Reviewerly/exHarmony huggingface dataset:
Software Auto Trigger Recording for Super Slow Motion Videos Using Statistical Change Detection

Handheld devices like smartphones have an inherent disadvantage in recording high frame rate (greater than 480 fps) videos because of limited RAM, limited CPU and GPU capabilities and memory. Almost all handheld devices cannot record such videos indefinitely and have a time limit (usually around 1 s) to keep the system from straining and crashing. In this time limit, anywhere around 480 or 960 or more full HD (resolution 1920 × 1080) frames are recorded per second (around 1 GB data per second is encoded and stored). Most handheld devices that have the capability to record high frame-rate videos and are designed for the common user, use hardware sensor trigger to automatically detect events and record slow motion videos. High framer-rate videos are generally used to record high speed events, the user cannot be expected to press/start the recording manually by timing the event. Therefore the need arises for a method to automatically detect events and trigger the recording. Hardware sensor triggers are basically a camera sensor with an attached circuit board that detects events in the scene. However hardware triggers are expensive, have limited peripheral memory, supports fixed frame rate, cannot overcome global motion, are incompatible with heterogeneous architecture, supports fixed sensor type and inefficient in low lighting conditions. Such limitations are efficiently overcome with the proposed method without compromising on performance and accuracy. Proposed method uses statistical inference along with global motion vectors to detect event in the scene (video frames) for Super Slow Motion (SSM) video capture. The qualitative performance prior to testing on device is elucidated along with quantitative analysis with leaderboard motion detection methods.
The Knowability Paradox and Unsuccessful Updates

Abstract In this paper we undertake an analysis of the knowability paradox in the light of modal epistemic logics and of the phenomena of unsuccessful updates. The knowability paradox stems from the Church-Fitch observation that the plausible knowability principle, according to which all truths are knowable, yields the unacceptable conclusion that all truths are known. We show that the phenomenon of an unsuccessful update is the reason for the paradox arising. Based on this diagnosis, we propose a restriction on the knowability principle which resolves the paradox.
A Multi-view Learning Method Based on Feature Correlation

The multi-view learning can improve the generalization performance of the classifier by using features from different sources. However, many multi-view data sets are constructed by unsupervised methods, which may cause uncertain to the classifier. In this paper, a supervised multi-view learning method based on the correlation between features is proposed, which mainly includes two parts: view evaluation and view construction. First of all, canonical correlation analysis and correlation matrix based on mutual information are employed to extract the correlation information between different feature sets. After that, a supervised data view construction algorithm, named data view construction (DVC), is proposed. Compared with traditional view construction methods, DVC can be used as a general view construction method to construct multi-view data set. The experimental results show that DVC achieves higher generalization compared with other view construction algorithms.
Updated Analysis of Detection Methods for Phishing Attacks

Phishing attacks use social engineering and some technical tricks to obtain users’ personal identity data, account credentials and details of your bank cards to impersonate users on the network. Organizations are not immune to these attacks, so they should implement an orderly phishing detection plan, with the aim of reducing risks from direct exposure. Phishing is perpetrated in various telematic services such as email, web, social networks and instant messaging, among others. This paper brings an updated study of the main existing mechanisms for the detection of phishing. Additionally, the most effective solutions in the literature will be highlighted, matching solutions for different services will be identified and the most effective solutions will be featured, with the aim of applying these approaches in future integrated solutions for the detection of phishing attacks.
Influence of MRI modality on accuracy of deep learning segmentation of abdominal organs

Learning to segment MRI sequences is an exciting current application of deep learning networks. Most recent related work on this issue propose architecture modifications and ensembles with voting to try to improve the quality of the result. One relevant issue that also deserves further investigation is how the MRI modality influences quality of segmentation. In this paper we discuss two well-known MRI modalities (T1-DUAL and T2-SPIR) and compare the quality of segmentation with each. We build and train three segmentation network architectures, then evaluate them with the two modalities to evaluate. We conclude that segmentation of T1-DUAL modality achieves around 6% higher IoU than T2-SPIR for our experimental dataset. Based on the results we conclude that, from the perspective of segmentation performance, T1-DUAL exhibits better contrast to individualize abdominal organs.
SiamKey: Siamese Local Keypoints Matching Network for Visual Tracking

Siamese Network has been widely researched in the tracking framework due to its simple structure and easy expansion. Most Siamese-based Network trackers use a depthwise cross-correlation (DW-XCorr) to embed the template information into the search branch. However, this method has many limitations in Siamese Network. On the one hand, the size of template needs to be set in advance. If pre-fixed size is larger than actual target size, lots of background noise will be brought in. On the contrary, there will be losing lots of foreground information. On the other hand, DW-XCorr is a handcrafted region-based correlation module, and lots of background information will be brought in and overwhelms the feature of the target, making it hard to distinguish the target from similar objects in background. In order to solve above problems, this paper proposes a simple local keypoints matching network (SiamKey) implementing keypoint-based correlation. We select top-K keypoints of illumination and rotation invariance from template feature by peakedness measurement, and embed kepoints feature into search branch adaptively. Our SiamKey can significantly reduce the background noise and improve the robustness without pre-fixed template size. Experiments on challenging benchmarks including VOT2018 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[1]</sup> , GOT-10k <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[2]</sup> and OTB-100 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[3]</sup> , demonstrate that the proposed SiamKey outperforms many state-of-the-art trackers.
Point-Counting on Elliptic Curves Belonging to One Prominent Family: Revisited

An approach to computing orders of elliptic curves from the family {D <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a</sub> : y <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> = x <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> + ax (mod p), a ≠ 0} with p being a prime number, is presented. The method combines an explicit formula for the orders of curves reduced modulo p with the well-known Hasse bound. Besides its efficiency (the algorithm takes at most O(log <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> p) bit operations), this approach allows to determine in a transparent way the spectrum of curve orders when p is fixed.
Developing and implementing an English-Spanish literary parallel audio-textual corpus for data-driven ESL learning

ABSTRACT The purpose of this paper is to present the LITTERA corpus, an English-Spanish literary parallel speech corpus created for the purpose of language learning, and to sketch out a few pedagogical applications for the study of English phonology by Spanish-speaking language learners. It is composed of 25 literary texts that have been aligned with the Spanish translation and are accompanied by audio from the corresponding audiobooks. In this article, we will detail its conception, composition and features at length, as well as provide a few examples of how LITTERA can be applied in language learning, particularly within the realm of oral comprehension and speech production.
Study of Geological Setting of Naftalan-Northern Naftalan Area by Use of Attribute Analysis of 3D Seismic Data

The paper is devoted to use of attribute analysis of 3D seismic data to more detailed study of geologic and tectonic setting of Naftalan-Northern Naftalan area of Ganja oil and gas region. For this, several attributes such as RMS amplitude, Acoustic relative impedance, Envelope have been calculated and analyzed. Aim of the study consists in selection of more effective attributes for identification and tracing of faults of various amplitude and disjunctive dislocations. In addition, the paper gives information about Naftalan field considered as the oldest brachianticline type field in Azerbaijan. The history of geological and geophysical studies covered the field is considered in brief. It has been noted in particular that two types of oil are extracted from the field: heavy oil with curative effect and light oil of commercial value. The aim of the study consisted in more detailed research on geological setting of the field in order to identify the intervals and zones of accumulation of light oil. The study resulted in compiling of a structural map for top of the I horizon of Maykop.
Monitoring and Prevention: How Technical Debt is Managed by Software Practitioners

Technical Debt is a concept to describe the gap between current solutions and optimal solutions, which could hurt system quality. The presence of technical debt is inevitable, becoming more realistic to deal with it than try to avoid it. Therefore, it is relevant to understand how software development teams are dealing with technical debt. This study aims to characterize the practices used to monitor and prevent technical debt by software practitioners. A corpus of responses from a survey of 226 software practitioners from Chile and Colombia is used to collect and analyze these practices. Results were mostly based on the point of view of developers (87; 38.5%), project managers (49; 21.7%), and software architects (48; 21.2%) and showed that weekly/daily team meetings was the most cited practice to monitor TD items. Adoption of good practices and well-defined architecture were selected as the best practices to avoid the presence of TD in software projects.
PROFESSIONAL SELF-REALIZATION OF PERSONALITY AS A DETERMINANT OF SCIENTIFIC AND TECHNICAL AND SOCIO-ECONOMIC DEVELOPMENT OF SOCIETY

In the article, the process of professional self-realization of the individual is interpreted as determinants of the scientific, technical andsocio-economic development of society from the standpoint of dialectical materialism as a theoretical and methodological basis of domestic psychological science. Self-realization is considered as a process of subjects` realization in a professional activity of universally-active abilities, that ensuring the creation of social values which determine the progressive cultural-historical and socio-economic development of society. The basics of the study of the problem of self-realization which began intensively developed in the domestic psychological science since the 1970`s were laid in the works of S.L. Rubinstein, formulated in the 1920`s principle of creative independent activity, in which person creating socially valuable products purposefully manifests and develops his own essential forces, enriches society with cultural results of life activity, contributing to its development. A self-fulfilling person is characterized by an active civic position, manifested in self-determination, initiative, self-discipline, responsibility, independence in activities aimed at creating of public goods, a reflective type of construction of one’s being, different from a reactive type of existence, the main purpose of which is only adaptation to living conditions for implementation own needs without taking into account the needs of society. The scientific, technical and socio-economic development of the country as a priority objective of domestic state policy, the implementation of which will ensure the leading position of the Russian Federation among the scientific, technical and socio-economically highly developed powers of the world, is possible only if the subject of being realized his essential forces as universal activity abilities in labor determined by public meanings. The alienation of person from work determines the alienation of an individual from its essential uniqueness, which in turn causes a violation of the course of human phylogenetic development and the cultural and historical development of civilization.
Identification of technology-based mixed reality devices

Abstract The purpose of this research is to identify technology-based mixed reality devices used in education. A variety of operating systems such as Windows, Android, and iOS used by the devices are reviewed in this study. It has been revealed that Hololens and HTC Vive are the most widely used devices, particularly in vocational education. Hololens mainly uses virtual reality (mixed reality) with a complete holographic computer. In the meantime, HTC Vive is a set of devices enabling its users to experience virtual reality. It is recommended that industry-driven education utilize those devices so that they are capable of adapting with industrial revolution 4.0.
Educational Effect of Simultaneous Interactive Lessons that can Connect with First-year Students

This paper reports the educational effect of simultaneous interactive lessons when compared to on-demand lessons. In this year, due to the influence of COVID-19, many universities have had to give online lessons. While there were many lessons by video distribution, I gave simultaneous interactive lessons using the Web conference system. From the results of the questionnaire, it was found that the satisfaction level, comprehension level, and learning motivation of the simultaneous interactive lessons were higher than those of the video distribution lessons, and it was also found that the factors were the connection with teachers and classmates.
Robust Ordinal Regression: User Credit Grading with Triplet Loss-Based Sampling

With the development of social media sites, user credit grading, which served as an important and fashionable problem, has attracted substantial attention from a slew of developers and operators of mobile applications. In particular, multi-grades of user credit aimed to achieve (1) anomaly detection and risk early warning and (2) personalized information and service recommendation for privileged users. The above two goals still remained as up-to-date challenges. To these ends, in this article, we propose a novel regression-based method. Technically speaking, we define three natural ordered categories including BlockList , GeneralList , and AllowList according to users’ registration and behavior information, which preserve both the global hierarchical relationship of user credit and the local coincident features of users, and hence formulate user credit grading as the ordinal regression problem. Our method is inspired by KDLOR ( kernel discriminant learning for ordinal regression ), which is an effective and efficient model to solve ordinal regression by mapping high-dimension samples to the discriminant region with supervised conditions. However, the performance of KDLOR is fragile to the extreme imbalanced distribution of users. To address this problem, we propose a robust sampling model to balance distribution and avoid overfit or underfit learning, which induces the triplet metric constraint to obtain hard negative samples that well represent the latent ordered class information. A step further, another salient problem lies in ambiguous samples that are noises or located in the classification boundary to impede optimized mapping and embedding. To this problem, we improve sampling by identifying and evading noises in triplets to obtain hard negative samples to enhance robustness and effectiveness for ordinal regression. We organized training and testing datasets for user credit grading by selecting limited items from real-life huge tables of users in the mobile application, which are used in similar problems; moreover, we theoretically and empirically demonstrate the advantages of the proposed model over established datasets.
Cross-modal Memory Fusion Network for Multimodal Sequential Learning with Missing Values

Information in many real-world applications is inherently multi-modal, sequential and characterized by a variety of missing values. Existing imputation methods mainly focus on the recurrent dynamics in one modality while ignoring the complementary property from other modalities. In this paper, we propose a novel method called cross-modal memory fusion network (CMFN) that explicitly learns both modal-specific and cross-modal dynamics for imputing the missing values in multi-modal sequential learning tasks. Experiments on two datasets demonstrate that our method outperforms state-of-the-art methods and show its potential to better impute missing values in complex multi-modal datasets.
The Application of Improved Genetic Algorithm to the Back Analysis of Foundation Pit Construction

Abstract The improved genetic algorithm is combined into the foundation pit finite element method. On the basis of the measured lateral displacement data on site, the parameters of the foundation soils were obtained by the back analysis. Then the lateral deformation of the surrounding soil was analyzed on the condition of each different step under the foundation pit excavation according to the inversion parameters, and the calculation and analysis of lateral displacement of cement-soil pile with anchorage retaining structure was did. The results reveal that the calculated value is agreed with to the measured value, which indicates that the improved genetic algorithm is efficient to the related constructions.
Learning Efficient Photometric Feature Transform for Multi-view Stereo

We present a novel framework to learn to convert the per-pixel photometric information at each view into spatially distinctive and view-invariant low-level features, which can be plugged into existing multi-view stereo pipeline for enhanced 3D reconstruction. Both the illumination conditions during acquisition and the subsequent per-pixel feature transform can be jointly optimized in a differentiable fashion. Our framework automatically adapts to and makes efficient use of the geometric information available in different forms of input data. High-quality 3D reconstructions of a variety of challenging objects are demonstrated on the data captured with an illumination multiplexing device, as well as a point light. Our results compare favorably with state-of-the-art techniques.
On the Performance Analyses of a Modified Force Field Algorithm and Neural Network Approach for Obstacle Avoidance in Swarm Robotics

Abstract Obstacle avoidance is a major hurdle when implementing mobile robots and swarm robots. Swarm robots work in groups and therefore require an efficient and functional obstacle avoidance algorithm to stay collision free between themselves and their surroundings. This paper reviews previous research in obstacle avoidance implementation using the force field method (FFM), also known as potential field method (PFM) and a neutral network approach. Moreover, this paper aims to execute simulations using a modified force field algorithm and a neural network approach and compare them. The obtained results are analyzed to identify the performance characteristics and the time taken to perform tasks using a singular mobile robot against a swarm robot environment consisting of four and ten robots, respectively, in both simulation cases. Simulations showed that the algorithm was successful in navigating obstacles for both single and swarm robot environments. A single robot was found to take up to 340% longer to arrive at the required location compared to the first robot in the experiment. Moreover, it was found that the neural network approach showed ~ 27% improvement over the modified force field algorithm when it comes to cases where more than four robots are being used.
New QC Codes over GF(11)

A linear code of length n, dimension k and minimum distance d over GF (q) is denoted by [n, k, d] <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">q</sub> . One of the main problem in linear coding theory is - for given length and dimension to construct codes with best possible minimum distances. In this talk we present 10 new QC codes.
Bengali Place Name Recognition - Comparative Analysis Using Different CNN Architectures

Optical Character Recognition (OCR) has been deployed in the past in different application areas such as automatic transcription and indexing of document images, reading aid for the visually impaired persons, postal automation etc. However, the performance in many cases has not been impressive due to the fact that character segmentation is itself an error-prone and difficult operation, which leads to the poor performance of the system due to erroneous segmentation of characters. Hence, for many applications (like document indexing, Postal automation) where full character-wise transcription is not required, word recognition is the preferred method these days. This article investigates recognition of Bengali place names as word images using 5 different traditional architectures. Experiments on word images (of Bengali place names) from 608 classes were conducted. Encouraging results were obtained in all instances.
Domain Adaptation Based Technique for Image Emotion Recognition Using Image Captions

Images are powerful tools for affective content analysis. Image emotion recognition is useful for graphics, gaming, animation, entertainment, and cinematography. In this paper, a technique for recognizing the emotions in images containing facial, non-facial, and non-human components has been proposed. The emotion-labeled images are mapped to their corresponding textual captions. Then the captions are used to re-train a text emotion recognition model as the domain-adaptation approach. The adapted text emotion recognition model has been used to classify the captions into discrete emotion classes. As image captions have a one-to-one mapping with the images, the emotion labels predicted for the captions have been considered the emotion labels of the images. The suitability of using the image captions for emotion classification has been evaluated using caption-evaluation metrics. The proposed approach serves as an example to address the unavailability of sufficient emotion-labeled image datasets and pre-trained models. It has demonstrated an accuracy of 59.17% for image emotion recognition.