Miracle-dz/UniFAR-FTUs huggingface dataset:
CO2Vec: Embeddings of Co-Ordered Networks Based on Mutual Reinforcement

We study the problem of representation learning for multiple types of entities in a co-ordered network where order relations exist among entities of the same type, and association relations exist across entities of different types. The key challenge in learning co-ordered network embedding is to preserve order relations among entities of the same type while leveraging on the general consistency in order relations between different entity types. In this paper, we propose an embedding model, CO2Vec, that addresses this challenge using mutually reinforced order dependencies. Specifically, CO2Vec explores in-direct order dependencies as supplementary evidence to enhance order representation learning across different types of entities. We conduct extensive experiments on both synthetic and real world datasets to demonstrate the robustness and effectiveness of CO2Vec against several strong baselines in link prediction task. We also design a comprehensive evaluation framework to study the performance of CO2Vec under different settings. In particular, our results show the robustness of CO2Vec with the removal of order relations from the original networks.
Bayesian Modeling Approach in Big Data Contexts: an Application in Spatial Epidemiology

In this work we propose a novel scalable Bayesian modeling approach to smooth mortality risks borrowing information from neighbouring regions in high-dimensional spatial disease mapping contexts. The method is based on the well-known "divide and conquer" approach, so that the spatial domain is divided into D subregions where local spatial models can be fitted simultaneously. Model fitting and inference has been carried out using the integrated nested Laplace approximation (INLA) technique. Male colorectal cancer mortality data in the municipalities of continental Spain have been analyzed using the new model proposals. Results show that the new modeling approach is very competitive in terms of model fitting criteria when compared with a global spatial model, and it is computationally much more efficient.
Economic Worth-Aware Word Embeddings

Knowing the perceived economic value of words is often desirable for applications such as product naming and pricing. However, there is a lack of understanding on the underlying economic worths of words, even though we have seen some breakthrough on learning the semantics of words. In this work, we bridge this gap by proposing a joint-task neural network model, Word Worth Model (WWM), to learn word embedding that captures the underlying economic worths. Through the design of WWM, we incorporate contextual factors, e.g., product’s brand name and restaurant’s city, that may affect the aggregated monetary value of a textual item. Via a comprehensive evaluation, we show that, compared with other baselines, WWM accurately predicts missing words when given target words. We also show that the learned embeddings of both words and contextual factors reflect well the underlying economic worths through various visualization analyses.
Innovations in Mobility and Logistics: Assistance of Complex Analytical Processes in Visual Trend Analytics

A variety of new technologies and ideas for businesses are arising in the domain of logistics and mobility. It can be differentiated between fundamental new approaches, e.g. central packaging stations or deliveries via drones and minor technological advancements that aim on more ecologically and economic transportation. The need for analytical systems that enable identifying new technologies, innovations, business models etc. and give also the opportunity to rate those in perspective of business relevance is growing. The users’ behavior is commonly investigated in adaptive systems, which is considering the induvial preferences of users, but neglecting often the tasks and goals of the analysis. A process-related supports could assist to solve an analytical task in a more efficient and effective way. We introduce in this paper an approach that enables non-professionals to perform visual trend analysis through an advanced process assistance based on process mining and visual adaptation. This allows generating a process model based on events, which is the baseline for process support feature calculation. These features in form of visual adaptations and the process model enable assisting non-experts in complex analytical tasks.
A Duffing Detection Method of Eliminating Noise False Alarm Based on Detection Statistics

Duffing chaotic systems are sensitive to certain signals and immune to noise, the properties of which demonstrate their potential application in weak signal detection. Determination of critical value for abrupt change of the Duffing system is important theory basis for Duffing oscillator system. A solving formula of the critical value of Duffing oscillator system is obtained by using harmonic balance analysis method. The simulation results show that the theoretical solution and numerical simulation are preferably coherence. Noise can cause the false alarm detection. Based on the sensitivity of damping ratio, the variable damping ratio Duffing oscillator detection method with variable amplitude coefficients is introduced to reduce the false alarm caused by noise. To overcome the shortcomings of the phase diagram discrimination method in quantitative analysis, a detection statistics identification method is proposed. The results of the experiment verify the feasibility and accuracy of the new approach.
Dynamic Frame-Slotted Aloha in RFID Using Received Signal Strength Information

Received signal strength information based dynamic frame-slotted Aloha (RSSI-DFSA) RFID is proposed to replace random slot access where idle and collided slots lead to low system efficiency. Tag coordinates are calculated from received signal strength information, and the slot is chosen based on the coordinates. Simulations show that RSSI-DFSA system efficiencies are 14.9%, 2.4%, and 2.1% higher, and the time efficiencies are 8.3%, 2.5%, and 1.8% higher than those of existing algorithms, when the path loss exponent is 4 and the standard deviations of the shadowing are 1, 2, and 3 dB.
Machine Learning-Based Task Clustering for Enhanced Virtual Machine Utilization in Edge Computing

Edge computing provides cloud-like services at the network edge near mobile users. Due to the prosperity of smart applications that involve computing-intensive tasks, edge devices are intended to provide sufficient amounts of resources in order to accommodate the increasing computing demands. However, computing resources could also suffer being underutilized which leads to both resource and energy wastage. In this paper, heterogeneous virtual machine (VM) allocation in edge computing is considered to cope with the different computing demands at each edge device. To this end, an unsupervised machine learning technique, namely, the K-means is used to cluster incoming tasks into three different categories according to their processing requirements. Afterwards, tasks belonging to each cluster will be allocated the appropriate type of VMs to better utilize the computing resources. Results show the effectiveness of the proposed scheme in clustering computing tasks and improving resource utilization in edge devices.
Battery Storage System Optimization for Multiple Revenue Streams and Savings

Battery storage systems (BSSs) can be employed for a variety of energy services. Saving in utility charges is one of the revenue streams that can be achieved using a BSS. Demand charges (DC)—one of the major utility charges, especially for large electricity customers—can be reduced using BSSs. Furthermore, a BSS can gain revenues from other services such as demand response (DR) and energy arbitrage (EA). This paper presents an optimization formulation for the sizing and scheduling of the BSS to minimize the monthly energy bill through minimization of DC. Moreover, it counts for EA's revenue as well as the participation in winter and summer DR programs. Based on the market regulations of Ontario (Canada), this study investigates the potential of using BSSs for DC minimization and profit maximization from both EA and DR using real data for a large class-A Canadian electricity customer in Ontario. The results demonstrate the effectiveness of the proposed BSS deployment algorithm in minimizing the overall energy bills of the class-A customers.
Evaporative self-assembly of ordered complex structures

Drying a Sessile Droplet - Imaging and Analysis of Transport and Deposition Patterns (H Hu & R Larson) Convective Assembly of Patterned Media (M Ghosh & K Stebe) Materials Deposition in Evaporating Menisci - Fundamentals and Engineering Applications of the Convective Assembly Process (B Prevo & O Velev) Organized Structures Formation Driven by Interfacial Instability at the Three Phase Contact Line - Langmuir-Blodgett Patterning (X D Chen & L F Chi) Patterning and Assembling Nanomaterials by Dip Coating (J Kim et al.) Fabrication of Nano/Microstructured Organic Polymer Films Using the Process of Condensation: Self-Assembly of Breath Figures (M Srinivasarao) Self-Assembly of Highly Ordered Structures Enabled by Controlled Evaporation of Confined Microfluids (M H Byun & Z Q Lin) Guided Assembly by Surface Controlled Dewetting and Evaporation (J J Guan & J L Lee).
Primary Frequency Control in Islanded Microgrids Using a Novel Smart Load

Islanded microgrids are an inexpensive way to supply electricity to consumers in remote areas. They consist of synchronous diesel generators, distributed generators (DGs) that use renewable energy resources, energy storage systems (ESSs), and loads. The low inertia of the DGs; however, interfaces increase the complexity of frequency control in islanded microgrids and increases the possibility of system instability. In this paper, a new frequency control framework that is based on using single-stage AC-DC converters as virtual synchronous machine (VSM) smart loads to emulate the behavior of a synchronous machine (SM) is proposed to increase system inertia and reduce frequency oscillations. The AC-DC converter is based on a new topology that has been proposed by the authors. In this paper, the topology and the control system are explained and an IEEE 37 bus test feeder with the smart loads is simulated as an islanded microgrid. It is shown that using the proposed smart load results in frequency fluctuations being damped considerably faster.
A Cooperative Spectrum Sensing Architecture and Algorithm for Cloud- and Big Data-based Cognitive Radio Networks

Cognitive Radio Network (CRN) was designed to lessen the shortage of radio resources. The Secondary Users (SUs) can opportunistically utilize any available spectrum when the Primary Users (PUs) are inactive. Some of the challenges of CRN include the service interruption loss, complexity of processing and exchange of large amount of data, limited available memory to SUs and the non-real-time exchange of spectrum sensing data. These challenges can lead to significant degradation in the performance of a CRN. Therefore, there is a need to seek solutions that will alleviate these problems. The Cloud system incorporated with Big Data Analytics algorithm can be a potential solution. In this paper, we propose a Cloud-based Cooperative Spectrum Sensing model for CRN that allows the SUs to aggregate their individual spectrum sensing data into a cloud environment, where it can be analyzed using a proposed expanded Apache Spark algorithm incorporated with the hybridization of three machine learning methods-ensemble classifier approach that can effectively and efficiently analyze the spectrum sensing data for easy access, real-time analysis, deep insight and on-demand decision support for the SUs. In addition, the two-layer Fusion Center design proposed introduces redundancy by using the cloud as a secondary Fusion Center while still maintaining a primary land-based Fusion Center.
3D NAND Memory and Its Application in Solid-State Drives: Architecture, Reliability, Flash Management Techniques, and Current Trends

In the past 20 years, NAND flash has un der gone dramatic scaling, with 2D NAND feature size going from 400 nm in 1998 to 15 nm in 2012 and 3D NAND increasing from 24 layers to 128 layers in just the past seven years. NAND density, expressed in terms of gigabits per square millimeter, has increased 100,000 times in the past 22 years [1]. Aside from physical scaling, NAND logical scaling has also undergone a transformation, evolving from singlelevel cell (SLC) to two bits per cell (MLC) to triplelevel cell (TLC) and now four bits per cell (QLC), which has been incorporated into many products. NAND die capacity has grown from 128 Mb in 2000 to 1.33 Tb in 2020.
Prediction of Protein Tertiary Structure Using Pre-Trained Self-Supervised Learning Based on Transformer

Information of 3D protein structure plays an important role in various fields, including health, biotechnology, biomaterials, and so on. Knowledge of the 3D structure of proteins will help in understanding the interactions that can take place with other molecules such as drug molecules and other effector molecules. To get the 3D protein structure, it is generally carried out experimentally using x-ray diffraction and NMR (Nuclear Magnetic Resonance) methods. The experimental method requires a relatively long time and expertise to handle the completion of the structure. Until now, not all protein structures can be determined because the level of complexity varies from one protein to another. One approach is to use machine learning that leverages evolution information and deep learning method. The results given can improve the accuracy of the prediction compared to the conventional approach. However, the level of accuracy is still influenced by the number of homologous proteins in the database. Therefore, this study propose to replace the process of extracting the evolutionary information from Multiple Sequence Analysis (MSA) into Transformer-based self-supervised pre-trained. To test these changes, an experiment was carried out on a 3D protein structure prediction model based on Long Short-Term Memory (LSTM) and Universal Transformer. The proposed method results show a decrease in the value of distance Root-Mean Square Deviation (dRMSD) of 0.561 Angstrom and Root Mean Square Error (RMSE) torsion angle of 0.11 degree on the Universal Transformer predictor. The results of the T-Test show that the decrease in the two indicators shows a significant result. Therefore, pre-trained data can be used as an evolutionary information only for the Universal Transformer predictor.
Ontology Construction for Security Analysis of Network Nodes

Security related data in cyberspace is multi-source and heterogeneous, so it cannot be effectively used in network security analysis. To solve this problem, a network security ontology model for network node security analysis is proposed. The model reflects the security status of network nodes through three key sub domains: network assets, security information, defense strategy, and modeling concepts and relationships in a hierarchical and structured form. Ontology related language is used for query, and user-defined rules are used to enhance the reasoning ability of ontology, so as to assist network security analysis. Experiments and quality evaluation results show that the ontology model can integrate multi-source data, query and reason the network security status, and has higher quality evaluation than the same type of ontology.
Evaluation of RF Energy Harvesting by Mobile D2D Nodes Within a Stochastic Field of Base Stations

Radio frequency energy harvesting can prolong the battery life and improve energy efficiency of device-to-device (D2D) communication. In this paper, we analyze the performance of a mobile D2D device powered by EH from the transmissions of underlying cellular base stations (BSs), whose locations are modeled as a homogeneous Poisson point process. We model the movements of D2D nodes via a modified random waypoint model. Log-distance path loss and Rayleigh fading are considered, and EH takes place solely within harvesting zones surrounding each BS and each D2D user harvests energy for a fixed number of charging time slots before attempting to transmit. We derive the probability of a D2D device being within an EH region surrounding BSs after multiple movements, and the probability of being within the fully charged state using a Markov-chain approach taking into account temporal effects. Moreover, the statistics of the harvested energy are characterized, and subsequently, the outage probability of a D2D transmission utilizing the harvested energy is derived. We show that the number of movements required to be within a harvesting region increases significantly when the harvesting threshold power increases, and that the number of harvesting time slots should be selected judiciously.
Heart Rate Extraction from Photoplethysmography Signal: A Multi Model Machine Learning Approach

The purpose of this research is to estimate the heart rate (HR) from wearable gadgets, for example, fingertip gadgets. As the skin of finger-tip is slight, it is not difficult to separate pulse from that point. An optimistic component in this day, HR checking is Photoplethysmography (PPG). Moreover, during physical workout HR extraction precision is truly influenced by clamor and movement artifact (MA). To extract HR variability there are numerous ordinary techniques. In this research, a novel way is utilized to extract HR which is known as a multi-model machine learning technique. In this study, initially training and testing of our developed algorithm is done for various features and various dataset. In addition, separation of noisy and non noisy information is done by K means clustering. Then, the machine gain information from noisy and non noisy dataset. The Linear Regression model is utilized to estimate HR by using dataset. In this study, the feature engineering is also done, as it were, we choose an alternate set of features and know their conduct with our recommended technique and we discover error percentage for each set of features. There were 12 subject from where trial dataset were recorded. The root mean square (RMS) and the mean absolute error of HR was extracted. The lowest absolute mean error we find in this research is 3.06 beats per minute (BPM).
Structural-Algorithmic Synthesis of Single Phase Voltage Source Inverter with Pulse-Width Modulation for Solar Power System

The structural-algorithmic synthesis (SAS) of single-phase voltage source inverter (SPVSI) with pulse-width modulation (PWM) in this area of application is determined for the solar power system with small distortion level of output voltage. To solve this problem in this paper consider the possibility as the SAS of using the principle of multi-channel conversion (MCC) of the energy flow. At that each of the M channels uses single-phase voltage source inverter (SPVSI) with pulse-width modulation (PWM) of the output voltage. Today they are often referred to as multichannel SPVSI. When all channels are fed from a common power supply source, the outputs of the M channels are connected in parallel through transfilters, which sum up the currents of the channels and partially perform the function of voltage filtering. This concept is characterized by significantly smaller difference in neighboring quantization levels both in the output voltages of SPVSIs and the resulting output voltage of SPVSIs with MCC. Feature of multichannel conversion by PWM in these solutions is the use of voltage levels optimized for minimum total harmonic distortion (THD) of output voltage and as well as modified solutions of transfilters. This reduces the installed capacity of the equipments. The results and recommendations obtained in the basis of computer simulation are presented for their comparative assessment and design.
Watch ’n’ Check: Towards a Social Media Monitoring Tool to Assist Fact-Checking Experts

We present an ongoing collaboration between computer science researchers and fact-checking experts in a broad-cast corporation to develop Watch ’n’ Check, a social media monitoring tool that assists fact-checkers to detect and target misinformation online. The lean methodology followed in our collaboration has helped us to better understand how information access tools can assist fact-checking experts. We report initial results and discuss our plan for further development, as well as the open challenges identified so far.
Rapid Design Method for Generating Power System Stability Databases in SPS for Machine Learning

Today, the electrical networks are subjected to various internal or external disturbances that are likely to affect their stability. In view of their complexities and sizes, it is important to understand and rapidly predict the behavior of power grids in reaction to these disturbances during their operations. Big data presents itself as an alternative for the fast and efficient prediction of the stable or unstable state of electrical networks. In this work, we will be presenting a simple and fast technic for generating a large amount of data stability on Matlab/Simscape Power System (SPS) for deep learning. This is to facilitate the use of machine learning in the power system, which is necessary for the extraction and classification of stability criteria. By performing a simulation from the pseudo code of $i$ fault on each $j$ lines of the IEEE 68 bus, we obtain large matrices of $n$ time response signals per generator, for each of the $p$ generation-load patterns in our database.
Vehicle Damage Classification and Fraudulent Image Detection Including Moiré Effect Using Deep Learning

Image-based vehicle insurance processing and loan management has large scope for automation in automotive industry. In this paper we consider the problem of car damage classification, where categories include medium damage, huge damage and no damage. Based on deep learning techniques, MobileNet model is proposed with transfer learning for classification. Moreover, moving towards automation also comes with diverse hurdles; users can upload fake images like screenshots or taking pictures from computer screens, etc. To tackle this problem a hybrid approach is proposed to provide only authentic images to algorithm for damage classification as input. In this regard, moiré effect detection and metadata analysis is performed to detect fraudulent images. For damage classification 95% and for moiré effect detection 99% accuracy is achieved.