Reviewerly/exHarmony huggingface dataset:
Performance Comparison of Recognition System Using I-Vector Based on Different Conditioning Methods

This paper focuses on the analysis of the i-vector paradigm, a compact representation of speaker utterances that is used by most of the state of the art speaker verification systems. The i-vector subspace modeling is one of the recent methods that has become the state of the art technique in this domain. This method largely provides the benefit of modelling both the intra-domain and inter-domain variabilities into the same low dimensional space. In this study, 2656 syllables bio-acoustic signals from 55 species of frog taken from 5 sources of database for frog identification system. Parameters of the system are initially tuned such as GMM component size (16, 32, 64 and 128 Gaussians) and experimented for 3 different conditioning methods which are whitening, Linear Discriminant Analysis (LDA) and Within Class Covariance Normalization (WCCN). This work was mainly motivated by the need to quantify the impact of their steps on the final performance, especially their ability to model data according to a theoretical Gaussian framework. To the end, we assess the effect of the parameter tuned and observe the recognition rate. We observed that, the accuracy for smaller GMM component and i-vector with WCCN outperform others with result 86.67%. These investigations allow highlighting the key points of the approach, in particular a core conditioning procedure that leads to the success of the i-vector paradigm.
Factors in Failure of Achieving LBI KPI: Teachers’ Perception

The Literacy, Numeracy and screening (LINUS LBI) 2.0, an intervention program was first implemented in 2013.It aimed to enhance the rate of English literacy among lower primary ESL learners.However, the number of non-achievers increased every year.The effectiveness of this program depends largely on the teachers, as they play a significant role in implementation.To a noteworthy extent, the teachers' perception which covers the factors leads to the failure of this program will realize the dream of Ministry of Education.Thus, this study was embarked to investigate teacher's perceptions on failure in achieving LBI KPI in grade B national schools in Temerloh District.A mixed method study was adopted to carry out this research with 80 teachers from 36 grade B national schools in Temerloh district.The participants were questioned on unavailability of resources, lack of training and support and other contributing factors.Questionnaire and individual interview were used to gather data.The overall findings of the study were that unavailability of resources and facilities, lack of training and support, pupils' attendance, cooperation of stakeholders and pedagogical knowledge played a huge role in the failure in achievement of LBI KPI.The implication of the study is that Ministry needs to be made aware of these factors in order to reevaluate the program to be successfully implemented and to take into considerations of the factors for other programs implementation as well.
Formulating Knowledge-Based Cloud Identity Selection

Cloud identity is critical in the adoption of cloud computing. Identity providers provide identity as a service for the cloud customers to facilitate the access to resources, but often provide no informed choice of the supply arrangements. The cloud customer requires a say in the elements of service that impact their expectations and quality of service – particularly in relation to the security and privacy of information. In this paper, we provide a novel software solution for addressing the problem. It allows for selecting a most trustable provider, in terms of security, privacy, risk, and standards. The application architecture uses a trust evaluation model as a third-party agency for informing the service user of evidence for and against a choice. A case study and application build demonstrate the feasibility and usefulness of the proposed method in a real cloud environment.
Generalized n-grams

Another idea related to the non-linear construction of n-grams, i.e., using distinct elements or distinct order of their appearance in a text, is the idea of replacing words by their synonyms or by the generalized concepts that correspond to the words according to a certain ontology.
A Multiple Cyclic-Route Generation Method with Route Length Constraint Considering Point-of-Interests

With the spread of map applications, route generation has become a familiar function. Most of route generation methods search a route from a starting point to a destination point with the shortest time or shortest length, but more enjoyable route generation is recently focused on. Particularly, cyclic-route generation for strolling requires to suggest to a user more than one route passing through several POIs (Point-of-Interests), to satisfy the user's preferences as much as possible. In this paper, we propose a multiple cyclic-route generation method with a route length constraint considering POIs. Firstly, our proposed method finds out a set of reference points based on the route length constraint. Secondly, we search a non-cyclic route from one reference point to the next one and finally generate a cyclic route by connecting these non-cyclic routes. Compared with previous methods, our proposed method generates a cyclic route closer to the route length constraint, reduces the number of the same points passing through by approximately 80%, and increases the number of POIs passed approximately 1.49 times.
Development of an Intelligent Foreign Language Multimedia Teaching System Based on the “New Liberal Arts” Paradigm

The main features of the foreign language education in the information age are to realize the modernization, informatization, intellectualization and diversification of the foreign language teaching, and to promote the modernization of the foreign language teaching through the informatization of the foreign language teaching. The new concept of the leapfrogging development in the foreign language teaching should be a hot topic and trend in the study of the foreign language teaching theories and practice in the new era. Under the “New liberal arts” paradigm, students are expected to take part in various activities to promote the transformation of the classroom theoretical knowledge into the specific practical skills and enhance their abilities to solve the practical problems in the real world.
Detection of Lung Cancer by Modified Irregular Tree Structure Bayesian Network Model Based Image Segmentation

Identification of lung cancer at an initial stage is an important and crucial aspect of image processing. Different methods have been used to detect lung cancer at early stage. Medical image segmentation has got an important role in the medical research field. The Computed Tomography (CT) lung images of the patient can be classified into normal or abnormal depending upon the presence and absence of a tumor. Here a method has been presented which will diagnose lung cancer at an early stage using CT scan images. Automatic image segmentation can also be used for interactive segmentation. Initially, the watershed segmentation is used to identify the different regions based on the intensity variation. The relation between the different regions is found by Irregular Tree Structure Bayesian Network (ITSBN) model. Extracted features from different regions are fed into Radial Basis Function Neural Network (RBFNN), which identifies a tumor.
Expert System for Fault Diagnose Based on the Test Equipment for a Missile

The essay introduces the framework and principle of expert system for fault diagnose, analyzes in details the software system and the hardware system, studies the knowledge obtaining, knowledge expressing, knowledge reasoning and other key questions for expert repository separately under the accurate diagnose and fuzzy distinguish circumstances.
Video Based Person Re-Identification Through Selective Knowledge Distillation

The creation and deployment of face recognition models need to identify low-resolution faces with extremely low computational cost. To address this problem, a feasible solution is compressing a complex face model to achieve higher speed and lower memory at the cost of minimal performance drop. Inspired by that, this paper proposes a learning approach to recognize low-resolution faces via selective knowledge distillation in live video. In this approach, a two-stream convolution neural network (CNN) is first initialized to recognize high-resolution faces and resolution-degraded faces with a teacher stream and a student stream, respectively. The teacher stream is represented by a complex CNN for high-accuracy recognition, and the student stream is represented by a much simpler CNN for low-complexity recognition. To avoid significant performance drop at the student stream, we then selectively distil the most informative facial features from the teacher stream by solving a sparse graph optimization problem, which are then used to regularize the fine- tuning process of the student stream. In this way, the student stream is actually trained by simultaneously handling two tasks with limited computational resources approximating the most informative facial cues via feature regression, and recovering the missing facial cues via low-resolution face classification.
[papers] Viewers' Susceptibility to Image Blurs in Watching Ultra-high-definition TV Correlates with Their Dynamic Visual Acuity

A major problem in the subjective evaluation of TV image quality is individual variability among participants. The large individual differences in the susceptibility to image blurring result in imprecise evaluations and loss of power to detect statistically significant differences between experimental conditions. In image quality assessments of traditional televisions, the observers' visual acuities (VA) should be screened. For emerging TV systems with wide field-of-view (FOV), in which objects move quickly relative to the display frame, it is unclear whether screening viewers' VAs is sufficient or not. In the wide FOV TV image quality evaluations, screening of the dynamic visual acuity (DVA) might be effective to control for the individual differences. We show here that a significant correlation between the number of blurred frames reported and the observer's DVA. Therefore, DVA screening is important to avoid an imprecise evaluation and to detect statistical significance between experimental conditions.
node-indri: Moving the Indri Toolkit to the Modern Web Stack

We introduce node-indri, a Node.js module that acts as a wrapper around the Indri toolkit, and thus makes an established IR toolkit accessible to the modern web stack. node-indri exposes many of Indri's functionalities and provides direct access to document content and retrieval scores for web development (in contrast to, for instance, the Pyndri wrapper). This setup reduces the amount of glue code that has to be developed and maintained when researching search interfaces, which today tend to be developed with specific JavaScript libraries such as React.js, Angular.js or Vue.js. The node-indri repository is open-sourced at https://github.com/felipemoraes/node-indri .
Objective Aesthetic Quality Assessment for Photos

Nowadays, the acquisition of digital photos is becoming easier and easier, and the demand for objective assessment of the aesthetic quality of photos is growing, with the development of computer vision and pattern recognition technology, this demand is gradually being explored. After studying the effect of gray interval distribution on the aesthetic quality of photos, an objective assessment method for the aesthetic quality of photographs which considers both global and local features is proposed. For the whole features, the gray interval and line angles features are extracted. The subject areas are extracted from the photos through clarity detection method, then calculate the clarity, lighting and the calculation of rule of thirds in the subject area, as to extract the local features. A total of 18 features including the whole and the local features, using LIBSVM to calculate and train, and to test this method on CUHKPQ image dataset, then we get a good performance. It can be concluded that the combination of 18 features in the objective assessment of photos aesthetic quality can meet the demand of practical application.
Blockchain as a trust layer for more efficient finance market

This article covers a variety of topics on blockchain and the impact that digital encryption and social engineering will have on finance society. In modernized mortgage market, exchange would happen naturally, allowing users to benefit from a web of trust, where parties do not need to be in a direct relationship with each other to reap rewards from referrals that originate from multiple parties. Pseudonymity and honesty are properties that blockchains handle natively, and this is a candidate solution to the “transparency” problem in lending and borrowing, which acts as a trust inhibitor at scale for many interactions.
Proactive Service Management in Disaster Recovery Phase with Non-Destructive Benchmarking Technique

In this paper, we introduce a network management system for autonomous service management that can be effectively utilized in emergency situations. In order to alleviate the problem of a shortage of human resources in disaster situations, approaches for autonomous service management are helpful. Such approaches require knowledge of maximum load capacity of managed server systems so that a benchmark test should be conducted to acquire knowledge. However, in a disaster recovery phase, a configuration of the server system changes frequently due to the occurrence of the secondary disaster or fluctuating user demand. Therefore, a benchmark technique should be established that can be applied even to running server systems. In our proposal, we describe a non-destructive benchmarking technique that can acquire knowledge of the maximum load capacity of the server system without imposing heavy load. A prototype system based on our proposal is designed and implemented for experiments. The results of the experiments confirm that the proposed system can stably operate a service by proactively controlling the amount of computing resource of the server system.
A Printing Image Restoration Method for Multiple Degradation Factors

A printing image restoration method for multiple degradation factors which includes Salt & Pepper noise, Gaussian noise and blur is proposed in this paper. First, considering the noise density of printing image is not very high, a two-step algorithm based on gray-scale range criterion and local difference criterion are used for detecting and removing Salt & Pepper noise. The test results show that the proposed algorithm can achieve good results for restoration of most test images. For the purpose of removing Gaussian noise and image blurring, considering the principle of the edge-reserving smoothing filter, the bilateral filter and guided filter are applied to the image restoration, and on this basis, the second-time guided filter for detail-enhancement is designed and applied. The superiority of the second-time guided filter is verified by evaluating the five image restoration approaches.
Current State of Art

There has been a wide array of domains ranging from fast-moving consumer products to political events where sentiment analysis has numerous applications. Several large companies have their own in-built capabilities in this area. These innumerable applications and interests have been the driving source towards sentiment analysis research. Several social networks and microblogs have provided strong platforms for users’ information exchange and communication. The social networks and microblogs provide trillions of pieces of multimodal information.
An Adversarial Learning and Canonical Correlation Analysis Based Cross-Modal Retrieval Model

The key of cross-modal retrieval approaches is to find a maximally correlated subspace among multiple datasets. This paper introduces a novel Adversarial Learning and Canonical Correlation Analysis based Cross-Modal Retrieval (ALCCA-CMR) model. For each modality, the ALCCA phase finds an effective common subspace and calculates the similarity by canonical correlation analysis embedding for cross-modal retrieval. We demonstrate an application of ALCCA-CMR model implemented for the dataset of two modalities. Experimental results on real music data show the efficacy of the proposed method in comparison with other existing ones.
New Polynomial-Time Algorithm Around the Scaffolding Problem

We describe in this paper an approximation algorithm for the scaffolding problem, which is part of genome inference in bioinformatics. The aim of the problem is to find a maximum weighted collection of disjoint alternating cycles and paths covering a particular graph called scaffold graph. The problem is known to be NP-complete, and we describe further result concerning a special class of graphs aiming to be close to real instances. The described algorithm is the first polynomial-time approximation algorithm designed for this problem on non-complete graphs.
Battling baseline again

With the Government inviting schools to pilot baseline testing materials in Reception at the beginning of the next academic year, the early years sector is being outspoken in its opposition.
&lt;b&gt;High versatility and potential of spatial data analysis with R programmin&lt;/b&gt;&lt;b&gt;g &lt;/b&gt;

R is a free programming language that has been widely used by statisticians and data miners for statistical software development and data analysis. The number of contributed packages for handling and analyzing spatial data has significantly increased over the last 15 years. This paper reviews the potential for spatial data analysis using R programming. The packages related mainly to geographic information system (GIS), such as sp, sf, rgdal, raster, ggmap, tmap, gstat, and RQGIS, are selected for specific tasks along with useful examples. By referring to these examples, new R users can examine how R handles spatial data and what types of problems it can be applied to. R provides several functions that can import, export, and manipulate vector and raster data. For spatial data analysis, R acts as a GIS tool because it can perform GIS procedures effectively from basic to advanced levels. For visualization and mapping, R can produce various 2D or 3D maps from spatial data using either customized or flexible approaches. A user community needs to be developed to enhance the benefits of R programming for the public and private sectors in Japan, particularly in the field of geoinformatics.
Case Studies of Deviance in Social Media

In this chapter, we provide a high-level view of various case studies that include deviant acts Deviant acts , deviant events Deviant events , and deviant groups Deviant groups . This chapter utilizes the concepts, tools, and methodologies that we presented throughout this book to study, analyze, and have a better understanding of many real-world deviant events, acts, and groups. Here, we briefly highlight case studies of the work we conducted during many deviant cyber campaigns that were projected against NATO NATO forces. In addition to these cases, we shed light on the deviant cyber campaigns conducted by the so-called Islamic State, also known as ISIL, ISIS, or Daesh Islamic State ISIS Daesh . We will highlight the major findings of these cases and point the interested reader for more details to other published literature.
Secure Database Handling

Onset of cloud computing allowed various IT services to be outsourced to cloud service providers (CSP). This includes the management and storage of users’ structured or unstructured data called Database as a Service (DBaaS). However, users need to trust the CSP to protect their data, which is inherent in all cloud-based services. Enterprises and Small-to-Medium Businesses (SMB) see this as a roadblock in adopting cloud services because they do not have full control of the security of the stored data on the cloud. Due to these security issues, one possible solution for the data owners is to store their sensitive data in encrypted form. However, to take full advantage of cloud database as service, FHE enabled SQL processing is a promising solution, which will be elaborated in this chapter.
Integration of Images into the Patent Retrieval Process

The importance of using patent drawings during the patent retrieval process has been analyzed before. Empirical research shows that the patent experts explicitly want patent drawings to be included. However, so far the inclusion of the patent images has had limited success. This includes the effectiveness of image processing methods as well as the adoption of images in search systems in a more prominent role. There are varying solutions presented by different entities, but few focused on a user centered integration of patent drawings into the search process. This is the goal of this research. It deals with the integration of images, specifically patent drawings, into the patent retrieval process based on a user-centered approach. This work presents the main theoretical foundations, the methodology as well as the current status of the doctoral project.
PEDAGOGICAL IDEAS OF THE PROMINENT RUSSIAN PUBLIC FIGURE N. V. SHELGUNOV

The article presents the facts of the biography of the prominent Russian public figure, pedagogical journalist of the XIX century Nikolai Vasil’evich Shelgunov (1824-1891), characterizes his socio-political and pedagogical ideas. N. V. Shelgunov is shown as a “thought leader” of the student youth in 1870-1880. The paper reveals the content of the most significant N. V. Shelgunov’s pedagogical works, assesses his contribution to the national pedagogy. He remained in the history of the Russian social pedagogical thought as one of the brightest representatives of its democratic wing.
The role of modern information technologies in improving the quality of education

The article considers the role and importance of new technologies, including information technologies, in increasing students' interest in learning, motivation, expanding their knowledge and improving the quality of their education in the modern information society.
Virtual Prototyping of Area-Based Fast Image Stitching Algorithm

This work presents a virtual prototyping design approach for an area-based image stitching hardware.The virtual hardware obtained from virtual prototyping is equivalent to the conceptual algorithm, yet the conceptual blocks are linked to the actual circuit components including the memory, logic gates, and arithmetic units.Through the proposed method, the overall structure, size, and computation speed of the actual hardware can be estimated in the early design stage.As a result, the optimized virtual hardware facilitates the hardware implementation by eliminating trail design and redundant simulation steps to optimize the hardware performance.In order to verify the feasibility of the proposed method, the virtual hardware of an image stitching platform has been realized, where it required 10,522,368 clock cycles to stitch two 1280 × 1024 sized images.Furthermore, with a clock frequency of 250MHz, the estimated computation time of the proposed virtual hardware is 0.877sec, which is 10x faster than the software-based image stitch platform using MATLAB.
Noisy Detector? Good! Analysis of Trusted-Receiver Scenario in Continuous-Variable Quantum Key Distribution

In CV-QKD the trusted-receiver assumption allows for a significant improvement in terms of key rate and achievable transmission distance. Moreover, as we demonstrate, sometimes detection noise can even be beneficial for the key rate.