Reviewerly/exHarmony huggingface dataset:
Self-Supervised Tuning for Few-Shot Segmentation

Few-shot segmentation aims at assigning a category label to each image pixel with few annotated samples. It is a challenging task since the dense prediction can only be achieved under the guidance of latent features defined by sparse annotations. Existing meta-learning method tends to fail in generating category-specifically discriminative descriptor when the visual features extracted from support images are marginalized in embedding space. To address this issue, this paper presents an adaptive tuning framework, in which the distribution of latent features across different episodes is dynamically adjusted based on a self-segmentation scheme, augmenting category-specific descriptors for label prediction. Specifically, a novel self-supervised inner-loop is firstly devised as the base learner to extract the underlying semantic features from the support image. Then, gradient maps are calculated by back-propagating self-supervised loss through the obtained features, and leveraged as guidance for augmenting the corresponding elements in embedding space. Finally, with the ability to continuously learn from different episodes, an optimization-based meta-learner is adopted as outer loop of our proposed framework to gradually refine the segmentation results. Extensive experiments on benchmark PASCAL-$5^{i}$ and COCO-$20^{i}$ datasets demonstrate the superiority of our proposed method over state-of-the-art.
Study of the Indicators of Climate Change in Mysore District, Karnataka, India

The study aims to find out the changes of climate change parameters for Mysore district.To analyse the variations in climatic parameters like Rainfall and Temperature, data were used for a period of 1986-2016, and Humidity data were used for a period of 2000-2016 to study the nature of climate change in the region.The findings of the study show that the climate variability and climate change for Mysore district, the monthly Rainfall is increased and monsoon Rainfall shows a decrease in trend.And the monthly, summer, monsoon minimum Temperature shows that decrease.Generally, the monthly maximum Temperature has decreased, but in case of summer and monsoon seasons increased respectively, and the monthly Relative Humidity has increased (positive).There is a decrease in Relative Humidity from January to April, and increase in the Relative Humidity during Monsoon season for Mysore District.The Annual average of Relative Humidity shows that it is increased for Mysore District.
Towards a Semantic Annotation of Pedagogical Unstructured Documents

The proliferation of information in the form of unstructured documents has steadily increased in different areas. However, in the education sector, most e-learning systems do not include any mechanism that can support this diversity. The interest of ontologies in the field of education has been emphasized because it allows more efficient description and retrieval of content, thus facilitating exchange and sharing between teachers and learners. To achieve this goal, a scalable architecture for the educational field becomes paramount, based on the standardization of data to transform them into FAIR data using semantic annotations. In this paper, we examine key contributions related to the development and use of ontologies in e-learning systems. We provide a critical framework for these studies, explaining how ontologies can be used to describe learning objects, and we propose a new ontology that will annotate unstructured documents, and we will talk towards the end of our future work.
Resources: A Safe Language Abstraction for Money

Smart contracts are programs that implement potentially sophisticated transactions on modern blockchain platforms. In the rapidly evolving blockchain environment, smart contract programming languages must allow users to write expressive programs that manage and transfer assets, yet provide strong protection against sophisticated attacks. Addressing this need, we present flexible and reliable abstractions for programming with digital currency in the Move language [Blackshear et al. 2019]. Move uses novel linear [Girard 1987] resource types with semantics drawing on C++11 [Stroustrup 2013] and Rust [Matsakis and Klock 2014]: when a resource value is assigned to a new memory location, the location previously holding it must be invalidated. In addition, a resource type can only be created or destroyed by procedures inside its declaring module. We present an executable bytecode language with resources and prove that it enjoys resource safety, a conservation property for program values that is analogous to conservation of mass in the physical world.
Online Constrained Model-based Reinforcement Learning.

Applying reinforcement learning to robotic systems poses a number of challenging problems. A key requirement is the ability to handle continuous state and action spaces while remaining within a limited time and resource budget. Additionally, for safe operation, the system must make robust decisions under hard constraints. To address these challenges, we propose a model based approach that combines Gaussian Process regression and Receding Horizon Control. Using sparse spectrum Gaussian Processes, we extend previous work by updating the dynamics model incrementally from a stream of sensory data. This results in an agent that can learn and plan in real-time under non-linear constraints. We test our approach on a cart pole swing-up environment and demonstrate the benefits of online learning on an autonomous racing task. The environment's dynamics are learned from limited training data and can be reused in new task instances without retraining.
An Algebraic Approach for the Search Space of Permutations with Repetition

We present an algebraic approach for dealing with combinatorial optimization problems based on permutations with repetition. The approach is an extension of an algebraic framework defined for combinatorial search spaces which can be represented by a group (in the algebraic sense). Since permutations with repetition does not have the group structure, in this work we derive some definitions and we devise discrete operators that allow to design algebraic evolutionary algorithms whose search behavior is in line with the algebraic framework. In particular, a discrete Differential Evolution algorithm which directly works on the space of permutations with repetition is defined and analyzed. As a case of study, an implementation of this algorithm is provided for the Job Shop Scheduling Problem. Experiments have been held on commonly adopted benchmark suites, and they show that the proposed approach obtains competitive results compared to the known optimal objective values.
THE ROLE OF STUDENT YOUTH IN THE CONSTRUCTION OF ANTI-CORRUPTION CIVIL SOCIETY: ILLUSION OR REALITY?

&#x0D; The article discusses the role and participation of students in building a «new» type of civil society from the perspective of an anti-corruption component. According to the authors, the most important criterion for the structure and construction of statehood is the propaganda of a negative attitude to corruption as a phenomenon that undermines public confidence in government bodies, government institutions, and public social institutions. The problem of the fight against corruption is relevant and at the present stage of the formation of our state requires active role participation. An important role and significance is assigned to students in the formation of the basic and key areas of modern state strategies in the anti-corruption vector. The authors substantiate the special role of students in the construction of a new formation by the absence of stable stereotypes due to age. The article presents and analyzes the results of a study on the attitude of students to fundamental changes in the Government of the Russian Federation, reveals ideas about the program for the development of the state and society in the near future from the perspective of students' opinions.
QISS: An Open Source Image Similarity Search Engine

Qwant Image Similarity Search (QISS) is a multi-lingual image similarity search engine based on a dual path neural networks that embed texts and images into a common feature space where they are easily comparable. Our demonstrator, available at http://research.qwant.com/images , allows real-time searches in a database of approximately 100 million images.
QSystem: bitwise representation for quantum circuit simulations

We present QSystem, an open-source platform for the simulation of quantum circuits focused on bitwise operations on a Hashmap data structure storing quantum states and gates. QSystem is implemented in C++ and delivered as a Python module, taking advantage of the C++ performance and the Python dynamism. The simulators API is designed to be simple and intuitive, thus streamlining the simulation of a quantum circuit in Python. The current release has three distinct ways to represent the quantum state: vector, matrix, and the proposed bitwise. The latter constitutes our main results and is a new way to store and manipulate both states and operations which shows an exponential advantage with the amount of superposition in the systems state. We benchmark the bitwise representation against other simulators, namely Qiskit, Forest SDK QVM, and Cirq.
Decoupled Gradient Harmonized Detector for Partial Annotation: Application to Signet Ring Cell Detection

Early diagnosis of signet ring cell carcinoma dramatically improves the survival rate of patients. Due to lack of public dataset and expert-level annotations, automatic detection on signet ring cell (SRC) has not been thoroughly investigated. In MICCAI DigestPath2019 challenge, apart from foreground (SRC region)-background (normal tissue area) class imbalance, SRCs are partially annotated due to costly medical image annotation, which introduces extra label noise. To address the issues simultaneously, we propose Decoupled Gradient Harmonizing Mechanism (DGHM) and embed it into classification loss, denoted as DGHM-C loss. Specifically, besides positive (SRCs) and negative (normal tissues) examples, we further decouple noisy examples from clean examples and harmonize the corresponding gradient distributions in classification respectively. Without whistles and bells, we achieved the 2nd place in the challenge. Ablation studies and controlled label missing rate experiments demonstrate that DGHM-C loss can bring substantial improvement in partially annotated object detection.
Novel and Diverse Recommendations by Leveraging Linear Models with User and Item Embeddings

Nowadays, item recommendation is an increasing concern for many companies. Users tend to be more reactive than proactive for solving information needs. Recommendation accuracy became the most studied aspect of the quality of the suggestions. However, novel and diverse suggestions also contribute to user satisfaction. Unfortunately, it is common to harm those two aspects when optimizing recommendation accuracy. In this paper, we present EER, a linear model for the top-N recommendation task, which takes advantage of user and item embeddings for improving novelty and diversity without harming accuracy.
Unsupervised Ensemble of Ranking Models for News Comments Using Pseudo Answers

Ranking comments on an online news service is a practically important task, and thus there have been many studies on this task. Although ensemble techniques are widely known to improve the performance of models, there is little types of research on ensemble neural-ranking models. In this paper, we investigate how to improve the performance on the comment-ranking task by using unsupervised ensemble methods. We propose a new hybrid method composed of an output selection method and a typical averaging method. Our method uses a pseudo answer represented by the average of multiple model outputs. The pseudo answer is used to evaluate multiple model outputs via ranking evaluation metrics, and the results are used to select and weight the models. Experimental results on the comment-ranking task show that our proposed method outperforms several ensemble baselines, including supervised one.
Enabling Incremental Knowledge Transfer for Object Detection at the Edge

Object detection using deep neural networks (DNNs) involves a huge amount of computation which impedes its implementation on resource/energy-limited user-end devices. The reason for the success of DNNs is due to having knowledge over all different domains of observed environments. However, we need a limited knowledge of the observed environment at inference time which can be learned using a shallow neural network (SHNN). In this paper, a system-level design is proposed to improve the energy consumption of object detection on the user-end device. An SHNN is deployed on the user-end device to detect objects in the observing environment. Also, a knowledge transfer mechanism is implemented to update the SHNN model using the DNN knowledge when there is a change in the object domain. DNN knowledge can be obtained from a powerful edge device connected to the user-end device through LAN or Wi-Fi. Experiments demonstrate that the energy consumption of the user-end device and the inference time can be improved by 78% and 71% compared with running the deep model on the user-end device.
Changes in the professional functions of the teacher in modern school

The enhancements of education place new demands on the professional functions of the teacher. As nowadays the teacher plays an important role in forming a new, informational type of society, they ought to know the variety of scientific methodological approaches, to use efficiently advanced information technologies and to implement new pedagogical ideas into the educational process. The article examines the changes that have occurred in the professional functions of the teacher under the influence of the social and educational environment. Innovation processes reveal the need to point out the academic function as the most important one for now.
Adopting the JPEG systems layer to create interoperable imaging ecosystems

JPEG Systems aims to lay the groundwork to build uniform applications and extensions on top of the various JPEG coding standards. In 1992 JPEG released the first JPEG standard which is still the most commonly used image file format to date. However, over the years, JPEG developed several other standards that became popular in specific domains. JPEG 2000 for example is widely adopted in medical imaging, digital cinema and geographical information systems. The original JPEG file format supported extensions by using app marker segments. These app marker segments allow to embed chunks of data in an image file. The content will be ignored if the it is not recognized by the decoder. Hence, it allows to build extensions while retaining backwards compatibility with legacy decoders. JPEG 2000 and several other newer formats on the other hand use box based formats. Boxes are containers of data with a specific type that allow more flexibility with respect to length and structure compared to marker segments. Again, if the decoder doesn't recognize the particular box type the content will be ignored. JPEG XT defines extensions to the legacy JPEG file format such as transparency, HDR support and a lossless mode. To this end, JPEG XT introduced a file format which allows to embed boxes in app marker segments. Using this principle, new extensions can be uniformly defined across the various JPEG file formats, i.e. by defining boxes. Therefore, JPEG Systems defines tools and extensions by the definition of boxes. The JPEG Universal Metadata Box Format (JUMBF) allows to embed any type of metadata in any type of JPEG image file and supports the possibility to reference this data from within or outside this image. Using this structure, JPEG Privacy and Security, 360 and JLINK define mechanisms to support privacy and security tools, 360 images and linked images respectively. This paper gives an overview of the JPEG Systems architecture and illustrates its working by introducing JPEG 360 and the recently initiated activity JLINK.
Coupled Training of Sequence-to-Sequence Models for Accented Speech Recognition

Accented speech poses significant challenges for state-of-the-art automatic speech recognition (ASR) systems. Accent is a property of speech that lasts throughout an utterance in varying degrees of strength. This makes it hard to isolate the influence of accent on individual speech sounds. We propose coupled training for encoder-decoder ASR models that acts on pairs of utterances corresponding to the same text spoken by speakers with different accents. This training regime introduces an L2 loss between the attention-weighted representations corresponding to pairs of utterances with the same text, thus acting as a regularizer and encouraging representations from the encoder to be more accent-invariant. We focus on recognizing accented English samples from the Mozilla Common Voice corpus. We obtain significant error rate reductions on accented samples from a large set of diverse accents using coupled training. We also show consistent improvements in performance on heavily accented samples (as determined by a standalone accent classifier).
THE PROBLEM OF QUALITATIVE METHODS APPLICABLE IN THE PSYCHOLOGY OF PERSONALITY

The article argues the historical confrontation and the mutual additions of qualitative and quantitative methods in psychology, and it presents the important systematized methodological aspects as for the application of qualitative methods in personality psychology. These aspects are: substantiation of qualitative research as the most appropriate methodology for the holistic study of a person as a personality from the standpoint of the systematic approach; combination of the diagnostics and psychological influences during dialogic interactions of a psycho­logist-researcher and a psychologist-practitioner (as two roles of one person) with a client in a psychotechnical situation; the characteristic features of qualitative research (holism, identified meanings and their interpretations, contextuality, reflexivity, interactivity, absence of directives, inductive approach to data, correspondence to situations, attention to individual cases, etc.); the types of qualitative studies - diagnostic, exploitative, verificatory or exploitative-verificatory - and variants of their combination; classification of methodological approaches that help chose certain qualitative methods based on their philosophical foundations, research purpose and objects, cognition principles and technologies; the methodological principles of qualitative research – conceptual sensi­tivity, interpretive reconstruction, reflexivity; the validity criteria for qualitative methods with taking into account a research stage (a level in the structure) – planning and data collecting, their analysis, data interpretation and presentation, ethical validity – and the structure of a final scientific work prepared by the research results (requirements for introduction, literature review and theoretical analysis, an empirical part, conclusions, possible applications). The conclusion is made about the relevance of qualitative researches at the modern meta-modern stage of the psychological science on personality&#x0D;
Immersiveness and Usability in VR: AComparative Study of Monstrum and Fruit Ninja

VR is a new medium, and standards and techniques for understanding the power of VR are just beginning to be adopted. The aim of this study is to look at the immersive potential of VR games physiologically and perceptually, using the horror game Monstrum and the arcade game Fruit Ninja. Players were run through Monstrum while talking aloud about the gameplay and then surveyed about their experience. To make a comparative study of VR and non-VR experiences, another study saw participants play Fruit Ninja Mobile and Fruit Ninja VR while recording galvanic skin response (GSR) during gameplay. It was found that, while Monstrum is immersive, issues like unintuitive UI and unappealing game AI dampened the experience, with motion sickness being a chief complaint. Meanwhile, Fruit Ninja VR was conducive for player engagement and immersion, with comparable instances of action gameplay sequences showing elevated numbers of GSR peaks per minute over the 2D Mobile version. This shows that emotional arousal for the same game is elevated in VR. Qualitative user responses corroborate the idea of a more immersive VR experience due to increased role taking over the 2D version, giving rise to possible design strategies for VR.
Exploring the Proportion of Content Represented by the Metadata of Research Articles

In this era, to find out relevant research articles is considered an important task to track the state-of-the-art-work, and it is termed as research paper recommender system. Considering the massive increase in research corpora, the research community has turned its focus towards finding the most relevant research papers. Researchers have adopted different techniques that are bibliographic information based, content-based, and collaborative filtering based. The most common approach for the research paper recommender system is content-based. According to a survey, 55% of research paper recommender systems use a content-based approach. On the other hand, due to the unavailability of the full text of research papers, researchers started utilizing the Meta-data. But it is still unclear that what proportion of full content can be represented by the Meta-data. This research explored the significant portion of the full content contained by the Metadata of research articles. We applied two different techniques; in the first technique, we implemented the TF-IDF over Metadata and full content and considered the intersection of key terms. Secondly, similarity scores of Meta-data and full content were calculated by applying cosine similarity. This approach was assessed on a dataset of 271 research articles that were automatically downloaded from CiteseerX. The results revealed that the Meta-data of research articles could effectively represent the 47% proportion.
Comparison of Modern and Traditional Ways of Perceiving Information about Selected Cultural Heritage Objects

Abstract This paper maps out the possibilities of using virtual and augmented reality in the context of virtual museums and galleries. In addition to the many advantages that virtual reality offers in new knowledge acquisition and presentation of cultural heritage objects, we also describe some possible disadvantages or problems directly related to this technology. Next, we try to find the answer to whether the presentation of selected objects of cultural heritage through virtual reality brings better results compared to the presentation in a traditional, museal form in the research part of the article. In conclusion, we summarise and present the results of the conducted research based on the statements of 138 students who participated in our testing.