Reviewerly/exHarmony huggingface dataset:
3M2RNet: Multi-Modal Multi-Resolution Refinement Network for Semantic Segmentation

One of the most important steps towards 3D scene understanding is the semantic segmentation of images. The 3D scene understanding is considered as the crucial requirement in computer vision and robotic applications. With the availability of RGB-D cameras, it is desired to improve the accuracy of the scene understanding process by exploiting the depth along with appearance features. One of the main problems in RGB-D semantic segmentation is how to fuse or combine these two modalities to achieve more advantages of the common and specific features of each modality. Recently, the methods that encounter deep convolutional neural networks have reached the state-of-the-art results in dense prediction. They are usually used as feature extractors as well as data classifiers with an end-to-end training procedure. In this paper, an efficient multi-modal multi-resolution refinement network is proposed to exploit the advantages of these modalities (RGB and depth) as much as possible. This refinement network is a type of encoder-decoder networks with two separate encoder branches and one decoder stream. The feature abstract representation of deep networks is performed by down-sampling operations in encoder branches leading to some resolution loss in data. Therefore, in the decoder branch, the occurred resolution loss must be compensated. In the modality fusion process, a weighted fusion of “clean” information paths of each resolution level of the two encoders is utilized via the skip connection by the aid of the identity mapping function. The extensive experimental results on the three main challenging datasets of NYU-V2, SUN RGB-D, and Stanford 2D-3D-S show that the proposed network obtains the state-of-the-art results.
Outliners Detection Method for Software Effort Estimation Models

Outliner detection methods are studied as an approach for simulated in-house dataset creation. In-house datasets are understood as an approach for increasing the estimation accuracy of the functional points-based estimation models. The method which was selected as the best option for outliners' detection is the median absolute deviation. The product delivery rate was used as a parameter for the median absolution deviation method. The estimation accuracy was compared for a public dataset and simulated in-house datasets, using stepwise regression models. Results show that in-house datasets increase estimation accuracy.
Relay dueling network for visual tracking with broad field‐of‐view

A deep reinforcement‐learning‐based method is presented for visual object tracking tasks. The key objective is to generate a sequence of actions which can move or scale the bounding box in the previous frame to track the target in the current frame. Two intelligent agents are trained to accomplish the above task with a special dueling deep Q‐learning network (Dueling DQN), referred to as a relay dueling network. The proposed model is divided into two agents: the movement agent and the scaling agent. The former performs horizontal or vertical movements and the latter generates scaling actions to change the size of the bounding box. The model has multiple inputs that cover both the bounding box region and the enlarged search region to improve the agents’ perception of the surroundings. The proposed method has a broader field of vision than other similar trackers and its distribution of actions makes it easy to train and improve its tracking performance. The proposed network is tested on popular standard tracker benchmarks and its performance is compared with state‐of‐the‐art trackers. The proposed network is found to be competitive in tracking accuracy and execution effectiveness when compared to conventional methods.
Paired-D GAN for Semantic Image Synthesis

Semantic image synthesis is to render foreground (object) given as a text description into a given source image. This has a wide range of applications such as intelligent image manipulation, and is helpful to those who are not good at painting. We propose a generative adversarial network having a pair of discriminators with different architectures, called Paired-D GAN, for semantic image synthesis where the two discriminators make different judgments: one for foreground synthesis and the other for background synthesis. The generator of paired-D GAN has the encoder-decoder architecture with skip-connections and synthesizes an image matching the given text description while preserving other parts of the source image. The two discriminators judge foreground and background of the synthesized image separately to meet an input text description and a source image. The paired-D GAN is trained using the effective adversarial learning process in a simultaneous three-player minimax game. Experimental results on the Caltech-200 bird dataset and the Oxford-102 flower dataset show that Paired-GAN is capable of semantically synthesizing images to match an input text description while retaining the background in a source image against the state-of-the-art methods.
MonitorApp: a web tool to analyze and visualize pollution data detected by an electronic nose

The analysis of air quality data may reveal the quality of life and can prevent dangers for the citizen health. Assuming that some chemical compounds in the air produce a bad smell, people may detect that something is going wrong acting as sensors that alerts potential risks. This work presents a visual analytics approach to support air quality experts in the analysis of data produced by electronic nose devices. The approach consists in setting workflows to manage and transform raw data offering clustering and visualization techniques to analyze such information. The analysis is supported by calendar, map and line graph visualization techniques also maneuvering the clustering attributes. The interactive map is used to show the position of monitoring stations in order to support making hypothesis related to the data source locations.
Recommendation algorithm based business english writing training strategy

In order to strengthen the training of English writing, this paper proposed a new business English writing training strategy based on some state-of-the-art recommendation algorithm. Firstly, we introduced the development of business English writing briefly, and then we studied how the recommendatio n algorithm is used to construct the English writing training model for the teachers, which can better assist students in business English writing training and reduce the common mistakes in writing. In this paper, we proposed an optimization and update scheme by the state-of-the-art recommendation algorithms. Furthermore, we designed a useful identification model to evaluate the continuous state of business English writing. The experimental results show that the coding accuracy of English vocabulary and the efficiency of the model can archive a good performance, which can effectively help students to write. This model deserves further promotion and application.
A cognitive network management system to improve QoE in stereoscopic IPTV service

Summary New Internet Protocol Television (IPTV) services are including new technologies such as Stereoscopic TV and three‐dimensions (3D) HDTV. As well, increased ubiquitous networking and promoting in smart devices have led to high demand IPTV (over networks). Stereoscopic content required higher data flow to support these emerging TV services, and there are higher requirements at the network layer to provide good quality of service and quality of experience to the end users in delivering stereoscopic IPTV. In this paper, we propose a new concept of cognitive network management algorithm and protocol based on 3D coding techniques for delivering of stereoscopic IPTV service. The proposed approach explains how the management algorithm observes the network performance to guarantee the quality of the stereoscopic IPTV services, by measuring the performance of quality of service (QoS) parameters (delay, jitter, and packets loss) and quality of experience (QoE) metrics such as Peak Signal‐to‐Noise Ratio (PSNR), Moving Image Videography (MIV), and Mean Opinion Score (MOS). Those parameters are monitored in order to take appropriate codification decision for IPTV service provider. Moreover, the codification decision uses K‐mean classification to select the better codification for the end users. Therefore, both kinds of 3D coding formats such as Stereo Video Coding (SVC) format and 2D + Z Coding format (2D‐plus‐Depth) are selected in our experiments. As a result, our proposal successfully ensures the appropriate quality of service and quality of experience to the end users when the service of stereoscopic IPTV is being delivered.
Greedy Action Selection and Pessimistic Q-Value Updating in Multi-Agent Reinforcement Learning with Sparse Interaction

AbstractAlthough multi-agent reinforcement learning (MARL) is a promising method for learning a collaborative action policy, enabling each agent to accomplish specified tasks, MARL has a problem of exponentially increasing state-action space. This state-action space can be dramatically reduced by assuming sparse interaction. We previously proposed three methods (greedily selecting actions, switching between Q-value update equations on the basis of the state of each agent in the next step, and their combination) for improving the performance of coordinating Q-learning (CQ-learning), a typical method for multi-agent reinforcement learning with sparse interaction. We have now modified the learning algorithm used in a combination of these two methods to enable it to cope with interference among more than two agents. Evaluation of this enhanced method using two additional maze games from three perspectives (the number of steps to a goal, the number of augmented states, and the computational cost) demonstrated that the modified algorithm improves the performance of CQ-learning.reinforcement learningmulti agentsparse interactionfully cooperativemaze games
Video-based recipe retrieval

Recipe retrieval has received great attention in the research community, which focuses on retrieving a textual recipe given a text or an image as the query. However, cooking is an interesting activity, and many useful elements are hidden in the dynamic videos, which might be omitted in the statistic texts and images. On the other hand, although a number of video-based retrieval methods have been investigated in the past, existing technologies mainly focus on general applications and seldom take the domain-specific feature into account. To bridge the above gap, we investigate a new problem of video-based recipe retrieval, which refers to retrieving a cooking video from a list of video candidates given a textual recipe as the query, or the reverse side. In this work, we first propose a hierarchical attention network to learn the representations of textual recipe and its cooking procedures. Moreover, we employ reinforcement learning to dynamically locate a video moment given a cooking procedure as the query. Thereafter, the representations of video moments and cooking procedures are projected into a common space and optimized with a pairwise ranking loss, which is able to distinguish the matched and unmatched video moment-cooking procedure pairs. Therefore, the retrieval process between cooking videos and textual recipes is performed as the assembling matching results of video moments and cooking procedures. By experimenting on a self-collected dataset, we demonstrate the effectiveness and rationality of our proposed solution on the scope of both overall performance comparison and micro-level analyses.
Prediction Demand for Classified Ads Using Machine Learning

Classified ads prediction is a very interesting activity for organizations in order to increase the purchase quantity of a product and thereafter the possibility of sale. Used goods predicting can be done by calculating the probability of sale for each selected product. In this paper, we conduct an empirical analysis on classified ads prediction of Avito dataset in order to develop prediction models using three individual machine-learning techniques and five ensemble learners. We compare and evaluate the performance of the proposed models using Root Mean Square Error (RMSE) measure. The stacked generalization method was also used to combine the best-performed models to select the best one. The results show that the Extreme Gradient Boosting Machine algorithm (XGBoost) is the most accurate model with an RMSE value of 0.2253.
Practical Indexing of Repetitive Collections Using Relative Lempel-Ziv

We introduce a simple and implementable compressed index for highly repetitive sequence collections based on Relative Lempel-Ziv (RLZ). On a collection of total size n compressed into z phrases from a reference string R[1..r] over alphabet [1..σ] and with hth order empirical entropy H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">h</sub> (R), our index uses rH <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">h</sub> (R)+o(r log σ)+O(r+z log n) bits, and finds the occ occurrences of a pattern P[1..m] in time O((m+occ) log n). This is competitive with the only existing index based on RLZ, yet it is much simpler and easier to implement. On a 1GB collection of 80 yeast genomes, a variant of our index achieves the least space among competing structures (slightly over 0.1 bits per base) while outperforming or matching them in time (1-10 microseconds per occurrence found). Our largest variant (below 0.3 bits per base) offers the best search time (1-3 microseconds per occurrence) among all structures using space below 1 bit per base.
A Lossless Polynomial-Based Secret Image Sharing Scheme Utilizing the Filtering Operation

As a basic method, Shamir’s polynomial-based secret sharing (PSS) is utilized to achieve polynomial-based secret image sharing schemes (PSISSs) with other better performances, such as meaningful shares, two-in-one property and et al. Since the modulus of the sharing polynomial must be a prime, e.g., 251 for PSISSs, Shamir’s PSS cannot deal with pixel values more than 250 well. On account of the importance of precise recovery in the image encryption field, there exist several solutions to achieve precise recovery for PSISSs, which have natural drawbacks, such as large computational costs, random shape changes and extra division for sharing. In this paper, we utilize a simple filtering operation to achieve the proposed lossless PSISS, namely LPSISS, without any side effects. We choose 257 rather than 251 as the sharing modulus, and filter sets of shared values to abandon sets with pixel value more than 255. This solution to lossless recovery is simple but effective, which is valuable as a basic technique to achieve better performances. Experimental results are conducted to prove the effectiveness of the proposed scheme.
Emotional computing based on cross-modal fusion and edge network data incentive

In large-scale emotional events and complex emotional recognition applications, how to improve the recognition accuracy, computing efficiency, and user experience quality becomes the first problem to be solved. Aiming at the above problems, this paper proposes an emotional computing algorithm based on cross-modal fusion and edge network data incentive. In order to improve the efficiency of emotional data collection and the accuracy of emotional recognition, deep cross-modal fusion can capture the semantic deviation between multi-modal and design fusion methods through non-linear cross-layer mapping. In this paper, a deep fusion cross-modal data fusion method is designed. In order to improve the computational efficiency and user experience quality, a data incentive algorithm for edge network is designed in this paper, based on the overlapping delay gaps and incentive weights of large data collection and error detection. Finally, the edge network is mapped to a finite data set space from the set of emotional data elements inspired by heterogeneous emotional events. In this space, all emotional events and emotional data elements are balanced. In this paper, an emotional computing algorithm based on cross-modal data fusion is designed. The results of simulation experiments and theoretical analysis show that the proposed algorithm is superior to the edge network data incentive algorithm and the cross-modal data fusion algorithm in recognition accuracy, complex emotion recognition efficiency, and computation efficiency and delay.
Re-identification of Vessels with Convolutional Neural Networks

In order to perform a reliable vessel behavior analysis for maritime surveillance, re-identification of previously detected vessels, passing through new camera locations, is of vital importance. However, challenging outdoor conditions of the maritime environment heavily restrict the application of conventional methods. Additionally, vessels are large objects and capturing a vessel from different viewpoints may provide entirely different visual appearances. To address these challenges, this paper proposes an Identity Oriented Re-identification network (IORnet) for the re-identification of vessels. This CNN-based approach incorporates the triplet loss method combined with a new loss function, which leads to improved vessel re-identification. Experimental results on our real-world evaluation dataset reveal that the proposed method achieves 81.5% and 91.2% on mAP and Rank1 scores, respectively. As an additional contribution, we also provide our annotated vessel re-identification dataset to the open public access.
Performance Comparison of Object Detection Algorithms with different Feature Extractors

In this work, speed vs accuracy of different Neural Network architectures using alternate feature extractors in the field of Object Detection is being computed, thereby finding the fastest and most accurate architecture out of the lot in order to carry out Object Detection. We made use of three architectures and three extractors to build different combinations of models in order to compute mAP, which is the metric used or commenting upon accuracy. COCO data-set has been used to extract sample images and the work is implemented on TensorFlow library.
Imbalanced Internet Traffic Classification Using Ensemble Framework

Machine learning (ML)-based traffic classification has been gaining increasing importance due to the declining port-based and payload-based approaches. However, the packet-level method is the most common measure used in the previous works, which requires additional hardware device to manage and monitor, increasing cost as well as extra works of the network personnel. In this paper, we propose a methodology to build an efficient classifier from NetFlow which is the widely applied monitoring solution among network operators in the form of flow-level. First, we analyze the per-application performance through the C4.5 decision tree with the features derived from the NetFlow records. The result shows that the accuracy obtained is as good as the packet-level method. We further propose the ensemble feature selection (FS) method to improve the classification accuracy and to reduce the computational complexity. Lastly, we present the clustering-based under-sampling combining with synthetic minority over-sampling technique (SMOTE) approach to solving the problem of the concept drift and the imbalanced dataset, and we extract insights and recommendation for practical application. With the combination of proposed methods, the experiment result reports high F-measure over two traces containing the wide range of applications with low computational complexity.
Implementation of Threshold Comparator Using Cartesian Genetic Programming on Embryonic Fabric

Recent research in the area of evolvable design clearly indicates its advantage in the electronics system domain. This biologically inspired approach for design automation and reconfiguration is required for the hardware that needs online adaptation. The Cartesian Genetic Programming (CGP) represents a circuit genotype in the form of grid of nodes. In satellite for safe mode detection, a threshold comparator circuit is used. The comparator circuit can be evolved using CGP architecture by evolutionary algorithm. An evolutionary algorithm (EA) is designed and applied on the CGP pattern of comparator. The evolved comparator in the cascaded form can be further implemented on embryonic fabric. The embryonic fabric has cellular structure that makes it suitable for self-healing and self-replication. The CGP approach to generate configuration data for embryonic array is better than the LUT based data generation approach. In this paper a 2-bit comparator is evolved using customized evolutionary algorithm. The implementation of cascaded 8-bit threshold comparator on the embryonic array is also demonstrated. This comparator design needs configuration data for 2-bit and the data can be cloned to next cells for scalable design on embryonic fabric.
Formal Verification of Security Critical Hardware-Firmware Interactions in Commercial SoCs

We present an effective methodology for formally verifying security-critical flows in a commercial System-on-Chip (SoC) which involve extensive interaction between firmware (FW) and hardware (HW). We describe several HW-FW interaction scenarios that are typical in commercial SoCs. We highlight unique challenges associated with formal verification of security properties of such interactions and discuss our approach of property-specific abstraction and software model checking to circumvent those challenges. To the best of our knowledge, this is the first exposition on formal co-verification of security-specific HW-FW interactions in the context and scale of a commercial SoCs. Despite traditional scalability challenges, we demonstrate that many such flows are amenable to effective formal verification.
An Exact Penalty Method for Top-k Multiclass Classification Based on L0 Norm Minimization

Top-k multiclass classification is one of the central problems in machine learning and computer vision. It aims at classifying instances into one of three or more classes and allows k guesses to evaluate classifiers based on the top-k error. However, existing solutions either simply ignore the specific structure of the top-k error by directly solving the top-1 error minimization problem or consider convex relaxation approximation for the original top-k error model. In this paper, we formulate the top-k multiclass classification problem as an ℓ0 norm minimization problem. Although the model is NP-hard, we reformulate it as an equivalent mathematical program with equilibrium constraints and consider an exact penalty method to solve it. The algorithm solves a sequence of convex optimization problems to find a desirable solution for the original nonconvex problem. Finally, we demonstrate the efficacy of our method on some real-world data sets. As a result, our method achieves state-of-the-art performance in term of accuracy
A new efficient variational model for multiplicative noise removal

In this paper, based on the approximation of the Taylor expansion, a novel fidelity term is formulated. We propose a new variational image restoration model to recover an image from its multiplicative noised version. We show that the proposed model is strictly convex and has a unique solution so that the model can be performed by the primal–dual method. Compared with other models, the proposed model is able to not only deal with various problems of different type noises, such as the multiplicative Beta noise, F noise, Gaussian noise, but also take significantly less computational time.