Reviewerly/exHarmony huggingface dataset:
Outsourcing multiauthority access control revocation and computations over medical data to mobile cloud

With recent advances in cloud computing, mobile devices are increasingly being used to record patient physiological parameters, and transfer them to a cloud-based hospital information system, for access control mediation over a variety of stakeholders. In such a cloud-based architecture, the patient must specify an access policy for a group of authorized parties towards its outsourced data. Multiauthority ciphertext-policy attribute-based encryption (CP-ABE) was provided as an innovative cloud-based access control cryptographic primitive to tackle the key escrow issue in a centralized architecture, and boost flexibility through cross-domain attributes management. Existing works, however, still have glaring drawbacks. First, they still rely on a trusted authority to generate and distribute user secret keys. Second, they do not simultaneously provide encryption, decryption, or revocation outsourcing, resulting in high processing and communication cost for both the data sender and the data receiver. Third, they do not support both user and attribute revocation, and the integrity of ciphertext downloaded from the cloud is not always verified at the user end. As a result, this paper exploits the dummy attribute technique and introduces a novel, efficient, and secure multiauthority ciphertext-policy ABE method for mediating access control over medical data, in the mobile cloud. The ciphertext access policy enforcement, partial ciphertext decryption, and both the user and attribute indirect revocation updates are safely outsourced to the cloud server in this study. Theoretical analysis demonstrates that our scheme is efficient and verifiable, and we prove that our construction is secure under the decisional bilinear Diffie-Hellman assumption.
Story points changes in agile iterative development

Abstract Story Points (SP) are an effort unit that is used to represent the relative effort of a work item. In Agile software development, SP allows a development team to estimate their delivery capacity and facilitate the sprint planning activities. Although Agile embraces changes, SP changes after the sprint planning may negatively impact the sprint plan. To minimize the impact, there is a need to better understand the SP changes and an automated approach to predict the SP changes. Hence, to better understand the SP changes, we examine the prevalence, accuracy, and impact of information changes on SP changes. Through the analyses based on 19,349 work items spread across seven open-source projects, we find that on average, 10% of the work items have SP changes. These work items typically have SP value increased by 58%-100% relative to the initial SP value when they were assigned to a sprint. We also find that the unchanged SP reflect the development time better than the changed SP. Our qualitative analysis shows that the work items with changed SP often have the information changes relating to updating the scope of work. Our empirical results suggest that SP and the scope of work should be reviewed prior or during sprint planning to achieve a reliable sprint plan. Yet, it could be a tedious task to review all work items in the product (or sprint) backlog. Therefore, we develop a classifier to predict whether a work item will have SP changes after being assigned to a sprint. Our classifier achieves an AUC of 0.69-0.8, which is significantly better than the baselines. Our results suggest that to better manage and prepare for the unreliability in SP estimation, the team can leverage our insights and the classifier during the sprint planning. To facilitate future studies, we provide the replication package and the datasets, which are available online.
Psychological Adjustment and Emotional Health Care Strategies in the Teaching Process of College Music Teachers Based on Big Data Analysis

In music teaching in high schools, teachers should not only impart corresponding music knowledge to students but also focus on cultivating students’ comprehensive quality and quality. In the process of music teaching in universities, we should focus on students’ psychological adjustment and emotional health care. In order to quantify the application effect of big data technology in music teaching reform, this paper takes piano teaching in universities as an example to conduct research and mainly analyzes the physiological and psychological prediction effects involved in the teaching process. This paper introduces three typical big data technologies to predict and analyze the data involved in the existing piano teaching in universities. The analysis results show that the prediction effect based on the fuzzy neural inference system is the best. In addition, the three-dimensional display of the prediction data shows that the prediction effect obtained by big data technology shows good consistency and continuity, which indicates that the prediction method based on big data technology is suitable for psychological adjustment and emotional health care in music teaching in universities.
Conversational AI for multi-agent communication in Natural Language

Research at the Interaction Lab focuses on human-agent communication using conversational Natural Language. The ultimate goal is to create systems where humans and AI agents (including embodied robots) can spontaneously form teams and coordinate shared tasks through the use of Natural Language conversation as a universal communication interface. This paper first introduces machine learning approaches to problems in conversational AI in general, where computational agents must coordinate with humans to solve tasks using conversational Natural Language. It also covers some of the practical systems developed in the Interaction Lab, ranging from speech interfaces on smart speakers to embodied robots interacting using visually grounded language. In several cases communication between multiple agents is addressed. The paper surveys the central research problems addressed here, the approaches developed, and our main results. Some key open research questions and directions are then discussed, leading towards a future vision of conversational, collaborative multi-agent systems.
Focus on hierarchical features: Soft-weighted hierarchical features network

Methods of multi-scale context are commonly used for semantic segmentation. Existing methods suffer from two flaws: (1) Underutilization of backbone-derived multi-scale feature information. (2) Mismatch between small objects and large-scale encodings. To solve the above issues, we propose a novel network to better represent high-level features, named as Soft-weighted Hierarchical Features Network (SWHF-Net) consisting of Semi-atrous Transform Feature Pyramid Module (ST-FPM) and Hierarchical Features Fusion Module (HF2M). Specifically, we propose a hierarchy-driven feature transformation function strategy to reconstruct the traditional feature pyramid module as ST-FPM. ST-FPM strengthens the properties of hierarchical features, which is beneficial for extracting the semantic representation of multi-scale objects. Simultaneously, HF2M focuses on the characteristics of features at different hierarchies, and adaptively calculates the attention map of multi-scale objects, greatly improving the efficiency. On Cityscapes, Pascal Context, and ADE20K, we achieve outstanding performance compared to the state-of-the-art methods with fewer computational costs.
Robust skin microrelief depth estimation using a mobile stereo system

The skin surface becomes wrinkled and rough due to various internal and external factors. A three-dimensional (3D) analysis of the skin is required to improve skin conditions. Stereophotogrammetry, a noninvasive 3D analysis method, is easy to install and use, but most stereo systems have a fixed baseline and scale. Previous stereo systems are not suitable for observing micro-range skin features. Therefore, we suggest the optimal conditions and methods for the 3D analysis of skin microrelief using a multi-conditioned stereo system.We constructed a nonconvergence model using a mobile device and acquired stereo images under multiscale and multi-baseline conditions. We extracted 3D information of the skin through our process: preprocessing, skin feature extraction, feature matching, and actual depth mapping. We improved the accuracy of the 3D analysis of the skin by using disparity values instead of disparity maps. We compared and analyzed the performances of six local feature detector and descriptor algorithms. In addition, we suggested depth-mapping formulas to estimate the actual depth of the skin microrelief.We confirmed that stereo images with a working distance of 70-75 mm and a baseline of 4-8 mm are effective for the 3D analysis of skin microrelief. In addition, accelerated KAZE exhibited the best performance for features extraction and stereo matching. Finally, the extracted 3D information was converted to the actual depth, and the performance of the 3D analysis was verified.The proposed system and method that provide texture information are effective for 3D skin disease analysis and evaluation.
Predicting User Preferences of Dimensionality Reduction Embedding Quality

A plethora of dimensionality reduction techniques have emerged over the past decades, leaving researchers and analysts with a wide variety of choices for reducing their data, all the more so given some techniques come with additional hyper-parametrization (e.g., t-SNE, UMAP, etc.). Recent studies are showing that people often use dimensionality reduction as a black-box regardless of the specific properties the method itself preserves. Hence, evaluating and comparing 2D embeddings is usually qualitatively decided, by setting embeddings side-by-side and letting human judgment decide which embedding is the best. In this work, we propose a quantitative way of evaluating embeddings, that nonetheless places human perception at the center. We run a comparative study, where we ask people to select "good" and "misleading" views between scatterplots of low-dimensional embeddings of image datasets, simulating the way people usually select embeddings. We use the study data as labels for a set of quality metrics for a supervised machine learning model whose purpose is to discover and quantify what exactly people are looking for when deciding between embeddings. With the model as a proxy for human judgments, we use it to rank embeddings on new datasets, explain why they are relevant, and quantify the degree of subjectivity when people select preferred embeddings.
Single Hidden Layer CEFYDRA: Cluster-first Explainable FuzzY-based Deep self-Reorganizing Algorithm

This paper summarizes the mathematical demonstration of the update rules for the first hidden layer of a double-layered network of cluster-first fuzzy-based regression algorithms. The proposed architecture is a necessary step for the generalization of this type of fuzzy system to deep learning configurations with multiple hidden layers. At every unit of the network, the inputs are grouped in fuzzy clusters (whose degree of membership is defined through Cauchy membership functions), and mapped with multidimensional logistic functions. The output of a unit is obtained by merging the predictions of all the clusters using a Takagi-Sugeno-Kang approach. All the parameters are optimized following a gradient descent learning. We also cover the reasons to believe that this algorithm, named CEFYDRA, is explainable, and we briefly mention its ability for plastic reorganization.
Initialization and Plasticity of CEFYDRA: Cluster-first Explainable FuzzY-based Deep self-Reorganizing Algorithm

The CEFYDRA is a network of units whose outputs are obtained using a fuzzy Takagi-Sugeno-Kang approach. At each unit, the information is clustered in fuzzy sets and then mapped using logistic functions and Cauchy membership functions. There are two primary contributions in this paper. The first is a set of suggestions for the initialization criteria of the parameters of a CEFYDRA. The second is a proposal for the self-reorganizing algorithm that modifies the location of the clusters of each unit as the algorithm is trained with gradient descent.
Multisource Information Fusion Algorithm for Personalized Tourism Destination Recommendation

In this paper, the existing scenic spot recommendation algorithms ignore the implicit trust and trust transmission of users when dealing with user relationships, and the lack of historical browsing behavior data of users in new city scenes leads to an inaccurate recommendation. In this paper, a personalized scenic spot recommendation method combining user trust relationship and tag preference is proposed. Firstly, the trust degree is introduced when the recommendation quality is poor only considering the similarity of users. By mining the implicit trust relationship of users, the problem that the existing research cannot make recommendations when the direct trust is difficult to obtain is solved, and the data sparsity and cold start problems are effectively alleviated. Secondly, in the process of user interest analysis, the relationship between scenic spots and tags is extended to the relationship among users, scenic spots and tags, and users’ interest preferences are decomposed into long-term preferences for different scenic spots tags, which effectively alleviates the problem of poor recommendation quality when users’ historical tour records are lacking. The personalized tourism recommendation method proposed in this paper effectively integrates many features of social networks and effectively alleviates the problems of data sparseness and feature learning in tourism recommendation based on social networks by using vectorization and deep learning technology. Its research has very important usage scenarios and commercial value in the tourism industry. This model can efficiently mine the association rules between scenic spots in multisource information data. The experimental results show that mining the correlation between the scenic spots selected by tourists can provide effective information for tourism decision-making.
Analysis and Application of Crosscultural Knowledge System Structure in English Teaching Based on Hierarchical Correlation Analysis

With the prosperity of the economy, China’s foreign exchanges are becoming more and more frequent, and English is becoming more and more important as the common language of international communication. For decades, Chinese educators and scholars have achieved great success in teaching English, but there are also some problems, including neglecting the teaching of crosscultural knowledge in English classrooms. And in the crosscultural English teaching based on hierarchical correlation analysis, its specific structure system is not complete, and its application scope is also less. Therefore, this paper analyzes hierarchical correlation analysis, teaching mode, etc., which lays the foundation for subsequent research. In addition, combined with the current research status, this paper analyzes the past crosscultural knowledge system and proposes a new structural model. A large number of experiments have verified the application effect of the improved knowledge architecture model in crosscultural English teaching under the hierarchical correlation analysis. The new model can improve students’ crosscultural awareness and crosscultural communication competence and improve the previous teaching system structure. There is a problem that both hierarchical teaching and overall planning cannot be taken into account. This paper studies the crosscultural issues in English teaching and conducts research from the following four aspects: first, it discusses the cultivation of crosscultural communicative competence of college students in English teaching and proposes some feasible solutions to the problem of neglecting the integration of crosscultural knowledge in English teaching. Sexual suggestions cultivate students’ crosscultural awareness of English learning and improve the efficiency of English learning. The second is to point out that college school students should pay attention to the differences in cultural communication between China and foreign countries and develop the awareness and ability of crosscultural communication. The third is to explore ways to improve students’ crosscultural communicative competence and cultivate all-round English talents for the country and society. The fourth is how to achieve crosscultural communication, so that people with different cultural backgrounds can communicate and learn smoothly. Based on the above problems, this paper adopts the method of case analysis to discuss the problem of imperfect cross-cultural knowledge system in English teaching in China, and the educational structure is incomplete.
IFLA-UNESCO Public Library Declaration 2022

This study presents the Turkish translation of IFLA-UNESCO Public Library Declaration 2022 published by IFLA and UNESCO
Low distortion and adaptive image steganography by enhancing DBSCAN, Sobel operator, and XOR coding

Image steganography is concealing digital messages in an image in a matter that the secret data is not revealed. An effective practice to reduce the variations between the cover and stego images is to hide the secret data in the edge areas. The clustering approach can be applied to detect the edge pixels of the cover image. DBSCAN is a density-based clustering algorithm and is applicable for this purpose. This algorithm, however, may fail to detect edge areas if its parameters are not tuned properly. Furthermore, to reduce the distortion of the stego image, coding techniques should be applied in the data embedding process. The existing coding schemes have low performance and there is still scope for improvement and to propose more promising approaches. In this paper, we propose an effective edge-based image steganography algorithm. The proposed method convolves the cover image using an enhanced Sobel operator prior to edge detection. This approach places more emphasis on the edge pixels. For detecting edge areas, an adaptive clustering method based on the DBSCAN algorithm is proposed, which adjusts its sensitivity against edge pixels according to the cover image structure and message size. As opposed to the traditional DBSCAN, this method is adaptive and can adjust the values of parameters dynamically. Additionally, a novel enhanced XOR coding technique for message concealment which reduces the variations of the edge pixels to 12.5%, is proposed. The experimental results demonstrate that the proposed method improves PSNR and wPSNR by 3.98db and 2.18db compared to other state-of-the-art schemes.
Natural Language Processing in Chatbots

Natural language processing (NLP) was utilized to include for the most part mysterious corpora with the objective of improving phonetic examination and was hence improbable to raise ethical concerns. As NLP gets to be progressively widespread and uses more information from social media. The main goal is to develop a fully useful voice-based mechanization framework for the home that uses the "Internet of Things, artificial intelligence, and natural language processing" (NLP) to provide an inexpensive and efficient way to connect to home appliances for work. Chatbots could be virtual individuals who can successfully make conversation with any human being utilizing intuitively literary abilities. As of now, there are numerous cloud base chatbots administrations that are accessible for the advancement and change of the chatbot segment such as "IBM Watson, Microsoft bot, AWS Lambda, Heroku," and many others. We displayed useful engineering that we propose to construct a brilliant chatbot for wellbeing care help. Our paper provides an outline of cloud-based chatbots advances together with the programming of chatbots and the challenges of programming within the current and upcoming period of chatbots.
Material Translation Based on Neural Style Transfer with Ideal Style Image Retrieval

The field of Neural Style Transfer (NST) has led to interesting applications that enable us to transform reality as human beings perceive it. Particularly, NST for material translation aims to transform the material of an object into that of a target material from a reference image. Since the target material (style) usually comes from a different object, the quality of the synthesized result totally depends on the reference image. In this paper, we propose a material translation method based on NST with automatic style image retrieval. The proposed CNN-feature-based image retrieval aims to find the ideal reference image that best translates the material of an object. An ideal reference image must share semantic information with the original object while containing distinctive characteristics of the desired material (style). Thus, we refine the search by selecting the most-discriminative images from the target material, while focusing on object semantics by removing its style information. To translate materials to object regions, we combine a real-time material segmentation method with NST. In this way, the material of the retrieved style image is transferred to the segmented areas only. We evaluate our proposal with different state-of-the-art NST methods, including conventional and recently proposed approaches. Furthermore, with a human perceptual study applied to 100 participants, we demonstrate that synthesized images of stone, wood, and metal can be perceived as real and even chosen over legitimate photographs of such materials.
Dual attention network for unsupervised medical image registration based on VoxelMorph

An accurate medical image registration is crucial in a variety of neuroscience and clinical studies. In this paper, we proposed a new unsupervised learning network, DAVoxelMorph to improve the accuracy of 3D deformable medical image registration. Based on the VoxelMorph model, our network presented two modifications, one is adding a dual attention architecture, specifically, we model semantic correlation on spatial and coordinate dimensions respectively, and the location attention module selectively aggregates the features of each location by weighting the features of all locations. The coordinate attention module further puts the location information into the channel attention. The other is introducing the bending penalty as regularization in the loss function to penalize the bending in the deformation field. Experimental results show that DAVoxelMorph achieved better registration performance including average Dice scores (0.714) and percentage of locations with non-positive Jacobian (0.345) compare with VoxelMorph (0.703, 0.355), CycleMorph (0.705, 0.133), ANTs SyN (0.707, 0.137) and NiftyReg (0.694, 0.549). Our model increases both model sensitivity and registration accuracy.
Resilient Service Authentication for Smart City Application Using IoT

Internet of Things (IoT) support for smart city systems improves service scales by ignoring various user congestion.People are looking for different security features for reliable and robust applications.Here, the Permanent Denial of Service (PDoS) problem arises from improper user identification.This article introduces the Service-Reliant Application Authentication (SRAA) to prevent PDoS attacks in a smart area of the city.In this authentication method, the security of the application is ensured through the distribution of guarded access.The supervised access distribution uses user interface features and sync with the user device.Abnormality in linking user device, application, and authentication is seen in Back Propagation (BP) readings.BP learning reduces given weights based on abnormalities trained during the access distribution process.The oddity is reflected in the sequence from previous training sessions to ensure consistent synchronization of distributed services.From PDoS, the web device displays a few unattended loads on the service, which reduces service failure.The effectiveness of the proposed verification method is verified using delays to verify metric accuracy, false standard, sync failure, and bit rate.