Reviewerly/exHarmony huggingface dataset:
The ethical dilemma of mobile phone data monitoring during COVID-19: The case for South Korea and the United States

Governments across the world have integrated a variety of advanced technologies to respond to the COVID-19 crisis. In particular, the use of surveillance programs that leverage data and tools from mobile phones have become important components of public health strategies to contain the spread of SARS-CoV-2 across the globe. Currently, big technology companies around the world are helping governments evaluate the effectiveness of their social distancing protocols by examining and analyzing movements of millions of mobile phone users in order to determine how the virus is spreading across the various geographic locations, and the effectiveness of the various social distancing methods that have been implemented. The collection and use of individual mobile phone data as a public health surveillance tool presents tensions between several ethical priorities. Such a dilemma resides in the tensions between public health ethics goals and clinical ethics goals. While public health ethics pursues goals that seek to ensure the good of the community, such goals are often achieved at the expense of clinical ethics goals which emphasize individual autonomy and civil liberty. In using persons' mobile phone data as a tool to fight the COVID-19 pandemic, we must address the tensions associated with weighing the needs of "the many" with ensuring the rights of the individual.
An efficient model for copy-move image forgery detection

Purpose This study aims to solve problems of detecting copy-move images. With input images, the problem aims to: Confirm the original or forgery of the images, evaluate the performance of the detection and compare the proposed method’s effectiveness to the related ones. Design/methodology/approach This paper proposes an algorithm to identify copy-move images by matching the characteristics of objects in the same group. The method is carried out through two stages of grouping the objects and comparing objects’ features. The classification and clustering can improve processing time by skipping groups of only one object, and feature comparison on objects in the same group improves accuracy of the detection. YOLO5, the latest version of you only look once (YOLO) developed by Ultralytics LLC, and K-means are applied to classify and group the objects in the first stage. Then, modified Zernike moments (MZMs) and correlation coefficients are used for the features extraction and matching in the second stage. The Open Images V6 data set is used to train the YOLO5 model. The combination of YOLO5 and MZM makes the effectiveness of the proposed method for copy-move image detection with an average accuracy of 94.26% for images of benchmark and MICC-F600 and 95.37% for natural images. The outstanding feature of the method is that it can balance both processing time and accuracy in detecting duplicate regions on the image. Findings The problem is then solved by doing the following steps: Build a method to detect objects and compare their features to find the similarity if they are copy-move objects; use YOLO5 for the object detection and group the same category objects; ignore the group having only one object and extract the features of the other groups by MZMs; detect copy-move regions using K-means clustering; and calculate and compare the detection accuracy of the proposed method and related methods. Originality/value The main contributions of this paper include: Reduce the processing time by using YOLO5 in objects detection and K-means in clustering; improve the accuracy by using MZM to extract features and correlation coefficients to matching them; and implement and prove the effectiveness of the proposed method for three copy-move data sets: benchmark, MICC-F600 and author-built images.
Twenty-five years of the IPCC Data Distribution Centre at the DKRZ and the Reference Data Archive for CMIP data

Abstract. The Data Distribution Centre (DDC) of the Intergovernmental Panel on Climate Change (IPCC) celebrates its 25th anniversary in 2022. The DKRZ (German Climate Computing Center; German: Deutsches Klimarechenzentrum) is the only remaining DDC Partner from the original group jointly managing the DDC. In spite of changes in prioritization, it has been supporting the IPCC Assessments and preserving the quality-assured, citable climate model data underpinning the Assessment Reports over these years over the long term. An active and engaged collaborative community achieved advances in data standardization, data management best practices, and infrastructure developments. These evolving standards are reflected in the activities of the DDC. The introduction of the IPCC FAIR Guidelines into the current Sixth IPCC Assessment Report (AR6) has significantly changed the role of the DDC Partner DKRZ from an independent partner for long-term data preservation into an active partner involved in the IPCC's Sixth Assessment cycle. As a result, the DDC has gained exposure and visibility, posing a challenge and an opportunity to operationalize the IPCC's FAIR Guidelines and long-term preservation approaches. While the value of DDC services has been recognized, DDC sustainability remains unresolved and is currently being discussed within the IPCC as part of a general AR6 review process to formulate recommendations for the AR7 data management.
Investigation of Diabetes Care in Elder Individuals Using Artificial Intelligence

The term blockchain is mainly regarded as the distributed transaction which is mainly comprised of different blocks, and each set tends to represent the data that are being associated with the previous blocks. The blockchain is mainly managed through peer-to-peer networks which comparatively involves in adhering to the protocol of authenticating various blocks to form the blockchain. The usage of blockchain technology has been increasingly used in different fields, and healthcare services are now using blockchain for better patient delivery, detecting disease, and other aspects. The scope of the proposed study is that this study has exploited the function of a blockchain-enabled big data network to support medical professionals in giving better treatment modalities and delivering better patient care. The application of a new generation of smart block chains such as Ethereum and NEM is now offering better services and features in creating blockchain-based healthcare data management and hence support healthcare centers, medical practitioners, nurses, radiologists, and patients for better healthcare management. The application of blockchain technology in big data networks supports adding more value as it results in enhanced data quality, accessibility, and support in creating better security and safety of data and information, which is highly essential in the medical industry. Blockchain technology enables big data technologies enabled in supporting medical practitioners in addressing various healthcare ailments; one of the major diseases impacting many people around the world is diabetes. Patients with such ailments tend to generate more data and information related to the disease and health-related aspects. Hence, this information requires being maintained and analyzed, so that superior healthcare services can be provided. This study is more involved in the investigation of blockchain technology through a big data network enabled in offering better care for elderly individuals who have been affected due to diabetes, the researchers propose to choose a questionnaire method to collect the data from nearly 169 respondents, and these data were then analyzed using SPSS data package. The analyst used percentage analysis, correlation analysis, and chi-square test to analyze the data which has been collated by the researchers. The results and discussion show in detail the major aspects of blockchain technology in supporting healthcare professionals for better diabetes care management for elderly individuals.
DNformer: Temporal Link Prediction with Transfer Learning in Dynamic Networks

Temporal link prediction (TLP) is among the most important graph learning tasks, capable of predicting dynamic, time-varying links within networks. The key problem of TLP is how to explore potential link-evolving tendency from the increasing number of links over time. There exist three major challenges toward solving this problem: temporal nonlinear sparsity, weak serial correlation, and discontinuous structural dynamics. In this article, we propose a novel transfer learning model, called DNformer, to predict temporal link sequence in dynamic networks. The structural dynamic evolution is sequenced into consecutive links one by one over time to inhibit temporal nonlinear sparsity. The self-attention of the model is used to capture the serial correlation between the input and output link sequences. Moreover, our structural encoding is designed to obtain changing structures from the consecutive links and to learn the mapping between link sequences. This structural encoding consists of two parts: the node clustering encoding of each link and the link similarity encoding between links. These encodings enable the model to perceive the importance and correlation of links. Furthermore, we introduce a measurement of structural similarity in the loss function for the structural differences of link sequences. The experimental results demonstrate that our model outperforms other state-of-the-art TLP methods such as Transformer, TGAT, and EvolveGCN. It achieves the three highest AUC and four highest precision scores in five different representative dynamic networks problems.
Computationally Efficient Context-Free Named Entity Disambiguation with Wikipedia

The induction of the semantics of unstructured text corpora is a crucial task for modern natural language processing and artificial intelligence applications. The Named Entity Disambiguation task comprises the extraction of Named Entities and their linking to an appropriate representation from a concept ontology based on the available information. This work introduces novel methodologies, leveraging domain knowledge extraction from Wikipedia in a simple yet highly effective approach. In addition, we introduce a fuzzy logic model with a strong focus on computational efficiency. We also present a new measure, decisive in both methods for the entity linking selection and the quantification of the confidence of the produced entity links, namely the relative commonness measure. The experimental results of our approach on established datasets revealed state-of-the-art accuracy and run-time performance in the domain of fast, context-free Wikification, by relying on an offline pre-processing stage on the corpus of Wikipedia. The methods introduced can be leveraged as stand-alone NED methodologies, propitious for applications on mobile devices, or in the context of vastly reducing the complexity of deep neural network approaches as a first context-free layer.
Optimizing vision and visuals

The evolution of the internet is underway, where immersive virtual 3D environments (commonly known as metaverse or telelife) will replace flat 2D interfaces. Crucial ingredients in this transformation are next-generation displays and cameras representing genuinely 3D visuals while meeting the human visual system's perceptual requirements.
Virtual enhanced education in counselor education: Is this for real?

Abstract Most applications of virtual reality and technology used in counseling occur in the context of client intervention and treatment. This article presents how virtual enhanced education tools such as virtual reality, augmented reality, and 360° videos provide a stage for constructing creative opportunities for experiential learning in counselor education.
Information security investment with budget constraint and security information sharing in resource-sharing environments

Nowadays, business connection between firms becomes rather common so that one firm stores not only its information asset but also some of other firms’ information asset. The management of information security is vital for these resource-sharing firms. This paper constructs a game-theoretic model between two resource-sharing firms and one hacker to examine their strategic interaction when the firms face budget constraint on security investment. We consider security information sharing between the firms, which can improve their overall security effort but meantime facilitate the hacker’s learning to reduce attack costs. We find that although a tight budget constraint can help save investment cost, the firms always suffer from a poorly secure environment. It shows that although security information sharing is usually encouraged, the firms may be hurt when security information sharing is excessive so that fierce cyber-attacks are induced. We finally design an optimal compensation mechanism, in which the compensation fund is shown to increase with the degree of resource sharing.
What Users Want From Cloud Deletion and the Information They Need: A Participatory Action Study

Current cloud deletion mechanisms fall short in meeting users’ various deletion needs. They assume all data is deleted the same way—data is temporally removed (or hidden) from users’ cloud accounts before being completely deleted. This assumption neglects users’ desire to have data completely deleted instantly or their preference to have it recoverable for a more extended period. To date, these preferences have not been explored. To address this gap, we conducted a participatory study with four groups of active cloud users (five subjects per group). We examined their deletion preferences and the information they require to aid deletion. In particular, we explored how users want to delete cloud data and identify what information about cloud deletion they consider essential, the time it should be made available to them, and the communication channel that should be used. We show that cloud deletion preferences are complex and multi-dimensional, varying between subjects and groups. Information about deletion should be within reach when needed, for instance, be part of deletion controls. Based on these findings, we discuss the implications of our study in improving the current deletion mechanism to accommodate these preferences.
Reasonable object detection guided by knowledge of global context and category relationship

The mainstream object detectors usually treat each region separately, which overlooks the important global context information and the associations between object categories. Existing methods model global context via attention mechanism, which requires ad hoc design and prior knowledge. Some works combine CNN features with label dependencies learned from a pre-defined graph and word embeddings, which ignore the gap between visual features and textual corpus and are usually task-specific (depend on RoIPool/RoIAlign). In order to get rid of the previous specific settings, and enable different types of detectors to refine detection results with the help of prior knowledge, in this paper, we propose KROD (Knowledge-guided Reasonable Object Detection), which consists of the GKM (Global Category Knowledge Mining) module and CRM (Category Relationship Knowledge Mining) module, to improve detection performance by mimicking the processes of human reasoning. For a given image, GKM introduces global category knowledge into the detector by simply attaching a multi-label image classification branch to the backbone. Meanwhile, CRM input the raw detection outputs to the object category co-occurrence based knowledge graph to further refine the original results, with the help of GCN (Graph Convolutional Network). We also propose a novel loss-aware module to distinctively correct the classification probability of different detected boxes. Without bells and whistles, extensive experiments show that the proposed KROD can improve different baseline models (both anchor-based and anchor-free) by a large margin (1.2% ∼ 1.8% higher AP) with marginal loss of efficiency on MS COCO.
Adaptive gradients and weight projection based on quantized neural networks for efficient image classification

Quantization of weights and activations has been introduced into optimization methodologies of deep neural networks (DNNs) to address issues like memory consumption and massive computational demands that impede edge applications of neural networks. There are multiple approaches to quantization which can probably all be classified into two categories: uniform quantization and non-uniform quantization. The key difference between these methods is whether unequal quantization intervals are used to match the non-uniform distribution of weights. However, conventional techniques train weights layer by layer to suit previously determined quantization points, which poses difficulty for reaching optimal points. We proposed a framework called Deep Projection (DP) to train quantized weights with adaptive gradients. Gradients and weights are projected to high dimensional training space through projection layers, resulting in more complicated update paths and randomness, which benefit the generalization of models. Renewal of weights in the network is conducted by values composited by self-updating high dimensional tensors, which means that the resulting gradients are softer and more adaptive to network training. We applied our method via a uniform quantization approach, and the results showed improvements even when we limited the first and last layers with lower bit-width. The results could be comparable with the non-uniform quantization method with 4-bit precision. Distributions of weights in different quantized networks are analyzed to display the advantages of our method. Besides, all of these steps can be realized in a conventional training process. There are no limitations to quantization bit-width and function. Namely, our framework is readily implemented with a concise architecture.
Radio Frequency Database Construction and Modulation Recognition in Wireless Sensor Networks

Current modulation recognition methods in wireless sensor networks rely too much on simulation datasets. Its practical application effect cannot reach the expected results. To address this issue, in this paper we collect a large amount of real-world wireless signal data based on the software radio device USRP 2920. We then propose a real radio frequency (RF) database architecture and preprocessing operators to manage real-world wireless signal data, conduct signal preprocessing, and export the dataset. Based on different feature datasets derived from the RF database, we propose a multidimensional feature hybrid network (MFHN), which is used to identify unknown signals by analyzing different kinds of signal features. Further, we improve MFHN and design a multifeatured joint migration network (MJMN) to identify small-sample targets. The experimental results show that the recognition rates for unknown target signals of the MFHN and MJMN are 82.7% and 93.2%, respectively. The proposed methods improve the recognition performance in the single node of wireless sensor networks in complex electromagnetic environments, which provides reference for subsequent decision fusion.
Implementation of Encryption and Data Hiding in E-Health Application

This chapter presents the possibilities of applying cryptography and steganography in design advanced methods of medical software. The proposed solution has two modules: medical data encryption and medical data hiding. In the first module for the encryption of patient data a Catalan crypto-key is used combined with the LatticePath combinatorial problem. In the second module for hiding patient data, the Catalan stego-key and medical image is used. The objective of the second part is to explain and investigate the existing author's method to steganography based on the Catalan numbers in the design of medical software. The proposed solution is implemented in the Java programming language. In the experimental part, cryptanalysis and steganalysis of the proposed solution were given. Cryptanalysis is based on time and storage complexity, leaking information and machine learning-based identification of the encryption method. Also, steganalysis is based on the amount of information per pixel in stego image, approximate entropy and bit distribution in stego-images.
TeGraph: A Novel General-Purpose Temporal Graph Computing Engine

Temporal graphs attach time information to edges and are commonly used for implementing time-critical applications that can not be effectively processed by traditional static and dynamic graph processing engines. State-of-the-art solutions that target temporal path problems remain ad-hoc and often suboptimal. A unified and high-performance solution that could efficiently process general temporal path problems via a universal optimization strategy and relieve practitioners from heavy optimization efforts is in urgent demand. In this paper, we make two key observations: (1) temporal path problems can be described as topological-optimum problems and solved by a universal single scan execution model; and (2) data redundancy commonly occurs in the native format of the transformed temporal graphs, which is unnecessary for information propagation and can be eliminated for better memory utilization and execution efficiency. Based on these core insights, we propose TegRaph, the first general-purpose temporal graph computing engine to provide a unified optimization strategy and execution model for general temporal path problems and their applications. TegRaph not only presents temporal information-aware graph representation that naturally fits temporal graphs but also offers general system-level supports such as out-of-core execution. Extensive evaluation reveals that TegRaph can achieve significant speedups over the state-of-the-art designs with up to two orders of magnitude (241×) with the throughput of two hundred million edges per second.
Multi-Task Learning with Calibrated Mixture of Insightful Experts

Multi-task learning has been established as an important machine learning framework for leveraging shared knowledge among multiple different but related tasks, with the generalization performance of models enhanced. As a promising learning paradigm, multi-task learning has been widely adopted by various real-world applications, such as recommendation systems. Multi-gate Mixture-of-Experts (MMoE), a well-received multi-task learning method in industry, based on the classic and inspiring Mixture-of-Experts (MoE) structure, explicitly models task relationships and learns task-specific functionalities, generating significant improvements. However, in our applications, negative transfer, which confuses considerable existing multi-task learning methods, is still observed to happen to MMoE. In this paper, an in-depth empirical investigation into negative transfer is launched. And it reveals that, incompetent experts, which play fundamental roles under the learning framework of MoE, are the key technique bottleneck. To tackle this dilemma, we propose the Calibrated Mixture of Insightful Experts (CMoIE), with three novel modules (Conflict Resolution, Expert Communication, and Mixture Calibration), customed for multi-task learning. Hence a group of insightful experts are constructed with enhanced diversity, communication and specialization. To validate the proposed method CMoIE, experiments are conducted on three public datasets and one real-world click-through-rate prediction dataset we construct based on traffic logs collected from a large-scale online product recommendation system. Our approach yields best performance across all of these benchmarks, demonstrating the superiority of it.