Miracle-dz/UniFAR-FTUs huggingface dataset:
Improvement and Performance Evaluation of GPSR-Based Routing Techniques for Vehicular Ad Hoc Networks

Geographic routing has been widely studied over the years as an effective solution for Vehicular Ad Hoc Networks (VANETs), especially because of the availability of wireless devices and global positioning system services. Given the unpredictable behavior of VANETs, selecting the next relay node has been proved a very challenging task. Therefore, in order to maintain acceptable network performance, the routing algorithm needs to be carefully designed to adapt to the fast network changes. The Geographic Perimeter Stateless Routing (GPSR) protocol is a widely adopted position-based routing protocol for VANETs, which makes it a good benchmark candidate. In this paper, we analyze the shortcomings of GPSR and propose a new strategy named Path Aware GPSR (PA-GPSR), which includes additional extension tables in the Neighbors’ Table to select the best path and bypass the nodes that have delivered such previous packets in recovery mode. Moreover, our proposed algorithm can eliminate packet routing loops avoiding the delivery of the same packet to the same neighbor node. These PA-GPSR features can, for instance, help to overcome link-breakage due to the unavoidable reasons, such as road accidents or dead-end roads. We used the Simulation of Urban MObility (SUMO) and Network Simulator-version 3 (NS-3) platform to compare our proposed algorithm to the traditional GPSR and Maxduration-Minangle GPSR (MM-GPSR) in scenarios varying the number of nodes as well as the number of source-destination pairs. Our results show that the proposed PA-GPSR strategy performed better than the traditional GPSR and MM-GPSR when packet loss rate, end-to-end delay, and network yield are considered as performance metrics.
Model-Free Volt-Var Control Based on Measurement Data Analytics

This paper proposes a new data-driven model-free Volt-Var control (VVC) scheme for the operation of radial power distribution systems. Unlike existing methods, the proposed VVC is neither reliant on model-based computer simulations nor limited to intuition/experience-driven rules for the operation of switchable capacitors and voltage regulators inside a feeder. Despite the model-free feature, the proposed scheme can still give optimal performances by using statistical estimations on measurement data such as the one provided by an advanced metering infrastructure system. Effectiveness and feasibility of the proposed idea is demonstrated by simulation studies on an IEEE 123 nodes test feeder. After few days of initial exploration, the proposed VVC is capable to give satisfactory results near to that of a model-based optimization technique. The promising outcome of this study suggests a novel application for emerging measurement and communication technologies in the operation of modern grids.
Lp Samplers and Their Applications

The notion of Lp sampling, and corresponding algorithms known as Lp samplers, has found a wide range of applications in the design of data stream algorithms and beyond. In this survey, we present some of the core algorithms to achieve this sampling distribution based on ideas from hashing, sampling, and sketching. We give results for the special cases of insertion-only inputs, lower bounds for the sampling problems, and ways to efficiently sample multiple elements. We describe a range of applications of Lp sampling, drawing on problems across the domain of computer science, from matrix and graph computations, as well as to geometric and vector streaming problems.
Review of Differential Power Processing Converter Techniques for Photovoltaic Applications

Differential power processing (DPP) converters are utilized in photovoltaic (PV) power systems to achieve high-efficiency power output, even under uneven lighting or mismatched PV cell situations. Since this DPP concept has been introduced for PV systems, various topologies and control algorithms have been proposed and validated, showing the benefits of DPP converters systems over existing series string and full power processing converter solutions. However, DPP systems are highly coupled and can be challenging to control. Various architectures, topologies, and control strategies for both series and parallel DPP architectures are reviewed and compared. Tradeoffs of different DPP converters and topologies are discussed. Also, the power curve for the PV connected to bus, PV to PV, and PV to independent port series DPP architectures are evaluated in terms of inverter interaction. To date, the PV to PV series DPP systems have been most widely implemented and robust system-level control for all architectures has been a major research focus. Furthermore, research and development is still needed, particularly for commercialization and parallel DPP approaches for emerging PV applications.
The Allocation of Valenced Percepts Onto 3D Space

Research on the metaphorical mapping of valenced concepts onto space indicates that positive, neutral, and negative concepts are mapped onto upward, midward, and downward locations, respectively. More recently, this type of research has been tested for the very first time in 3D physical space. The findings corroborate the mapping of valenced concepts onto the vertical space as described above but further show that positive and negative concepts are placed close to and away from the body; neutral concepts are placed midway. The current study aimed at investigating whether valenced perceptual stimuli are positioned onto 3D space akin to the way valenced concepts are positioned. By using a unique device known as the cognition cube, participants placed visual, auditory, tactile and olfactory stimuli on 3D space. The results mimicked the placing of valenced concepts onto 3D space; i.e., positive percepts were placed in upward and close-to-the-body locations and negative percepts were placed in downward and away-from-the-body locations; neutral percepts were placed midway. These pattern of results was more pronounced in the case of visual stimuli, followed by auditory, tactile, and olfactory stimuli. Significance Statement Just recently, a unique device called “the cognition cube” (CC) enabled to find that positive words are mapped onto upward and close-to-the-body locations and negative words are mapped onto downward and away-from-the-body locations; neutral words are placed midway. This way of placing words in relation to the body is consistent with an approach-avoidance effect such that “good” and “bad” things are kept close to and away from one’s body. We demonstrate for the very first time that this same pattern emerges when visual, auditory, tactile, and olfactory perceptual stimuli are placed on 3D physical space. We believe these results are significant in that the CC can be used as a new tool to diagnose emotion-related disorders.
On-chip FPGA Debug Instrumentation for Machine Learning Applications

FPGAs provide a promising implementation option for many machine learning applications. Although simulations or software models can be used to explore the design space of these applications, often the final behaviour can not be evaluated until the design is mapped to the FPGA and integrated into the target system. This may be because long run-times are required, or because the environment can not be adequately described using a software model. Once unexpected behaviour is observed, on-chip debug is notoriously difficult; typically a design is instrumented with on-chip trace buffers that record the run-time behaviour for later interrogation. In this paper, we describe instrumentation that can accelerate the process of debugging machine learning applications implemented on an FPGA. Unlike previous work, our instrumentation is optimized to take advantage of characteristics of this application domain. Our instruments gather useful domain-specific information about the observed variables instead of recording the raw values of those elements. Results show that the proposed instruments provide at least 17.8x longer visibility in the most conservative of our experiments at a low area and latency cost.
A High-Voltage Generation Charge-Pump IC Using Input Voltage Modulated Regulation for Neural Implant Devices

This brief presents a high-voltage generation charge pump integrated circuit (IC) with a novel regulation scheme for neural stimulation applications. Instead of using a widely used pulse frequency modulation approach with a variable clock frequency for regulation, an input voltage modulated regulation with a low drop-out regulator at the input of the feedforward path is used to maintain the charge pump output voltage with varying load current between <inline-formula> <tex-math notation="LaTeX">$10~{\mu }\text{A}$ </tex-math></inline-formula> to 1 mA. The proposed charge pump IC, implemented with 0.18-<inline-formula> <tex-math notation="LaTeX">$\mu \text{m}$ </tex-math></inline-formula> standard low-voltage CMOS process, reliably generates 12.8-V output voltage from a 2.8-V input and achieves more than 80% power efficiency at 1 mA load condition. The proposed IC occupies 0.6 mm<inline-formula> <tex-math notation="LaTeX">$^{2}$ </tex-math></inline-formula> of core die area.
A Novel Fuzzy-Based Convolutional Neural Network Method to Traffic Flow Prediction With Uncertain Traffic Accident Information

As a key part of the method of improving traffic capacity, traffic flow prediction is becoming a research hot-spot of traffic science and intelligent technology, in which the accuracy of traffic flow prediction is particularly concerned. In this paper, a novel fuzzy-based convolutional neural network (F-CNN) method is proposed to predict the traffic flow more accurately, in which a fuzzy approach has been applied to represent the traffic accident features when introducing uncertain traffic accidents information into the CNN at the first time. First, for the sake of extracting the spatial-temporal characteristics of the traffic flow data, this paper divides the whole area into small blocks of $32\times32$ and constructs three trend sequences with inflow and outflow types. Second, uncertain traffic accident information is generated from the real traffic flow data by utilizing a fuzzy inference mechanism. Finally, the F-CNN model is realized to train the internal information of the trend sequence, the uncertain traffic accident information, and the external information. Moreover, pre-training and fine-tuning strategies are efficiently developed to learn the parameters of the F-CNN. At last, the real Beijing taxicab trajectory and the meteorology datasets are employed to show that the proposed method has superior performance compared with the state-of-the-art approaches.
The Synthesis of Kynurenic Acid in Mammals: An Updated Kynurenine Aminotransferase Structural KATalogue

Kynurenic acid (KYNA) is a bioactive compound that is produced along the kynurenine pathway (KP) during tryptophan degradation. In a few decades, KYNA shifted from being regarded a poorly characterized by-product of the KP to being considered a main player in many aspects of mammalian physiology, including the control of glutamatergic and cholinergic synaptic transmission, and the coordination of immunomodulation. The renewed attention being paid to the study of KYNA homeostasis is justified by the discovery of selective and potent inhibitors of kynurenine aminotransferase II, which is considered the main enzyme responsible for KYNA synthesis in the mammalian brain. Since abnormally high KYNA levels in the central nervous system have been associated with schizophrenia and cognitive impairment, these inhibitors promise the development of novel anti-psychotic and pro-cognitive drugs. Here, we summarize the currently available structural information on human and rodent kynurenine aminotransferases (KATs) as the result of global efforts aimed at describing the full complement of mammalian isozymes. These studies highlight peculiar features of KATs that can be exploited for the development of isozyme-specific inhibitors. Together with the optimization of biochemical assays to measure individual KAT activities in complex samples, this wealth of knowledge will continue to foster the identification and rational design of brain penetrant small molecules to attenuate KYNA synthesis, i.e., molecules capable of lowering KYNA levels without exposing the brain to the harmful withdrawal of KYNA-dependent neuroprotective actions.
Teaming Up Pre-Trained Deep Neural Networks

With the rapid growth of big data applications, the training process of deep neural networks is getting more expensive in terms of the computational cost. In this paper, we propose an algorithm to exploit the reliability of existing convolutional neural networks that has been gained during earlier training processes. We use fuzzy integrals to perform late fusion on the classification decisions taken by pre-trained classifiers. The proposed method was evaluated using the ImageNet benchmark with ten different pre-trained state-of-the-arts Convolutional Neural Networks (CNN) models. The evaluation results show that the proposed fuzzy-based fusion method could achieve better performance than the best of the contributing models, in terms of recognition accuracy. The accuracy improvement ranges from 8% to 30% better than the used pre-trained classifiers.
Deep Learning Approach To Update Road Network using VGI Data

In our earlier work, we worked on extraction of the total width of road by agents traversing in the direction guided by Volunteered Geographic Information (VGI). The only downfall of VGI approach is its inability to update the new road developments. In this paper, we introduce deep learning approach to update the road network. We make use of the output of our previous work which forms as an input to train the Convolutional Neural Network (CNN). Then, further post processing is performed to remove non-road segments (such as buildings, vegetation, etc) on the output of CNN and finally, obtain the updated road map.
Interacting Multiple Model Estimator for Networked Control Systems: Stability, Convergence, and Performance

In this paper, we study the interacting multiple model (IMM) estimator for networked control systems with packet loss but without packet acknowledgment (ACK). The ACK is a signal sent by the actuator to inform the estimator of whether control packets are lost or not. A system with ACK is usually called a transmission control protocol (TCP) like system; otherwise, it is called a user datagram protocol (UDP) like system. We show that the stability of the IMM estimator for UDP-like systems is determined by the observation packet arrival rate (p.a.r.) and is independent of the control p.a. r. and control inputs. The IMM estimator is stable if the observation p.a. r. is greater than a critical value. We show that this critical value is the same as the critical value for the stability of the optimal estimator for its corresponding TCP-like system. If control inputs eventually tend to zero, the error covariance of the IMM estimator converges to that of the optimal estimator for TCP-like systems. We characterize the impact of the control/observation p.a. r. and the control input on estimation performance. Finally, we prove that the average estimation performance of the IMM estimator approximates that of the optimal estimator within a finite bound, and is superior to that of the linear minimum mean square error estimator.
Learning From the Optical Spectrum: Failure Detection and Identification

The availability of coarse-resolution cost-effective optical spectrum analyzers (OSAs) allows their widespread deployment in operators’ networks. In this paper, we explore several machine learning approaches for soft-failure detection, identification, and localization that take advantage of OSAs. In particular, we present three different solutions for the two most common filter-related soft-failures: filter shift and tight filtering, which noticeably deform the expected shape of the optical spectrum. However, filter cascading is a key challenge as it affects the shape of the optical spectrum similarly to the tight filtering; the approaches are specifically designed to avoid the misclassification of properly operating signals when normal filter cascading effects are present. The proposed solutions are as follows: First, the multi-classifier approach, which uses features extracted directly from the optical spectrum; second, the single-classifier approach, which uses preprocessed features to compensate for filter cascading; and third, the residual-based approach, which uses a residual signal computed from subtracting the signal acquired by OSAs from an expected signal synthetically generated. Extensive numerical results are ultimately presented to compare the performance of the proposed approaches in terms of accuracy and robustness.
Thinning Satellite Communication Antenna Arrays for Dual Band Operation

In this paper a design method is proposed to construct a dual-band operation antenna array, interleaving two arrays, operating at Ku and Ka-band frequencies, on the same real estate. Sharing the same antenna aperture is required to utilize a limited real estate for multiple tasks. First, each array configuration is examined, to achieve optimal initial guess to the thinning algorithms. Triangular lattice is applied for both arrays and overlapping elements are shifted along major axes to allow interleaving of both arrays. Circular aperture is used for the larger Ku array. To analyze the configuration performance, reference arrays are constructed, with rectangular aperture and rectangular lattice. Moreover, genetic algorithm is used to further thinning the Ku array and maintaining a required property of the application. All the analysis is taken considering a scan range of $35^{\mathrm {o}} \bullet 65^{\mathrm {o}}$, to achieve a design that is applicable for satellite communication purposes. The optimal Ku array directivity after thinning at scan angle $\theta =50^{\mathrm {o}}$ is 32.53 dBi (1 dB decrease relative to reference array), using only 74% of the reference arrays elements (rectangular aperture and rectangular lattice). Side lobes level (SLL) remained at -22.19 dB (SLL requirement is -22 dB). The results for the requested scan range shows no grating-lobes in radiation patterns, and satisfactory results comparing to the reference arrays.
Prevention of Educational Institutions from Terrorist Attacks using IoTs

Internet of Things (IoTs), due to its dynamic nature, efficiency and accuracy led to the hasty and unrestrained explosion of modern intelligent security systems, playing a vital role in monitoring various industries and organizations. Developing countries like Pakistan have been facing terrorism for decades and implementing their ideas and efforts to survive in the worst circumstances. Specially, educational institutions bear great losses due to such attacks and even they are still under these threats. In this paper, an efficient and effective IoTs based security system has been proposed to handle these situations. Its attacking and defensive mechanism makes it possible to detect terrorist activities using behavior analysis from videos of human activities and help in preventing organizations from such attacks. Initially, the system is trained using the pre-defined and self-generated patterns. A prototype has been developed to implement and test the performance of the system.
Citrus sinensis MYB Transcription Factor CsMYB85 Induce Fruit Juice Sac Lignification Through Interaction With Other CsMYB Transcription Factors

Varieties of Citrus are commercially important fruits that are cultivated worldwide and are valued for being highly nutritious and having an appealing flavor. Lignification of citrus fruit juice sacs is a serious physiological disorder that occurs during postharvest storage, for which the underlying transcriptional regulatory mechanisms remain unclear. In this study, we identified and isolated a candidate MYB transcription factor, CsMYB85, that is involved in the regulation of lignin biosynthesis in Citrus sinensis, which has homologs in Arabidopsis and other plants. We found that during juice sac lignification, CsMYB85 expression levels increase significantly, and therefore, suspected that this gene may control lignin biosynthesis during the lignification process. Our results indicated that CsMYB85 binds the CsMYB330 promoter, regulates its expression, and interacts with CsMYB308 in transgenic yeast and tobacco. A transient expression assay indicated that Cs4CL1 expression levels and lignin content significantly increased in fruit juice sacs overexpressing CsMYB85. At4CL1 expression levels and lignin content were also significantly increased in Arabidopsis overexpressing CsMYB85. We accordingly present convincing evidence for the participation of the CsMYB85 transcription factor in fruit juice sac lignification, and thereby provide new insights into the transcriptional regulation of this process in citrus fruits.
Leveraging Machine Learning to Improve Software Reliability

Finding software faults is a critical task during the lifecycle of a software system. While traditional software quality control practices such as statistical defect prediction, static bug detection, regression test, and code review are often inefficient and time-consuming, which cannot keep up with the increasing complexity of modern software systems. We argue that machine learning with its capability in knowledge representation, learning, natural language processing, classification, etc., can be used to extract invaluable information from software artifacts that may be difficult to obtain with other research methodologies to improve existing software reliability practices such as statistical defect prediction, static bug detection, regression test, and code review. This thesis presents a suite of machine learning based novel techniques to improve existing software reliability practices for helping developers find software bugs more effective and efficient. First, it introduces a deep learning based defect prediction technique to improve existing statistical defect prediction models. To build accurate prediction models, previous studies focused on manually designing features that encode the statistical characteristics of programs. However, these features often fail to capture the semantic difference of programs, and such a capability is needed for building accurate prediction models. To bridge the gap between programs’ semantics and defect prediction features, this thesis leverages deep learning techniques to learn a semantic representation of programs automatically from source code and further build and train defect prediction models by using these semantic features. We examine the effectiveness of the deep learning based prediction models on both the open-source and commercial projects. Results show that the learned semantic features can significantly outperform existing defect prediction models. Second, it introduces an n-gram language based static bug detection technique, i.e., Bugram, to detect new types of bugs with less false positives. Most of existing static bug detection techniques are based on programming rules inferred from source code. It is known that if a pattern does not appear frequently enough, rules are not learned, thus missing many bugs. To solve this issue, this thesis proposes Bugram, which leverages n-gram language models instead of rules to detect bugs. Specifically, Bugram models program tokens sequentially, using the n-gram language model. Token sequences from the program are then assessed according to their probability in the learned model, and low probability sequences are marked as potential bugs. The assumption is that low probability token sequences in a program are unusual, which may indicate bugs, bad practices, or unusual/special uses of code of which developers may want to be aware. We examine the effectiveness of our approach on the latest versions of 16 open-source projects. Results