Reviewerly/exHarmony huggingface dataset:
An Expression Recognition Method on Robots Based on Mobilenet V2-SSD

This paper adopts a combination of MobileNet and SSD method to recognize facial expressions, and then apply it to Nao robots. MobileNet is a lightweight deep network model for mobile devices. The standard convolution is decomposed by depthwise separable convolution to decompose the calculation and reduce the amount of computation. The SSD model evolves from the VGG16 model and maintains excellent object detection performance despite a sharp decrease in the number of parameters. This paper combines MobileNet V2 with SSD for expression recognition, which not only meets the real-time requirements, but also keeps high recognition accuracy. Since the robot's processor performance is limited, while the deep neural network can automatically extract the image feature for accurate classification, it is of great significance to use lightweight deep neural network to apply the real-time detection and recognition of the Nao robot.
PROBLEMS OF CONTEXT AND CONCEPTUAL MANAGEMENT IN EDUCATION: PSYCHOLOGICAL, SOCIAL AND ECONOMICAL ASPECTS

The purpose of the article is a brief description of the experience of analyzing the conceptual foun-dations of governance in education and other spheres in post-Soviet Russia in comparison with developed democracies; a description of typical approaches to the implementation (imitation) of strategic planning in management, as well as the consequences and specific traits of contextual, a-strategic management. Conceptual management in education and in the context of others is oppose to contextual management. Modern management concepts are externally presented in Russian education and in other areas of management discourse and practice. The system of professional standards developed in the country is designed to solve a number of managerial tasks. However, the non-conceptual use of professional standards leads to an imbalance in subject-object interaction and in organization in whole. This article lists two categories of workers. Non-compliance with the professional requirements is typical to the first category of workers. Failure to comply with professional standards also take part to the professional, partners and personal deformations. These deformations are illustrated by the text of management strategies. Having strategies is an incentive for development. However, the lack of an algorithm for the compilation and evaluation of such documents, the analysis of the importance of this aspect in the process of evaluating the activities of authorities leads to the fact that this strategic management remains unclaimed in the territorial and organizational management system. The authors consider two leading aspects of the problem of territorial strategization: (1) substantiation of the reasons for poor-quality strategic developments; (2) the search for errors in the conceptual management methodology. The roots of the first problem should be found in the imbalances in the formation and development of the subject of management, starting with the problem of improper selection and de-professionalization of managerial personnel. To solve the second problem, the authors propose SEO analysis tools as a methodology for evaluating strategy texts. This tool solves the problem of audit of regional strategies in the context of administrative, socio-psychological and linguistic aspects of public servants in the field of education. The data obtained can also be disseminated to non-governmental educational institutions: in particular, in additional or global education it is extremely important to know its subject, object, purpose and objectives, etc. Otherwise, as in the case of state educational institutions, imitations will arise that are more likely to harm education and society than support it.
Body-Part-Aware and Multitask-Aware Single-Image-Based Action Recognition

Action recognition is an application that, ideally, requires real-time results. We focus on single-image-based action recognition instead of video-based because of improved speed and lower cost of computation. However, a single image contains limited information, which makes single-image-based action recognition a difficult problem. To get an accurate representation of action classes, we propose three feature-stream-based shallow sub-networks (image-based, attention-image-based, and part-image-based feature networks) on the deep pose estimation network in a multitasking manner. Moreover, we design the multitask-aware loss function, so that the proposed method can be adaptively trained with heterogeneous datasets where only human pose annotations or action labels are included (instead of both pose and action information), which makes it easier to apply the proposed approach to new data on behavioral analysis on intelligent systems. In our extensive experiments, we showed that these streams represent complementary information and, hence, the fused representation is robust in distinguishing diverse fine-grained action classes. Unlike other methods, the human pose information was trained using heterogeneous datasets in a multitasking manner; nevertheless, it achieved 91.91% mean average precision on the Stanford 40 Actions Dataset. Moreover, we demonstrated the proposed method can be flexibly applied to multi-labels action recognition problem on the V-COCO Dataset.
Efficient image noise estimation based on skewness invariance and adaptive noise injection

The precise estimation of the noise level is a crucial issue in image processing. In this study, the authors propose a new method for noise standard deviation (STD) estimation from natural images based on skewness-scale invariance in the transform domain and an adaptive noise injection strategy. The method is divided into two steps. The first step assumes that the natural clean image has the property of constancy of skewness in the transform domain. Then, a preliminary noise estimation method based on skewness invariance is designed by solving a constrained non-linear optimisation problem. The second step involves noise rectification via noise injection. According to the phenomenon that compared with the high-noise circumstance, the error of preliminary estimation is more serious under a low amount of noise, the noise STD is re-estimated by injecting another noise for which the STD is known. In addition, the threshold model with respect to image complexity is established to identify whether a second estimation is needed. The experimental results demonstrate the efficacy of the proposed method and performance is superior to other state-of-the-art methods.
Crime-GAN: A Context-based Sequence Generative Network for Crime Forecasting with Adversarial Loss

Grasping the dynamics of crime situation is a long standing but significant problem and plays an instructive role in the field of security and protection. Traditional methods approach the crime forecasting via stochastic equations based on physics or statistics, which may be interpretable but less efficient in real applications. Recently, some data-driven models, especially sequence generative networks, seem to be promising in capturing spatio-temporal dynamics with massive dataset available. In this paper, we process some regional crime dataset of recent fifteen years in the crime situation awareness graphs and learn latent representations with variational auto-encoder. And then Crime Generative Adversarial Network (Crime-GAN) is formulated as a new crime forecasting model for four types of crime, integrating sequence to sequence structure and Wasserstein adversarial loss. In comparison to other typical algorithms, such as Conv-RNN, Crime-GAN shows superior forecasting performance for multi-type crime in spatio-temporal scale.
A 4D grid based approach for efficient conflict detection in large-scale multi-UAV scenarios

This paper proposes an algorithm to detect conflicts among UAVs from a strategic point of view. The method is based on the representation of the airspace as a 4D grid of cells. Given a set of UAVs with their scheduled 4D trajectories (their flight plans), the whole scenario is discretized as a 4D grid and the problem is solved by filling the appropriate cell for each way-point from the trajectories and checking the neighboring cells. The proposed method is tested and compared against a traditional algorithm getting a significantly better performance. The proposed method also scales very well with increasing number of UAVs and way-points per trajectory.
Partial Observer Decision Process Model for Crane-Robot Action

The most common use of robots is to effectively decrease the human’s effort with desirable output. In the human-robot interaction, it is essential for both parties to predict subsequent actions based on their present actions so as to well complete the cooperative work. A lot of effort has been devoted in order to attain cooperative work between human and robot precisely. In case of decision making , it is observed from the previous studies that short-term or midterm forecasting have long time horizon to adjust and react. To address this problem, we suggested a new vision-based interaction model. The suggested model reduces the error amplification problem by applying the prior inputs through their features, which are repossessed by a deep belief network (DBN) though Boltzmann machine (BM) mechanism. Additionally, we present a mechanism to decide the possible outcome (accept or reject). The said mechanism evaluates the model on several datasets. Hence, the systems would be able to capture the related information using the motion of the objects. And it updates this information for verification, tracking, acquisition, and extractions of images in order to adapt the situation. Furthermore, we have suggested an intelligent purifier filter (IPF) and learning algorithm based on vision theories in order to make the proposed approach stronger. Experiments show the higher performance of the proposed model compared to the state-of-the-art methods.
Text-Independent Speaker Verification with Adversarial Learning on Short Utterances

A text-independent speaker verification system suffers severe performance degradation under short utterance condition. To address the problem, in this paper, we propose an adversarially learned embedding mapping model that directly maps a short embedding to an enhanced embedding with increased discriminability. In particular, a Wasserstein GAN with a bunch of loss criteria are investigated. These loss functions have distinct optimization objectives and some of them are less favoured for the speaker verification research area. Different from most prior studies, our main objective in this study is to investigate the effectiveness of those loss criteria by conducting numerous ablation studies. Experiments on Voxceleb dataset showed that some criteria are beneficial to the verification performance while some have trivial effects. Lastly, a Wasserstein GAN with chosen loss criteria, without finetuning, achieves meaningful advancements over the baseline, with 4% relative improvements on EER and 7% on minDCF in the challenging scenario of short 2second utterances.
Effective Utilization of External Knowledge and History Context in Multi-turn Spoken Language Understanding Model

At present, spoken language understanding (SLU) in multi-turn dialogue is a research hotspot, which mainly includes intent detection and slot filling. SLU models trained by large-scale corpus can learn good superficial semantic and grammatical information. But they lack the ability for modeling the knowledge needed to understand language. In order to further understand the deep semantic information of the dialogue, external knowledge needs to be modeled and incorporated into the SLU model. In addition, utilizing the correlation between history dialogue and current utterance is able to understand dialogue in multi-turn SLU. Thus, this paper proposes a joint model of intent detection and slot filling based on history context and external knowledge. This model constructs history dialogue encoder to obtain history context. Meanwhile, it constructs knowledge attention over context module. This module selects external knowledge according to the context information in current utterance and obtains knowledge representation. Finally, the history context and external knowledge representation are incorporated into the intent detection and slot filling joint model. The result of experiments on the common dataset demonstrate that with the help of external knowledge and history context, the performance of our model has a significant improvement.
A Character-Level BiGRU-Attention for Phishing Classification

Online phishing usually tricks victims by showing fake information which is similar to the legitimate one, so that the phishers could elevate their privileges. In order to guard users from fraudulent information and minimize the loss caused by visiting phishing websites, a variety of methods have been developed to filter out phishing websites. At present, there are several phishing detection methods continually being updated, but the experimental results of them are not enough satisfactory. To fill these gaps, an improved model based on attention mechanism bi-directional gated recurrent unit, named BiGRU-Attention model, will be introduced. The basic mechanism of this model is that it obtains the characters before and after a particular character through the BiGRU, and then calculates score for that character by the Attention. Since the final score depends on the composition of the input, the more similar between phishing and legitimate websites, the more difficult it is to be distinguished. By utilizing this model, most of the phishing URLs will be tested out. Also, an explanation of why phishing and legal websites can be distinguished will be given. Based on the experimental results, the BiGRU-Attention model achieves an accuracy of 99.55%, and the F1-score is 99.54%. Besides, the effectiveness of deep neural network in anti-phishing application and cybersecurity will be demonstrated. Keywords Phishing Detection, BiGRU-Attention Model, Important Characters, The Difference Between similar URLs.
Image Memorability Prediction Using Depth and Motion Cues

Memorability is one of the intrinsic image properties, which enables one to quantify to what extent images are memorable to the human cognitive system. Many works have shed light on various visual factors that influence the image memorability. Recently, one such study showed the influence of image depth and motion information on image memorability [3]. Based on these findings, this article proposes a deep learning-based prediction model, which utilizes depth and motion cues to predict the image memorability scores. The proposed model contains three deep CNN networks. Each of these three networks is individually trained to utilize one of the three visual factors: 1) visual depth information; 2) optical flow information; and 3) fine-tuned scene- and object-related features. In the end, all three networks are ensembled to predict the final memorability scores for the given image. An extensive set of experiments are conducted on large scale image memorability data set LaMem. From the experimental results, it is observed that the proposed model performs better than the current state-of-the-art model [3] by 2.44%.
Metapath Enhanced Graph Attention Encoder for HINs Representation Learning

In this paper, we propose a novel representation learning framework, named MEGAE, for heterogeneous information networks. To investigate the rich semantic information in heterogeneous information networks, we use metapaths to complete implicit links between nodes. A graph attention encoder is further used to learn graph structural information with shared weight parameters. The attention mechanism, on the other hand, provides us an intuition of how the representation is learned and improves the interpretability of our model. Furthermore, a multitask learning of node classification and link prediction is trained to achieve more robust generalization ability. To validate our ideas, extensive experiments on three real-world datasets show that our model achieves state-of-the-art results on node classification and link prediction tasks in HINs.
Multi Criteria based Resource Score Heuristic for Cloud Workflow Scheduling

Scheduling scientific workflows modelled by Directed Acyclic Graphs (DAG) is an NP complete problem. Cloud computing provides reliable Quality of Service defined in terms of Service Level Agreements (SLA). To schedule scientific workflows in cloud environment, where resources are shared, it is important to manage the cloud resources efficiently by maximizing utilization. The dynamic nature of cloud resources, due to sharing, heterogeneity, virtualization and workload variations offer a host of challenges in terms of resource availability and performance. This may have a significant impact on task execution times and data transfer times, thus introducing delay in overall execution time called makespan, In this paper, Multi Criteria based Resource Score Heuristic for Cloud Workflow Scheduling is proposed, with an objective of minimizing makespan considering the probability of temporal availability of resources in a cloud computing environment. To choose the best virtual machine with optimal value of maximum resource availability and minimum task execution time, a method of compensatory aggregation of conflicting criteria, is used for scoring each resource. The simulation results demonstrate that the proposed heuristic, can generate schedules with better makespan minimization and is found to be more reliable since resource availability factor is considered while mapping tasks to virtual machines.
Impact and Applications of Fuzzy Cognitive Map Methodologies

Since their introduction in 1986, Fuzzy Cognitive Maps (FCMs) have been comprehensively studied, applied, and extended with growing interest and are still expanding in use. This chapter discusses the impact of Fuzzy Cognitive Maps as a knowledge acquisition, knowledge reasoning and modeling methodology, on its own, and in synergy with other soft computing, computational intelligence and knowledge-based methodologies. It discusses the general structure and development of FCMs and their topologies as well as extensions to fill specific problem needs. The extensive application areas are also presented along with future research directions.
Reconciling Lambek’s restriction, cut-elimination and substitution in the presence of exponential modalities

Abstract The Lambek calculus can be considered as a version of non-commutative intuitionistic linear logic. One of the interesting features of the Lambek calculus is the so-called ‘Lambek’s restriction’, i.e. the antecedent of any provable sequent should be non-empty. In this paper, we discuss ways of extending the Lambek calculus with the linear logic exponential modality while keeping Lambek’s restriction. Interestingly enough, we show that for any system equipped with a reasonable exponential modality the following holds: if the system enjoys cut elimination and substitution to the full extent, then the system necessarily violates Lambek’s restriction. Nevertheless, we show that two of the three conditions can be implemented. Namely, we design a system with Lambek’s restriction and cut elimination and another system with Lambek’s restriction and substitution. For both calculi, we prove that they are undecidable, even if we take only one of the two divisions provided by the Lambek calculus. The system with cut elimination and substitution and without Lambek’s restriction is folklore and known to be undecidable.
Robust Coreset Construction for Distributed Machine Learning

Motivated by the need of solving machine learning problems over distributed datasets, we explore the use of \emph{coreset} to reduce the communication overhead. Coreset is a summary of the original dataset in the form of a small weighted set in the same sample space. Compared to other data summaries, coreset has the advantage that it can be used as a proxy of the original dataset. However, existing coreset construction algorithms are each tailor-made for a specific machine learning problem. Thus, to solve different machine learning problems, one has to collect coresets of different types, defeating the purpose of saving communication overhead. We resolve this dilemma by developing robust coreset construction algorithms based on k-means/median clustering, that give a provably good approximation for a broad range of machine learning problems with sufficiently continuous cost functions. Through evaluations on diverse datasets and machine learning problems, we verify the robust performance of the proposed algorithms.
Sentiment Analysis of English-Punjabi Code Mixed Social Media Content for Agriculture Domain

In India, more than 70% of the population is dependent on agriculture. Since the independence of India, the people involved in agriculture mostly stay in rural areas. The government has taken numerous efforts for the improvement of conditions of farmers. Still the condition is not improved to acceptable rate. Currently, it has been easy to extract the reviews of farmers from micro-blogging websites. Since decades, a trend has been seen that multilingual speakers often switch between more than one languages to express themselves on social media networks. Multiple languages are mixed with different rules of grammars, which in itself is the challenging task. In this paper, the authors have extracted the agriculture-related comments having code-mixing property with English-Punjabi mixed content. Further, the performed language identification, normalization, and creation of English-Punjabi code-mixed dictionary. After that, we have tested various models trained on English-Punjabi code mixed data using Support Vector Machine and Naive Bayes techniques for sentiment analysis, tested the pipeline for unigram predictive model. Later experimented for n-gram and performance was found to be better in our implemented model.
Instance Mask Embedding and Attribute-Adaptive Generative Adversarial Network for Text-to-Image Synthesis

Existing image generation models have achieved the synthesis of reasonable individuals and complex but low-resolution images. Directly from complicated text to high-resolution image generation still remains a challenge. To this end, we propose the instance mask embedding and attribute-adaptive generative adversarial network (IMEAA-GAN). Firstly, we use the box regression network to compute a global layout containing the class labels and locations for each instance. Then the global generator encodes the layout, combines the whole text embedding and noise to preliminarily generate a low-resolution image; the instance embedding mechanism is used firstly to guide local refinement generators obtain fine-grained local features and generate a more realistic image. Finally, in order to synthesize the exact visual attributes, we introduce the multi-scale attribute-adaptive discriminator, which provides local refinement generators with the specific training signals to explicitly generate instance-level features. Extensive experiments based on the MS-COCO dataset and the Caltech-UCSD Birds-200-2011 dataset show that our model can obtain globally consistent attributes and generate complex images with local texture details.