Reviewerly/exHarmony huggingface dataset:
Learning Discriminative Representations and Decision Boundaries for Open Intent Detection

Open intent detection is a significant problem in natural language understanding, which aims to identify the unseen open intent while ensuring known intent identification performance. However, current methods face two major challenges. Firstly, they struggle to learn friendly representations to detect the open intent with prior knowledge of only known intents. Secondly, there is a lack of an effective approach to obtaining specific and compact decision boundaries for known intents. To address these issues, this paper presents an original framework called DA-ADB, which successively learns distance-aware intent representations and adaptive decision boundaries for open intent detection. Specifically, we first leverage distance information to enhance the distinguishing capability of the intent representations. Then, we design a novel loss function to obtain appropriate decision boundaries by balancing both empirical and open space risks. Extensive experiments demonstrate the effectiveness of the proposed distance-aware and boundary learning strategies. Compared to state-of-the-art methods, our framework achieves substantial improvements on three benchmark datasets. Furthermore, it yields robust performance with varying proportions of labeled data and known categories. The full data and codes are available for use at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/thuiar/TEXTOIR</uri> .
Mendability Index: a new metric for estimating the effort required for manually editing auto-segmentations of objects of interest

Auto-segmentation of medical images is critical to boost precision radiology and radiation oncology efficiency, thereby improving medical quality for both health care practitioners and patients. An appropriate metric to evaluate auto-segmentation results is one of the significant tools necessary for building an effective, robust, and practical auto-segmentation technique. However, by comparing the predicted segmentation with the ground truth, currently widely-used metrics usually focus on the overlapping area (Dice Coefficient) or the most severe shifting of the boundary (Hausdorff Distance), which seem inconsistent with human reader behaviors. Human readers usually verify and correct auto-segmentation contours and then apply the modified segmentation masks to guide clinical application in diagnosis or treatment. A metric called Mendability Index (MI) is proposed to better estimate the effort required for manually editing the auto-segmentations of objects of interest in medical images so that the segmentations become acceptable for the application at hand. Considering different human behaviors for different errors, MI classifies auto-segmented errors into three types with different quantitative behaviors. The fluctuation of human subjective delineation is also considered in MI. 505 3D computed tomography (CT) auto-segmentations consisting of 6 objects from 3 institutions with the corresponding ground truth and the recorded manual mending time needed by experts are used to validate the performance of the proposed MI. The correlation between the time for editing with the segmentation metrics demonstrates that MI is generally more suitable for indicating mending efforts than Dice Coefficient or Hausdorff Distance, suggesting that MI may be an effective metric to quantify the clinical value of auto-segmentations.
Design and Evaluation of an Online Workplace Promotion Decision Analysis Training Game With Provided Simulation Context

Appropriate appraisal methods and promotion conditions are one of the most important issues that companies must face in their sustainable development. The promotion decision team also faces the problem of different audit experiences and decision-making. This study designed a highly realistic remote educational game to simulate the promotion of a company manager as the theme
Controlling gain with loss: Bounds on localizable entanglement in multiqubit systems

We investigate the relation between the amount of entanglement localized on a chosen subsystem of a multi-qubit system via local measurements on the rest of the system, and the bipartite entanglement that is lost during this measurement process. We study a number of paradigmatic pure states, including the generalized GHZ, the generalized W, Dicke, and the generalized Dicke states. For the generalized GHZ and W states, we analytically derive bounds on localizable entanglement in terms of the entanglement present in the system prior to the measurement. Also, for the Dicke and the generalized Dicke states, we demonstrate that with increasing system size, localizable entanglement tends to be equal to the bipartite entanglement present in the system over a specific partition before measurement. We extend the investigation numerically in the case of arbitrary multi-qubit pure states. We also analytically determine the modification of these results, including the proposed bounds, in situations where these pure states are subjected to single-qubit phase-flip noise on all qubits. Additionally, we study one-dimensional paradigmatic quantum spin models, namely the transverse-field XY model and the XXZ model in an external field, and numerically demonstrate a quadratic dependence of the localized entanglement on the lost entanglement. We show that this relation is robust even in the presence of disorder in the strength of the external field.
Is dc:subject enough? A landscape on iconography and iconology statements of knowledge graphs in the semantic web

Purpose In the last few years, the size of Linked Open Data (LOD) describing artworks, in general or domain-specific Knowledge Graphs (KGs), is gradually increasing. This provides (art-)historians and Cultural Heritage professionals with a wealth of information to explore. Specifically, structured data about iconographical and iconological ( icon ) aspects, i.e. information about the subjects, concepts and meanings of artworks, are extremely valuable for the state-of-the-art of computational tools, e.g. content recognition through computer vision. Nevertheless, a data quality evaluation for art domains, fundamental for data reuse, is still missing. The purpose of this study is filling this gap with an overview of art-historical data quality in current KGs with a focus on the icon aspects. Design/methodology/approach This study’s analyses are based on established KG evaluation methodologies, adapted to the domain by addressing requirements from art historians’ theories. The authors first select several KGs according to Semantic Web principles. Then, the authors evaluate (1) their structures’ suitability to describe icon information through quantitative and qualitative assessment and (2) their content, qualitatively assessed in terms of correctness and completeness. Findings This study’s results reveal several issues on the current expression of icon information in KGs. The content evaluation shows that these domain-specific statements are generally correct but often not complete. The incompleteness is confirmed by the structure evaluation, which highlights the unsuitability of the KG schemas to describe icon information with the required granularity. Originality/value The main contribution of this work is an overview of the actual landscape of the icon information expressed in LOD. Therefore, it is valuable to cultural institutions by providing them a first domain-specific data quality evaluation. Since this study’s results suggest that the selected domain information is underrepresented in Semantic Web datasets, the authors highlight the need for the creation and fostering of such information to provide a more thorough art-historical dimension to LOD.
An Eye Movement Study in Unconventional Usage of Different Software Tools

One of the main challenges of Human-Computer Interaction is the creation of UIs that enable the use of different systems in an easy and understandable method. The study analyses the student audience who uses software tools differently from the basis. In the research, two languages supporting UI implementation related to .NET technology, XAML and classic C#, were compared in terms of the cognitive load of test subjects. The results of the traditional knowledge level assessment tests and the answers to the questionnaires show that the UI implementation described in XAML is easier to read and understand than the same description in classic C#. When viewing the source codes, the eye movement parameters of the test subjects were also recorded and then evaluated, where a significant difference in the number and duration of fixations was observed, i.e., the interpretation of the classic C# source code showed a larger cognitive load. Overall, the results of the eye movement parameters supported the results of the other two measurement methods when comparing the different types of UI descriptions. The results established in the study and its conclusion may have an impact on programming education as well as industrial software development in the future, and also clearly shows the importance of choosing the development technology that best suits the person or development team.
Bulut Bilişimin Gelişmesi ile Büyük Verinin Tekilleştirilmesinde Yapay Zekâ Öneminin İncelenmesi

With the start of Industry 4.0 in 2011, new concepts and technologies have entered the IT literature. Some of these technologies are virtualization, modularity, big data and deduplication. Big data can be defined as data of a magnitude that exceeds the ability of traditional database systems to collect, store, manage and analyze data. Today, data is diverse, large and rapidly changing. This situation cannot be solved with the traditional database structure. With the emergence of big data, it has become difficult to process data with the algorithms used for data processing. Therefore, new algorithms and technologies have been developed. The most important of these technologies is data deduplication. Deduplication backs up data by dividing it into variable or fixed sizes. In this way, it aims to save storage space by storing only one copy of many repeated data. Today, "deduplication and compression" is an indispensable feature for data storage in both server-storge and hyper-converged architecture systems. Recently, artificial intelligence technologies are advancing very rapidly and their application areas are expanding. Therefore, Artificial Intelligence is a technology that will be very important for the industry and our lives in the future. The purpose of this paper is to give an idea about the relationship between deduplication technology and artificial intelligence by examining various deduplication systems and algorithms. Studies in the literature show that deduplication provides significant savings in storage space, the importance of data security, and the use of artificial intelligence and deduplication as a whole.
The Dynamics of Learning Loss for Elementary Students Jakarta in the New Normal

This study aims to determine the occurrence of learning loss in the new normal era due to sudden changes in the online learning process for elementary school students in Jakarta. This study uses a case study qualitative research method to analyse the causes of learning loss in elementary school students. There are five research stages: test questions, observation, questionnaires, interviews, and documentation. The research subjects were 30 grade IV elementary school students in Jakarta and one grade IV teacher. The results of this study stated that as many as 33.33% of elementary school students did not experience learning loss, and as many as 66.67% of students experienced learning loss.
Automatic Selective Encryption of DICOM Images

Securing DICOM images is essential to protect the privacy of patients, especially in the era of telemedicine and eHealth/mHealth. This increases the demand for rapid security. Nevertheless, a limited amount of research work has been conducted to ensure the security of DICOM images while minimizing the processing time. Hence, this paper introduces a selective encryption approach to reduce the processing time and sustain the robustness of security. The proposed approach selects regions within medical images automatically in the spatial domain using the pixel thresholding segmentation technique, then compresses and encrypts them using different encryption algorithms based on their importance. An adaptive two-region encryption approach is applied to single and multi-frame DICOM images, where the Region of Background (ROB) is encrypted using a light encryption algorithm, while the Region of Interest (ROI) is encrypted using a sophisticated encryption algorithm. For multi-frame DICOM images (Approach I), additional time-saving has been achieved by almost 10,000 times faster than the Naïve encryption approach, and 100 times better compression ratio, using one segmentation map based on a pre-defined reference frame for all the DICOM frames. For single-frame DICOM image (Approach II), a multi-region selective encryption approach is proposed, where the ROI is further split into three regions based on potential security threats, using a mathematical model that guarantees shorter encryption time in comparison with the Naive and the two-region encryption approaches, with almost 47% and 14% saving times, respectively. Based on the estimated processing time, Approach I outperformed Approach II noticeably. Further, cryptanalysis metrics are utilized to evaluate the proposed approaches, which indicate good robustness against a wide variety of attacks.
Dynamic Editing Distance-based Extracting Relevant Information Approach from Social Networks

Online social networks, such as Facebook, Twitter, LinkedIn, etc., have grown exponentially in recent times with a large amount of information. These social networks have huge volumes of data especially in structured, textual, and unstructured forms which have often led to cyber-crimes like cyber terrorism, cyber bullying, etc., and extracting information from these data has now become a serious challenge in order to ensure the data safety. In this work, we propose a new, supervised approach for Information Extraction (IE) from Web resources based on remote dynamic editing, called EIDED. Our approach is part of the family of IE approaches based on masks extraction and is articulated around three algorithms: (i) a labeling algorithm, (ii) a learning and inference algorithm, and (iii) an extended edit distance algorithm. Our proposed approach is able to work even in the presence of anomalies in the tuples such as missing attributes, multivalued attributes, permutation of attributes, and in the structure of web pages. The experimental study, which we conducted, on a standard database of web pages, shows the performance of our EIDED approach compared to approaches based on the classic edit distance, and this with respect to the standard metrics recall coefficient, precision, and F1-measure.
Modified ECC for Secure Data Transfer in Multi-Tenant Cloud Computing

Cloud computing technologies comprise various kinds of significant desirable constrains such as security, liability, government surveillance, telecommunications capacity, anonymity and privacy. The usage of cipher text technology is considered as a desired technique for performing the process of encryption in order to solve the issue of granting security to the data that are shared in the cloud. Similarly, the architecture of multi-tenant in the cloud computing system grants benefits to both the service providers and end-users which shares a common cloud platform to multiple tenants (i.e.) users and suitable resources are also computed by implementing proposed architecture. Therefore in this research work, the concept of cipher text multi-tenant are integrated for providing enhanced security to the data shared in the cloud environment. Hence a Modified Elliptic Curve Cryptography (MECC) based on Diffee Hellman algorithm is proposed in this research paper which provides enhanced security using alternate key generation. The encryption, decryption, upload and download time are calculated and it is concluded that the algorithm that is proposed in this research paper consumes less time for all these measures when comparing with other existing algorithms. Characteristics like less memory, high operational performance, small sized keys and rapid key generation process, and effective resource savings enable the modified ECC to obtain high efficiency. The encryption time of the proposed MECC system was 51ms with the key length 4096 bits, whereas existing method had 92ms as encryption time. Likewise With the key length of 4096 bits, the decryption time for the proposed model was 159ms. Accordingly the proposed system has reduced cipher text key size of 836KB when compared with the existing AES, Blow Fish, and Two Fish. Additionally, the key generation time (35 s) also seems to be considerably reduced when compared with the existing methods. These statements reveal that the proposed system outshine the state of art methods in terms of key generation time, encryption and decryption time and computational complexity.
A vehicle classification method based on improved ResNet

While autonomous driving has made great strides, there are still some scenarios that need to be solved. For different kinds of vehicles, it is necessary to classify them accurately so as to adopt different driving strategies. At present, due to the good performance of deep learning in the field of image recognition, this method is mainly used for image classification. Therefore, facing the problem of vehicle classification, this paper adopts the method of combining EcaNet and ResNet to classify ten common vehicles in automatic driving perception. The experimental results show that the classification accuracy of the proposed method is 75.83%, compared with 66.46% of the comparison method.
Test case classification via few-shot learning

Crowdsourced testing can reduce testing costs and improve testing efficiency. However, crowdsourced testing generates massive test cases, requiring testers to select high-quality test cases for execution. Consequently, crowdsourced test cases require much effort to perform labeling due to the costly manual labor and domain knowledge. Existing methods usually fail to consider the crowdsourced testing scenario’s inadequate and imbalanced data issues. We aim to effectively and efficiently classify many crowdsourced test cases for developers to alleviate manual efforts. In this paper, we propose a test case classification approach based on few-shot learning and test case augmentation to address the limitations mentioned above. The proposed approach generates new test cases by the large pre-trained masked language model and extracts embedding representation by training word embedding models. Then a Bidirectional Long Short-Term Memory (BiLSTM)-based classifier is designed to perform test case classification by extracting the in-depth features. Besides, we also apply the attention mechanism to assign high weights to words that represent the test case category by lexicon matching. To verify the effectiveness of the classification framework, we select 1659 test cases from three crowdsourced testing projects to conduct in-usability evaluation experiments. The experimental results show that the proposed approach has a higher accuracy and precision rate than existing classification methods. It can be concluded that (1) the proposed approach is an effective test case classification technique for crowdsourced testing; (2) the proposed approach is practical to help developers select high-quality test cases quickly and effectively.
FNSAM: Image super-resolution using a feedback network with self-attention mechanism

BACKGROUND: High-resolution (HR) magnetic resonance imaging (MRI) provides rich pathological information which is of great significance in diagnosis and treatment of brain lesions. However, obtaining HR brain MRI images comes at the cost of extending scan time and using sophisticated expensive instruments. OBJECTIVE: This study aims to reconstruct HR MRI images from low-resolution (LR) images by developing a deep learning based super-resolution (SR) method. METHODS: We propose a feedback network with self-attention mechanism (FNSAM) for SR reconstruction of brain MRI images. Specifically, a feedback network is built to correct shallow features by using a recurrent neural network (RNN) and the self-attention mechanism (SAM) is integrated into the feedback network for extraction of important information as the feedback signal, which promotes image hierarchy. RESULTS: Experimental results show that the proposed FNSAM obtains more reasonable SR reconstruction of brain MRI images both in peak signal to noise ratio (PSNR) and structural similarity index measure (SSIM) than some state-of-the-arts. CONCLUSION: Our proposed method is suitable for SR reconstruction of MRI images.
Structured Cluster Detection from Local Feature Learning for Text Region Extraction

The detection of regions of interest is commonly considered as an early stage of information extraction from images. It is used to provide the contents meaningful to human perception for machine vision applications. In this work, a new technique for structured region detection based on the distillation of local image features with clustering analysis is proposed. Different from the existing methods, our approach takes the application-specific reference images for feature learning and extraction. It is able to identify text clusters under the sparsity of feature points derived from the characters. For the localization of structured regions, the cluster with high feature density is calculated and serves as a candidate for region expansion. An iterative adjustment is then performed to enlarge the ROI for complete text coverage. The experiments carried out for text region detection of invoice and banknote demonstrate the effectiveness of the proposed technique.
Medical image segmentation based on self-supervised hybrid fusion network

Automatic segmentation of medical images has been a hot research topic in the field of deep learning in recent years, and achieving accurate segmentation of medical images is conducive to breakthroughs in disease diagnosis, monitoring, and treatment. In medicine, MRI imaging technology is often used to image brain tumors, and further judgment of the tumor area needs to be combined with expert analysis. If the diagnosis can be carried out by computer-aided methods, the efficiency and accuracy will be effectively improved. Therefore, this paper completes the task of brain tumor segmentation by building a self-supervised deep learning network. Specifically, it designs a multi-modal encoder-decoder network based on the extension of the residual network. Aiming at the problem of multi-modal feature extraction, the network introduces a multi-modal hybrid fusion module to fully extract the unique features of each modality and reduce the complexity of the whole framework. In addition, to better learn multi-modal complementary features and improve the robustness of the model, a pretext task to complete the masked area is set, to realize the self-supervised learning of the network. Thus, it can effectively improve the encoder’s ability to extract multi-modal features and enhance the noise immunity. Experimental results present that our method is superior to the compared methods on the tested datasets.