Reviewerly/exHarmony huggingface dataset:
Resource Optimization in Quantum Access Networks

In this paper, low-complexity channel allocation methods are proposed for quantum access networks. We consider dense-wavelength-division-multiplexing passive optical network (DWDM-PON) structures that enable users to exchange secret keys, in addition to data transmission. We consider two main sources of noise in such systems, Raman scattering and four-wave mixing, and examine optimal channel allocation in different scenarios. We also take into account finite-key effects in the quantum key distribution (QKD) channels. Our numerical results show that the proposed wavelength assignment methods can significantly enhance the secret key generation rate of users.
Essential element-region driven model in image recognition

The performance of image recognition depends on not only algorithms but also image factors, especially the essential elements and regions. This paper presents an essential element-region (EssER) recognition method. Firstly, we propose a series of contrast experiments to estimate the differences of the elements which include part, structure, texture and optical field. And the result shows that the key parts are influential in recognition tasks. Then for each input image, we apply a backtracking approach modeled by Convolutional Neural Networks (CNN) to calculate the essential regions in images. When only feeding the essential region instead of the original image into convolutional networks as input, the recognition result shows the performance decreases limitedly. At last, we propose a knowledge-guided attention network (KGAN) based on the EssER model to solve the problem of fine-grained classification and compared to traditional methods. Our model improves the performance at 4.7%, 6.0% than original baseline neural network with close computational cost on Stanford Cars, CUB-2011. Our method can reach the state-of-the-art performance in fine-grained classification.
A Recursive Bayesian Model for Extreme Values

In this paper, we propose a new approach for analyzing extreme values such as large losses in financial markets. Our goal is to compute the predictive distribution of extreme events that are clustered in time. We apply a stochastic parametrization of the generalized extreme value distribution to model the asymptotic behavior of the block-maximum and derive a Rao-Blackwellized particle filter. This reduces the parameter space, and we derive a concise, recursive solution. Using the filter, the predictive distribution, conditioned on the past data, is computed at each sample-time. We introduce a new risk-measure, pVaRα, that is a more robust estimate of the true nature of value-at-risk, and illustrate our results using both simulated data and actual stock market returns from 1928-2017.
Mobile Application Development Methodologies Adopted in Omani Market: A Comparative Study

Popularity of mobile phones and huge growing for mobile applications make developers in need for flexible software process, which can deal with many challenges facing the mobile app development process. These challenges include: volatility of requirements, strong user involvement, development time tightness, process simplicity, and production of valuable software in low cost. This research study investigates the current mobile app development approaches adopted in Omani market and provides a comparison between existing methods. The results reveal that Agile approach is the most popular model for mobile software engineering in Omani, as it naturally fits most of the applications required in this market. The study also discusses various agile process models such as Scrum, XP, Lean, DSDM, and others. It is concluded that XP model is the most preferable model used by Omani developers due to its dynamic and adaptive nature for different mobile app processes. The study provides also a series of recommendations for mobile app developers which should help in selecting the most appropriate method that suits the targeted market sector
Integrating Geochemical Anomaly and Remote Sensing Methods to Predict Oil- and Gas-Bearing Areas in the Yanchang Oil Field, Ordos Basin, China

Oil seepage is one of the most important characteristics of hydrocarbon formation, and understanding oil seepage is crucial for oil-gas exploration and the assessment of petroleum resources. Remote sensing and geochemical methods have the same material and theoretical bases for extracting oil and gas information from underlying strata and the identification of media features. As an emerging exploration method, hyperspectral remote sensing is efficient compared with traditional geochemistry because it is a finer, and sometimes more directly quantitative method for determining the specific mineral anomaly content. Hence, the use of both methods together is important. This paper describes the analysis of hyperspectral remote sensing data and the extraction of abnormal index information, including the level of carbonate alteration and the content of acidolytical hydrocarbons, pyrolysis hydrocarbons, headspace gas, and ferric and ferrous ions. The two methods have mutual authentication, and they are complementary and are useful in oil-bearing areas. When these methods are integrated, the acidolytical hydrocarbon index is the most effective geochemical index and is better at characterizing the oil field distribution than other indices. Also, hydrocarbon geochemical anomalies occurring in oil fields generally show continuous distribution points and are consistent with oil reservoirs. Consequently, a 3D model was established to comprehensively utilize hyperspectral remote sensing and geochemical data to determine the distribution of petroleum reservoirs efficiently as well as to delineate oil- and gas-bearing prospects. There is great potential for determining oil- and gas-bearing fields through the integration of hyperspectral and geochemical data.
A Reference Architecture for Ontology Engineering Web Environments

Ontology authoring, maintenance and use are never easy tasks, mostly due to the complexity of real domains and how they dynamically change as well as different background possessed by modellers about methodologies and formal languages. However, although the needs for ontologies are well-understood, not less important is to provide editing tools to manipulate and understand them. In this context, this work proposes and documents a reference architecture for such tools running in web environments. Moreover, it provides the rationale for boosting the collaborative development of a novel tool based on this architecture, named crowd. Previous surveys reveal that few Web-based ontology engineering environments have been developed and in addition, almost all of them are mere visualisers, with limited graphical features and lacking inference services
OPIEC: An Open Information Extraction Corpus

Open information extraction (OIE) systems extract relations and their arguments from natural language text in an unsupervised manner. The resulting extractions are a valuable resource for downstream tasks such as knowledge base construction, open question answering, or event schema induction. In this paper, we release, describe, and analyze an OIE corpus called OPIEC, which was extracted from the text of English Wikipedia. OPIEC complements the available OIE resources: It is the largest OIE corpus publicly available to date (over 340M triples) and contains valuable metadata such as provenance information, confidence scores, linguistic annotations, and semantic annotations including spatial and temporal information. We analyze the OPIEC corpus by comparing its content with knowledge bases such as DBpedia or YAGO, which are also based on Wikipedia. We found that most of the facts between entities present in OPIEC cannot be found in DBpedia and/or YAGO, that OIE facts often differ in the level of specificity compared to knowledge base facts, and that OIE open relations are generally highly polysemous. We believe that the OPIEC corpus is a valuable resource for future research on automated knowledge base construction.
Generation of Image Caption Using CNN-LSTM Based Approach

Image captioning is gaining attention due to the recent developments in the deep neural architectures. But the gap between semantic concepts and the visual features is a major challenge in image caption generation. In this paper we have developed a method to use both visual features and semantic features for the caption generation. We discuss briefly about the various architectures used for visual feature extraction and Long Short Term Memory (LSTM) for caption generation. An object recognition model has been developed to identify the semantic tags in the images. These tags are encoded along with the visual features for the captioning task. We have developed an Encoder-Decoder architecture using the semantic details along with the language model for the caption generation. We evaluated our model with standard datasets like Flickr8k, Flickr30k and MSCOCO using standard metrics like BLEU and METEOR.
Distributed storage system based on permissioned blockchain

Readily available blockchain technologies allow to improve reliability and availability of existing cloud applications.
A Secure Framework for File Encryption Using Base64 Encoding

File security is the practice of preventing unauthorized access, use, disclosure, disruption, modification, inspection, or destruction of files stored on a database. Nowadays, security has become a very challenging issue, as the number of attacks on cyber security has increased which has affected the confidentiality, authentication, integrity, and availability of files. Here, we are assuring confidentiality and integrity aspects. To cope up with today’s security demands, we propose a secure method to provide security to all types of file formats using AES (Rijndael) and Base64 encoding, which will be later stored on MySQL database server in cloud. After thorough research, we did not find a framework which provides dynamic encryption. The model consists of three layers. First layer performs encoding and encryption. The critical aspect of using Base64 is for encoding binary data into ASCII text which can be encrypted, stored, and transmitted. Hereafter, the encoded data is encrypted by AES (Rijndael) algorithm. Second, the encrypted file is stored into MySQL Server, and finally in third step processing is done through cloud, which protects the critical information from larceny, data leakage, and deletion, thereby providing a total of three security layers.
Auto-Summarization: A Step Towards Unsupervised Learning of a Submodular Mixture

We introduce an approach that requires the specification of only a handful of hyperparameters to determine a mixture of submodular functions for use in data science applications. Two techniques, applied in succession, are used to achieve this. The first involves training an autoencoder neural network constrainedly so that the bottleneck features have the following characteristic: the larger a feature's value, the more an input sample should have an automatically learnt property. This is analogous to bag of-words features, but where the “words” are learnt automatically. The second technique instantiates a mixture of submodular functions, each of which consists of a concave composed with a modular function comprised of the learnt neural network features. We introduce a mixture weight learning approach that does not (as is common) directly utilize supervised summary information. Instead, it optimizes a set of meta-objectives each of which corresponds to a likely necessary condition on what constitutes a good summarization objective. While hyperparameter optimization is often the bane of unsupervised methods, our approach reduces the learning of a summarization function (which most generally involves learning 2n parameters) down to the problem of selecting only a handful of hyperparameters. Empirical results on three very different modalities of data (i.e., image, text, and machine learning training data) show that our method produces functions that perform significantly better than a variety of unsupervised baseline methods.
To the Discussion on Academic Philosophy: What, How, and What for

The article discusses how to implement common cultural competencies in the course of teaching philosophy. The authors believe that the optimal content of the course is the history of philosophy, which, when being creatively taught, can become a school of critical thinking and education and give direction to those who are trying to consciously shape their world view and educate their mind. The historical-philosophical approach organically incorporates the problematic one, makes it possible to discuss current issues of the day, based on classical examples of the philosophical culture. Some techniques for the development of cognitive skills that correspond to the strategies of liberal education are considered. The authors draw attention to the benefits of the work of a student with a philosophical text, the need for the development of written and oral speech, organically associated with the development of thinking. In the conditions of increasing bureaucratic regulation of the educational process and limited time, it is important to preserve the space of academic freedom, to build teaching taking into account the individual-personal contact with this or that audience. University philosophy is regarded as a valuable experience in overcoming the limitations of everyday consciousness, it seems to be a source of ideas that provides support in a situation of epistemological uncertainty and value relativism. The article covers both philosophical and theoretical, and applied aspects of the problem of teaching philosophy in higher education institution.
A multi-shape hybrid symmetric encryption algorithm to thwart attacks based on the knowledge of the used cryptographic suite

This research paper introduces a novel symmetric encryption algorithm that provides the confidentiality security service without leaking enough information about the exact encryption algorithm or other cryptographic parameters to anyone except the legitimate receiver. This is achieved by utilizing a set of sound existing symmetric encryption algorithms, namely, the Advanced Encryption Standard,MARS,RC6, Serpent and Twofish. The identity of the algorithm used to secure the current message, along with other cryptographic parameters (e.g. mode of operation, key length), are hidden from anyone except the legitimate receiver. These parameters are shared securely between communicating parties by utilizing some of the symmetric key bits. The cipher suite in addition to the encryption key, changes systematically and securely with every new message, leaving the opponent with no enough information to launch any effective attack. The algorithm performance is tested, and the overall performance is close to the performance of conventional encryption with a delay of about 16.52 CPU Cycles per message. From a security point of view, the possibility of cryptanalysis is diminished significantly, as no party except communicating parties has any knowledge of the cryptographic parameters in use.
Framework of Sequence Chunking for Human Activity Recognition Using Wearables

Human activity recognition (HAR) is the main research area in ubiquitous computing, and most of existing approaches are based on the frameworks of sliding window segmentation and dense labeling. However, existing frameworks have some problems. For example, sliding window segmentation will cause the problem of label inconsistency, and dense labeling cannot model relationship between activities explicitly. In our paper, we propose a new framework to deal with the problems caused by these frameworks, in which HAR is treated as a sequence chunking problem and divided into the subtasks of segmentation and labeling. The purpose of the segmentation is to segment a raw sequence into different chunks that represent the corresponding activities respectively, and labeling is used to predict the corresponding label for each chunk based on segmentation results. We propose an encoder-decoder model based on convolutional neural networks to implement the proposed framework. The encoder segments a sequence to chunks based on BIO labels, and the decoder treats a chunk as a basic unit to predict the corresponding label. We conduct experiments and show that the proposed model achieves the state-of-the-art performance on both Opportunity and Hand Gesture datasets.
Does Pair Programming Pay Off?

Pair programming means two developers working together closely on the same software development task on a single computer. It is not obvious whether this is an efficient practice: While a pair can often work faster and better, it also occupies two team members. We have studied this for several years by analyzing recordings of dozens of actual industrial pair programming sessions. Our observations suggest that pair programming that pays off will typically (need to) exhibit high process fluency. They also suggest that it is going to pay off when the pair members’ knowledge is highly complementary.
Densifying Assumed-Sparse Tensors

Neural machine translation - using neural networks to translate human language - is an area of active research exploring new neuron types and network topologies with the goal of dramatically improving machine translation performance. Current state-of-the-art approaches, such as the multi-head attention-based transformer, require very large translation corpuses and many epochs to produce models of reasonable quality. Recent attempts to parallelize the official TensorFlow “Transformer” model across multiple nodes have hit roadblocks due to excessive memory use and resulting out of memory errors when performing MPI collectives. This paper describes modifications made to the Horovod MPI-based distributed training framework to reduce memory usage for transformer models by converting assumed-sparse tensors to dense tensors, and subsequently replacing sparse gradient gather with dense gradient reduction. The result is a dramatic increase in scale-out capability, with CPU-only scaling tests achieving 91% weak scaling efficiency up to 1200 MPI processes (300 nodes), and up to 65% strong scaling efficiency up to 400 MPI processes (200 nodes) using the Stampede2 supercomputer.
Research on Art Education Digital Platform Based on Big Data

In the context of the era of big data, many universities have put our courses on the network platform, art education is also actively developing online open courses. Students take advantage of these courses to study online and achieve global synchronization of education. The future online learning platform can make up for the lack of face-to-face communication and guidance from teachers, and even generate a qualitative and quantitative complementary online learning self-evaluation platform to improve the deficiencies of the original teaching system. In this regard, education concept and paradigm of art discipline will also be changed: digital media technology will replace the original paper and electronic media, electronic textbooks, digital lectures, online schools and so on will provide personalized learning approaches for teenagers in the 21st century, and promote the diversified development of education experience.
Bias CRLB in Sine Space for a Three-Dimensional Sensor

As bias estimation methods are developed, it becomes necessary to obtain the bound on bias estimation for more complex bias and sensor models. Three-dimensional (3-D) sensors, such as radars commonly used in applications, contain both scale and additive biases in sine space which result in a nonlinear estimation problem that may have poor observability and accuracy depending on the geometry of the sensors. By converting the sine space and range measurements to Cartesian using an unbiased conversion, it is possible, via creation of pseudomeasurements, to eliminate the need to estimate the target's state thereby reducing the sensor bias estimator complexity. The present paper evaluates the Cramér-Rao lower bound (CRLB) for estimating scale and additive biases in sine space for 3-D sensors and compares it with a maximum likelihood formulation implemented via iterated least squares, which is thereby shown to be statistically efficient. Additionally, the importance of measurement diversity is investigated with respect to the CRLB.
Zonohedral Approximation of Spherical Structuring Element for Volumetric Morphology

Performing dilation and erosion using large structuring elements can be computationally slow – a problem especially pronounced when processing volumetric data. To reduce the computational complexity of dilation/erosion using spherical structuring elements, we propose a method for approximating a sphere with a zonohedron. Since zonohedra can be created via successive dilations/erosions of line segments, this allows morphological operations to be performed in constant time per voxel. As the complexity of commonly used methods typically scales with the size of the structuring element, our method significantly improves the run time. We use the proposed approximation to detect large spherical objects in volumetric data. Results are compared with other image analysis frameworks demonstrating constant run time and significant performance gains.
vIIS

Training and assessment of a new worker is the most vital part of any vocational industry. The landscape of jobs and work is changing rapidly, thanks to the emerging new technology and the advancement of knowledge in the scientific fields. This technological and scientific revolution presents an opportunity in the creation of new industries and occupations, enhanced productivity and quality of work life, and the potential for more people to participate in the workforce. But these come at risk and disadvantage of an increased cost of training as well as lack of proper training in a few industries. In this paper, we propose to build a vIIS framework - A Vocational Interactive Immersive Storytelling system that uses storytelling in an Interactive virtual environment to train and assess a worker a new skill. The major advantage of this vIIS system is that it provides constant feedback in an engaging immersive virtual reality environment and has better memory retention and recall of the trained task. For assessment, we intend to collect data such as task completion time, accuracy, error rate, qualitative feedback and also measure transfer learning.
Practice of Teaching Reform of Network Security Technology Course under the Background of Internet+

Network security technology is an important professional course for computers and related majors.It has the characteristics of wide content, pre-requisite knowledge, fast curriculum knowledge update and strong practical practice.In this paper, the teaching content, teaching methods and assessment methods of the course have been reformed for the problems existing in the teaching of network security technology courses.Practice has shown that reforms have stimulated students' interest in learning and improved their ability to apply knowledge.