Miracle-dz/UniFAR-FTUs huggingface dataset:
Electrical Appliance Classification using Deep Convolutional Neural Networks on High Frequency Current Measurements

Monitoring the energy demand of appliances can raise consumer awareness and therefore reduce energy consumption. Using a single-point measurement of mains energy consumption can keep costs and hardware complexity to a minimum. This data stream of raw voltage and current measurements can be used in machine learning tasks to extract information. We apply Deep Convolutional Neural Networks on an electrical appliance classification task, using raw high frequency start up events from two datasets. We further present Data Augmentation techniques to improve the model performance and evaluate different data normalization techniques. We achieve a perfect classification on WHITED and a Fl-Score of 0.69 on PLAID.
Metamorphic Testing for Machine Translations: MT4MT

Automated machine translation software and services have become widely available and increasingly popular. Due to the complexity and flexibility of natural languages, automated testing and quality assessment of this type of software is extremely challenging, especially in the absence of a human oracle or a reference translation. Furthermore, even if a reference translation is available, some major evaluation metrics, such as BLEU, are not reliable on short sentences, the type of sentence now prevailing on the Internet. To alleviate these problems, we have been using a metamorphic testing technique to test machine translation services in a fully automatic way without the involvement of any human assessor or reference translation. This article reports on our progress, and presents some interesting preliminary experimental results that reveal quality issues of English-to-Chinese translations in two mainstream machine translation services: Google Translate and Microsoft Translator. These preliminary results demonstrate the usefulness and potential of metamorphic testing for applications in the natural language processing domain.
Towards Emergent Security in Low-Latency Smart Grids with Distributed Control

IT security management in distributed Smart Grids has been discussed in a number of recent works, mainly from the viewpoint of how to prevent unauthorized access, either organizationally or cryptographically. In this paper, we aim to extend this viewpoint by introducing the concept of emergent security in cyber-physical energy systems. The main contribution of this paper is the description of four architectural layers, providing a structured approach for security assessment and evaluation. The proposed emergent security layer shifts the perspective of IT security towards incorporating real-time measurements, decoupling feedback of components, increasing topology-awareness, and implementing distributed counter-measures and participant reputation systems. We propose an iterative process for designing resilient distributed control algorithms for Smart Grids. This process acknowledges the new challenges for security management in critical infrastructures due to the recent advances in inter connectivity of systems. The proposed approach is validated using an implementation of Smart Grid control and a co-simulation of a cyber-physical energy system, indicating the importance of the introduced emergent security layer.
Design of a Multi-Wavelength LED Photo-Rejuvenating System

To realize cosmetic phototherapy, a multi-wavelength LED cold light source design method is proposed in this work, which combines the 455nm, 595nm, 630nm, 661nm, and 828nm LED chips into a single light module. Compared with the conventional phototherapies using laser or intense pulsed light, the proposed light source has the advantages of improved safety and longer life time. The electrical design to drive the system is also proposed. The system can be used in different photo-rejuvenating applications, e.g. whitening and tendering skin, treating acne, by choosing different wavelengths. Another feature of this system is that the temperature of skin area exposed to the light can be monitored in real time. Experiments have been implemented to test the radiant power density and uniformity. The results show that the maximally achievable power density of the system is about 75mW/cm2; and the uniformity of spot is above 80%.
CFSES Optimization Feature Selection with Neural Network Classification for Microarray Data Analysis

The DNA microarray data are enabling to measure the countenance levels of genes simultaneously, so long as an excessive for tuitous for cancer prediction. The number of features or genes is often more than thousands but in another side, the number of instances or subjects is less than hundreds. For that, it is required and vital to perform gene selection for classification. Feature selection moderates this problem by eradicating irrelevant genes or features from data. So it optimizes the computational cost with concentrated data. At here, we propose a new methodology for feature selection to determine marker genes that are relevant to a type of cancer. A different bio-stimulated procedure entitled as CFSES (Correlation-based Feature Selection Elephant Search) is projected in this work. We use the Linear Support Vector Machine (Linear-SVM), SMO-SVM and MLP to make classification with use of the selected marker genes. The performance of marker gene selection and classification are illustrated in two real data analysis.
Matching-Theory-Based Resource Allocation for Underlay Device to Multi-Device Communications

In underlay device-to-device multicast communication (D2MD), a group of users can communicate directly by reusing cellular resources blocks (RB) to share common content. This paradigm brings great benefits to cellular networks in term of energy and spectral efficiency. However, D2MD or cellular communication quality may degrade or blocked due to harmful mutual interference between cellular, D2MD users sharing the same communication resources. Therefore, to fully achieve the advantages of D2MD communication, resource management and power control became critical. In this paper, we model the join power and resource allocation as a mixed integer nonlinear problem (MINLP) to maximize network global energy efficiency (GEE). To coup with NP-hard nature of the problem, we investigate a two stages solution. At first, we propose a scheme based on matching theory to solve resource allocation sub-problem. Here, we introduce a reuse and a split factor to control the aggregated interference on CUE from D2MD and the number of RB used by each group. Second, we apply the framework of fractional programming to optimally solve the power control sub-problem subject to QoS constraints. Finally, GEE metric is analysed via extensive numerical simulations with a spatial Poisson process for the users’ locations and applying different clustering algorithms as K-nearest neighbour, distance limit.
Extracting Textual Features of Financial Social Media to Detect Cognitive Hacking

Social media are increasingly reflecting and influencing the behavior of human and financial market. Cognitive hacking leverages the influence of social media to spread deceptive information with an intent to gain abnormal profits illegally or to cause losses. Measuring the information content in financial social media can be useful for identifying these attacks. In this paper, we developed an approach to identifying social media features that correlate with abnormal returns of the stocks of companies vulnerable to be targets of cognitive hacking. To test the approach, we collected price data and 865,289 social media messages on four technology companies from July 2017 to June 2018, and extracted features that contributed to abnormal stock movements. Preliminary results show that terms that are simple, motivate actions, incite emotion, and uses exaggeration are ranked high in the features of messages associated with abnormal price movements. We also provide selected messages to illustrate the use of these features in potential cognitive hacking attacks.
ScienceSearch: Enabling Search through Automatic Metadata Generation

Scientific facilities are increasingly generating and handling large amounts of data from experiments and simulations. Next-generation scientific discoveries rely on insights derived from data, especially across domain boundaries. Search capabilities are critical to enable scientists to discover datasets of interest. However, scientific datasets often lack the signals or metadata required for effective searches. Thus, we need formalized methods and systems to automatically annotate scientific datasets from the data and its surrounding context. Additionally, a search infrastructure needs to account for the scale and rate of application data volumes. In this paper, we present ScienceSearch, a system infrastructure that uses machine learning techniques to capture and learn the knowledge, context, and surrounding artifacts from data to generate metadata and enable search. Our current implementation is focused on a dataset from the National Center for Electron Microscopy (NCEM), an electron microscopy facility at Lawrence Berkeley National Laboratory sponsored by the Department of Energy which supports hundreds of users and stores millions of micrographs. In this paper, we describe a) our search infrastructure and model, b) methods for generating metadata using machine learning techniques, and c) optimizations to improve search latency, and deployment on an HPC system. We demonstrate that ScienceSearch is capable of producing valid metadata for NCEM's dataset and providing low-latency good quality search results over a scientific dataset.
Chinese Entity Relation Extraction Based on Syntactic Features

Entity Relation Extraction (ERE) is an important research topic in the field of information extraction. However, to the best of our knowledge, only a few ERE works have been done for Chinese corpus. Because the syntactic features of Chinese sentences and English sentences are very different, existing algorithms for English corpus cannot be directly applied to Chinese corpus. Thus, in this paper, we propose a novel Chinese entity extraction system based on syntactic features (named SF-CERE). The basic idea of SF-CERE is given as follows. Firstly, we extract candidate relation triples based on verbs and verb-nouns as relation keywords to avoid pre-defining relation types. Secondly, the triples are filtered using the positional constraints between relation keywords and entity pairs. Thirdly, we summarize four major Chinese syntactic features to expand the identified relation triples and improve accuracy. Finally, we use the method of relation transfer to mine and infer implicit relation triples. The experimental results on two real-world dataset (i.e., the encyclopedia dataset and the news dataset) show that SF-CERE effectively improves the quality of the relation triples and obtains good extraction performance.
Establishing Thermal Model for the Design of the High-Power LED Headlight Cooling Device

A study of the thermal performance of a novel radiator in high-power LED (light-emitting diode) headlight is presented. The thermal resistance model is built to analyze the factors which have impacts on the radiator temperature. The analyses are based on the experiment combined with computations using the CFD software ANSYS Icepak. The prediction results are validated with the experimental one. The temperature distribution of the LED module and radiator are obtained. The maximum temperature of the radiator is reduced by about 25°C compared with a certain headlight while the input power is 16 W. The effects of the number of fins, substrate spacing and fin thickness on the radiator temperature are also examined. The Results indicate that the optimum parameters are: number of fins=36, substrate spacing=3 mm and fin thickness=2.5°. Meanwhile, as the input power increases to 22 W, the maximum temperature of the chip is only 85.01 °C, therefore, the heat dissipation of headlight designed in this paper is greatly enhanced.
Proposal for Control Dependency White-Box Test Coverage Metrics for Inheritance

Several white-box test coverage metrics have been proposed in the literature in the context of object-oriented programs. However, these are not appropriate for the objectoriented feature inheritance. The main reason for this situation is the fact that a class is derived from another class in inheritance. Though there are a large number of well established test coverage metrics have been reported to capture the coverage of objectoriented programs, white-box test coverage metrics to capture the coverage due to inheritance have rarely been reported. In this paper, we propose a set of novel unit test coverage metrics for object-oriented feature inheritance based on execution of control dependences of the JSysDG (Java System Dependency Graph).
Service Abstraction Layer for Building Operating Systems: Enabling portable applications and improving system resilience

For large scale implementation of Demand Response programs, applications enabling Demand Response would need to be portable between buildings. Building Operating Systems are an essential strategic piece for enabling portable applications, which work with several hardware implementations from different vendors. These building operating systems can effectively be used to implement large-scale Demand Response implementations. Currently, though, the service layer of building operating systems requires direct integration with specific services, and thereby have requirements for specific implementations of services to be present in a specific building. This paper proposes the introduction of a Service Abstraction Layer to decouple the application from the specific implementations of services, as well as to introduce the concept of redundancy in service responsibility areas. These changes would allow for application portability between buildings but also allow for building operating system resiliency.A prototype abstraction layer is implemented and tested. Results show the introduction of a Service Abstraction Layer has promising benefits for building operating systems, and successfully decouples the applications from the building operating system implementation, as well as improving system reliability and resiliency. For smart grid applications, the addition of a service abstraction layer allows for large-scale portable applications, but would still require some form of standardization of communication protocols for different service types.
Bit Error Rate Analysis of Non-Orthogonal Multiple Access (NOMA) Technique in 5G with Different Power and User Scenarios

Non-orthogonal multiple access (NOMA) is a potential multiple access technique for the fifth-generation (5G) and beyond wireless networks. By allowing multiple users to share the same time and frequency blocks by dividing their power levels, NOMA can scale up the number of served users, increase the spectral efficiency (SE), and improve bit error rate (BER) compared to existing orthogonal multiple access (OMA) techniques. This paper discusses the performance and challenges of NOMA with various power allocation ratios and a different number of users, comparatively. Simulation results are provided at the end of the paper in terms of BER at the receiver end using different power proportions and number of users that share the same frequency resources.
Crop Anomaly Identification with Color Filters and Convolutional Neural Networks

Monitoring crops is a time consuming yet important task to ensure production quality. In this paper we present a comparison of convolutional neural network-based methods, evaluating model complexity and performance based on multiple metrics for the binary classification task of segmenting trees from the environment. An Unmanned Aerial Vehicle (UAV) is used to obtain RGB video of orange crops in different altitudes. Keyframes are extracted based on drone trajectory and speed for training and evaluation of the models. The effect on performance of multiple data augmentation techniques is also evaluated. The preferred model is then applied to a reconstruction of a region from multiple images and a color filter is applied for anomaly detection. Experimental and visual results show that these methods are able to segment the environment efficiently without any feature engineering, being a viable pre-processing method for reducing noise in disease identification applications.
FaultVisor2: Testing Hypervisor Device Drivers Against Real Hardware Failures

Hardware failures are inevitable, especially in cloud environments where there are many hardware devices. To improve the hypervisor's reliability, hypervisor device drivers must handle hardware failures appropriately. Our goal is to allow cloud vendors to test closed-source hypervisor device drivers against failures of their real hardware. Previous studies either require source code, can only test against virtual hardware, or cannot be applied to hypervisors. In this paper, we propose FaultVisor2, a hypervisor device driver testing framework that combines fault injection and nested virtualization. To test closed-source hypervisor device drivers, we inject pseudo faults to the I/O data returned from hardware to hypervisor device drivers. To test against real hardware, we allow the target hypervisors pass-through access to the physical hardware and manipulate I/O data of the target devices by intercepting I/O access. To apply to hypervisors, we exploit nested virtualization and run a small hypervisor underneath the target hypervisor to inject pseudo faults. We omit some nested virtualization functions, including nested paging virtualization, to achieve a close to real execution environment and reduce runtime overhead. In our experiment using the VMWare ESXi hypervisor, we found three types of errors which led to critical system failures.
A Framework for 3D Object Segmentation and Retrieval Using Local Geometric Surface Features

Robotic vision and in particular 3D understanding has attracted intense research efforts the last few years due to its wide range of applications, such as robot-human interaction, augmented and virtual reality etc, and the introduction of lowcost 3D sensing devices. In this paper we explore one of the most popular problems encountered in 3D perception applications, namely the segmentation of a 3D scene and the retrieval of similar objects from a model database. We use a geometric approach for both the segmentation and the retrieval modules that enables us to develop a fast, low-memory footprint system without the use of large-scale annotated datasets. The system is based on the fast computation of surface normals and the encoding power of local geometric features. Our experiments demonstrate that such a complete 3D understanding framework is possible and advantages over other approaches as well as weaknesses are discussed.
A Low-Cost Amperometric Glucose Sensor Based on PCB Technology

The most successful electrochemical sensor is the glucose sensor, which has transformed the management of diabetes. Microfabrication, inkjet and screen printing are used for the realization of electrochemical sensors. However, these can be costly, time-consuming, non-standardized processes, which may be hard to adapt for low-cost mass fabrication. In this paper, we extend our previous work on PCB-based electrochemical sensing, to the realization of amperometric glucose sensors. In vitro results are presented from a multielectrode platform with on-board counter and reference electrodes, in an effort to develop low-cost, PCB-based multiparametric, multi-modal sensor arrays. A sensitivity of 3.13 µAcm−2 mM−1 was achieved.
Influence of Zero Sequence Voltage Injection on Circulating Current Control of Full-Bridge Modular Multilevel Converter for Capacitor Voltage Ripple Minimization

With dc short circuit fault ride through capabilities, full-bridge submodule (FBSM) based modular multilevel converter (FB-MMC) has gained much interests as the FBSM capacitance can be reduced significantly when the modulation index equals to 1.414. In this paper, the analytic expression of the injected circulating current, which minimizes the FBSM voltage ripple of the FB-MMC is derived. Based on these derivations, the impact of zero sequence voltage injection (ZSVI) on the injected circulating current is analyzed in detail. It is shown that, with ZSVI, the amplitude of the injected circulating current can be reduced about 14% for achieving FBSM voltage ripple minimization, which has benefits to decrease the current stress of the semiconductor devices. Finally, the correctness of the presented analysis is verified by comparative simulation results on a ± 200kV/600 MW FB-MMC system.
Non-Intrusive Human Motion Recognition Using Distributed Sparse Sensors and the Genetic Algorithm Based Neural Network

Due to the rapid development of sensing technology and the increasing ratio of elderly population, many research activities have been performed to develop human motion detection and recognition systems. Various camera and wearable sensor-based human recognition systems have been developed; however, they are either not privacy protective or not practical for longterm monitoring. In this paper, we present a non-intrusive indoor human recognition system using distributed sensors and Genetic algorithm (GA) based neural network. Pyroelectric infrared (PIR) sensors are chosen using masks with random sampling windows to sense the human body thermal variations. The time domain statistical features are extracted to train classification algorithm in order to recognize human motion. Total of 200 samples are collected from volunteers performing two actions, i.e., walking normal and abnormally. A number of classification algorithms have been trained to recognize human motion. The outcome indicates that the QFAM-GA method outperforms other state-of-the-art methods, such as KNN, SVM, CART, NB and Fuzzy Min-Max.
Advanced Single-Phase Onboard Chargers with Small DC-Link Capacitors

In this paper, a single-phase non-isolated onboard battery charger with small DC-link capacitors is proposed, where the low-voltage battery charger is utilized as an active power filter to reduce the inherent second-order ripple power when the high-voltage (HV) battery is charged from the grid. In this scheme, the large DC-link capacitor banks of the HV battery charger can be eliminated without additional switches, leading to the reduction of cost and volume of the onboard battery charger. Also, this structure of EV chargers has distinctive features of three different modes such as grid-to-vehicle (G2V), vehicle-to-grid (V2G), and HV-to-LV battery (H2L) modes. The simulation and experimental results have verified the effectiveness of the proposed topology.
Driver Generation for Java EE Web Applications

Program analyses typically need to identify a single entry method that triggers program executions. However, many modern applications do not have such a single entry point. Instead, they have to be deployed in a container that interacts with them through an application programming interface (API). We present a tool that generates a driver that supplies such an entry method for Java EE web applications. The generated driver simulates an arbitrary client interacting with the web application through the container. We describe the modular design of the driver generator, and existing support for features like Java server pages, servlets, filters and listeners. We report on our experience of using the driver generator for large real-world web applications.