Miracle-dz/UniFAR-FTUs huggingface dataset:
Real-time Communication Systems For Automation Over Wireless: Enabling Future Interactive Tech

Author(s): Narasimha Swamy, Vasuki | Advisor(s): Sahai, Anant | Abstract: The density and frequency of interaction between society and technology is increasing and this presents us with opportunities to improve lives and livelihoods, and also to address the inequities in our society holistically. The Internet of Things (IoT) and 5G wireless com- munication technologies are the future technologies that envision to bring us closer to these opportunities. These technologies leverage the ubiquitous sensing, actuation, and comput- ing capabilities to enable smart devices to perform interesting tasks and to gain knowledge about the environment. These may include futuristic healthcare systems, affordable preci- sion agriculture systems, smart energy-efficient cities, and advanced flexible manufacturing. However, the present understanding of wireless communication is not enough to get us to this future.In this dissertation, we look at designing wireless communication frameworks for interac- tive applications like drone swarms, and industrial automation that require fast and reliable communication. We focus on designing frameworks that make wireless communication highly reliable while also maintaining the latency requirements of the systems being targeted. This is the missing link needed to leverage the power of wireless communication for the next generation of interactive applications.One of the key contributions of this thesis is the design of cooperative communication based protocol frameworks that leverage a combination of diversity techniques to achieve the target reliability and latency. The framework uses spatial diversity to combat multipath channel fading and repetitions in time and frequency to shield against unmodeled error events. We analyze these protocols using the communication-theoretic delay-limited-capacity framework and consider their sensitivity to different modeling assumptions.Another key contribution of this thesis is an in-depth exploration of the dynamics of wireless channels in the context of ultra-reliable low-latency communication (URLLC). We revisit some standard concepts such as coherence time and question whether some of the modeling assumptions made in the context of cellular and WLAN communications make sense in the context of URLLC. We find that our cooperative communication frameworks are robust to the nominal dynamic channel models, especially spatial dependence. However, events such as synchronization mismatch or sudden change in the channels due to shadow- causing objects need to be protected against and therefore we build in frequency and time margins.The final contribution of this thesis is bringing together the temporal model of channel dynamics and machine learning to build intelligent relay selection strategies. This essentially provides the reliability needed by smartly selecting a small set of relays instead of relying on using every single relay available to combat fading. Finally, we present some preliminary experimental findings that pave the way to making these systems practical.
Arabic Sentiment Analysis: An Empirical Study of Machine Translation's Impact

The largest amount of Sentiment Analysis has been carried out for English language. To deal with Arabic sentiment analysis, machine translation of English resources or Arabic texts may be applied to built Arabic sentiment analysis systems. In this paper, we translate Arabic dataset into English and study the impact of machine translation while considering a standard Arabic system as a baseline. Experiments show that sentiment analysis of Arabic content translated into English reach a competitive performance with respect to standard sentiment analysis of Arabic texts. This suggests that machine translation can successfully transfer the expression of sentiment or polarity. Moreover , we explored the multi-domain extending of training data in order to enhance performance and we show that we should have, in the training set, data whose domain is the same as the domain of evaluation dataset.
Towards Practical Active Learning for Classification

In recent decades, the availability of a large amount of data has propelled the field of machine learning enormously. Machine learning, however, relies heavily on the availability of annotated data, typically labels indicating to which class a data instance belongs. With the huge amounts of data, this raises the question of how to efficiently annotate data, certainly when having limited resources. This thesis addresses the particular challenge of using as few annotations as possible, while at the same time, maintaining a good learning performance. For that we utilize active learning, which iteratively chooses the most valuable instances as to obtain the labels froman oracle (e.g. a human expert). Though many studies have demonstrated that active learning can reduce the annotation cost, there are still several issues that limit its practical use. This thesis makes a further step forwards making active learning more practical for real-world applications. We first provide a benchmark and comparison of six different categories of active learning algorithms built on logistic regression. This work provides a better understanding of the underlying characteristics of various active learners and illustrates the potential benefits of using such techniques, but it also provides many cases for which active learning fails to outperform passive learning (i.e. randomly selecting instances for labeling). Those failed cases motivate us to propose two novel active learning methods that show a clear advantage over passive learning. The first one proposes to weight the so-called retraining-based criteria with an uncertainty score that is measured by the estimated posterior probability. The second one measures the usefulness of unlabeled instances according to the variance of the predictive probability. This method takes an additional step towards practical active learning, clearly outperforming current state of the art on binary andmulti-class classification tasks. We further consider two realistic issues when applying active learning to real-world problems. One is how to find an initial set that contains at least one instance per class to start the active labeling cycle. The other one is dealing with the absence of human annotators in the interactive labeling loop. We propose new approaches to tackle the above problems and observe good performance compared to existing methods. This thesis concludes with an analysis of the contributions and limitations of our work, as well as research directions that deserve further studies. We hope that this thesis also inspires others to make active learning more suitable for real-world applications.
Towards a Software Requirements Change Classification using Support Vector Machine

Requirements are the basis for all software projects. Thus, the requirements phase needs much more attention in order to specify the problems that the software system is intended to solve. However, identifying correctly and completely the software requirements encompasses many issues due mainly to their inconsistencies, ambiguities, incompleteness, and instability. In addition, requirements change requests are inevitable during the software life-cycle (SLC). Change request expressed in natural language format are hard to analyze since they may affect different types of software requirements. To provide an appropriate response to a change request, this paper aims to: (i) investigate how well machine learning techniques are used in the classification of software requirements as well as requirements change requests, and (ii) give an overview of our research that proposes to use the natural language processing and Support Vector Machine (SVM) classifier to automatically classify the requirements change requests into mainly two categories (functional change and technical change).
Recognizing Textual Entailment for Arabic using semantic similarity and Word Sense Disambiguation

In this paper, we propose a semantic method for recognizing textual entailment in Arabic. The proposed method serves to detect the directional semantic entailment relationship between text/hypothesis pairs. More specifically, we work at the sentence level, conducting semantic similarity measure and word sense disambiguation process in order to detect entailment relationship in the context of Arabic question/answering system. The results obtained are encouraging. Our method has reached an accuracy of 70%.
An Attention-Based Word-Level Interaction Model for Knowledge Base Relation Detection

Relation detection plays a crucial role in knowledge base question answering, and it is challenging because of the high variance of relation expression in real-world questions. Traditional relation detection models based on deep learning follow an encoding-comparing paradigm, where the question and the candidate relation are represented as vectors to compare their semantic similarity. Max- or average-pooling operation, which is used to compress the sequence of words into fixed-dimensional vectors, becomes the bottleneck of information flow. In this paper, we propose an attention-based word-level interaction model (ABWIM) to alleviate the information loss issue caused by aggregating the sequence into a fixed-dimensional vector before the comparison. First, attention mechanism is adopted to learn the soft alignments between words from the question and the relation. Then, fine-grained comparisons are performed on the aligned words. Finally, the comparison results are merged with a simple recurrent layer to estimate the semantic similarity. Besides, a dynamic sample selection strategy is proposed to accelerate the training procedure without decreasing the performance. Experimental results of relation detection on both SimpleQuestions and WebQuestions datasets show that ABWIM achieves the state-of-the-art accuracy, demonstrating its effectiveness.
Wideband Full-Corporate-Feed Waveguide Continuous Transverse Stub Antenna Array

A Ka-band waveguide continuous transverse stub antenna array with full-corporate-feed network is presented in this paper. Multiple-section stepped-impedance transformers are used for wideband impedance matching of the radiation slots. The required high-quality quasi-TEM wave excitation is generated by a wideband plane-wave generator (WPWG). Compared with the conventional pillbox structure, the WPWG produces a quasi-TEM wave with more uniform amplitude distribution. By using the combination of these design features, significant increases both in bandwidth and antenna aperture efficiency are achieved. For demonstration, a prototype is designed, fabricated, and measured. Excellent agreement is achieved between simulations and measurements. The antenna is matched over the entire Ka-band of 26–40 GHz. A high antenna aperture efficiency of over 75% and a low cross-polarization of less than −46 dB are achieved over the entire bandwidth. The demonstrated features of wideband, high aperture efficiency, and low cross-polarization have great potentials for millimeter-wave applications.
Infrared Patch-Tensor Model With Weighted Tensor Nuclear Norm for Small Target Detection in a Single Frame

The robust and efficient detection of infrared small target is a key technique for infrared search and track systems. Several robust principal component analysis (RPCA)-based methods have been developed recently, which have achieved the state-of-the-art performance. However, there are still two drawbacks: 1) the false alarm ratio would raise under the heavy background clutters and noises and 2) these methods are usually time-consuming and not suitable for real-time processing. To solve this problem, an infrared patch-tensor (IPT) model based on weighted tensor nuclear norm is proposed in this paper. First, the infrared image is transformed into the IPT. Considering the sum of nuclear norms adopted in the IPT model is not the convex envelope of the tensor rank, and the solution is substantially suboptimal. The tensor nuclear norm is adopted to recover the underlying low-rank background tensor and sparse target tensor, and the computation complexity can be reduced dramatically with the help of the tensor Singular Value Decomposition. Moreover, to further suppress the background clutters, a weight tensor is incorporated with tensor nuclear norm to preserve the background edges better. Then, the separation between target and background is formulated as a convex weighted tensor RPCA model. Finally, the proposed model can be solved by the alternating direction method of multipliers. Extensive experiments demonstrate that the proposed model outperforms the other state-of-the-arts in terms of performance and efficiency.
Regularized Capon Beamformer Using $\ell_{1}$-Norm Applied to Photoacoustic Imaging

Delay-and-Sum (DAS), as a non-adaptive beamforming method, is one of the most common algorithms used in Photoacoustic imaging due to its simple implementation. The results obtained from this algorithm suffer from low resolution and high sidelobes. The adaptive Minimum variance (MV) method improves the image quality compared to DAS in terms of resolution and contrast. In this paper, it is proposed to add a $\ell_{1}$-norm regularization term to the conventional MV minimization problem and create a new sparse beamforming method, named Modified-Sparse-Mv (ms-Mv)algorithm. In fact, the sparsity of the output is forced to the beampattern by adding this new sparse added term, which results in more noise reduction and sidelobe suppression compared to MV. The minimization problem is convex, and therefore, it can be solved using an iterative algorithm. The results show that the proposed MS-MV method improves the signal-to-noise-ratio for about 5.36 $dB$ and 6.44 $dB$ compared to DAS and MV, respectively, for the designed wire phantom.
Vehicle Detection and Width Estimation in Rain by Fusing Radar and Vision

While much effort has been devoted to deep learning object detection, relatively limited attention has been paid to object detection in bad weather, e.g. rain, snow or haze. In heavy rain, the raindrop on the front windshield can make it difficult to detect object from an in-car camera. The conventional way to cope with this has been to use radar as the main detection sensor. However, radar is highly susceptible to false positives. Furthermore, many entry level radar sensors only return the centroid of each detected object, rather than its size and extent. In addition, due to lack of texture input, radar cannot discriminate a vehicle from a non-vehicle object, e.g. roadside pole. This motivates us to detect vehicle by fusing radar and vision. In this paper, we first calibrate the radar and camera with respect to the ground plane. The radar detections are then projected to the camera image for target width estimation. Empirical evaluation on a large database shows that there is a natural synergy in both sensors, as the image based estimation is found to be greatly facilitated by the accuracy of the radar detection.
Observers and Invariant Sets for Lure–type Nonlinear Systems using LPV–Embedding

This paper deals with observers for Lure–type nonlinear systems designed so that the associated estimation error dynamics are bounded in an invariant set. The design uses the LPV–embedding technique, whereby the nonlinearity is redefined in terms of a time-varying parameterised linear function, called the ‘LPV parameter’. For LPV parameters with bounded variation, invariant sets for the original nonlinear system are computed by embedding the LPV system into a convex polytopic description. The trajectories of the estimation error are guaranteed to be bounded and remain in the computed invariant set, which exists if a condition in terms of the system matrices and parameters is satisfied. We show that arbitrarily small estimation error bounds can be achieved for some classes of systems. We illustrate the results via a simulation example of a flexible manipulator.
Thermal Analysis of Averaging Times in Radio-Frequency Exposure Limits Above 1 GHz

This paper considers the problem of choosing an appropriate “averaging time” in radiofrequency (RF) exposure limits to protect against thermal hazards, focusing on the RF frequency range above 3–10 GHz. Analysis is based on examination of the dynamic properties of thermal models for tissue using Pennes’ bioheat equation. Three models are considered: a baseline model consisting of a uniform half space with dielectric and thermal properties similar to those of human skin with adiabatic boundary conditions; a layered 1D model with dielectric and thermal properties similar to those of skin, fat, and underlying muscle, with convective boundary conditions appropriate for room environments; and exposures to the head of an anatomically detailed image-based model (“Taro”). RF exposure consisted of plane wave radiation incident on the two planar models, and radiation from resonant dipoles located 1.5 cm from the head model, at frequencies ranging from 1 to 300 GHz. The dynamic properties of the models were explored by analytic solution of the baseline model, and from numerical solutions of the thermal responses of the layered and head models. From the step responses of the models (increases in surface temperature to a suddenly imposed exposure), the impulse and frequency responses of the models were obtained. In the frequency domain, the thermal models exhibit extreme lowpass characteristics with cutoff (−3 dB response) frequencies below 1 mHz. The impulse response to millimeter wave radiation (30–300 GHz) shows a sharp peak at zero time, due to short term accumulation of heat near the surface, which dissipates quickly as heat is conducted into deeper layers of tissue. Simple analytical results of a further simplified model assuming purely surface heating agree well with results of a more detailed assessment for millimeter waves. Response of the model to pulse trains and to single maximum fluence “big bang” pulses in which all allowable energy over a 6-min averaging time is delivered in one short pulse raises the possibility of excessive transient temperature increases at the tissue surface from exposure to short high-fluence pulses at mm-wave frequencies. Such exposures are not produced by current technologies apart from certain military weapons systems but may occur from future high-power mm-wave technology. By contrast, simulations of exposure from a communications waveform at 1.9 GHz show extremely tiny transient temperature fluctuations. The results generally confirm the present choice of 6 min for an averaging time in the current generation of RF exposure limits but suggest the need for additional limits on fluence for brief high-fluence pulses at mm-wave frequencies. This paper addresses thermal hazards only, and a larger range of evidence would need to be evaluated as well in revising exposure limits.
Multi-wavelength Brillouin Raman Erbium Fiber Laser utilizing Captured Residual Raman Pump Power

We experimentally demonstrate a multi-wavelength Brillouin Raman Erbium fiber laser (MBREFL) by using captured residual Raman pump to pump an erbium doped fiber. Results show that our MBREFL is capable of producing up to 48 Brillouin Stokes lines (BSL) at Brillouin and Raman pump power of +6 dBm and 600 mW, respectively. The proposed MBREFL is tunable free of self-lasing modes for 14.5 nm from 1574.5 with BSLs between 33 and 48.
Tensor Nuclear Norm-Based Low-Rank Approximation With Total Variation Regularization

Some existing low-rank approximation approaches either need to predefine the rank values (such as the matrix/tensor factorization-based methods) or fail to consider local information of data (e.g., spatial or spectral smooth structure). To overcome these drawbacks, this paper proposes a new model called the tensor nuclear norm-based low-rank approximation with total variation regularization (TLR-TV) for color and multispectral image denoising. TLR-TV uses the tensor nuclear norm to encode the global low-rank prior of tensor data and the total variation regularization to preserve the spatial-spectral continuity in a unified framework. Including the hyper total variation (HTV) and spatial-spectral total variation (SSTV), we propose two TLR-TV-based algorithms, namely TLR-HTV and TLR-SSTV. Using the alternating direction method of multiplier, we further propose two simple algorithms to solve TLR-HTV and TLR-SSTV. Extensive experiments on simulated and real-world noisy images demonstrate that the proposed TLR-HTV and TLR-SSTV outperform the state-of-the-art methods in color and multispectral image denoising in terms of quantitative and qualitative evaluations.
A Triple-Branch Neural Network for Knowledge Graph Embedding

The distributed representation of knowledge graphs (KGs), which embeds the structured graphs into low-dimensional embedding spaces, is widely used to facilitate various applications of AI, such as information retrieval and question answering. The primary elements of KGs, the entities viewed as nodes and the relations regarded as links between entities, naturally make up the local embedding context for each other, which is called the multi-restriction property of KGs. However, this property is not fully explored by previous models, where either only part of the multi-restriction is captured, or the capability of the embedding model is limited to a fixed function without clear interpretation. To address this issue, we propose TBNN, a triple-branch neural network to learn the embeddings of KGs. In particular, the embedding of any element of a KG is determined by its multi-restriction via an interaction layer followed by parallel branched layers. Thus, the entities and relations can be treated equivalently in spite of their seeming differences in the original KG. We define the loss function of TBNN based on the confidence score of the three elements of each triple. In addition, we propose using the log-sum-exp pairwise loss to smooth the hinge loss, which results in better performance. Empirically, we evaluate our model on the tasks of link prediction and triple classification with the subsets of WordNet and Freebase. Experiment results show that our model performs better than the baselines, especially providing stable performance for relations with different mapping properties.
Evaluation Index System for Clearing Models of Different Electricity Price Policies

The electricity price policy of a region directly determines the operation of the electricity market in the region. The clearing model is the core problem of electricity price policy and marginal clearing pricing and high-low matching pricing are two popular mechanisms of electricity price clearing. The innovation of this paper is to propose a complete evaluation index system for the clearing model of different electricity price policies. The index system evaluates the operational effects of the electricity market from five aspects of market economy scale, market structure benefits, market security, environmental friendliness and social welfare. In this paper, the clearing models are presented. And then, the evaluation index system for the clearing model is proposed. Finally, it is introduced that the application of the analytic hierarchy process (AHP) and the entropy weighted TOPSIS. Based on the actual trading results, each evaluation index is calculated. The marginal clearing pricing and high-low matching pricing are evaluated by the AHP and the entropy weighted TOPSIS, respectively. It is verified that the evaluation index system proposed can effectively evaluate the clearing model of different electricity price policies.