Reviewerly/exHarmony huggingface dataset:
An Efficient Mapping Approach to Large-Scale DNNs on Multi-FPGA Architectures

FPGAs are very attractive to accelerate the deep neural networks (DNNs). While single FPGA can provide good performance for small-scale DNNs, support for large-scale DNNs is limited due to higher resource demand. In this paper, we propose an efficient mapping approach for accelerating large-scale DNNs on asymmetric multi-FPGA architectures. In this approach, the neural network mapping can be formulated as a resource allocation problem. We design a dynamic programming-based partitioning to solve this problem optimally. Experimental results using the large-scale ResNet-152 demonstrate that our approach deploys sixteen FPGAs to provide an advantage of 16.4x GOPS over the state-of-the-art work.
Spectral Metric for Dataset Complexity Assessment

In this paper, we propose a new measure to gauge the complexity of image classification problems. Given an annotated image dataset, our method computes a complexity measure called the cumulative spectral gradient (CSG) which strongly correlates with the test accuracy of convolutional neural networks (CNN). The CSG measure is derived from the probabilistic divergence between classes in a spectral clustering framework. We show that this metric correlates with the overall separability of the dataset and thus its inherent complexity. As will be shown, our metric can be used for dataset reduction, to assess which classes are more difficult to disentangle, and approximate the accuracy one could expect to get with a CNN. Results obtained on 11 datasets and three CNN models reveal that our method is more accurate and faster than previous complexity measures.
Document/query expansion based on selecting significant concepts for context based retrieval of medical images

In the medical image retrieval literature, there are two main approaches: content-based retrieval using the visual information contained in the image itself and context-based retrieval using the metadata and the labels associated with the images. We present a work that fits in the context-based category, where queries are composed of medical keywords and the documents are metadata that succinctly describe the medical images. A main difference between the context-based image retrieval approach and the textual document retrieval is that in image retrieval the narrative description is very brief and typically cannot describe the entire image content, thereby negatively affecting the retrieval quality. One of the solutions offered in the literature is to add new relevant terms to both the query and the documents using expansion techniques. Nevertheless, the use of native terms to retrieve images has several disadvantages such as term-ambiguities. In fact, several studies have proved that mapping text to concepts can improve the semantic representation of the textual information. However, the use of concepts in the retrieval process has its own problems such as erroneous semantic relations between concepts in the semantic resource. In this paper, we propose a new expansion method for medical text (query/document) based on retro-semantic mapping between textual terms and UMLS concepts that are relevant in medical image retrieval. More precisely, we propose mapping the medical text of queries and documents into concepts and then applying a concept-selection method to keep only the most significant concepts. In this way, the most representative term (preferred name) identified in the UMLS for each selected concept is added to the initial text. Experiments carried out with ImageCLEF 2009 and 2010 datasets showed that the proposed approach significantly improves the retrieval accuracy and outperforms the approaches offered in the literature.
SemEval-2019 Task 2: Unsupervised Lexical Frame Induction

This paper presents Unsupervised Lexical Frame Induction, Task 2 of the International Workshop on Semantic Evaluation in 2019.Given a set of prespecified syntactic forms in context, the task requires that verbs and their arguments be clustered to resemble semantic frame structures.Results are useful in identifying polysemous words, i.e., those whose frame structures are not easily distinguished, as well as discerning semantic relations of the arguments.Evaluation of unsupervised frame induction methods fell into two tracks: Task A) Verb Clustering based on FrameNet 1.7; and B) Argument Clustering, with B.1) based on FrameNet's core frame elements, and B.2) on VerbNet 3.2 semantic roles.The shared task attracted nine teams, of whom three reported promising results.This paper describes the task and its data, reports on methods and resources that these systems used, and offers a comparison to human annotation.
Vehicle Detection Based on Drone Images with the Improved Faster R-CNN

With the increasing number of vehicles, traffic management has put forward higher requirements for vehicle monitoring, thus the technology of vehicle detection based on drone images has received increasing attention. Firstly, we construct a new vehicle detection data set of 600 drone images so that to solve the vehicle detection tasks in real world. Secondly, aiming at the problem of false detection and missed detection in vehicle detection, the Faster R-CNN is improved by using ResNet and constructing Feature Pyramid Networks (FPN) to extract the image features. Finally, based on the vehicle detection data set, the improved Faster R-CNN can be used to detect vehicle targets. The experiment results show that the accuracy of improved method is 96.83%, which is 3.86% higher than that of the original Faster R-CNN method.
A Survey on Lightweight Authenticated Encryption and Challenges for Securing Industrial IoT

Embedded systems are widely deployed nowadays in various domains like smart cards, automobiles, telecommunications, home automation systems, computer networking, digital consumer electronics, defense and aerospace. IoT is the technology enabling the inter-connection of these embedded devices (composed of sensors, actuators etc.) through the internet to exchange data, optimize processes, monitor devices in order to generate benefits for the industry, the economy, and the end user. These operations typically consists of sensitive or critical information that needs to be protected against outside world. Therefore their security comes as a primary concern. However the main challenges while providing security for these devices are resource constrained environment in terms of computing power, memory capacity, chip area and the power usage. The limited capabilities of these devices necessitate the adoption of Lightweight Cryptography (LWC). Lightweight cryptography is a field dealing with cryptographic algorithms or cryptographic protocols specially designed for the usage in constrained environments which includes RFID tags, contactless smart cards, sensors, embedded systems, health-care devices and so on. This work provides a survey of existing lightweight authenticated encryption algorithms. We surveyed 17 lightweight AE schemes (LWAE), out of which 9 schemes are from the ongoing CAESAR competition.
Real-Word Errors in Arabic Texts: A Better Algorithm for Detection and Correction

Real-word (also known as semantic or context-sensitive) spelling error is a class of error that escapes the typical spell checker which relies on dictionary look-up. This kind of error occurs when a user types a correctly spelled word-by mistake-when another is intended, e.g., “I want a peace (piece) of cake.” Further, these errors commonly arise in text written by people with dyslexia. Real-word errors are harder to detect as we need to consider the context. In this paper, we propose a spell checker that detects and corrects real-word errors for the Arabic language. Our method avoids predefined confusion sets-a simple approach used by many works tackling this problem-which limits the list of words that can be detected and corrected. Thus, our system can detect and correct a larger set of real-word errors. For the detection phase, we employ word and stem n-gram (n = 1-3) language model along with machine learning, achieving a precision and recall of 83.5% and 99.2%, respectively. And for the correction phase we use n-gram, which results in an accuracy of 98%. Our scheme is robust, with an excellent performance even when the percentage of real-word error words is high. This makes the system suitable for handling errors in post OCR recognition of Arabic text.
Design and implementation of a secure and flexible access-right delegation for resource constrained environments

With the emergence of the Internet of Things (IoT) technology, devices are becoming more ubiquitous and openly accessible to the users. Commonly, IoT devices are constrained in nature (e.g. in memory, computational and battery capacity) and supposed to perform in constrained environment (e.g. restricted communication bandwidth). In these constrained environments, access control is a major challenge. Among other concerns, delegation of access right is crucial for securing flexible access to resources. Previous approaches to delegation in the context of the IoT are mostly static in nature and do not consider the distributed essence of IoT systems. Moreover, they consider a highly resourceful environment for delegation. In this paper, we present a novel capability-based access right delegation framework designed specifically for IoT systems. Our design, in particular, relies on light weight symmetric-key cryptography. We provide a detailed system description and compare the performance of the proposed framework with both centralized and decentralized implementations. Our results indicate that our design efficiently provides a fine-grained, dynamic and secure access right delegation for IoT devices in constrained environments.
Hybrid job offer recommender system in a social network

Abstract Recommender systems (RSs) play a very important role in web navigation, ensuring that the users easily find the information they are looking for. Today's social networks contain a large amount of information and it is necessary that they employ a mechanism that will guide users to the information they are interested in. However, to be able to recommend content according to user preferences, it is necessary to analyse their profiles and determine their preferences. The present work proposes a job offer RS for a career‐oriented social network. The recommendation system is a hybrid, it consists of a case‐based reasoning (CBR) system and an argumentation framework, based on a multi‐agent system (MAS) architecture. The CBR system uses a series of metrics and similar cases to decide whether a job offer is likely to be recommended to a user. Besides, the argumentation framework extends the system with an argumentation CBR, through which old and similar cases can be obtained from the CBR system. Finally, a discussion process is established amongst the agents who debate using their experience from past cases to take a final decision.
Hierarchical residual learning for image denoising

In recent years, residual learning based convolutional neural networks have been applied to image restoration and achieved some success. To avoid network degradation, deep layers in these methods are identity mappings, which are not easy to be learned as observed in recent image recognition work. In this paper, we propose a novel residual learning based CNN framework for image denoising, which does not need to learn identify mappings while avoiding network degradation. The proposed CNN network contains three kinds of sub-networks: feature extraction sub-network, inference sub-network and fusion sub-network. The feature extraction sub-network is first used to densely extract patches and represent them as high dimensional feature maps. Multiple inference sub-networks are then cascaded to learn noise maps by exploiting multi-scale information in a hierarchical fashion, which makes our method have a strong ability of toleraing errors in noise estimation. Finally, the fusion sub-network fuses the noise maps to obtain the final noise estimation. The proposed hierarchical residual learning network can tackle with multiple general image denoising tasks such as Gaussian denoising and single image super-resolution. Experimental results on several datasets show that our hierarchical residual learning based image denoising method outperforms many state-of-the-art ones.
Using Virtual Reality to Compare Design Alternatives Using Subjective and Objective Evaluation Methods

Objective: This study sought to develop a method that supports a more evidence-based approach to evaluating multiple design options in virtual reality (VR), combining subjective insights gathered using traditional approaches and objective feedback gathered using the VR platform. Additionally, this study sought to understand how objective data garnered from the VR platform could be used to compliment traditional evaluation strategies. Background: VR can be a viable research platform for supporting evidence-based design practices. Prior studies have predominately utilized experiential user feedback. While able to provide valuable subjective insights, these approaches are less effective in making objective comparisons between multiple designs alternatives. Method: A repeated measures study was conducted with nursing faculty. User feedback was captured through surveys, interviews, and the VR platform. Results: The survey, interview, and the objective VR data converged in terms of identifying the highest performing design option. Survey data showed that Room 2 performed best in terms of perceived physical access to supplies, unobstructed movement, and availability of space to accommodate additional equipment. VR data showed that participants in Room 2 had significantly higher visibility to both patient and care partners throughout their simulated interaction. Conclusion: Simulation-based evaluations in VR that use a combination of users’ subjective insights and objective data obtained from VR can be an effective tool for helping designers evaluate multiple design options. The use of scenario-based simulations provided a structured and clinically relevant approach to comparing three preoperative rooms, supporting a more robust assessment of users’ physical response to a simulated healthcare environment.
Real-time hierarchical facial performance capture

This paper presents a novel method to reconstruct high resolution facial geometry and appearance in real-time by capturing an individual-specific face model with fine-scale details, based on monocular RGB video input. Specifically, after reconstructing the coarse facial model from the input video, we subsequently refine it using shape-from-shading techniques, where illumination, albedo texture, and displacements are recovered by minimizing the difference between the synthesized face and the input RGB video. In order to recover wrinkle level details, we build a hierarchical face pyramid through adaptive subdivisions and progressive refinements of the mesh from a coarse level to a fine level. We both quantitatively and qualitatively evaluate our method through many experiments on various inputs. We demonstrate that our approach can produce results close to off-line methods and better than previous real-time methods.
True-Color and Grayscale Video Person Re-Identification

Person re-identification is an important task in forensics applications. Most existing person re-identification methods focus on matching persons captured by different true-color cameras. In practice, the captured pedestrian videos may be grayscale in some cases due to camera malfunction or special treatment for gray mode. In these cases, the person re-identification between true-color and grayscale pedestrian videos, which we call color to gray video person re-identification (CGVPR), will be needed. Since the color information that is very important to represent a pedestrian is usually intensity information and monochrome in grayscale videos, the CGVPR problem is very challenging. To relieve the difficulties in CGVPR, we propose an asymmetric within-video projection based Semicoupled Dictionary Pair Learning (SDPL) approach. SDPL simultaneously learns two within-video projection matrices, a pair of true-color and grayscale dictionaries, as well as a semi-coupled mapping matrix. The learned within-video projection matrices can make each video (true-color or grayscale) more compact. The learned dictionary pair and the mapping matrix can work together to bridge the gap between features of true-color and grayscale videos. To date there exists no true-color and grayscale pedestrian video dataset, so we contribute a new one, called true-color and grayscale video person re-identification dataset (CGVID). Our dataset is collected under a real-world scenario and consists of over 50K frames. Extensive evaluations demonstrate that the collected CGVID dataset is very challenging and can be used for further research on person re-identification. The experimental results show that our approach outperforms the compared methods on the CGVPR task.
VCG: Exploiting visual contents and geographical influence for Point-of-Interest recommendation

The rapid development of location-based social networks (LBSNs) provides a substantial amount of image data which not only reveals visual contents of POIs but also users’ visual preferences. We argue that the combination of visual content and other side information (e.g., geographical influence) can lead to a more accurate and personalized recommendation performance. In this paper, we enhance POI recommendation by proposing a unified framework named VCG, which incorporates visual contents and geographical influence in LBSNs. Specifically, we employ an overlapping community detection method to capture heterogeneous relations between POIs. Then high-level visual features are leveraged to model two types of POI relations in communities (i.e., POI–POI and POI–Community). Moreover, we design an objective function with social regularization terms based on weighted matrix factorization to learn latent vectors of users, POIs and communities. In terms of geographical influence, an improved power-law probabilistic model is proposed to discover users’ geographical preferences. Finally, we develop a fused POI recommendation framework which joints user preferences with POI visual relations and geographical influences. We evaluate VCG on two real-world datasets: Yelp and Breadtrip, and experimental results show that the proposed framework is effective for the POI recommendation task.
A memetic imperialist competitive algorithm with chaotic maps for multi-layer neural network training

The performance of artificial neural networks (ANNs) is largely dependent on the success of the training process. Gradient descent-based methods are the most widely used training algorithms but have drawbacks such as ending up in local minima. One approach to overcome this is to use population-based algorithms such as the imperialist competitive algorithm (ICA) which is inspired by the imperialist competition between countries. In this paper, we present a new memetic approach for neural network training to improve the efficacy of ANNs. Our proposed approach - memetic imperialist competitive algorithm with chaotic maps (MICA-CM) - is based on a memetic ICA and chaotic maps, which are responsible for exploration of the search space, while back-propagation is used for an effective local search on the best solution obtained by ICA. Experimental results confirm our proposed algorithm to be highly competitive compared to other recently reported methods.
Kinodynamic planning for spherical tensegrity locomotion with effective gait primitives

Tensegrity-based robots can achieve locomotion through shape deformation and compliance. They are highly adaptable to their surroundings, and are lightweight, low cost, and physically robust. Their high dimensionality and strongly dynamic nature, however, can complicate motion planning. Efforts to date have primarily considered quasi-static reconfiguration and short-term dynamic motion of tensegrity robots, which do not fully exploit the underlying system dynamics in the long term. Longer-horizon planning has previously required costly search over the full space of valid control inputs. This work synthesizes new and existing approaches to produce dynamic long-term motion while balancing the computational demand. A numerical process based upon quasi-static assumptions is first applied to deform the system into an unstable configuration, causing forward motion. The dynamical characteristics of the result are then altered via a few simple parameters to produce a small but diverse set of useful behaviors. The proposed approach takes advantage of identified symmetries on the prototypical spherical tensegrity robot, which reduce the number of needed gaits but allow motion along different directions. These gaits are first combined with a standard search method to achieve long-term planning in environments where the developed gaits are effective. For more complex environments, the various motion primitives are paired with the fall-back option of random valid actions and are used by an informed sampling-based kinodynamic motion planner with anytime properties. Evaluations using a physics-based model for the prototypical robot demonstrate that modest but efficiently applied search effort can unlock the utility of dynamic tensegrity motion to produce high-quality solutions.
Adaptive online time series prediction based on a novel dynamic fuzzy cognitive map

This paper proposes a fuzzy cognitive map scheme for real-time online data prediction. The fuzzy cognitive maps (FCMs) are constructed on the basis of abstracting a numerical time series into a limited number of nodes (concepts), and used as a modeling tool for predicting time series. By represent ing time series in terms of information granules constructed in the space of amplitude and change of amplitude of the time series, a fuzzy cognitive map is dynamically constructed by using the set of information granule, where the particle swarm optimization (PSO) is utilized to study the parameters. In order to find better weights in the global search process, each parameter of the particle swarm algorithm (PSO) is not set to a fixed value but adaptively changes. In this paper, a dynamic fuzzy C-means clustering algorithm is used to online adjust the cluster center and weight according to the impact of the incoming data at the current moment such that the model can capture real-time changes in the data information. The proposed approach is illustrated in detail by a series of experiments using a collection of publicly available data.
Sentiment Analysis of Film Review Texts Based on Sentiment Dictionary and SVM

The sentiment analysis of the film review text is to extract and analyze the hidden sentiment information in the text data, thereby helping the network personnel such as the media platform to analyze the audience's preference for the film. Based on this, this paper proposes a film review text sentiment analysis method based on SVM classification technology in sentiment dictionary and maching learning. Firstly, the basic sentiment dictionary, the domin sentiment dictionary, the negative word dictionary and the degree adverb dictionary are constructed. The four dictionaries are combined to realize the expansion of the dictionary. Secondly, the SVM model training set is construcetd by calculating the combination of sentiment weight and user scoring. Finally, using the test data for sentiment classification experiments, the results show that the method has higher accuracy of sentiment classification than the method based on a basic sentiment dictionary.