Miracle-dz/UniFAR-FTUs huggingface dataset:
A High-Speed Successive-Cancellation Decoder for Polar Codes Using Approximate Computing

Polar codes are a new class of forward-error-correction codes, which have been proved asymptotically capacity-achieving for symmetric memoryless channels. Successive-cancellation (SC) decoders are low complexity while suffering from low speed due to their serial processing nature. Based on a newly published fast simplified SC algorithm, we propose a more efficient decoder than the prior art in this brief. Well-optimized components in the decoder core are developed. Additionally, several efficient approximate computing units (Apx-CUs) are introduced to substitute their accurate counterparts in the decoder to achieve even higher operating frequency. Field-programmable gate-array implementation results show that our design is about 1.5 times faster than the prior art, and the proposed Apx-CUs can bring an additional 19% processing speed.
Demo: Automatic Deployment and Dynamic Scaling of NFV Service Chaining on Bare Metal (SCBM)

Network functions virtualization (NFV) based service chaining is one of the key enablers to speed up innovations of 5G networks for differential service needs in terms of network slicing. In this demonstration, we showcase automatic deployment of a service chain or slice (i.e., a load-balanced web-database system with memory cache), consisting of 4 different virtual network functions (VNFs) connected with proper mutual relations, on a x86-PC bare metal server running with Linux. Dynamic scaling-up of such a service chain without service interruption is also demonstrated by adding one more web server, during the run time, based on the fact that its relations with the others can be automatically copied and established. Our stress tests show the load*duration responses with the stress level before and after the scaling-up, and their difference becomes dramatically amplified as the stress level gets high.
A Review of Electric Vehicles Charging Standard Development: Study Case in Indonesia

The transportation is a sector that consumes high fossil fuels and has a major impact on the environment such as global warming Electric vehicles are considered an alternative to reducing dependence on fossil fuel consumption and minimizing environmental impact. Issues related to the development of electric vehicles are adequate charging station (CS) infrastructure. There are three charging methods for electric vehicles, namely conductive, inductive, and battery exchange. Several international standards have been developed to accommodate the development needs of the charging stations. As an initial user of electric vehicles, Indonesia is faced with the problem of determining the charging system. Indonesia needs to choose the suitable charging system to have clear guidance on CS infrastructure development and maximize the use of electric vehicles. So it is necessary to develop a standard framework for charging stations to support the commercialization of EV in Indonesia. The purpose of this paper is to analyze the potential development of electric vehicle charging stations in Indonesia. Moreover, the result also provides an overview of the standard requirements for CS. Hence, that it can maximize utilize of charging stations on a wider scale and allow the community to benefit from the benefits that can be achieved by this rule.
Non-Cooperative Beacon Power Control for VANETs

In vehicle-to-vehicle communication, every vehicle broadcasts its status information periodically in its beacons to create awareness for surrounding vehicles. However, when the wireless channel is congested by beaconing activity, many beacons are lost due to packet collision. This paper presents a distributed beacon transmission power control algorithm to control channel usage at a desired level. The algorithm is based on game theory, for which the existence and uniqueness of the Nash equilibrium is proven. The proposed algorithm is then compared with other congestion control mechanisms using simulation. The results of the simulations indicate that the proposed algorithm is stable and performs better than the others in terms of fairness, bandwidth usage, and the ability to meet application requirements.
Motion-Adaptive Image Capture in a Body-Worn Wearable Sensor

Any body-mounted wearable sensor that takes periodic pictures is susceptible to motion blur in the captured images. Motion blur may render images useless, and capture of these images consumes battery power. In this paper, an attempt is made to decrease the number of blurred images captured by an eyeglass-mounted camera and increase battery life of the device. The camera motion was detected using an onboard accelerometer and motion derived metrics were used to control image capture. A total of 825 images with corresponding accelerometer data were collected during 2.3 hours in different lighting conditions to train several capture models. Further 1564 images (4.3 hours) were used to test and compare the capture models. The performance of the best model was assessed in an independent experiment, where two devices, one taking pictures at a fixed frequency and one using the motion-adaptive capture were used to collect 650 images (1.8 hours). The motion-adaptive algorithm captured the same number of blur-free images, but reduced power consumption by 12%. The algorithm was found to perform better in the conditions with higher chances of motion blur e.g. in low lighting conditions.
Unified Representation and Registration of Heterogeneous Sets of Geometric Primitives

Registering models is an essential building block of many robotic applications. In case of three-dimensional data, the models to be aligned usually consist of point clouds. In this letter, we propose a formalism to represent in a uniform manner scenes consisting of high-level geometric primitives, including lines and planes. Additionally, we derive both an iterative and a direct method to determine the transformation between heterogeneous scenes (solver). We analyzed the convergence behavior of this solver on synthetic data. Furthermore, we conducted comparative experiments on a full registration pipeline that operates on raw data, implemented on top of our solver. To this extent we used public benchmark datasets and we compared against state-of-the-art approaches. Finally, we provide an implementation of our solver together with scripts to ease the reproduction of the results presented in this letter.
Non-Linear Associations Amongst the Oil Price, Gold Price and Stock Market Returns in Pakistan: A Vector Error Correction Model

The objectives of this research are to study the interrelationship among oil, gold price and stock returns. In this study, data from secondary source is used to examine association amongst crude oil, gold price and stock return. Daily prices from 1986 to 2016 were selected. The stock price return was collected from Pakistan Stock Exchange (PSE) and gold and crude oil prices were selected from World Bank. We used Vector Error Correction Model (VECM) to estimate the short and long term association among the variables. Our research result analysis indicates that there exists the short run and as well the long run bi-directional causal association among the selected variables.
Building Candidate Monolingual Parallel Corpus from Scientific Papers

Paraphrase extraction is the method of collecting words, phrases or sentences that have same meaning from free text or structured documents. Candidate monolingual pararel corpus was build from scientific papers by clustering techniques. The corpus contain couples of citation sentences that mention same index. The corpus evaluted using weighting techniques and proximity measurements based on the density of each sentence cluster. The results of this reserach are expected to simplify paraphrase labeling by language and computer science experts.
Privacy-Preserving Distributed Deep Learning with Privacy Transformations

Distributed Deep Learning (DDL) allows disparate sites or entities to use their local data to collaboratively learn a model at a central server. To protect data privacy, existing approaches like fully homomorphic encryption and differential privacy are either computationally prohibitive or insecure. In this paper, we proposed applying a privacy-preserving transformation (PPT) before sending the transformed data to the server. The design goals of PPT include computation efficiency, privacy preservation, and good learnability at the server with maximal reuse of DL software infrastructure. After analyzing the security model and possible attacks, we evaluated simple PPTs including scrambling, random linear transforms, and Advanced Encryption Standard (AES). While AES is more secure than the others, it significantly degrades the learning performance. To address this challenge, we proposed a novel random deep neural network as PPT. Our experiments showed that the random weights and connections provide adequate security and good learning performances at the server.
IT Security in Lübeck – The design of a modern and future-proof security curriculum

With the increasing reliance on digitally connected systems, the need for secure and dependable systems continues to rise in lockstep with the need for well trained security professionals that can address this rising demand. The University of Lubeck ¨ established a new degree program in IT Security in the Fall of 2016. The creation of the degree program results from a high demand for specialists both nationally and internationally, along with the already existing strengths in security and dependability related research areas at the institutes of the university. The educational goals of the new degree program are the training of computer scientists with profound practical skills in building and protecting secure and dependable IT systems as well as all the necessary theoretical background to create systems according to the security by design principle on a large scale.
A novel Turbo ICI cancellation technique for FBMC-OQAM trough a doubly selective channel

Filter bank multicarrier modulation with Offset Quadrature Amplitude (FBMC-OQAM), is a promising Multi-Carrier Modulation (MCM) candidate for the 5th generation (5G), as it provides a flexible spectrum use compared to orthogonal frequency division multiplexing (OFDM). In addition, FBMC is able to achieve higher data rates within a given radio spectrum bandwidth through the use of OQAM Modulation. However, in doubly selective channel FBMC-OQAM suffers from Inter-carrier Interference (ICI), which severely degrades the system performance. In order to reduce the doubly selective channel effect we propose an iterative ICI cancellation technique based on turbo structure to improve the performance of the conventional scheme. Simulation results prove that the proposed Turbo ICI cancellation system outperforms the conventional scheme in terms of Bit Error Rate (BER) and Signal to Interference Ratio (SIR).
Efficient Real-Time Control Design for Automatic Train Regulation of Metro Loop Lines

This paper systematically investigates the efficient real-time control design for automatic train regulation (ATR) of metro loop lines subjected to frequent minor disruptions. To describe the dynamic evolution of the trains periodically operating on the loop line, a train traffic model related to the departure time is proposed based on a state-space equation, where the number of the station increases from circle to circle. Under the frequent minor disruptions, a dynamic optimal control model is developed to determine the ATR strategy to improve the punctuality and the regularity of the metro operation with the safety and control constraints. To solve the formulated optimal control model with the updated information, a real-time control algorithm based on a model predictive control approach is designed, which splits the original optimization problem into a set of convex quadratic programming problems, which can be numerically calculated efficiently and satisfy the real-time control requirement. Numerical examples are given to illustrate the effectiveness of the proposed method.
Simple Vector Representations of E-Commerce Products

Product similarity is a fundamental function in many aspects of e-commerce. To calculate the similarity, first, we need to build a representation that draws similar products closer in a vector space, i.e. embedding. In our experiments, we create product embeddings of n-dimension using unstructured texts like product names as the dataset. After constructing the word embeddings, using Word2Vec, we combine the vectors with two different methods: 1) unweighted average and 2) weighted average with noise removal. For evaluation, we use two different tasks, which are calculating product similarity and integrating the embedding as a feature for product category prediction. The result shows that the second method is better at doing simple task of calculating product similarity with more than 92 % accuracy. As for product category prediction, the first method consistently gives the best result with around 71 % accuracy and 86 % recall.
Co-saliency Detection via Extremely Weakly Supervised Convolutional Neural Network

A new computational intelligence approach called extremely weakly supervised learning (EWS) has been proposed for co-saliency detection in this paper. Most co-saliency detection algorithms in the literature are quite ineffective in highlighting the entire salient object. Further, they fail in providing high saliency values to some parts of the salient object. In addition, most learning-based algorithms require voluminous training datasets in order to enable the learning of the common object features. The EWS approach proposed in this paper is aimed at circumventing these limitations. The approach involves the training of a one-dimensional convolutional neural network with a careful refinement of foreground and background superpixels and the use of a single image in order to extract the common salient object from an image group. Experiments on Imagepair and iCoseg, public-domain co-saliency benchmark datasets, show that the EWS approach is quite effective in comparison with the state-of-the-art methods.
Relevant Frequency Band Selection using Sequential Forward Feature Selection for Motor Imagery Brain Computer Interfaces

In order to provide basic communication abilities to people with motor disability, motor imagery brain computer interface is one of most widely used technique. In this paper, we present a novel algorithm (Composite Filter bank based stationary CSP) for determining subject as well as task specific discriminative frequency bands for classification of motor imagery tasks. It is noted in the literature that while performing any motor imagery tasks, two major frequency band of EEG spectrum i.e mu (7-12 Hz) as well as beta (12-30 Hz) bands are actively involved. Hence, in most of the literature work EEG signals were filtered using a frequency band of 7-30 Hz usually before using CSP transformation. However, it is possible that some of the frequencies may not provide useful features to distinguish motor imagery tasks. In this paper, we propose a novel approach to select a subset of relevant frequency bands using sequential forward feature selection method from a composite filter bank which consists of Prior-known EEG frequency bands and a set of variable size overlapping frequency bands to improve the performance of motor imagery tasks classification. Experimental results of the proposed work on publicly available datasets validate the effectiveness of the proposed method. Friedman statistical test conducted further shows that the proposed approach significantly outperforms the existing methods.
Deep Learning Based Biomedical NER Framework

This paper describes a novel deep learning-based framework for biomedical name entity recognition. Bio-Entity name entity recognition task based on three different deep learning techniques: Feedforward Networks (FFNs), Recurrent Neural Networks (RNNs), and Hybrid Convolutional Neural Networks (CNNs), has allowed better latent feature learning and discovery for the complex NLP task. The performance evaluation of the proposed framework with the BioNLP dataset corresponding to biomedical entity recognition task, has led to promising performance, when assessed in terms of F-measure, Recall and Precision. The best performing deep learner based on Hybrid CNN approach has resulted in an F-score of 70.32%, and surpassed the performance reported by other participants in the Challenge task.
Controlling Concurrent Change: Managing In-Field Change in Critical Embedded Systems

Recent trends in the automotive domain show a fast development towards automated driving functions with growing complexity that comes along with increasing computing power of automotive platforms. Due to the long lifetime of automobiles compared to the innovation cycles of functionalities, in future, new functionality will have to be added by software updates. However, performing such updates on a safety-critical system requires a methodologically safe approach. In this paper, we outline how this is tackled by the DFG research unit "Controlling Concurrent Change" that formalizes the integration part of a V-model like development process aiming at in-field integration.
A Low-Power, Differential Relaxation Oscillator With the Self-Threshold-Tracking and Swing-Boosting Techniques in 0.18- $\mu$ m CMOS

This paper presents a fully integrated, 8.2-MHz relaxation oscillator with a self-threshold-tracking loop and swing-boosting technique for improving its long-term frequency stability and noise performance. The proposed latch-based relaxation oscillator increases the transition speed to reduce the static power consumption. To decrease the process–voltage–temperature dependence, we propose a self-threshold-tracking loop to ensure that the transition point of the inverter-based comparator is set to a fixed ratio of the supply voltage. In addition, the comparator delay can be compensated by the tracking loop, relaxing the requirements of comparator power consumption. The design is implemented in a 0.18-<inline-formula> <tex-math notation="LaTeX">$\mu \text{m}$ </tex-math></inline-formula> CMOS process. The design achieves a period jitter of 7.66 ps<sub>rms</sub>, the phase noise of −109 dBc/Hz at an offset frequency of 100 kHz, and an Allan deviation noise floor of 1.56 ppm. The resultant figure of merit is 160.8 dBc/Hz, while only consuming 46.3 <inline-formula> <tex-math notation="LaTeX">$\mu \text{W}$ </tex-math></inline-formula>. The power efficiency of the design is 5.6 kHz/nW. As for the supply sensitivity, the design achieves 0.9 %/0.1 V, which is 10<inline-formula> <tex-math notation="LaTeX">$\times $ </tex-math></inline-formula> lower than the design with no compensation loop. The measured temperature coefficient of the proposed oscillators is 123 ppm/°C from −20 °C to 100 °C without any trimming process.
The evaluation and analysis for the interoperability of EV charging coupler*

The excellent electrical performance of EV charging coupler is the foundation for ensuring charging safety and reliability. In view of the limitations of the current evaluating methods, combined with the practical use conditions, the interoperability testing methods and evaluation indicator were proposed. The specific performance of each products were tested and researched. Expermental results indicated that proposed test method is effective and practical, it is useful for the research& design of EV charging coupler, and also useful for the formulation & revision of relevant standard.
Combining analog and digital electronics in a hands-on introductory course

A first-year course has been developed for the joint program of the Faculty of Engineering Technology of KU Leuven and Hasselt University. This course should motivate freshmen for a further study of engineering in general and should spur interest in electrical and computer engineering in particular. The philosophy is that hands-on laboratory and experiences build student confidence, stimulate curiosity, and demonstrate the relevance of electrical engineering. The practical experiments are supported by traditional lectures in combination with other learning methods. Further, a variety of formative and summative evaluation methods are used. This paper describes the structure of the new course.
Opinion QA-Pairs Generation From Indonesian Twitter

One objective of opinion mining is to provide knowledge reasoning from sentences given to the system. It requires an understanding for the meaning of each word including the out-of-vocabulary words and the relation between words in that sentence. Twitter is one of social media networking that allows users to broadcast short text message called tweets. Compared to English, Bahasa Indonesia has a different characteristic on its structure in which it has many words coming from local language that can be categorized as the out-of-vocabulary words. This paper is aimed to explain how to generate the question-answer pairs from opinion statements from Twitter in Bahasa Indonesia. Those generated question-answer pairs will be used to build a question-answer opinion corpus. The proposed approach is the transformation of a pattern-based opinion sentences, focused on the identification of objects, features of objects, and opinions of objects. The final result of this research showed the QA-pairs as a knowledge base for the question and answer opinion system with accuracy reaching 81.7%.
Hierarchical Reinforcement Learning for Playing a Dynamic Dungeon Crawler Game

This paper describes a novel hierarchical reinforcement learning (HRL) algorithm for training an autonomous agent to play a dungeon crawler game. As opposed to most previous HRL frameworks, the proposed HRL system does not contain complex actions that take multiple time steps. Instead there is a hierarchy of behaviours which can either execute an action or delegate the decision to a sub-behaviour lower in the hierarchy. The actions or sub-behaviours are chosen by learning the estimated cumulative reward. Since each action only takes one time step and the system starts at the top of the hierarchy at every time step, the system is able to dynamically react to changes in its environment. The developed dungeon crawler game requires the agent to take keys, open doors, and go to the exit while evading or fighting with enemy units. Based on these tasks, behaviours are constructed and trained with a combination of multi-layer perceptrons and Q-learning. The system also uses a kind of multi-bjective learning that allows multiple parts of the hierarchy to simultaneously learn from a chosen action using their own reward function. The performance of the system is compared to an agent using MaxQ-learning that shares a similar overall design. The results show that the proposed dynamic HRL (dHRL) system yields much higher scores and win rates in different game levels and is able to learn to perform very well with only 500 training games.
Little Engineers: Young Children's Learning Patterns in an Educational Robotics Project

This short paper aimed to investigate how young children as little computer engineers learned in an educational robotics project. Student participants were 11 third graders at one public elementary school in Taiwan. The project in an after-school learning program lasted for five weeks. During weekly learning session, each student needed to use one visual block programming called Ozoblockly to design action patterns for their robots (Ozobot). The study adopted a case study methodology to examine the specific learning scenario. The principal researcher used a participant observation method to observe students’ learning behaviors, informal after-class interviews to collect students’ learning responses, and a portfolio analysis approach to examine students’ programming works. The findings showed that students might obtain a basic understanding of a computer engineer after the instructional intervention. Furthermore, some specific learning patterns from young children’s programming process were identified.