Reviewerly/exHarmony huggingface dataset:
Thematic spectral remote sensing data in land covers' monitoring over test region

In this work a project for the implementation of remote sensing research activities for the acquisition of new knowledge and encouraging the participation of the PhD students of Remote Sensing Systems /RSS/ Department at SRTI-BAS in these activities is presented. The goal of the project is collecting data through spectral measurements for land cover monitoring in a selected test region in Bulgaria and create an open access spectral database. The first task of the work to collecting spectral measurements data is related to the methodology of acquiring in-situ spectral data of land covers in test site. Methodology follows the next steps of 1) collecting samples and additional information; 2) laboratory and field spectrometric measurements; 3) spectral data verification. For the implementation of the steps the test region is selected meeting the following requirements: i) Offers a wide variety of objects from the adopted nomenclature; ii) Has spectral data from Earth Observation device systems; iii) Has the possibility to perform regular measurements with available spectrometric systems. According to the described conditions the test region around the town of Novi Iskar is chosen. In CORINE Land Cover database for this area the presence of 12 classes of land covers has been verified which has to be characterized in detail on the basis of the received data. Each one will be recorded in the created database which is the next project task. This will allow the data received in the experiments to be considered reliable and representative. For monitoring purposes the data could be interpolated for larger areas with similar land covers to trace the dynamics of objects using spectral data.
P‐8.1: An Optimized Sub‐pixel Rendering Algorithm

Aiming at the display application of displaying FHD‐sized pictures on HD‐sized screens, further optimizations were carried out to solve the existing problems of existing algorithms, such as contour blur and color slant, etc., and the results of detailed processing that could be observed at high magnification could not meet the requirements of human eyes. An optimized sub‐pixel rendering algorithm is proposed, constructed the sampling and reconstruction model, the algorithm adopts weighted rendering and joins the special texture detection, respectively for six kinds of vertical lines, six kinds of horizontal lines and two kinds of checkerboard texture to build special sampling area model which can realize the good display effect of FHD format pictures on HD size AMOLED screen. The main performance of this algorithm is that it has good consistency for each texture processing. There is no obvious lack of detail information and no color bias.
Correlation of Column Sequences from the Arrays of Sidelnikov Sequences of Different Periods

We show that the non-trivial correlation of two properly chosen column sequences of length q-1 from the array structure of two Sidelnikov sequences of periods qe-1 and qd-1, respectively, is upper-bounded by $(2d-1)\sqrt{q} + 1$, if $2\leq e < d < \frac{1}{2}(\sqrt{q}-\frac{2}{\sqrt{q}}+1)$. Based on this, we propose a construction by combining properly chosen columns from arrays of size $(q-1) \times \frac{q^e-1}{q-1}$ with e=2,3,...,d. The combining process enlarge the family size while maintaining the upper-bound of maximum non-trivial correlation. We also propose an algorithm for generating the sequence family based on Chinese remainder theorem. The proposed algorithm is more efficient than brute force approach.
Dynamic Unit Surgery for Deep Neural Network Compression and Acceleration

Successful deep neural network models tend to possess millions of parameters. Reducing the size of such models by pruning parameters has recently earned significant interest from the research community, allowing more compact models with similar performance level. While pruning parameters usually result in large sparse weight tensors which cannot easily lead to proportional improvement in computational efficiency, pruning filters or entire units allow readily available off-the-shelf libraries to harness the benefit of smaller architecture. One of the most well-known aspects of network pruning is that the final retained performance can be improved by making the process of pruning more gradual. Most existing techniques smooth the process by repeating the technique (multi-pass) at increasing pruning ratios, or by applying the method in a layer-wise fashion. In this paper, we introduce Dynamic Unit Surgery (DUS) that smooths the process in a novel way by using decaying mask values, instead of multi-pass or layer-wise treatment. While multi-pass schemes entirely discard network components pruned at the early stage, DUS allows recovery of such components. We empirically show that DUS achieves competitive performance against existing state-of-the-art pruning techniques in multiple image classification tasks. In CIFAR10, we prune VGG16 network to use 5% of the parameters and 23% of FLOPs while achieving 6.65% error rate with no degradation from the original network. We also explore the method's application to transfer learning environment for fine-grained image classification and report its competitiveness against state-of-the-art baseline.
Functional characteristics of higher educational institution’s socio-cultural environment under transformation conditions of higher education system in the modern russian society

The article is devoted to the questions of socio-cultural environment formation in the modern Russian higher educational institution. In the course of the study, the author analyses theoretical aspects of this social phenomenon, its forming elements and functional characteristics in the course of higher education system’s transformation. Based on the analysis results, the author comes to the conclusion about integration of functions, which allow managing innovations in the educational sphere effectively.
Exploring the Potential of Elastic Computing Clusters in Geo-Distributed Data Centers with Fast Fabric Interconnection

Large-scale enterprise computing systems are growing rapidly, to address the increasing demand for data processing; however, in many cases, the computing resources in a single data center may not be sufficient for critical data-centric workloads, and important factors, such as space limitations, power availability, or company policies, limit the possibilities of expanding the data center's resources. In this paper, we explore the potential of harvesting spare computing resources across geo-distributed data centers with fast fabric interconnection for real-world enterprise applications. We specifically characterize the computing resource utilization of four large-scale production data centers, and we show how to efficiently combine local storage and computing clusters with remote and elastic computation resources. The primary challenge is incorporating the available remote computing resources efficiently. To achieve this goal, we propose leveraging the capabilities of Kubernetes-based elastic computing clusters to utilize the spare computing resources across geo-distributed data centers for Big Data applications. We also provide an experimental performance evaluation based on real-use case scenarios via an empirical execution and a simulation, which shows that the proposed system can accelerate Big Data services by employing existing computing resources more efficiently across geo-distributed data centers.
Lazy Caterer’s Approach in Construction of Base Units for Combined Hybrid DWDM System

We propose the mapping of Lazy Caterer’s series in construction of Base Units (BUs) for the combined hybrid DWDM system. The performance in terms of percentage non-uniformity between BUs, and reduced FWM intensities is reported.
Distantly Supervised Relation Extraction through a Trade-off Mechanism

Distantly supervised relation extraction can label large amounts of unstructured text without human annotations for training. However, distant supervision inevitably accompanies with the wrong labeling problem, which can deteriorate the performance of relation extraction. What's more, the entity-pair information, which can enrich instance information, is still underutilized. In the light of these issues, we propose TMNN, a novel Neural Network framework with a Trade-off Mechanism, which combines the feature of text and entity pair on the sentence level to predict relations. Our proposed trade-off mechanism is a probability generation module to dynamically adjust the weights of text and corresponding entity pair for each sentence. Experimental results on a widely used dataset show that the proposed method reduces the noisy labels and achieves substantial improvement over the state-of-the-art methods.
Real-Time Home-Devices Control Based on Pupil Position

This paper proposes a Real-Time Home Devices Control System based on the processing of the user’s pupil position. A low-cost method is proposed to assist people with reduced mobility, unable to handle any type of device manually. The system was tested using a television and the software “Plaphoons” as test devices. It is capable of identifying up to six different action commands. The method allows to identify if the user is looking towards the screen and uses this information to determine if you want to perform some command. Each time a command is identified, a signal is sent to an electronic interface that allows to operate a universal remote control adapted for that purpose.The system was successfully tested in real cases by patients of the Cilsa ONG institution. The tests performed showed that the system is capable of being used successfully by users of different ages and with different degrees of motor disability.
QIMS: QoE-Centric Information-Agnostic Mix-Flows Scheduling in SD-WAN

Software defined wide area network (SD-WAN) is a promising technique in scheduling mix-flows with different quality of service (QoS) requirements due to the decoupling of control and data planes, if the flow information, e.g., the flow size, is pre-known. In many cases, it is almost impossible to pre-know the flow size of the mix-flows before the transmission starts. In this context, how to efficiently schedule the information-agnostic mix-flows to provide SD-WAN users with better quality of experience (QoE) is a burning problem. In this paper, we propose an information-agnostic mix-flows scheduling mechanism, called QIMS, to minimize flow completion time (FCT). QIMS leverages a two stage priority scheduling mechanism, which is implemented on both the SD-WAN users and switch side. In the first stage, nondeadline flows are assigned with the highest priority, while each deadline flow is assigned with the lowest priority at its start and its priority is gradually promoted according to its urgency. More specifically, a flow is more urgent if it is closer to its deadline. In the second stage, QIMS keeps the priority of non-deadline flows unchanged, but reverses each deadline flow's priority which is assigned in the first stage to avoid packet reordering. Furthermore, we formulate the relationship between priority and urgency as a nonlinear sum-of-ratios problem with queuing theory, and derive the optimal priority promotion thresholds. The experiment results show that QIMS reduces the average FCT by 18% and improves the system throughput by 8.9%.
Collaborative and Privacy-Preserving Machine Teaching via Consensus Optimization

In this work, we define a collaborative and privacy-preserving machine teaching paradigm with multiple distributed teachers. We focus on consensus super teaching. It aims at organizing distributed teachers to jointly select a compact while informative training subset from data hosted by the teachers to make a learner learn better. The challenges arise from three perspectives. First, the state-of-the-art pool-based super teaching method applies mixed-integer non-linear programming (MINLP) which does not scale well to very large data sets. Second, it is desirable to restrict data access of the teachers to only their own data during the collaboration stage to mitigate privacy leaks. Finally, the teaching collaboration should be communication-efficient since large communication overheads can cause synchronization delays between teachers. To address these challenges, we formulate the collaborative teaching as a consensus and privacy-preserving optimization process to minimize teaching risk. We theoretically demonstrate the necessity of collaboration between teachers for improving the learner's learning. Furthermore, we show that the proposed method enjoys a similar property as the Oracle property of adaptive Lasso. Empirical study illustrates that our teaching method can deliver significantly more accurate teaching results with high speed, while the non-collaborative MINLP-based super teaching becomes prohibitively expensive to compute.
46.4: Compact Stereoscopic Augmented Reality Goggle with Single Display Panel

A light path for stereoscopic near eye display utilizing single display panel has been proposed and embedded in an existing snow goggle for AR display function with its original form factor and ID design intact. The prototype demonstrates sufficient quality to display numbers or symbols for data information or guiding.
Analysis on Development and Utilization of Standard Archive Information Resources

In this paper, the author demonstrates the importance of development and utilization of standard archive information resources on development of standard archiving, management of standard preparation and revision, cultivation of talents in standardization, promotion and popularization of standardization, and puts forward ideas for researches on development and utilization of standard archive information resources by analyzing the current status, existing problems, as well as the progress of researches on development and utilization of standard archive information resources in and out of China.
A Way to Detect the Opinion Sentences from Short Texts by the Vote-AdaBoost Combining Classify Method

Because of the sparse sentiment features of network short texts, the identify result of common classification methods which are efficacious in texts with tradition structures are unsatisfactory. This article tries to combine multiple classifiers combination and integrated learning methods to solve this problem. Vote-AdaBoost combining classify method is constructed to optimize the appropriate classifier as the voting combination in iterative learning process. Finally, an effective classification method to detect the opinion sentences of short texts is obtained, and the effectiveness of this method is verified by experiments.
Legal, Ethical, and Professional Aspects of Testing

Legal aspects of testing are concerned with the application of the legal system to the computing field. It includes intellectual property law such as patents, copyright, trademarks, and trade secrets. Patents provide legal protection for intellectual ideas; copyright law protects the expression of an idea, and trademarks provide legal protection of names or symbols. There are potential legal impacts to an organization if the software has been inadequately tested, and if the quality of the testing is deemed to be negligent. Software testers are professionals and need to behave professionally and ethically at all times during testing.
Spatial and Channel Restraint for Complementary Feature Learning

Convolutional neural networks (CNNs) have been widely used in the field of computer vision in recent years, and have greatly improved the effect. At present, most of the CNNs extract the features from the network, obtain the probability distribution of the categories through the fully connected layer, then obtain and optimize the softmax loss functions. In this process, the networks gradually put the focus on discriminative features that are helpful for classification, while other features of the object may be ignored by the network. In order to help the networks, in addition to focusing on discriminative features of the object, also focus on other features of the object in training, we proposed spatial restraint module and channel restraint module. Our methods impose constraints on the discriminative features which have high response during the training process, forcing the network to mine useful complementary features. Our methods could improve the generalization performance of the model. Compared with baselines, our CNNs can achieve performance boost in both image classification task and weakly supervised object localization task.
A new Parallelization for p3Enum and Parallelized Generation of Optimized Pruning Functions

Since quantum computers will be able to break all public-key encryption schemes employed today efficiently, quantum-safe cryptographic alternatives are required. One group of candidates are lattice-based schemes since they are efficient and versatile. To make them practical, their security level must be assessed on classical HPC systems in order to determine efficient but secure parameterization.In this paper, we propose a novel parallelization strategy for the open source framework p3Enum which is designed to solve the important lattice problem of finding the shortest non-zero vector in a lattice (SVP). We also present the p3Enum extreme pruning function generator (p3Enum-epfg) which generates optimized extreme pruning functions for p3Enum's pruned lattice enumeration by employing a parallelized simulated annealing approach. We demonstrate the quality of the pruning functions delivered. Combining the new parallelization with optimized pruning functions speeds up p3Enum by a factor up to 3 compared to the previous version.Additionally, we compare the required runtime to solve the SVPs with state-of-the art tools and, for the first time, also visualize the statistical effects in the runtime of the algorithms under consideration. This allows a considerably better understanding of the behavior of the implementations than previous average-value considerations and demonstrates the relative stability of p3Enum's parallel runtimes which improve reproducibility and predictability. All these advancements make it the fastest SVP solver for lattice dimensions 66 to 92 and a suitable building block as SVP-oracle in lattice basis reduction.
Multi-strategies Method for Cold-Start Stage Question Matching of rQA Task

Sentence Semantic Equivalence Identification (SSEI) plays a key role in the Retrieval-based Question Answering (rQA) systems. Nevertheless, for the resource limitation of many real applications, even the best SSEI models may underperform. To enhance the performance, this paper firstly proposes a novel deep neural network named Densely-connected Fusion Attentive Network (DFAN). The key idea behind our model is to learn the interactive semantic information with densely connection and fusion attentive mechanism. Secondly, for the limitation of the available corpus for the given domain, we add an auxiliary classification task, which categorizes questions into domain-specific classes. And pre-trained sentence embeddings learned from large unlabeled pairs are integrated as the weakly supervised learning strategy. We conduct experiments on datasets SNLI, Quora, and the domain corpus provided for a real rQA system, achieving competitive results on all. For the domain corpus, as the best F1 value of 93.29% reached by the proposed DFAN model with additional strategies, the measure hit@1 for the real rQA systems is 52.02%, which outperforms all compared methods. This result also shows that, getting satisfied performance for a real rQA system remains a challenging natural language processing task.
Above the Clouds: A Brief Study

Cloud Computing is a versatile technology that can support a broad-spectrum of applications. The low cost of cloud computing and its dynamic scaling renders it an innovation driver for small companies, particularly in the developing world. Cloud deployed enterprise resource planning (ERP), supply chain management applications (SCM), customer relationship management (CRM) applications, medical applications, business applications and mobile applications have potential to reach millions of users. In this paper, we explore the different concepts involved in cloud computing and we also examine clouds from technical aspects. We highlight some of the opportunities in cloud computing underlining the importance of clouds showing why that technology must succeed and we have provided additional cloud computing problems that businesses may need to address. Finally, we discuss some of the issues that this area should deal with.
Remove Reflections using Motion and ClusteringTechniques

Images taken via transparent glass mediums are ubiquitous in many daily scenarios and resulted in undesired reflections that create ambiguity in image analysis applications and loss of information. These images are superimposed of background object behind glass and reflected an object in front of the glass. Removing undesired reflections is of broad application to various computer vision tasks. This paper addresses extracting background and reflection images from two images that taken from different viewpoints in front of transparent glass mediums, background and reflection edge detection is based on clustering and computing motion between the two images using Scale Invariant Feature Transform flow (SIFT flow). Experimental results on real-world images proved the outperformance of the proposed than state of the art algorithm using a quantitative and visual quality comparison.
ERROR ANALYSIS OF ESL LEARNERS AT TYUMEN INDUSTRIAL UNIVERSITY

Purpose: In this study, we reviewed the errors encountered in the English text of the students. That the Russian language was their mother tongue and the English language was their foreign language.&#x0D; Methodology: The aims of the present pilot study were to the analysis of foreign language tests in groups in the direction of "Oil and Gas Business", "Automobile Industry" in the Branch of IUT in Surgut, Tyumen Industrial University. We analyzed the written tests of the students involved in the research, applying the error manual of Dagneaux, which was altered in accordance with the purpose of our paper.&#x0D; Result: Our findings will constitute a database necessary to conceive a more effective teaching and learning process that focuses not only on translation classes but also on the acquisition of English as a foreign language in general.&#x0D; Applications: This research can be used for universities and higher education students.&#x0D; Novelty/Originality: In this research, the model of ESL teaching is presented in a comprehensive and complete manner.
Towards Automatic Normalization of the Moroccan Dialectal Arabic User Generated Text

Today social media is an important way of communication between people in the world. As the case of other countries, Moroccan people use several languages in their web communication leaving behind a considerable amount of user-generated text. The latter presents several opportunities for extracting useful information. However, processing this content is very challenging especially when facing the Moroccan Dialectal Arabic content in social media. This is to several factors such as scripts diversity (Arabic and Arabizi), orthographic errors and writing rules lack. In this context, the present work is a first attempt towards addressing the problem of Moroccan Dialectal Arabic spelling inconsistency in social media. We conduct a deep study that uses a systematic approach where we report on a series of experiments performed on Moroccan Dialectal social media text. The most interesting findings that have emerged is the orthographic inconsistency existing in written Moroccan Dialectal Arabic regarding both Arabic and Latin scripts. This phenomenon affects an important amount of texts in social media and proved the need of exploiting available Arabic tools in addition to building a customized spelling correction system.