Reviewerly/exHarmony huggingface dataset:
Prune Deep Neural Networks With the Modified &lt;inline-formula&gt; &lt;tex-math notation="LaTeX"&gt;$L_{1/2}$ &lt;/tex-math&gt; &lt;/inline-formula&gt; Penalty

Demands to deploy deep neural network (DNN) models on mobile devices and embedded systems have drastically grown in recent years. When transplanting DNN models to such platforms, requirements pertaining to computation and memory use are bottlenecks. To overcome them, network pruning has been carefully studied as a method of network compression. The effectiveness of network pruning is significantly affected by incorrect pruning on important connections. In this paper, we propose a network pruning method based on the modified <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$L_{1/2}$ </tex-math></inline-formula> penalty that reduces incorrect pruning by increasing the sparsity of the pretrained models. The modified <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$L_{1/2}$ </tex-math></inline-formula> penalty yields better sparsity than the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$L_{1}$ </tex-math></inline-formula> penalty at a similar computational cost. Compared with the past work that numerically defines the importance of connections and re-establishes important weights when incorrect pruning occurs, our method achieves faster convergence by using a simpler pruning strategy. The results of experiments show that our method can compress LeNet300-100, LeNet-5, ResNet, AlexNet, and VGG16 by factors of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$66\times $ </tex-math></inline-formula> , <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$322\times $ </tex-math></inline-formula> , <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$26\times $ </tex-math></inline-formula> , <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$21\times $ </tex-math></inline-formula> , and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$16\times $ </tex-math></inline-formula> , respectively, with negligible loss of accuracy.
Comparison of the Rayleigh and K-Distributions for Application in Incoherent Change Detection

The aim of this letter is to compare two incoherent change-detection algorithms for target detection in low-frequency ultrawideband (UWB) synthetic aperture radar (SAR) images. The considered UWB SAR operates in the frequency range from 20 to 90 MHz. Both approaches employ a likelihood ratio test according to the Neyman-Pearson criterion. First, the bivariate Rayleigh probability distribution is used to implement the likelihood ratio test function. This distribution is well known and has been used for change-detection algorithms in low-frequency UWB SAR with good results. Aiming to minimize the false alarm rate and taking into consideration that low-frequency UWB SAR images have high resolution compared to the transmitted wavelength, the second approach implements the test by using a bivariate K-distribution. This distribution has scale and shape parameters that can be used to adjust it to the data. No filter is applied to the data set images, and the results show that with a good statistical model, it is not needed to rely on filtering the data to decrease the number of false alarms. Therefore, we can have a better tradeoff between resolution and detection performance.
CarvingNet: Content-Guided Seam Carving Using Deep Convolution Neural Network

We propose an improved content-aware image resizing method that uses deep learning. The proposed method is extended from seam carving, which is another image resizing method. Seam carving uses the energy map from an image. It also removes a seam where the energy is the minimum. We propose a method for creating a deep energy map using an encoder–decoder convolution neural network. A deep energy map preserves important parts or boundaries in an image, without distortion. Furthermore, it has the characteristic that uniform intensity of edges is displayed for all images. Four well-known resizing methods and our proposed method were evaluated in terms of aspect ratio similarity. In such an objective evaluation, the proposed method demonstrated better results than the other four algorithms. Our proposed method can reduce the size of an image without damaging the overall structure or losing important information in the image.
Vehicle joint make and model recognition with multiscale attention windows

Abstract Vehicle Make and Model Recognition (VMMR) deals with the problem of classifying vehicles whose appearance may vary significantly when captured from different perspectives. A number of successful approaches to this problem rely on part-based models, requiring however labor-intensive parts annotations. In this work, we address the VMMR problem proposing a deep convolutional architecture built upon multi-scale attention windows. The proposed architecture classifies a vehicle over attention windows which are predicted to minimize the classification error. Through these windows, the visual representations of the most discriminative part of the vehicle are aggregated over different scales which in fact provide more representative features for the classifier. In addition, we define a loss function accounting for the joint classification error across make and model. Besides, a training methodology is devised to stabilize the training process and to impose multi-scale constraints on predicted attention windows. The proposed architecture outperforms state-of-the-art schemes reducing the model classification error over the Stanford dataset by 1.7 % and improving the classification accuracy by 0.2 % and 0.3 % on model and make respectively over the CompCar dataset.
Local Semantic-Aware Deep Hashing With Hamming-Isometric Quantization

Hashing is a promising approach for compact storage and efficient retrieval of big data. Compared to the conventional hashing methods using handcrafted features, emerging deep hashing approaches employ deep neural networks to learn both feature representations and hash functions, which have been proven to be more powerful and robust in real-world applications. Currently, most of the existing deep hashing methods construct pairwise or triplet-wise constraints to obtain similar binary codes between a pair of similar data points or relatively similar binary codes within a triplet. However, we argue that some critical local structures have not been fully exploited. So, this paper proposes a novel deep hashing method named local semantic-aware deep hashing with Hamming-isometric quantization (LSDH), aiming to make full use of local similarity in hash function learning. Specifically, the potential semantic relation is exploited to robustly preserve local similarity of data in the Hamming space. In addition to reducing the error introduced by binary quantizing, a Hamming-isometric objective is designed to maximize the consistency of similarity between the pairwise binary-like features and corresponding binary codes pair, which is shown to be able to improve the quality of binary codes. Extensive experimental results on several benchmark datasets, including three single-label datasets and one multi-label dataset, demonstrate that the proposed LSDH achieves better performance than the latest state-of-the-art hashing methods.
Deep Reservoir Neural Networks for Trees

Tree structured data are a flexible tool to properly express many forms of hierarchical information. However, learning of such data through deep recursive models is particularly demanding. We will show through the introduction of the Deep Tree Echo State Network model (DeepTESN) that the randomized Neural Networks framework offers a formidable approach to allow an efficient treatment of learning in tree structured domains by deep architectures. Theoretical properties, for the Reservoir Computing setup constraints, and empirical behavior of the proposed approach are analyzed, showing its feasibility and accuracy.
Automated Abstraction of Manipulation Domains for Cost-Based Reactive Synthesis

When robotic manipulators perform high-level tasks in the presence of another agent, e.g., a human, they must have a strategy that considers possible interferences in order to guarantee task completion and efficient resource usage. One approach to generate such strategies is called reactive synthesis. Reactive synthesis requires an abstraction, which is a discrete structure that captures the domain in which the robot and other agents operate. Existing works discuss the construction of abstractions for mobile robots through space decomposition; however, they cannot be applied to manipulation domains due to the curse of dimensionality caused by the manipulator and the objects. In this work, we present the first algorithm for automatic abstraction construction for reactive synthesis of manipulation tasks. We focus on tasks that involve picking and placing objects with possible extensions to other types of actions. The abstraction also provides an upper bound on path-based costs for robot actions. We combine this abstraction algorithm with our reactive synthesis planner to construct correct-by-construction plans. We demonstrate the power of the framework on a UR5 robot, completing complex tasks in face of interferences by a human.
Intent Detection and Slots Prompt in a Closed-Domain Chatbot

In this paper, we introduce a methodology for predicting intent and slots of a query for a chatbot that answers career-related queries. We take a multi-staged approach where both the processes (intent-classification and slot-tagging) inform each oth-er's decision-making in different stages. The model breaks down the problem into stages, solving one problem at a time and passing on relevant results of the current stage to the next, thereby reducing search space for subsequent stages, and eventually making classification and tagging more viable after each stage. We also observe that relaxing rules for a fuzzy entity-matching in slot-tagging after each stage (by maintaining a separate Named Entity Tagger per stage) helps us improve performance, although at a slight cost of false-positives. Our model has achieved state-of-the-art performance with F1-score of 77.63% for intent-classification and 82.24% for slot-tagging on our dataset that we would publicly release along with the paper.
Near-Optimal Path Planning for a Car-Like Robot Visiting a Set of Waypoints With Field of View Constraints

This letter considers two variants of a shortest path problem for a car-like robot visiting a set of waypoints. The sequence of waypoints to be visited is specified in the first variant while the robot is allowed to visit the waypoints in any sequence in the second variant. The shortest path problem is first solved for two waypoints with heading angle constraints at the waypoints using the Pontryagin's minimum principle. Using the results for the two point problem, tight lower and upper bounds on the length of the shortest path are developed for visiting n points by relaxing the requirement that the arrival angle must be equal to the departure angle of the robot at each waypoint. Theoretical bounds are also provided on the length of the feasible solutions obtained by the proposed algorithm. Simulation results verify the performance of the bounds for instances with 20 waypoints.
Towards evaluation of cloud ontologies

Many enterprises consider that cloud computing will continue to play a key role due to its ability to deliver various types of on-demand IT services, and according to customer needs. Although cloud technology has characteristics that distinguish it from other technologies, unfortunately, services in this technology suffer from the heterogeneity issue. As a result, cloud services have many challenges, such as service description, interoperability, and service discovery. Many studies have contributed dealing with such challenges using ontology, as it can participate as a mapping layer to present such services in a unified description. Cloud ontologies included in these studies can be classified into three types of ontologies; ontologies that focus on representing functional features of cloud services, ontologies that focus on non-functional features, and ontologies that focus on both function and non-function features. These ontologies are not unified in representing such features, where they agree in some features and differ in others. By using techniques developed to evaluate ontologies related to the other technologies, it is difficult to decide which cloud ontology captures cloud services in the right way. We have noticed that there is not a framework or a benchmark to evaluate the cloud ontologies. In this survey, the current cloud ontologies are analyzed to identify a set of observations that represent their strengths and weaknesses. According to these observations, a set of suggested rules are introduced and verified to be considered during the construction or the evaluation of cloud ontologies. The work in this paper can be considered as the fundamental step for developing a formal framework to automatically evaluate cloud ontologies.
Blockchain applications in healthcare and the opportunities and the advancements due to the new information technology framework

The promise of blockchain extends into healthcare allowing a transformation of our current system and its use of information technology. The current use of information technology in healthcare can be advanced with blockchain. The fundamental values that blockchain will bring include decentralization due to its distributed ledger technology, interoperability and increased security and immutability. These benefits enable a healthcare system that is integrated, seamless and more secure and with an increased level of compliance. Blockchains offer the opportunity to deliver secure and highly efficient transaction processing between patient, provider and payer and other healthcare parties in a more efficient and seamless way. The use of smart contracts in healthcare is the catalyst by which blockchains can deliver these transactions in a transformative way. The application of blockchains combined with the Internet of Things (IoT) is also a catalyst that will enable these transactions to be processed to disrupt healthcare from its current state at this time. We explore ways that blockchain and its associated information technology will revolutionize our healthcare system that we believe would allow for a more efficient and fluid environment with improved cost efficiencies.
Factivity and subject extraction in Jordanian Arabic

This paper investigates subject extraction from embedded clauses in Jordanian Arabic (JA). Firstly, it shows that JA maintains the division between factive vs. non-factive verbs with respect to (subject) extraction. We argue that non-factive clausal complements are not islands for extraction, and, hence, their subject can undergo movement to the left periphery of the main clause. We use Starke's (2001) proposal of Relativized Minimality to account for the ensuing restriction that bans the object and/or locative adjuncts of the factive clausal complement to appear pre-verbally (while the subject is extracted). We propose that this restriction is due to the richly-featured content of the subject wh-word that acts as a barrier against the movement of elements that fall under its c-command domain. As for factive clausal complements (i.e., embedded under factive verbs), we prove that such complements are strong islands for extraction. We follow Kiparsky and Kiparsky (1970) and related works, that factive complements are, with updated terminology, DPs which are absolute islands in Arabic. With this being the case, this paper challenges several recent approaches to factive complements that have argued either for a reduced left periphery for factive complements (e.g., Haegeman, 2006; de Cuba, 2007) or for the presence of an operator that has the effect to block movement out of these clauses (e.g., Zubizaretta, 2001; Haegeman, 2012).
Machine Learning Nowcasting of PV Energy Using Satellite Data

Satellite-measured radiances are obviously of great interest for photovoltaic (PV) energy prediction. In this work we will use them together with clear sky irradiance estimates for the nowcasting of PV energy productions over peninsular Spain. We will feed them directly into two linear Machine Learning models, Lasso and linear Support Vector Regression (SVR), and two highly non-linear ones, Deep Neural Networks (in particular, Multilayer Perceptrons, MLPs) and Gaussian SVRs. We shall also use a simple clear sky-based persistence model for benchmarking purposes. We consider prediction horizons of up to 6 h, with Gaussian SVR being statistically better than the other models at each horizon, since its errors increase slowly with time (with an average of 1.92% for the first three horizons and of 2.89% for the last three). MLPs performance is close to that of the Gaussian SVR for the longer horizons (with an average of 3.1%) but less so at the initial ones (average of 2.26%), being nevertheless significantly better than the linear models. As it could be expected, linear models give weaker results (in the initial horizons, Lasso and linear SVR have already an error of 3.21% and 3.46%, respectively), but we will take advantage of the spatial sparsity provided by Lasso to try to identify the concrete areas with a larger influence on PV energy nowcasts.
GPS: Factorized group preference-based similarity models for sparse sequential recommendation

One of the key tasks for recommender systems is the prediction of personalized sequential behavior. There are two primary means of modeling sequential patterns and long-term user preferences: Markov chains and matrix factorization, respectively. Together, they provide a unified approach to predicting user actions. In spite of their strengths in tackling dense data, however, these methods struggle with the sparsity issues often present in real-world datasets. In approaching this problem, we propose combining similarity-based methods (demonstrably helpful for sequentially unaware item recommendation) with Markov chains to offer individualized sequential recommendations. This approach, called GPS (a factorized group preference-based similarity model), further leverages the idea of group preference along with user preference to introduce a greater array of interactions between users—which in turn eases the problem of data sparsity and cold users and cuts down on the assumption of a strong independency within various factors. By applying our method to a range of large, real-world datasets, we demonstrate quantitatively that GPS outperforms several state-of-the-art methods, particularly in cases with sparse datasets. Regarding qualitative findings, GPS also grasps personalized interactions and can provide recommendations that are both on-target and meaningful.
Two sides of the same coin: session types and game semantics: a synchronous side and an asynchronous side

Game semantics and session types are two formalisations of the same concept: message-passing open programs following certain protocols. Game semantics represents protocols as games, and programs as strategies; while session types specify protocols, and well-typed π-calculus processes model programs. Giving faithful models of the π-calculus and giving a precise description of strategies as a programming language are two difficult problems. In this paper, we show how these two problems can be tackled at the same time by building an accurate game semantics model of the session π-calculus. Our main contribution is to fill a semantic gap between the synchrony of the (session) π-calculus and the asynchrony of game semantics, by developing an event-structure based game semantics for synchronous concurrent computation. This model supports the first truly concurrent fully abstract (for barbed congruence) interpretation of the synchronous (session) π-calculus. We further strengthen this correspondence, establishing finite definability of asynchronous strategies by the internal session π-calculus. As an application of these results, we propose a faithful encoding of synchronous strategies into asynchronous strategies by call-return protocols, which induces automatically an encoding at the level of processes. Our results bring session types and game semantics into the same picture, proposing the session calculus as a programming language for strategies, and strategies as a very accurate model of the session calculus. We implement a prototype which computes the interpretation of session processes as synchronous strategies.
Incremental Market Behavior Classification in Presence of Recurring Concepts

In recent years, the problem of concept drift has gained importance in the financial domain. The succession of manias, panics and crashes have stressed the non-stationary nature and the likelihood of drastic structural or concept changes in the markets. Traditional systems are unable or slow to adapt to these changes. Ensemble-based systems are widely known for their good results predicting both cyclic and non-stationary data such as stock prices. In this work, we propose RCARF (Recurring Concepts Adaptive Random Forests), an ensemble tree-based online classifier that handles recurring concepts explicitly. The algorithm extends the capabilities of a version of Random Forest for evolving data streams, adding on top a mechanism to store and handle a shared collection of inactive trees, called concept history, which holds memories of the way market operators reacted in similar circumstances. This works in conjunction with a decision strategy that reacts to drift by replacing active trees with the best available alternative: either a previously stored tree from the concept history or a newly trained background tree. Both mechanisms are designed to provide fast reaction times and are thus applicable to high-frequency data. The experimental validation of the algorithm is based on the prediction of price movement directions one second ahead in the SPDR (Standard & Poor's Depositary Receipts) S&P 500 Exchange-Traded Fund. RCARF is benchmarked against other popular methods from the incremental online machine learning literature and is able to achieve competitive results.
Towards an automatic filament detector with a Faster R-CNN on MAST-U

In the present magnetically confined plasmas, the prediction of particle loading on material surfaces is a primary concern in view of the protection of plasma facing components for next step devices. Thus, an understanding of filament dynamics is needed. In this context, this work aims to develop an automatic detector for filaments arising in the MAST-U plasma. The identification of the filaments has been done starting from 2D images acquired with a fast visible camera. Therefore, it can be faced as an image object recognition problem. Currently, the object recognition is a key output of deep learning and machine learning algorithms. In this paper, a database of several thousands of images generated by a synthetic diagnostic, which reproduces the statistical properties of experimental filaments in terms of position, size and intensity has been used. The synthetic images are pre-processed by mapping them onto the toroidal midplane of the machine. Then a Faster R-CNN is customized to the problem of identifying the filaments. In particular, in order to enhance the performance of the detector, a suitable definition of the target-boxes defining the filament positions and sizes is adopted with good results.