Reviewerly/exHarmony huggingface dataset:
Which Apple Keeps Which Doctor Away? Colorful Word Representations With Visual Oracles

Recent pre-trained language models (PrLMs) offer a new performant method of contextualized word representations by leveraging the sequence-level context for modeling. Although the PrLMs generally provide more effective contextualized word representations than non-contextualized models, they are still subject to a sequence of text contexts without diverse hints from multimodality. This paper thus proposes a visual representation method to explicitly enhance conventional word embedding with multiple-aspect senses from visual guidance. In detail, we build a small-scale word-image dictionary from a multimodal seed dataset where each word corresponds to diverse related images. Experiments on 12 natural language understanding and machine translation tasks further verify the effectiveness and the generalization capability of the proposed approach. Analysis shows that our method with visual guidance pays more attention to content words, improves the representation diversity, and is potentially beneficial for enhancing the accuracy of disambiguation.
Research on Reading Promotion of University Digital Library in the New Media Era

research-article Research on Reading Promotion of University Digital Library in the New Media Era Share on Authors: Wei Yang Army Academy of Artillery and Air Defense, China Army Academy of Artillery and Air Defense, ChinaView Profile , Xingwei Zhang Army Academy of Artillery and Air Defense, China Army Academy of Artillery and Air Defense, ChinaView Profile , Wenbo Fan Army Academy of Artillery and Air Defense, China Army Academy of Artillery and Air Defense, ChinaView Profile Authors Info & Claims ICISCAE 2021: 2021 4th International Conference on Information Systems and Computer Aided EducationSeptember 2021 Pages 1918–1922https://doi.org/10.1145/3482632.3484067Online:22 November 2021Publication History 0citation6DownloadsMetricsTotal Citations0Total Downloads6Last 12 Months6Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
An image cipher system based on networked chaotic map with parameter <i>q</i>

This paper models a networked coupling chaotic system with a fractional-like local map based on network topology and fractional theory. The proposed model contains more system parameters and a more relaxed coupling relationship. The entropy analysis also shows that the proposed model is more sensitive to the changes in the coupling parameter. Based on this model, an effective image cipher system is designed using universal scrambling and diffusion based on DNA encoding. The proposed method utilizes the generated pseudo-random signals from the networked chaotic map. The secret keys can be generated in parallel and the security is also improved due to the rise of complexity of the new chaotic map. Analysis and simulations confirm that the new chaotic map as well as the DNA encoding technology improves the effectiveness and robustness of the proposed encryption algorithm.
Selecting Ontologies for Reuse: Case of Constructing Hotel Room Ontology

In line with the goal of promoting interoperability among applications, ontology reuse is an encouraged practice. Selecting appropriate ontologies for reuse when constructing a domain ontology is a critical activity. It calls for the use of formal methodologies and quality metrics in order to provide a comprehensive rationale for the selection process. This research employs NeOn methodological guidelines and CLeAR in selecting and assessing ontologies for reuse while building a domain-specific ontology, the Hotel Room Ontology. Results of the ontology selection and assessment exercise led to the selection of Hontology, DogOnt and SOSA/SSN. The integrated approach to ontology selection gives a justifiable basis for decisions made in identifying ontologies for reuse.
IAD: Indirect Anomalous VMMs Detection in the Cloud-Based Environment

Server virtualization in the form of virtual machines (VMs) with the use of a hypervisor or a Virtual Machine Monitor (VMM) is an essential part of cloud computing technology to provide infrastructure-as-a-service (IaaS). A fault or an anomaly in the VMM can propagate to the VMs hosted on it and ultimately affect the availability and reliability of the applications running on those VMs. Therefore, identifying and eventually resolving it quickly is highly important. However, anomalous VMM detection is a challenge in the cloud environment since the user does not have access to the VMM. This paper addresses this challenge of anomalous VMM detection in the cloud-based environment without having any knowledge or data from VMM by introducing a novel machine learning-based algorithm called IAD: Indirect Anomalous VMMs Detection. This algorithm solely uses the VM’s resources utilization data hosted on those VMMs for the anomalous VMMs detection. The developed algorithm’s accuracy was tested on four datasets comprising the synthetic and real and compared against four other popular algorithms, which can also be used to the described problem. It was found that the proposed IAD algorithm has an average F1-score of 83.7% averaged across four datasets, and also outperforms other algorithms by an average F1-score of 11%.
Hourly Day-ahead Solar Energy Prediction For Supporting Smart Grid Implementation in Semau Island

Knowledge of solar power prediction is an important aspect for power generation planning as well as supporting operations in grid area that apply photovoltaic (PV) generators. Such an aspect is even more critical in the case of limited availability of measurement data. This paper presents a novel model for predicting daily specific solar radiation using predetermined artificial neural networks. Daily meteorological data are used to enrich the conventional artificial neural networks technique. For simulation purposes, solar data was collected at two locations with different meteorological conditions, namely Kupang, Timor Island and Waingapu, Sumba Island, both in East Nusa Tenggara province, Indonesia. Finally, the proposed method shows good performance so as to achieve the above mentioned prediction purposes.
Research on Security Technology Requirement for Zero Trust Security Product

Like “machine learning” and “AI,” Zero Trust has become one of cybersecurity’s latest buzzwords. Zero Trust security product becomes a new kind of network security products. Due to the lack of professional testing and evaluation system for Zero Trust security product, the basic security of Zero Trust security product cannot be guaranteed. Research on security technology requirement of Zero Trust security product is provided, and the security technology requirement would be used in the actual product testing, to further promote the development of network border security.
Experience in the development of operating models of microsatellites in the framework of scientific and educational programs

The article presents an analysis of a set of scientific and educational activities to increase the motivation of modern youth to engage in technical creativity in the aerospace industry. Special attention is paid to the experience of the European Space Agency and, in particular, the Norwegian Center for Space Education. The results and plans for the deployment of multilevel training of young specialists in collaboration with industrial enterprises of St. Petersburg, which, on the basis of modern technologies, organize youth projects for the development of microsatellites layouts are considered. The experience of participation in international youth competitions "CanSat" and the organization of similar competitions in Russia is noted.
Interaction Design for the Next Billion Users

As the first pandemic wave of Covid-19 hit India and the Indian Government announced a nationwide lockdown in March 2020, [] it left millions of interstate migrant workers/daily-wage earners without a source of livelihood. Many of them had to return to their hometowns, often hundreds of kilometers away, with many walking a major part or the entirety of the distance [].This situation called for a design that could empower migrant workers to thrive in the new normal. An app was designed to enable migrant workers to search for jobs, learn about micro-entrepreneurship, get mental health assistance and develop skills.In terms of usage and interaction, this app proposes several unique features - a result of researching about and understanding our target demographic. At the core of the interaction is a chatbot. It asks questions to the user through text and audio, but the user replies through clickable buttons only. This makes it easy, even for those who may not be literate. The keyboard has been removed from this app. Another unique feature is the usage of interactive videos to explain various things, such as micro-entrepreneurship, skill training, etc. These videos make use of buttons for the users to make choices.In this paper, we discuss these two new interactions that will introduce a visual input method and an interactive video interface which will be inclusive of those less-privileged in terms of literacy in our ever-expanding tech world.The contents of the paper are targeted toward helping the next billion users. However, some of the things proposed may go on to improve interactions for all.
Research on Oversampling Algorithm for Imbalanced Datasets Based On ARIMA Model

Imbalanced data classification is an important research topic in machine learning field. Conventional classification algorithms are not ideal for imbalanced datasets. In this paper, we propose an oversampling algorithm based on time series forecasting model. Based on the randomness of the data, the minority class data are transformed into time series. Then according to the particularity of time series forecasting, a series of tests before modeling are performed on the minority class data, which ensure that the conversion sequences conform to the principle of time series modeling. After that, the minority class data are oversampled through the fitted ARIMA model, so that the dataset is balanced. Finally, selecting eight datasets from UCI and KEEL repositories, the proposed algorithm is compared with other oversampling algorithms and the decision tree classifier is used to perform classification experiments. Experimental results show that the proposed algorithm is more effective than other algorithms.
An Overview of Relevant Literature on Different Approaches to Word Sense Disambiguation

WSD (Word Sense Disambiguation) is a common issue in Natural Language Processing (NLP) and Machine Learning technology. In NLP, word sense disambiguation is described as the capacity to detect which meaning of a word is activated by its use in a specific context. WSD is a solution to the uncertainty that occurs when words have different meanings in different contexts. Contextual word meaning plays an important role in various applications such as sentiment analysis, search engine, information extraction, machine translation etc. It is a challenge for these systems to detect and overcome the uncertainty that emerges from the lexical ambiguity. Many studies have been conducted over the decades to propose various approaches to the WSD problem. In this manuscript, a comparative study of three approaches namely LESK algorithm, embedding techniques, and Neural Network techniques based on the text collected from children's story books is performed. We explored an approach that combines Bi-LSTM neural network with Knowledge Graph to predict contextual word meaning. Our study shows that the combined approach accuracy is 80.34approaches
A New Ship Target Detecting Method Based on Saliency in SAR Image

SAR (Synthetic Aperture Radar) is a kind of active microwave sensor, and has been widely used in the field of ocean ship detection. Because SAR imaging is affected by the speckle noise, sea clutter and complex environment, it is still a challenging task about ship target detection in SAR image processing. In this paper a fast ship target detecting method based on saliency is proposed. Firstly, the SAR image is separated to land and sea in order to remove the influence of land scattering. In the next step, the sea candidate area is segmented by super pixels, the global and local contrast of super pixels are calculated by using Bhattacharyya dissimilarity measure, and then combine them to generate the saliency map. Finally, the Otsu algorithm is used to separate the target regions in the saliency maps. The experimental results show that the proposed method effectively suppressed the influence of sea and land clutter, enhanced the saliency of the target, which greatly improves the detection efficiency and accuracy.
IWPT 2021 Shared Task Data and System Outputs

This package contains data used in the IWPT 2021 shared task. It contains training, development and test (evaluation) datasets. The data is based on a subset of Universal Dependencies release 2.7 (http://hdl.handle.net/11234/1-3424) but some treebanks contain additional enhanced annotations. Moreover, not all of these additions became part of Universal Dependencies release 2.8 (http://hdl.handle.net/11234/1-3687), which makes the shared task data unique and worth a separate release to enable later comparison with new parsing algorithms. The package also contains a number of Perl and Python scripts that have been used to process the data during preparation and during the shared task. Finally, the package includes the official primary submission of each team participating in the shared task.
Tracking Position of Airborne Target on SPx-Radar-Simulator Using Probabilistic Data Association Filter

Radar has various functions, one of which is tracking the position of targets in the air. In tracking the target position, there are obstacles, namely the uncertainty of data associations. To overcome the uncertainty of data associations, the concept of data association can be used where one of the algorithms that use this concept is the Probabilistic Data Association Filter (PDAF). A single target position tracking system in the air will be tested on radar using the PDAF with our proposed maintenance track algorithm. The data used for testing comes from simulating two motions on the SPx-Radar-Simulator. False alarms originating from interference (clutter) will be generated in the environment around the simulated target. The test results of the target tracking system using the PDAF algorithm that has been designed can track targets well, maintain tracking conditions in an environment that has clutter, and maintain track on multitarget environments. The error between the original data and the prediction on the validated target has a relatively small value, although there is a relatively significant difference in the error of the altitude data when the motion has varying altitude conditions.
A Solution to Improve Productivity for Remote Students and E-Learners

The background of "improving productivity for remote workers and students" is based on a worldwide survey conducted by AACSB. 79% of schools have suspended face-to-face activities (that is, for all students) and converted all face-to-face courses to online or other platforms. Only 21% of schools only convert courses involving students affected by travel restrictions into online education. In the face of COVID-19's failure to start school normally, the school's evolving online teaching and the decline in learning efficiency, the author decided to provide a solution for students who want to make breakthroughs in self-study, as well as teachers and remote workers who need feedback in online live broadcast Solution-Develop a plug-in that can help students and teachers improve the efficiency of learning online courses. The currently selected basic functions are human face concentration recognition and monitoring, scientific learning models based on multiple learning methods such as the Pomodoro Technique and Ebbinghaus curve, web page locking, personalized anthropomorphic AI companion reading growth reward mechanism, real-time note centre and so on.
Research on Dynamic Mechanism of Integration of Blockchain and Enterprise Network

Based on the application of blockchain technology and the problem of enterprise network governance, this paper takes the dynamic factors and mechanism of the integration of blockchain and enterprise network as the research content, and uses grounded theory to identify seven dynamic factors of the integration of blockchain and enterprise network, which play the role of generating power, developing power and supporting power. The research result is of great significance for promoting the application and practice of blockchain in the field of enterprise entities and promoting the sustainable development of enterprises and regional economy.
A study of the theory of chain-network integration between blockchain and enterprise network

The Internet network system extends the connection objects to the entire economic entity system, the entire industry chain, and the entire value chain. It can realize the ubiquitous and deep interconnection of the entire chain of human, financial, and material from R&D and design to production, management, and service. Promoting the chain-network integration of blockchain and enterprise networks is expected to solve the dilemma of efficiency and risk. We adopt a comprehensive literature research method to focus on the theoretical evolution and application development of enterprise networks and blockchains, explain the main logic of the integration of blockchain and enterprise network theory and chain networks from a theoretical perspective, give the definition of enterprise networks, and innovate to establish chain- network integration model and correlation measurement model. It provides basic theoretical support and practical reference in order to open up the information, trust and credit barriers between the network and the network based on the blockchain technology for the industrial Internet, complex enterprise networks or hyper-networks, and to improve the overall operating efficiency.
Study and realization of a solar tracker using the Arduino board for a solar photovoltaic installation

This paper tackles about design of a device allowing a photovoltaic solar installation to follow the flat movement of sun in order to optimize electrical energy production. This device called solar tracking system is a structure that allows tracking the apparent movement of the sun in real time. To achieve this, we have chosen to use the active or slaved tracker. The improvement of the energy performances by using a double axis solar tracker is also studied. The observation of the power curves according to the influence of the received light intensity shows a maximum of 180W in the form of a plateau between 10: 50 and 14: 00 (for 3 hours) local time for the double axis system while a maximum of 175W is recorded from 12: 50 to 13: 50 (1 hour) for the single axis tracker and a maximum of 170W (for a few minutes) at noon for the fixed system. From the analysis of these results, a significant gain of about 26.20% for the dual axis tracker compared to a fixed installation was obtained.
An Adaptive Horizontal Vertical Gradient Feature Extraction Algorithm for Face Recognition

Face recognition system encounters different challenges due to variation in pose, ageing, illumination and angle. The paper proposes an adaptive horizontal vertical gradient (AHVG) feature extraction algorithm for face recognition that is robust to age and pose changes. This approach begins by segmenting the face region and then extracts the feature by an adaptive approach. The AHVG feature extraction process extracts the adaptive features in both row-wise and column-wise directions and the gradient is estimated. During the feature reduction process, the vertical and horizontal gradients are mixed and the number of features extracted from the image is reduced during the feature reduction process in two stages. The complexity of the classification method is reduced by feature reduction along row-wise and column-wise. The feature thus extracted from train and test image is classified using K-nearest neighbour algorithms. The proposed framework outperformed conventional face recognition algorithms with an average recognition rate of 92.7%, an average feature extraction time of 249 s, and an average face recognition time of 0.15 s, using three separate datasets such as YALE, MORPH and FGNET.
Low-Density Parity-Check Codes Achieve List-Decoding Capacity

Related DatabasesWeb of Science You must be logged in with an active subscription to view this.Article DataHistorySubmitted: 10 September 2020Accepted: 16 August 2021Published online: 23 November 2021KeywordsLDPC codes, Gallagher codes, list-decoding, threshold, local properties, random linear codesAMS Subject Headings94B05Publication DataISSN (print): 0097-5397ISSN (online): 1095-7111Publisher: Society for Industrial and Applied MathematicsCODEN: smjcat
FPGA Based Hardware Accelerator Design for Convolution Process in Convolutional Neural Network

Developments of hardware accelerators for deep learning has increased rapidly due to the demand for flexibility to be applied to various deep learning architectures. The architecture that is widely marketed in recent years is GPU-based architecture where developers meet many difficulties for accelerators to be applied to different architectures. In this paper, the author will design an FPGA-based accelerator that will be used to handle processes in the convolution layer of Convolutional Neural Network (CNN). The system is designed with a base clock of 10 ns capable of providing a throughput of 1Gbyte/sec. The test results using a kernel with a size of 3 × 3 completed with an iteration time of 2683.92 us with a latency of 7930 ns. Furthermore, testing using a 2×2 kernel was completed with an iteration time of 2643 us with a latency of 5930 ns. The use of accelerators to complete the convolution process is proven to speed up the process of completing the convolution process with time difference of up to ±1400 us compared to the process carried out in Matlab.
On the structural stability of phase-coded quantum cryptography against detector-blinding attacks

Abstract Quantum key distribution (QKD) systems are open systems. Thus, an eavesdropper can actively influence the elements of the system through the communication line, altering their regular operation. One known attack is an attack with detector blinding. With such an attack, the eavesdropper can obtain the entire key and remain undetectable. The paper proposes a fundamentally new method of detecting and protecting quantum cryptography systems with phase coding against the attack with avalanche detector blinding. The proposed method is based on diverting a part of the input signal in the receiving terminal to bypass the interferometer and deliver it to the same avalanche detectors. Measuring the detector response in different time windows, both for the bypassed signal and the normal operation range, one can effectively identify the ongoing detector blinding attack. The method does not require radical changes in existing quantum cryptography systems and is quite universal, since it can be used for any phase-coded quantum key distribution protocol. The method provides a fundamental strategy of detecting and protecting against the attack at the level of the quantum key distribution protocol, and not at the level of technical ‘patches’.