Miracle-dz/UniFAR-FTUs huggingface dataset:
Supervised Learning of Single-Layer Spiking Neural Networks for Image Classification

The traditional artificial neural networks encode information through the spike firing rate. Spiking neural networks fall into the third-generation artificial neural network models, which use the precisely timed spike trains to encode neural information. The computational models can accurately simulate the neural network activities of human brain, and provide powerful capabilities of signal processing to solve the complex problem. In this paper, we propose a supervised learning algorithm for single-layer spiking neural networks based on the spike train kernel function, which can implement the complex spatio-temporal pattern learning of spike trains. Furthermore, a pattern classifier based on single-layer spiking neural networks is constructed for image recognition problem. We test the learning performance of the proposed algorithm by the image classification task on the LabelMe dataset. The experimental results show that the proposed algorithm has got good image classification accuracy for the test dataset, and the different sizes of receptive fields influence classification accuracies significantly.
IIoT Edge Analytics: Deploying Machine Learning at the Wellhead to Identify Rod Pump Failure

Oil and Gas operators now have the possibility to collect and leverage significant amounts of data directly at the extremities of their production networks. Data combined with Industrial Internet of Things (IIoT) architecture is an opportunity to improve maintenance of assets, increase their up-time, reduce safety risks and optimize operational costs. However, to turn data into meaningful insights, Oil and Gas industry needs to fully take benefit of Machine Learning (ML) models which are able to consume real-time data and provide insights in isolated locations with scarce connectivity. These ML models need to be precise, robust and compatible with Edge computing capabilities. This paper presents an analytics solution for rod pumps, capable of automated Dynagraph Card recognition at the wellhead leveraging an ensemble of ML models deployed at the Edge. The proposed solution does not require Internet connectivity to generate alarms and addresses confidentiality requirements of Oil and Gas industry. An overview of the employed ML models as well as the computing and communication infrastructure is given. We believe the given outline is insightful for the petroleum industry on its road to digitization and optimization of Artificial Lift systems.
Fog Computing-Assisted Trustworthy Forwarding Scheme in Mobile Internet of Things

The interaction between mobile Internet of Things (IoT) devices is based on a hybrid communication architecture. To improve packet delivery ratio, reduce end-to-end delay, and protect data privacy, designing an efficient data forwarding scheme is crucial in guaranteeing the quality of data transmission. Fog computing, a novel distributed computing framework, can decrease the amount of data transmission on the Internet and improve quality of services. In this paper, we propose a fog computing-assisted trustworthy forwarding (FCTF) scheme. To the best of our knowledge, this paper is the first study that investigates the role of fog infrastructure nodes (FINs) in forwarding scheme design. FCTF first selects the contact probability and the service degree as the basic trustworthy metrics between node pairs, and combines high-performance optimization algorithms to design a dynamic detection model of overlapping trustworthy communities (DOTCs). Then, we construct logical joint edge community structures on the basis of the results of DOTC and the distribution of FINs at each timestamp. Finally, based on the logical joint edge community, we define the forwarding utilities for FINs and mobile devices, respectively, and design self-adaptive forwarding rules. The experimental results prove that our FCTF scheme can enhance the delivery ratio, and decrease the latency and average hop-count more effectively, and has better routing quality compared with some popular forwarding models in mobile IoT applications.
Image-guided Placement of Magnetic Neuroparticles as a Potential High-Resolution Brain-Machine Interface

We are developing methods of noninvasively delivering magnetic neuroparticles™ via intranasal administration followed by image-guided magnetic propulsion to selected locations in the brain. Once placed, the particles can activate neurons via vibrational motion or magnetoelectric stimulation. Similar particles might be used to read out neuronal electrical pulses via spintronic or liquid-crystal magnetic interactions, for fast bidirec- tional brain-machine interface. We have shown that particles containing liquid crystals can be read out with magnetic resonance imaging (MRI) using embedded magnetic nanoparticles and that the signal is visible even for voltages comparable to physiological characteristics. Such particles can be moved within the brain (e.g., across midline) with- out causing changes to neurological firing.
Stabilization of Wind Farm by Using PMSG Based Wind Generator Taking Grid Codes into Consideration

This paper presents a new operational strategy for a large-scale wind farm (WF) which is composed of both fixed speed wind turbines with squirrel cage induction generators (FSWT-SCIGs) and variable speed wind turbines with permanent magnet synchronous generators (VSWT-PMSGs). FSWT-SCIGs suffer greatly from meeting the requirements of fault ride through (FRT), because they are largely dependent on reactive power. Integration of flexible ac transmission system (FACTS) devices is a solution to overcome that problem, though it definitely increases the overall cost. Therefore, in this paper, a new method is proposed to stabilize FSWT-SCIGs by using VSWT-PMSGs in a WF. This is achieved by injecting the reactive power to the grid during fault condition by controlling the grid side converter (GSC) of PMSG. The conventional proportional-integral (PI)-based cascaded controller is usually used for GSC which can inject small amount of reactive power during fault period. Thus, it cannot stabilize larger rating of SCIG. In this paper, a suitable fuzzy logic controller (FLC) is proposed in the cascaded controller of GSC of PMSG in order to increase reactive power injection and thus improve the FRT capability of WF during voltage dip situation due to severe network fault. To evaluate the proposed controller performance, simulation analyses are performed on a modified IEEE nine-bus system. Simulation results clearly show that the proposed method can be a cost-effective solution which can effectively stabilize the larger rating of SCIG compared to conventional PI based control strategy.
Rare Events Simulations with Applications to the Performance Evaluation of Wireless Communication Systems

Rare Events Simulations with Applications to the Performance Evaluation of Wireless Communication Systems Nadhir Ben Rached The probability that a sum of random variables (RVs) exceeds (respectively falls below) a given threshold, is often encountered in the performance analysis of wireless communication systems. Generally, a closed-form expression of the sum distribution does not exist and a naive Monte Carlo (MC) simulation is computationally expensive when dealing with rare events. An alternative approach is represented by the use of variance reduction techniques, known for their efficiency in requiring less computations for achieving the same accuracy requirement. For the right-tail region, we develop a unified hazard rate twisting importance sampling (IS) technique that presents the advantage of being logarithmic efficient for arbitrary distributions under the independence assumption. A further improvement of this technique is then developed wherein the twisting is applied only to the components having more impacts on the probability of interest than others. Another challenging problem is when the components are correlated and distributed according to the Log-normal distribution. In this setting, we develop a generalized hybrid IS scheme based on a mean shifting and covariance matrix scaling techniques and we prove that the logarithmic efficiency holds again for two particular instances. We also propose two unified IS approaches to estimate the left-tail of sums of independent positive RVs. The first applies to arbitrary distributions and enjoys the logarithmic efficiency criterion, whereas the second satisfies the bounded relative error criterion under a mild assumption but is only applicable to the case of independent 5 and identically distributed RVs. The left-tail of correlated Log-normal variates is also considered. In fact, we construct an estimator combining an existing mean shifting IS approach with a control variate technique and prove that it possess the asymptotically vanishing relative error property. A further interesting problem is the left-tail estimation of sums of ordered RVs. Two estimators are presented. The first is based on IS and achieves the bounded relative error under a mild assumption. The second is based on conditional MC approach and achieves the bounded relative error property for the Generalized Gamma case and the logarithmic efficiency for the Log-normal case.
An Annealing Memetic Algorithm for Instructional Scheduling

In order to solve the problems of large-scale instructional scheduling, many constraints, and complex interrelation, it is studied based on the changes in the scheduling problem and the adjustment of related algorithms. Firstly, the problem of instructional scheduling is described as a whole, and the relationship between each stage, in particular the impact of the combined plan on scheduling is also described, and an optimization plan is proposed. Then the optimal scheduling scheme is solved with Memetic algorithm. According to the constraints, the fitness value function in the algorithm is designed flexibly. Finally, the convergence of the algorithm is proved based on the Banach compression mapping theorem. Practice has proved that: Memetic algorithm can effectively solve the problem of inadequate teaching resources and difficult of instructional scheduling. Therefore, the Memntic algorithm can be used in instructional scheduling.
KeNet: a comprehensive Turkish wordNet and its applications in text clustering

In this thesis, we summarize the methodology and the results of our efforts to construct a comprehensive WordNet for Turkish. Most languages have access to comprehensive language resources. Traditional resources like bilingual dictionaries, monolingual dictionaries, thesauri and lexicons are developed by lexicographers. As computer processing of languages gain popularity, a new set of resources become necessary. One such resource is WordNet which was initially constructed for English language in Princeton University. A WordNet contains much of the information contained in a classic dictionary, but it also contains additional relationship information. These relations go beyond synonym relation and give information about relations such as a word being“is-a” or “is-a-part-of” another. These semantic relations are used in many text analysis tasks. A WordNet also categorizes words under common concepts. These concepts are called as synsets. As a result of all these, WordNet is a comprehensive dictionary which is readable by the computers and a useful language resource for text analysis and other research based on human language. In Turkish language, our WordNet is not the first. The previous WordNet is part of BalkaNet project which is a multilingual WordNet including Turkish and Balkan languages. BalkaNet contains only common words between these languages, as such BalkaNet does not contain all Turkish words and suffers from top-down constructing method disadvantages. BalkaNet project has not been updated or expanded in recent years. In this work we construct a Turkish WordNet from scratch using a bottom-up method. In general there are two methods for constructing WordNets. Bottomup method means that we create the WordNet from scratch while top-down approach uses other WordNets by translating them. We use Turkish Contemporary Dictionary (CDT) which is an online Turkish dictionary provided by Turkish Language Institute. Bottom-up approach has its own difficulties, since constructing a WordNet from scratch requires more resources and a lot of effort.
Transforming adversity into an ally: A qualitative study of “feeding your demons” meditation

Contemplative science has made great strides in the empirical investigation of meditation practices, such as how mindfulness, compassion, and mantra practices impact health and well-being. However, meditation practices from the Vajrayana Buddhist tradition that use mental imagery to transform distressing beliefs and emotions have been little explored. We examined the “Feeding Your Demons” meditation, a secular adaptation of the traditional Tibetan Vajrayana Buddhist meditation practice of Chöd (“Severance”) in a pilot, randomized controlled trial in which 61 community adults from the U.S. with prior meditation experience and moderate levels of depressive and anxiety symptoms (70% female)were randomly assigned to one month (15 meditation sessions) of “Feeding Your Demons” practice or a waitlist control group. Written diary entries were collected immediately after each meditation session. We used an Interpretive Phenomenological Analysis approach to examine qualitative responses to two questions which probed (1) how participants made meaning of each meditation session and (2) how they thought it may impact their future thoughts and intentions for action. Five major themes were identified based on 20 codes developed through an inductive review of written responses across all participants. The themes included an enhanced sense of self-worth and confidence, empathy for the “demon” or rejected parts of oneself, increased self-awareness, an active-oriented “fierce” self-compassion, and an acceptance form of self-compassion. Overall, participants expressed an ability to reframe, or transform, their relationship to distressing thoughts, emotions, and experiences as they gained personal insights, self-compassion, and acceptance through the meditation process which in turn shaped their future intentions for action in the world. This research suggests that a secular form of a Vajrayana Buddhist practice may be beneficial for Western meditation practitioners with no prior training in Vajrayana Buddhism. Future research is warranted to understand its longer-term impacts on health and well-being.
Pengembangan Sistem VOIP Menggunakan Server Issabel Versi 4.0 dan Tunnel EOIP pada OMNI Hospital Alam Sutera

The VoIP communication system at OMNI Hospital Alam Sutera uses the Elastix 2.5 server with the Centos 5.11 operating system. Elastix 2.5 by the developer has been declared End of Life. The server security system is a serious concern considering that VoIP servers can be accessed from the internet. Iptables and fail2ban applications are applications that are used to limit and counteract those who try to attack the VoIP server. One application that can be used as an open source VoIP server is the Issabel Application version 4.0. The migration process from Elastix 2.5 application to Issabel 4.0 by backing up all configurations in the Elastix 2.5 application through a web browser including the configuration of endpoints, fax, e-mail, asterisk. After the backup file is downloaded then upload the backup file to the Issabel 4.0 application then run the migration process. Adding a backup path as a failover connection is needed because the VoIP communication protocol between the OMNI Hospitals Group still uses one path so that when there is a problem in the connection path, the communication protocol will stop. The tunnel EoIP is a protocol used as a backup path between the OMNI Hospitals Group site.
A multi scale guided filter for illumination estimation in retinex algorithm

The method of illumination estimation for images affects the effect of retinex algorithm and causes information loss and halo artefact. A multi scale guided filter is proposed to estimate the illumination. In this method, bidimensional empirical mode decomposition is combined with guided filter. Firstly, the image is decomposed into multiple bidimensional intrinsic mode functions and a trend function by bidimensional empirical mode decomposition. Secondly, we use guided filter to estimate illumination information of each bidimensional intrinsic mode functions. Then illuminance component is formed by combining the illuminance information and the trend function. Finally, guided filter is again used to achieve a more accurate illuminance component. The simulation results show that multi scale guided filter can estimate the illumination component deeply and ensure the integrity of the reflection component. In the effect image of retinex algorithm, the contrast is raised by 1, the luminance is raised by 1, the information entropy is raised by 0.18, the clarity is raised by 0.001, and the visual effect is improved as a whole.
USING REAL SIGNALS WITH SIMULATED SYSTEMS

Students often find real systems more interesting than simulations, but using real systems in a course can be impractical. One compromise between using real systems and pure simulation is to capture signals from real systems and have the students process them using simulated systems. The advantages of this approach include exposing the students to deviations from the ideal such as noise and timing imperfections, and allowing them to experiment with different solutions in software. An example of this method is presented where a caller identification signal is captured from the telephone system, and is demodulated using the numeric computation package MATLAB.
Flexible image analysis for law enforcement agencies with deep neural networks to determine: where, who and what

Due to the increasing need for effective security measures and the integration of cameras in commercial products, a huge amount of visual data is created today. Law enforcement agencies (LEAs) are inspecting images and videos to find radicalization, propaganda for terrorist organizations and illegal products on darknet markets. This is time consuming. Instead of an undirected search, LEAs would like to adapt to new crimes and threats, and focus only on data from specific locations, persons or objects, which requires flexible interpretation of image content. Visual concept detection with deep convolutional neural networks (CNNs) is a crucial component to understand the image content. This paper has five contributions. The first contribution allows image-based geo-localization to estimate the origin of an image. CNNs and geotagged images are used to create a model that determines the location of an image by its pixel values. The second contribution enables analysis of fine-grained concepts to distinguish sub-categories in a generic concept. The proposed method encompasses data acquisition and cleaning and concept hierarchies. The third contribution is the recognition of person attributes (e.g., glasses or moustache) to enable query by textual description for a person. The person-attribute problem is treated as a specific sub-task of concept classification. The fourth contribution is an intuitive image annotation tool based on active learning. Active learning allows users to define novel concepts flexibly and train CNNs with minimal annotation effort. The fifth contribution increases the flexibility for LEAs in the query definition by using query expansion. Query expansion maps user queries to known and detectable concepts. Therefore, no prior knowledge of the detectable concepts is required for the users. The methods are validated on data with varying locations (popular and non-touristic locations), varying person attributes (CelebA dataset), and varying number of annotations.
Big Data and the Illusion of Choice: Comparing the Evolution of India’s Aadhaar and China’s Social Credit System as Technosocial Discourses

India and China have launched enormous projects aimed at collecting vital personal information regarding their billion-plus populations and building the world’s biggest data sets in the process. However, both Aadhaar in India and the Social Credit System in China are controversial and raise a plethora of political and ethical concerns. The governments claim that participation in these projects is voluntary, even as they link vital services to citizens registering with these projects. In this study, we analyze how the news media in India and China—crucial data intermediaries that shape public perceptions on data and technological practices—framed these projects since their inception. Topic modeling suggests news coverage in both nations disregards the public interest and focuses largely on how businesses can benefit from them. The media, institutionally and ideologically linked with governments and corporations, show little concern with violations of privacy and mass surveillance that these projects could lead to. We argue that this renders citizens structurally incapable of making a meaningful “choice” about whether or not to participate in such projects. Implications for various stakeholders are discussed.
Study of Vulnerabilities of ARP Spoofing and its detection using SNORT

As the organizational dependency on information exchange has increased exponentially the threats to information security have also increased. The organization has to spend a generous amount of resources to protect the information from the intruders. Attackers exploit the vulnerabilities of the network applications to have unauthorized access to organizational information. These vulnerabilities come into being because of poor application code or design. In this paper we will be discussing one such type of design level vulnerability that is ARP Spoofing. ARP Spoofing has been used to perform many types of attacks, including session hijacking, and cookie hijacking. We have used Snort for detecting ARP Spoofing. Performance of the snort for detecting ARP Spoofing has been studied experimentally on a real network by varying number of the target.
Single-Switch Differential Power Processing PWM Converter to Enhance Energy Yield of Photovoltaic Panels under Partial Shading

the chapter Abstract The partial shading on a photovoltaic (PV) panel consisting of multiple substrings poses serious issues of decreased energy yield and occurrence of multiple maximum power points (MPPs). Although various kinds of differential power processing (DPP) converters have been proposed to prevent the partial shading issues, multiple switches and/or magnetic components in proportion to the number of substrings are necessary, hence increas- ing the circuit complexity and volume. This chapter proposes a novel single-switch DPP PWM converter to achieve simplified circuit. The proposed DPP converter is essentially the combination of a forward/flyback resonant inverter (FFRI) and voltage multiplier (VM). The fundamental operation analysis is performed, and the current sensorless con- trol strategy suitable for the proposed DPP converter is also discussed. A 30-W prototype of the proposed DPP converter was built, and various kinds of experimental verification tests were performed emulating partial shading conditions. With the proposed DPP converter, local MPPs of a partially shaded PV panel were successfully eliminated, and energy yield was significantly enhanced, demonstrating the efficacy and performance of the proposed DPP converter. The 30-W prototype of the proposed DPP converter was built, and its fundamental operation performance was measured. Experimental equalization tests emulating partial shading con-ditions were performed using solar array simulators or a real PV panel. With the prototype of the proposed DPP converter, local MPPs disappeared, and extractable maximum powers significantly increased, demonstrating the efficacy and performance of the proposed DPP converter.