Miracle-dz/UniFAR-FTUs huggingface dataset:
High-performance supercapacitors based on electrospun multichannel carbon nanofibers

Multichannel carbon nanofibers (MCNFs) have been fabricated by annealing electrospun PAN/PS immiscible polymer nanofibers. The obtained mechanically flexible mats of MCNFs, possessing an integrative architecture, are used as an electrode directly with no binder and without any activation processes. Owing to the binder-free characteristic, unique multichannel structure and high specific surface area, the MCNFs show high capacitance (270 F g−1 at a current density of 0.5 A g−1), perfect cycling stability (the capacitance does not decrease after 5000 cycles at a current density of 1 A g−1), excellent rate capability (89% retention at a current density of 20 A g−1) and high energy and power density. These results demonstrate that the electrode material has a promising prospect in applications of energy storage.
Improved Centralized XML Query Processing Using Distributed Query Workload

eXtensible Markup Language (XML) is used widely to transfer data among a wide variety of systems. Due to an increase in query workloads and management of larger datasets, centralized processing is no longer feasible for XML query processing. To address this issue, we propose a technique that improves XML query processing through query workload distribution. An effective distributed XML query processing can be affected by several criteria such as indexing, fragmentation, distribution strategy, and well as the query handling in the distributed servers. However, we believe that an efficient labeling mechanism and an inexpensive centralized query processors or a pruning method at dedicated servers contribute greatly to the overall performance of a distributed query processor. In this paper, we present an effective centralized pruning technique that is adopted into our proposed distributed XML query processing technique to process XML queries robustly. Experimental evaluations showed that the proposed distributed query processor superseded the performance of centralized query processor.
Profit Maximization Model for the Task Assignment Problem in 2-Tier Fog/Cloud Network Environments

This letter proposes an exact mathematical model to determine the optimal profit for task allocation within a 2-tier fog/cloud network environment. The model determines the optimal profit for fog computing networks while considering green computing concepts. This provides the exact operations needed to optimize for both fog node state manipulations and task allocation to the fog computing network. The proposed model uses complete knowledge of all its inputs to provide exact solutions. As a result, the model can be used to benchmark various real-time resource allocation algorithms by using historic trace data of demanded resources from a given network topology.
Ultra-Low Power Wake-Up for Long-Term Biodiversity Monitorin

This paper presents a mixed analog-digital always-on ultra low-power wake-up based on pulse pattern analysis. It is used for triggering a high performance multi-channel recorder only when necessary. Its architecture makes the most of ultra-low power analog primitives coupled with an embedded digital low power system for fine tuning the pulse detector in order to maximise its efficiency. Such system allows long-term biodiversity study, as most of the bioacoustic energy is pulsed. We illustrate the efficiency of our system with a case study on Sperm Whale biosonar. The architecture and features extraction using analog primitives are first detailed, followed by embedded digital implementation of the automatic gain control for the pulse detector. Always-on current consumption of this intelligent wake-up is 12. 5μA, with an area under the ROC curve equal to 75%. This allows an autonomy of 2 years on a single CR2032 battery cell. Finally perspectives towards biodiversity monitoring are discussed.
Verifying Artificial Neural Network Classifier Performance Using Dataset Dissimilarity Measures

The specification and verification of algorithms is vital for safety-critical autonomous systems which incorporate deep learning elements. In this paper, we introduce a novel measure for quantifying the dissimilarity between the datasets used for training ANN-based image classification algorithms, and the test datasets used for verifying and evaluating classifier performance. The novel dissimilarity measure is defined in terms of each neuron’s distribution of output values during training. This measure and its related variants, allow performance metrics, such as accuracy, to be placed into context by characterizing the test datasets employed for classifier evaluation. A system-level requirement could specify the permitted form of the functional relationship between classifier performance and the dissimilarity measures - placing demands on the classifier’s capability to generalize to data progressively more distant from the training dataset; such a requirement can be verified by dynamic testing. Empirical results, obtained using publicly available datasets, suggest that the measures have relevance to real-world practice for both quantifying dataset dissimilarity, and specifying and verifying classifier performance.
Dengue Prediction using Machine Learning Algorithms

Dengue is an arboviral disease caused by the Aedes mosquito-borne dengue viruses (DENVs). The World Health organization (WHO) reports an annual incidence of around 100-400 million infections in 2019 which is the largest number of dengue cases ever reported globally and prompted WHO to declare the virus as the world’s top 10 public health threats. It can be turned into life-threatening dengue hemorrhagic fever which further evolves into dengue shock syndrome. Indispensable useful tools that precisely distinguish dengue and its subtypes in the early stage of disease progression are essential to convenient well-timed supportive care and therapy. In recent years, Bangladesh has seen a hike in the dengue outbreak and 101,000 cases were reported to the WHO in 2019. Such an outbreak can create havoc in society. Because of the lack of vaccine and antiviral drugs, a timely prediction of the dengue outbreak is, therefore, crucial to reducing the casualty. In this paper, we proposed a new machine learning approach to predict dengue fever. A patient dataset, containing information of the patient’s diagnosis report, medical history, and symptoms, was constructed through collecting real-time raw data samples of various types of dengue fever patients from the Medicine Department of Chittagong Medical College Hospital and Dhaka Medical College Hospital, Bangladesh. The whole dataset was split into 70:30 ratios using 70% for training and 30% for test purposes. We applied machine learning algorithms, namely decision tree (DT) and random forest (RF) in the proposed classification model. Finally, the decision tree resulted in an average accuracy of 79%, which is higher than the random forest.
A Context-Policy-based Approach to Access Control for Healthcare Data Protection

Fueled by the emergence of IoT-enabled medical sensors and big data analytics, nations all over the world are widely adopting digitalization of healthcare systems. This is certainly a positive trend for improving the entire spectrum of quality of care, but this convenience is also posing a huge challenge on the security of healthcare data. For ensuring privacy and protection of healthcare data, access control is regarded as one of the first-line-of-defense mechanisms. As none of the traditional enterprise access control models can completely cater to the need of the healthcare domain which includes a myriad of contexts, in this paper, we present a context-policy-based access control scheme. Our scheme relies on the eTRON cybersecurity architecture for tamper-resistance and cryptographic functions, and leverages a context-specific blend of classical discretionary and role-based access models for incorporation into legacy systems. Moreover, our scheme adheres to key recommendations of prominent statutory and technical guidelines including HIPAA and HL7. The protocols involved in the proposed access control system have been delineated, and a proof-of-concept implementation has been carried out – along with a comparison with other systems, which clearly suggests that our approach is more responsive to different contexts for protecting healthcare data.
Impact of DFT Based Speckle Reduction Filter on Classification Accuracy of Synthetic Aperture Radar Images

Speckle in SAR images makes it difficult to interpret the image thus reducing the effectiveness of image processing. In remote sensing, image scene classification is an elementary problem which aims to label an image automatically with a specific semantic category. The classification performance of SAR data with speckle is inadequate for many applications. Thus, speckle removal becomes an important pre-processing step for SAR data classification. This study investigates the impact and importance of speckle filtering for classification using ALOS-PALSAR-2 data on San Fran-cisco area. Wishart classifier is chosen for classification of filtered and unfiltered SAR data. The influence of DFT based speckle reduction framework is investigated in terms of classification accuracy.
A Sentiment-based Similarity Model for Recommendation Systems

Recommendation Systems are tools that interpret the users' preferences in an attempt to generate fitting suggestions. Studies in this domain of research tend to conclude that the numerical user ratings are not powerful enough to truly express the users' preferences. The best way to overcome this is by extending the analysis to other elements provided by the user, such as text-based reviews of items. This data is believed to reveal a deeper understanding of the user's sentiment regarding a certain item. The goal of the proposed paper is to exploit the valuable information offered by the textual reviews, by mixing Sentiment Analysis techniques into the recommendation process. The contributions of this paper bring two major improvements to the traditional $\boldsymbol{k}$ Nearest Neighbors collaborative filtering algorithm. As a first step, a sentiment rating approach is developed based on calculated sentiment scores for each item. The resulting sentiment ratings replace the numerical ones in the recommendation process. Next, a sentiment based user similarity measure is defined taking into account three factors of similitude: the attractiveness, relevance, and popularity of reviews and users. Several experimental setups using two different datasets demonstrate that the newly proposed similarity measure outperforms some of the traditional ones and can be successfully used in the recommendation process.
SafetyNet: Interference Protection via Transparent PHY Layer Coding

Overcrowded wireless devices in unlicensed bands compete for spectrum access, generating excessive cross-technology interference (CTI), which has become a major source of performance degradation especially for low-power IoT (e.g., ZigBee) networks. This paper presents a new forward error correction (FEC) mechanism to alleviate CTI, named SafetyNet. Designed for ZigBee, SafetyNet is inspired by the observation that ZigBee is overly robust for environment noises, but insufficiently protected from high-power CTI. By effectively embedding correction code bits into the PHY layer SafetyNet significantly enhances CTI robustness without compromising noise resilience. SafetyNet additionally offers a set of unique features including (i) transparency, making it compatible with millions of readily-deployed ZigBee devices and (ii) zero additional cost on energy and spectrum, as it does not increase the frame length. Such features not only differentiate SafetyNet from known FEC techniques (e.g., Hamming and Reed-Solomon), but also uniquely position it to be critically beneficial for today’s crowded wireless environment. Our extensive evaluation on physical testbeds shows that SafetyNet significantly improves ZigBee’s CTI robustness under a wide range of networking settings, where it corrects 55% of the corrupted packets.
Valid Prediction Intervals for Course Grades with Conformal Prediction

Conformal prediction is a statistical-learning framework that complements predictions with a reliable measure of confidence [22]. It allows the construction of prediction intervals for any type of regression models under the assumption that the data are exchangeable. Conformal prediction intervals have provably valid frequentist coverage for finite data; i.e. they contain the true value of the response variable for any test data with probability at least 1 – ϵ for any significance level ϵ. Due to the validity of its prediction intervals, we argue that the conformal-prediction framework has to be used for course-grade prediction. In this paper, we experiment with two conformal predictors: Inductive Conformal Machines (ICMs) [11] and Cross-Conformal Machines (CCMs) [21]. They are compared in terms of informational efficiency (width and stability of prediction intervals) and computational efficiency on course-grade prediction tasks from a liberal education program of Maastricht University, the Netherlands. We show that ICMs enjoy substantial computational benefits while CCMs has a better informational efficiency. Moreover, CCMs extend the applicability of the conformal-prediction framework to course-grade prediction tasks for data sets of as few as 30 instances.
Application of Traffic Weighted Multi-Map Optimization Strategies to Traffic Assignment

Traffic Assignment Problem (TAP) is a critical issue for transportation and mobility models that deals mainly with the calculus and delivery of best-cost routes for the trips in a traffic network. It is a computationally complex problem focused on finding user equilibrium (UE) and system optimum (SO). The Traffic Weighted Multi-Maps (TWM) technique offers a new perspective for TAP calculus, based on routing decisions using different traffic network views. These TWM are complementary cost maps that combine physical traffic networks, traffic occupation data, and routing policies. This paper shows how evolutionary algorithms can find optimal cost maps that solve TAP from the SO perspective, minimizing total travel time and providing the best-cost routes to vehicles. Several strategies are compared: a baseline algorithm that optimizes the whole network and two algorithms based on extended k-shortest path mappings. Algorithms are analyzed following a simulation-optimization methodology over synthetic and real traffic networks. Obtained results show that TWM algorithms generate solutions close to the static UE traffic assignment methods at a reasonable computational cost. A crucial aspect of TWM is its good performance in terms of optimal routing at the system level, avoiding the need for continuous route calculus based on traffic status data streaming.
Emotion Recognition using Deep Convolutional Neural Network on Temporal Representations of Physiological Signals

Human emotion recognition using Electroencephalography (EEG) signals has become paramount in the field of affective computing. These signals received considerable recognition from researchers, as it delivers low cost and simple results for recognizing emotions manually as well as automatic. In this research paper, we present a novel approach for emotion classification based on electroencephalographic signals taken from the AMIGOS dataset. The proposed work extract features 14 channels of EEG signals using a variant of a deep convolutional neural network on spectrogram images of signals containing time and frequency. The proposed model outperforms the previous deep learning models. Accuracy of arousal comes out to be 0.875 with an f1 score of 0.75789 while the accuracy of valence comes out 0.750 with an f1 score of 0.49693.
A Query-based Routing Table Update Mechanism for Content-Centric Network

Due to the popularity of network applications, such as multimedia, online shopping, Internet of Things (IoT), and 5G, the contents cached in the routers are frequently replaced in Content-Centric Networking (CCN). Generally, cache miss causes numerous propagated packets to get the required content that deteriorates network congestion and delay the response time of consumers. Many caching strategies and routing policies were proposed to solve the problem. This paper presents an alternative solution by designing a query-based routing table update mechanism to increase the accuracy of routing tables. By adding an additional query content in interest packets, our approach real-time explores the cached content in routers and updated the routing table accordingly. This paper uses a general network simulator, ndnSIM, to compare basic CCN and our approach. The results show that our approach improves the response time of consumers and network congestion and is compatible with general forwarding strategies.
Robust, Fine-Grained Occupancy Estimation via Combined Camera & WiFi Indoor Localization

We describe the development of a robust, accurate and practically-validated technique for estimating the occupancy count in indoor spaces, based on a combination of WiFi & video sensing. While fusing these two sensing-based inputs is conceptually straightforward, the paper demonstrates and tackles the complexity that arises from several practical artefacts, such as (i) over-counting when a single individual uses multiple WiFi devices and under-counting when the individual has no such device; (ii) corresponding errors in image analysis due to real-world artefacts, such as occlusion, and (iii) the variable errors in mapping image bounding boxes (which can include multiple possible types of human views: {head, torso, full-body}) to location coordinates. We develop statistical techniques to overcome these practical challenges, and finally propose a novel fusion algorithm, based on inexact bipartite matching of these two streams of independent estimates, to estimate the occupancy in complex, multi-inhabitant indoor spaces (such as university labs). We experimentally demonstrate that this estimation technique is robust and accurate, achieving less than 20% error, in an approx. 85m2 lab space (with the error staying below 30% in a smaller 25m2 area), across a wide variety of occupancy conditions.
DeepSafety: A Deep Learning Framework for Unsafe Behaviors Detection of Steel Activity in Construction Projects

In the field of construction, the safety of construction workers on construction sites has been a major problem over the years. According to statistic of previous study, approximately 88% of these accidents are caused by unsafe behaviors. In recent years, the development and application of deep learning have attracted considerable research interest such as object detection, human pose estimation, etc. Meanwhile, the object detection techniques have been applied to detect whether workers wear or use proper equipment. However, using proper equipment doesn’t mean performing correct operation. Therefore, this paper proposes a deep learning framework, DeepSafty, to detect unsafe behaviors of construction workers through their postures to reduce their mortality rate. In our proposed DeepSafty, the object detection model YOLOv3 is used to locate construction workers precisely. Then, human pose estimation technology is used to determine various postures of construction workers. There are 17 joints in the poses. In other words, a 51-dimensional vector will be produced from the neural network, as unsafe behaviors are continual movements in the field of construction. Finally, a model that deals with time series in deep learning, i.e., long short-term memory, is used to solve any classification problems of time-dependent joint vectors. Finally, we conduct a comprehensive experimental study based on a dataset collected through a CCTV of a real construction site. The results demonstrate the effectiveness of our deep learning approach and show the strength of taking both object detection and human pose estimation into account in unsafe behaviors of construction workers.
The Path Forward for GaN Power Devices

GaN power devices, discrete transistors and integrated circuits, have been in production for over 10 years and have made significant inroads in many applications that benefit from the smaller size and the faster switching speed. These devices are several times smaller than their aging silicon MOSFET ancestors and, largely because of this size advantage, have also become comparatively less costly to produce. In this paper we will discuss cost, power density, and integration challenges facing GaN producers. We will attempt to quantify the impact and set a reasonable timetable for implementation.
Determination of anionic groups in wood by time-of-flight secondary ion mass spectrometry and laser ablation-inductively coupled plasma-mass spectrometry

Abstract A novel method for labelling of anionic groups on wood surfaces was developed and applied to spruce and aspen wood sections in the native state, and after acid washing and alkaline treatment. The metal ions Mg2+, Cu2+, Sr2+, and Zn2+ were used as markers and their patterns of attachment to anionic groups were assessed by time-of-flight secondary ion mass spectrometry (ToF-SIMS). Sr2+ was found to be a very suitable marker for labelling of anionic groups. In addition to ToF-SIMS, laser ablation-inductively coupled plasma-mass spectrometry (LA-ICP-MS) was used for semi-quantitative analysis of labelled anionic groups in wood samples. ToF-SIMS imaging of Sr2+-labelled anionic groups revealed abundant anionic groups in bordered pit tori and ray parenchyma cell walls in spruce wood. LA-ICP-MS revealed that the relative concentration of Sr2+ in tori of bordered pits was approximately eight-fold higher than in fibre cell walls. After alkaline treatment the concentration of anionic groups in the fibre cell wall was approximately three-fold higher than in the original acid-washed spruce. In aspen wood, anionic groups were located mainly in pits between vessels and contact ray parenchyma cells, in ray cell walls and in certain fibre wall layers.