Reviewerly/exHarmony huggingface dataset:
Parameter tuning for meta-heuristics

These days meta-heuristic algorithms are gaining lot of popularity. The performance of the meta-heuristics depends upon the suitable selection of user dependent parameters. Finding the most suitable values for the parameters (fine tuning) is a challenging problem. This paper proposes a generalized strategy to find the most suitable value of any parameter for a meta-heuristic algorithm. The approach is based on the relation between algorithm’s performance and functional landscape. The proposed approach is evaluated by applying it to a recent meta-heuristic algorithm, Gravitational Search Algorithm (GSA). The parameter α which plays a vital role in the convergence of GSA search process, is fine tuned using the proposed strategy. Obtained values of α, change the nature of gravitational coefficient G from monotonic to non-monotonic for a cluster free diversified search. The proposed strategy is tested over CEC-2015 test suite. Various statistical tests have been applied to compare the obtained results with recent variants of GSA and other state-of-the-art meta-heuristics. Results confirm that the parameters obtained using proposed approach significantly improve the results.
Analysis of Multi-Types of Flow Features Based on Hybrid Neural Network for Improving Network Anomaly Detection

Security issues of large-scale local area network are becoming more prominent and the anomaly detection for the network traffic is the key means to solve this problem. On the other hand, it is a challenge to extract effective and accurate traffic features for anomaly detection. In order to resolve this challenge, multi-types of network flow features are designed and analyzed in the present study. These features include sequence packet features, general statistical features and environmental features, which can profile the characteristics of network flows accurately. Moreover, a method based on the hybrid neural network is proposed to detect anomaly by analyzing these features. One-dimensional convolutional network is implemented to analyze the sequence features in the hybrid neural network, while deep neural networks are utilized to learn the characteristics of high-dimension feature vectors including general statistical features and environmental features. The method can make comprehensive analysis for network anomaly detection. Two datasets of ISCX-IDS-2012 and CIC-IDS-2017 are carried out to evaluate the performance of the proposed method and other similar algorithms. The present study shows that the comprehensive performances of the proposed method are better than that for others algorithms. It is concluded that the proposed method can be applied for the anomaly detection applications with reasonable performance.
Multiple Real-time object identification using Single shot Multi-Box detection

Real time object detection is one of the challenging task as it need faster computation power in identifying the object at that time. However the data generated by any real time system are unlabelled data which often need large set of labeled data for effective training purpose. This paper proposed a faster detection method for real time object detection based on convolution neural network model called as Single Shot Multi-Box Detection(SSD).This work eliminates the feature resampling stage and combined all calculated results as a single component. Still there is a need of a light weight network model for the places which lacks in computational power like mobile devices( eg: laptop, mobile phones, etc). Thus a light weight network model which use depth-wise separable convolution called MobileNet is used in this proposed work. Experimental result reveal that use of MobileNet along with SSD model increase the accuracy level in identifying the real time household objects.
Hyperspectral inversion of soil organic matter content in cultivated land based on wavelet transform

Soil organic matter (SOM) is one of the most important indicators of cultivated land fertility and greatly influences other soil nutrient factors and physicochemical characteristics. This study aimed to develop a universal method to detect SOM content within the plough layer of cultivated land using ground hyperspectral data. The hyperspectral data was decomposed using the wavelet transform algorithm. The sensitivity of the high-frequency information increased with the degree of the wavelet decomposition. SOM content was retrieved using the high-frequency coefficients created with the wavelet transform and random forest algorithm. The validation model showed a R2 of 0.748 and RMSE of 0.254. The predictive accuracy of the model based on the random forest algorithm was improved by 10.2% compared to that of the math transformations. Therefore, the high-frequency information decomposed by the wavelet technology effectively enhanced the predictive accuracy of the SOM content by coupling the wavelet technology and random forest algorithm.
Self-Sovereign Identity: Use-cases, Technologies, and Challenges for Industrial IoT

Industrial Internet of Things applications are distributed Cyber-Physical systems with long life-cycles, with strict security requirements, that are being pervasively adopted with the proliferation of Industry 4.0. A fundamental aspect of security, in such applications, is a trustworthy identity to avoid impersonation attacks. Classical approaches for identity management usually rely on some variation of a centralized system, either based in a Public Key Infrastructure or in a central identity database. These solutions have some well-known drawbacks, notably the single point of failure in the central node and the limited scalability. Self-Sovereign Identity is a novel paradigm for digital identity allowing individuals and devices to fully own and manage their digital identities. According to this paradigm, identity artifacts can be stored by their owners and freely presented whenever a transaction requires some proof of identity, without relying on a central entity. This paper provides a review and discussion of the use-cases, technologies, and challenges that Self-Sovereign Identity faces in the context of Industrial Internet of Things' applications. The potential advantages of using this new paradigm are discussed in detail, together with the key challenges that still need to be addressed.
Robust adaptive steganography based on generalized dither modulation and expanded embedding domain

Sharing images on social network platforms (SNPs) from mobile intelligent devices is becoming more and more popular and has great potential for covert communication. However, images will be processed by lossy social network channels, such as JPEG compression, which reduces image quality and destroys message extraction. Previous robust steganographic schemes using reverse engineering or anti-compression domain for SNPs suffer from some security flaws or have only small capacity and low security level. The purpose of this paper is to refine the robust steganographic scheme by considering asymmetric costs for different modification polarities and expanding the embedding domain for digital images, aiming to aggregate the modifications on the elements with small costs. Such a new strategy that utilizes asymmetric distortion for dither modulation to implement ternary embedding can be regarded as generalized dither modulation in substantial sense. Compared with the original Dither Modulation-based robust Adaptive Steganography (DMAS), the proposed scheme selects more DCT coefficients as cover elements and we call it Generalized dither Modulation-based robust Adaptive Steganography (GMAS). Extensive experiments demonstrate that the proposed GMAS gains significant performance improvements in terms of robustness and security when compared with DMAS.
An FPGA-Based Hardware Accelerator for CNNs Using On-Chip Memories Only: Design and Benchmarking with Intel Movidius Neural Compute Stick

During the last years, convolutional neural networks have been used for different applications, thanks to their potentiality to carry out tasks by using a reduced number of parameters when compared with other deep learning approaches. However, power consumption and memory footprint constraints, typical of on the edge and portable applications, usually collide with accuracy and latency requirements. For such reasons, commercial hardware accelerators have become popular, thanks to their architecture designed for the inference of general convolutional neural network models. Nevertheless, field-programmable gate arrays represent an interesting perspective since they offer the possibility to implement a hardware architecture tailored to a specific convolutional neural network model, with promising results in terms of latency and power consumption. In this article, we propose a full on-chip field-programmable gate array hardware accelerator for a separable convolutional neural network, which was designed for a keyword spotting application. We started from the model implemented in a previous work for the Intel Movidius Neural Compute Stick. For our goals, we appropriately quantized such a model through a bit-true simulation, and we realized a dedicated architecture exclusively using on-chip memories. A benchmark comparing the results on different field-programmable gate array families by Xilinx and Intel with the implementation on the Neural Compute Stick was realized. The analysis shows that better inference time and energy per inference results can be obtained with comparable accuracy at expenses of a higher design effort and development time through the FPGA solution.
POINet

Multi-person pose tracking aims to jointly estimate and track multi-person keypoints in the unconstrained videos. The most popular solution to this task follows the tracking-by-detection strategy that relies on human detection and data association. While human detection has been boosted by deep learning, existing works mainly exploit several separated stages with hand-crafted metrics to realize data association, leading to great uncertainty and feeble adaption in complex scenes. To handle these problems, we propose an end-to-end pose-guided ovonic insight network (POINet) for the data association in multi-person pose tracking, which jointly learns feature extraction, similarity estimation, and identity assignment. Specifically, we design a pose-guided representation network to integrate pose information into hierarchical convolutional features, generating a pose-aligned person representation for person, which helps handle partial occlusions. Moreover, we propose an ovonic insight network to adaptively encode the cross-frame identity transformation, which can cope with the tough tracking cases of person leaving and entering the scene. In general, the proposed POINet provides a new insight to realize multi-person pose tracking in an end-to-end fashion. Extensive experiments conducted on the PoseTrack benchmark demonstrate that our POINet outperforms the state-of-the-art methods.
AdvGAN++: Harnessing Latent Layers for Adversary Generation

Adversarial examples are fabricated examples, indistinguishable from the original image that mislead neural networks and drastically lower their performance. Recently proposed AdvGAN, a GAN based approach, takes input image as a prior for generating adversaries to target a model. In this work, we show how latent features can serve as better priors than input images for adversary generation by proposing AdvGAN++, a version of AdvGAN that achieves higher attack rates than AdvGAN and at the same time generates perceptually realistic images on MNIST and CIFAR-10 datasets.
“Context collapse” on a small island

Abstract Commentators and analysts in new media studies have taken inspiration from Goffman’s ‘dramaturgical’ approach to interaction as performance, as well as his concepts of ‘face’ and ‘impression management’. Goffman is specifically invoked in discussions of a particular source of interactional trouble that is seen as generated in and by the structure of mediated communication in digital spaces: so-called “context collapse.” Context collapse represents “a crisis of self-presentation” ( Wesch, 2008 ) that is brought about by the ability of digital platforms like Twitter and Facebook to “flatten multiple audiences into one” ( Marwick &amp; boyd, 2010 , p. 9). Returning to Goffman’s unpublished PhD dissertation ( Goffman, 1953 ) – based on fieldwork on the remote island of Unst in the Shetlands – presents an opportunity to understand more fully both the online phenomenon of “context collapse” and the promise and limitations of Goffman’s work for the study of interaction in digital environments.
Learning Object-Action Relations from Bimanual Human Demonstration Using Graph Networks

Recognizing human actions is a vital task for a humanoid robot, especially in domains like programming by demonstration. Previous approaches on action recognition primarily focused on the overall prevalent action being executed, but we argue that bimanual human motion cannot always be described sufficiently with a single action label. We present a system for framewise action classification and segmentation in bimanual human demonstrations. The system extracts symbolic spatial object relations from raw RGB-D video data captured from the robot's point of view in order to build graph-based scene representations. To learn object-action relations, a graph network classifier is trained using these representations together with ground truth action labels to predict the action executed by each hand. We evaluated the proposed classifier on a new RGB-D video dataset showing daily action sequences focusing on bimanual manipulation actions. It consists of 6 subjects performing 9 tasks with 10 repetitions each, which leads to 540 video recordings with 2 hours and 18 minutes total playtime and per-hand ground truth action labels for each frame. We show that the classifier is able to reliably identify (action classification macro F1-score of 0.86) the true executed action of each hand within its top 3 predictions on a frame-by-frame basis without prior temporal action segmentation.
Explainable Video Action Reasoning via Prior Knowledge and State Transitions

Human action analysis and understanding in videos is an important and challenging task. Although substantial progress has been made in past years, the explainability of existing methods is still limited. In this work, we propose a novel action reasoning framework that uses prior knowledge to explain semantic-level observations of video state changes. Our method takes advantage of both classical reasoning and modern deep learning approaches. Specifically, prior knowledge is defined as the information of a target video domain, including a set of objects, attributes and relationships in the target video domain, as well as relevant actions defined by the temporal attribute and relationship changes (i.e. state transitions). Given a video sequence, we first generate a scene graph on each frame to represent concerned objects, attributes and relationships. Then those scene graphs are associated by tracking objects across frames to form a spatio-temporal graph (also called video graph), which represents semantic-level video states. Finally, by sequentially examining each state transition in the video graph, our method can detect and explain how those actions are executed with prior knowledge, just like the logical manner of thinking by humans. Compared to previous works, the action reasoning results of our method can be explained by both logical rules and semantic-level observations of video content changes. Besides, the proposed method can be used to detect multiple concurrent actions with detailed information, such as who (particular objects), when (time), where (object locations) and how (what kind of changes). Experiments on a re-annotated dataset CAD-120 show the effectiveness of our method.
UM-Adapt: Unsupervised Multi-Task Adaptation Using Adversarial Cross-Task Distillation

Aiming towards human-level generalization, there is a need to explore adaptable representation learning methods with greater transferability. Most existing approaches independently address task-transferability and cross-domain adaptation, resulting in limited generalization. In this paper, we propose UM-Adapt - a unified framework to effectively perform unsupervised domain adaptation for spatially-structured prediction tasks, simultaneously maintaining a balanced performance across individual tasks in a multi-task setting. To realize this, we propose two novel regularization strategies; a) Contour-based content regularization (CCR) and b) exploitation of inter-task coherency using a cross-task distillation module. Furthermore, avoiding a conventional ad-hoc domain discriminator, we re-utilize the cross-task distillation loss as output of an energy function to adversarially minimize the input domain discrepancy. Through extensive experiments, we demonstrate superior generalizability of the learned representations simultaneously for multiple tasks under domain-shifts from synthetic to natural environments. UM-Adapt yields state-of-the-art transfer learning results on ImageNet classification and comparable performance on PASCAL VOC 2007 detection task, even with a smaller backbone-net. Moreover, the resulting semi-supervised framework outperforms the current fully-supervised multi-task learning state-of-the-art on both NYUD and Cityscapes dataset.
Estimation of daily dew point temperature by using bat algorithm optimization based extreme learning machine

Capabilities of the bat algorithm optimized extreme learning machine (Bat-ELM) model for dew point temperature (Tdew) estimation were evaluated in this study, in comparison with the kernel-based nonlinear extension of Arps decline model (KNEA), the genetic algorithm optimized ELM (GA-ELM), the particle swarm optimization ELM (PSO-ELM), and six other non-hybrid machine learning models. Daily meteorological data [including mean temperature (Tmean), maximum temperature (Tmax), minimum temperature (Tmin), mean relative humidity (RHmean), maximum relative humidity (RHmax), minimum relative humidity (RHmin) and atmospheric pressure (Pa)] during 2014–2017 at the Yangling station of China were collected for model evaluation, by using six different input combinations and a 10-fold cross-validation. Results showed that all models exhibited a poor accuracy with Tmean as the only input, but had a relatively good accuracy under the combination of three meteorological parameters (i.e., Tmax, Tmin and Pa) that can be easily acquired. Under the combination of Tmax, Tmin, RHmax, RHmin and Pa, model performances were similar or even slightly worse when compared with the combination of Tmax, Tmin, RHmax and RHmin. Overall, for estimating daily Tdew, our results suggest that Bat-ELM would be the optimal model while Tmax, Tmin, RHmax and RHmin would be the best input combinations.
A New Approach for Transformation-Based Fuzzy Rule Interpolation

Fuzzy rule interpolation (FRI) is of particular significance for reasoning in the presence of insufficient knowledge or sparse rule bases. As one of the most popular FRI methods, transformation-based fuzzy rule interpolation (TFRI) works by constructing an intermediate fuzzy rule, followed by running scale and move transformations. The process of intermediate rule construction selects a user-defined number of rules closest to an observation that does not match any existing rule, using a distance metric. It relies upon heuristically computed weights to assess the contribution of individual selected rules. This process requires a move operation in an effort to force the intermediate rule to overlap with an unmatched observation, regardless of what rules are selected and how much contribution they may each make. It is, therefore, desirable to avoid this problem and also to improve the automation of rule interpolation without resorting to the user's intervention for fixing the number of closest rules. This article proposes such a novel approach to selecting a subset of rules from the sparse rule base with an embedded rule weighting scheme for the automatic assembling of the intermediate rule. Systematic comparative experimental results are provided on a range of benchmark datasets to demonstrate statistically significant improvement in the performance achieved by the proposed approach over that obtainable using conventional TFRI.
Multi-Colony Ant Colony Optimization Based on Generalized Jaccard Similarity Recommendation Strategy

Ant Colony Optimization has achieved good results in solving Traveling Salesman Problem (TSP), it has a tendency to fall into local optima and the convergence speed is limited.To address this problem, multi-colony ant colony optimization based on the generalized Jaccard similarity recommendation strategy (JCACO) is proposed.Firstly, two classical ant populations, Ant Colony System and Max-Min Ant System are selected to form heterogeneous multi-colony.Secondly, attribute-based collaborative filtering recommendation mechanism is proposed to balance the diversity and convergence of the algorithm, three strategies have been implemented under this recommendation mechanism: The attribute cross-learning strategy is used to highlight the effect of excellent attributes and improve the attribute comprehensive performance; According to the diversity results of the population measured by information entropy, the attribute recommendation learning strategy is used to enrich the diversity of the population adaptively; The pheromone reward strategy is implemented on the public path to accelerate the convergence speed; Among which, according to the generalized Jaccard similarity coefficient, the most suitable communication object is recommended in order to achieve the best learning efficiency.Finally, when the algorithm stagnates, the elite reverse learning mechanism is used to jump out of the local optimum.Experimental results show that JCACO has good performance and high stability in TSP instances, especially in large-scale TSP instances.
Spear: Optimized Dependency-Aware Task Scheduling with Deep Reinforcement Learning

Modern data parallel frameworks, such as Apache Spark, are designed to execute complex data processing jobs that contain a large number of tasks, with dependencies between these tasks represented by a directed acyclic graph (DAG). When scheduling these tasks, the ultimate objective is to minimize the makespan of the schedule, which is equivalent to minimizing the job completion time. With task dependencies, however, minimizing the makespan of the schedule is non-trivial, especially when tasks in the DAG have different resource demands with respect to multiple resource types. In this paper, we present Spear, a new scheduling framework designed to minimize the makespan of complex jobs, while considering both task dependencies and heterogeneous resource demands at the same time. Inspired by recent advances in artificial intelligence, Spear applies Monte Carlo Tree Search (MCTS) in the specific context of task scheduling, and trains a deep reinforcement learning model to guide the expansion and rollout steps in MCTS. With deep reinforcement learning, search efficiency can be significantly improved by focusing on more promising branches. With both simulations and experiments using traces from production workloads, we compare the scheduling performance of Spear with state-of-the-art job schedulers in the literature, and Spear can outperform those approaches by up to 20%. Our results have validated our claims that MCTS and deep reinforcement learning can readily be applied to optimize the scheduling of complex jobs with task dependencies.