Reviewerly/exHarmony huggingface dataset:
Multi-scene ancient chinese text recognition

Multi-scene ancient Chinese text recognition (MACR) is a challenging task for ordinary people without relevant professional knowledge. Due to language barriers and the lack of related open datasets, there is little research on MACR. In this paper, a multi-scene ancient Chinese text (MACT) dataset, formed by handwritten text, calligraphy, natural scene text in ancient fonts, is established that includes synthetic samples generated for training and real scene samples collected for testing. We first perform experiments on some CNN structures as the baseline method, and the top-1 recognition result, 66.94%, is approximately 13.96% higher than subjective human recognition results. Furthermore, based on these models and confidence score from the baseline, a multi-model ensemble (MME) method is proposed, which adopts auxiliary datasets and a feature extraction method to augment data before training, utilizes different hyper-parameters to optimize in training, and integrates multiple models to improve recognition accuracy. The top-1 accuracy results of the MME method reach 73.36% and other top-n results also greatly surpass the baseline results. The MACT dataset is publicly available on the website1.
On Managing Contextual Knowledge of Digital Document Ecosystems, characterized by Alphanumeric Textual Data

The multidisciplinary textual-data are often disorganized and misinterpreted in many documents, which can obscure the information retrieval and its interpretation in company networks and even the World Wide Web. Managing textual information in particular with large-size alphanumeric data sources is challenging and at times can preclude the prompt delivery of good quality document services to diverse customers. Optimizing the words, sentences and alphanumeric characters of a script is the purpose of research, without losing intelligibility, semantics, perception, content flow and the contextual scenarios, represented as dimensions. We interpret the manuscript as a document ecosystem, within which different dimensions are construed. We choose different lexes, sentences, paragraphs and pages that possess frequent alphanumeric characters, interpreted in multiple domains and contexts. The ontologies of alphanumeric textual-data dimensions and their metaphors are presented in several data schemas, connecting various contexts of document ecosystems. The domain ontologies that can deliver text-mining, the semantic and schematic information of textual data, can expedite the textual-data integration process in the multidimensional warehouse modelling procedure. Diverse views and contexts that are generic within the document ecosystems are analysed for contextual knowledge. The ontologically structured document ecosystems that can facilitate more legibility and reproducibility to a variety of document designers are research outcomes. Data analysts, text mining experts and document managers can benefit the current research.
Dunuen: A User-Friendly Formal Verification Tool

Formal verification allows checking the design and the behaviour of a system. One of the main limitations to the adoption of formal verification techniques is the process of model creation using specification languages. For this reason a tool supporting this activity is necessary. Actually, there are several tools allowing analysts to verify models expressed into specification languages. These tools provide support for automatically checking whether a system satisfies a property. However, to use such tools it is important to deeply know a precise notation for defining a system, i.e., the Calculus of Communicating Systems. Since systems are often expressed as time-series, to overcome this problem, we provide an user-friendly tool able to automatically generate a system model starting from the CSV - Comma-Separated Values format (the most widespread format considered to release dataset). In this way we hide the details about the model construction form the analyst, which can only focus immediately on the properties to verify. We introduce Dunuen, a tool allowing the user to firstly perform a kind of pre-processing operation starting from a CSV file, as discretization or removing attributes; subsequently it automatically creates a formal model from the pre-processed CSV file and, by invoking the model checker we embedded in Dunuen, it finally verifies whether the generated model satisfies a property expressed in temporal logic through a graphic interface, proposing formal methods as an alternative to machine learning for classification tasks.
Target Acquisition Performance as a Criterion of Camouflage Pattern Effectiveness

In this paper we present results and analysis of a photosimulation digital camouflage assessment trial. The amount of human observers were employed to get a robust statistical data. We have conducted a study to investigate possible improvements in camouflage patterns effectiveness determination. The aim was to prepare a study of potential of target acquisition performance as a Figure of merit of camouflage pattern effectiveness.
Linear Scheduling of Big Data Streams on Multiprocessor Sets in the Cloud

Nowadays, there is an accelerating need to efficiently and timely handle large amounts of data that arrives continuously. Streams of big data led to the emergence of Distributed Stream Processing Systems (DSPS) that assign processing tasks to the available resources (dynamically or not) and route streaming data between them. Efficient scheduling of processing tasks of data flows can reduce application latencies and eliminate network congestion. In this work, we propose a linear complexity scheme for the task allocation and scheduling problem to improve system’s performance, load balancing and memory efficiency, in applications where there is need for heavy communication (all-to-all) between the tasks assigned to pairs of components.
Unmanned aerial vehicle (UAV) image haze removal using dark channel prior

Abstract The unmanned aerial vehicle (UAV) image taken in foggy or haze weather usually has lower contrast and fidelity, the quality of the image is seriously degraded. In this paper, we propose a dehazing model based on dark channel prior to dehaze the UAV image. We use a quad-tree hierarchical searching method to estimate the atmospheric light value, it can effectively avoid the influence of white objects in the image on the estimation of atmospheric light values. In the process of refining the medium transmission map, we propose a new regularization optimization scheme. Experimental results show that the proposed approach effectively recover the clear UAV image.
Reinforcement Learning for Resource Allocation in Cloud Datacenter

Cloud technologies provide capabilities that can guarantee to the end user high availability, performance and scalability. However, the growing use of IoT technologies and devices, have made the applications not only more computationally intensive, but also data intensive. Because of this, dynamically scaling applications running on clouds can lead to varied and unpredictable results due to highly time-varying workloads distinguishes this new kind of applications. These applications are also often composed of different independent modules that could be easily moved across devices. Automatic scheduling and allocation of these modules is not an easy task, because there could be many conditions that prevent the design of a smart solutions. Thus determining appropriate scaling policies in a dynamic non-stationary environment is non-trivial, as a problem arises concerning resource allocation. Decision making about which resources should be added and removed, when the underlying performance of the resource is in a constant state of flux, becomes an issues. In this work we model both the applications and the infrastructure in order to formulate e Reinforcement Learning problem for automatically find the best configuration for the applications modules, taking into account the environment in which they are placed and the applications already running.
Big data execution time based on Spark Machine Learning Libraries

The paper focuses on exploring the time consumption of supervised and unsupervised models of Apache Spark framework in massive datasets. Big Data analytics has been relevant in the industry due to the need to convert information into knowledge. Among the challenge of big data is the creation of strategies to improve the execution costs of running machine learning models to make a prediction. Apache Spark is a powerful in-memory platform that offers an extensive machine learning library for regression, classification, clustering, and rule extraction. This investigation, from a computation cost perspective, performs different experiments using real datasets. The main contribution of the paper is to compare the execution time of different machine learning models, such as random forests, decision tree, logistic regression, linear support vector machine, and kNN. The present work expects to combine the areas of big data and machine learning, comparing the results with different configurations and the use of the optimization methods, cache and persist. The evaluation experiments show that logistic regression performed the shortest execution time of the Spark MLlib models.
An improvement of a theorem of Carlitz

We improve a result of Carlitz about the number of variables needed for a system of polynomial equations with coefficients in Fq[X] to have non-trivial solutions by considering the p-weight degree of the polynomials. By providing infinite families of polynomials we illustrate that our improvement is significant and, in general, is tight.
IST - Style Transfer with Instance Segmentation

We present a technique to highlight people on images in different styles. Our instance style transfer model (IST) combines an instance segmentation model and a stylization model. Image parts that contain people are found by a segmentation network and then stylized by a style transfer network. The modified image parts are combined with the original image. The resulting image contains people highlighted in different ways, while the background remains unchanged.
Research Data Management Among Researchers in Higher Learning Institutions of Sub-Saharan Africa

Advancement in information and communication technologies has made it easier for researchers to capture and store myriad data at a higher level of granularity. Higher education institutions (HEIs) worldwide are incorporating research data management (RDM) services to enable researchers to work with their data properly. This chapter focuses on creating awareness amongst researchers on how researchers and HEIs can form strategies, design and restrict data management plan (DMP), integrate research data life cycle, and ensure quality data sharing, as well as integrate with developed RDM policies and guidelines to curb challenges prohibiting the practice of RDM in HEIs.
Intrahour Cloud Tracking Based on Optical Flow

Drifting of the clouds may shade the sun which causes significant power output fluctuations in solar power systems. Forecasting the location of clouds could provide sufficient time to smoothen the power output. A method for identification and tracking cloud pixels from a commercial All-Sky camera is presented. Sky images taken every 5s were processed to determine the optimal threshold of different criterion to identify the six classes of clouds as well as clear-sky pixels. Cloud boundaries are tracked using Lucas-Kanade optical flow estimation method. The time when clouds will shade the sun can be forecasted 30s ahead with an acceptable accuracy. The research results demonstrate that the method presented has the potential for tracking different type of clouds with high variability.
Smart Contracts for Research Lab Sharing Scholars Data Rights Management over the Ethereum Blockchain Network

The data sharing is the claim of actual scholars datasets to share and reuse in the future from any domain. The rise of blockchain technology has to increase universally and enhancement in share and reuse of scholars datasets. Despite there are numbers of security management frameworks for share data securely. However, those frameworks is a centralize based to make data share digitally. Its has restriction and owned by third party authority. The access and reuse of research datasets have a variety of issues it misinterpretation. In this aspect, the researcher or publisher has not to share data publicly due to reuse and perceive the risk in a data sharing environment. Preparing and storing data is difficult in contents sharing. To overcome the limitation and restriction, we proposed distributed data sharing management based on blockchain network (peer to peer P2P network). To signify on Ethereum framework, we proposed the case study of data sharing on the Ethereum smart contract platform to achieve the access.
Patterns of media coverage repeated in online abuse on high-profile criminal cases

What relationship do the mainstream media have with online abuse on high-profile criminal cases? This article hopes to make a start at answering this question by examining tweets containing the #McCann hashtag, utilised by a highly engaged community of users to comment on all matters related to the disappearance of British child Madeleine McCann. On #McCann, the child’s parents and other players are often singled out as the perpetrators of her disappearance and other crimes, in a blend of harassment, defamation and insults with conspiracy theories, disinformation and a strong anti-establishment vein typical of the posttruth era. Through an experimental digital ethnography blending elements of content and discourse analysis, this research has observed the #McCann conversation and analysed 500 tweets with the hashtag, observing that some of the most offensive theories posted by users on Twitter reprised themes seen in the mainstream media at the time of the disappearance, which resulted in defamation lawsuits by the McCanns and in complaints about unethical reporting at the Leveson Inquiry. This raises questions about the mainstream media’s responsibility and duty of care towards people they report on in the digital age, and showcases a symbiotic yet diffident relationship between anti-establishment online users and traditional news media.
Gravity Aided Inertial Navigation Path Planning Algorithm based on Underwater Vehicle Constraint and Bezier Curve

In gravity aided inertial navigation, a good path planning, which makes the path pass the area where the gravity changes drastically, in matching area will improve the matching accuracy effectively. The underwater vehicle is restricted by kinematics and other constraints, and cannot move in any direction. However, The path planned by the traditional path planning algorithm is a broken line and is not suitable for actual underwater vehicle navigation. Aiming at underwater vehicle path planning problem, this paper proposes a gravity aided inertial navigation path planning algorithm which is based on vehicle constraints and Bezier curves. The $A^{\ast}$ algorithm is optimized by vehicle constraints and gravity characteristics. The Bezier curve is used to smooth the planned path, making the planned path more suitable for the actual situation. Simulation experiments show that the matching accuracy is effectively improved by the planned path based on vehicle constraints and Bezier curve.
Semantic Analysis in Soccer Videos Using Support Vector Machine

A tremendous increase in the video content uploaded on the internet has made it necessary for auto-recognition of videos in order to analyze, moderate or categorize certain content that can be accessed easily later on. Video analysis requires the study of proficient methodologies at the semantic level in order to address the issues such as occlusions, changes in illumination, noise, etc. This paper is aimed at the analysis of the soccer videos and semantic processing as an application in the video semantic analysis field. This study proposes a framework for automatically generating and annotating the highlights from a soccer video. The proposed framework identifies the interesting clips containing possible scenes of interest, such as goals, penalty kicks, etc. by parsing and processing the audio/video components. The framework analyzes, separates and annotates the individual scenes inside the video clips and saves using kernel support vector machine. The results show that semantic analysis of videos using kernel support vector machines is a reliable method to separate and annotate events of interest in a soccer game.
Open-Ended Continuous Learning of Compound Goals

Continuous learning of increasingly difficult tasks is envisaged to provide learning autonomy to robots. The tasks, however, are often human-designed or require significant external intervention. This article proposes a domain-independent goal generation mechanism to generate goals at different levels of complexity. Using a mobile robot application, we demonstrate how an agent generates compound goals by combining the state space attributes from the states that it has experienced during exploration and uses task-independent reward functions to learn the solutions to those goals. Finally, the whole process is repeated when its environment changes, thus, forming a continuous learning architecture. Results from the experiments show how the agent can combine complementary and contradictory groups of state attributes to form expressive goals and learn behaviors akin to wall following, avoiding obstacle and lane following without any previous knowledge of its environment.
Face Dynamic Modeling Based on Deep Learning and Feature Extraction

Abstract Facial modeling is a key step to model visual effects in special movie effects and computer games. In this paper, a method based on the combination of deep learning and feature extraction is proposed for the modeling of 3D face model. Firstly, the face region is located for the captured face image. And then, the facial feature points are extracted by the landmark algorithm and the Convolutional Neural Network (CNN) is used to classify the facial expressions. Next, a special expression 3D face model is created by the deformation of the standard 3D face model based on the facial expressions classification result. Finally, the 3D face model and the extracted facial feature points are combined to perform personalized adjustment of the 3D model to complete a 3D facial expression animation system. The experimental results show that the proposed method can effectively perform the dynamic 3D face modeling which has high reality.
Agentless Automation Model for Post Exploitation Penetration Testing

In a world where even the most mundane of tasks requires the intervention of the cyberspace, it is particularly important to be aware of the risks and vulnerabilities that come with cyberspace. This has led to the development of a field better referred to as vulnerability assessment and penetration testing (VAPT). There are multiple parts to perform penetration testing, in this paper we will be focusing on the Post Exploitation part. Post exploitation outlines the activities to perform in order to breach the security of a target system by leveraging a discovered exploit. In this paper we propose an agentless model for the automation of post exploitation activities, where agentless means that no third party software is required to be installed on the target machine.
Model-Driven Joint Optimization of Power and Latency Guarantee in Data center Applications

Cloud data centers try to achieve two primary but competing goals for latency-sensitive services: meeting latency service-level objective (SLO) and reducing energy utilization. In this paper, we perform a joint optimization of both goals, such that given a tail latency constraint and request rate, the number of active servers and their corresponding core frequencies which cause the least energy overhead can be derived. Both the server sleep mode and setup delay are taken into account. Based on the assumption that requests arrive following a Poisson process, we provide a numerical method to compute the optimal number of active servers and a closed-form expression for their corresponding core frequency. We propose how the optimization technique can be used for real, bursty, non-Poisson data center traffic.
Probability Representation of Quantum Channels

Using the known possibility to associate the completely positive maps with density matrices and recent results on expressing the density matrices with sets of classical probability distributions of dichotomic random variables we construct the probability representation of the completely positive maps. In this representation, any completely positive map of qubit state density matrix is identified with the set of classical coin probability distributions. Examples of the maps of qubit states are studied in detail. The evolution equation of quantum states is written in the form of the classical-like kinetic equation for probability distributions identified with qubit state.
Design and Implementation of Monitoring Controller for Switch Machine Gap Based on Image Processing

The switch machine is a vital device for operating railway turnouts and plays an important role in safe running of train. Abnormal working state of switch machine will affect safety of railway transportation. Therefore, it is necessary to monitor the working state of switch machine timely. The switch machine gap is an important indication of its working state, due to the complicated external environment, the traditional methods and the image-based methods for monitoring switch machine gap are hard to satisfy the application demands. To solve this problem, this paper proposes a new detection algorithm for switch machine gap. Firstly, the raw image is downsampled and the detection region of the gap is determined by matching the characteristic of the detection column and indication rod in the downsampled image. Secondly, multiple image processing techniques and the RANSAC (Random Sample Consultation) algorithm are used to obtain the gap's pixel distance in the detection region. Finally, the gap's real distance is calculated by the mathematical relationship between its pixel distance and its real distance. Based on the proposed algorithm, a monitoring controller for the switch machine gap is designed and evaluated on the ZD(J)9 switch machine. The experimental results show that the performance of the monitoring controller satisfies the application requirements, and the switch machine gap can be monitored in real-time and accurately, which makes the railway transportation more reliable and safety.
High Resolution 3D Surface Imaging Using Variable Structured Illumination via Lissajous Scanning MEMS Mirror Module

We report a high resolution 3D imaging using variable structured illumination via Lissajous scanning MEMS mirror module. The Lissajous scanning MEMS mirror was operated at pseudo-resonant frequencies within the bandwidth. The structured illumination pattern density was controlled by selecting the greatest common divisor (GCD) of two scanning frequencies. Scanning frequency selection with high GCD provides a coarse illumination pattern. The constant pattern was obtained by modulating a laser beam at the least common multiple (LCM) of the two selected scanning frequencies. A low-Q inner mirror provides wide frequency tuning range as well as enables variable structured illumination. 3D stereoscopic image has been successfully obtained by using a variable structured illumination and stereoscopic camera. Structured illumination effectively enhances 3D imaging resolution. This structured illumination module can provide a new breakthrough for 3D MEMS imaging applications.