Reviewerly/exHarmony huggingface dataset:
Modeling Human Motives and Emotions from Personal Narratives Using External Knowledge And Entity Tracking

The ability to automatically understand and infer characters’ motivations and emotional states is key to better narrative comprehension. In this work, we propose a Transformer-based architecture, referred to as , to model characters’ motives and emotions from personal narratives. Towards this goal, we incorporate social commonsense knowledge about the mental states of people related to social events and employ dynamic state tracking of entities using an augmented memory module. Our model learns to produce contextual embeddings and explanations of characters’ mental states by integrating external knowledge along with prior narrative context and mental state encodings. We leverage weakly-annotated personal narratives and knowledge data to train our model and demonstrate its effectiveness on publicly available dataset containing annotations for character mental states. Further, we show that the learned mental state embeddings can be applied in downstream tasks such as empathetic response generation.
Evaluation of Artificial Neural Network Inference Speed and Energy Consumption on Embedded Systems

Digitalization and automation have been the driving force of the fourth industrial revolution and with them comes a vast amount of data that is collected and needs to be processed. Because of the high costs involved with operating data centers, the task of processing this data has been moved onto edge devices, which process the data on the spot and can act independently. In this paper, an accelerator for artificial neural network inference on edge devices, namely the Intel Neural Compute Stick (NCS), will be evaluated in terms of speed, energy consumption, and performance/cost ratio after which it will be compared to other similar solutions. It has been concluded that the inference speed is significantly better by using the Intel NCS than without it. This solution can be used in private projects and prototyping because of its low cost and low power consumption.
Earthquake prediction, forecasting, &amp; early warning

This chapter is an introduction to earthquake prediction studies. Although no one can yet predict the time, location, or size of impending earthquakes, broadly defined, earthquake prediction remains important. The chapter includes a description of key conceptual models (time- and slip-predicable, etc.) that build on the theory of elastic rebound and provide a set of tools for discussing earthquake occurrence and recurrence. Paleoseismic and geodetic observations are introduced because any discussion of long-term deformation patterns and earthquake interactions requires observations from a range of earthquake-related geosciences. Seismic hazard estimation is also introduced along with discussions of earthquake interactions and triggering. The chapter includes a review of precursory phenomena that have captured attention for many years but remain variable any individual event. Finally, we review earthquake early warning systems that have been developed, deployed, and refined over the last few decades.
Minimizing Energy and Cost Through VM Placement Using Meta-Heuristic Algorithm in Cloud Data Center

Nowadays, cloud computing is an essential commodity for the computational world which offers a large number of server capabilities through virtualized services. The data centers which promise their clients to deliver services contain a large number of computational nodes which are responsible for consuming a large amount of energy. Virtual machine (VM) in somehow solves this problem. Through VM consolidation, the number of physical machines (PM) in the data centers is minimized and so the energy consumption. Optimized VM placement is the medium for VM consolidation. In this paper, we proposed a mathematical model to reduce the power consumption as well as active VMs and then used meta-heuristic algorithm which uses GA for generating an optimized solution. The proposed model is compared with first fit, first fit decreasing and permutation pack algorithms and found our meta-heuristic algorithm is performing well compared to others.
Design and Realization of Animation Composition and Tone Space Conversion Algorithm

In recent years, with the development of society and the rapid development of the animation industry, people are paying more and more attention to and requirements for animation production. As an indispensable part of animation production, picture composition plays a major role in animation production. It can give full play to the application of color matching and light and shadow design and enhance the depth and space of the animation screen. Tone space conversion refers to the conversion or representation of color data in one color space into corresponding data in another color space. Its purpose is to distinguish and process color components such as hue and saturation in an image. This article first introduces the domestic and foreign research status of digital image preprocessing and analyzes the basic principles of several color space conversions in detail. Then, several color space conversion algorithms are studied, and the performance of the algorithms is compared and analyzed. The paper focuses on the hardware implementation and optimization of the algorithm for converting RGB color space into HSI color space to meet the real-time requirements. This article focuses on the mutual conversion between the RGB tone space and the HSI tone space and describes in detail how each color component in the HSI tone space is converted from the three RGB color components from a geometric perspective, and then the conversion is derived, and several general conversion methods of RGB to HSI tone space are introduced; two conversion methods of geometric derivation method and standard modulus algorithm are implemented in the software, and the comparison verification is carried out, and the comparison is made from the perspective of hardware implementation. The pros and cons of the two methods are discussed. Finally, the paper summarizes the shortcomings in the design and proposes further research directions in the future.
Traffic monitoring in smart cities using internet of things assisted robotics

In recent era, IoT-assisted robotics (IoRT) plays a major part in collaborating IoT sensor nodes, robots and human beings to establish a collaborative communication. In this paper, we develop an IoRT system with a modern architecture that encompasses the integration of IoT sensor nodes and robots in smart cities. The study develops a cloud platform to access smart vehicle mobility data in smart cities without collision. A framework is then developed to assist the robots and IoT sensor node in managing and making quick decisions on traffic collision. Hence, it is developed to connect, share, and exchange the information between the cloud and the IoT nodes/robots. The experimental validation shows improved traffic control and collision free traffic in smart cities than other smart city traffic management mechanism.
Robust Modelling of Static Hand Gestures using Deep Convolutional Network for Sign Language Translation

The efforts have been made in the field of Sign Language Recognition research from the last few decades. Sign language is basically an instrument for idea/information propagation through gestures, using hand, lip, and facial expressions. These features are in the form of sign's and every sign pattern has a very different meaning. Typical used gestures are hand gestures. These gestures corresponding the different signs are modelled for automatic detection & recognition of signs. This helps in automatic sign language translation. In this paper, a brief introduction of hand gestures and its modelling approach is covered. The main intent of the effort is to make a natural and robust system that translates sign language into meaningful information. Work is done on creating our own dataset that contains 10,500 images of static signs corresponding to 25 English alphabets ('A'-'Y'). CNN is used to classify these signs into their respective classes. Our proposed CNN model is inspired from the VGG16 base architecture, trained with over 8000 training and 500 validation images. In addition to these, 2000 test images are used to measure the performance of the proposed system. This paper also shows empirical comparisons among trained models and achieves up to 96.7% testing accuracy.
Convolutional neural network for estimating physical parameters from Newton’s rings

By analyzing Newton’s rings, often encountered in interferometry, the parameters of spherical surfaces such as the rings’ center and the curvature radius can be estimated. First, the classical convolutional neural networks, visual geometry group (VGG) network and U-Net, are applied to parameter estimation of Newton’s rings. After these models are trained, the rings’ center and curvature radius can be obtained simultaneously. Compared with previous analysis methods of Newton’s rings, it is shown that the proposed method has higher precision, better immunity to noise, and lower time consumption. For a Newton’s rings pattern of <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mn>640</mml:mn> </mml:mrow> </mml:mrow> <mml:mo>×<!-- × --></mml:mo> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mn>480</mml:mn> </mml:mrow> </mml:mrow> </mml:math> pixels comprising <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mo>−<!-- − --></mml:mo> </mml:mrow> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mn>5</mml:mn> </mml:mrow> </mml:mrow> <mml:mspace width="thickmathspace" /> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mi mathvariant="normal">d</mml:mi> <mml:mi mathvariant="normal">B</mml:mi> </mml:mrow> </mml:mrow> </mml:math> Gaussian noise or 60% salt-and-pepper noise, the parameters can be estimated by the VGG model in 0.01 s, the error of the rings’ center is less than one pixel, and the error of curvature radius is lower than 0.5%.
Identification of Accounting Fraud Based on Support Vector Machine and Logistic Regression Model

The authenticity of the company’s accounting information is an important guarantee for the effective operation of the capital market. Accounting fraud is the tampering and distortion of the company’s public disclosure information. The continuous outbreak of fraud cases has dealt a heavy blow to the confidence of investors, shaken the credit foundation of the capital market, and hindered the healthy and stable development of the capital market. Therefore, it is of great theoretical and practical significance to carry out the research on the identification and governance of accounting fraud. Traditionally, accounting fraud identification is mostly based on linear thinking to build the fraud identification model. However, more and more studies show that fraud has typical nonlinear characteristics, and the multiobjective of fraud means also determines the limitations of using the linear model for identification. Considering that the traditional identification methods may have the defects of model setting error and insufficient information extraction, this paper constructs the support vector machine and logistic regression model to identify accounting fraud. The support vector machine is used to improve the learning ability and generalization ability of unknown phenomena, and the explanatory power of each variable to the whole model is identified by the logistic regression model. This paper breaks through the linear constraint hypothesis and explores the model setting form which is more suitable for the law of corporate fraud behaviour to extract the fraud identification information more fully and provide more powerful support for investors to effectively identify fraud.
A price-aware congestion control protocol for cloud services

Abstract In current infrastructure-as-a service (IaaS) cloud services, customers are charged for the usage of computing/storage resources only, but not the network resource. The difficulty lies in the fact that it is nontrivial to allocate network resource to individual customers effectively, especially for short-lived flows, in terms of both performance and cost, due to highly dynamic environments by flows generated by all customers. To tackle this challenge, in this paper, we propose an end-to-end Price-Aware Congestion Control Protocol (PACCP) for cloud services. PACCP is a network utility maximization (NUM) based optimal congestion control protocol. It supports three different classes of services (CoSes), i.e., best effort service (BE), differentiated service (DS), and minimum rate guaranteed (MRG) service. In PACCP, the desired CoS or rate allocation for a given flow is enabled by properly setting a pair of control parameters, i.e., a minimum guaranteed rate and a utility weight, which in turn, determines the price paid by the user of the flow. Two pricing models, i.e., a coarse-grained VM-Based Pricing model (VBP) and a fine-grained Flow-Based Pricing model (FBP), are proposed. The optimality of PACCP is verified by both large scale simulation and small testbed implementation. The price-performance consistency of PACCP are evaluated using real datacenter workloads. The results demonstrate that PACCP provides minimum rate guarantee, high bandwidth utilization and fair rate allocation, commensurate with the pricing models.
Abstractive Summarization of Russian News Learning on Quality Media

Summarization is becoming a demanded task in the modern world of ever-increasing document flow. This task allows to compress existing text while maintaining all salient information. However, building a neural summarization model requires training data which is scarce in some languages. In this work, we consider the problem of abstract summarization of news texts in Russian. We propose a new method for obtaining training data that uses the news leads of high-quality media that publishes news in accordance with the classical model. We prove dataset eligibility for training by building an abstractive summarization framework based on pre-trained language models and comparing summarization results with extractive baselines.
Remote sensing and structural studies of the Central Asir Shear Zone, Western Arabian Shield: Implications for the late Neoproterozoic E-W Gondwana assembly

The Central Asir Shear Zone (CASZ) is a N- to NNE-oriented inhomogeneous strain zone parallel to the Nabitah shear belt in southern Saudi Arabia. It is ~200 km long and 20 km wide, and traverses the Asir terrane. The deformed terrains in the vicinity of the CASZ have experienced a prolonged history involving three successive phases of deformation: arc-collision, dextral transpression and sinistral strike-slip faulting. Remote-sensing data analysis and field investigation provide insights into the geological and structural features along the CASZ. Image transformation and enhancement reveal faults and folds, and discriminate between granitoids, metavolcanics, gabbros and younger (Ablah Group) sediments. The obtained evidence points to polyphase deformation of the area, characterized by narrow zones of high strain, folding and shearing enclosing slices of less deformed rocks. Lineament analysis shows predominantly NNE, NNW, N and NE trends of folds and shear zones, and NW, NE and E fracture trends. The CASZ has a >100 m.y. long deformation history, including a change from dextral to sinistral shearing. Both shearing senses are found on the older N-S and younger NW-SE fault systems. N-S oriented shearing was accompanied by intrusion of granitoids and localized hydrothermal alteration along N to NNE trending shear zones. Strain analysis shows low strain in the Ablah basin sediments, consistent with Ablah deposition after the tectonic climax in the region.
Continuous matching of evolving patterns over dynamic graph data

Abstract Nowadays, the scale of various graphs soars rapidly, which imposes a serious challenge to develop processing and analytic algorithms. Among them, graph pattern matching is the one of the most primitive tasks that find a wide spectrum of applications, the performance of which is yet often affected by the size and dynamicity of graphs. In order to handle large dynamic graphs, incremental pattern matching is proposed to avoid re-computing matches of patterns over the entire data graph, hence reducing the matching time and improving the overall execution performance. Due to the complexity of the problem, little work has been reported so far to solve the problem, and most of them only solve the graph pattern matching problem under the scenario of the data graph varying alone. In this article, we are devoted to a more complicated but very practical graph pattern matching problem, continuous matching of evolving patterns over dynamic graph data , and the investigation presents a novel algorithm for continuously pattern matching along with changes of both pattern graph and data graph. Specifically, we propose a concise representation of partial matching solutions, which can help to avoid re-computing matches of the pattern and speed up subsequent matching process. In order to enable the updates of data graph and pattern graph, we propose an incremental maintenance strategy, to efficiently maintain the intermediate results. Moreover, we conceive an effective model for estimating step-wise cost of pattern evaluation to drive the matching process. Extensive experiments verify the superiority of .
Non-Complex CNN Models for Colorectal Cancer (CRC) Classification Based on Histological Images

Colorectal cancer become a significant public health issue and is the world’s second leading cause of death from cancer. Cancer becomes a very dangerous disease, because it gives no visible signs at an early stage. Signs of cancer will usually only be seen if it is in the third stadium or the last stadium, where the cancer has spread to surrounding organs. Early diagnosis of colorectal cancer is highly needed because treatment choices are decided and the period of survival is heavily affected. This paper proposes the Convolutional Neural Network (CNN) for detecting four classes of colon cancer. The data-set consists of 2500 images, divided into Tumor, Complex, Lymphoma and Stroma. This data set represents a selection from the Institute of Pathology, University of Heidelberg, Germany, consist of 150 × 150 px textures in histological pictures. The proposed system consist of two hidden convolutional layers, a fully connected layer and use Adam Optimizer with learning 0.001, and trained 10 times (epochs = 10). The result of the proposed system is 83% accuracy.
Intelligent Vehicle Navigation System Based on Visual Detection and Positioning

Abstract This design is a set of intelligent vehicle navigation system which relies on visual detection and positioning. The image is processed and the coordinates of beacon and car body are extracted by the camera hanging above. The coordinate information is transmitted to the intelligent vehicle and fused with the intelligent vehicle data to determine its own environment and running state. The system integrates image processing, wireless communication and intelligent path planning to realize the task of accurate recognition and automatic tracing.
Design of Solder Quality Inspection System Based on Machine Vision

The electronic water pump for vehicles adjusts the working state of the water pump with the help of the electronic control unit, and then pressurizes the coolant, to circulate the coolant in the cooling system to ensure that the engine temperature does not exceed the limit threshold and can work normally. Therefore, it is a very important link to inspect internal circuit boards in the production of water pumps. Traditional manual inspection has limited working time, while automatic machine vision inspection can perform 24-hour uninterrupted inspection. Compared with manual inspection, it is more reliable and stable, which has advantages in speed, and has a wide range of industrial applications. According to this, this paper designs a solder joint inspection system based on machine vision, which can detect the status of solder joints and feed back the current soldering results to the workers. The method solves the problem of automatically detecting the welding quality of the circuit board in the manual welding process, greatly improves the production efficiency of the workshop production line, and shortens the product production cycle.
Design and Implementation of Smart Card based Secure Key Storage The Blockchain E-voting Application

E-voting is an application of in the election process. E-voting was developed to make the election process easier, safer and more practical. Blockchain technology is explained as a safe technology because of its distributed nature. Tso et al's e-voting is one of the e-voting mechanism that uses the ethereum smart contract blockchain. Tso et al e-voting application use asymmetric cryptography for the safety of its users in the form of Paillier's algorithm. This study proposed use of the AES-256 algorithm to secure key data on smart cards for e-voting process, SHA-256 for authentication, and secure communication protocols to secure the key in sending process. The key is stored in a ciphertext format with AES-256 encryption and SHA-256 as authentication algorithm. The results of implementing smart card prototypes as key storage in the Tso et al e-voting application shows that the proposed e-voting scheme has a key storage time of 0.774 seconds, a memory capacity of 572 bytes, with 0.472 seconds generation speed.
Virtual Machine Security Problems and Countermeasures for Improving Quality of Service in Cloud Computing

Cloud computing is widely used across industries due to its benefits such as scalability, availability and powerful resource integration. Though it is based on a cost model, it became affordable due to virtualization technology. However, it has caused many security issues both at Hypervisor (HV) level and Virtual Machine (VM) level. Security issues arise due to vulnerabilities that are exploited by adversaries to launch different kinds of attacks leading to deterioration of Quality of Service (QoS) in cloud computing. Apart from virtualization related security issues, cloud computing has evidenced data level and communication level security challenges. This paper focuses on the attacks and countermeasures that are associated with virtualization. Also, the proposed research work provides useful insights on the current state of the art and find challenges to be addressed with respect to three kinds of attacks. They are known as VM side channel attack that involves a class of attacks due to shared usage of hardware by VMs, hypervisor attacks occur due to compromised VMs and attacks targeting VM live migration exploiting dynamic VM allocation schemes. The findings in this paper motivate in further investigation into these specific attacks to improve the state of the art.
Research and application of security baseline in business information system

Security baseline is an important part of information network security application research. It is a basic requirement for business system security, and is also a prerequisite for conducting security assessments and solving information system security problems. This article studies the basic configuration, state management and security vulnerabilities of the security baseline to design a security baseline research plan. According to the environment configuration and classified protection of the business system, it carding the safety baseline strategy that fits the business system. The security baseline strategy checks the configuration of the business information system, and then performs security hardening on the system, which reducing business system security risks. The research and application of security baselines ensure the normal operation of business systems and reduce the cost of daily system security maintenance.