Reviewerly/exHarmony huggingface dataset:
A Few Shot Image Ensemble Classification Algorithm Based on Meta-Learning

Aiming at the problem of decreased accuracy in model classification resulting from the drift of data types in the few shot image classifications, this paper proposes a few shot image ensemble classification method based on meta-learning. Applying feature extraction network on the basis of attention mechanism to extract the features in the image, this method is able to input the feature vector extracted to meta-learner and generate the weight parameter of similarity measurement network in the corresponding channel herewith. And then integrate the similarity distance calculated by multiple weak classifiers in all channels to get the classification results of target sample. The experiment on the miniImageNet data set indicates that the tasks on the 5way1shot and 20way1shot have improved 11.8% and 13.41% respectively, corresponding to the control method, and that when applying the model parameters trained by MiniImageNet data set to perform the test on the CUB200 and Clatech101 data set, the classification accuracy of ECML proposed here is also higher than it of control model. Thus it can be seen that to generate a similarity measurement network by building a meta-learner on a meta-task not only improves the model's generalization ability, but also effectively solves the problem of performance degradation of few shot image classification when the data sample type drifts.
Model-based Variational Autoencoders with Autoregressive Flows

Variational autoencoders are employed to provide a framework for learning deep latent state representation. Inverse autoregressive flow is a type of normalizing flow that is employed to provide strategies for flexible variational inferences of posteriors over latent variables. The study aimed to prove that the agent can find a solution faster and at a lower cost. The proposed architecture comprises three basic methods, whereby the first one initiates the parameters and other layers of the TensorFlow framework; the second one is the build method that develops a layer using the Kera Library, and the last method, transform, determines the next sequence in the chain and changes the input. The model was then tested on a car racing simulator from OpenAI Gym. It was concluded that the proposed model is fast because it achieved a score of 928 ± 14 over 100 random trials, which is the best in the tested environment.
Nonsingular zeros of polynomials defined over finite fields

The aim of this paper is to study the existence of nontrivial, nonsingular zeros of a nonhomogeneous polynomial defined over a finite field. To accomplish this, we determine conditions that guarantee the existence of a prescribed number of nonsingular zeros of a homogeneous form f over a finite field k that are not zeros of a homogeneous form h when f, h are relatively prime. The cases of quadratic and cubic polynomials are considered in detail. This extends previous results that have usually considered only the homogeneous case.
Data Visualization Tools and Data Modelling

Information provides a secure and powerful way to gain large amount of knowledge. Due to big data and IoT the huge amount of data is generated and data analytics is used to take benefit of this data. Understanding of data is required to make sense out of data and to do so visualization techniques help to understand data in depth.
Application of Analytic Hierarchy Process in the Performance Evaluation of Network Course Instructor

NOTICE OF RETRACTION: While investigating potential publication-related misconduct in connection with the ICIMTech 2021 Conference Proceedings, serious concerns were raised that cast doubt on the integrity of the peer-review process and all papers published in the Proceedings of this Conference. The integrity of the entire Conference has been called into question. As a result, of its investigation, ACM has decided to retract the Entire Conference Proceedings and all related papers from the ACM Digital Library.
Entanglement-Assisted Absorption Spectroscopy

We propose a practical transmitter-receiver system that exploits entanglement to achieve a provable quantum advantage over all spectrometers based on classical sources. The quantum advantage is robust against noise and loss.
A case study of an autonomous model between a real and a virtual environment

In this short article and accompanying visual essay, I trace the development and implementation of a dialogic site-specific performance in public space. I propose a scenographic method for how to make an empty, abandoned urban public space ‘speak’: how to choose the site for the performance and how to construct a narrative based on the history of the site. The actual ‘set’ of the performance, the analogue AR (Augmented Reality) boxes, played with some of the central features of AR technology. These characteristics were, in several respects, consistent with the site-specific performance characteristics presented in the case study. The concept raised in the study builds on the meeting points of these two. The performance created a blurring of real and virtual space through an analogue technique.
Temporal modelling of first-person actions using hand-centric verb and object streams

Analysis of first-person (egocentric) videos involving human actions could help in the solutions of many problems. These videos include a large number of fine-grained action categories with hand–object interactions. In this paper, a compositional verb–noun model including two complementary temporal streams is proposed with various fusion strategies to recognize egocentric actions. The first step is based on construction of verb and object video models as decomposition of actions with a special attention on hands. Particularly, the verb video model that is the spatial–temporal encoding of hand actions and the object video model that is the object scores with hand–object layout are represented as two separate pathways. The second step is the fusion stage to identify action category, where distinct verb and object models are combined to give their action judgments. We propose fusion strategies with recurrent steps collecting verb and object label judgments along a temporal video sequence. We evaluate recognition performances for individual verb and object models; and we present extensive experimental evaluations for action recognition over recurrent-based fusion approaches on the EGTEA Gaze+ dataset.
Big Data in Rope Skipping Rehabilitation Training for Drug Addicts in Judicial Administration

NOTICE OF RETRACTION: While investigating potential publication-related misconduct in connection with the ICIMTech 2021 Conference Proceedings, serious concerns were raised that cast doubt on the integrity of the peer-review process and all papers published in the Proceedings of this Conference. The integrity of the entire Conference has been called into question. As a result, of its investigation, ACM has decided to retract the Entire Conference Proceedings and all related papers from the ACM Digital Library.
A Voice Dialog System without Interfering with Human Speech Based on Turn-taking Detection

In this study, we developed a new rule-based dialog system that includes both turn-taking detection and response-switching functions. The traditional rule-based part allows the system user to design and customize topics and conversation transitions easily. In addition to the traditional rule-based part, the turn-taking detection function can detect the human speaker’s continuous speech and control the system’s speech response according to the detected continuous speech. This mechanism enables conversations without interfering with the speech of the human speaker. The turn-taking detection was created using a deep neural network (DNN). Using the turn-taking detection result, the response switching function can change three types of response transitions: a back-channeling response, topic changing, and normal conversation transition.To confirm that our developed functions could improve the impressions of rule-based dialog systems, an experiment comparing the dialog system with and without our developed functions was conducted. The system with both turn-taking detection and response switching functions provided a relatively better impression to the participants. Although some improvements are needed for the dialog system, the results suggest that the use of a turn-taking detection function may enable a relatively smooth conversation even with a rule-based dialog system.
Approximate MDS Property of Linear Codes

In this paper, we study the weight spectrum of linear codes with super-linear field size and use the probabilistic method to show that for nearly all such codes, the corresponding weight spectrum is very close to that of a maximum distance separable (MDS) code.
Influencing Factors and Strategies of the Flow of Academic Professionals in Colleges and Universities Based on Convolutional Neural Networks

The influencing factors of academic professional mobility in colleges and universities are complex and diverse, and the intensity of each influencing factor has obvious characteristics of differentiation. However, scholars have few relevant studies based on the intensity of factors affecting the flow of academic professionals in colleges and universities. Therefore, this article aims to study and analyze the influence of different factors on the flow of academic professionals in different types of colleges and universities, which is of great significance for promoting the rational and orderly flow of academic professionals. This paper proposes a comprehensive application of multiple methods, such as questionnaire surveys and data statistics, based on ERG theory, based on the four types of universities and colleges: research-oriented, teaching research, teaching, and application. The influence intensity of the flow-influencing factors is comparatively studied. It clarified the differences in the influence of spiritual factors, economic factors, and social factors on the mobility of academic professionals in colleges and universities, established a mobility factor model, and put forward policy recommendations for colleges and universities to promote the rational and orderly mobility of academic professionals. A total of 2042 questionnaires on “Policy Improvement Factors Affecting the Flow of Academic Experts in Universities” were released, four academic experts of different levels were assigned to universities, and 1,561 were effectively searched. Among them, there were 336 research universities, 157 educational research universities, 404 educational universities, and 164 applied universities. The experimental results of this article show that the factors affecting the flow of academic professionals in universities include economic strength factor of 0.4945, social strength factor of 0.5456, and intellectual strength factor of 0.52. Therefore, the factors affecting the mobility of university scholars can be used in strategic research.
What are the critical security flaws in my system?

Delivering secure software is a challenge that every software engineering team needs to face and solve. Methods based on static analysis can help programmers identify security risks in the software. Security checkers built using static analysis methods are a great help but they can overload the users with their findings. Today there is no security checker for Erlang that understands the severity of the found vulnerability and uses the information to prioritise the found vulnerabilities when presenting the results to the programmers.
Dynamic Public Key Certificates with Forward Secrecy

Conventionally, public key certificates bind one subject with one static public key so that the subject can facilitate the services of the public key infrastructure (PKI). In PKI, certificates need to be renewed (or revoked) for several practical reasons, including certificate expiration, private key breaches, condition changes, and possible risk reduction. The certificate renewal process is very costly, especially for those environments where online authorities are not available or the connection is not reliable. A dynamic public key certificate (DPKC) facilitates the dynamic changeover of the current public–private key pairs without renewing the certificate authority (CA). This paper extends the previous study in several aspects: (1) we formally define the DPKC; (2) we formally define the security properties; (3) we propose another implementation of the Krawczyk–Rabin chameleon-hash-based DPKC; (4) we propose two variants of DPKC, using the Ateniese–Medeiros key-exposure-free chameleon hash; (5) we detail two application scenarios.
Rapid parameter estimation of discrete decaying signals using autoencoder networks

Abstract In this work we demonstrate the use of neural networks for rapid extraction of signal parameters of discretely sampled signals. In particular, we use dense autoencoder networks to extract the parameters of interest from exponentially decaying signals and decaying oscillations. By using a three-stage training method and careful choice of the neural network size, we are able to retrieve the relevant signal parameters directly from the latent space of the autoencoder network at significantly improved rates compared to traditional algorithmic signal-analysis approaches. We show that the achievable precision and accuracy of this method of analysis is similar to conventional algorithm-based signal analysis methods, by demonstrating that the extracted signal parameters are approaching their fundamental parameter estimation limit as provided by the Cramér–Rao bound. Furthermore, we demonstrate that autoencoder networks are able to achieve signal analysis, and, hence, parameter extraction, at rates of 75 kHz, orders-of-magnitude faster than conventional techniques with similar precision. Finally, our exploration of the limitations of our approach in different computational systems suggests that analysis rates of <?CDATA $\gt$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mo>&gt;</mml:mo> </mml:math> 200 kHz are feasible using neural networks in systems where the transfer time between the data-acquisition system and data-analysis modules can be kept below ∼3 µ s.
Target-specific Sentiment Analysis of Dual-channel Interactive Attention Network

research-article Target-specific Sentiment Analysis of Dual-channel Interactive Attention Network Share on Authors: Kai Wen Chongqing University of Posts and Telecommunications and Research Center of New Telecommunication Technology Applications, Chongqing University of Posts and Telecommunications, China Chongqing University of Posts and Telecommunications and Research Center of New Telecommunication Technology Applications, Chongqing University of Posts and Telecommunications, ChinaView Profile , Xuhong Zhang Chongqing University of Posts and Telecommunications and Research Center of New Telecommunication Technology Applications, Chongqing University of Posts and Telecommunications, China Chongqing University of Posts and Telecommunications and Research Center of New Telecommunication Technology Applications, Chongqing University of Posts and Telecommunications, ChinaView Profile , Na Huo Chongqing University of Posts and Telecommunications and Research Center of New Telecommunication Technology Applications, Chongqing University of Posts and Telecommunications, China Chongqing University of Posts and Telecommunications and Research Center of New Telecommunication Technology Applications, Chongqing University of Posts and Telecommunications, ChinaView Profile , Yuhui Wan Chongqing University of Posts and Telecommunications and Research Center of New Telecommunication Technology Applications, Chongqing University of Posts and Telecommunications, China Chongqing University of Posts and Telecommunications and Research Center of New Telecommunication Technology Applications, Chongqing University of Posts and Telecommunications, ChinaView Profile Authors Info & Claims ICAIIS 2021: 2021 2nd International Conference on Artificial Intelligence and Information SystemsMay 2021 Article No.: 93Pages 1–6https://doi.org/10.1145/3469213.3470295Online:18 August 2021Publication History 0citation11DownloadsMetricsTotal Citations0Total Downloads11Last 12 Months11Last 6 weeks1 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
The challenge of transdisciplinarity: Design methods for agri-food innovation and sustainability

This is not a book about food design. Instead, it focuses on the transdisciplinarity of design and its application in the agri-food sector. This book presents design as a combination of methods for teaching, learning, experimenting, and implementing to fulfill the requirements of the transformation and innovation we see in the fields of food systems and sustainability. By exploring a collection of different cases, this book explains the concept of design in a way that is accessible even to scholars and practitioners who are not designers, as an approach that forces to set aside technology and helps to process and problem-solve in a transdisciplinary and creative way. This selection of cases will be helpful for educators and students of any subject seeking to broaden their perspective and improve their professional careers. It will also be useful for professors and instructors who require inspirational tools and concepts to introduce complex subjects, such as transdisciplinarity and food sustainability, in their classrooms.
A Diagnostic Study Of Visual Question Answering With Analogical Reasoning

The deep learning community has made rapid progress in low-level visual perception tasks such as object localization, detection and segmentation. However, for tasks such as Visual Question Answering (VQA) and visual language grounding that require high-level reasoning abilities, huge gaps still exist between artificial systems and human intelligence. In this work, we perform a diagnostic study on recent popular VQA in terms of analogical reasoning. We term it as Analogical VQA, where a system needs to reason on a group of images to find analogical relations among them in order to correctly answer a natural language question. To study the task in depth, we propose an initial diagnostic synthetic dataset CLEVR-Analogy, which tests a range of analogical reasoning abilities (e.g. reasoning on object attributes, spatial relationships, existence, and arithmetic analogies). We benchmark various recent state-of-the-art methods on our dataset and compare the results against human performance, and discover that existing systems fall shorts when facing analogical reasoning involving spatial relationships. The dataset and code will be publicly available to facilitate future research.
Industrial Symbiosis: Context-Aware Strategies for Automated Negotiation of Smart Contracts in Peer-to-Peer Markets of Prosumers

Industrial Symbiosis can evolve from a simple concept to a revolutionary practical approach where prosumer companies produce goods and provide services through automated negotiation of Smart Contracts by means of new technologies like Internet-of-Things (IoTs) and Blockchain in a Peer-to-Peer (P2P) Market. However, a symbiotic scheme of prosumers exchanging utility surpluses and industrial services gives rise to a great challenge: each company will only participate in the market if the motivation of increasing its own profits exists. I n t his work, each prosumer company is represented by a software agent that resorts to previously learned context-aware strategies for defining prior distributions. Then, strategies are adapted to different opponents when negotiating utility contracts by properly accounting for opponents’ preferences (private) and key contextual (public) information. Agents negotiating Smart Contracts can be readily implemented using Distributed Applications (dApps) over Ethereum/Solidity and digital currencies as the Ether.
SynTime: Token Types and Heuristic Rules

According to the five common characteristics of time expressions, we propose a type-based approach named SynTime for time expression recognition. Specifically, we define three main syntactic token types, namely time token, modifier, and numeral, to group time-related token regular expressions. On the types we design general heuristic rules to recognize time expressions. In recognition, SynTime first identifies time tokens from raw text, then searches their surroundings for modifiers and numerals to form time segments, and finally merges the time segments to time expressions. As a light-weight rule-based tagger, SynTime runs in real time, and can be easily expanded by simply adding keywords for the text from different domains and different text types. Evaluation on benchmark datasets and tweets data shows that SynTime outperforms state-of-the-art methods.
Pattern recognition from cyclist under influence (CUI) crash events: application of block cluster analysis

Background Alcohol impairment in traffic crashes is a critical safety concern. Alcohol impairment in non-motorist crashes has been rising in recent years. However, there is not much research focused on cyclist under influence (CUI).Method This study applied block cluster analysis to Louisiana traffic crash data from 2010 to 2016 to identify the key contributing attributes and association patterns of CUI crashes.Results The findings identified eight column clusters: hit and run crashes during cloudy conditions, impaired cyclist crashes in open country locations, younger impaired cyclist crashes at lighted intersections, elderly cyclist crashes during inclement weather, intersection crashes on low-speed roadways, segment-related crashes on undivided two-way roadways, fatal cyclist crashes at dark with no lighting, and collision with a vehicle on business locality.Conclusions The findings of this study can be beneficial in the synchronization of regional and local behavioral safety efforts to lessen the occurrence and injury level of CUI crashes.
Tensions and Techniques in Investigating Longitudinal Experiences with Slow Technology Research Products

How can technologies be created that take on a long-term place in people's lives and that coevolve with them over time? What kinds of qualities should designers consider in crafting such kinds of computational things? And, how should we study and evaluate such new technologies through a longer temporal frame? In this chapter, we draw on examples of longitudinal field studies of the Photobox and Olly research products to explore these questions and to detail tensions and techniques that emerged across these two cases. Our findings reveal key tensions that researchers ought to be wary of when conducting longitudinal field studies of slow technology research products and techniques that can be applied to mitigate them.