Reviewerly/exHarmony huggingface dataset:
Editing k-Nearest Neighbor Reference Set by Growing from Two Extreme Data Points

The k-nearest neighbor rule (k-NN) requires significant computational time and memory during recall. Hence it is difficult to apply k-NN in real-time applications. Several approaches have attempted to speed it up by editing the reference set. Most of them achieved the primary goal of reducing the reference set size, thus reducing recall time. However, many of them not only incurred the overhead of significant editing time, albeit only once, but also suffered from a decrease in accuracy in ensuing recall mode. In this paper, we set out to reduce the reference set without sacrificing classification accuracy and without incurring a significantly long editing time. One artificial and one real world data sets were used to compare the performance of the proposed method with that of existing methods from the literature. The research results indicate that the proposed method achieved the objective and thus merits further development.
Channel analysis of free-space quantum key distribution with untrusted node

Various optical quantum key distribution protocols have been proposed and examined to implement a global quantum communication system both in fiber-based network and in free-space systems. Employing a satellite as a quantum relay is a possible solution to increase the secure covered distance, which is available for sharing secure keys between two remote ground stations. In this paper, combining with the idea of measurement device independent quantum key distribution, a modified free-space quantum communication protocol is proposed to build a QKD network with untrusted nodes, where the parties on the ground send quantum signals to the untrusted satellite relay that perform a joint measurement on the incoming signals. Our results demonstrate that though the modified protocol suffers higher link attentuation than the entanglement-based quantum communication protocol owing to higher effects of atmospheric turbulence for uplink, our modified protocol provides secure quantum communications with untrusted relays. Furthermore, the complex and non-space-qualified sources remain on the ground in our scheme , which makes our scheme more practical for bridging a seamless connection form ground to space.
Theoretical development of multi-domain command and control

We are developing an agent-based model that allows us to explore control and distributed machine learning concepts in a systems-of-systems framework. The model captures the complex interactions between vehicles with very different operational tempos (such as satellites and unmanned aerial vehicles (UAVs)) and a variety of environmental elements (communication towers, objects of interest, etc.). Treating the model as a complex adaptive system, we can explore issues of controllability and observability, such as the constraints needed to maintain multi-vehicle formations under diverse conditions, and scaling questions, such as the data rates among vehicles and control centers under diverse system parameters.
A comprehensive evaluation of audio-visual behavior in various modes of interviews in the wild

Behavioral interviews are a crucial part of any candidate selection process. Automatic assessment of communication skill in such interviews is a key issue in Social Computing. With the evolution of technology, interactive online platforms have enabled conducting interviews online either in the form of video interviews (asynchronous interviews without an interviewer) or online face-to-face interviews without the need of collocation of both interviewer and interviewee. In this work, we automatically assess the communication skill score of job applicants giving real interface-based interviews and online face-to-face interviews with a model trained on the video interviews collected in a controlled environment. Web-based platforms are developed to collect interviews asynchronously and face-to-face. The web-based platform for face-to-face is Skype-like that allows recording and streaming the audio-visual content of both interviewer and interviewee. We address this problem by capturing the audio-visual behavior of the participant in various modes. To this end, we have collected 251 video interviews (asynchronous) in controlled environment for training, 38 real video interviews in the wild and 45 face-to-face interviews in the wild for evaluation. This work is novel in evaluating the interviews in the wild, with the model trained in a complementary environment. Results obtained indicate that models trained on data collected in controlled environment generalize for the interviews conducted in real uncontrolled environment, with diversity in people and audio recording hardware.
GPU Sparse Ray-Traced Segmentation

This paper introduces a real-time region growing segmentation algorithm, designed for graphics processing units (GPUs), which labels only a fraction of the input elements. Instead of searching locally around each element for strong similarity, like state-of-the-art segmentation and pre-segmentation methods do, the proposed algorithm searches both locally and remotely, using a unique ray tracing-based search strategy, which quickly covers the segmentation search space. The presented algorithm fully exploits the parallelism of the GPUs, sparsely segmenting high-resolution images (4K) in real-time on low range laptops and other mobile devices, approximately 5× times faster than the state-of-the-art simple linear iterative clustering (SLIC). While this paper demonstrates the results with images, the algorithm is trivially modifiable to work with input sets of any dimension. In contrast to the state-of-the-art real-time GPU methods, this algorithm doesn't require additional merging steps, as pre-segmentation methods do, and it produces complete segmentation. Additionally, post-segmentation optional stages for complete labeling and region merging on the GPU are also provided, although they are not always necessary.
Simultaneous high-speed measurement of 3D surface shape and temperature

Pattern projection-based three-dimensional (3D) measurement systems are widely used for contactless, nondestructive, and full-field optical 3D shape measurements. 3D reconstruction is performed between one camera and the projector or between two cameras by detection and triangulation of corresponding image points. In order to record fast processes, such as people in motion or even explosions and crashes, we have recently developed a 3D stereo sensor consisting of two high-speed cameras and a GOBO projection-based high-speed pattern projector. The system, which works in the visible wavelength range (VIS), enables us to successfully reconstruct 3D point clouds of athletes in action, crash tests, or airbag inflations. However, as such processes usually exhibit local temperature changes, simultaneously measuring the surface temperature would be of great benefit. Therefore, we have extended our existing high-speed 3D sensor by including an additional high-speed longwave infrared (LWIR) camera, which detects radiation in the spectral range between 7.5 and 12 &mu;m. The setup allows us to map the recorded temperature data onto the reconstructed 3D points. We present the design of this novel 5D (three spatial coordinates, temperature, and time) sensor and the process of simultaneously calibrating the VIS cameras and the LWIR camera in a common coordinate system. Moreover, we show first promising measurements of an inflating airbag and a basketball player conducted at a frame rate of 1 kHz.
A FIR Digital Filter for Pulse Signal Processing

The purpose of digital filtering the signal is to eliminate the glitches and distortions that appear in some abnormal situations. This requires a good accuracy and stability of the filter because the FIR filter mainly uses a non-recursive structure. Therefore, in theory and practical finite precision calculations, the error is relatively small, and the design is also more flexible. The FIR filter does not contain feedback and is faster at the same order, and it can achieve real-time processing of blood cell count signals to ensure the detection speed of the system. And the FIR filter is a linear phase causal system that can satisfy the phase requirement of the signal in further processing. And the FIR filter is a linear phase causal system that can satisfy the phase requirement of the signal in further processing.
Uncertain Evidence for Probabilistic Relational Models

Standard approaches for inference in probabilistic relational models include lifted variable elimination (LVE) for single queries. To efficiently handle multiple queries, the lifted junction tree algorithm (LJT) uses a first-order cluster representation of a model, employing LVE as a subroutine in its steps. LVE and LJT can only handle certain evidence. However, most events are not certain. The purpose of this paper is twofold, (i) to adapt LVE, presenting LVE $$^{evi}$$ , to handle uncertain evidence and (ii) to incorporate uncertain evidence for multiple queries in LJT, presenting LJT $$^{evi}$$ . With LVE $$^{evi}$$ and LJT $$^{evi}$$ , we can handle uncertain evidence for probabilistic relational models, while benefiting from the lifting idea. Further, we show that uncertain evidence does not have a detrimental effect on completeness results and leads to similar runtimes as certain evidence.
The Role of Convolutional Neural Networks in the Automatic Recognition of the Hepatocellular Carcinoma, Based on Ultrasound Images

Hepatocellular carcinoma (HCC) represents the most frequent form of liver cancer. It evolves from cirrhosis, as the result of a restructuring phase at the end of which dysplastic nodules appear. HCC is the main cause of death in people affected by cirrhosis. The most reliable method for HCC diagnosis, the golden standard, is the needle biopsy, but this is an invasive technique, dangerous for the human body. We develop computerized methods, based on ultrasound images, in order to perform automatic diagnosis of HCC in a noninvasive manner. In our previous research, we elaborated the textural model of HCC, based on classical and advanced texture analysis methods, in combination with traditional classification techniques, which led to a satisfying accuracy. We aim to improve this performance in our current and further research. In this article, we analyzed the role of specific deep learning techniques concerning the automatic recognition of HCC from ultrasound images. We chose the Convolutional Neural Networks (CNN), this method being well known for its performance in the field of image recognition. Thus, CNN are based on artificial neural networks, they also performing image processing operations, such as inner convolutions. In order to evaluate the newly adopted technique, we assessed the classification accuracy achieved in the cases of distinguishing HCC from the cirrhotic parenchyma on which it had evolved, respectively when differentiating HCC from the hemangioma benign liver tumor. We compared the accuracy of CNN with our previous results, based on texture analysis methods and it resulted that CNN yielded better recognition rates than the classical texture analysis techniques, respectively comparable with those provided by the advanced texture analysis methods. However, further improvements will be necessary, which we mention within the last section of this article.
The Evolution of Electronic Education in English Language in Russian Higher Education Institutions

Modern technologies are creating new opportunities for higher education and life-long learning. Electronic education changes the landscape of higher education: institutions around the world have to respond to the growing demand for digital learning in both formal and non-formal education. Online universities have already appeared worldwide, the online university model is noted as one of five models in the project on the export of Russian education. Russian online education is developing not so long ago - since 2013. The Russian government has adopted a number of projects aimed at the development of online education, especially in higher education, including the project “Development of the export potential of the Russian education system” (2017), aimed to expand digital education market and export of Russian educational programs at the international level. The authors studied the data from Russian universities that had launched online courses in foreign languages, developed criteria for assessing these educational products, the success of their implementation, as well as the main strategies for promoting online courses in foreign languages. In this article infrastructure, regulatory, information, organizational and marketing tools are used.
Automatic Initialization of 3D Active Models for Lobe Segmentation in Thorax CT Images

In several applications involving medical image analysis, the process of image segmentation, be it automatic or manual, is a present task. An accurate segmentation provides information for inspection of anatomical structures, to identify diseases and monitoring of its progress, and even for surgical planning and simulation. Thus, the role of image segmentation is essential in any medical image analysis system. Among the segmentation techniques in the literature, the active models technique is one of the most popular approaches of the last two decades and has been widely used in medical image segmentation, achieving considerable success. Active models that are applied on three-dimensional applications are called Active Surfaces Methods (ASM), which has been widely used in the segmentation of 3D objects, evolving under the influence of their energy to converge to the desired surface. So, knowing how essential surface extraction is to obtain an accurate segmentation, this paper proposes an automatic initialization method for ASM applied in lobes segmentation in CT images. The proposed method has achieved significant results, with overall accuracy rate of 94%.
Vision based pointing error estimation for mobile eye-tracking system

Translating environmental knowledge from bird’s eye view perspective, such as a map, to first person egocentric perspective is notoriously challenging, but critical for effective navigation and environment learning. Pointing error, or the angular difference between the perceived location and the actual location, is an important measure for estimating how well the environment is learned. Traditionally, errors in pointing estimates were computed by manually noting the angular difference. With the advent of commercial low-cost mobile eye trackers, it becomes possible to couple the advantages of automated image processing based techniques with these spatial learning studies. This paper presents a vision based analytic approach for calculating pointing error measures in real-world navigation studies relying only on data from mobile eye tracking devices. The proposed method involves three steps: panorama generation, probe image localization using feature matching, and navigation pointing error estimation. This first-of-its-kind application has game changing potential in the field of cognitive research using eye-tracking technology for understanding human navigation and environment learning and has been successfully adopted by cognitive psychologists.
INFORMATIONAL BALANCE METHOD FOR DISPERSIVE AND NON-DISPERSIVE REMOTE SENSING SIGNALS FOR DETECTING OIL CONTAMINATION OF SOIL IN COASTAL AREAS USING HYPERSPECTROMETERS

The article presents a method for the detection of soil oil contamination in coastal areas using onboard hyperspectrometers - the signals informational balance method of dispersive and non-dispersive probing, used respectively for basic and validation measurements.Remote sensing is a potentially promising method for detecting various types of coastal environment pollution, including pollution by petroleum hydrocarbons. The general principle of remote presentation is the obligatory carrying out of remotely validation obtained results.The principles and methods of carrying out validation measurements are diverse and can be classified according to the following features: 1) synchronization of basic and validation measurements; 2) autonomy validation measurements; 3) select the height of the validation measurements.The proposed method operational algorithm is described. A variant of joint and synchronous basic and validation measurements using a hyperspectrometer installed on a low-altitude aircraft is considered.The mathematical model of the used method is given. Based on the information balance condition, an expression is obtained for determining the signal-to-noise ratio in the system in order to further calculate the correlation between the narrow-band oil index and the broadband signature-based sign of the heavy metals anomalous concentration.
Differential Evolution Algorithms Used to Optimize Weights of Neural Network Solving Pole-Balancing Problem

Differential evolution (DE) has been successfully used to solve difficult optimization problems. Every year, novel DE algorithms are developed to outperform the previous versions. The JADE is a famous DE algorithm using a mutation strategy current-to-pbest and the adaptation of control parameters. The SHADE has been developed to eliminate some bottlenecks of the JADE, especially its tendency to a premature convergence. The performance of these algorithms has been demonstrated on various benchmarks. The goal of this work is to compare the performance of the selected DE algorithms which are used to optimize the weights of the artificial neural network solving the pole-balancing problem.
Image Retrieval Uses SVM-Based Relevant Feedback for Imbalance and Small Training Set

There are many image retrieval systems that use the SVM-based relevance feedback approach to reduce the gap between low-level visual features and high-level semantic concepts. However, the performance of these systems is low due to the lack of two issues: first, the imbalance of the training set. Second, the size of the training set is very small compared to the dimension of the feature. In this paper, we propose the image retrieval method, SVMITS (SVM-based relevant feedback for class imbalance training set), to overcome the above limitations. Our proposed method solves the first problem through resampling techniques and the second is through reducing the number of dimensions of the features. Finally, we incorporate resampling techniques, dimension reduction, and ensemble-based learning techniques to improve the accuracy of image retrieval using SVM-based relevant feedback. To illustrate the effectiveness of our proposed method, we provide empirical results on a database of 10800 images.
Research on the Construction of the Multivariate English Teaching Mode Combined with the Cloud Technology

At present, there are various teaching modes adopted by universities in China, but none of them has achieved satisfactory results. Each teaching method has its own advantages and disadvantages, and cannot be applied to all parts of our country. According to the local resources and the cultural characteristics in various regions, different universities adopted different teaching modes. Therefore, there is an urgent need to create a teaching mode that can be applied to the college English teaching in the most widely possible range.
The Reform and Practice of Computer Public Teaching in University

At present, Chinese secondary schools have set up basic computer courses. After the end of the course, students have to take a computer academic level examination. This shows that the students have a certain computer basis before they go to university. In view of this, how to reform the teaching of computer public courses in universities is worth exploring. It is found that the basic and application level of the college freshmen vary greatly. In order to teach students in accordance with their aptitude, a uniform teaching content, a uniform teaching schedule and a single teaching form can not be used in the computer teaching. This article mainly takes a university as an example to promote the reform of the teaching of the university computer public course. Through the reform of the examination method, the improvement of teaching methods and the optimization of the teaching content, the reform of the teaching of the public computer is explored. The practice shows that the reform is effective.
Symmetry-Aware Face Completion with Generative Adversarial Networks

Face completion is a challenging task in computer vision. Unlike general images, face images usually have strong semantic correlation and symmetry. Without taking these characteristics into account, existing face completion techniques usually fail to produce a photo-realistic result, especially for the missing key components (e.g., eyes and mouths). In this paper, we propose a symmetry-aware face completion method based on facial structural features using a deep generative model. The model is trained with a combination of a reconstruction loss, a structure loss, two adversarial losses and a symmetry loss, which ensures pixel faithfulness, local-global contents integrity and symmetrical consistency. We conduct a dedicated symmetry detection technique for facial components and show that the symmetrical attention module significantly improves face completion results. Experiments show that our method is capable of synthesizing semantically valid and visually plausible contents for the missing facial key parts from random mask. In addition, our model outperforms other methods for detail completion of facial components.
CS-IBC: Cuckoo search based incremental binary classifier for data streams

The act of classifying data streams is widely studied in the literature over the last decade. Incremental or progressive learning strategies are adapted to classify the data streams by many research contributions in recent literature. The contemporary affirmation of recent literature indicate that issues like timeliness, linearity of computational complexity, incremental update of the classifier, and concept drift adaptation in data stream classification are still significant constraints. And there is a need for an algorithm to provide good classification performance with a reasonable response time and maximal classification accuracy. In order to arrive at this, Cuckoo Search Based Incremental Binary Classifier (CS-IBC) has been devised in this manuscript. The contributions of the CS-IBC is to define class labels from training data and fasten the class search through bio inspired strategy called “CUCKOO Search”. A periodical update of the classifier is also proposed to update the classifier if a set of new labelled records are given. The CS-IBC is tested on KDDCUP data that contains records, which are labelled as attack prone or normal. Metrics such as classification error rate, latency of the classification strategy and classification accuracy deterioration were assessed to estimate the scope of the CS-IBC as binary classifier. The experimental study indicates that the proposed CS-IBC is robust and scalable.
Computing the multi-string BWT and LCP array in external memory

Indexing very large collections of strings, such as those produced by the widespread next generation sequencing technologies, heavily relies on multi-string generalization of the Burrows–Wheeler Transform (BWT): large requirements of in-memory approaches have stimulated recent developments on external memory algorithms. The related problem of computing the Longest Common Prefix (LCP) array of a set of strings is instrumental to compute the suffix-prefix overlaps among strings, which is an essential step for many genome assembly algorithms. In a previous paper, we presented an in-memory divide-and-conquer method for building the BWT and LCP where we merge partial BWTs with a forward approach to sort suffixes. In this paper, we propose an alternative backward strategy to develop an external memory method to simultaneously build the BWT and the LCP array on a collection of m strings of different lengths. The algorithm over a set of strings having constant length k has O(mkl) time and I/O volume, using O(k+m) main memory, where l is the maximum value in the LCP array.