Miracle-dz/UniFAR-FTUs huggingface dataset:
SRMC: A Multibit Memristor Crossbar for Self-Renewing Image Mask

A recent surge of research on deep convolutional neural network (DCNN) has given a challenge on existing computation architecture which has the flows in speed and memory bottleneck. In the pretreatment of deep learning, mask operation is frequently used to remove noise or fetch information. However, under data-intensive conditions, applying mask frequently can put an extremely heavy memory/communication burden on computing system. An efficient substrate of DCNN for ameliorating mask operation is urgently needed. In this paper, we present a self-renewing mask circuit (SRMC) to alleviate/solve earlier problems. First, a new approach to apply mask is presented based on computation-in-memory architecture that implements high-performance processor and high-density memory in the same physical location. Second, we designed the peripheral circuit which can provide self-renewing function to further avoid the data exchange with an external space. As opposed to most other computational element, which calculates two 1-bit data, the proposed SRMC storage multibit value and calculate several of them parallel. The calculating ability of SRMC is significantly superior to those of computational element in general architectures. Moreover, mean filter and edge detector are implemented to illustrate the effectiveness and scalability of the proposed circuit. Finally, we discussed two possible methods to enhance the practicability of our scheme.
Analysis of a Novel Consequent-Pole Flux Switching Permanent Magnet Machine With Flux Bridges in Stator Core

In this paper, a novel flux switching permanent- magnet machine (FSPM) with consequent-pole magnets and flux bridges in the stator core is proposed to improve the torque capability of FSPMs with high pole ratios. The stator yoke and flux bridges adjacent to the magnets can provide magnetic paths for the main working harmonic field, thus the magnetic reluctance of the working harmonic field is reduced, and the flux modulation effect, winding back-electromotive force (EMF), and output torque of the proposed FSPM can be significantly improved compared to the regular FSPM with high pole ratios. The operation principles and stator–slot/rotor–slot combinations of the proposed FSPM are introduced. Then, the influences of pole ratio, split ratio, slot opening ratio, and PM width on the torque density of the proposed FSPM is investigated using finite element analysis. Moreover, after the investigations, a proposed FSPM is compared to a regular FSPM in terms of back-EMF, rated torque, power factor, etc. Finally, to verify the theoretical analysis, a 12-stator–slot/14-rotor–slot FSPM is built and tested.
Defect Tolerance in as-deposited Selenium-alloyed Cadmium Telluride Solar Cells

The efficiency of cadmium telluride (CdTe) solar cells is limited primarily by voltage, which is known to depend on the carrier concentration and carrier lifetimes within the absorber layer of the cell. Here, cathodoluminescence measurements are made on an as-deposited CdSeTe/CdTe solar cell that show that selenium alloyed CdTe material luminesces much more strongly than non-alloyed CdTe. This reduction in non-radiative recombination in the CdSeTe suggests that the selenium gives it a certain defect tolerance. This has implications for carrier lifetimes and voltages in cadmium telluride solar cells.
Using a Fully Connected Convolutional Network to Detect Objects in Images

The paper describes a new object detection algorithm which uses a convolutional network with a convolution kernel of the Network in Network (NiN) type. Detection refers to the simultaneous localization of objects on an image and their recognition. Detector can operate with images of arbitrary sizes. The algorithm proposed has a high computational efficiency, so when processing HD frame on a single CPU core, the operating time is about 300 ms. High degree of uniformity of network operations allows to massively parallel data processing on GPU, which is likely to reduce the operating time to less than 10 ms. Our algorithm is robust to small overlaps and the average quality of images of detected objects. It represents an end-to-end learner model, which output is delimited by boundaries and classes of objects throughout an image. An open access image database from car recorders is used to evaluate the algorithm for object detection. This algorithm is not limited to the use of one type of objects, it is able to simultaneously detect a mixture of objects.
Vulnerability Measure of a Sentence

A sentence is a group of words that must expresses a complete idea or sense or meaning. The meaning of a sentence depends primarily on the use of appropriate words. Confusion arises when there are multiple options (synonyms) available for a word. In this work, we introduce a vulnerability measure of a sentence that tries to define the amount of ambiguity due to the various options available for words used in the sentence. This measure facilitates the authors or copy editors to identify the vulnerable sentences in a document. Though the selection of appropriate words depends on the context, here we have just considered the synonyms of the words and defined the measure.
Quantifying the Diversity in Users Activity: An Example Study on Online Music Platforms

Whether it be through a problematic related to information ranking (e.g. search engines) or content recommendation (on social networks for instance), algorithms are at the core of processes selecting which information is made visible. Those algorithmic choices have in turn a strong impact on users activity and therefore on their access to information. This raises the question of measuring the quality of the choices made by algorithms and their impact on the users. As a first step into that direction, this paper presents a framework to analyze the diversity of the information accessed by the users. By depicting the activity of the users as a tripartite graph mapping users to products and products to categories, we analyze how categories catch users attention and in particular how this attention is distributed. We then propose the (calibrated) herfindahl diversity score as a metric quantifying the extent to which this distribution is diverse and representative of the existing categories. In order to validate this approach, we study a dataset recording the activity of users on online music platforms. We show that our score enables to discriminate between very specific categories that capture dense and coherent sub-groups of listeners, and more generic categories that are distributed on a wider range of users. Besides, we highlight the effect of the volume of listening on users attention and reveal a saturation effect above a certain threshold.
Twitter Sentiment Analysis of Movie Reviews Using Information Gain and Naïve Bayes Classifier

The opinion of Twitter users related with movie is a valuable asset to be studied. This is because business and organization casts always want to know the public and consumers' opinions about their movies and consumers also want to know others' opinion before purchasing movie products. Sentiment analysis was applied in this research to gain information whether a tweet is positive opinion, negative opinion, or neutral opinion. Naive Bayes Classifier was chosen as the algorithm due to its strength on accuracy. In order to increase the run time efficiency, it was combined with Information Gain as feature selection method. By collecting tweets from 12 popular movie titles as dataset, the experimental evaluations show that the proposed techniques have the accuracy 82.19% with 0.006 as the optimal threshold of gain.
Analyzing News from Electronic Media and Topics Discussed on Social Media Using Ontology

Nowadays Social Networking sites contribute significantly in representing the thoughts of public on many issues, not only this but many news channels also broadcast news via social media and people comment on them with their own opinions as well. Furthermore, as people are becoming the largest sensor network, trending topics are not only led by media, but also by the public, and hence it is worth pondering how they affect each other. Majority of the population are converging toward the social media to discuss news on the social platforms as well. In our paper our focus is to find out the difference in topics which are initiated and discussed by the public on Social media and the topics news media are more willing to cover hence assessing the general perception of public regarding the biasness shown by the mainstream media outlets. To perform our analysis, we made news ontology containing data from Social Media and Electronic Media and answered following two questions: Are topics discussed on social media different from mainstream news media of Pakistan? and Is mainstream media of Pakistan biased towards specific topics and tend to report them more frequently while ignoring certain topics as compared to social media? To answer these questions, we formulated SPARQL queries and performed analysis on the results obtained. Our ontology provides basic model to analyze news gathered through different sources which can be utilized in future for analyzing various subjects for example comparing different news channels and biasness among them.
Identifying Severity Level of Cybersickness from EEG signals using CN2 Rule Induction Algorithm

One of the typical gaming disorder is cybersickness. Cybersickness is the condition that occurs during or after exposed by the virtual environment. The increasing of cybersickness symptoms in gamers can lead to the poor health condition. Prior studies in investigating cybersickness employ subjective self-reports questionnaire, i.e., simulator sickness questionnaire (SSQ). However, the objective measurement is required to determine the actual condition of subjects due to cybersickness severity level. Therefore, this paper proposed identification of cybersickness severity level using electroencephalograph (EEG) signals. From the EEG, we extract the best feature such as percentage change (PC) of power percentage (PP) in beta and theta frequency band from pre- to post-stimulation. We found a specific pattern of cybersickness that marked by the sudden decreasing of $\mathbf{PP}\beta$ during the recording between baseline segment (4 minutes) and the last part (4 minutes) of game playing. Unlike previous studies, this paper proposed the rules-based algorithm i.e. CN2 Rules Induction for identifying cybersickness severity level. This giving ease for medical-expert to determine appropriate diagnosis and treatment towards patients. The classification yields the best accuracy of 88.9% using the CN2 rule induction. It is outperforming other classifiers accuracies such as decision tree (72.2%) and SVM (83.3 %). According to the results, incorporating PC of the $\mathbf{PP}\beta$ feature with the rules-based algorithm is working well for identifying cybersickness severity level from EEG.
Android Dashboard for Past and Present Processes

We use Android devices for many activities beyond making phone calls. Yet, rarely are we aware of the activities happening inside it. A savvy user would like to know all this. As of now, we do not have any service that monitors the Android OS from within and tells us what the running processes are doing and what the old processes that are no longer alive did do. This paper presents a dashboard of the provenance of present and past processes. It analyses the process behavior with strace and ltrace. These details are logged and are uploaded to the cloud. The logged data are analyzed to provide the provenance of a process. An application ‘Android Dashboard for Past and Present’ is developed to provide the end user with the details of all the happenings inside Android device by analyzing the processes. The source code and related documents of this paper are available at https://github.com/anurp/DashboardOfProcess.
Electrical Tree Growth Under Very Low Frequency (VLF) Voltage Excitation

Electrical trees are a mechanism of failure of high voltage polymeric insulation. Their growth is associated to partial discharges (PD). Conventionally, PD are analyzed using phase-resolved PD plots and statistical parameters. An alternative is to exploit the tools of nonlinear time series analysis to analyze PD as a nonlinear dynamical process. Standards for field testing of power cable include the measurement of PD under very low frequency (VLF) voltage excitation. Here we calculated the correlation dimension of the reconstructed dynamic object obtained from the time series of PD during tree propagation. Also, conventionally PD analysis was carried out to compare with the alternative approach. Electrical trees grown at 0.1 Hz 14, 16 and 18 kV have been analyzed, along a tree grown at 50 Hz 14 kV for comparison. Different PD dynamics were found, depending on the applied voltage and frequency, and the stage of tree-growth. The results show that the approach using nonlinear time series analysis can be an alternative method of analyzing tree growth and PD under VLF voltage excitation.
Asymptotic Stability and Attractivity for 2D Linear Systems

Asymptotic stability by definition encapsulates two well-known properties usually called Lyapunov stability and attractivity. In the realm of the linear 1D systems however, it is a well-known property that attractivity is equivalent to asymptotic stability. This result has been assumed to be true when working with multidimensional systems but a definitive proof is missing in the literature. In this paper, we present for the first time a complete proof showing that for a linear discrete 2D model (whether a Fornasini-Marchesini or a Roesser model), asymptotic stability is indeed equivalent to attractivity.
An Investigation on the Effects of Contact in MEMS Oscillators

We investigate the effects of contact in MEMS oscillators by fabricating, testing, and simulating a simple device featuring bilinear stiffness. When the shuttle oscillation exceeds a given threshold, contact with an elastic beam is activated. We first discuss the standard one-cycle response that displays a strongly hardening hysteresis loop. Then, we show that other periodic solutions exist, characterized by multiple cycles and impacts. These are stable only for limited ranges of forcing amplitudes and frequencies and are associated to period doubling bifurcations. The spectrum of the measured and simulated displacements shows an increasingly rich collection of tones similar to what is known as frequency combs in optics. [2018-0175]
A Proactive Plastic Deformation Method for Fine-Tuning of Metal-Based MEMS Devices After Fabrication

An innovative plastic deformation method is proposed to tune metal-based MEMS devices after fabrication. The plastic deformation is a well-known failure mechanism in MEMS devices, which includes a phenomenon that the devices do not return original position after repeated actuation. Here, in contrast to this common experience, we exploit the plastic deformation for the first time to tune the performance of MEMS devices, by producing deliberate, controlled plastic deformation with the help of Joule heating. The proposed plastic deformation method is precisely and predictably controlled by the model we developed here, and it was successfully demonstrated and evaluated using a fabricated MEMS device. Unlike conventional plastic deformation methods, the proposed method was accomplished solely by electrical control, which means that the method can be employed in integrated devices. Tuning resolution was demonstrated at a 10-nm level, which is remarkably smaller than that of conventional plastic deformation methods, where the tuning resolution is typically several micrometers. We confirmed that the proposed method does not deteriorate the basic performance of the fabricated MEMS devices. Finally, we proposed and demonstrated a method for calibrating MEMS devices after fabrication and suggested an interesting application, which is recovering the performance of aged MEMS devices. [2017-0129]
A New Approach of Multiple Line Outage Identification Using Phasor Measurement Unit (PMU) with Bad Data

Nowadays line outage identification problem has become a crucial issue to the power system planners, researchers, engineers and academicians to ensure a secure and reliable power systems. Line outage identification problem is a tedious task with the increase in the system size and number of line outages. This paper presents a new algorithm for multiple line outage identification in presence of bad data due to faulty Phasor Measurement Units (PMUs). However with the enormous advancement of storage facility phasor angles can be easily stored intact for several outage cases through load flow study. These phasor angles for various outage cases are to be utilized when actual line outage occurs. A new model has been developed based on comparison between the simulated phasor angles for various outage cases and measured phasor angles obtained from Phasor Measurement Units (PMUs). Also, the proposed algorithm is able to detect line outages accurately by considering bad data. The performance of the proposed algorithm has been tested on standard IEEE 5-bus, IEEE 14-bus, IEEE 30-bus systems to check the effectiveness and viability of the proposed algorithm.
Electrolaryngeal Speech Enhancement with Statistical Voice Conversion based on CLDNN

An electrolarynx (EL) is a widely used device to mechanically generate excitation signals, making it possible for laryngectomees to produce EL speech without vocal fold vibrations. Although EL speech sounds relatively intelligible, is significantly less natural than normal speech owing to its mechanical excitation signals. To address this issue, a statistical voice conversion (VC) technique based on Gaussian mixture models (GMMs) has been applied to EL speech enhancement. In this technique, input EL speech is converted into target normal speech by converting spectral features of the EL speech into spectral and excitation parameters of normal speech using GMMs. Although this technique makes it possible to significantly improve the naturalness of EL speech, the enhanced EL speech is still far from the target normal speech. To improve the performance of statistical EL speech enhancement, in this paper, we propose an EL-to-speech conversion method based on CLDNNs consisting of convolutional layers, long short-term memory recurrent layers, and fully connected deep neural network layers. Three CLDNNs are trained, one to convert EL speech spectral features into spectral and band-aperiodicity parameters, one to convert them into unvoiced/voiced symbols, and one to convert them into continuous $F_{0}$ patterns. The experimental results demonstrate that the proposed method significantly outperforms the conventional method in terms of both objective evaluation metrics and subjective evaluation scores.
Review on Injection of False Data Attack into Power System State Estimation

The process of state estimation is threatened to false data injection attacks and diagnosing such kind of bad-natured attacks has significant impacts on reliable operation of power system. Recent studies show that false data injection attack which is one kind of cyber-attack can bypass conventional methods of bad data detection (chi-square WLSE, maximum residual, Hypothesis testing), introducing dangerous effects on operation of power system state estimation. In this report detection-based methods are discussed to address the limitations of existing methods. One method is Kullback-Leibler Distance (KLD) which calculate the distance between two probability distributions p & q derived from variations of measurements (where, $q$ is the probability distribution of measurements variation from historical data. $p$ is the probability distribution of measurements variation between current measurements and previous measurements). Second method is absolute distance method which also used to calculate distance index. By calculating distance index between two distributions of measurements, injection of false data can be detected. If the distance index is larger compare to previous one then newly received measurements are likely to be manipulated.
Single LED-Based Indoor Positioning System Using Multiple Photodetectors

This paper presents a visible light positioning system using a single light-emitting diode (LED) and a novel receiver. In the proposed system, the receiver is composed of a horizontal photodetector (PD) and two tilted PDs, and a known calibration point is deployed to improve the accuracy of the positioning. Based upon the multi-PDs’ relative positions and the received signal strength, the location of the receiver can be estimated regardless of the LED's and PDs’ characteristics. The influences of tilted PDs on the positioning errors are discussed, i.e., the polar angle <italic>α</italic> and the difference <italic>θ</italic> between two azimuth angles. The experimental results show that high accuracy can be achieved when <italic>α</italic> = 20° and <italic>θ</italic> ranges from 30° to 90°. The average and maximum errors are 2.15 and 4.01 cm, respectively, in the case of <italic>α</italic> = 20° and <italic>θ</italic> = 45°. The proposed system avoids the intercell interference caused by different transmitters and can be applied in scenarios with limited LEDs.
Improving robustness of classifiers by training against live traffic

Deep learning models are known to be overconfident in their predictions on out of distribution inputs. This is a challenge when a model is trained on a particular input dataset, but receives out of sample data when deployed in practice. Recently, there has been work on building classifiers that are robust to out of distribution samples by adding a regularization term that maximizes the entropy of the classifier output on out of distribution data. However, given the challenge that it is not always possible to obtain out of distribution samples, the authors suggest a GAN based alternative that is independent of specific knowledge of out of distribution samples. From this existing work, we also know that having access to the true out of sample distribution for regularization works significantly better than using samples from the GAN. In this paper, we make the following observation: in practice, the out of distribution samples are contained in the traffic that hits a deployed classifier. However, the traffic will also contain a unknown proportion of in-distribution samples. If the entropy over of all of the traffic data were to be naively maximized, this will hurt the classifier performance on in-distribution data. To effectively leverage this traffic data, we propose an adaptive regularization technique (based on the maximum predictive probability score of a sample) which penalizes out of distribution samples more heavily than in distribution samples in the incoming traffic. This ensures that the overall performance of the classifier does not degrade on in-distribution data, while detection of out-of-distribution samples is significantly improved by leveraging the unlabeled traffic data. We show the effectiveness of our method via experiments on natural image datasets.