Miracle-dz/UniFAR-FTUs huggingface dataset:
Intellevator: A Context-Aware Elevator System for Assisting Passengers

The development of ambient assisted living environments requires the growth of the associated technologies of the inside devices. In this article, we propose the Intellevator, an intelligent elevator system in smart buildings that could pro-actively provide passengers assistive information based on the real-time context sensing and awareness. We firstly introduce the embedded sensing approach to upgrade a conventional elevator to become Internet of Things-enabled. Then, after transferring and increasing the computational capability to the agent server, we prototyped an end-user centered application that provides the real-time context visualization and intelligence for assisting users, such as the advices of riding at other floor for reducing passengers' waiting time. We experimented this proposal by integrating with a real conventional elevator and conducted a 22-participants attended case study for evaluation. The results demonstrated that the system usability and user experience of the Intellevator is beyond that of the existing conventional elevator. Furthermore, valuable feedbacks for advice making and context visualization have been collected; they will serve as an informed reference for developing the future elevator system.
A Log-Periodic Antenna for Long Range Communication Within a Wireless Sensor Network System for Sea Water Quality Monitoring

In this paper the design of a printed log-periodic antenna for long range communication within a wireless sensor network system for sea water quality monitoring is presented. The difficulties in using the design procedure for log-periodic dipole antennas proposed by Carrel are discussed and some limitations are pointed out. New design hints are given and a preliminary example of a printed antenna is presented. Simulations are carried out not only for free-space but also in presence of sea and wave motion. Resuts show that printed log-periodic is actually a good candidate for the intended application.
Case-Based Teaching on Designing and Realizing an Innovation Algorithm

This paper presents concepts on designing and realizing a novel algorithm which can be of significance for teaching purpose. In order to interpret novelty in any new algorithm, in this work a new conceptual case-based is introduced based on tree biomechanics. Firstly, core terminologies such as initialization, exploration and exploitation paradigm, and termination criterion are explained thoroughly. Moreover, a detailed mathematical formulation is constructed. Hence, the mathematical derivation and explanation on core algorithmic terminologies will be of high value in teaching and designing new algorithm. The stepwise procedure of mathematic is deduced for the experimental and statistical analysis. Thus, the experiment was carried out on 13 set of constrained benchmark optimization problems. Consequently, the results obtained by the proposed method are compared with results of other methods from the literature. The proposed method has shown effectiveness in solving constrained optimization problems and it is comparable or better than other methods in the literature.
Case Study of Process Variation-Based Domain Partitioning of GPGPUs

We can unlock higher performance in general purpose graphics processing unit (GPGPU) chips by adhering to local environment conditions while setting a timing (clock) reference. We propose partitioning the GPU chip into smaller independent domains to enable finer control of the chip voltage frequency (V/F) settings. Post silicon (Si) analysis is used to classify the domains as fast, ’typical’, or slow. This information is then provided to the system management unit (SMU) to enable the appropriate settings for power optimization. In this paper, we present an initial analysis of power/performance associated with fine-grain domains in GPUs.
Data-Driven Smart Bike-Sharing System by Implementing Machine Learning Algorithms

This paper aims to solve a real-life problem: the bike-sharing management system arises the requirement of offering the customers the accessibility of the bikes in different bike-stations concerning the potential demands in every time-slice. The prediction of needs is critical to the distribution of the limited resources (bikes and empty slots to place the bikes) and the management of the system. We propose addressing this problem by using the regression model, which is trained by the raw data collecting from the different sensors. Thanks to the wide distribution of the edge devices, the machine learning algorithms, and the advanced computing ability, we may incorporate the intelligence to the database-related system. We will demonstrate that the boosting gradient method as a predictor to forecast the quantities of rentals and returns of bikes, outperforming the other means, e.g., random forest, support vector machine, etc. It reaches a promising result; the average accuracy reaches 75%.
Automatically Detecting Incivility in Online Discussions of News Media

Detecting biased language in written discourse is a highly relevant area of research in political communication and other social sciences, given the large quantity of information exchanged in public online platforms. In this abstract, we discuss an approach based on the concept of "incivility"-assessing biased text on the Facebook pages of established news media. News outlets are forced to put increasing efforts into preventing heated debates from turning into disrespectful discussions on their social media platforms. By scaling the analysis from a few thousand manually coded samples to more than a million comments, we take a step towards supporting media outlets in (semi-)automatizing the detection of uncivil comments and enable a much broader analysis of the latter.
Inertial and Magnetic-Field Sensor Arrays - Capabilities and Challenges

Today, thanks to the development of micro-scale low-cost accelerometers, gyroscopes, and magnetometers, it is possible to construct compact motion-sensor arrays with hundreds of sensing elements. These arrays, as well as providing a high performance-to-price ratio, can also provide new measurement capabilities and enable the development of smart sensors that adapt to the usage conditions. This paper gives an overview of the capabilities enabled by the stochastic, component, temporal, and spatial information diversity that an array of inertial or magnetic field sensors provide. The main challenges in the practical realization of these types of arrays are described.
A Compact Three-Port DC-DC Converter for Integrated PV-Battery System

In this paper, a new non-isolated three-port DC-DC converter (NITPC) to integrate a battery storage with a PV module is proposed. The intermittency of renewable energy and the unpredictable load demand are eliminated by firming a backup battery with the PV module to supply extra power when it is required. The proposed converter is reconfigurable and able to operate as a conventional boost converter, a buck-boost converter or a forward converter in different modes to support several power flow combinations and achieve power conditioning and regulation among the PV module, battery and an output port simultaneously. Nevertheless, the converter only consists of two switches, one coupled inductor, one diode and two capacitors. Thus, the system size and number of components are reduced compared with the traditional DC-DC converters. High output regulated voltage is achieved by using a coupled inductor and by combining the PV module and the battery in series. Simulation and experiment are carried out to verify the proposed circuit.
Evaluation of Several Denial of Service Attack Methods for IoT System

Recently, the online cheating and attacking posed a threat to net safety. As an important component of the development of the information era, the Internet of Things (IoT) would cause serious property losses once it is attacked. This experiment is to use 3 devices to simulate the principle of the Denial of Service (DoS) attack. The attack is launched by Kali Linux in different ways. Furthermore, this paper lists the changed variables of the experiment and shows how the influence can be caused.
A Bayesian Classifier Based on Constraints of Ordering of Variables for Fraud Detection

Fraud detection is important for financial institutions and the society. Supervised machine learning techniques were applied for fraud detection. However, mostly discriminative techniques were applied on these problems. Probabilistic graphical models can also detect fraud, providing also a graphical representation of its reasoning scheme as a graph. We proposed a method to generate a probabilistic graphical model for fraud detection, using constraints related to the domain. We achieved 99.272% of accuracy and we outperformed other baselines techniques of probabilistic graphical models. We demonstrated that constraints are important to tackle complex problem such a fraud detection.
Improving Apache Spark's Cache Mechanism with LRC-Based Method Using Bloom Filter

Memory-and-Disk caching is a common caching mechanism for temporal output in Apache Spark. However, it causes performance degradation when memory usage has reached its limit because of the Spark's LRU (Least Recently Used) based cache management. Existing studies have reported that replacement of LRU-based cache mechanism to LRC (Least Reference Count) based one that is a more accurate indicator of the likelihood of future data access. However, frequently used partitions cannot be determined because Spark accesses all of partitions for user-driven RDD operations, even if partitions do not include necessary data. In this paper, we propose a cache management method that enables allocating necessary partitions to the memory by introducing the bloom filter into existing methods. The bloom filter prevents unnecessary partitions from being processed because partitions are checked whether required data is contained. Furthermore, frequently used partitions can be properly determined by measuring the reference count of partitions. We implemented two architecture types, the driver-side bloom filter and the executor-side bloom filter, to consider the optimal place of the bloom filter. Evaluation results showed that the execution time of the driver-side implementation was reduced by 89% in a filter-test benchmark based on the LRC-based method.
CNN-Based InSAR Denoising and Coherence Metric

Interferometric Synthetic Aperture Radar (InSAR) imagery for estimating ground movement, based on microwaves reflected off ground targets is gaining increasing importance in remote sensing. However, noise corrupts microwave reflections received at satellite and contaminates the signal's wrapped phase. We introduce Convolutional Neural Networks (CNNs) to this problem domain and show the effectiveness of autoencoder CNN architectures to learn InSAR image denoising filters in the absence of clean ground truth images, and for artefact reduction in estimated coherence through intelligent preprocessing of training data. We compare our results with four established methods to illustrate superiority of proposed method.
SRPA: SDN-Based Routing Protocol for Ad Hoc Networks

For distributed routing algorithms in ad hoc networks, many control messages exchanges bring about excessive network delay and high-energy consumption. These weaknesses seriously deteriorate network performance and shorten lifetime of wireless nodes. Further, distributed operations are not good for updates of protocols and services, so that ad hoc networks are under restrictions in network management. This paper presents a novel SDN-based routing protocol named SRPA for ad hoc networks. SRPA removes routing decisions from general nodes to the controller. This strategy is able to detect shortest paths with low latency and reduce control messages exchanging effectively. We conduct experiments in OMNet++. The experimental results show that the route discovery time (RDT) and the round trip time (RTT), especially the first packet RTT, are shorter in SRPA than in AODV.
Sentiment Identification in Football-Specific Tweets

Sports fans generate a large amount of tweets which reflect their opinions and feelings about what is happening during various sporting events. Given the popularity of football events, in this work, we focus on analyzing sentiment expressed by football fans through Twitter. These tweets reflect the changes in the fans’ sentiment as they watch the game and react to the events of the game, e.g., goal scoring, penalties, and so on. Collecting and examining the sentiment conveyed through these tweets will help to draw a complete picture which expresses fan interaction during a specific football event. The objective of this work is to propose a domain-specific approach for understanding sentiments expressed in football fans’ conversations. To achieve our goal, we start by developing a football-specific sentiment dataset which we label manually. We then utilize our dataset to automatically create a football-specific sentiment lexicon. Finally, we develop a sentiment classifier which is capable of recognizing sentiments expressed in football conversation. We conduct extensive experiments on our dataset to compare the performance of different learning algorithms in identifying the sentiment expressed in football related tweets. Our results show that our approach is effective in recognizing the fans’ sentiment during football events.
Aerial Manipulation with Six-Axis Force and Torque Sensor Feedback Compensation

This paper presents a framework for aerial manipulation based on the feedback compensation of torques and forces measured by a six-axis F/T sensor, placed between the UAV and the robotic arm attached below it. An attitude controller that compensates for the sensed torques and a novel position controller are presented. The framework capabilities are evaluated by simulating the robot doing a pouring task. Results showed a system capable of successfully doing the pouring task with a payload representing 10% of the whole system weight, in less than 20 seconds.
Modelling Implicit Content Networks to Track Information Propagation Across Media Sources to Analyze News Events

With the rise of the Internet as the premier news source for billions of people around the world, the propagation of news media online now influences many critical decisions made by society every day. Fake news is now a mainstream concern. In the context of news propagation, recent works in media analysis largely focus on extracting clusters, news events, stories or tracking links or conserved sentences at aggregate levels between sources. However, the insight provided by these approaches is limited for analysis and context for end users. To tackle this, we present an approach to model implicit content networks at a semantic level that is inherent within news event clusters as seen by users on a daily basis through the generation of semantic content indexes. The approach is based on an end-to-end unsupervised machine learning system trained on real-life news data that combine together with algorithms to generate useful contextual views of the sources and the inter-relationships of news events. We illustrate how the approach is able to track conserved semantic context through the use of a combination of machine learning techniques, including document vectors, k-nearest neighbors and the use of hierarchical agglomerative clustering. We demonstrate the system by training semantic vector models on realistic real-world data taken from the Signal News dataset. We quantitatively evaluate the performance against existing state of the art systems to demonstrate the end-to-end capability. We then qualitatively demonstrate the usefulness of a news event centered semantic content index graph for end-user applications. This is evaluated with respect to the goal of generating rich contextual interconnections and providing differential background on how news media sources report, parrot and position information on ostensibly identical news events.
SPLIT: A Secure and Scalable RPL routing protocol for Internet of Things

Due to recent notorious security threats, like Mirai-botnet, it is challenging to perform efficient data communication and routing in low power and lossy networks (LLNs) such as Internet of Things (IoT), in which huge data collection and processing are predictable. The Routing Protocol for low power and Lossy networks (RPL) is recently standardized as a routing protocol for LLNs. However, the lack of scalability and the vulnerabilities towards various security threats still pose a significant challenge in the broader adoption of RPL in LLNs.To address these challenges, we propose SPLIT, a secure and scalable RPL routing protocol for IoT networks. SPLIT effectively uses a lightweight remote attestation technique to ensure software integrity of network nodes. To avoid additional overhead caused by attestation messages, SPLIT piggybacks attestation process on the RPL’s control messages. Thus, SPLIT enjoys the low energy consumption and scalability features of RPL protocol, which are essential in resource-constrained large scale networks such as IoT. The simulation results for different IoT scenarios show the effectiveness of SPLIT compared to the state-of-the-art in presence of different types of attacks, concerning metrics such as packet delivery ratio and energy consumption.
Microns: Commands for Building Bubble Microservices

Microns are bean-like command objects with structural and behavioral properties that are naturally aligned with microservices design principles and architectural goals. A micron based microservice implementation revolves around building every microservice operation as a micron command. Microns are then logically grouped to virtually realize a bubble microservice. Bubbles facilitate postponing the need for physically defining service domain boundaries until deployment and are meant to be more agile and scalable than traditional microservices implementation approaches. Microns are not limited to building microservices; they are adequate for building many parts of the architecture as long-lived software assets that can be reused in realizing existing and future architecture trends. This paper attempts to re-explore commands through microns and discusses their suitability for building microservices and many other parts of the architecture with the ultimate goal of increasing system's agility, scalability, performance, and consistency. The paper also presents some micron-based applications and designs, where microns serve as high-quality architectural elements that can non-invasively make existing as well as new snippets of logic accessible over the web and other channels with minimal effort.
A Client Based Anomaly Traffic Detection and Blocking Mechanism by Monitoring DNS Name Resolution with User Alerting Feature

Malware has become one of the most critical targets of network security solutions nowadays. Many types of malware receive further instructions from the C&C servers and the attack targets may be instructed by IP addresses which causes direct attacks without DNS name resolution from the malware-infected computers. In the meanwhile, several programs that are hidden from the users (e.g. malware, virus, etc.) may perform DNS name resolutions for cyber attacks or other communications. In this paper, we propose a client based anomaly traffic detection and blocking mechanism by monitoring DNS name resolution per application program. In the proposed mechanism, by the collaboration of DNS proxy and packet filter, DNS traffic is monitored on the client and the traffic destined to the IP addresses obtained without DNS name resolution or the traffic from unrecognized programs will be detected and blocked. In addition, in order to mitigate false positive detection, an alert-window will be shown to let the users decide whether to allow the traffic or not. We implemented a prototype system on a Windows 7 client and confirmed that the proposed mechanism worked as expected.
Polymer Based Hybrid Membrane-Flexure Nanomechanical Piezoresistive Sensor

In this paper, a novel Hybrid Membrane-Flexure Nanomechanical (HMF-NM) Piezoresistive Sensor with SU-8 as structural material and Indium Tin Oxide (ITO) as piezoresistor is designed and simulated using commercial finite element analysis (FEA) software. SU-8/ITO microcantilever has been fabricated to electromechanically extract the piezoresistive property of ITO which has been considered for simulation. The simulated device consists of a circular membrane coated with receptor layer suspended by inverse trapezoidal flexures with embedded piezoresistors. Surface stress induced on the membrane due to target binding is transduced as a uniaxial stress in the flexures which is then sensed using piezoresistors. A conventional polymer U-shaped piezoresistive cantilever is also designed and simulated for comparison. The surface stress sensitivity of HMF-NM sensor is extracted as 4.01 ppm/[mN/m] which is more than 20 times that of a conventional cantilever.
Modeling Single-Gyroid Structures in Surface Mesh Models for 3D Printing

How to improve strength-to-weight ratio of printed models is an important topic in 3D printing. We in this paper propose a novel structure modeling method based on the implicit function technique and the finite element method (FEM). Our method first obtains a set of sampled points in a given surface mesh model by using a probability-based strategy, and generates an adaptive tetrahedral mesh from the points. FEM is then used to analyze the stress of the tetrahedral mesh and a stress map of the input model is created. The method finally builds a result model composed of a shell and an interior single-gyroid lattice. The lattice is defined by a piecewise 3D implicit function, and has several special structural properties just like the structure of the light but strong butterfly wing. The lattice together with the shell forms a natural 3D structure for 3D printing. Local thickness of lattice rods in the structure adaptively changes with stress distribution for withstanding external loads. Experimental results show that our method can deal with various surface mesh models in rapid way, and the resulted models for 3D printing have high strength-to-weight ratios.
A 512 × 512 SPAD Image Sensor With Integrated Gating for Widefield FLIM

In this paper, we report on SwissSPAD2, an image sensor with 512 × 512 photon-counting pixels, each comprising a single-photon avalanche diode (SPAD), a 1-b memory, and a gating mechanism capable of turning the SPAD on and off, with a skew of 250 and 344 ps, respectively, for a minimum duration of 5.75 ns. The sensor is designed to achieve a frame rate of up to 97 700 binary frames per second and sub-40 ps gate shifts. By synchronizing it with a pulsed laser and using multiple successive overlapping gates, one can reconstruct a molecule's fluorescent response with picosecond temporal resolution. Thanks to the sensor's number of pixels (the largest to date) and the fully integrated gated operation, SwissSPAD2 enables widefield fluorescence lifetime imaging microscopy with an all-solid-state solution and at relatively high frame rates. This was demonstrated with preliminary results on organic dyes and semiconductor quantum dots using both decay fitting and phasor analysis. Furthermore, pixels with an exceptionally low dark count rate and high photon detection probability enable uniform and high-quality imaging of biologically relevant fluorescent samples stained with multiple dyes. While future versions will feature the addition of microlenses and optimize firmware speed, our results open the way for low-cost alternatives to commercially available scientific time-resolved imagers.