Reviewerly/exHarmony huggingface dataset:
Pepsi quietly joining Facebook ad boycott: sources

PepsiCo, Inc. is quietly joining the growing number of companies pulling ad dollars from Facebook, people close to the matter tell FOX Business.
Refinement for Multiagent Protocols

An interaction protocol specifies a decentralized multiagent system operationally by specifying constraints on messages exchanged by its member agents. Engineering with protocols requires support for a notion of refinement, whereby a protocol may be substituted without loss of correctness by one that refines it. We identify two desiderata for refinement. One, generality: refinement should not restrict enactments by limiting protocols or infrastructures under consideration. Two, preservation: to facilitate modular verification, refinement should preserve liveness and safety. We contribute a novel formal notion of protocol refinement based on enactments. We demonstrate generality by tackling the declarative framework of information protocols. We demonstrate preservation by formally establishing that our notion of refinement is safety and liveness preserving. We show the practical benefits of refinement by implementing a checker. We demonstrate that it is less time-intensive to check refinement (and thereby gain safety and liveness) than to recheck safety and liveness of a composition.
Heuristic Approach for Low Light Image Enhancement using Deep Learning Technique

Due to low photon count and low Signal to Noise Ratio (SNR), low light imaging becomes more challenging. Images taken in a short exposure get affected by noise while images taken in a long exposure can be blurry. Different methods like image denoising, deblurring, and image enhancement are existing, but at extreme conditions their effectiveness is limited. A dataset that includes raw short exposure low light images and corresponding long exposure images is used for the development of a learning-based pipeline. DNN based approach operates on raw sensor data and works effectively. It outperforms the traditional image processing pipeline which shows poor results on such raw sensor data.
Non-Anthropocentric Design : From Thinking the Other to Ahuman Love

In my PhD project Living Things – Design beyond Human Exceptionalism I am exploring and proposing everyday reconfigurations of human- nonhuman relations. In this paper I position my work in the con ...
Analysis of Trending Topics and Text-based Channels of Information Delivery in Cybersecurity

Computer users are generally faced with difficulties in making correct security decisions. While an increasingly fewer number of people are trying or willing to take formal security training, online sources including news, security blogs, and websites are continuously making security knowledge more accessible. Analysis of cybersecurity texts can provide insights into the trending topics and identify current security issues as well as how cyber attacks evolve over time. These in turn can support researchers and practitioners in predicting and preparing for these attacks. Comparing different sources may facilitate the learning process for normal users by persisting the security knowledge gained from different cybersecurity context. Prior studies neither systematically analysed the wide-range of digital sources nor provided any standardisation in analysing the trending topics from recent security texts. Although LDA has been widely adopted in topic generation, its generated topics cannot cover the cybersecurity concepts completely and considerably overlap. To address this issue, we propose a semi-automated classification method to generate comprehensive security categories instead of LDA-generated topics. We further compare the identified 16 security categories across different sources based on their popularity and impact. We have revealed several surprising findings. (1) The impact reflected from cyber-security texts strongly correlates with the monetary loss caused by cybercrimes. (2) For most categories, security blogs share the largest popularity and largest absolute/relative impact over time. (3) Websites deliver security information without caring about timeliness much, where one third of the articles do not specify the date and the rest have a time lag in posting emerging security issues.
Action Recognition with Deep Multiple Aggregation Networks

Most of the current action recognition algorithms are based on deep networks which stack multiple convolutional, pooling and fully connected layers. While convolutional and fully connected operations have been widely studied in the literature, the design of pooling operations that handle action recognition, with different sources of temporal granularity in action categories, has comparatively received less attention, and existing solutions rely mainly on max or averaging operations. The latter are clearly powerless to fully exhibit the actual temporal granularity of action categories and thereby constitute a bottleneck in classification performances. In this paper, we introduce a novel hierarchical pooling design that captures different levels of temporal granularity in action recognition. Our design principle is coarse-to-fine and achieved using a tree-structured network; as we traverse this network top-down, pooling operations are getting less invariant but timely more resolute and well localized. Learning the combination of operations in this network -- which best fits a given ground-truth -- is obtained by solving a constrained minimization problem whose solution corresponds to the distribution of weights that capture the contribution of each level (and thereby temporal granularity) in the global hierarchical pooling process. Besides being principled and well grounded, the proposed hierarchical pooling is also video-length and resolution agnostic. Extensive experiments conducted on the challenging UCF-101, HMDB-51 and JHMDB-21 databases corroborate all these statements.
Optimal Temporal Plan Merging

Agents can individually devise plans and coordinate to achieve common goals. Methods exist to factor planning problems into separate tasks and distribute the plan synthesis process, while reducing the overall planning complexity. However, merging distributedly generated plans becomes computationally costly when task plans are tightly coupled, and conflicts arise due to dependencies between plan actions. Existing methods either scale poorly as the number of agents and tasks increases, or do not minimize makespan, the overall time necessary to execute all tasks. A new algorithm, the Temporal Optimal Conflict Resolution Algorithm (TCRA*), is introduced to merge independently generated plans and optimally minimize the resulting makespan. A proof of optimality is provided and the algorithm is empirically evaluated across two heterogeneous multiagent domains against two baseline algorithms. The TCRA* results in better makespan across the problems solved, and a search relaxation constant allows the TCRA* to generate better plans with competitive processing time and memory usage.
Enhancing the Educational Activities of Law Students as One of the Ways to Improve Efficiency and Quality of the Professional Training

The article urges the problem of enhancing the academic activity of law students as one of the ways of improving the efficiency and quality of professional training. The nature and structure of academic activity of future lawyers are covered. Thus, academic activity is a form of a human activity, which structure is viewed through the prism of mastering a set of knowledge and methods of activity, moral and ethical values, as well as social relations. Academic activity of student youth promotes mastering of basic methods and practical experience of solving professional problems, development of planetary thinking and creativity, that is, it is a professional-oriented activity. In order to enhance the academic activity of law students, the features of its structure were investigated. In particular, the following components were selected: motivational, cognitive and practical.A questionnaire on the topic “Why did I choose the profession of a lawyer?” was conducted. The pedagogical experiment and psychological research based on two methodologies, Ilyina's "Motivation to study in higher educational institutions" and Ayzenko's method of figurative thinking "Find regularity", have been involved.An assessment of the impact done by interactive teaching methods of students, who are studying at the National Aviation University, the specialty "Law", has also been provided.The main ways to solve the problem of intensifying the educational activities of future lawyers have been identified. They include the development of communication, mental processes, motivation to study, active involvement of students in educational activities, and taking responsibility for studying in the higher educational establishment. Problems of interaction between teachers and students while training and increasing students' independence need further development.
Text Detection on Roughly Placed Books by Leveraging a Learning-based Model Trained with Another Domain Data

Text detection enables us to extract rich information from images. In this paper, we focus on how to generate bounding boxes that are appropriate to grasp text areas on books to help implement automatic text detection. We attempt not to improve a learning-based model by training it with an enough amount of data in the target domain but to leverage it, which has been already trained with another domain data. We develop algorithms that construct the bounding boxes by improving and leveraging the results of a learning-based method. Our algorithms can utilize different learning-based approaches to detect scene texts. Experimental evaluations demonstrate that our algorithms work well in various situations where books are roughly placed.
Research Guides: Time Magazine Archive : How to Search

This guide shows techniques on searching the Time Magazine Archive, containing all issues from 1923 to 2000.
Application of web-resources in the student students education process higher education

Organichna himiya v shemah: navch.posibnik u 3 ch.[Organic chemistry in the schemes
Ivan Pesin (to his 90th anniversary)

The note is devoted to biography and achievements of Ukrainian mathematician Ivan Pesin (1930--1993).
The Evolution and Application of Design in Visual Communication and its Influence in Social Behavior

Today with the evolution of technology, interactive processes and transmedia narratives, social behavior in communication is changing. It is growing rapidly and influencing consumers of content and for audiences, the main objective is to study how communication is becoming increasingly iconic to the point that feelings begin to be plotted, such is the case of emoticons or emojis in short messages. For this purpose, a methodology based on scientific and bibliographical research on communicative language types and individual behavior is used, coupled with an exploratory field analysis based on user experiences on mobile devices. These results of the research will strengthen the study of trans-disciplinary contents that focus specifically on the application and generation of creative ideas in the field of production, interaction or realization of a communicative or interactive product. That using technology as a support and platform, the communicator or creator of communicative products creates needs in the users.&#x0D; Something very different happens in creativity, this is emotional, an awareness of what is wanted to be informed and how can it be informed, ¿what is its functionality? how do we want to reach the consumer this kind of creativity comes through the evolution of experiences.
DiDi Labs’ End-to-end System for the IWSLT 2020 Offline Speech TranslationTask

We describe the DiDi Labs system submitted for the IWSLT 2020 Offline Speech Translation Task (Ansari et al., 2020).We trained an end-to-end system that translates audio from English TED talks to German text, without producing intermediate English text.Our base system used the S-Transformer architecture (Di Gangi et al., 2019b), trained using the MuST-C dataset (Di Gangi et al., 2019a).We extended the system via decoder pre-training, pre-trained speech features, and text translation, but these extensions did not yield improved results.
Game-Theoretic Perspectives and Algorithms for Cybersecurity

Information plays a key role in many games, and game theory includes reasoning about how agents should perceive signals, and how they should strategically decide what signals to send. This can involve complex tradeoffs about how revealing certain information will affect the beliefs and actions of other players. I will overview some basic approaches for modeling information in game theory, such as signaling games, and applications to games such as Poker. The second part of the talk with focus on our work applying game theoretic models and algorithms in cybersecurity. I will discuss how we apply game theory to optimize strategies for deception in cybersecurity, including honeypots, honey traffic, and other deceptive objects. I will also cover work that considers dynamic deception using sequential models that capture uncertainty. Finally, I will discuss some recent work in adversarial learning and connections between this area and game theory.
Trust Execution Environment and Multi-party Computation for Blockchain e-Health Systems

Blockchain is a rich and attractive domain for researchers since it is independent of “third party” such as Bank or government. This “open” phenomenon does not respect all the security criteria such as private data protection and confidentiality; hence, we cannot trust this approach despite its contributions. Blockchain technology has gained considerable progress in recent years in fields such as e-health. The medical data contains personal and sensitive information that must be preserved. The current Blockchain systems suffer from serious practical limitations, e.g. poor performance, high-energy consumption and lack of confidentiality. On the other hand, Trust Execution Environment TEE is imperfect; it is based on the centralization of data. To avoid data centralization and its limitations, an approach based on collecting the necessary data from distributed database is presented in this paper. Our goals are to protect the user’s privacy and to execute it in TEE combined with Multi-party computation MPC. We proof by security analysis that our new solution meets the fundamental criteria of security such as confidentiality and privacy.
Making Sound Decisions in Game Audio

The audio department needs to have ownership of the game – having access to the build is essential to ensuring that the audio is at its best quality. In the realm of indie game audio, voice is often regarded as a tertiary consideration in comparison with music and sound design. However, good voice acting can make or break a game. The addition of voice to game developers game project can hugely add to their game theme and identity. In narrative-heavy games such as games from Bioware, a great voice performance is essential to player immersion. Determining the behaviour of sound playback in-game is a core role of implementation and also an integral part of game audio in general. The main difference between game audio and linear audio for films, TV and podcasts is its interactivity; if one watches a film, both the audio and visuals will play out exactly as intended at the exact timecode it was told to play.
Online Management and Query of Multimodal Human Motion and Shape Data

Recent research and development in artificial intelligence for robotic systems, such as self-driving cars, point to the increasing need for integrated multi-modal human signature data. In this paper, we introduce the developmental effort on a prototype multimodal human Motion and Shape Analysis System (MSAS). MSAS stores and manages physically collected multi-modal human shape and motion data as well as additional augmented modality data such as simulated LIDAR (Light Detection and Ranging) partial point clouds created through data synthetization. We highlight cross-modality metadata integration and search as well as online 3D content-based retrieval and visualization. The goal of MSAS is to harvest the synergy among various modalities by bringing them together under a standard and structured framework to support data exploitation.
Caracterization of the Green Pointer Laser Beam Value as an Obstacle Sensor on Various Background Colors

Abstract Obstacle detection sensor is an important development tool of autonomous vehicles in recognizing its environment. Green laser pointer is an example of a sensor in the form of visibe light beam to detect the obstacles that are in the track of the tractor. This research aimed to Determine the variation of green laser beam as an obstacle sensors in different backgrounds and different intensity of light. This system used a CMOS camera and cctv lense 0-60 mm of focal length as the prime sensor and a green laser pointer at 532±10 nm with a maximum of 98 0 mW output power. Asicap software used to capture the image of the green laser pointer and then the image processed by using ArcMap 10.5 to determine the coordinat of the green laser and its RGB pixels. The camera is capable to detect the green laser pointer beam with intensities up to 50000 lux° with Temperature 51.6 ° C and 40.3% of humadity from 2 meters until 7 meters from Potition captured. From the tests carried out it appears that the normalization value of green laser light, especially for the maximum and minimum values of the components of green on various types of objects look the same. Especially for the minimum green value. This states that the various types of green laser beam sighting objects have the same minimum value, so the coding program will be the same.
AN EFFICIENT METHOD FOR REMOVAL OF MULTIPLE NOISES USING CASCADE FILTERING AND HYBRID SEGMENTATION OF BRAIN ABNORMAL GROWTH (TUMOR) FROM MAGNETIC RESONANCE IMAGES

Image denoising and fragmentation are the two most testing fields in clinical image processing mainly when it is application precise. The occurrence of clamor not only corrupts the visual eminence but also enormously affects the accurateness of segmentation which is necessary for medical diagnosis procedure. The brain tumor recognition using Magnetic Resonance Imaging (MRI) is very significant but difficult task which further used in medical field for the recognition of tumor. In the projected method, a superior procedure for denoising and division of 2-D magnetic resonance brain images for detecting the abnormal growth (tumor) is discussed. A new and well-organized fusion decision based filtering procedure for the elimination of high compactness Impulse Noise and Additive White Gaussian noise in images is proposed. The algorithm implements two stages were the first stage is prepared to remove the noise and restore the image. Here a cascaded filter employing a Modified adaptive based Median Filter for elimination of high concentration impulse noise and a novel algorithm that removes Gaussian noise and Blurredness in the image. The output of the first stage is given as input to the second stage, where a Hybrid fragmentation method is implemented to differentiate the brain tumor region in the medical image. To articulate the efficiency of proposed technique, the outcomes are compared with the existing filters such as Adaptive, median and Average filter where the proposed procedure produces a better Peaksignal to Noise Ratio (PSNR). Disadvantage in present methods is rectified in our projected method. It suppresses noise, protect image characteristics, and also enhance visual quality. From the results, it has been experimental that designed technique has worked efficiently by preserving the edges and fine lines.
Complex Emotional Intelligence Learning Using Deep Neural Networks (Student Abstract)

Emotion recognition and mining tasks are often limited by the availability of manually annotated data. Several researchers have used emojis and specific hashtags as forms of training and supervision.This research paper proposes a new textual and social corpus, the corpus labeled using basic emotions following Plutchik's theory. Thus, This paper propose a first study for the representation and interpretation of complex emotional interactions, using deep neural networks.
Simultaneous diagonalization of incomplete matrices and applications

We consider the problem of recovering the entries of diagonal matrices $\{U_a\}_a$ for $a = 1,\ldots,t$ from multiple incomplete samples $\{W_a\}_a$ of the form $W_a=PU_aQ$, where $P$ and $Q$ are unknown matrices of low rank. We devise practical algorithms for this problem depending on the ranks of $P$ and $Q$. This problem finds its motivation in cryptanalysis: we show how to significantly improve previous algorithms for solving the approximate common divisor problem and breaking CLT13 cryptographic multilinear maps.
Pengembangan e-disposisi Menggunakan Metode Exreme Programming (Studi Kasus BPKAD Kotawaringin Timur)

Abstract – The process of disposition of letters in BPKAD Kotawaringin Timur so far still relies on a manual process. The manual process will certainly not escape human error or the difficulty of tracking the disposition of letter history and can complicate the archiving process. Of these problems, of course an information system application is needed that can be used to handle the letter disposition process. The choice that is very likely to be implemented is to develop the system by direct appointment with a small number of programmers according to their budget.To develop the information system, it is necessary to determine a development method that is appropriate to the conditions of the agency. Of the several methods available, Extreme Programming is a software engineering process that tends to use an object oriented approach and the target of this method is a team formed on a small to medium scale. The extreme programming method can be used to create e-disposition applications with all the limitations of resources both in terms of HR and budget. The test results show that this application meets the acceptance criteria set forth in the user stories, so this application is fit for usewriting English title with a maximum of 10 words. Abstract is written here with a single paragraph. Abstract cover of introduction, methods and results achieved. Abstract includes reasons for selecting a topic or topics the importance of research, research methods and summary of results without any reference to the bibliography should describe research done explicitly by the sentences straightforward and clear. Abstracts must end with remarks about the importance of the results or conclusions shortly. A maximum of 200 words in Indonesian Times New Roman 10 point
A Tag Management System for Cross Search among Data Repositories

近年, 世界では論文や研究データを始めとした研究成果などに対してアクセスを容易にし, データの発信, 共有, 再利用を促すことを目的としたオープンサイエンスと呼ばれる試みが活発になってきているこれに伴って国内でも既存のリポジトリソフトウェアを利用して，論文だけでなく，研究データを対象としたデータリポジトリの構築が行われている．しかし，データリポジトリとして利用することを考えた場合，メタデータを画一化できないことなどから，ユーザが所望したデータにたどりつけないなど，現状では共有，再利用は容易ではない．そこで, 本研究ではこの問題を解決しより利便性を向上させるインターフェースの開発を目的としている. 本インターフェースは, ユーザがより容易にアイテムを共有, 再利用できるよう，アイテムにタグを割り振ることによって，メタデータに欠けているものを補完し管理を行うシステムである.