Reviewerly/exHarmony huggingface dataset:
Trident SSD: A Trident Single-Shot Multibox Object Detector with Deconvolution

Abstract Scale variation is one of the key challenges in the object detection area, which limits the precision of detection methods like Single shot multibox detector (SSD). This paper proposes a detection method based on SSD, which focuses on handling scale variation and better detection performance of small objects. Our method, called TridentSSD, have an architecture with three branches, which are respectively responsible for detecting different scales of objects, solving the problem of scale variation while training. Then we augment small object branch with deconvolution module and feature fusion methods to improve precision, especially for small object detection. During training, we modify the original matching rules to generate training samples. Consequently, we can use objects of different scales to train the corresponding branches respectively. Finally, Experiments have done on both PASCAL VOC2007 and VOC2012 datasets. Results show that, with an input size of 300×300, our TridentSSD achieves better performance compared to the benchmark method SSD.
Integrating Formal Methods for Security in Software Security Education

As the number of software vulnerabilities discovered increases, the industry is facing difficulties to find specialists to cover the vacancies for security software developers.Considering relevant teaching and learning theories, along with existing approaches in software security education, we present the pedagogic rationale and the concrete implementation of a course on security protocol development that integrates formal methods for security research into the teaching practice.A novelty of the framework is the adoption of a conceptual model aligned with the level of abstraction used for the symbolic (high-level) representation of cryptographic and communication primitives.This is aimed not only at improving skills in secure software development, but also at bridging the gap between the formal representation and the actual implementation, making formal methods and tools more accessible to students and practitioners.
The Use of SMART Complexes in the Methodological System of Modern Informational and Educational Technologies

The main aspects of using SMART complexes in the methodological system of modern informational and educational technologies have been considered in the article. The analysis of the legal framework concerning the use of informational technologies in the educational process has been conducted, the main characteristics of the informational society have been described, and the interaction between education and society has been specified. The general approaches to the implementation of mechanisms of reforming Ukrainian education have been pointed out. It has been figured out that education quality depends on the forms and methods that are used while organizing training activities. It has been highlighted that the majority of modern methodologies are connected with the use of digital (informational and educational) technologies. The effectiveness of the implementation of a competence-based approach during future specialists` training has been emphasized. The notions «digital competence», «informational and educational environment» have been defined. The essence of the concept «SMART complex» as an interconnected combination of standard, educational and methodical materials that exist in the informational and educational environment of an educational institution and are necessary for the effective formation of competences as a programmed result of acquiring academic subjects has been defined. The individual conception of SMART-education and the structure of the methodological system using SMART complexes have been presented. It has been proven that the interaction between structural components of the given methodological system occurs on two levels: the content-based and the activity-based ones. General functions of SMART complexes that promote qualitative training of future specialists have been specified.
Creating high-resolution 360-degree single-line 25K video content for modern conference rooms using film compositing techniques

360-degree image and movie content have been gaining popularity in the last few years all over the media industry. There are two main reasons for this development. On the one hand, it is the immersive character of this media form and, on the other hand, the development of recording and presentation technology has made great progress in terms of resolution and quality. 360-degree panoramas are particularly widespread in VR and AR technology. Despite the high immersive potential, these forms of presentation have the disadvantage that the users are isolated and have no social contact during the presentation. Efforts have therefore been made to project 360-degree content in specially equipped rooms or planetariums to enable a shared experience for the audience. One area of application for 360- degree single-line cylindrical panorama with moving imagery included are modern conference rooms in hotels, which create an immersive environment for their clients to encourage creativity. The aim of this work is to generate high-resolution 25K 360-degree videos for projection in a conference room. The creation of the panoramas uses the single-line cylinder technique and is based on composition technologies that are used in the film industry. Video sequences are partially composed into a still image panorama in order to enable a high native resolution of the final film. A main part of this work is the comparison of different film, video and DSLR cameras, in which different image parameters are examined with respect to the quality of the images. Finally, the advantages, disadvantages and limitations of the procedure are examined.
Latin BERT: A Contextual Language Model for Classical Philology

We present Latin BERT, a contextual language model for the Latin language, trained on 642.7 million words from a variety of sources spanning the Classical era to the 21st century. In a series of case studies, we illustrate the affordances of this language-specific model both for work in natural language processing for Latin and in using computational methods for traditional scholarship: we show that Latin BERT achieves a new state of the art for part-of-speech tagging on all three Universal Dependency datasets for Latin and can be used for predicting missing text (including critical emendations); we create a new dataset for assessing word sense disambiguation for Latin and demonstrate that Latin BERT outperforms static word embeddings; and we show that it can be used for semantically-informed search by querying contextual nearest neighbors. We publicly release trained models to help drive future work in this space.
Development of the Modern Russian Market of Online Education in Terms of the Knowledge Society

Introduction. In recent years, an online education market has emerged in Russia with proven technologies and significant financial turnover. The range of courses offered to the consumer is quite wide (marketing, management, humanities, IT, etc.). The purpose of the article is to present a classification of the online courses offered on the Russian market by subject matter and technical tools on the basis of the results of the study conducted, as well as to trace the dynamics of the market capacity for 2014–2021. Materials and Methods. Russian online education market at the present stage of its development was the object of the research. The empirical material included: a statistical analysis of the thematic diversity of online courses on the Russian market; a secondary analysis of the Online Education Barometer questionnaire for 2019, RBC’s ranking of the 35 largest Russian private companies providing services in the field of online education; education market studies for 2017, 2018 and 2020. Results. The Russian online education market capacity rapidly rose on 90 times for 2014–2019, from 0.4 to 36.5 billion rubles, which indicates the explosive growth of online education popularity. The thematic diversity of online courses is determined by the main educational society needs (programming, it-technologies, marketing and business administration). The thematic focus on the Russian “state” platforms corresponds to the education areas of the Russian offline universities. Discussion and Conclusion. The market for online education in Russia exponentially grows. On the one hand, the high demand for online courses can be explained by increasing of knowledge-intensive work number. On the other hand, labor realizes that knowledge and skills are a guarantee of employment. The diversity of online disciplines correlates with the idea of mutual incentives of supply and demand. Indeed, the most of courses are business, IT and design orientated. The article will be useful to universities’ chiefs and private educational companies planning to expand their online courses; for those who are planning to enter the Russian online education market and looking for perspective directions; for researchers and business analytics of the education market.
CROWD CONGESTION DETECTION IN VIDEOS

Automatic detection of crowd congestion in high density crowds is a challenging problem, with substantial interest for safety and security applications. In this paper, we propose a method that can automatically identify and localize congested regions in crowded videos. Our proposed method is based on the notion that pedestrians in the congested region follow a particular behavior. Pedestrians in the congested areas cannot move freely due to space unavailability and tend to undergo lateral oscillations. In our method, we first extract trajectories by using particle advection technique and then compute oscillatory features for each trajectory. Trajectories with higher oscillation values and with less proximity are clustered, indicating the congested regions. We perform experiments on a diversity of challenging scenarios. From the experimental results, we show that our method provides precise localization of congested regions in crowd videos.
The Application of Presentation Practice Production Method for Teaching Speaking Skill: The Perception of Teachers and Students

The research is to know the application of teaching English by Using “Presentation Practice Production” method and its effect on teachers and students in MI Masyhudiyah, Gresik, East Java. Qualitative research was used by combining questionnaire, interview and secondary data, teachers’ lesson plan of all English Teachers. The theory was based on (Larsen, 2003), (Jordan, Carlile, &amp; Stack, 2008) and (Brown, 2000). The finding and discussion were, first, the application on teachers created: 1) the students tent to be more having bravery to do such a conversation in English, getting more focus and stable to have talks among. 2) The students got easily imitate and follow the English lesson by what the teacher taught in the classroom. Second, the effects on teachers were 1) teachers got the easiness in conveying the teaching materials in the classroom, improving the speaking skill, and getting more focus on teaching materials. 2) Teachers could see directly the results from the teaching at the day. The last, the effect on students are 1) Students could directly use the words, utterances and conversation at the day and more active, more focus and more perceptive in studying English. 2) The students were impressed by using English fingers.
Lifelogging caption generation via fourth-person vision in a human–robot symbiotic environment

Abstract Automatic analysis of our daily lives and activities through a first-person lifelog camera provides us with opportunities to improve our life rhythms or to support our limited visual memories. Notably, to express the visual experiences, the task of generating captions from first-person lifelog images has been actively studied in recent years. First-person images involve scenes approximating what users actually see; therein, the visual cues are not enough to express the user’s context since the images are limited by his/her intention. Our challenge is to generate lifelog captions using a meta-perspective called “fourth-person vision”. The “fourth-person vision” is a novel concept which complementary exploits the visual information from the first-, second-, and third-person perspectives. First, we assume human–robot symbiotic scenarios that provide a second-person perspective from the camera mounted on the robot and a third-person perspective from the camera fixed in the symbiotic room. To validate our approach in this scenario, we collect perspective-aware lifelog videos and corresponding caption annotations. Subsequently, we propose a multi-perspective image captioning model composed of an image-wise salient region encoder, an attention module that adaptively fuses the salient regions, and a caption decoder that generates scene descriptions. We demonstrate that our proposed model based on the fourth-person concept can greatly improve the captioning performance against single- and double-perspective models.
Science knowledge, libraries and the Internet. The interaction at the current stage

The authors address the issue of impact of the Internet on academic libraries providing information support of science research and studies. Based on the 20+–year experience of the RAS Library for Natural Sciences, they demonstrate how the Library interacts with the Internet and what impact it has got. The key Library functions are specified for the current period. The authors argue that academic libraries preserve their functions in science information support though modifying them in some way. The libraries are to accomplish the same tasks though on the basis of their extended collection comprising the virtual segment accessible online. The library collections now comprise traditional sources (printed and digital) and new ones (primarily, various databases). Still the libraries train their users (formerly interpreted as readers) to use arrays of science information now comprising digital formats. Besides, the academic libraries have been gaining new functions related to bibliometrical analyses of research and science studies of individual users and organizations being served. For certain degree, this makes the academic libraries a player in scientific communication.
A First Look at Contact Tracing Apps

Today's smartphones come with a large number of powerful additional devices such as a low power Bluetooth sensor, powerful embedded sensors such as the digital compass, accelerometer, GPS sensors, Wi-Fi capabilities, microphone, humidity sensors, health tracking sensors, and camera. These value-added sensors revolutionize many sectors of today's community including tracking, social networking, healthcare, manufacturing, and monitoring of the environment, etc. These additional embedded sensors could be used for large-scale personal, group, and community sensing applications. Governments and regulators are turning to use these features of the smartphone technology to trace the people thought to have symptoms of certain diseases or virus e.g. COVID-19. The outbreak of COVID-19 in December 2019, has seen a surge of the mobile applications for tracing, tracking and isolating the persons showing COVID-19 symptoms in-order to overcome the contagious disease. As the embedded sensors could disclose private information of the users thus potentially bring threat to the privacy and security of users. In this paper, we analyzed a large set of smartphone applications that have been designed by the governments to deal with the COVID-19 outbreak and bring the people back to normal life. Specifically, we analyzed what type of permission these smartphone apps require, whether these permissions are necessary for the track and trace, how data from the user devices is transported to the analytics center, what security measures apps have deployed, and what privacy threats these features could have.
Expanding the responsibility of an offshore team and sustainably increasing business value using SAFe

The paper outlines a four-year journey of a software engineering team based in Bangalore, India, which transitioned from having the responsibility of different modules to complete engineering responsibility of the range of software products. The paper also talks about the proactive steps taken to transition to Scaled Agile Framework (SAFe) [1] successfully and discusses the challenges related to people and team culture. The paper also summarizes lessons learned from the journey.
ANALYSIS OF RELATIONSHIP BETWEEN NUMBER OF BEACH USERS AND PROBABILITY OF BEACH USER’S ENTRANCE INTO RIP AREA USING AI

わが国の多くの海水浴場では，ライフセーバーによる監視救助活動が行われているが，繁忙期は，数万人の利用者に対して数十人のライフセーバーで活動を行っていることから，溺水事故を完全に防ぐことは難しい．溺水事故の発生を極力減らすためには，ライフセーバーには利用者数の増減による柔軟な監視体制が求められる．一方, わが国の溺水事故の主要因は離岸流であるが，利用者数の増減と離岸流事故の発端となる離岸流エリアへの利用者の立ち入りの関係は明らかになっていない．本研究は，AIを用いて海水浴場の定点撮影画像から利用者数の経時変化を算出し，利用者数の増減と離岸流発生エリアへの人立入りの関係性を統計解析によって調べた．その結果，離岸流エリアへの人立入り有の場合，利用者数が多いことを明らかにし，利用者数の増減による離岸流エリアへの立ち入り確率を示した．
A priori estimates for classification problems using neural networks

We consider binary and multi-class classification problems using hypothesis classes of neural networks. For a given hypothesis class, we use Rademacher complexity estimates and direct approximation theorems to obtain a priori error estimates for regularized loss functionals.
Enhanced character embedding for Chinese named entity recognition

Traditional named entity recognition methods mainly explore the application of hand-crafted features. Currently, with the popularity of deep learning, neural networks have been introduced to capture deep features for named entity recognition. However, most existing methods only aim at modern corpus. Named entity recognition in ancient literature is challenging because names in it have evolved over time. In this paper, we attempt to recognise entities by exploring the characteristics of characters and strokes. The enhanced character embedding model, named ECEM, is proposed on the basis of bidirectional encoder representations from transformers and strokes. First, ECEM can generate the semantic vectors dynamically according to the context of the words. Second, the proposed algorithm introduces morphological-level information of Chinese words. Finally, the enhanced character embedding is fed into the bidirectional long short term memory-conditional random field model for training. To explore the effect of our proposed algorithm, experiments are carried out on both ancient literature and modern corpus. The results indicate that our algorithm is very effective and powerful, compared with traditional ones.
Multi-Pass Transformer for Machine Translation

In contrast with previous approaches where information flows only towards deeper layers of a stack, we consider a multi-pass transformer (MPT) architecture in which earlier layers are allowed to process information in light of the output of later layers. To maintain a directed acyclic graph structure, the encoder stack of a transformer is repeated along a new multi-pass dimension, keeping the parameters tied, and information is allowed to proceed unidirectionally both towards deeper layers within an encoder stack and towards any layer of subsequent stacks. We consider both soft (i.e., continuous) and hard (i.e., discrete) connections between parallel encoder stacks, relying on a neural architecture search to find the best connection pattern in the hard case. We perform an extensive ablation study of the proposed MPT architecture and compare it with other state-of-the-art transformer architectures. Surprisingly, Base Transformer equipped with MPT can surpass the performance of Large Transformer on the challenging machine translation En-De and En-Fr datasets. In the hard connection case, the optimal connection pattern found for En-De also leads to improved performance for En-Fr.
Prediction of Sepsis from Clinical Data Using Long Short-Term Memory and eXtreme Gradient Boosting

Developing an objective and efficient computer-aided tool for early detection of sepsis has become a promising research topic.In this paper, we present two methods for early prediction of sepsis from clinical data.One is neural network-based method and the other is eXtreme Gradient Boosting (XGBoost) based method.Considering the temporal relationship between clinical data from sepsis patients in the ICU, we built a Long Short-Term Memory (LSTM) network to extract the intrinsic relation between different indicators in clinical data and meanwhile model the temporal dependencies, which only uses the previous information not future information to predict the results.Neural networks have made great achievements in unstructured data, such as image processing and speech processing, while traditional machine learning methods are better at processing structured data than neural networks.Thus, we trained an XGBoost model on the pre-processed data for improving the prediction accuracy.In official phase, we only used the first seven vital signs in our network, on test set A, the LSTM-based method has the utility score is 0.267 and the score of XGBoost-based method is 0.392.We submit the latter method as the final entry and the official final test utility score is 0.313.Our team name is CQUPT_Just_Try, and the ranking is 15th.
QoE-Aware Stable Adaptive Video Streaming Using Proportional-Derivative Controller for MPEG-DASH

In video distribution services such as video streaming, the providers must satisfy the various quality demands of the users. One of the human-centric indexes used to assess video quality is the quality of experience (QoE). In video streaming, the video bitrate, video freezing time, and video bitrate switching are significant determiners of QoE. To provide high-quality video streaming services, adaptive streaming using the Moving Picture Experts Group dynamic adaptive streaming over Hypertext Transfer Protocol (MPEG-DASH) is widely utilized. One of the conventional bitrate selection methods for MPEG-DASH selects the bitrate such that the amount of buffered data in the playback buffer, i.e., the playback buffer level, can be maintained at a constant value. This method can avoid buffer overflow and video freezing based on feedback control; however, this method induces high-frequency video bitrate switching, which can degrade QoE. To overcome this issue, this paper proposes a bitrate selection method in an adaptive video steaming for MPEG-DASH to improve the QoE by minimizing the bitrate fluctuation. To this end, the proposed method does not change the bitrate if the playback buffer level is not around its upper or lower limit, corresponding to the full or empty state of the playback buffer, respectively. In particular, to avoid buffer overflow and video freezing, the proposed method selects the bitrate based on proportional-derivative (PD) control to maintain the playback buffer level at a target level, which corresponds to an upper or lower threshold of the playback buffer level. Simulations confirm that, the proposed method offers better QoE than the conventional method for users with various preferences.