Reviewerly/exHarmony huggingface dataset:
Toward Accurate Visual Reasoning With Dual-Path Neural Module Networks

Visual reasoning is a critical stage in visual question answering (Antol et al., 2015), but most of the state-of-the-art methods categorized the VQA tasks as a classification problem without taking the reasoning process into account. Various approaches are proposed to solve this multi-modal task that requires both abilities of comprehension and reasoning. The recently proposed neural module network (Andreas et al., 2016b), which assembles the model with a few primitive modules, is capable of performing a spatial or arithmetical reasoning over the input image to answer the questions. Nevertheless, its performance is not satisfying especially in the real-world datasets (e.g., VQA 1.0& 2.0) due to its limited primitive modules and suboptimal layout. To address these issues, we propose a novel method of Dual-Path Neural Module Network which can implement complex visual reasoning by forming a more flexible layout regularized by the pairwise loss. Specifically, we first use the region proposal network to generate both visual and spatial information, which helps it perform spatial reasoning. Then, we advocate to process a pair of different images along with the same question simultaneously, named as a "complementary pair," which encourages the model to learn a more reasonable layout by suppressing the overfitting to the language priors. The model can jointly learn the parameters in the primitive module and the layout generation policy, which is further boosted by introducing a novel pairwise reward. Extensive experiments show that our approach significantly improves the performance of neural module networks especially on the real-world datasets.
AdaBoostNet: An Efficient Hierarchical Neural Network for Image Classification

Hierarchical Neural Networks [e.g., deep neural networks (DNNs)] have recently gained increasing attention for image classification task. Most of the previous works devote to stack multiple levels of feature learning modules with the hope that higher level modules can represent more abstract semantics of the image data. However, training of such hierarchical network is typically cast as a time-consuming non-convex optimization problem, and its effectiveness for image feature representation critically depends on expertise in parameter tuning with various ad hoc tricks. To address these issues, we advocate a biologically-inspired hierarchical neural network. One key philosophy is that the higher-level modules in the network should correct misclassification induced by the lower ones. Given this idea, a sequential stacking strategy of basic feature learning modules is presented. In practical, an efficient layer wise learning and decision aggregation method is applied to boost the hierarchical network, which is different from naive layers cascading and end-to-end finetuning, and we term it as AdaboostNet. Moreover, the basic feature learning module is set as extreme learning machine (ELM), an effective and cheaply-optimized model. Experiments are conducted on benchmark datasets to evaluate our claims. It shows that the proposed network achieves comparable or better image classification accuracy and training efficiency than traditional DNNs and hierarchical ELMs.
The construction of the Kazakh language thesauri in automatic word processing system

In the paper presents an overview of existing electronic Kazakh-language thesauri and their automatic methods of construction and application. The author analyzed the main characteristics of open access thesauri for scientific research, evaluated the dynamics of their development and effectiveness in solving problems of natural language processing. Statistical and linguistic methods of thesaurus construction were studied, which allow to automate the development and reduce the labor costs of expert linguists. It is considered algorithms for selecting key terms from texts and semantic thesaurus links of all types, as well as the quality of application of the resulting thesauri. For illustrate the features of various methods of building thesaurus links, a combined method was developed that generates a specialized thesaurus completely automatically based on the corpus of domain texts and several existing linguistic resources.
Container Cluster Scheduling Strategy Based on Delay Decision Under Multidimensional Constraints

With the rise of online applications such as machine learning, stream processing, and interactive data-intensive applications in shared clusters, container cluster scheduling in data centers is facing new challenges. In order to solve the problem that application performance and economic cost cannot be balanced in a container cluster deploying a hybrid application, this paper proposes a container cluster scheduling strategy based on delay decision under multi-dimensional constraints. Formal language-based application placement constraints were introduced, and a task reorder model was established based on delayed decision-making. The experiments show that this strategy improves application performance and cluster utilization.
Resolving the Symbol-Subsymbol Debates

The symbol spatialization process creates a new geometric layer between the connectionist layer and the symbolic layer. Connectionist networks produce vectors; Geometric layers promote these vectors into \(\mathscr {N}\)-Balls in higher dimensions.
Missing Frame Detection of Surveillance Videos Based on Deep Learning in Forensic Science

The identification of road traffic accidents plays an essential role in the litigation process of complicated traffic cases. Speed appraisement is the main part among all kinds of judicial identification in the area of road traffic accident. When evaluating the speed of vehicles, video frame time is an important parameter. The situation of missing frames may cause an imprecision of speed inspection. In this paper, we propose a method to detect missing frames by the movement of objects in video based on deep learning techniques. The method is based on object detection neural network. A derived distance of target object is calculated and applied to detect missing frames. We then confirm the performance of proposed method on dataset consisted of collected surveillance videos. It can find missing frames accurately and rapidly, which effectively reduces calculation errors of vehicle speed and promotes the authenticity of forensic investigation.
Multi-Scale Real-Time Object Detection With Densely Connected Network

In this paper, we are interested in the object detection problem with high accuracy and fast prediction. To achieve this, we have modified the existing object detection algorithm, we design a new feature extraction network, predict detection target based on multiple feature maps and improve the anchor box. Particularly, since DenseNet can combine the output of previous convolutional layers by using skip connection operation to make prediction more accurate, we introduce DenseNet to improve the YOLOv3-Tiny network. Furthermore, we add a prediction layer to the improved network to detect more true targets. Finally, we clustered the ground truth of specific dataset to get the anchor box for training to improve the detection results of the improved network. We demonstrate the effectiveness of our network by using Pascal VOC datasets. The experimental results show that the improved network increases the detection accuracy by 14% and the detection speed is 63 frames per second in the video detection. The detection accuracy is improved to a higher level and the detection speed still meeting the real-time detection requirement, which shows that the proposed method is robust and effective. Anchor box, DenseNet, Video detection
An Analysis of Adversarial Cyber Testing Practice

Researchers at the Johns Hopkins University Applied Physics Laboratory recently conducted a series of structured interviews with a cross section of professionals engaged in adversarial cyber (AC) testing. The team analyzed interview transcripts using grounded theory techniques to discover emergent AC testing themes. This paper presents observations on the current state of AC practice, including its strengths and weaknesses. Overall, researchers noted a diversity of practice in the AC test discipline, reinforcing a need to further study the reproducibility of AC test results and the strength of conclusions that an organization may draw based on the outcome of any single AC test engagement.
A Group Authentication Scheme with Privacy-preserving for D2D Communications in 5G HetNets

The next-generation mobile communication, which is 5G wireless mobile networks, has become the paradigm to bring not only solutions for the rising demand for huge data traffic, and massively connected devices such as IoT but also new services. One of the promising solutions is that device-to-device communications are expected to play a key role to improve efficiency and have low latency. Most importantly, D2D communications will be the key enabler for group-based services. However, the new case scenarios and new system architecture of 5G Heterogeneous Networks are vulnerable against various attacks since the 5G is still in the early stage. It is utmost important to have a secure and privacy-preserving scheme for D2D communications in 5G HetNets. Even though various research studies have been done by the researchers for D2D communications but many of them fail to address the heterogenous access scenarios, identity protection, and group authentication. In this paper, we propose a certificateless privacy-preserving authentication and key agreement scheme for D2D communications in 5G HetNets using identity-based encryption and elliptic curve digital signature algorithm (ECDSA) to achieve secure and robust communication. The proposed scheme has been analyzed in terms of security and performance. The result of the formal verification using AVISPA and performance analysis shows that the scheme is secure, lightweight and efficient.
Gaussian state entanglement witnessing through lossy compression

We propose a method to witness entanglement between two continuous-variable systems in a Gaussian state. Its key ingredient is a local lossy state transfer from the original spatially separated systems onto two spatially separated qubits. The qubits are initially in a pure product state, therefore by detecting entanglement between the qubits we witness entanglement between the two original systems. This method greatly simplifies entanglement witnessing in complex systems.
Massive Cloud Computing and Multimedia Framework for Modern Inventive Talent Guiding

Massive cloud computing and robust multimedia framework for modern online accounting inventive talent guiding is designed and implemented in this paper. With artificial intelligence as support, the defects and problems in accounting management system can be effectively compensated. This paper has the following innovations. (1) Comparison with the original data processing method, cloud of unstructured data processing procedure has structural features. (2) The main method of RTCP protocol is to implement communication behavior periodically. Effective transmission of the control packets based on the data packet distribution and delivery mechanism. (3) Cloud computing technology provides standardized and also integrated infrastructure. The performance is validated through the robust test. We compare with the state-of-the-art models to analyze.
Building a Classification System for Failed Test Reports: Industrial Experience

Running complex test suites against a financial transaction system produces huge amounts of responses, both expected and unexpected. In this article, we outline our experience of using ML for reliable automatic extraction of "that" unexpected response from a big number of same type messages produced a by system under test. We describe classification approaches and data manipulations we have tried, and explain the final choices. Also we outline business constraints and final design decisions for the resultant tool.We also address the task of classifying difference patterns between expected and actual responses in attempt to provide automated pre-judgement on a reason for test failure. We outline clustering considerations and results achieved.
Key Concepts of the Future of Artificial Intelligence

The future of artificial intelligence (AI) and its application in medicine are concomitantly challenging and remarkable. While there is some hype around the portfolio of AI tools and their deployment in medicine, especially with medical image interpretation, the long-term dividend remains underestimated. The myriad of emerging tools that are relevant to AI in medicine are the next-generation 5G connectivity; augmented, virtual, and mixed reality (medical extended reality); blockchain and its implications for cybersecurity and data privacy; brain–computer interface as a dimension of increased human cognition in AI; capsule network and the next phase of deep learning; cloud computing imbued with AI technology; edge computing and embedded AI as part of the evolution toward Internet of Everything; fuzzy cognitive maps combined with neural networks to form fuzzy cognitive maps; generative query network and its capability to learn on its own; hypergraph database as a disruptive innovation in databases; low-shot learning as a novel way to learn from small data; neuromorphic computing; quantum computing as a futuristic approach to computing; recursive and spiking neural networks as neural network variants with cognitive elements; swarm intelligence as a mechanism to incorporate crowd wisdom; temporal convolutional nets as a superior tool to recurrent neural network; and finally, transfer learning as a way to render learning more efficient. As promising as these developments and advances are, innovation also needs to be part of health -care data and databases as this is the foundation of future AI applications in medicine and health care.
Artificial Intelligence-Based Knowledge Representation and Reasoning

The quality of higher education can be enhanced only by upgrading the content and skills towards knowledge. Hence, knowledge representation and reasoning play a chief role to represent the facts, beliefs, and information, and inferring the logical interpretation of represented knowledge stored in the knowledge bases. This chapter provide a broad overview of knowledge, representation, and reasoning along with the related art of study in the field of higher education. Various artificial intelligent-based knowledge representation and reasoning techniques and schemes are provided for better representation of facts, beliefs, and information. Various reasoning types are discussed in order to infer the right meaning of the knowledge followed by various issues of knowledge representation and reasoning. .
Generative Model for Node Generation

We present a generative model applied to graph-structured data for node generation by incorporating the graph convolutional architecture and semi-supervised learning with variational auto-encoder. This idea is motivated by successful applications of deep generative models for images and speeches. However, when applied to graph-structured data, especially social network data, existing deep generative models usually don't work: these models can not learn underlying distributions of social network data effectively. In order to address this problem, we construct a deep generative model, using architectures and techniques that prove to be effective for modelling network data in practice. Experimental results show that our model can successfully learn the underlying distribution from the social network dataset, and generate reasonable nodes, which can be altered by varying latent variables. This provides us a way to study social network data in the same way we study image data.
A Real-time Anomaly Detection Algorithm for Taxis Based on Trajectory Big Data

In order to prevent taxi drivers from deliberately detouring or other fraudulent behaviors, a real-time anomaly trajectory detection algorithm based on spatial-temporal big data of taxi is proposed to obtain the real-time detection results with fast feedback. Firstly, the road network map is divided into grid and numbered, and the feature points obtained with direction angle, then transform original trajectory into feature grid trajectory sequence by preprocessing such as matching and completion. Then, the common sequence of the trajectory is obtained from a large number of taxi historical data, and the standard K trajectory between the start point and the end point is obtained by clustering. When the real-time trajectory detection, it is matched with the standard K trajectory, only edit distance of two trajectory need to be calculated considering the spatial-temporal threshold. Finally, based on the real data set of taxi GPS trajectory in Beijing from March to May 2011, a large number of experiments are carried out, and verified the effectiveness and efficiency of the proposed algorithm.
Longitudinal Analysis of Cyber-Related Articles

Cyber attacks have a massive impact on a worldwide economy that is ever-growing in its reliance on technology. With the increase in internet connectivity, more individuals, as well as enterprises, are vulnerable to cyber attacks. Also, in recent years, cyber-related news attracted significant attention from news outlets as well as viewers. Currently, there are many news outlets dedicated to covering technology news in general, and cyber news in particular. In this work, we conduct a correlation analysis over four years of cyber-related news articles obtained from the Global Data on Events, Location, and Tone data source. We apply both supervised and unsupervised text analysis techniques to understand spatial, temporal and distributional topic patterns. Experimental results show interesting trends with respect to cyber-attacks such as ransomware, data breach and denial of service attacks as well as more general cyber-related concepts such as cryptocurrency. This work helps practitioners in understanding an increasingly evolving spectrum of cyber events.
Probabilistic Measures and Integrals: How to Aggregate Imprecise Data

This paper develops the theory of probabilistic-valued measures and integrals as a suitable aggregation tool for dealing with certain types of imprecise information. The motivation comes from Moore’s interval mathematics, where the use of intervals in data processing is due to measurement inaccuracy errors. In case of rounding, the intervals can be considered in distribution function form linked to random variables uniformly distributed over the relevant intervals. We demonstrate how the convolution of distribution functions is taken into account, and integration with respect to probabilistic-valued measures is converted into convolving certain distribution functions. We also improve some existing features of the integral and investigate its convergence properties.
Image Super-Resolution via Residual Blocks and Non-Negative Matrix Decomposition

Abstract In order to solve the problems that the existing super-resolution algorithms based on convolutional neural network do not fully utilize the feature information at all levels, which results in low reconstruction accuracy, and the deep network may resulting in the gradient disappearing, an image super-resolution reconstruction algorithm based on residual blocks and non-negative matrix decomposition is proposed. The proposed residual block can gradually learn the characteristics of the input image, the computational burden of the network is shared on each single residual block, thus the network complexity decreases. The input image and output image are fused by non-negative matrix decomposition to obtain more detail information of high-resolution image. Experiments on the benchmark dataset show that the proposed algorithm improves the PSNR, structural similarity and visual effect.
Project Management and Customer Activation

In a digitally (em)powered age, customers expect a service and product experience in line with continuously evolving expectations. This induces great potential for organisations that shape engagement before, during, or after the main customer touch points. Powered by insights coming from the CRM-driven 360° view, they entail even more value when enabling a company to quickly and continuously learn from its experiences. This chapter will illustrate that project managers need to master a dual dynamic to attain through activated customer engagement. On the one hand, new types of projects, changing expectations, and shifting habits offer humbling challenges. On the other hand, governance, change, and delivery continue to be the foundational baseline. By integrating theoretical insights and real-life cases, the author wants to stimulate project managers. Rather than seeing the digital era as a transformational tsunami for customer engagement, they should see it as an opportunity to go beyond things in a reality where rapidly changing demand entails growth, learning, and great value.
A service‐oriented method for domain and business process modelling

Abstract In this paper, we present Precise SOM (Precise Service Oriented Modelling) —a novel lightweight method for integrated domain and business process modelling—which follows the service‐oriented paradigm, uses a UML profile as notation and provides detailed workflows to guide the production of the models. In our method, the UML models are precisely defined by means of a metamodel and of a set of constraints, and by restricting UML to the essential language constructs, to help modellers to avoid common mistakes and to guarantee, by construction, a good quality. Precise SOM has been validated by detailing how it can be used in various modelling tasks, some of them illustrated by a (industrial) case study.
Adaptive Image Defogging Algorithm Based on DCNN

Under the influence of special weather conditions such as heavy fog, haze, sand and dust, the images taken outdoors are gray and white due to the reflection of light by cluttered particles in the air. Most existing image defogging algorithms are single-layer network feature extraction, feature information is seriously lost, and transmittance calculation is inaccurate. In view of the above problems, an adaptive image defogging algorithm based on deep convolutional neural network is proposed. The implementation of this algorithm is still based on the atmospheric scattering model. There are three kinds of fully convolutional networks: shallow extraction, parallel extraction and deep fusion, which extract the shallow features of the image, extract the deep features and fuse the deep and shallow features together to make the transmission image more accurate. After experimental testing, compared with the traditional defogging algorithm, this deep convolution defogging algorithm has a better defogging effect on outdoor outdoor fog images, especially the defogging effect of details.
Distortion Analysis in Mammogram Images using Spectral Deviation Mapping

This paper presents a spectral mapping based image coding approach for suspicious region detection using architectural region detection. In the analysis of detecting suspicious regions in mammogram image, segmentation approaches were applied. In the process of region selection, accuracy of decision depends on the coding algorithm, segmentation, and feature descriptor and classification algorithm. The existing approach of suspicious region detection is based on the content, which get distorted during capturing, transmission, or storage process. The distortion added to the image impact the accuracy of region detection and classification. In this paper, to improve the performance of detection, a new spectral domain deviation in textural content of the mammogram region is presented. The direct image mapping of spatial content doesn’t reveal the nature of variation, which tends to misclassification in decision. To improve the accuracy of region selection a spectral domain mapping of energy deviation in textural representation is proposed.