Reviewerly/exHarmony huggingface dataset:
A new methodology for thermoelastic model identification in composite materials using digital image correlation

Local strain measurement techniques, such as strain gauges or extensometers, have been broadly utilized as input data source for thermoelastic effect model identification in fiber-reinforced polymers, despite their well-known high heterogeneity. This experimental setup strongly limits the possibility of assigning thermoelastic models in a local-based manner for posterior Thermoelastic Stress Analysis. This issue has been addressed herein through proposing a novel method for spatial identification of thermoelastic models in composite structures using full-field experimental measurements. The proposed concept is validated by conducting tests on laminated tensile coupons with various stacking sequences. To this end, the displacements and thermal data collected from Digital Image Correlation and infrared camera, respectively, are interpolated to a mutual background mesh using a Smoothing Element Analysis. It is shown that this procedure results in a continuous strain field and a reconstructed thermal map for the laminate domain at various loading stages. The resulting smoothed strain map is used as the input for three different thermoelastic models, followed by a comparison of the calculated analytic temperature variations for each model against the infrared camera’s measurements. The relative error associated to this model assignment process is underlined, and it is revealed a significant effect of material properties variability in the accuracy of the method. It is shown that the proposed methodology can circumvent inaccuracies of the conventional Thermoelastic Stress Analysis method, while providing a viable and computationally efficient method for the selection of appropriate thermoelastic models at the local level.
Communication-Efficient and Fine-Grained Forward-Secure Asynchronous Messaging

In recent years, motivated by the revelation of long-term and widespread surveillance of personal communications, extensive efforts have been putting into <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">store-and-forward</i> asynchronous messaging systems (e.g., email and SMS) for providing critical security guarantees. Of particular interest among them is forward security, which makes past messages remain secure in the event that the secret key gets exposed. Traditional forward-secure public key encryption can provide forward security for asynchronous scenarios, but it is not flexible enough for instant messaging systems. This is mainly because that, after updating his/her secret key, the user totally loses the decryption capacity of ciphertexts that have not been received. In this paper, to achieve practical forward-security of asynchronous messaging systems, we investigate the construction of a new primitive named forward-secure puncturable encryption (FSPE) that captures fine-grained forward security. Namely, the user can maintain the decryption capacity of those encrypted messages that have not been received yet. Meanwhile, even if the secret key is disclosed, those received messages can still remain secure. Specifically, we propose a communication-efficient FSPE scheme for achieving fine-grained forward-secure asynchronous messaging. Moreover, to improve the efficiency of asynchronous messaging built upon FSPE, we extend it to support outsourced decryption. We also implement the proposed scheme and evaluate a proof-of-concept of main algorithms, so as to increase confidence on its correctness and practicability.
Anomaly Detection using IoT Sensor-Assisted ConvLSTM Models for Connected Vehicles

The safety and security of connected autonomous vehicles’ (CAVs) passengers are crucial for the autonomous vehicle industry. A zero-loss and accident-free strategy is leading the way, not only considering a luxurious development and design of the automotive industry but also avoiding the cyber attacks against the vehicle intrusion detection system (IDS) that may be at its best performance keeping attackers from maliciously altering or corrupting the flow of data within the vehicle’s internal communication bus. In the Vehicle-To-Everything (V2X) age, there is no guarantee, , that faulty data streams out of critical Electronic Control Units (ECUs) can be kept from leading the autonomous vehicle astray without external help from different sets of IoT sensors in pedestrian-held devices, passenger-held devices, or side-road infrastructure. Therefore, in this research, ConvLSTM deep learning models are proposed to recognize corrupted data streams bundled together with the autonomous vehicle navigation and powertrain data. The efficiency and generalization of the supervised ConvLSTM deep learning models are tested on anomalies within a stack of different in-vehicle sensors assisted by IoT navigation sensor data (e.g. Global Navigation Satellite System (GNSS) location, motion, and orientation data). The proposed models are evaluated using a dataset that consists of many IoT sensors that, although, was not collected through an IoT network infrastructure, being collected through real-world vehicle ride can very well represent a benchmark for any V2X IoT streaming bundle that is used for autonomous vehicle positioning applications in the future. In this paper, we achieved anomaly detection with 0.98 F1-score using different ConvLSTM model designs which is much higher than most state-of-the-art approaches and on par with state-of-the-art deep learning LSTM models.
Semantic Segmentation of Underwater Images using UNet architecture based Deep Convolutional Encoder Decoder Model

With rapid development of deep neural network-based image processing techniques, many areas like augmented reality, robotic vision are adopting image segmentation technique for addressing image analytics problems. Efforts are also being made in exploring underwater environments which are precious with vast biological gene banks and marine biological resources. Study of these forms of ecosystem helps human in interpreting environmental effects such as climatic change, effect of pollution and so on. Deep convolutional neural networks based architectures have paved rapid development in the field of segmentation. This paper tries to explore underwater objects by applying semantic image segmentation. Proposed model adapted UNet based semantic segmentation network as the basic frame work and extended it for accurate segmentation of underwater fish4knowledge image dataset. Training and fine tuning the model by choosing appropriate hyperparameters achieved an average IoU scores of 0.8583 on Acanthurus nigrofuscus class 08 from Fish4Knowledge image dataset.
Twin support vector machines with privileged information

In the field of machine learning, collected data always have additional features which are always referred as privileged information. Privileged information learning is mainly used to help train the classifier in the training process, and predict the unseen example by the learned classifier. In this paper, we propose a new method named twin support vector machines with privileged information (TWSVM-PI). In the proposed method, we first introduce the privileged information into twin SVMs so as to construct a model for prediction, and then utilize the Lagrangian multiplier method to optimize the proposed objective function. Thus, we obtain two nonparallel classification hyperplanes by solving two smaller sized quadratic programming problems (QPPs), which can shorter the computational time and improve the accuracy of the prediction. Finally, we conduct extensive experiments to evaluate the performance of the proposed TWSVM-PI method. The results have shown that our proposed method can obtain a better performance compared with state-of-the-art methods.
Semantic manifold modularization-based ranking for image recommendation

As the Internet confronts the multimedia explosion, it becomes urgent to investigate personalized recommendation for alleviating information overload and improving users' experience. Most personalized recommendation approaches pay their attention to collaborative filtering over users' interactions, which suffers greatly from the highly sparse interactions. In image recommendation, visual correlations among images that users consumed provide a piece of intrinsic evidence to reveal users' interests. It inspires us to investigate image recommendation over the dense visual graph of images instead of the sparse user interaction graph. In this paper, we propose a semantic manifold modularization-based ranking (MMR) for image recommendation. MMR leverages the dense visual manifold to propagate users' historical records and infer user-image correlations for image recommendation. Especially, it constrains interest propagation within semantic visual compact groups by manifold modularization to make a tradeoff between users' personality and graph smoothness in propagation. Experimental results demonstrate that user-consumed visual correlations play actively to capture users' interests, and the proposed MMR can infer user-image correlations via visual manifold propagation for image recommendation.
Analysis of a multi-objective hybrid system to generate power in different environmental conditions based on improved the Barnacles Mating Optimizer Algorithm

Industrial development, increased demand for energy, limited fossil fuels, and the prevention of environmental damage have led to the use of renewable energy to produce power. The combined system includes renewable wind and solar energy systems or other renewable energy. The hybrid system has been widely used in recent decades to provide energy. In this research, an optimal integrated system has been used to generate electricity. The proposed system includes wind, hydropower, and photovoltaic systems. to estimate the maximum power generation and reduce fluctuations in electricity generation, three proposed algorithms have been used and three common solutions have been applied to solve the optimization problem. The performance of the combined system in different weather conditions has also been assessed. The results showed that among the three proposed algorithms, the model improved the Barnacles Mating Optimizer Algorithm (IBMO) has the best distribution and convergence. Among the three common solutions, a common solution has the most logical solution to the optimization problem. And among the different hydrological conditions, the highest electricity generation is related to the wet season. the most important role in the hybrid system related the hydropower so that despite the reduction of electricity produced in the wind and photovoltaic power plants, the hydropower plant compensates for the drop in production of these systems and causes the production flow to continue.
Optimized cuckoo search algorithm using tournament selection function for robot path planning

Acceptability of mobile robots in various applications has led to an increase in mobile robots’ research areas. Path planning is one of the core areas which needs to be improvised at a higher level. Optimization is playing a more prominent role these days. The nature-inspired algorithm is contributing to a greater extent in achieving optimization. This article presents the modified cuckoo search algorithm using tournament selection function for robot path planning. Path length and Path time are the algorithm’s parameters to validate the effectiveness and acceptability of the output. The cuckoo search algorithm’s fundamental working principle is taken as the baseline, and the tournament selection function is adapted to calculate the optimum path for robots while navigating from its initial position to final position. The tournament selection function is replacing the concept of random selection done by the cuckoo search algorithm. The use of tournament selection overcomes local minima for robots while traversing in the configuration space and increases the probability of giving more optimum results. The conventional cuckoo search algorithm whose random selection mechanism may lead to premature convergence may fall into the local minima. The use of tournament selection function increases the probability of giving better results as it allows all the possible solution to take part in the tournament. The results are analysed and compared with other relevant work like cuckoo search algorithm and particle swarm optimization technique and presented in the article. The proposed method produced a better output in terms of path length and path time.
Understanding the collinear masking effect in visual search through eye tracking

Recent research has reported that, while both orientation contrast and collinearity increase target salience in visual search, a combination of the two counterintuitively masks a local target. Through eye-tracking and eye-movement analysis with hidden Markov models (EMHMM), here we showed that this collinear masking effect was associated with reduced eye-fixation consistency (as measured in entropy) at the central fixation cross prior to the search display presentation. As a decreased precision of saccade landing position is shown to be related to attention shift away from the saccadic target, our result suggested that the collinear masking effect may be related to attention shift to a non-saccadic-goal location in expectation of the search display before saccading to the central fixation cross. This attention shift may consequently interfere with attention capture by the collinear distractor containing the target, resulting in the masking effect. In contrast, although older adults had longer response times, more dispersed eye-movement pattern, and lower eye-movement consistency than young adults during visual search, the two age groups did not differ in the masking effect, suggesting limited contribution from ageing-related cognitive decline. Thus, participants' pre-saccadic attention shift prior to search may be an important factor influencing their search behavior.
Knowledge-Enhanced Domain Adaptation in Few-Shot Relation Classification

Relation classification (RC) is an important task in knowledge extraction from texts, while data-driven approaches, although achieving high performance, heavily rely on a large amount of annotated training data. Recently, many few-shot RC models have been proposed and yielded promising results in general domain datasets, but when adapting to a specific domain, such as medicine, the performance drops dramatically. In this paper, we propose a Knowledge-Enhanced Few-shot RC model for the Domain Adaptation task (KEFDA), which incorporates general and domain-specific knowledge graphs (KGs) to the RC model to improve its domain adaptability. With the help of concept-level KGs, the model can better understand the semantics of texts and easily summarize the global semantics of relation types from only a few instances. To be more important, as a kind of meta-information, the manner of utilizing KGs can be transferred from existing tasks to new tasks, even across domains. Specifically, we design a knowledge-enhanced prototypical network to conduct instance matching, and a relation-meta learning network for implicit relation matching. The two scoring functions are combined to infer the relation type of a new instance. Experimental results on the Domain Adaptation Challenge in the FewRel 2.0 benchmark demonstrate that our approach significantly outperforms the state-of-the-art models (by 6.63% on average).
Misspelling Correction with Pre-trained Contextual Language Model

Spelling irregularities, known now as spelling mistakes, have been found for several centuries. As humans, we are able to understand most of the misspelled words based on their location in the sentence, perceived pronunciation, and context. Unlike humans, computer systems do not possess the convenient auto complete functionality of which human brains are capable. While many programs provide spelling correction functionality, many systems do not take context into account. Moreover, Artificial Intelligence systems function in the way they are trained on. With many current Natural Language Processing (NLP) systems trained on grammatically correct text data, many are vulnerable against adversarial examples, yet correctly spelled text processing is crucial for learning. In this paper, we investigate how spelling errors can be corrected in context, with a pre-trained language model BERT. We present two experiments, based on BERT and the edit distance algorithm, for ranking and selecting candidate corrections. The results of our experiments demonstrated that when combined properly, contextual word embeddings of BERT and edit distance are capable of effectively correcting spelling errors.
Automated Configuration of Genetic Algorithms by Tuning for Anytime Performance

Finding the best configuration of algorithms' hyperparameters for a given optimization problem is an important task in evolutionary computation. We compare in this work the results of four different hyperparameter tuning approaches for a family of genetic algorithms on 25 diverse pseudo-Boolean optimization problems. More precisely, we compare previously obtained results from a grid search with those obtained from three automated configuration techniques: iterated racing, mixed-integer parallel efficient global optimization, and mixed-integer evolutionary strategies. Using two different cost metrics, expected running time and the area under the empirical cumulative distribution function curve, we find that in several cases the best configurations with respect to expected running time are obtained when using the area under the empirical cumulative distribution function curve as the cost metric during the configuration process. Our results suggest that even when interested in expected running time performance, it might be preferable to use anytime performance measures for the configuration task. We also observe that tuning for expected running time is much more sensitive with respect to the budget that is allocated to the target algorithms.
DOING L2 SPEECH RESEARCH ONLINE: WHY AND HOW TO COLLECT ONLINE RATINGS DATA

Abstract Listener-based ratings have become a prominent means of defining second language (L2) users’ global speaking ability. In most cases, local listeners are recruited to evaluate speech samples in person. However, in many teaching and research contexts, recruiting local listeners may not be possible or advisable. The goal of this study was to hone a reliable method of recruiting listeners to evaluate L2 speech samples online through Amazon Mechanical Turk (AMT) using a blocked rating design. Three groups of listeners were recruited: local laboratory raters and two AMT groups, one inclusive of the dialects to which L2 speakers had been exposed and another inclusive of a variety of dialects. Reliability was assessed using intraclass correlation coefficients, Rasch models, and mixed-effects models. Results indicate that online ratings can be highly reliable as long as appropriate quality control measures are adopted. The method and results can guide future work with online samples.
Evaluating Temporal Queries Over Video Feeds

Recent advances in Computer Vision and Deep Learning have made possible the efficient extraction of structured information from frames of video feeds. As such, a stream of objects and their associated classes along with unique object identifiers derived via object tracking can be generated, providing unique objects as they are captured across frames. In this paper we initiate a study of temporal queries involving objects and their co-occurrences in video feeds. For example, queries that identify video segments during which the same two red cars and the same two humans appear jointly for five minutes are of interest to many applications ranging from law enforcement to security and safety. We take the first step and define such queries in a way that they incorporate certain physical aspects of video capture such as object occlusion. We present an architecture consisting of three layers, namely object detection/tracking, intermediate data generation, and query evaluation. We propose two techniques, Marked Frame Set (MFS) and Sparse State Graph (SSG), to organize all detected objects in the intermediate data generation layer, which effectively, given the queries, minimizes the number of objects and frames that have to be considered during query evaluation. We also introduce an algorithm called SSG-CM that processes incoming frames against the SSG and efficiently prunes objects and frames unrelated to query evaluation, while maintaining all states required for succinct query evaluation. We present the results of a thorough experimental evaluation utilizing both real and synthetic data, establishing the trade-offs between MFS and SSG. We stress various parameters of interest in our evaluation and demonstrate that the proposed query evaluation methodology coupled with the proposed algorithms is capable to evaluate temporal queries over video feeds efficiently, achieving orders of magnitude performance benefits.
Part-Based Object Tracking Using Multiple Adaptive Correlation Filters

Object tracking is challenging, and recently, correlation filters methods have been proposed for this task. Most of these methods focus on the central portion of the target and are negatively affected by changes in the target size and shape. This work proposes a collaborative scheme using several local correlation filters combined with a global correlation filter for improving the performance of object tracking methods based on correlation filters. The proposed correlation filter used in this scheme is based on features extracted from multiple layers of deep convolutional neural networks, and a strategy to identify when these models should also be updated is presented. Experiments show that the proposed scheme tends to be consistent and to achieve better results than other comparative tracking approaches. The proposed collaborative approach can be applied to other correlation filters, which tends to further improve the tracker performance.
SSPNet: Learning spatiotemporal saliency prediction networks for visual tracking

We present SSPNet, a novel method for learning the spatiotemporal saliency of a target for visual tracking. State-of-the-art trackers typically track targets by predicting the target state, ie coordinates of a bounding box encompassing the target, from the target candidates sampled around a previous target state. However, they have two limitations: 1) vulnerability to tracking distractors present in a frame and 2) the strong bias of the target state estimation to the initial frame. The proposed method addresses this problem by predicting the spatiotemporal features of the target so-called current and future target saliencies. Given a frame, the current target saliency represents the spatial aspect of the target, whereas the future target saliency depicts how the target will appear in the next frame based on the temporal features. This technique improves tracking accuracy in two ways: we can exploit the similarity between the current and the future target saliencies to detect the distractors. Further, SSPNet provides better prior knowledge about the current target state compared to using only the previous frame, which mitigates the bias to the initial frame and occlusion problem. We show that SSPNet outperforms the state-of-the-art trackers, particularly in challenging sequences.