Miracle-dz/UniFAR-FTUs huggingface dataset:
Infrastructure Costs in Dynamic Optical Networks

In this paper a cost model for the evaluation of the infrastructure costs of dynamic optical networks is provided. For an expected traffic demand, the cost model uses a bottom-up approach for the dimensioning of transponders and optical nodes, namely, reconfigurable optical add/drop multiplexers (ROADM). The infrastructure costs are calculated as the sum of the costs of ROADMs, transponders and optical amplifiers. The approach is applied to the calculation of the network costs for two ROADM implementations, namely, colorless and colorless & directionless. For selected network topologies, the results show that the lowest costs are obtained by colorless ROADMs. However, by this solution the network cannot automatically reroute traffic in case of link failures. This is overcome by adding the directionless property to colorless ROADMs. Although the resulting solution is more costly, it allows efficient utilization of network resources. In fact, a colorless & directionless network reduces the number of transponders needed to serve an expected traffic demand.
A Novel Tweets Clustering Method using Word Embeddings

Twitter users share a variety of information discussing multiple topics. Clustering-based methods have become an effective solution to group together tweets related to the same topics. However, due to the lack of context, short texts are challenging to model. Most of the existing clustering methods use the Vector Space Model (VSM) to transform tweets into a structured form. However, this representation do not consider the semantic relationships between words and suffers from high dimensionality and sparsity. To deal with these issues, we propose a new approach that aims to group tweets into topically coherent clusters by preserving the semantic links between words using word embeddings and low dimensional vector representations. The experimental results show the superior performance of the proposed method compared to existing ones.
Effects of Proton Radiation-Induced Defects on Optoelectronic Properties of MoS2

We report on photoluminescence (PL) spectroscopy and transmission electron microscope imaging of suspended and substrate-supported flakes of the 2-D semiconductor MoS<sub>2</sub> before and after exposure to 100-keV proton radiation with fluences of <inline-formula> <tex-math notation="LaTeX">$6 \times 10^{13}$ </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">$6\times 10^{14}$ </tex-math></inline-formula>, and <inline-formula> <tex-math notation="LaTeX">$6 \times 10^{15}$ </tex-math></inline-formula> p/cm<sup>2</sup>, respectively, and subsequent annealing. An indirect-to-direct bandgap transition is observed, which is preserved after annealing. This transition is accompanied by an unexpected increase in PL intensity after radiation exposure of multilayer samples, which is attributed to higher radiative efficiency of the direct-gap transition.
Educating and Raising Awareness on Cyber Security Social Engineering: A Literature Review

Social engineering, due in part to the increasing popularity and advancements in information technology and ubiquity of devices, has emerged as one of the most challenging cyber security threats in the contemporary age. In the context of cyber security, social engineering is the practice of taking advantage of human weaknesses through manipulation to accomplish a malicious goal. This literature review identifies various social engineering cyber security threats in diverse environments. Exploiting humans as the weakest security link in such environments, as opposed to technical vulnerabilities and system protocols, has led to increased calls for raising information security awareness among users. One of the most straightforward solutions is through effective training and education programs. As such, the paper details how innovative information security education programs can effectively increase user/employee awareness and ultimately reduce cyber security incidents.
Text-based Authorship Identification - A survey

The virtual world provides criminals with an anonymous environment to conduct malicious activities such as mal-ware, sending ransom messages, spamming, theft intellectual property and sending ransom e-mails. All these activities are text in somehow. Therefore, there is a need for a tool in order to identify the author or creator of this illegal activity by analyzing the text. Text-based Authorship Identification techniques are used to identify the most possible author from a group of potential suspects of text. This paper is meant to explore the text-based authorship identification researches within the period 2007–2017. The researches were classified based on the application into email authorship, source code authorship, online text authorship, gender identification and online messages authorship. Also, the paper reviews and reports the datasets which used in the experiments of text-based authorship identification techniques. Finally, it reported the techniques which were used in authorship identification.
Implementation of SHE-PWM Technique for Parallel Voltage Source Inverters Employed in Uninterruptible Power Supplies

This paper proposes a new selective harmonic elimination pulse-width modulation (SHE-PWM) technique with fixed and low switching frequency for paralleled voltage source inverters (VSIs). The proposed SHE-PWM technique involves the use of using different switching patterns for each inverter module instead of using the same switching pattern for each module, which is the standard practice. This allows more harmonics to be eliminated from the output AC waveforms and helps reduce circulating current in inverters, but also increases the complexity and the number of non-linear equations that need to be solved. The proposed new SHE-PWM technique is based on recent advances in genetic algorithm (GA) optimization methods that are well suited to solving multi-objective problems so that the optimum firing angles needed to minimize harmonic content and circulating current in paralleled VSIs modules can be achieved. Simulation and experimental results obtained from a laboratory prototype are presented and analyzed to demonstrate the features of the proposed control strategy, such as its ability to reduce circulating current and output harmonic content.
The State of Charge Estimation of Lithium-Ion Battery Based on Temperature-Compensated Method with Adaptive Extended Kalman Filter

In this paper, the state of charge (SOC) estimation for lithium-ion (Li-ion) battery is considered. In order to reduce the SOC estimation error caused by temperature effect, a temperature-compensated method is proposed. The temperature-compensated method is based on the ohmic resistance of the equivalent circuit model (ECM) of the Li-ion battery. According to the ohmic resistance, a proper temperature model can be chosen. The adaptive extended Kalman filter (AEKF) is then applied to estimate the SOC of Li-ion battery based on the chosen temperature model. Experimental results show that the proposed temperature-compensated SOC estimation method performs better estimation accuracy than that method without temperature-compensated.
Machine Learning For Security: The Case of Side-Channel Attack Detection at Run-time

This paper presents experimental evaluation and comparative analysis on the use of various Machine Learning (ML) models for detecting Cache-based Side Channel Attacks (CSCAs) in Intel's x86 architecture. The paper provides performance evaluation of ML models based on run-time detection accuracy, speed, computational overhead, and distribution of error in terms of false positives and false negatives. Experiments are performed using state-of-the-art CSCAs namely; Flush+Reload and Flush+Flush attacks, under realistic load conditions on RSA and AES crypto-systems. The paper provides quantitative & qualitative analysis of at least 12 ML models being used for CSCA detection for the first time.
Parallel-Coupled-Line Quasi-Elliptic Single-Ended and Balanced Bandpass Filters

This paper presents the design of parallel-coupled-line quasi-elliptic single-ended and balanced bandpass filters (BPF). Parallel-coupled-line quasi-elliptic BPFs are a brand new type of BPF that has a simple design procedure and layout. Thanks to the design formulas that explicitly determine the characteristic impedances of the coupled lines, we can systematically design single-ended and balanced BPFs for desired filter orders and responses. Two proof-of-concept prototypes are fabricated on an RO4003 substrate. Compared to the recent works in the literature, the proposed BPFs achieve competitive performance in terms of the selectivity and circuit size.
53% PAE 32W Miniaturized X-Band GaN HEMT Power Amplifier MMICs

We have developed a miniaturized high efficiency X-band power amplifier MMIC with $\pmb{0.25} \ \pmb{\mu}\ \mathbf{m} \ \mathbf{AlGaN}\mathbf{/GaN}$ HEMT on SiC technology. Class-F two-stage MMICs are designed and fabricated, and the chip size is $\pmb{3.0} \ \mathbf{mm} \ \mathbf{x}\ \pmb{3.3}\ \mathbf{mm. }$ The MMICs had an output power of 45.1 dBm (32.4W), a power added efficiency of 53.4% and an associated gain of 15.1 dB with a drain voltage of 36 V at 9.3GHz.
ClubBT: Leveraging BitTorrent for Content Distribution in Ad-Hoc Social Networks

We introduce the ClubBT protocol which is intended to be used for content distribution in wireless ad-hoc social networks. This protocol combines some functions of BitTorrent with knowledge regarding social relationships that is inferred from social networks. The peer selection strategy, used by ClubBT to share content, is to give priority to the nodes that connect the largest number of friend nodes. We show that this strategy reduces the downloading time not only for friend nodes, but also for stranger nodes.
Pitfalls in Environmental Endurance Testing of Electrical Connectors – Problem Overview and Discussion

Corrosion tests, fretting tests and other environmental endurance tests are performed to provide information on performance in use or to demonstrate compliance with standards or specifications. In this paper, pitfalls concerning the use of tests to predict the durability of electrical connectors in the field are discussed. The discussion covers corrosion testing resulting in unrealistic chemistries, fretting tests concentrating on incorrect failure mechanisms, self-healing effects during testing resulting in misleading results, as well as lack of evaluation of effects of testing, such as contact resistance measurements under conditions not revealing surface films formed. Testing under unrealistic conditions or without proper evaluation of results may not only risk not detecting a poor design but also to reject a good one.
Risks from Spam Attacks on Blockchains for Intemet-of-Things Devices

There has been increased interest in the use of blockchains to control Internet of Things devices either directly, or through smart contracts. Many blockchains, such as Ethereum and Fabric, have support for smart contracts. The use of public blockchains while attractive due to their decentralization and availability, do pose challenges, such as unpredictable transaction latencies and cryptocurrency price fluctuations. Transactions in the Ethereum network, such as invokations of smart contracts used to control an IoT device, have no fairness or eventuality guarantees. In this work we describe a “spam attack” method available to parties with sufficient cryptocurrency reserves to delay a statistically significant portion of transactions submitted to the Ethereum network. This paper derives estimations on the costs and effects of such an attack, and is based on an analysis of historical transactions.
An ASIC Design of Multi-Electrode Digital Basket Catheter Systems with Reconfigurable Compressed Sampling

This paper presents an Application Specific Integrated Circuit (ASIC) design with reconfigurable compressed sampling (CS) for multi-electrode basket catheter systems that acquire intracardiac electrograms (IEGMs). This work adopts a reconfigurable CS (ReCS) encoder for near-electrode processing to enable sub-Nyquist sampling rate thus improve the system capacity. The ReCS encoder is designed to work with a reconfigurable compression cycle as well as a reconfigurable compression ratio, which makes it suitable for a wide range of different signals. This digital ASIC chip is placed at the distal end of the catheter close to electrodes, so that all signals have been digitalized and encoded before transmitting to an external receiver. Such architecture ensures serial data transmission, reducing number of traces and size of the catheter, as well as fabrication complexity. Evaluated area cost of total digital circuits is 0.046 mm2 and the power consumption is 49.1 $\mu \mathbf{W}$ with 4 MHz clock frequency in 65 nm process.
Semantic Indexing-Based Data Augmentation for Filtering Undesired Short Text Messages

In the last years, spammers have taken advantage of the popularity of electronic media to spread undesired text messages. These may cause direct and indirect damages, such as dissatisfaction and exposure of users to misleading information and malicious content that can result in significant financial losses. Automatic filtering short text messages is a challenging problem nowadays because labeled datasets generally contain few instances and messages may have an insufficient amount of terms to be classified. In addition, the messages are rife with abbreviations, slang, and misspelled words making it difficult to generate a good computational representation. In this study, we propose an automatic data augmentation technique to increase the number of labeled instances and to improve the quality of the computational representation of short and noise text messages. We also proposed an ensemble approach to combine the predictions obtained by the classifiers using the messages generated by this technique. Experiments with three text representation techniques demonstrated that the ensemble approach improves the results obtained in the detection of undesired short text messages when the number of training instances is small.
Forca: Fast and Atomic Remote Direct Access to Persistent Memory

For promising performance boost, recent trends of modern data centers tend to use Persistent Memory (PM) as storage and utilize the one-sided feature of Remote Direct Memory Access (RDMA) to directly access PM for I/O requests. However, accessing PM through one-sided RDMA faces two challenges: one-sided data races and remote data crash consistency. Existing systems that employ server-bypass mechanism either abandon server-bypass write to avoid the challenges at the cost of extra server loads, or support both server-bypass read/write through inefficient concurrency control without ensuring remote crash consistency. In this paper, we propose a novel server-bypass RDMA-to-PM framework, named Forca, to provide high concurrency and guarantee remote crash consistency at the same time. In Forca, we design an optimized log-structured mechanism to eliminate in-place updates, removing race conditions of one-sided RDMA and providing atomicity for each update simultaneously. We implement Forca as a generic module to support server-bypass RDMA to PM, and conduct experiments on a Forca-based key-value store called ForcaKV. The experiments show that Forca achieves higher throughput than state-of-the-art techniques by up to 1.4x under high concurrency scenarios, and its crash consistency mechanism incurs only 14.7%-19.1% time overhead.
Implementation of Victims Detection Framework on Post Disaster Scenario

Disasters are prone to occur in Indonesia due to geographical factors, such as tectonic plate movements, which can cause an earthquake. Earthquakes are one of the most frequent disasters, they have broad impacts in a short time and are unpredictable. Thus, an extensive search process in a short time is highly critical to determine the victims location. In this paper, a victims detection framework is developed starting from acquiring images using an unmanned aerial vehicle and further processing using convolutional neural network (CNN) to locate victims robustly on post-disaster. Input images are then sent to victim detector dedicated ground station server for further high processing robustly locating the possibility of victims. A simulation system mimicking a real environment is developed to test our framework in real time. A transmission protocol is also developed for effectively transmitting data between the robot and the server. The treatment on the detection process of the victim is different from the normal human detection, some pre-processing stages are applied to increase the variation of the given dataset. An embedded system is used for taking images and additional sensors data, such as location and time using Global Navigation Satellite System.
Immersive Learning Explored: Subjective and Objective Factors Influencing Learning Outcomes in Immersive Educational Virtual Environments

Recent studies in Immersive Learning show that different factors influence the learning outcomes in Educational Virtual Environments (EVEs). An underlying theory about learning in EVEs from an educational perspective is yet lacking. To enhance immersive learning research, a systematic framework of immersive learning processes as a theoretic foundation is required in order to appropriately discuss these processes. This paper summarizes the factors influencing learning in EVEs and introduces their localization in the Helmke's pedagogical supply-use-model. Presence as the subjective feeling of “being there” is emphasized as the central criterion influencing immersive learning. Presence is influenced by objective and subjective factors. The objective factors which are provided by the technology are summarized under the term immersion; the subjective factors consist of the motivational, cognitive, and the emotional factors. They are summarized as (immersive) learning potential. The localization of these predictor variables of learning outcome in Helmke's supply-use-model results in the educational framework for immersive learning (EFiL), which provides an educational understanding of immersive learning as “learning activities initiated by a mediated or medially enriched environment that evokes a sense of presence”. It constitutes a first educational reflection of immersive learning processes which has to be pursued and evaluated by empirical research.
Improving the Robustness of Myoelectric Control System Using Linear Regression Classifier

In the application of electromyography-based pattern recognition (EMG-PR) approach, the stability and robustness of EMG-PR control system against EMG description methods like feature extraction and electrode configuration was scarcely investigated. Aiming at developing a robust, stable, and accurate EMG-PR system, a new pattern recognition method, Linear Regression Classifier (LRC) is proposed in this study. The results among 12 TBI patients show that the proposed LRC scheme achieved significantly higher average classification accuracy (achieved above 99% when using 56 monopolar electrodes) in comparison to the commonly used LDA and KNN. Moreover, the LRC scheme was robust to the selection of feature sets, and to the electrode configurations especially when TD and TDAR feature sets were used. These outcomes suggest the comparative advantage of the LRC.
Improving Feedback and Discussion in MOOC Peer Assessement Using Introduced Peers

Massive Open Online Courses (MOOCs) often use peer grading which enables the use of open ended questions in assignments at scale. Open ended questions enhance students' creativity and improve cognition. However, this peer grading includes drawback such as missing or low quality feedback, and inconsistency between peer graders. In design studios and highly connected MOOCs, a common practice is to use open feedback, visible to everyone in the community. Drawing inspirations from this technique, we hypothesize that introducing peers and allowing them to communicate with each other during peer assessment in a structured open learning environment will increase the quality of feedback. We tested this approach in a controlled study, comparing open to blind peer review, and found the technique led to higher quality feedback. We discuss implications using open identity grading in learning environments as a potential stimuli for students to interact more.
Reversible Data Hiding in Encrypted Images Using Prediction-Error Encoding

In this paper, we propose a reversible data hiding method in encrypted images (RDHEI) using prediction-error encoding (PE-RDHEI). It uses a weighted checkerboard based prediction to predict 3/4 of the pixels in an original image. The obtained prediction-error values and the unmodified pixels are encrypted separately. The data hider then embed secret data into the encrypted prediction-error values using the prediction-error encoding method. At the receiver side, the secret data and original image can be completely extracted and recovered. Compared with existing RDHEI methods, PE-RDHEI significantly improves the embedding rate. Experimental results are provided to show the excellent performance of our proposed algorithm.
Use Online Dictionary Learning to Get Parts-Based Decomposition of Noisy Data

A huge amount of data is generated every day. Extracting interpretable features from the data is becoming important. Meanwhile, dimension reduction and low-rank approximation are also becoming important as people want to factorize big matrix into smaller ones that are easy to handle. Sparse coding is such a technique that can factorize a matrix into sparse linear combinations of basis elements. We found that through Online Dictionary Learning, an efficient sparse coding method, we can decompose large data matrix with noise into interpretable dictionary atoms. Such atoms are useful in reconstructing a denoised data matrix.
Evaluating the Impact of Repetition, Redundancy, Scrubbing, and Partitioning on 28-nm FPGA Reliability Through Neutron Testing

SRAM-based field-programmable gate arrays (FPGAs) are widely deployed in space and high-radiation environments, but they exhibit vulnerability to radiation effects. Designs can be hardened against radiation effects with design-side countermeasures such as redundancy, scrubbing, and partitioning. Through neutron tests, we investigate the impact of these design-side countermeasures on 28-nm FPGAs. We specifically address not only the provided radiation hardness but also the resource utilization and performance overheads. In addition, we evaluate the efficacy of repeating the operation after error detection. The results show that using coarse-grained and fine-grained triple modular redundancy (TMR) over dual modular redundancy (DMR) improves the failure cross section by <inline-formula> <tex-math notation="LaTeX">$3.29\times $ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$11.49\times $ </tex-math></inline-formula>, respectively. The partitioning scheme that we used does not show a significant effect on radiation hardness. Using an internal scrubber and repeating the operation after a failure further decreases DMR, coarse-grained TMR, and fine-grained TMR cross sections by <inline-formula> <tex-math notation="LaTeX">$5.10\times $ </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">$1.85\times $ </tex-math></inline-formula>, and <inline-formula> <tex-math notation="LaTeX">$1.18\times $ </tex-math></inline-formula>, respectively.