Reviewerly/exHarmony huggingface dataset:
Graph fusion based hyperspectral image classification

In hyperspectral image classification, small number of labeled samples versus high dimensional data is one of major challenges. Semi-supervised learning has shown potential to relieve the dilemma. Compared with its supervised learning counterpart, semi-supervised learning exploits both intrinsic structure of labeled and unlabeled samples. In this work, we proposed a graph-fusion based semi-supervised learning method for hyperspectral image classification. More specially, two graphs are constructed from spectral-spatial Gabor features and original spectral signatures, respectively, and then are integrated using an affine combination. Experimental results from an AVIRIS hyperspectral dataset verify the excellent classification performance of our method.
Cross-platform Identity-based Cryptography using WebAssembly

The explosive spread of the devices connected to the Internet has increased the need for efficient and portable cryptographic routines.Despite this fact, truly platform-independent implementations are still hard to find.In this paper, an Identitybased Cryptography library, called CryptID is introduced.The main goal of this library is to provide an efficient and opensource IBC implementation for the desktop, the mobile, and the IoT platforms.Powered by WebAssembly, which is a specification aiming to securely speed up code execution in various embedding environments, CryptID can be utilized on both the client and the server-side.The second novelty of CrpytID is the use of structured public keys, opening up a wide range of domain-specific use cases via arbitrary metadata embedded into the public key.Embedded metadata can include, for example, a geolocation value when working with geolocation-based Identity-based Cryptography, or a timestamp, enabling simple and efficient generation of singleuse keypairs.Thanks to these characteristics, we think, that CryptID could serve as a real alternative to the current Identitybased Cryptography implementations.
Source Code Comprehension and Appropriation by Novice Programmers: Understanding Novice Programmers’ Perception about Source Code Reuse

Software development practices rely extensively on reusing source code written by other programmers. One of the recurring questions about such practice is how much programmers, acting as users of somebody else’s code, really understand the source code that they inject it in their programs. The question is even more important for novices, who are trying to learn what programming is and how it should be practiced on a larger scale. In this paper we present the results of an ongoing research using a semiotic approach to investigate how novice programmers reuse source code, and how, through messages inscribed in the source code of the programs they write or reuse, they communicate, implicitly or explicitly, what such source code "means" to them and others. We carried out three studies with novice programmers, and results suggest that source code reuse may impact what programmers take their source code to mean.
Study of UGS Target Recognition Method based on Recurrent Neural Network and Peak Value Region Extraction

In order to improve the accuracy of Unattended Ground Sensors (UGS) for human activity recognition, this paper proposes a target recognition method based on Recurrent Neural Network (RNN) and peak value region extraction. The ground seismic signal is obtained from the seismic sensor, and the peak value region of the signal is extracted and trained by Recurrent Neural Network (RNN) to form the target recognition network model. Simulation results show that the proposed approach has an obvious improvement in the accuracy of human target recognition compared with the existing seismic target recognition methods.
QoE Prediction Model with Personalized Parameters in IPTV Domain

With the rapid development of multimedia services, such as Internet Protocol Television (IPTV), Quality of Experience (QoE) has increasingly attracted the attention of service providers. In this paper, we propose a QoE prediction model to map users QoE, which combines IPTV viewing records and personalized parameters. We simplify the model by identifying the effective features through data mining technology. The simulations demonstrate that the proposed model can achieve high accuracy of QoE prediction.
Knowledge Organization, Data and Algorithms: The New Era of Visual Representations

This article shows how visual representations have progressively taken the lead over classical language-based models of knowledge organization (KO). The paper adopts a theoretical and historical perspective and focuses on the consequences of the changes in the volume of data generated by data production on the KO models. Until now, data visualization tools have been used mainly by researchers with expertise in textual data processing or in computational linguistics. But now, these tools are accessible to a greater number of users. Thus, there are new issues at stake for KO, other professions and institutions for gathering data that contribute to defining new standards and KO representations.
Energy Efficient Green Consolidator for Cloud Data Centers

In today’s era of computing, cloud computing is most efficient medium to deliver information and services to people and businesses. The key challenge is to serve such kind of huge requirements. Cloud computing is currently most promising technology to decrease overall infrastructure cost by virtualization technology. It not only reduces huge infrastructure cost but increase reliability and availability of services. On other hand considering huge energy requirements for cloud data centers, it is very important to achieve green cloud computing for reducing energy consumption of cloud data center. In this paper we discussed the approach of dynamically consolidating the VMs to save energy of cloud data center. This paper implements a novel computing approach which will create negligible performance overheads during and after VM live migration. So it will achieve seamless live migration which makes this approach superior and implementable than most of current VM consolidation approaches. It will save nearly 45 % of energy of cloud data centers.
Object Detection Boosting using Object Attributes in Detect and Describe Framework

Different objects have unique attributes, visual appearances and physical properties which help human visual system to recognize them better. But can object attributes help improve the object detection performance in computer vision? To answer this very question, we carry out extensive experimentation in this research work and claim that, indeed, object attributes improve the object detection performance significantly. We train feature pyramid networks to learn deep convolutional features for objects and their attributes. When used in combination with each other to infer bounding boxes and class scores for objects, these convolutional features show that object detection boosts significantly. We present a new method to boost the performance of object detection using object attributes in Detect-and-Describe (DaD) framework. We explain multiple approaches for boosting of object detection using their attributes. In these approaches, the convolutional features of attributes are merged with convolutional features of bounding box and class labels using different feature merging techniques to boost object detection. To report the performance of object detection boosting using DaD framework, we train our experimental models on aPascal train split and report performance on aPascal test split. Our results show that object attributes can help boost mean average precision (mAP) of object detection as significant as 2.68%.
Longest property-preserved common factor: A new string-processing framework

We introduce a new family of string processing problems. Given two or more strings, we are asked to compute a factor common to all strings that preserves a specific property and has maximal length. We consider three fundamental string properties: square-free factors, periodic factors, and palindromic factors under three different settings, one per property. In the first setting, we are given a string x and we are asked to construct a data structure over x answering the following type of online queries: given a string y, find a longest square-free factor common to x and y. In the second setting, we are given k strings and an integer 1<k′≤k and we are asked to find a longest periodic factor common to at least k′ strings. In the third one, we are given two strings and we are asked to find a longest palindromic factor common to the two strings. We present linear-time solutions for all settings. This is a full and extended version of a paper from SPIRE 2018.
A Lion Optimization Algorithm for an Efficient Cloud Computing With High Security

Cloud computing is a developing paradigm to provide reliable and resilient infrastructure allowing the users (data owners) to store their data and the data consumers (users) can access the data from cloud servers.This paradigm reduces storage and maintenance cost of the data owner.However, cloud data storage still gives rise to security related problems.In case of shared data, the data face both cloud-specific and insider threats.In this work, we propose lion optimization algorithm (LOA) optimized centrality measure fragmentation and replication of data in the cloud for optimum performance and security that consider both security and performance issues.The Lion Optimization Algorithm (LOA) is introduced.Special lifestyle of lions and their cooperation characteristics has been the basic motivation for development of this optimization algorithm.In our methodology, Mating that refers to deriving new solutions and Territorial Defense and Territorial Takeover intend to find and replace the worst solution by new the best solution.Likewise, we divide a data files and replicate the fragmented data over the cloud nodes using LOA optimized centrality measures.Each of the nodes stores only a single data fragment of a particular data file that guarantees that even in case of a successful attack, no significant information is revealed to the attacker.We also proposed a fused technique which is used for the encryption and decryption to enhance the high security for the data in the cloud environment.This can be implemented after the LOA performance and also protects the information in the cloud efficiently.We also compare the performance of the LOA methodology and fused encryption and decryption techniques with other standard replication schemes.The higher level of security with improved performance overhead was observed.
Efficient and Secure Communication of Quantum Image Steganography using 3-way’s of Authentication

The concept of Cryptography and Steganography is not a new technique, even though it is ancient but not static. In Cryptography and Steganography the new emerging techniques are done using Quantum Mechanics and Quantum Physics. In this paper, mainly works on how to use Quantum Technique in cryptography system and in field of steganography. Steganography is a process of hiding information of the different types of media files (audio, text, image and video) in another media files. In this paper, mainly the quantum steganography works for two imagery files. The quantum secret image will be hiding in the Quantum Cover image. In this process of hiding of information or image, need to select the pixels. The Qbits used to store the secret image are two most and least significant Qbits. The secret image will be scrambled first, using Arnold’s cat map technique, and then will be embedded into Quantum Cover picture. The Stegno Image is sufficient to extract the secret information. The NEQR quantum representation model is used for converting image into quantum states. The simulation result and analysis demonstrates that the proposed scheme has good visibility and high embedding capacity.
An LP-Based Approach for Goal Recognition as Planning

Goal recognition aims to recognize the set of candidate goals that are compatible with the observed behavior of an agent. In this paper, we develop a method based on the operator-counting framework that efficiently computes solutions that satisfy the observations and uses the information generated to solve goal recognition tasks. Our method reasons explicitly about both partial and noisy observations: estimating uncertainty for the former, and satisfying observations given the unreliability of the sensor for the latter. We evaluate our approach empirically over a large data set, analyzing its components on how each can impact the quality of the solutions. In general, our approach is superior to previous methods in terms of agreement ratio, accuracy, and spread. Finally, our approach paves the way for new research on combinatorial optimization to solve goal recognition tasks.
Analisis Perbandingan Klasifikasi Support Vector Machine (SVM) dan K-Nearest Neighbors (KNN) untuk Deteksi Kanker dengan Data Microarray

Cancer is a disease that can cause human death in various countries. According to WHO in 2018, cancer causes 9.6 million human deaths worldwide. Globally, about 1 in 6 deaths is due to cancer. Therefore, we need a technology that can be used for cancer detection with high acuration so that cancer can be detected early. Microarrays technique can predict certain tissues in humans and can be classified as cancer or not. However, microarray data has a problem with very large dimensions. To overcome this problem, in this study use one of the dimension reduction techniques, namely Partial Least Square(PLS) and use Support vector Machine (SVM) and K-Nearest Neighbors as a classification method, which will be used to compare which is better.The system built was able to reach 98.54% in leukemia data with PLS-KNN, 100% in lung data with KNN, 66.52% in breast data with PLS-KNN, and 85.60% in colon data with PLS- SVM. KNN is able to get the best in three data from four valued data.
Logical Instrumentalism and Concatenation

Logical pluralism is the theory that there is more than one right logic. Logical instrumentalism is the view that a logic is a correct logic if it can be used to fruitfully pursue some deductive inquiry. Logical instrumentalism is a version of logical pluralism, since more than one logic can be used fruitfully. In this paper, I will show that a logical instrumentalist must accept linear logic as a correct logic, since linear logic is useful for studying natural language syntax. I further show that this means that the logical instrumentalist must accept a wide range of connectives, in particular concatenation. I end by explaining why this is a feature rather than a bug.
The Chicago sound show at the smart museum of art, the University of Chicago

The Chicago Sound Show mounted by the Smart Museum of Art at the University of Chicago invited a number of local artists to produce site-specific sound installations that revealed both the possibilities and limitations of the medium.
THE PRECISION OF GOOGLE EARTH MAP ANALYSIS WITH THE COORDINATES OF IGS STATIONS

Abstract. This paper identifies the location of global IGS reference station on Google Earth map, and raises a method of identifying the location and analysis the precise position accuracy. The Google Earth map supports WGS-84 geodetic coordinates. Acquire the WGS-84 Precise Geodetic Coordinates of the Global IGS reference station on IGS Website. The IGS reference station is labeled on the Google Earth map exactly in KML markup language. Using Google Earth ruler tool, calculated the map distance, ground distance and azimuth angle of the Google Earth map position and the labeled position from the precision IGS reference station. Identifying the deviations between the exactly point location of IGS station and the recognized location of Google Earth. About 1,054 IGS stations which covering all over the world are using as samples for data statistics. With a series of statics graph, analysis the accuracy and recognition rate. It shows that the recognition rate is 71.8% and the average accuracy is about 4.38 meters. The accuracy from 2m to 4m occupied 18.76%; the accuracy from 4m to 6m occupied 41.0%; the accuracy from 6m to 8m occupied 6.34%; the accuracy from 8m to 10m occupied 3.70% etc. The accuracy in China region is about 1.5–2 meters and it’s reach up to 1 meter in the cities of the United States, Europe and Japan etc. So that Google Earth map could be used for high-precision positioning services, such as, field reconnaissance, survey network design and the description of station etc.
Autonomous cars in science fiction

We view autonomous cars just like the ones in science fiction, and that could be a problem.
On the Cultivation of Autonomous Learning Ability of College English

&lt;p&gt;In college education, more emphasis is placed on the subjectivity and freedom of students, so weak self-control often affects the final learning effect in English learning. Learning foreign language needs patience, so how to cultivate college students' self-learning ability is one of the problems worth thinking about in the current education. The cultivation of students' autonomous learning ability will directly affect the teaching efficiency of teachers, so in the process of teaching, teachers should consciously cultivate students' autonomous awareness of English learning, so as to improve students' self-control ability and optimize the effect of English learning. This paper is mainly based on the current situation of college students' autonomous learning ability, and gives corresponding optimization strategies, in order to better enhance students' enthusiasm in English learning.&lt;/p&gt;
Automatic assignment of labels in Topic Modelling for Russian Corpora

The main goal of this paper was to improve topic modelling algorithms by introducing automatic topic labelling, a procedure which chooses a label for a cluster of words in a topic.Topic modelling is a widely used statistical technique which allows to reveal internal conceptual organization of text corpora.We have chosen an unsupervised graph-based method and elaborated it with regard to Russian.The proposed algorithm consists of two stages: candidate generation by means of PageRank and morphological filters, and candidate ranking.Our experiments on a corpus of encyclopedic texts on linguistics has shown the advantages of labelled topic models for NLP applications.
ELECTRONIC LEARNING TOOLS AS A MEANS OF INCREASING THE EFFECTIVENESS OF INCLUSIVE EDUCATION

Today, the education of young people with disabilities is given special attention. Therefore, inclusive education becomes the main focus of teaching such children. It is based on the use of learning technologies allowing for all children to obtain knowledge in ordinary schools, taking into account the peculiarities of their development. This opportunity is provided by the active use of electronic learning tools. Therefore, there is a need to create and implement integrated electronic educational resources that will have the focus of tasks on a specific subject for the purpose of teaching children with special needs. The authors indicate a comparative analysis of thematic sites as one of the leading methods in the study of this problem; the limitedness of use of electronic educational resources in inclusive education was revealed on the basis of this method. The main results of the study include the development of an electronic educational resource on the topic "Number Systems" and methodological recommendations on the organization of the educational process with its use in inclusive education. Their significance lies in the fact that they can be recommended for use by teachers, and can also become a tool for self-study of this topic. The results of the research fill in the existing gaps in the theory of general education, contribute to the solution of questions about the motivation of teaching computer science to special needs children
Robust quantization of deep neural networks

We studied robust quantization of deep neural networks (DNNs) for embedded devices. Existing compression techniques often generate DNNs that are sensitive to external errors. Because embedded devices may be affected by external lights and outside weather, DNNs running on those devices must be robust to such errors. For robust quantization of DNNs, we formulate an optimization problem that finds the bit width for each layer minimizing the robustness loss. To efficiently find the solution, we design a dynamic programming based algorithm, called Qed. We also propose an incremental algorithm, Q* that quickly finds a reasonably robust quantization and then gradually improves it. We have evaluated Qed and Q* with three DNN models (LeNet, AlexNet, and VGG-16) and with Gaussian random errors and realistic errors. For comparison, we also evaluate universal quantization that uses equal bit width for all layers and Deep Compression, a weight-sharing based compression technique. When tested with increasing size of errors, Qed most robustly gives correct inference output. Even if a DNN is optimized for robustness, its quantizations may not be robust unless Qed is used. Moreover, we evaluate Q* for its trade off in execution time and robustness. In one tenth of Qed’s execution time, Q* gives a quantization 98% as robust as the one by Qed.
Storage and Communication Security in Cloud Computing Using a Homomorphic Encryption Scheme Based Weil Pairing

With introduction of smart things into our lives, cloud computing is used in many different areas and changes the communication method. However, cloud computing should guarantee the complete security assurance in terms of privacy protection, confidentiality, and integrity. In this paper, a Homomorphic Encryption Scheme based on Elliptic Curve Cryptography (HES-ECC) is proposed for secure data transfer and storage. The scheme stores the data in the cloud after encrypting them. While calculations, such as addition or multiplication, are applied to encrypted data on cloud, these calculations are transmitted to the original data without any decryption process. Thus, the cloud server has only ability of accessing the encrypted data for performing the required computations and for fulfilling requested actions by the user. Hence, storage and transmission security of data are ensured. The proposed public key HES-ECC is designed using modified Weil-pairing for encryption and additional homomorphic property. HES-ECC also uses bilinear pairing for multiplicative homomorphic property. Security of encryption scheme and its homomorphic aspects are based on the hardness of Elliptic Curve Discrete Logarithm Problem (ECDLP), Weil Diffie-Hellman Problem (WDHP), and Bilinear Diffie-Helman Problem (BDHP).
The Digital Mindset as an Approach to Education forthe Millenial Generation

Education in the millennium requires strategies and adjustments in the preparation and implementation of the teaching and learning process.The situation that surrounds the world of education also changes because of the strong influence of digitalization in all fields.Therefore, one of the educational strategies for the millennium is the need to develop digital narratives in the education process itself.Because the millennials rely heavily on digital technology, millennial education achieved through a pattern of technological determinism.An education system is a design in a teaching approach that adapts to learning styles and media technology so that it becomes more familiar and following the current culture.Digitizing the education system, as intended in Digital Narrative, will be in vain without a change in the mindset of all stakeholders in the education world.Therefore, in the millennial generation learning effort, the first step needed is to develop an educational system instrument, including learning strategies, which depart from the digital mindset.The next step is to integrate all these educational instruments into a technology-based education system.This article was prepared to discuss and criticize millennial education strategies with a digital narrative approach.The discussion was conducted qualitatively by referring to some theories and libraries.