technique. The alterations of interstitial cells of Cajal, the SCF/c-kit pathway, and smooth muscle cells were also investigated. Results (1) Gastric contractions were significantly improved in the DM + GES group compared with those in the DM group. (2) The damage of interstitial cells of Cajal was prevented in the DM + GES group in contrast to the DM group. Moreover, long-pulse GES increased the expression of the SCF/c-kit pathway. More proliferated interstitial cells of Cajal in muscle layers were observed obviously in the DM + GES group. (3) The number of smooth muscle cells in the DM group was not significantly decreased compared with that in the control group. However, ultrastructural changes were distinctly damaged in the DM group. The application of GES protected against the alteration of the ultrastructures of smooth muscle cells. Conclusions Long-pulse GES improves gastric contraction possibly by enhancing the proliferation of interstitial cells of Cajal and restoring the injury of smooth muscle cells.
Peak-Hour Subway Passenger Flow Forecasting: A Tensor Based Approach

Passenger flow forecasting plays a critical role in intelligent transportation systems. Existing studies mainly focused on the accuracy of forecasting. In this paper, we propose a tensor-based framework combined with “priori modeling” and “posterior analysis”, which can forecast peak-hour passenger flow with both accuracy and interpretability. This framework consists of three parts: 1) construct 5-way tensor model based priori features of passenger flow (e.g. multiple periodicity, temporal variabilities and spatial information); 2) design a tensor completion algorithm to forecast peak-hour passenger flow; 3) qualitatively and quantitatively assess the reliability and importance of priori features. Our framework is evaluated on a real world large-scale passenger flow dataset in the largest city in China. Experimental results showed that our approach achieves 25%-40% improvement over baselines. We also provided in-depth analysis for traffic meanings of modes of tensor and the rank of importance of priori features.
Adaptive Semi-autonomous Agents via Episodic Control

Shared autonomy is a situation in which agents adapt to users based on feedbacks to jointly accomplish tasks. In the case of semiautonomous agents such as mobile robots operational load can be reduced by adaptively automating their behavior. Machine learning is one of the method for adapting teleoperated agents to users as control tendency depends on users and environments. It is difficult to regard user inputs as supervisions because user controls are not always available during descisionmaking processes for agents that continually make decisions (such as navigation robots).
Mobility pattern recognition method: Segmentation and geographic projection

In this paper, a new spatial temporal pattern recognition technique specifically tailored to analyze mobility patterns is developed. The analysis is executed in the dimensions of time and space while taking into account the types of activities performed by population. The mobility patterns are transformed into sequences of strings through segmentation in the dimension of time, and a series of geographic projection operations in the dimension of space. The sequences are compared using Sequence Alignment Method (SAM) generating dissimilarity matrices and through data mining, clusters of mobility patterns along with their representative patterns are emerged. Using household travel survey data collected in New York City, we conduct an experiment to illustrate the application of the methodology and to demonstrate its capability in addressing questions regarding pattern disparity analysis and recognition on transportation data.
Unconditional Perseveration of the Short-Term Mental Set in Chunk Decomposition

A mental set generally refers to the brain’s tendency to stick with the most familiar solution to a problem and stubbornly ignore alternatives. This tendency is likely driven by previous knowledge (the long-term mental set) or is a temporary by-product of procedural learning (the short-term mental set). A similar problem situation is considered the factor required for perseveration of the long-term mental set, which may not be essential for the short-term mental set. To reveal the boundary conditions for perseveration of the short-term mental set, this study adopted a Chinese character decomposition task. Participants were asked to perform a practice problem that could be solved by a familiar loose chunk decomposition (loose solution) followed by a test problem, or they were asked to repeatedly perform 5–8practice problems followed by a test problem; the former is the base-set condition, and the latter is the enhanced-set condition. In Experiment 1, the test problem situation appeared to be similar to the practice problem and could be solved using the reinforced loose solution and also an unfamiliar tight chunk decomposition (tight solution) (a 2-solution problem). In Experiment 2, the test problem situation differed from the practice problem and could only be solved using an unfamiliar tight solution (a 1-solution problem). The results showed that, when comparing the enhanced-set and base-set conditions, both the accuracy rate and the response times for solving the test problem with a tight solution were worse in Experiment 1, whereas the response times were worse in Experiment 2. We concluded that perseveration of the short-term mental set was independent of the similarity between problem situations and discuss the differences in perseveration between two types of fixation.
Learning to Attend to Salient Targets in Driving Videos Using Fully Convolutional RNN

Driving involves the processing of rich audio, visual and haptic signals to make safe and calculated decisions on the road. Human vision plays a crucial role in this task and analysis of the gaze behavior could provide some insights into the action the driver takes upon seeing an object/region. A typical representation of the gaze behavior is a saliency map. The work in this paper aims to predict this saliency map given a sequence of image frames. Strategies are developed to address important topics for video saliency including active gaze (i.e. gaze that is useful for driving), pixel- and object-level information, and suppression of non-negative pixels in the saliency maps. These strategies enabled the development of a novel pixel- and object-level saliency ground truth dataset using real-world driving data around traffic intersections. We further proposed a fully convolutional RNN architecture capable of handling time sequence image data to estimate saliency map. Our methodology shows promising results.
Explainability by Parsing: Neural Module Tree Networks for Natural Language Visual Grounding

Grounding natural language in images essentially requires composite visual reasoning. However, existing methods over-simplify the composite nature of language into a monolithic sentence embedding or a coarse composition of subject-predicate-object triplet. They might perform well on short phrases, but generally fail in longer sentences, mainly due to the over-fitting to certain vision-language bias. In this paper, we propose to ground natural language in an intuitive, explainable, and composite fashion as it should be. In particular, we develop a novel modular network called Neural Module Tree network (NMTree) that regularizes the visual grounding along the dependency parsing tree of the sentence, where each node is a module network that calculates or accumulates the grounding score in a bottom-up direction where as needed. NMTree disentangles the visual grounding from the composite reasoning, allowing the former to only focus on primitive and easy-to-generalize patterns. To reduce the impact of parsing errors, we train the modules and their assembly end-to-end by using the Gumbel-Softmax approximation and its straight-through gradient estimator, accounting for the discrete process of module selection. Overall, the proposed NMTree not only consistently outperforms the state-of-the-arts on several benchmarks and tasks, but also shows explainable reasoning in grounding score calculation. Therefore, NMTree shows a good direction in closing the gap between explainability and performance.
Learning to Predict Pedestrian Intention via Variational Tracking Networks

We propose a new deep learning based system for short term prediction of pedestrian behavior in front of a vehicle. To achieve this, we first develop a framework for class-specific object tracking and short term path prediction based on a variant of a Variational Recurrent Neural Network (VRNN), which incorporates latent variables corresponding to a dynamic state space model. The low level visual features learned from this system were found to be highly informative for the discrete intention prediction task (i.e., predicting whether a pedestrian is stopping or crossing), and achieved high performance on the Daimler benchmark. This is despite a much smaller training dataset than is normally used for training deep learning models. To the best of our knowledge, we are the first to apply deep learning to this problem without using externally trained pedestrian pose estimation systems. Our system performs comparable to the state-of-the-art approach that relies on pose estimation, and runs in real time.
Robotic Arm Motion for Verifying Signatures

This paper proposes a novel set of function-based features for dynamic signature verification. They are inspired on the human stance and the variations in the angles of the arm joints when signing. Specifically, we propose to convert the trajectory and pen-tip altitude and azimuth from an on-line signature into the required sequence of an anthropomorphic robot poses to reproduce such signature. Then, these new robotic sequences are evaluated in an on-line automatic signature verifier. The robotic arm poses are defined by the angles of its joints. These values are worked out by means of the homogeneous transformation matrices between the different coordinate frames of each joint by using the Denavit-Hartenberg (DH) parameterization. The sequences of joint angles represent the new function-based feature space that we propose for signature verification. Our experimental results on the MCYT-100 corpus highlight the effectiveness of robotic-based features in signature verification. Moreover, competitive performances are achieved in random and skilled forgery experiments, compared to previous approaches.
Privacy Partition: A Privacy-Preserving Framework for Deep Neural Networks in Edge Networks

The rise of the Internet of Things (IoT) encourages an emerging computing paradigm - edge computing - which leverages innovations in "last mile" communications infrastructure to provide improved quality of service guarantees to compute-intensive services such as autonomous driving and improved support for connected devices. Many high-value edge computing applications benefit from an integration of privacy-sensitive resource-constrained local data streams and data-hungry resource-constrained analytic tools like deep neural networks. We propose a practical method for privacy-preservation in deep learning classification tasks based on bipartite topology threat modeling and an interactive adversarial deep network construction in the context of edge computing. We term this approach Privacy Partition. A bipartite topology consisting of a trusted local partition and untrusted remote partition provides an apt alternative to centralized and federated collaborative deep learning frameworks in the case of deployment contexts such as IoT smart spaces, where users would like to restrict access to high-resolution data streams due to privacy concerns but would still like to benefit from deep learning services and external computational resources such as remote cloud data centers.
IoTC2: A Formal Method Approach for Detecting Conflicts in Large Scale IoT Systems

Internet of Things (IoT) has become a common paradigm for different domains such as health care, transportation infrastructure, smart homes, smart shopping, and e-commerce. With its interoperable functionality, it is now possible to connect all domains of IoT together to provide comprehensive services to the users. Because numerous IoT devices can connect and communicate at the same time, there can be events that trigger conflicting actions for an actuator or an environmental feature. This paper provides a formal method approach, IoT Confict Checker (IoTC2), to ensure safety of controller and actuators’ behavior with respect to conflicts. Any policy violation results in detection of the conflicts. We define the safety policies for controller, actions, and triggering events and implement them in Prolog to prove the logical completeness and soundness. In addition to that, we have implemented the detection policies in Matlab Simulink Environment with its built-in Model Verification blocks. We created a smart home environment in Simulink and showed how the conflicts affect actions and corresponding features. The scalability, efficiency, and accuracy of our method are tested in this simulated environment.
Modeling Temporal Dynamics of User Preferences in Movie Recommendation

Users in movie recommender systems are likely to change their preferences over time. Modelling the temporal dynamics of user preferences is essential for improving the recommendation accuracy. In this paper, we propose an approach to model temporal dynamics of user preferences in movie recommendation systems based on a coupled tensor factorization framework. We weigh the past user preferences and decrease their importance gradually by introducing an individual time decay factor for each user according to the rate of his preference dynamics. We exploit users' demographics as well as the extracted similarities among users over time, aiming to enhance the prior knowledge about user preference dynamics, in addition to the past weighted user preferences to generate movie recommendations. Our experiments on the public benchmark dataset, MovieLens show that our model outperforms other competitive methods and is more capable of alleviating the problems of cold-start and data sparsity.
Fast Motif Discovery Using a New Motif Extension Algorithm

In biology, proteins are modeled as a long chain of amino acids in primary structure. Generally, each protein is composed of 20 types of amino acids and the number and the arrangement of amino acids vary among different proteins. A sequence motif is a repeated pattern of consecutive amino acids in the primary structure of proteins which can provide information about some important biological features such as transcription factor binding and protein-protein interaction sites. In this paper, we proposed a new motif extension algorithm to enhance the performance of de Bruijn which is one of the recent motif discovery algorithms. The proposed algorithm receives an initial set of candidate motifs and tries to extend them to a desired length using a two-sided approach. In the proposed algorithm, the problem state is limited by a similarity threshold which is given by the user as a constraint. The algorithm for the development of candidate motifs always selects a characters whose appearance are greater than that of the specified similarity threshold. We conducted some experiments on real hardware and real inputs to evaluate our algorithm. The results showed that the proposed algorithm is at least 20 times faster than the original de Bruijn algorithm. Furthermore, the average similarity of identified motifs to the input protein family was 28% higher than the counterpart.
Investigating the oscillation characteristics and mitigating its impact with low-penetration connected and automated vehicles

This paper studies the traffic oscillations under connected environment by connecting it with vehicles' car-following behaviors. The manual vehicles are calibrated with the intelligent driver model to reproduce the traffic oscillations by simulating heterogeneous vehicles driving on a single lane. The characteristics of traffic oscillations are then investigated with the second-order difference of cumulative data method. With the incorporation of different ratios of connected and automated vehicles (CAVs), we study its impact on traffic flow and propose several control strategies (responsive control without connection, proactive control with V2V connection, and traffic wave suppression control with V2I connection) with low-penetration CAVs. The performance of these control strategies are then compared, while the proposed control strategies can improve traffic flow operation by decreasing traffic oscillation occurrences.
Optimal Predictive Steering Control for Aircraft Ground Path Following

Whilst autopilot systems have been commonly used by aircraft in flight, the control of aircraft on the ground has received limited attention in the literature. This paper presents a real-time optimal controller based on linear quadratic (LQ) method with a road preview model incorporated with the aircraft model. The aircraft ground dynamics are captured by a 6-DOF nonlinear model which is linearized at a range of equilibrium points. The proposed LQ controller is compared with a reference, simple driver model in runway exit manoeuvres by following the centerline at different speeds. Taking advantage of the road preview mechanism and optimal control gain, the presented controller outperforms the driver model with lower track error.
Robust Lane Keeping Control in Automated Vehicles: A Driver-in-the Loop Approach

The shared control between human driver and autonomous controller for vehicles has been evaluated over several driving conditions such as cruise control, lane keeping/ lane change assist, highway driving etc. Typically, these approaches synthesize controllers based on linear vehicle models integrated with visual cues based driver model. In this work, to consider the aspect of tire-friction non-linearity, Brush-Tire force model has been considered. Further, to address conflict between human driver and autonomous controller during typical maneuvers, a novel sharing parameter was introduced in the proposed robust sliding mode based control design. Subsequently, the proposed shared control technique is validated thru numerical simulations over the Satory test track, with lateral wind force and road curvature considered as disturbance. Discussions on influence of varying road-friction conditions and sharing parameter proposed is also presented.
[Regular Paper] Low Cost Micro-Droplet Formation Chip with a Hand-Operated Suction Syringe

Removing syringe pump from microfluidics droplet formation devices has many advantages such as using a small amount of biomedical sample directly from pipette to the inlet port of the channel, furthermore the system becomes smaller in size and lower in cost by abolishing the cost of the syringe pump. In this study, a quite simple and low-cost, power-free pumping method to form micro-droplets in a PMMA double T-Junction microfluidic channel is presented. The device was fabricated by direct write laser technique using PMMA substrate. By taking benefits of the suction pressure that comes from a single hand-operated suction syringe, the device generates droplet's volume about 2 Nano-liter consequently no complicated system is required. The low cost of the PMMA sheet and the fabrication process causes that one chip is about 30 cent. This makes it disposable as a single -use which is recommended in many medical analyses.
Theory and simulation of a neuro-fuzzy controller for 20MW turbo generator control

This document focuses on the development of a learning tools for the dynamic model of a steam turbine 20MW capacity type able to accommodate the different variations of the machine’s operating parameters. Considering all the advantages and disadvantages of the optimization methods that we have studied (genetic algorithm and neural networks), the neuro-fuzzy option was remarkable in view of the results obtained in the modeling of the systems. The ANFIS model (Adaptive Neuro-Fuzzy Inference System), known for its performance in the learning domain of complex non-linear systems, is chosen for a better approximation of functions for modeling. The active power depending of the high pressure steam flow inlet turbine and the low pressure steam flow extraction as well as the voltage across the generator depending of the power factor and the active power shall be controlled from 2 ANFIS models mounted in parallel, their outputs acting respectively on the control of the HP (High Pressure) steam inlet valve opening for the variation of the HP steam flow inlet and the rotor excitation current for the variation of the voltage across the alternator. The dynamic model thus obtained from the adjustment of the parameters of the membership functions describes the behavior of the turbine to stabilize its operating point in the face of any disturbance.
A Wideband Fault Location Scheme for Active Distribution Systems

This paper proposes a fault location technique for future distribution systems with distributed generation. The technique considers the circuit analysis at the non-fundamental frequencies that are generated when a fault occurs. To consider both inductive and capacitive parameters of the line, the distributed parameter line model is used for the analysis. A general 5th order polynomial is derived to estimate the fault distance and can be used for different fault types. The IEEE 34-bus feeder which is an actual distribution system is simulated and used to assess the accuracy of the method. The results obtained from different test conditions emphasize the high accuracy of the method with almost all cases having an error in estimated distance of less than 100 m.
SysTaint: Assisting Reversing of Malicious Network Communications

The ever-increasing number of malware samples demands for automated tools that aid the analysts in the reverse engineering of complex malicious binaries. Frequently, malware communicates over an encrypted channel with external network resources under the control of malicious actors, such as Command and Control servers that control the botnet of infected machines. Hence, a key aspect in malware analysis is uncovering and understanding the semantics of network communications. In this paper we present SysTaint, a semi-automated tool that runs malware samples in a controlled environment and analyzes their execution to support the analyst in identifying the functions involved in the communication and the exchanged data. Our evaluation on four banking Trojan samples from different families shows that SysTaint is able to handle and inspect encrypted network communications, obtaining useful information on the data being sent and received, on how each sample processes this data, and on the inner portions of code that deal with the data processing.