Miracle-dz/UniFAR-FTUs huggingface dataset:
Hardware-in-the-loop based WCET analysis with KLEE

C programming dominates the mainstream of embedded development as of today. To aid the development, hardware abstractions, libraries, kernels, and light-weight operating systems are commonplace. However, these typically offer little or no help to automatic worst-case execution time (WCET) estimation, and thus manual test and measurement based approaches remain the de facto standard. For this paper, we take the outset from the Real-Time For the Masses (RTFM) framework, which is developed to facilitate embedded software development for IoT devices and provides highly efficient implementations, suitable to the mainstream of embedded system design. Although the Rust language plays currently a minor part in embedded development, we believe its properties add significant improvements and thus implement our RTFM framework in Rust. We present an approach to worst-case execution time estimation in the context of RTFM tasks and critical sections, which renders sufficient information for further response time and schedulability analysis. We introduce our test bench, which utilizes the KLEE tool for automatic test vector generation and subsequently performs cycle accurate hardware-in-the-loop measurements of the generated tests. The approach is straightforward and fully automatic. Our solution bridges the gap in between measurement based and static analysis methods for WCET estimation. We demonstrate the feasibility of the approach on a running example throughout the paper and conclude with a discussion on its implications and limitations.
An On-Chip Dynamically Obfuscated Wrapper for Protecting Supply Chain Against IP and IC Piracies

With the modern semiconductor supply chain, the ownership of both intellectual property (IP) and integrated circuit (IC) cannot be guaranteed. The IP piracy may take place at the untrusted IC designer or untrusted foundry without the knowledge of the original IP owner. The untrusted foundry can also perform IC piracy with reverse engineering of GDSII, overproducing the number of ICs, and shipping out-of-spec/defective devices. A holistic solution is proposed to protect the ownership of both IP owners and IC designers. In this solution, a dynamically obfuscated wrapper for split test (DOST) and a secure split test methodology together aim at preventing IP overusing at multiple abstraction levels and enabling IC designers to fully control the production, test, and authentication processes. DOST has been implemented and validated on video graphics array-liquid crystal display, floating-point and graphics unit, Leon3, and Leon3mp benchmarks. DOST enables the structural tests in the locked mode and the functional tests in the functionally unlocked mode. The results show that the proposed method is highly robust against IP and IC piracies with an insignificant area (1.381%) and power (1.276%) overhead.
Marginal Likelihood Training of BiLSTM-CRF for Biomedical Named Entity Recognition from Disjoint Label Sets

Extracting typed entity mentions from text is a fundamental component to language understanding and reasoning. While there exist substantial labeled text datasets for multiple subsets of biomedical entity types—such as genes and proteins, or chemicals and diseases—it is rare to find large labeled datasets containing labels for all desired entity types together. This paper presents a method for training a single CRF extractor from multiple datasets with disjoint or partially overlapping sets of entity types. Our approach employs marginal likelihood training to insist on labels that are present in the data, while filling in “missing labels”. This allows us to leverage all the available data within a single model. In experimental results on the Biocreative V CDR (chemicals/diseases), Biocreative VI ChemProt (chemicals/proteins) and MedMentions (19 entity types) datasets, we show that joint training on multiple datasets improves NER F1 over training in isolation, and our methods achieve state-of-the-art results.
Predicting Named Entity Location Using Twitter

A knowledge base contains a set of concepts, entities, attributes, and relations. Knowledge bases are increasingly critical to a wide variety of applications in both industry and academia. Yet despite all that, knowledge bases are greatly incomplete. As the world evolves, new entities are generated. Enriching existing knowledge bases with new entities and new location attribute values for them becomes more and more important. Twitter is one of the most popular micro-blogging platforms. Named entities are mentioned frequently in the huge collection of tweets which contain abundant geographical location knowledge. Given a named entity and a set of tweets where the entity appears, we are interested in predicting the entity city-level location using the knowledge embedded in tweets. This task is helpful for many applications such as knowledge base enrichment, tweet location prediction, and entity search. In this paper we propose NELPT, the first unsupervised framework for Named Entity city-level Location Prediction by leveraging the geographical location knowledge from Twitter. This framework leverages a Linear Neural Network model as the predictive model combining two categories of information: (1) local count information; (2) global distributional information. A learning algorithm based on the expectation-maximization (EM) method is proposed to automatically learn the parameters of the Linear Neural Network predictive model without requiring any training data. The experimental results on a real world Twitter data set show that our framework significantly outperforms the baselines in terms of accuracy, and scales very well.
Class Strength V2: An Adaptive Multilingual Tool for Tweet Classification

In this paper we present the second version of our multilingual tweet classification tool. ClassStrength v2 classifies tweets into 14 categories (Sports, Music, News&Politics etc.) using a distant supervision approach. The new version extends the initial set of five languages to ten (English, French, German, Chinese, Japanese, Arabic, Russian, Spanish, Portuguese and Polish). In addition, the classification models of each language get automatically updated every month to allow accurate classification over time. Our experimentation showed that the larger the time gap between the tweet and the data used for training the model, the worse the performance, which motivated for creating an adaptive version of ClassStrength that get its models updated periodically.
Operation of a Modular Multilevel Converter Controlled as a Virtual Synchronous Machine

This paper presents an implementation of a Virtual Synchronous Machine (VSM) based on a Modular Multilevel Converter (MMC). The control system relies on the internal simulation of an electromechanical swing equation, which emulates the inertia and damping effect of a synchronous machine and provides the frequency and phase angle needed for controlling the MMC to operate as a VSM. The ac-side control is based on cascaded voltage and current controllers in a synchronous reference frame defined by the virtual swing equation. The reference signals for the ac-side voltage control are generated from a reactive power control loop and a quasi-stationary virtual impedance which emulates the equivalent voltage drop over the stator windings of a synchronous machine. The control system also includes internal control loops for regulating the double frequency circulating currents of the MMC. The presented VSM implementation is tested in a laboratory environment using an MMC with 18 half-bridge sub-modules per arm. Experimental results demonstrate the operation of the VSM-controlled MMC when connected to an external ac grid and in islanded mode feeding a resistive load.
Fast Joint Object Detection and Viewpoint Estimation for Traffic Scene Understanding

Environment perception is a critical enabler for automated driving systems since it allows a comprehensive understanding of traffic situations, which is a requirement to ensure safe and reliable operation. Among the different applications, obstacle identification is a primary module of the perception system. We propose a vision-based method built upon a deep convolutional neural network that can reason simultaneously about the location of objects in the image and their orientations on the ground plane. The same set of convolutional layers is used for the different tasks involved, avoiding the repetition of computations over the same image. Experiments on the KITTI dataset show that our efficiency-oriented method achieves state-of-the-art accuracies for object detection and viewpoint estimation, and is particularly suitable for the recognition of traffic situations from on-board vision systems. Code is available at https://github.com/cguindel/lsi-faster-rcnn.
Clicker Supported Team-Based Learning Environment: A Qualitative Study

In recent years Clicker technology has been widely used at universities to provide interactive learning environments. It is used with suitable pedagogic approaches to obtain the expected learning outcome. The aim of the study is to specify student views about the environment developed as a result of the integration of Team-based learning strategy and clicker technology in education. The participants are 30 first-year engineering students taking Physics I at Near East University. A case study design was used in this research. After overviewing the literature, the researcher prepared a semi-structured interview form to collect the data. İnterviews at the voluntary base was given after a five-week application process. The data were analyzed through content analysis method. The findings indicated that student perception of clicker supported Team-Based learning was positive. The students admitted that this environment enhanced their learning and they even made suggestions. This study can help educationalists integrate clicker technology in Team-Based learning strategy.
A 112-GB/S PAM4 Transmitter in 16NM FinFET

This work reports a 112-Gb/s low power voltage-mode transmitter (TX) with four-tap feed forward equalization (FFE), designed and fabricated in 16nm FinFET technology. The design includes a hybrid impedance controller with dual regulator architecture for independent swing, common-mode and equalization control. The 56-Gb/s 4:1 multiplexer (MUX) array together with an automatic phase alignment technique is proposed to minimize output jitter. The TX consumes 345 mW and provides 0.6 to 1-Vpp output swing. The PAM4 TX achieves 130-fs random jitter (RJ) with ratio of level mismatch (RLM) > 0.963.
Integrating Fog With Long-Reach PONs From a Dynamic Bandwidth Allocation Perspective

Integrating fog computing with optical access networks is believed to form a highly capable fronthaul that will live up to the various requirements and challenges of tomorrow's access networks. Such integration combines the high capacity of optical fiber with closer-to-the-edge computing and storage capabilities. However, because optical access networks were not originally designed to carry offloaded traffic nor support edge-to-edge communications, the network architecture and employed bandwidth allocation both need to be reconsidered in this new setting. In this paper, we study the offloading performance in a long-reach optical access network when the underlying bandwidth allocation is either centralized or decentralized. We investigate how offloading can be supported in each paradigm and develop an analytical framework that is tested against numerical results. The effect of offloading on regular upstream traffic is also examined as well as the effects of cloudlet placements and network extension on the offloading performance.
Investigation of Dual Stator Spoke Type Vernier Machine for EV Application.

Recently, vernier machines have been nominated for EV applications numerously [1]–[3]. The attractive features of vernier machine for EV applications are a reduction of volume due to the removal of gearbox [4], and high power density owing to magnetic gear effect, also called as vernier effect. However, one of the critical issues for vernier machine on EV application is the low power factor. To enhance power factor of vernier machine, various methods and topologies were suggested continuously [5]. Especially, one stator-spoke type vernier machine was suggested to increase power factor with the use of flux focusing effect. However, it had flux bypass issue that reduced vernier effect [6]. To solve the issue, dual stator spoke type vernier machine (DSSTVM) uses dual stator to permit flux as additional flux path [7]. DSSTVM eventually realized flux focusing effect to boost torque thus high power factor. However, the study of DSSTVM had been limited to the combination of 12 slots and 20 poles [8]. In this paper, the comparative study of different slot/pole combination DSSTVMs with different winding configurations to find a suitable model for the EV application is done. Power density per volume and weight is a critical feature for light weighted EV. Additionally, good flux weakening capability for wide speed range and high efficiency for less energy usage is required for future EV application. Hence, comparisons of different models of DSSTVM with the aspect of back-emf, torque, power density, power factor, loss, and efficiency are done. The top priority of the work was to find the best model for EV application in overall performance and to discover various trade-off relationships for each machine with differences they are given. Finite element method (FEM) analysis is utilized for the overall performance comparison.
Opportunities for Leveraging Low-Voltage GaN Devices in Modular Multi-level Converters for Electric-Vehicle Charging Applications

Modular multi-level converters (MMCs), already well-established in high-voltage, high-power AC-DC conversion, can potentially bring advantages in lower-power applications, such as on-board chargers in electric vehicles (EVs). The availability of mature, high-quality GaN devices with low voltage ratings have made it worthwhile to consider the MMCs for these applications, due to its limited voltage gradients and higher AC-side power quality. To investigate these possibilities, a simulated 6-level MMC is compared against an experimentally-validated two-level EV charger. Both converters are designed for a maximum power level of 6.6 kW and compatible with 240 V and 400 V AC-side and DC-link voltages, respectively. The study reveals that the MMC offers great promise in terms of power-quality improvement and AC-side filtering requirements, and the need for large sub-module capacitances to maintain the module voltages is counterbalanced by the reduced requirements for EMI filtering and DC-link decoupling.
Associative Multichannel Autoencoder for Multimodal Word Representation

In this paper we address the problem of learning multimodal word representations by integrating textual, visual and auditory inputs. Inspired by the re-constructive and associative nature of human memory, we propose a novel associative multichannel autoencoder (AMA). Our model first learns the associations between textual and perceptual modalities, so as to predict the missing perceptual information of concepts. Then the textual and predicted perceptual representations are fused through reconstructing their original and associated embeddings. Using a gating mechanism our model assigns different weights to each modality according to the different concepts. Results on six benchmark concept similarity tests show that the proposed method significantly outperforms strong unimodal baselines and state-of-the-art multimodal models.
Real-Time Detection and Recognition of Road Traffic Signs using MSER and Random Forests

Real-time detection and recognition of road traffic signs plays an important role in advanced driving assistance system. Typically, the region of interest (ROI) method is effective in feature extraction but inefficient because it is sensitive to illumination changes. In this paper, we propose a maximally stable extremal regions (MSER) method with image enhancement to greatly improve ROI. Firstly, we employ gray world algorithm to process original images. And then potential areas of traffic signs are obtained through increasing the image contrast ratio and extracting the image-enhanced MSER. According to the characteristic variable and the geometry moment invariants, the geometric characteristics of traffic signs are extracted to obtain the ROIs. Finally, HSV-HOG-LBP feature is constructed and the random forests algorithm is used to identify the traffic signs. The experimental results show that our proposed method show strong robustness on illumination condition and rotation scale, and achieves a good performance by experiments with actual images and German traffic sign detection benchmark (GTSDB) data set.
A Multi-Objective Optimization Approach for Question Routing in Community Question Answering Services (Extended Abstract)

Community Question Answering (CQA) has increasingly become an important service for people asking questions and providing answers online, which enables people to help each other by sharing knowledge. Recently, with accumulation of users and contents, much concern has arisen over the efficiency and answer quality of CQA services. To address this problem, question routing has been proposed which aims at routing new questions to suitable answerers, who have both high possibility and high ability to answer the questions. In this paper, we formulate question routing as a multi-objective ranking problem, and present a multi-objective learning-to-rank approach for question routing (MLQR), which can simultaneously optimize the answering possibility and answer quality of routed users. In MLQR, realizing that questions are relatively short and usually attached with tags, we first propose a tagword topic model (TTM) to derive topical representations of questions. Based on TTM, we then develop features for each question-user pair, which are captured at both platform level and thread level. In particular, the platform-level features summarize the information of a user from his/her history posts in the CQA platform, while the thread-level features model the pairwise competitions of a user with others in his/her answered threads. Finally, we extend a state-of-the-art learning-to-rank algorithm for training a multi-objective ranking model. Extensive experimental results on real-world datasets show that our MLQR can outperform state-of-the-art methods in terms of both answering possibility and answer quality.
Modeling of Unbalanced Three-Phase Grid-Connected Converters with Decoupled Transfer Functions

The unbalanced three-phase passive filters or grid impedances lead to a multiple-input multiple-output (MIMO) dynamic model for grid-connected converters in the stationary αβ-frame. The MIMO system with the couplings between the α-axis and β-axis components complicates the stability analysis and current controller design. This paper thus presents a modeling method that decomposes the MIMO system into two single-input single-output (SISO) transfer functions, which enables to use the classical SISO system tools for the dynamic analysis and controller design. Time-domain simulations and experiments are performed and the results validate the effectiveness of the approach.
Rule Sharing for Fraud Detection via Adaptation

Writing rules to capture precisely fraudulent transactions is a challenging task where domain experts spend significant effort and time. A key observation is that much of this difficulty originates from the fact that such experts typically work as "lone rangers" or in isolated groups, or work on detecting frauds in one context in isolation from frauds that occur in another context. However, in practice there is a lot of commonality in what different experts are trying to achieve. In this paper, we present the GOLDRUSH system, which facilitates knowledge sharing via effective adaptation of fraud detection rules from one context to another. GOLDRUSH abstracts the possible semantic interpretations of each of the conditions in the rules at the source context and adapts them to the target context. Efficient algorithms are used to identify the most effective rule adaptations w.r. t a given cost-benefit metric. Our extensive set of experiments, based on real-world financial datasets, demonstrate the efficiency and effectiveness of our solution, both in terms of the accuracy of the fraud detection and the actual money saved.
An Efficient Design for Canny Edge Detection Algorithm Using Xilinx System Generator

Digital Image consists of some informative pixels and some redundant pixels. Edge Detection is the process of preserving informative pixels and eliminating redundant data which improves bandwidth and storage efficiency. A sudden change in pixel intensity level is defined as an Edge. Theoretically, from the literature it is seen that Canny edge detection is the most accurate algorithm. It is also insensitive to Noise. The only disadvantage of Canny algorithm is its high computational complexity which limits its maximum frequency of application with high latency and low throughput. In this paper, tradeoff between Accuracy and Complexity is studied. An efficient Canny algorithm is designed using Xilinx System Generator which utilizes JTAG Hardware co-simulation approach. An accuracy is compromised in order to make it more efficient in terms of resource utilization than the conventional one. Spartan-3E FPGA is used as a reconfigurable hardware platform for the designed algorithm.
Composition of Least Restrictive Controllers, With Application to Collision Avoidance in Multiagent Systems

A supervisor (of a continuous-time or hybrid system) is a controller in charge of modifying the input assigned by a user or set of users to a system, in order to enforce a given specification. This paper describes conditions under which multiple supervisors, designed to enforce different specifications, can be composed to obtain a supervisor enforcing the union of those specifications. As an application, we propose the composition of two supervisors, one enforcing collision avoidance of a large multiagent system, the other enforcing a second property, called sparsity, that allows efficient computation of the collision avoidance conditions.
Gamifying static analysis

In the past decades, static code analysis has become a prevalent means to detect bugs and security vulnerabilities in software systems. As software becomes more complex, analysis tools also report lists of increasingly complex warnings that developers need to address on a daily basis. The novel insight we present in this work is that static analysis tools and video games both require users to take on repetitive and challenging tasks. Importantly, though, while good video games manage to keep players engaged, static analysis tools are notorious for their lacking user experience, which prevents developers from using them to their full potential, frequently resulting in dissatisfaction and even tool abandonment. We show parallels between gaming and using static analysis tools, and advocate that the user-experience issues of analysis tools can be addressed by looking at the analysis tooling system as a whole, and by integrating gaming elements that keep users engaged, such as providing immediate and clear feedback, collaborative problem solving, or motivators such as points and badges.