Reviewerly/exHarmony huggingface dataset:
Hybrid Systems Verification with Isabelle/HOL: Simpler Syntax, Better Models, Faster Proofs.

We extend a semantic verification framework for hybrid systems with the Isabelle/HOL proof assistant by an algebraic model for hybrid program stores, a shallow expression model for hybrid programs and their correctness specifications, and domain-specific deductive and calculational support. The new store model yields clean separations and dynamic local views of variables, e.g. discrete/continuous, mutable/immutable, program/logical, and enhanced ways of manipulating them using combinators, projections and framing. This leads to more local inference rules, procedures and tactics for reasoning with invariant sets, certifying solutions of hybrid specifications or calculating derivatives with increased proof automation and scalability. The new expression model provides more user-friendly syntax, better control of name spaces and interfaces connecting the framework with real-world modelling languages.
Intelligent Question Answering Module for Product Manuals

Question Answering (QA) has been a well-researched NLP problem over the past few years. The ability for users to query through information content that is available in a range of formats - organized and unstructured - has become a requirement. This paper proposes to untangle factoid question answering targeting the Hi-Tech domain. This paper addresses issues faced during document question answering, such as document parsing, indexing and retrieval (identifying the relevant documents) as well as machine comprehension (extract spans of correct answers from the context). Our suggested solution provides a comprehensive pipeline comprised of document ingestion modules that handle a wide range of unstructured data across various sections of the document, such as textual, images, and tabular content. Our studies on a variety of “real-world” and domain-specific datasets show how current fine-tuned models are insufficient for this challenging task, and how our proposed pipeline is an effective alternative.
Efficient Word Size Modular Arithmetic

Published in "IEEE Transactions on Emerging Topics in Computing, Volume: 9, Issue: 3, JulySeptember 2021" and orally presented at ARITH 2021.
Data Extract: Hickey21_Expansive_Framing

Contains queries for generation of data sets for further analytic and qualitative analyses.
Application of Online and Offline Mixed Teaching Mode Based on ASP.NET Technology in Law Education in Colleges and Universities

With the rapid development of Internet technology, it has affected all aspects of society, including college education. The use of modern educational technology such as ASP.NET in college legal education can improve the intuitiveness and accuracy of the teaching process, enrich the teaching methods, and the integration of online and offline teaching mode can also better deepen students' understanding of knowledge. After analyzing the status quo of law teaching in traditional colleges and universities, this article finds deficiencies. Through experimentation and comparison, it is found that the use of ASP. NET network multimedia technology adopts a new teaching mode integrating online and offline, which can effectively control the learning process of students, enhance the teaching effect, and achieve the goal of continuous improvement.
Deep Sequoia corpus - PARSEME-FR corpus - FrSemCor

The Sequoia corpus is a set of 3,099 linguistically-annotated French sentences, originating from four sources (Europarl, European Agency Reports, French regional journal L'Est Republicain, and French wikipedia).Several types of annotations were added over the years.The current release comprises: - parts-of-speech (SEQUOIA ANR-08-EMER-013 project) - syntactic dependency trees - deep syntactic dependency graphs (Deep sequoia project) - multi-word expressions and named entities (PARSEME COST project and PARSEME-FR ANR-14-CERA-0001 project) - coarse semantic tags for nouns (FrSemCor project)See the deep sequoia page for a detailed description: https://deep-sequoia.inria.fr/
Travel Buddy: Travel Companion using Knowledge Graph Attention Networks

Tourism has always been an important sector in India. With industries gradually moving to the internet, the travel and tourism industry has followed suit, but still, it has failed to provide a full-fledged online solution for travellers or businesses. The objective of this paper is to create an all-in-one travelling application, Travel Buddy, that will facilitate the online tourism industry using the Knowledge Graph Attention Network model for recommendations. The app focuses on user interactions and personalized recommendations. The customer, i.e., the traveller can plan trips, compare hotels, reserve rooms and get relevant assistance during the trip along with personalized recommendations on each step. This app will give the customers curated suggestions and make their experience more memorable. It also features a community section for travellers with similar interests to interact through blogs. It acts as a medium for travel agencies to showcase their packages and local tourism-related businesses to list their business. The proposed system demonstrates this app that accommodates both the users and provides good quality recommendations using the state-of-the-art KGAT model.
Cryptocurrency-Based Payment Framework for EV Charging

The world is transforming from paper or plastic currency towards digital currency. Everyone is trying to benefit from the decentralized system, the anonymity, and lower transaction cost offered by cryptocurrencies. One of the hurdles in the ascension of electric vehicle is its charging and the transaction for charging. In this chapter, a cryptocurrency-based payment framework is formulated for electric vehicle charging to eliminate the third-party involvement as in the plastic currency system. In this payment framework, smart contracts are deployed which would ensure the conditions to be satisfied, like the amount sent by the consumer. Smart contract includes the transfer function that would transfer the amount of ether and set charging time according to the charging rate, amount of ether sent, and power delivered. Overview of the platform prototype of payment is presented to demonstrate the implementation of the framework in the real world. At last, the authors look at the security issues, challenges ahead, and also suggested some of the improvements that could be further done.
Uncertainty-aware Topic Modeling Visualization

Topic modeling is a state-of-the-art technique for analyzing text corpora. It uses a statistical model, most commonly Latent Dirichlet Allocation (LDA), to discover abstract topics that occur in the document collection. However, the LDA-based topic modeling procedure is based on a randomly selected initial configuration as well as a number of parameter values than need to be chosen. This induces uncertainties on the topic modeling results, and visualization methods should convey these uncertainties during the analysis process. We propose a visual uncertainty-aware topic modeling analysis. We capture the uncertainty by computing topic modeling ensembles and propose measures for estimating topic modeling uncertainty from the ensemble. Then, we propose to enhance state-of-the-art topic modeling visualization methods to convey the uncertainty in the topic modeling process. We visualize the entire ensemble of topic modeling results at different levels for topic and document analysis. We apply our visualization methods to a text corpus to document the impact of uncertainty on the analysis.
Using Code Generators when Creating Ready-Made Templates for Application Development

Code generation is basically about writing programs that write programs. Given today’s complex code-intensive frameworks, such as Java 2 Enterprise Edition (J2EE), Microsoft’s. NET and Microsoft Foundation Classes (MFC), it is becoming increasingly important that we use our skills to create programs which aid us in developing our applications. Generally speaking, the more complex the framework is, the more appealing you will find a code generation solution. Many object-oriented programming (OOP) languages lack reusability and flexibility, and require a similar code to be written repeatedly. This paper reviews the code generators that are most useful for implementation in applications for automatic code generations, their pros and cons, where they are most widely used nowadays, as well as their versatility. In Visual Studio we use the term “scaffolding” when we want to generate identities automatically. When we start a new ASP.NET project, it genrates a template where we start from.
Transient oscillations in steelpan drums tracked via machine learning

Applying a machine learning based object detector to high-speed videos of Caribbean steelpan drums illuminated by laser electronic speckle pattern interferometry (ESPI), we can track the development of sympathetic vibrations in response to drum strikes. This object detector was trained on a dataset of crowdsourced human-annotated images obtained in the Steelpan Vibrations Project on the Zooniverse platform. When the model is used to supply annotations to thousands of unlabeled video frames, we measure visual oscillations at frequencies consistent with audio recordings of these drum strikes. In this talk we share early physics results such as the unanticipated phenomenon of sympathetic oscillations of higher-octave notes that significantly precede the rise in sound intensity of the corresponding second harmonic tones; the mechanism responsible for this remains unidentified.
Adaptive central pattern generators to control human/robot interactions

The presentation will concern the use of bio-inspired robot controllers based on the functioning of specific biological sensorimotor loops that control biological systems. These loops are based on specific neural network structures, called central pattern generators (CPG) that are implied in the genesis and learning of adaptive rhythmic movements. Therefore, it is interesting to better understanding and modeling these structures to have humanoid robots able to learn rhythmic movements for locomotion or for interacting with humans. After a brief introduction on biological central pattern generators and the rhythmic movements, we will introduce the concept of synchronization a principle that underlies the rhythmic interaction between humans and the dynamic oscillators. Different models of central pattern generators based on dynamic oscillators will be introduced. The second part of the presentation will present several experiments of vision-based Human-Robot motor coordination using adaptive central pattern generators. Other experiments of robot teleoperation for industrial rhythmic tasks will be introduced. Several videos of simulations and experiments will illustrate the presentation. A conclusion and perspectives will conclude the talk.
Image haze removal based on rolling deep learning and Retinex theory

Multispectral remote sensing images are a very important data source, but its acquisition process is often affected by haze weather and other factors, resulting in the decline of image quality, blurred details and poor visual effect, which seriously affect their application and interpretation. To reduce the impact of haze on multispectral remote sensing image and improve the image clarity and the use value, a new haze removal method based on rolling deep learning and Retinex theory (RDLRT) was proposed here. It uses the rolling deep learning theory to realize the preliminary removal of image haze, and then calculates the peak signal-to-noise ratio (PSNR) value of the processed image, which is used to judge whether the obtained image is a stage effect image (SEI). Then, on the one hand, the SEI image is processed for colour saturation and brightness; on the other hand, the Retinex theory is used to further filter the haze of the SEI image. Finally, the results of the two processing are fused, and it is used as the restored image after removing the haze. A series of validation experiments were carried out with true multispectral remote sensing images, outdoor colour images and different methods, and good experimental results were obtained. The purpose of removing image haze and improving image quality is achieved. Through the comparison of these experiments, it is concluded that the RDLRT algorithm proposed in this paper can not only effectively remove haze in images, but also maintain good details and colour of the image, which has some popularization values for image pre-processing and restoration.
Object Segmentation based on Anti-disturb Network in Ecological Surveillance

Visual tele-observation is an effective way for ecological protection. Different from pedestrian or rigid body detection, wildlife recognition in natural scenes face more complex problems, such as the similarity of background and foreground, wild background clutter, deformation, and other factors, which will lead to a reduction in recognition accuracy. In this paper, We propose a novel segmentation network that is named T-S network to segment background and foreground. The network mainly consists of two parts: one is a tracking network. Considering the context similarity of images in the data set, the purpose of tracking is to ensure that the position of the object is accurately known when the same species in the images are synthesized into a video with interval frames; the other is the saliency network, which is the basis of the most likely position of the object obtained from the tracking network. Then, the saliency network is used to complete the object segmentation. To verify the effectiveness of the network and consider the size of the data set, a lightweight CNN is used to identify the network. Experiments show that the accuracy of task recognition is improved by 1.89% by using the segmentation method proposed in this paper.
ACADEMICIAN V.L. KOMAROV IN THE PERCEPTION OF CONTEMPORS

On the example of an interview with V.V. Bogatov and I.A. Urmina – by the authors of the book "Academician Komarov and His Time", prepared by the famous Moscow journalist Sergei Sharkshane, it is shown that the figure of Academician Vladimir Leontyevich Komarov, as the head of the USSR Academy of Sciences in 1931–1945, was not only underestimated in the public consciousness of his contemporaries, but also unjustifiably defamed, while archival documents suggest otherwise. In his article, Sergei Sharkshane suggested concentrating on the main thing - overcoming injustices.
Design and Implementation of college English Learning System based on Speech Recognition Technology

English is now the world's main international language, and the use of a wide range of English learning has been paid attention to by all countries. With the rapid development of Internet information technology, the use of multimedia in English teaching has become a hot topic in colleges and universities. At present, many English learning software in the market mostly integrate all kinds of resources, among which the speech recognition technology analyzes the function and performance of college English learning system. The college English learning system constructed in this paper aims at those learners who have poor mastery of basic English learning and want to improve their basic English learning. At the same time, as an auxiliary learning system, the system can also be used as a supplement to classroom teaching to provide help in tutoring and correcting the listening and pronunciation of learners in basic English learning, so as to improve the accuracy of learners' English learning.
Improving Semantic Association through Brainstorming Activities

Children with semantic difficulty have a very hard time understanding logical ideas relating to meaning. Enriching vocabularies to gain a better chance of expressing oneself meaningfully in oral and in written forms could be done with brainstorming activities. To understand better, there is a need to improve skills on semantics; the interpretation and meaning of words put together, sentence structures and symbols. The researcher found the use of brainstorming activities a means to improve students’ semantic association. Such activities give each group participant to bring out ideas openly with no hesitation to criticisms. These exercises serve as a tool in making students speak before a group to allow wilder ideas to be brought into open which unconsciously improve the semantic association.The researcher opts for a descriptive method through observation of the brainstorming activities, how students relate to the issues and how they will stand for their individual thoughts depending on their beliefs, educational background, cultural orientation, and social status.
Time Series Anomaly Detection Based on Graph Convolutional Networks

With the popularization of my country's electric vehicles and the continuous deepening of smart grid upgrades, the electricity consumption data of domestic power grid companies has increased significantly. Due to inadequate detection and clever power theft methods of users, a large number of abnormal power data appear in the power consumption data of power companies. Aiming at the power theft problem of power grid companies and abnormal power data, this article suggests a power data outlier detection model in view of the combination of graph convolution and characteristic pool. First, use the time a train of directed horizontal view algorithm to delegate the power data as graph data; then, use the graph convolutional neural network and feature pooling layer to detect the abnormality of the transformed graph data. The validity of the advanced model is proving by standard graph data set and power used record data set of the power grid company. The method proposed in this paper provides an effective solution for the power theft problem and abnormal power data in the power grid.
High quality recordings and transcriptions of speech via remote platforms

High quality recordings and transcriptions of speech are important to a wide variety of disciplines from linguistics and human language technology to biomedical screening, diagnosis and tracking. The wide availability of internet connections and powerful mobile devices offers low cost opportunities for collecting speech data at scale. But even in the era of open source software, numerous challenges remain. For example, video call applications such as Zoom have become widespread and allow for recording, but only provide lossy codecs with low frame rates, and often contain missing, repeated, or interpolated frames, as well as freezes, longer dropouts and other audio artefacts. LDC has recently developed a suite of tools to allow high quality internet-based audio recordings and transcription, with a premium on portability and flexibility, using secure cloud computing services for storage and back-end processing. We present here the current design and capabilities of our software, as well as availability in terms of open source code and app distribution. We also discuss future plans; planned capabilities include 2 + sided conversational recordings, connecting participants via the internet, a modern extension of Conversational Telephone Speech (CTS) collections.
Quantitative and Stream Extensions of Answer Set Programming.

Answer Set Programming has separately been extended with constraints, to the streaming domain, and with capabilities to reason over the quantities associated with answer sets. We propose the introduction and analysis of a general framework that incorporates all three directions of extension by exploiting the strengths of Here-and-There Logic and Weighted Logic.
EFFECT OF UTILIZING E-LEARNING AS A LEARNING MEDIA AND THE LEVEL OF DIGITAL LITERATURE ON STUDENT LEARNING OUTCOMES IN PRACTICUM COURSES

The purpose of this study was to investigate the effect of using e-learning as a learning medium and the level of digital literacy on student learning outcomes in practicum courses. The research was conducted with a quantitative approach with a survey method. The population in this study were STMIK Bina Patria students. Data collection techniques were carried out by giving questionnaires to respondents. The results showed that the use of e-learning as a learning medium and the level of digital literacy had a significant effect on student learning outcomes for practicum courses, namely the variable utilization of e-learning had a significance value of 0.000 (p-value &lt;0.05) and the variable digital literacy had a significant value. significance value of 0.035 (p-value &lt;0.05). The impact of this research can be a means of alternative solution for lecturer to teach practicum courses and improve the outcome of students learning.
A First Approach to Closeness Distributions

Probabilistic graphical models allow us to encode a large probability distribution as a composition of smaller ones. It is oftentimes the case that we are interested in incorporating in the model the idea that some of these smaller distributions are likely to be similar to one another. In this paper we provide an information geometric approach on how to incorporate this information and see that it allows us to reinterpret some already existing models. Our proposal relies on providing a formal definition of what it means to be close. We provide an example on how this definition can be actioned for multinomial distributions. We use the results on multinomial distributions to reinterpret two already existing hierarchical models in terms of closeness distributions.
Comparative Analysis of Network Forensic Tools on Different Operating Systems

This paper deals with the theoretical and practical elaboration of the mentioned topics, which gives an insight into digital forensics, network forensics, collection and analysis of digital evidence, and the tools with which the above is done. An insight into the comparison of tools is provided, which begins with the selection of tools according to the stated criteria and the description of the tools with the operating systems on which they run. The comparison itself begins with defining tasks to test functionality. Upon completion of the above tasks, the obtained results are recorded and later used to analyze the performance of the tool. After the tool comparison, the obtained results are scored and ranked, and then a conclusion is given about the acquired knowledge and experience during the preparation of this paper.
“Scaffolding” responses to digital forensic inquiries

Abstract As part of their role, digital forensic practitioners are often faced with case‐specific investigative inquiries which they must attempt to address and provide an appropriate answer to their client. Such investigative questions can take many forms wherein every instance, any response they offer must be reliable. To be reliable, any response to an inquiry must be derived from justifiable and robust processes and information, coined here as the underpinning “scaffolding.” This work offers a discussion of response scaffolding, what it is and why it is required in digital forensics with the three ways that this can be achieved outlined—via “past experience,” “targeted testing” or through reference to appropriate “existing bodies of knowledge.” Each one is examined in turn and their use is demonstrated via an overarching hypothetical example which is deployed and utilized throughout the analysis narrative for illustrative purposes. This article is categorized under: Digital and Multimedia Science &gt; Cybercrime Investigation