Reviewerly/exHarmony huggingface dataset:
Thinking Hallucination for Video Captioning

With the advent of rich visual representations and pre-trained language models, video captioning has seen continuous improvement over time. Despite the performance improvement, video captioning models are prone to hallucination. Hallucination refers to the generation of highly pathological descriptions that are detached from the source material. In video captioning, there are two kinds of hallucination: object and action hallucination. Instead of endeavoring to learn better representations of a video, in this work, we investigate the fundamental sources of the hallucination problem. We identify three main factors: (i) inadequate visual features extracted from pre-trained models, (ii) improper influences of source and target contexts during multi-modal fusion, and (iii) exposure bias in the training strategy. To alleviate these problems, we propose two robust solutions: (a) the introduction of auxiliary heads trained in multi-label settings on top of the extracted visual features and (b) the addition of context gates, which dynamically select the features during fusion. The standard evaluation metrics for video captioning measures similarity with ground truth captions and do not adequately capture object and action relevance. To this end, we propose a new metric, COAHA (caption object and action hallucination assessment), which assesses the degree of hallucination. Our method achieves state-of-the-art performance on the MSR-Video to Text (MSR-VTT) and the Microsoft Research Video Description Corpus (MSVD) datasets, especially by a massive margin in CIDEr score.
Region-adaptive Concept Aggregation for Few-shot Visual Recognition

Few-shot learning (FSL) aims to learn novel concepts from very limited examples. However, most FSL methods suffer from the issue of lacking robustness in concept learning. Specifically, existing FSL methods usually ignore the diversity of region contents that may contain concept-irrelevant information such as the background, which would introduce bias/noise and degrade the performance of conceptual representation learning. To address the above-mentioned issue, we propose a novel metric-based FSL method termed region-adaptive concept aggregation network or RCA-Net. Specifically, we devise a region-adaptive concept aggregator (RCA) to model the relationships of different regions and capture the conceptual information in different regions, which are then integrated in a weighted average manner to obtain the conceptual representation. Consequently, robust concept learning can be achieved by focusing more on the concept-relevant information and less on the conceptual-irrelevant information. We perform extensive experiments on three popular visual recognition benchmarks to demonstrate the superiority of RCA-Net for robust few-shot learning. In particular, on the Caltech-UCSD Birds-200-2011 (CUB200) dataset, the proposed RCA-Net significantly improves 1-shot accuracy from 74.76% to 78.03% and 5-shot accuracy from 86.84% to 89.83% compared with the most competitive counterpart.
A Study of Virtualization Techniques Applied in Cloud Computing Environment

Abstract: The fastest-growing internet technology today is cloud computing. On-demand network access to a shared pool of reconfigurable resources is possible. A fundamental component of cloud computing is virtualization, which enables real resources to be divided into virtual resources so they can be shared among other virtual machines. Utilizing this virtualized resource can help you use less real equipment, servers, and storage space. Modern technologies like cloud computing expand the functional possibilities, elastic resource management, and collaborative execution style of applications. Virtualization, which enables business or academic IT resources through ondemand allocation dynamically, is the core component of cloud computing. The resources come in a variety of shapes, including client, server, storage, network, and application. This essay focuses on how virtualization increases the resources' elasticity in a cloud computing context. This study also provides a thorough analysis of open source virtualization approaches, problems, and future research directions.
Leveraging Artificial Intelligence, Natural Language Processing, and Natural Language Generation in Medical Writing

Medical writing is a process that generates a variety of documents in the biomedical domain, including but not limited to clinical reports, regulatory reports, protocol documents, patient narratives, plain language summaries, and so on. Medical writing is complex and time-consuming because a writer must refer to multiple sources, sift through a large volume of documents, maintain data integrity, perform review of literature, do interpretation of results, summarize, and so on. These challenges can be addressed and minimized substantially by adopting artificial intelligence, specifically cognitive search, natural language processing (NLP), and natural language generation (NLG) models and other techniques. Given the recent advances in language models for NLG, the time is ripe for a product in the medical writing domain that integrates and automates search capabilities, provides cognitive processing, and generates content using NLG. This white paper takes scientific manuscript writing as an example to provide insights into the way NLP and NLG can augment, automate, and expedite the process of writing a wide variety of biomedical documents. It looks at the current limitations of technology and ways to address those. Finally, it provides recommendations on how these technologies can be used to create a single system or product. Such an approach has the potential to expand into multiple areas in the biomedical domain, with medical writing as the first challenge.
Questions and Answers from Freelance Pros

In the age of LinkedIn, how important is a website for a freelancer? What are the key elements that should be included in developing a website? What are your favorite web conferencing platforms and why? What are the etiquette rules for web conferencing platforms that you follow? Camera on or off and when? In this issue, our Freelance Focus contributors are: Melissa L. Bogen Lori De Milto Cathryn D. Evans Gail V. Flores
Collaborative representation with hole filling techniques for Kinect RGB-D face recognition

We present in this paper, three different filtering techniques based on kernel function to fill missing information in depth map images obtained from low resolution sensor such as Kinect to improve the performance accuracy of RGB-D face recognition systems. We propose in this study, an RGB-D face recognition scheme that combines depth map and colour image using wavelet average fusion followed by collaborative representation classifier (CRC) for comparison of reference and probe images. We present the evaluation results based on our GU-RGBD face database and IIIT-D face database to present the significance of our three different filters employed in hole filling. Further, our investigation presents an extensive experimental analysis using eight different feature extraction techniques independently across three different filters to demonstrate the potential of our proposed approach. The proposed approach of hole filling, improves the performance accuracy of RGB-D face recognition as compared to without employing filtering operations.
NLS: An accurate and yet easy-to-interpret prediction method

Over the last years, the predictive power of supervised machine learning (ML) has undergone impressive advances, achieving the status of state of the art and super-human level in some applications. However, the employment rate of ML models in real-life applications is much slower than one would expect. One of the downsides of using ML solution-based technologies is the lack of user trust in the produced model, which is related to the black-box nature of these models. To leverage the application of ML models, the generated predictions should be easy to interpret while maintaining a high accuracy. In this context, we develop the Neural Local Smoother (NLS), a neural network architecture that yields accurate predictions with easy-to-obtain explanations. The key idea of NLS is to add a smooth local linear layer to a standard network. We show experiments that indicate that NLS leads to a predictive power that is comparable to state-of-the-art machine learning models, but that at the same time is easier to interpret.
Blockchain and Game Theory Convergence for Network Slice Brokering

As a distributed ledger technology, blockchain has received significant attention in revolutionizing telecommunication and networking domains. This article proposes a blockchain-based network slice brokering mechanism for multioperator and multitenant environments of the envisioned 6G networks.
Discover and safe: an automated security management system for educational institutions

In this paper, we proposed discover and safe (DaS) automatic security system by the help of face recognition. The focus of this automated system is to provide high-level security to manage the entry of people with face detection. This paper proposes haar cascade algorithm with dip libraries to create a camera-based real-time security management system through face detection and recognition. Haar cascade algorithm is an object detection algorithm which is mainly used in identifying face of any image or a real-time video (by webcam or building camera). The DaS framework worked on two phases: 1) to locate whether the VM is an unintended security; 2) to secure mission critical applications. The DaS access system implemented the face encoding scheme to detect the face and eye which works effectively on light and illumination changes. The results show that DaS framework can Armor the VM from obscured security problems and steal hidden doors against the attackers.
MaxGNR: A Dynamic Weight Strategy via Maximizing Gradient-to-Noise Ratio for Multi-task Learning

When modeling related tasks in computer vision, Multi-Task Learning (MTL) can outperform Single-Task Learning (STL) due to its ability to capture intrinsic relatedness among tasks. However, MTL may encounter the insufficient training problem, i.e., some tasks in MTL may encounter non-optimal situation compared with STL. A series of studies point out that too much gradient noise would lead to performance degradation in STL, however, in the MTL scenario, Inter-Task Gradient Noise (ITGN) is an additional source of gradient noise for each task, which can also affect the optimization process. In this paper, we point out ITGN as a key factor leading to the insufficient training problem. We define the Gradient-to-Noise Ratio (GNR) to measure the relative magnitude of gradient noise and design the MaxGNR algorithm to alleviate the ITGN interference of each task by maximizing the GNR of each task. We carefully evaluate our MaxGNR algorithm on two standard image MTL datasets: NYUv2 and Cityscapes. The results show that our algorithm outperforms the baselines under identical experimental conditions.
Securely Similarity Determination of Convex Geometry Graphics Under the Malicious Model

With the development of big data technology, data privacy protection has been paid more and more attention, especially privacy computing. Secure multi-party computation is one of the important tools to realize privacy computing, and secure multi-party computational geometry is an important part of secure multi-party computation and the research is of great significance. Among them, the problem of securely judging plane graphic similarity is a new research field in secure multi-party computational geometry, which has a broad application prospect in many fields. Most of the existing solutions are limited to regular polygonal similarity and can not resist malicious attacks. Under the malicious model, this paper proposes a protocol for securely judging whether two convex geometry graphics are similar based on the Paillier encryption algorithm and uses the ideal/real model paradigm method to prove the security of the protocol. The analysis shows that the protocol is efficient and can resist malicious attacks, which has practical value.
An Improved Virtual Mouse for Public Computing Systems using Computer Vision

Libraries and other public places with interactive computing devices, such as boarding pass printers at the airport and e-books at libraries, were avoided during the pandemic. A range of people avoids using public computing systems due to hygiene concerns. Introducing technology so users can interact with the computing device without physically touching their input devices might resolve the issue. However, proposing such a system can have major drawbacks, as the model will obey the movement of any hand it detects, which will ease the task of intrusion. In order to eliminate such drawbacks, the proposed system is designed to only obey the hand movements of a correct user by tracking the proximity between the user's face and all hands that the camera captures. The proposed system was aimed to be computationally inexpensive and accurate with the constraint to work upon the input of a single webcam or video input device, which doesn't necessarily need to be of high-definition configuration.
Simulation-based Approach to Evaluate and Regulate the Reputation Score of a Software Agent in E-Market

Reputation is a crucial factor that governs the importance of a software agent in the agent-mediated e-market. In the e-market, various buyers and service providers are involved in buying and selling the products. A buyer agent (BA) acts on behalf of a buyer to buy the products from a service provider agent (SPA) preferably having a good reputation score (Rep-Score). The conventional customer rating mechanism for online transactions lacks adequate analysis and investigation of customer reviews and hence does not reflect the accurate reputation of the service providers. This research investigates the reputation of a software agent using customer feedback based on product attributes such as product quality, design, price, delivery time, and defects. A knowledge rule-set is formed to establish a link between customer feedback and the repute of a software agent. Further, a simulation-based approach using the Rosetta toolkit and the Fuzzy Control System is applied to quantify and fine-tune the reputation of a software agent. There could be a chance of an unfair relationship between the same buyer-seller pair due to recurrent transactions. The proposed work eliminates any chance of a conspiracy between a service provider and a buyer agent. In case, the buyer agent makes repeated transactions with a particular service provider agent, the value of the weight assigned to the reputation of the service provider agent is significantly diminished for each new transaction, hence decreasing the final value of the Rep-Score. As a result, this method guarantees the correctness of the reputation evaluation of a software agent. A performance analysis is performed to validate the proposed approach using mean squared error and standard deviation.
Xylem partnership offers cybersecurity for water utilities

Xylem Inc is partnering with Dragos Inc to bring industrial cybersecurity to critical infrastructure in the water sector.
Analysis of the Advantages and Disadvantages of Internet Hot Words in Teaching Chinese as a Foreign Language

As the language that carries Internet culture, Internet hot words are gradually becoming mature with the development of the times.With their increasing influence in social life, in recent years, scholars have aroused the research of Internet hot words, and achieved good results.certain results.Hot words on the Internet are part of Chinese vocabulary, and there is still a lot of controversy in their application to teaching Chinese as a foreign language.This article will analyze the advantages and disadvantages of online hot words in the classroom teaching of Chinese as a foreign language, and provide the majority of Chinese as a foreign language teachers with ideas for the selection of classroom teaching content.
Assessment of risk susceptibility score in future rescue technicians

The relevance of the paper is due to the growing need for qualified personnel in rescue teams qualified to effectively fulfill their professional duties, as well as due to modern trends to upgrade the system of secondary vocational education, which is bound to affect the training of future rescue technicians. The objective is to assess the risk preparedness among college students specializing in Emergency Protection within different courses of study. Methods . Individual risk preparedness assessment was carried out among 1 st to 4 th year college students utilizing the method by G. Schubert. Results and discussion . As a result of the study, significant difference in risk preparedness score was identified between 1st and 4th year students: 1 st year students are less inclined to take risks compared to their 4 th year fellows. Conclusion . Enrollment of new recruits requires that educational organizations exercise professional discretion and provide medical counseling throughout professional training across various disciplines to ensure that future rescuers successfully undergo professional adaptation, acquiring appropriate models of behavior and psychological risk preparedness from as early as their 1 st academic year.
Linguistic variation on Twitter: A case study of Italian loanwords in the Spanish of South America

No AccessLinguistic variation on Twitter: A case study of Italian loanwords in the Spanish of South AmericaOlga KellertOlga KellertSearch for more papers by this authorhttps://doi.org/10.14220/9783737015530.347SectionsPDF/EPUB ToolsAdd to favoritesDownload CitationsTrack Citations ShareShare onFacebookTwitterLinkedInRedditEmail About Previous chapter Next chapter FiguresReferencesRelatedDetails Download book coverWiener Arbeiten zur LinguistikVolume 7 1st editionISBN: 978-3-8471-1553-3 eISBN: 978-3-7370-1553-0HistoryPublished online:March 2023 PDF download
XL-BPMN Model-Based Service Similarity Measurement Technique

In service-oriented developments, existing studies do not give lots of efforts on a formalized and systematic method for measuring similarities between services for their reuse in business models. This deteriorates the reusability of the constructed service due to the developers’ intuition and informal service analyses. In this paper, we propose a technique for measuring similarity of services by analyzing syntax and semantics between services in the eXtended Layered business process modeling notation (XL-BPMN) model. First of all, the profiles of the formalized attributes for specifying services are defined, and the criteria for determining service similarities are established. To measure similarity between services, a technique both a syntactic similarity analysis facilitated by the XL-BPMN model-based edge counting method and a semantic similarity analysis based on meta data registry (MDR)-applied service attributes is specified. To automate analysis, a tool that can support the semantic similarity analysis technique is implemented. An online shopping mall system is investigated and evaluated to verify the effectiveness of the proposed technique. The similarity measurement technique, which is further formalized at upper business levels, can improve the accuracy of service analyses and enhance service reusability by distinguishing services with high similarity levels as common services.
A VR-based interactive teaching and practice environment for supporting the whole process of mining engineering education

Due to the complex and dangerous nature of underground industrial sites, there is an urgent need to improve practical teaching abilities. In this paper, a VR-based interactive teaching and practice environment is proposed for teaching mining equipment processes under laboratory conditions. This environment integrates a digitised design, a virtual simulation platform, and a VR interactive product operation. This VR interactive environment includes the structural cognition of mining equipment, the actual operating conditions, and the actual operation of electronic control. Moreover, this system integrates a series of virtual reality human–machine interaction hardware, so that the students can quickly learn the operating conditions and the states of the actual underground production. This system helps them comprehensively learn the mechanical, electrical, communication, control, and mining methods involved in the mining equipment. Finally, this system provides a virtual practice path for training innovative engineering technicians.
Development of Visual Perception Assessment in the Form of an Interactive Digital Book for Students with Mental Disabilities

Children with special needs will achieve good learning outcomes if educators conduct assessments. Assessment is a process to collect information about abilities, advantages and needs that will be used as considerations and decisions related to the preparation of academic programs. This study aims to develop a visual perception assessment product in the form of a digital book for students with intellectual disability which is expected to be able to measure the ability, difficulty and needs in aspects of the development of children's visual perception with cases of mental impairment needed as consideration in planning learning programs, especially in reading learning. The method used in this study adapts the Research and Development method initiated by Borg &amp; Gall. The validity of the resulting product is calculated by Aiken's V formula which compared with the table r value (0.78) obtained a material validity value of 0.95 (very valid), a media validity of 0.90 (very valid) and a system validity of 0.91(very valid). The reliability of the products developed proved to be reliable and consistent based on the Cronbach Alpha test which obtained a value of 0.744, higher than the minimum Cronbach Alpha score limit of 0.60. The implementation of the assessment by students with mental disabilities obtained a score of 96.9 percent, meaning that the product can be used very well. Based on the results of this study, the visual perception assessment in the form of interactive digital books for students with intellectual disability that have been developed is suitable for use.
Knowledge-Enriched Moral Understanding upon Continual Pre-training

The aim of moral understanding is to comprehend the abstract concepts that hide in a story by seeing through concrete events and vivid characters. To be specific, the story is highly summarized in one sentence without covering any characters in the original story, which requires the machine to behave more intelligently with the abilities of moral perception and commonsense reasoning. The paradigm of “pre-training + fine-tuning” is generally accepted for applying neural language models. In this paper, we suggest adding an intermediate stage to build the flow of “pre-training + continual pre-training + finetuning”. Continual pre-training refers to further training on task-relevant or domainspecific corpora with the aim of bridging the data distribution gap between pre-training and fine-tuning. Experiments are basing on a new moral story dataset, STORAL-ZH, that composes of 4,209 Chinese story-moral pairs. We collect a moral corpus about Confucius theory to enrich the T5 model with moral knowledge. Furthermore, we leverage a Chinese commonsense knowledge graph to enhance the model with commonsense knowledge. Experimental results demonstrate the effectiveness of our method, compared with several state-of-the-art models including BERT-base, RoBERTa-base and T5-base.
Effects of memory conversation and group identity on collective memory

2 南开大学周恩来政府管理学院社会心理学系, 天津 300350)