Reviewerly/exHarmony huggingface dataset:
Prediction of Thalassemia Based on SAELM Hybrid Algorithm

Thalassemia is a serious hereditary disease, which cannot be cured. It's valuable to study on thalassemia. The input weights and biases of Extreme Learning Machine (ELM) can be randomly initialized, which sometimes cannot get the better result. So a new algorithm named SAELM based on thalassemia is proposed by using the strong global optimization ability of Simulated Annealing (SA) algorithm, which can find the best weights and biases of ELM algorithm. Simulation results illustrated that the SAELM is better than ELM in the main evaluation indices, so SAELM algorithm can be used as a medical reference index in the screening of thalassemia.
Two-Stage Model Compression and Acceleration: Optimal Student Network for Better Performance

Convolutional neural networks(CNNs) have demonstrated its advanced ability in many fields. However, the calculations and parameters of the advanced CNNs are unaffordable for exiting intelligence devices. This problem mostly hinders the practical application of CNNs. In this paper, we propose a two-stage model compression and acceleration(abbreviated as STCA) method to solve this problem. The STCA is composed of supernet and subnet, the supernet is a large pre-trained neural network with superior performance, and the subnet is obtained by pruning the supernet. More specifically, the overall process of STCA includes the search and train stage. In the search stage, we first search and remove the unnecessary channels of the supernet based on channel importance pruning to get the pruned network. Then the weights in the pruned network are initialized to get the subnet. During the training stage, the subnet will learn from the training data and the supernet together. We will extract the knowledge from the supernet and transfer it to the subnet to improve the performance of the subnet. We have proved the effectiveness of STCA by implementing extensive experiments on several advanced CNNs (VGGNet, ResNet, and DenseNet). All subnet trained by STCA achieve significant performance, especially when selecting the VGGNet-19 as the supernet, the subnet only with about 1/10 parameters and 1/2 calculations achieves 94.37% and 74.76% accuracy on the CIFAR-10 and CIFAR-100 dataset, which are 0.84% and 2.31% higher than the accuracy of the supernet.
An Approach Using Linked Data for Open Tourist Data Exploration of Thua Thien Hue Province

In five domains of Digital Transformation, data plays an important role as a key tangible asset for value-creation. Nowadays, data is continuously generated everywhere, even the unstructured data is increasingly becoming usable and valuable. The challenge of data is turning it into valuable information and unleash its potentials for Digital Transformation. Motivated by Open Data initiatives in over the world which took the lead for publishing government data for public use, Thua Thien Hue Open Data Initiative (TTHODI) (Thua Thien Hue Open Data Portal: http://opendata.thuathienhue.gov.vn/ ) took first steps in public its local government data to create data-driven benefits of community and enterprise in the region. On this trend, Linked Data is naturally coming at the right time for us as an ideal standard for representing our open datasets thanks to its expressiveness in form of triples. Furthermore, with huge potentials for linking our open datasets to existing datasets in Linked Open Data Cloud for enriching our data cloud for an added value to TTHODI. This paper presents our approach using Linked Data for Tourist Open Data as a case study and also shows the first results of using Linked Open Data for TTHODI in Tourism.
Statistical Machine Translation, Ripple Down Rules and Hidden Markov Model for Burmese Romanization

Burmese Romanization is the phonetic translation of Burmese (Myanmar) language into their phonetic Latin script. Burmese Romanization is aimed the reader who is unfamiliar with the Burmese script to pronounce the Burmese language reasonably accurately. We developed a 15K Burmese Romanization parallel corpus based on the practical usage of several Romanizations of native Myanmar people. The experiments were performed using different SMT approaches (PBSMT, HPBSMT, OSM), Ripple Down Rules (RDR) and Hidden Markov Model (HMM) approaches to the task of Burmese Romanization. The results show that the OSM approach achieves the highest performance in Burmese to Romanized Burmese direction. On the other hand, HMM approach gives the highest results in Romanized Burmese to Burmese.
Research on New Model of Digital Copyright Service Based on Registration

In the current open network environment, with the widespread use and dissemination of digital content, digital copyright management and service methods have been paid more and more attention, and have become a current research hotspot. Based on the research and analysis of the existing copyright management and service models, a new digital copyright service model based on registration is proposed for the purpose of better realizing copyright management and infringement monitoring. Based on some of the core techniques such as copyright information registration, efficient extraction and comparison of media fingerprint, and blindly embedding and extraction of copyright identifier, a new model of network-based digital media copyright service based on registration, fingerprint comparison, and identifier tracking can be implemented to solve a series of market order chaos problems caused by online media piracy and infringement.
Using Collaborative Physics Technologies to Integrate Enterprise Cloud Services

The front of the global foundry industry growth slow times, strong competitors joined Samsung Electronics, Global Foundries, IBM, and other outstanding companies, especially to manufacture components companies are outsourcing services. This study investigates the emerging cloud services systems, analyzes the future trends for foundry collaboration that will impact physics factors and import gray relational theory, the SWOT survey results in the case company, to enhance foundry industry competitiveness and the advantages for industry reference.
Research on the Fast Solution Method of Pilots’ Head Attitude

Abstract Aiming at the problem of poor accuracy and poor real-time performance in the process of head pose calculation in pilot cabin simulation, a high accuracy and fast algorithm for head pose calculation is proposed. On the basis of traditional EPNP algorithm was improved, and combining with POSIT algorithm’s speed advantage, select face five key feature points as virtual control points, according to the mapping relationship between space and virtual control point coordinates, establish a system of linear equations, then the internal camera parameters is calculated using Zhengyou Zhang camera calibration method, finally using POSIT algorithm of calculating to get the head rotation matrix and translation vector, by conversion from the head of Euler angle in real time. The experimental results show that compared with DLT, EPNP, POSIT and other traditional methods, the proposed method has higher accuracy, better real-time performance and better robustness in solving pilots’ head attitude in continuous video.
A Novel Sentence Scoring Method for Extractive Text Summarization

Saliency based sentence ranking is a basic step of extractive text summarization. Saliency of a sentence is often measured based on the important words that the sentence contains. One of the drawbacks of such saliency-based sentence extraction method is that it extracts mainly the sentences related to the most common topic in the document. But the input document may contain multiple topics or events and the users may like to see in the summary the salient information for each different topic or event. To alleviate such problem, diversity-based re-ranking approach or sentence clustering-based approach is commonly used. But re-ranking or sentence clustering makes the summarization process slow. In this paper, we propose a novel summarization method that computes the score of a sentence by combining saliency and novelty of the sentence. Without using any re-ranker or clustering of sentences, the proposed approach can automatically take care of the diversity issue while producing a summary. We have evaluated the performance of the system on DUC 2001 and DUC 2002 benchmark single document summarization datasets. Our experiments reveal that it outperforms several existing state-of-the-art extractive summarization approaches.
Blockchain Platform Taxonomy

Now blockchain technology continues to develop – lots of new projects and platforms are being created. Some of these technological solutions are based on approved platforms, but other developers use their own new model types. The statistics show that blockchain technology is one of the future-proof technologies like artificial intelligence, Internet of Things and nanotechnology. However, the lack of standardisation in the field of the modern technologies can cause negative consequences for developers and financial institutes. The purpose of this study is to create the common open classification of existing blockchain platforms. Classification is a hierarchical structure consisting of six key characteristics: token, transaction, block, framework, network and communication. Each characteristic consists of groups (qualitative features), including sets. Such approaches allow to standardise the process of forming the blockchain platform architecture. Moreover, it is possible to create various scenarios of the network changes.
Basics of Scalar Random Processes

The chapter contains elements of theory of random processes required for time series analysis and for understanding its results. The time series discussed in this book belong to the class of stationary random processes, which means that their statistical properties averaged over an ensemble of realizations of the process do not depend upon the time origin. If averaging over an ensemble of realizations is equivalent to averaging over time of any single realization, the process is ergodic. If a stationary process is Gaussian, it is also ergodic. In our research, we mostly have just one sample realization (one time series) of finite length and by extending the properties estimated from a single time series to the entire process under the study, we assume that the process is ergodic. The most important characteristics of any time series include the covariance function and spectral density, which describe the time series properties in the time and frequency domains, respectively. The two stationary discrete random processes typical for climatology and other disciplines are white noise and Markov chains. Examples of the true and estimated correlation functions and spectral densities including their sampling variability are briefly discussed for simulated time series of length $$ N = 100 $$ .
Montale <i>in</i> e <i>per</i> Elena Salibra

Elena Salibra (1949–2014) is increasingly to be considered a major poet of her time. The present paper situates her alongside Eugenio Montale in order to determine his influence on her poetry. The correspondence between Montale and Salibra is intellectual and poetic, reflecting Salibra’s dual orientation towards the 20th century giant as both an academic and a poet. The paper beings with an outline of recent criticism of Salibra, and proceeds directly to the texts themselves. The totality of Salibra’s oeuvre is brought under consideration, noting that even if her academic concentration was dominantly on Pascoli and Gozzano, Montale nonetheless represents an enormous presence in her work. The paper proceeds by close analysis to consider first the paratesto and then focuses on specific quotations, implicit or explicit, of Montale in Salibra. The close linguistic reading of Salibra and Montale demonstrates, in the argument of this paper, that the younger poet found an excellent master in the elder.
Brazilian Forest Fire Analysis: An Unsupervised Approach

Forest fire is one of the most dangerous natural hazards of planet earth now. Here presenting an approach where it has been trying to analyze the danger of fire for the forests of Brazil. The dataset contains data from 1998 to 2017 for all the states of Brazil where a forest fire has been caught throughout the year. An unsupervised approach like—K-Means clustering, Fuzzy C-Means, and Apriori algorithm was used to do so. This is a large dataset containing 6454 unlabeled data, to build a model with them K-Means clustering seems helpful. It tries to build subgroups (clusters) of similar data points from a large group. Fuzzy C-Means is also an unsupervised algorithm and it’s working process is similar to K-Means clustering. By using K-Means clustering, Fuzzy C-Means, and Apriori method the regions which are in risk of fire danger were detected.
Exploring Generative Adversarial Networks for Entity Search and Retrieval

With the continuous growth of Web users and the variety of information needs, search engines face new challenges to satisfy users and provide them with accurate answers. Yet, it is remarkable that the majority of queries is related to real-world entities. For example, in Web search, queries such as: Microsoft founder or Bohemian Rhapsody are more frequent types of requests. This has lead to increase interest in entity search and retrieval. In its early steps, entity retrieval (ER) systems are benefited from the advances in ad hoc document retrieval and used existing models to retrieve entities considering similarities to documents. However, with the availability of structured data represented in knowledge bases, recent researches aim at building ER-oriented systems and studying entity representation and its characteristics. In this paper, we apply generative adversarial networks (GANs) to ER. Recently, GANs have shown outstanding results in some information retrieval tasks including Web search and question answering. We apply two variants of GANs to data from the benchmark test collection based on DBpedia-Entity (v.2). We use features from standard retrieval models such as language model, bm25 model, and the recent fielded sequential dependence model (FSDM). Within the experiments, we also explore performances on different types of queries and compare our results to some of the successful learning-to-rank (LTR) models.
Designing Unconventional Communication Systems for Long-Distance Relationships Using the Flexi Card Game: A Card-Based Design Toolkit

In this demo paper, we present the Flexi Card Game – a card-based generative design toolkit to support designers and non-designers with different backgrounds in participatory structures in the development of unconventional communication systems, with a particular focus on supporting couples in long-distance relationships.
Enhancing Secure Digital Communication Media Using Cryptographic Steganography Techniques

Data hiding technique is the process of anti-computer forensic for making the data difficult to accessible. Steganography is merging texts, files, or other multimedia files within another texts, files, or other multimedia files to reduce the visible attack and it is an approach of data hiding technique. Cryptography is changing the readable text to illegible information. This paper presents about secure communication media which is used in transferring text, multimedia or relevant digital file between sender and receiver securely. To have securing communication media, the media required to reduce the possible threats and vulnerabilities. Therefore, transferred media is main thing to consideration for having communication system firmly. Data hiding techniques are used to improve the security of communication media using salt encryption. This paper is proposed the methodology to develop the secure communication media using combination of cryptography and steganography techniques by describing experimental results from difference technical analysis.
Ensembles of optimal binary sequences of length 2<sup>2n</sup>

Abstract The problem of constructing ensembles of optimal binary sequences of arbitrary period is of great practical importance and urgency, as the electromagnetic spectrum is becoming increasingly congested. The solution to this problem will allow designers of asynchronous wireless communication systems to overcome this congestion, by using ensembles of sequences tailored to their system requirements. This work aims to develop a systematic method for constructing ensembles of sequences with optimal correlation with arbitrary period. Optimality can be strict, or asymptotic, as the period tends to infinity. The problem is of fundamental importance in information theory, with wide-ranging applications to wireless communications, navigation, radar, digital watermarking, and related fields.
Boltzmann Exploration for Deterministic Policy Optimization

Gradient-based reinforcement learning has gained more and more attention. As one of the most important methods, Deep Deterministic Policy Gradient (DDPG) has achieved remarkable success and has been applied to many challenging continuous scenarios. However, it still suffers from instable training on off-policy data and premature convergence to a local optimum. To deal with these problems, in this paper, we combine Boltzmann exploration with deterministic policy gradient. The candidate policy is represented by a Boltzmann distribution, and updated by Kullback-Leibler (KL) projection. By introducing the Boltzmann policy, the exploration is encouraged to effectively prevent the policy to collapse quickly. Experimental results show that the proposed algorithm outperforms DDPG on most tasks in MuJoCo continuous benchmark.
Novel Image Steganography Algorithm Using Two Hidden Thresholds

This paper describes a novel image steganography algorithm, with the central idea being the use of two hidden thresholds. By implementing the described method, various types of information (sub-images, numbers, symbols) can be hidden inside a usual image. The article presents how to apply this procedure for a gray image: after recovering the two threshold values, hidden inside the least significant bit of each of the first 16 pixels on the edges, an image can be processed by comparing the gray level to the threshold levels (making sure the gray level is between the thresholds) and thus reveal the secret message. The algorithm can be extended to different types of data and image representations and could also suggest another method of embedding metadata inside an image, such as the time of download, the original website from where it was downloaded or information on its author, etc.
A Mapping Data Prediction Algorithm based On Generalized Regression Neural Network

According to the historical accessing data of the edge routers, the data prediction is done based on neural network. The prediction results can give some hints to the data updating processes in the caches of border routers, and then the request latency can be reduced where applicable. In this paper, a data prediction algorithm based on generalized regression neural network (DP_GRNN) is proposed. Besides, the simulation experiment using the real data is provided to valid the DP_GRNN algorithm.
Accuracy Estimation for an Incrementally Learning Cooperative Inventory Assistant Robot

Interactive teaching from a human can be applied to extend the knowledge of a service robot according to novel task demands. This is particularly attractive if it is either inefficient or not feasible to pre-train all relevant object knowledge beforehand. Like in a normal human teacher and student situation it is then vital to estimate the learning progress of the robot in order to judge its competence in carrying out the desired task. While observing robot task success and failure is a straightforward option, there are more efficient alternatives. In this contribution we investigate the application of a recent semi-supervised confidence-based approach to accuracy estimation towards incremental object learning for an inventory assistant robot. We evaluate the approach and demonstrate its applicability in a slightly simplified, but realistic setting. We show that the configram estimation model (CGEM) outperforms standard approaches for accuracy estimation like cross-validation and interleaved test/train error for active learning scenarios, thus minimizing human training effort.
Troubled IS/IT projects: searching for the root causes

Purpose For decades, troubled information systems (IS)/information technology (IT) projects have continued to lose billions of dollars all over the world. The purpose of this paper is to look for the root causes of this widespread and important phenomenon. Design/methodology/approach This paper begins with a concise survey of empirical research and shows that a significant proportion of project failures are derived from the behavior of managers rather than from objective or technical obstacles. Although researchers have made significant efforts to improve the professional conduct of managers, the situation ameliorated only slowly. This raises the issue of a possible hidden cause lying behind the perceived determinants of failure; the authors, therefore, look for the root-cause of this broad noteworthy phenomenon and apply the techniques typical of “root-cause analysis”. Findings IS/IT managers handling troubled projects worldwide have nothing in common except for a similar cultural basis. As students, they learned the fundamental concepts of informatics, and it is, therefore, natural to hypothesize a specific cause and effect relationship between these lessons and the ineffective behaviors of managers. A careful analysis shows that computing theories are narrow, self-referential and abstract, and this negatively affects the conduct of managers, who have broad, practical responsibilities. Practical implications The cultural causes of runaway projects require advanced theoretical research on the foundations of computer science to create a comprehensive and consistent construct that can support the manifold duties of IS/IT project leaders. A future theoretical structure will underpin innovative courses in universities and professional organizations, making leaders more aware of their roles and more capable of managing them. Originality/value In general, when a problem is traced to its origin, it can be eradicated. This work offers evidence that the root causes of troubled projects lie in the management culture, and therefore, the professional preparation of managers needs to be improved so to remove the present difficulties in IS/IT projects.
An improved-Viterbi Based Chinese Sequence Labeling Enhancement Method

The performance of supervised-learning based Chinese sequence labeling(CSL) is usually limited by the quality of the training corpus. In order to reduce the lack of labeling corpus and make full use of the context information of corpus, For improving the performance of CSL, this paper gives a method that re-decode the sequence using the context labeling results. For re-decoding, we propose an improved-Viterbi algorithm that can utilize the context sequence's labeling results and the order of key information in the results. Experimental results demonstrate that our method can effectively optimize the labeling results for some kinds of Chinese plain text, especially for some domain-specific tasks such as drama scene labeling and resume key information extraction.