Miracle-dz/UniFAR-FTUs huggingface dataset:
Prediction-based Resource Allocation using LSTM and Minimum Cost and Maximum Flow Algorithm

Predictive business process monitoring aims at providing the predictions about running instances by analyzing logs of completed cases of a business process. Recently, a lot of research focuses on increasing productivity and efficiency in a business process by forecasting potential problems during its executions. However, most of the studies lack suggesting concrete actions to improve the process. They leave it up to the subjective judgment of a user. In this paper, we propose a novel method to connect the results from predictive business process monitoring to actual business process improvements. More in detail, we optimize the resource allocation in a non-clairvoyant online environment, where we have limited information required for scheduling, by exploiting the predictions. The proposed method integrates offline prediction model construction that predicts the processing time and the next activity of an ongoing instance using LSTM with online resource allocation that is extended from the minimum cost and maximum flow algorithm. To validate the proposed method, we performed experiments using an artificial event log and a real-life event log from a global financial organization.
Hysteresis Observation in Smart Material Actuators with Unstructured Uncertainty

Hysteresis phenomenon, which is prevalent in smart material actuators can affect control of the actuator, and should be compensated in the controller. In this paper its value is formulated as an internal state of the actuator, that is modeled by the Bouc-Wen model. A state observer, based on the position measurement is presented to estimate the hysteresis, which is not measurable by commercial sensors. The observer and convergence proof are presented using Lyapunov theorem, and the necessary constraints on design parameters are extracted. To facilitate these constraints, a lower order observer using velocity measurement is presented. The latter observer shows better transient and faster response. Both of the proposed observers are robust against model uncertainty. The effect of uncertainty in the estimated value is quantified and validated. An experimental setup using a piezoelectric actuator and an integrated strain gauge sensor is used as the test setup. Simulation and experimental results verify the effectiveness of the proposed schemes.
Land Subsidence Monitoring Along Highway And Railway in SU-XI-CHANG Area

Tremendous groundwater withdrawal has caused land subsidence disaster in Su-Xi-Chang area, China. In order to monitor land subsidence and reduced the potential damages to railway and highway, nn effective monitoring method is urgently needed. SBAS-InSAR method with SAR data provides an effective ability to detected land subsidence in recent years. In this work, we monitor land subsidence area along railway and highway in Su-Xi-Chang area from 2007 to 2012 by using SBAS-InSAR processing method with ALOS PALSAR SAR data. The InSAR result along highway and highway ranged from -50 mm/year to 16 mm/year, -50 mm/year to 26 mm/year, respectively. Analysis of land subsidence along highway in Wuxi area showes that the area with serious land subsidence distributes in the industrial concentrated area.
An Optimization Method for Cooling System Design of Traction Motors

This paper presents an optimization method for traction motor cooling system. The proposed method is generic and can cover a wide range of cooling systems used in e-mobility, railway, marine and aerospace applications. The optimization procedure utilizes both numerical and analytical modeling methods to estimate accurately thermal and cooling parameters in motor critical parts. Analytical approaches, in combination with numerical ones, enable accurate estimation of the motor cooling condition while keeping the computation time within a reasonable range. CFD calculations provide boundary conditions and heat transfer evaluation of the airflow inside the motor for developed finite element and lumped approach models where the most temperature sensitive parts are located. Several thermal and flow measurements are carried out on a 245 kW open self-ventilated induction motor to evaluate accuracy of different components of the optimization procedure. A good agreement between the thermal model, CFD calculation and experimental results is achieved.
ButtonTips: Design Web Buttons with Suggestions

Buttons are fundamental in web design. An effective button is important for higher click-through and conversion rates. However, designing effective buttons can be challenging for novices. This paper presents a novel interactive method to aid the button design process by making design suggestions. Our method proceeds in three steps: 1) button presence prediction, 2) button layout suggestion and 3) button color selection. We investigate two distinct but complementary interfaces for button design suggestion: 1) region selection interface, where the button will appear in a user-specific region; 2) element selection interface, where the button will be associated with a user-selected element. We compare our method with an existing website building tool, and show that for novice designers, both interfaces require significantly less manual efforts, and produce significantly better button design, as evaluated by professional web designers.
River Water Pollution Pattern Prediction using a Simple Neural Network

Rivers are an important element of its environment; river water sustains and prospers living beings in its surrounding. When river water becomes polluted, though, it becomes useless or even harmful to its ecosystem. This Paper proposes an IoT (Internet of Things) based system as a solution to counteract river pollution. The system is composed of a hardware that measures pH, temperature, and turbidity of the water – then transmitting the data via LPWAN (Low Power Wide Area Network), more specifically LoRa (Long Range. Successfully transmitted data will be used to train an ANN (Artificial Neural Network) which is used to recognize and predict patterns of river water pollution. The monitoring and prediction results will be accessible via a web app. This Paper has successfully designed and built a system that implements an ANN for recognizing patterns in river conditions, to predict potential river pollution. Early detection of river pollution can serve as vital information to act in preventing or anticipating river pollution.
Forecasting Interruptions in Power Supply Using Photovoltaics in Poland – Case Study of Safe Pedestrian Crossing

Power supply for autonomous systems is commonly provided by photovoltaics (PV), especially for civil-engineering applications, where seamless integration of PV with landscape is a valuable asset.The design rules for autonomous systems are always highly climate and application dependent. In the engineering practice, the systems are commonly over-sized or under-sized, and largely fail to meet the expectations in terms of economy or supply-autonomy.This paper investigates the application of PV for powering the safe pedestrian crossing, with all-night illumination, using detailed solar irradiance data for Polish climate. The results show the realistic forecast of energy deficits and give the design rules for optimal system size.
Filtering the Noise to Reveal Inter-Domain Lies

On the Internet, routers of Autonomous Systems (ASes) have to determine their preferred inter-domain route, i.e. control path (CP), for each IP prefix. The traffic is then forwarded AS after AS, following a data path (DP) that should match the CP for the same prefix. The underlying implicit trust that ASes advertise the paths they use for packet forwarding may be misplaced. Network operators may tweak CPs and DPs to carry out inter-domain lies that are visible when the two paths differ. Lies can be either unintended, due to misconfigurations or technical limitations, or deliberate, e.g. for economical gain. While lies globally mitigate the ability to troubleshoot and understand the root cause of connectivity issues, detecting them is not a trivial task as the ground data is noisy.In this paper, we propose a modular framework to measure and correctly quantify the discrepancies between CPs and DPs. We define several rules to overcome specific sources of noise inducing mismatches (MMs), e.g., incomplete traces, sibling ASes, IXPs or third-party addresses in general. We leverage the Peering testbed to conduct a measurement campaign at a scale never achieved before, and conclude that, while the upper bound of lies is significant, the lower bound is not negligible. This suggests that the noise interfering with collected traces is not the sole culprit for the MMs between CPs and DPs.
Design and Heuristic Optimization of a CMOS LNA for Ultra-Wideband Receivers

In this paper, an ultra-wideband (UWB) 3–10 GHz low noise amplifier (LNA) designed in a 0.18 μm RF-CMOS technology is presented. A noise cancelling technique has been used to improve the noise performance of the proposed UWB LNA. Moreover, the proposed LNA uses current reuse and inductive peaking techniques to provide low power dissipation and high flat power gain. An input impedance matching of 50 Ω is provided by a new network matching over the 3–10 GHz band. In addition, a new method for multiobjective optimization of the UWB-LNA using multi-objective inclined planes system optimization (MOIPO) is introduced. The proposed UWB-LNA has been simulated by Cadence Spectre-RF. The post-layout simulation results shows an input reflection coefficient (S11) below than −15 dB, flat power gain (S21) of 12.5 dB and noise figure (NF) of 3.8 dB over the whole bandwidth. The power consumption is about 5 mW at a 1.8 V power supply.
Transfer Learning for Alzheimer's Disease Detection on MRI Images

In this paper, we focus on Alzheimer's disease detection on Magnetic Resonance Imaging (MRI) scans using deep learning techniques. The lack of sufficient data for training a deep model is a major challenge along this line of research. From our literature review, we realised that one of the current trends is using transfer learning for 2D convolutional neural networks to classify subjects with Alzheimer's disease. In this way, each 3D MRI volume is divided into 2D image slices and a pre-trained 2D convolutional neural network can be re-trained to classify image slices independently. One issue here, however, is that the 2D convolutional neural network would not be able to consider the relationship between 2D image slices in an MRI volume and make decisions on them independently. To address this issue, we propose to use a recurrent neural network after a convolutional neural network to understand the relationship between sequences of images for each subject and make a decision based on all input slices instead of each of the slices. Our results show that training the recurrent neural network on features extracted from a convolutional neural network can improve the accuracy of the whole system.
A Muti-branch Convolutional Netural Network for Mobile Platform

We proposed a new type of light-weight convolutional neural network MRINet for low computing power requirements. This model is applied with strategies including depthwise separable convolution, Channel pruning and ELU activation. It greatly reduces the amount of calculation while getting high accuracy. MRINet can complete the training process and application on mobile phones and other mobile platforms. By integrating into the corresponding application, which can solve many real problems including self-medication. By training on the ISIC dataset, as compared to MobileNet, we improved training speed by 23%, while accuracy is increased 3.3%.
Singularity Avoidance Control of a Non-Holonomic Mobile Manipulator for Intuitive Hand Guidance

Mobile manipulators are robot systems capable of combining logistics and manipulation tasks. They thus fulfill an important prerequisite for the integration into flexible manufacturing systems. Another essential feature required for modern production facilities is a user-friendly and intuitive human-machine interaction. In this work the goal of code-less programming is addressed and an intuitive and safe approach to physically interact with such robot systems is derived. We present a natural approach for hand guiding a sensitive mobile manipulator in task space using a force torque sensor that is mount close to the end effector. The proposed control structure is capable of handling the kinematic redundancies of the system and avoid singular arm configurations by means of haptic feedback to the user. A detailed analysis of all possible singularities of the UR robot family is given and the functionality of the controller design is shown with laboratory experiments on our mobile manipulator.
Modeling Data, Information and Knowledge for Security Protection of Hybrid IoT and Edge Resources

Currently, with the growth of the Internet of Things devices and the emergence of massive edge resources, security protection content has not only empowered IoT devices with the accumulation of networked computing and storage as a flexible whole but also enabled storing, transferring and processing DIKW (data, information, knowledge, and wisdom) content at the edge of the network from multiple devices in a mobile manner. However, understanding various DIKW content or resources poses a conceptual challenge in unifying the semantics of the core concepts as a starting point. Through building metamodels of the DIKW framework, we propose to cognitively formalize the semantics of the key elements of the DIKW in a conceptual process. The formalization centers on modeling the perceived world only by relationships or semantics as the prime atomic comprising elements. Based on this cognitive world model, we reveal the difference between relationships and entities during the conceptualization process as a foundation for distinguishing data and information. Thereafter, we show the initial case for using this formalization to construct security protection solutions for edge computing scenarios centering on type conversions among typed resources formalized through our proposed formalization of the DIKW.
Combined Analytic-Linear-FE Approach for Fast Stress Analysis of High-Speed Interior PM Rotors

Because of the complex rotor assemblies, the design of high-speed interior permanent-magnet (IPM) machines has to deal with highly conflicting requirements arising from the electromagnetic and mechanical optimization. Suitable Finite Element (FE) models for stress analysis of IPM rotors have to incorporate non-penetration contact constraints at the PM-lamination interface then causing geometric nonlinearity which increases the computation time considerably. This paper presents a systematic mixed analytic-FE approach to derive realistic equivalent pressure distributions representing the contact interaction PM-lamination and to be applied to the lamination boundaries in a linear and homogeneous FE model. This allows fast FE analyses to be easily run using open-source general-purpose FE solvers and avoiding non-linear contact formulations. The analytical pressure distributions are implemented in the open source FE solver Freefem++ and validated by (geometric) non-linear static FE analyses with a commercial package implementing “real” non-penetration contact constraints.
Indoor Navigation Based on Model Switching in Overlapped Known Regions

This paper introduces a novel drone navigation algorithm based on overlapped known regions (OKR). Each OKR has associated a neural network model, which takes as input an RGB image from a camera located at the top of the drone. This model generates two outputs: the distance to the center of the region, and the orientation of the vector that points to the center of the region in the horizontal plane. These regions are constrained to overlap the center of neighbor regions. After training, the drone is able to navigate continuously through several regions by switching the model parameters once the center of each region is reached. Additionally, in order to significantly reduce the number of parameters of each model an adaptive convolutional kernel (ACK) is used, which is able to redefine the convolutional kernel during the inference time according to the input image.
Blockchain for Reliable and Secure Distributed Communication Channel

Distributed storage systems (DSS) have a potential of storing big amounts of data. Specialized DSS codes were defined with the main idea to enhance the reliability, availability, maintenance and data security in DSS systems. In this paper, we establish a new way of communication, named Distributed Communication Channel (DCC), that uses the coding mechanism that is similar to the one applied in the DSS. DCC represents a communication channel between two or more parties, that exchange information through multiple physical channels. It adapts the DSS concept using the benefits established with respect to the communication reliability and security against intruders and attacks. Moreover, with this concept we offer an increased security using the recent developments used in the Blockchain technology. The blockchain is used as a tool to securely bypass the transformation matrix or the exchange of encryption keys, between the sender and the receiver in the communication channel. The whole system uses the so-called spatial spreading mechanism to transfer the data through independent channels. Therefore, it is more reliable and secure, than the basic systems using the DSS algorithm.
Fast Style Transfer for Chinese Tranditional Ink Painting

By style transfer mechanism, the style of another image can be illustrated toward another target picture. However, calculating speed and quality are two tricky issues. In this paper, we propose an accelerated version of the transfer for Chinese traditional ink painting style. By reducing the size of the rendering network, the training speed can be increased to 1.27 times. It is about the similar quality as the image generated by the original model. This can also reflect the characteristics of ink painting: light ink, black and white.
Systematic Approach and Tools for Migrating from Text-Based Reports to Structured Reports: Based on the DICOM Structured Reporting Guidelines

A structured reporting system is a set of standard-compliant practices that guides the physicians in order to create structured documents, organizing and simplifying their workload. In spite of this advantages, many physicians still use conventional methods to write their findings, like free-text reports. One main reason for this is the little convention on how to disseminate a structured reporting routine in the report environment. This work proposes a systematic approach to migrate a system routine from free-text reports to structured reports, focusing on the DICOM Structured Reporting guidelines. To evaluate this proposal, a structured reporting system was created in the context of the Santa Catarina State Integrated Telemedicine and Telehealth System (STT/SC), in Brazil, and, in a case study covering obstetric ultrasonography reports, was evaluated by a group of experts using the AdEQUATE model, showing a high user perception from the system. The results are a set of defined premises and steps that turns a telemedicine system into a complete structured reporting environment.
Aberrant Brain Function in Active-Stage Ulcerative Colitis Patients: A Resting-State Functional MRI Study

Background: Patients with ulcerative colitis (UC) usually display cognitive impairments, such as memory loss, attention deficits, and declining executive functions, particularly during the active stage of the disease. However, the potential neurological mechanisms of these symptoms remain unclear. Method: Forty-one patients with mildly to moderately active UC, as well as 42 matched healthy controls, were recruited for an examination using psychological scales, cognitive function tests and resting-state functional magnetic resonance imaging (rs-fMRI). Seed points were identified via analysis of amplitude of low-frequency fluctuation (ALFF), and functional connectivity (FC) was calculated between these seed regions and other voxels in the whole brain. Correlation analyses were performed among clinical indexes, neuropsychological assessments and neuroimaging data. Result: Compared with the healthy controls, patients with UC exhibited lower ALFF values in the bilateral hippocampal/parahippocampal (HIPP/ParaHIPP) region and higher ALFF values in the left posterior cingulate cortex (PCC.L) and left middle frontal gyrus (MFG.L). With HIPP/ParaHIPP as the seed point, the strengths of the FC in the bilateral middle frontal gyri (MFG), anterior cingulate cortex (ACC), and left caudate nucleus (CAU.L) increased; using the PCC.L as the seed point, the strengths of the FC in the middle cingulate cortex (MCC) and the left angular gyrus (AUG.L) increased. These abnormal brain regions were mainly located in the limbic system. By analyzing the correlations between these brain regions and behavioral data, we observed a close correlation between decreased HIPP/ParaHIPP activity and memory loss; increased PCC activity and strength of FC with the AUG.L were related to dysfunction of executive function and attention network in patients with UC. Conclusion: Based on these results, the limbic lobe might be the core of the brain-gut axis (BGA) and play an important role in cognitive impairments, suggesting potential mechanisms for cognitive impairment in patients with UC in the active stage of the disease.
Architecture with Internet of Things for Counting Passengers in Urban Bus

This paper proposes an Architecture based on Internet of Things for passenger counting in bus. The Architecture provides informations about the number of passengers per route in a public transportation system that adopts integrated billing. Experiments were performed with microcontrollers boarded on buses and with the COAP and Ble 6LowPan protocols. Cameras were installed on the buses and Computational Vision algorithms in the microcontrollers analyzed the images to detect people. The results showed that the Internet of Things protocols, COAP and Ble 6LowPan offer low overhead and response time.
Transit Priority Signal Control for Interrelated Intersections

A traffic signal control model based on transit priority strategy, the Transit Priority Signal Control Model is proposed to optimize the signal control of the interrelated intersections. This is a model of two-layer architecture including the regional layer and the single intersection layer. The PTD (People Total Delay) is adopted as the performance index of the model. Short-term traffic flow forecasting is an important factor of the intersection signal control. In single intersection layer, the PSO-BPNN model is employed to forecast the short-term traffic flow. In a given scenario, the TPSCM is evaluated by using Matlab simulation tools. The simulation results show the validity of the TPSCM.
Product Quality Assessment using Opinion Mining in Persian Online Shopping

Human opinions and decisions are mainly inspired by other people's beliefs and experiences which give them a strong background of anything they've heard or read about. Accordingly, extracting and knowing what others think about a special subject or product have become extremely important for different types of service providers as well as the consumers. Opinion mining is the area of analyzing user's sentiments through the available reviews on the internet and has the beneficial application of guiding users for online shopping. Unfortunately, very few studies has been done for sentiment analysis in Persian online shopping and the existing works have many limitations in their performances, esp. in differentiating between a weak and a strong mood in sentimental sentences. This paper proposes a new lexicon-based opinion mining method for Persian online shopping which considers the effect of intensifier adjectives in extracting the exact opinion of a review. It has been applied on a real dataset extracted from Digikala11https://www.digikala. com and has achieved promising results compared to those of expert evaluators.