Reviewerly/exHarmony huggingface dataset:
Performance prediction of deep learning applications training in GPU as a service systems

Data analysts predict that the GPU as a service (GPUaaS) market will grow to support 3D models, animated video processing, gaming, and deep learning model training. The main cloud providers already offer in their catalogs VMs with different type and number of GPUs. Because of the significant difference in terms of performance and cost of this type of VMs, correctly selecting the most appropriate one to execute the required job is mandatory to minimize the training cost. Motivated by these considerations, this paper proposes performance models to predict GPU-deployed neural networks (NNs) training. The proposed approach is based on machine learning and exploits two main sets of features, thus capturing both NNs properties and hardware characteristics. Such data enable the learning of multiple linear regression models that, coupled with an established feature selection technique, become accurate prediction tools, with errors below 12% on average. An extensive experimental campaign, performed both on public and in-house private cloud deployments, considers popular deep NNs used for image classification and speech transcription. The results show that prediction errors remain small even when extrapolating outside the range spanned by the input data, with important implications for the models’ applicability.
Efficient Electronic Patient Information Hiding Scheme With Tamper Detection Function for Medical Images

With the rapid development of the electronic medical information systems and Internet technology, the frequency of electronic medical records (EMRs) and electronic patient information (EPIs) transmitted over the Internet and shared among authorized medical parties is gradually increased. Reversible data hiding in medical images is an efficient technique to embed an EPI or EMR into a medical image, such as X-ray, MRI etc. In this paper, a reversible data hiding scheme by using enhanced neighbor mean interpolation (ENMI) technology is proposed to expand each <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$2 \times 2$ </tex-math></inline-formula> block in the original image to a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$3\times 3$ </tex-math></inline-formula> block. The four original pixels remaining at the four corners in each expanded block. Five derived pixels by using the ENMI interpolation algorithm are used to carry 4-bits secret data and a 1-bit authentication code with our proposed efficient data hiding strategy. Experimental results confirm that the detection performance and the robustness of the hidden data with our proposed scheme are significantly higher than that of the Geeth & Geeth scheme and other six representative schemes.
Perturbed-Location Mechanism for Increased User-Location Privacy in Proximity Detection and Digital Contact-Tracing Applications

Future social networks will rely heavily on sensing data collected from users' mobile and wearable devices. A crucial component of such sensing will be the full or partial access to user's location data, in order to enable various location-based and proximity-detection-based services. A timely example of such applications is the digital contact tracing in the context of infectious-disease control and management. Other proximity-detection-based applications include social networking, finding nearby friends, optimized shopping, or finding fast a point-of-interest in a commuting hall. Location information can enable a myriad of new services, among which we have proximity-detection services. Addressing efficiently the location privacy threats remains a major challenge in proximity-detection architectures. In this paper, we propose a location-perturbation mechanism in multi-floor buildings which highly protects the user location, while preserving very good proximity-detection capabilities. The proposed mechanism relies on the assumption that the users have full control of their location information and are able to get some floor-map information when entering a building of interest from a remote service provider. In addition, we assume that the devices own the functionality to adjust to the desired level of accuracy at which the users disclose their location to the service provider. Detailed simulation-based results are provided, based on multi-floor building scenarios with hotspot regions, and the tradeoff between privacy and utility is thoroughly investigated.
DET: Enabling Efficient Probing of IPv6 Active Addresses

Fast IPv4 scanning significantly improves network measurement and security research. Nevertheless, it is infeasible to perform brute-force scanning of the IPv6 address space. Alternatively, one can find active IPv6 addresses through scanning the candidate addresses generated by state-of-the-art algorithms. However, the probing efficiency of such algorithms is often very low. In this paper, our objective is to improve the probing efficiency of IPv6 addresses. We first perform a longitudinal active measurement study and build a high-quality dataset, hitlist, including more than 1.95B IPv6 addresses distributed in 58.2K BGP prefixes and collected over 17 months period. Different from the previous works, we probe the announced BGP prefixes using a pattern-based algorithm. This results in a dataset without uneven address distribution and low active rates. Further, we propose an efficient address generation algorithm, DET, which builds a density space tree to learn high-density address regions of the seed addresses with linear time complexity and improves the active addresses’ probing efficiency. We then compare our algorithm DET against state-of-the-art algorithms on the public hitlist and our hitlist by scanning 50M addresses. Our analysis shows that DET increases the de-aliased active address ratio and active address (including aliased addresses) ratio by 10%, and 14%, respectively. Furthermore, we develop a fingerprint-based method to detect aliased prefixes. The proposed method for the first time directly verifies whether the prefix is aliased or not. Our method finds that 10.64% of the public aliased prefixes are false positive.
Medical Students’ Technology Use for Self-Directed Learning: Contributing and Constraining Factors

With medical education shifting towards competency-based models, medical students are expected to be self-directed lifelong learners. There is an urgent need to understand what technology students adopt for self-directed learning and what factors contributed to students’ self-initiated technology use. This study took place in a midwestern university medical school, which implements a flipped classroom model where students are required to learn all the course materials independently before class. Twenty-six first- and second-year medical students participated in a semi-structured interview about their self-directed learning with technology, and contributing factors towards technology use. A qualitative description methodology using thematic analysis was used to identify key themes from the interview data. Medical students reported using four types of technologies for learning video resources, self-assessment tools, management tools, and social media. Three key determinants of students’ self-directed technology use were identified, including perceived usefulness, subjective norms, and educational compatibility. By probing medical students’ self-initiated technology use and its determinants, this study suggested that in a self-directed learning environment, medical students used a variety of third-party resources to facilitate learning and develop self-directed learning skills. This study also provided important practical implications to better support students’ productive use of technologies for self-directed learning.
Cross-Frame Transformer-Based Spatio-Temporal Video Super-Resolution

In this paper, we explore the spatio-temporal video super-resolution task, which aims to generate a high-resolution and high-frame-rate video from an existing video with low resolution and frame rate. First, we propose an end-to-end spatio-temporal video super-resolution network chiefly composed of cross-frame transformers instead of traditional convolutions. Especially, the cross-frame transformer module divides the input feature sequence into query, key, value matrixes, and then obtains the maximum similarity and similarity coefficient matrixes between neighboring and current feature maps through self-attention processing operations. Next, we propose a multi-level residual reconstruction module, which could make full use of the maximum similarity and similarity coefficient matrixes obtained by the cross-frame transformer, to reconstruct the high resolution and frame rate results from coarse to fine. Qualitative and quantitative evaluation results show that our method offers better performance and requires fewer training parameters compared with the existing two-stage network.
Privacy-Preserving Image Watermark Embedding Method Based on Edge Computing

To solve the problem of privacy leakage and response latency in outsourced image watermark embedding in cloud computing, an efficient and privacy-preserving watermark embedding method for outsourced digital images was proposed by introducing edge computing technology. We had proposed a perturbing encryption method with homomorphism to ensure the information security and the correctness of discrete wavelet transformation in the encrypted domain. In addition, the framework was designed to guarantee the safety of singular value decomposition that edge server could not recover the original image matrix. The experimental results show that the proposed method is superior to similar secure watermarking schemes in terms of encryption/decryption time and ciphertext expansion. The proposed method enables the watermarking operation to be performed in an unsafe outsourced environment while achieving a watermarking effect similar to the plaintext equivalent.
Models of Language and Multiword Expressions

Traditional accounts of language postulate two basic components: words stored in a lexicon, and rules that govern how they can be combined into meaningful sentences, a grammar. But, although this words-and-rules framework has proven itself to be useful in natural language processing and cognitive science, it has also shown important shortcomings when faced with actual language use. In this article, we review evidence from language acquisition, sentence processing, and computational modeling that shows how multiword expressions such as idioms, collocations, and other meaningful and common units that comprise more than one word play a key role in the organization of our linguistic knowledge. Importantly, multiword expressions straddle the line between lexicon and grammar, calling into question how useful this distinction is as a foundation for our understanding of language. Nonetheless, finding a replacement for the foundational role the words-and-rules approach has played in our theories is not straightforward. Thus, the second part of our article reviews and synthesizes the diverse approaches that have attempted to account for the central role of multiword expressions in language representation, acquisition, and processing.
The devil is in the face: Exploiting harmonious representations for facial expression recognition

Despite the recent effort from computer vision community, facial expression recognition (FER) remains a largely unsolved problem. This is because the appearance of people’s face undergoes dramatic changes due to changes in view angle, pose, illumination plus ambiguous facial expressions and low-quality facial images. In this work, we show the advantage of feature representation learning by dynamically graph message propagating subject to FER discriminative learning constraints and minimizing the distance of expression-agnostic transformed instance feature pairs. Specifically, we formulate a novel Harmonious Representation Learning (HRL) model for joint learning of landmark-guided graph message propagation, and spatially invariant feature learning using only generic matching metrics. Extensive comparative evaluations demonstrate the superiority of our proposed approach for FER over a variety of state-of-the-art methods on three major benchmark datasets including SFEW 2.0, RAF-DB, and CK+.
The Potential Utilization of Blockchain Technology

The application of blockchain innovation in E-commerce has a benefit. Add a space be the need for closeness sense in carrying out the transaction process in a security framework is required to preserve the privacy of information. One of the technological marvels is a Blockchain. The application of Blockchain innovation is to supply security for certificate security within the computerized period 4.0 so as not to control by unreliable parties. By utilizing the strategy of issue detailing, inquire about the plan, information, information preparing &amp; introduction, examination &amp; research reports and investigate strategies seven writing ponders. The application of blockchain innovation in an E-commerce service may be a need for closeness sense in carrying out the exchange handle a security framework is required to preserve information secrecy. Numerous things are valuable in Blockchain innovation, counting observing monetary resources, wellbeing records, character, and planning the supply chain. However, many proposals lack criticism and have static thinking. So that it fails to get the main benefits of the blockchain.
Comparing the usability of a virtual reality manual wheelchair simulator in two display conditions

Virtual reality (VR) simulators can help train manual wheelchair skills. Transfer of skills from the virtual to the real world may depend on the sense of presence, or of being “in” the virtual environment. Objectives To compare 1) the usability (in terms of performance, overall experience, and satisfaction), as well as 2) the sense of presence, in a wheelchair simulator with two display conditions: a head-mounted display (HMD) or a computer monitor. Methods Sixteen healthy adults practiced in the wheelchair simulator, first with a computer monitor display and then with an HMD. Task performance, cybersickness, presence, and overall experience in VR were assessed. Results Four of the participants were unable to complete all tasks in the HMD condition. When comparing the two display conditions, performance was the same, except for one task (bathroom) which took longer with the computer monitor. The HMD condition was rated as significantly higher in terms of sense of presence and VR experience but provoked more intense symptoms of cybersickness. Discussion Use of an HMD increased symptoms of cybersickness, with small gains in wheelchair performance. Thus, the use of an HMD may be warranted for the training of wheelchair skills, if tolerated by participants.
Potential Blockchain Technology in Higher Education Learning Innovations in Era 4.0

In the world of education, the development of technology systems is increasingly rapid. The implementation of Blockchain continues to be used in various sectors. This study analyzed the use of Blockchain in higher education. The research discussion results lead to the use of blockchain technology, which is a new trend in higher education. Blockchain implementation in higher education creates problems in the use of blockchain technology. Based on the use of Blockchain in research, we are exploring the application of related technologies in higher education. The problem with technology that functions to record small transactions and data used for many sectors such as recording transactions, documentation, and even voting, which is also used in Higher Education, is unfortunately not well implemented. This supports research on the use of blockchain technology that can record transactions in many sectors. Documenting the process of transactions, documentation, and voting can even be used in Higher Education. To improve the blockchain technology system's performance in higher education, it is becoming more effective, efficient, and more accessible in recording transaction processes. It can become a new innovation for Higher Education.
Sharing medical data using a blockchain‐based secure EHR system for New Zealand

In New Zealand, the demand for healthcare services has grown gradually in the last decade, and it is likely to increase further. This had led to issues such as increasing treatment costs and processing time for the patients. To address the growing pressure in the healthcare sector, and its fragmented IT landscape that compounds the problems further, the New Zealand Ministry of Health aims to establish a shared Electronic Health Record (EHR) system that integrates all the major healthcare organisations such as hospitals, medical centres, and specialists. Due to its characteristics, blockchain technology could be a potential platform for building such large-scale health systems. Here, MedBloc, a blockchain-based secure EHR system that enables patients and healthcare providers to access and share health records while providing usability, security, and privacy is presented. MedBloc captures a longitudinal view of the patients’ health story and empowers the patients to regulate their own data by allowing them to give or withdraw consent for healthcare providers to access their records. To preserve the patients’ privacy and protect their health data, MedBloc uses an encryption scheme to secure records and smart contracts to enforce access control to prevent unauthorised access.
An Intelligent Traffic Entropy Learning-Based Load Management Model for Cloud Networks

This letter proposes a novel traffic entropy learning based load prediction and management model that envisages improvement of load distribution by minimization of performance degradation due to traffic prediction errors. The entropy determines the variance considering dynamic surge and plunge of the traffic periodically and suggests to acquire sufficient number of active physical machines (PMs) to render efficacious services. The experimental simulation and comparison of the proposed model with existing approaches reveal that it significantly improves resource utilization up to 21.5% with reduction of active servers and energy consumption up to 26.5% and 11.7%, respectively.
Mammographically occult breast cancers detected with AI-based diagnosis supporting software: clinical and histopathologic characteristics

Abstract Background To demonstrate the value of an artificial intelligence (AI) software in the detection of mammographically occult breast cancers and to determine the clinicopathologic patterns of the cancers additionally detected using the AI software. Methods By retrospectively reviewing our institutional database (January 2017–September 2019), we identified women with mammographically occult breast cancers and analyzed their mammography with an AI software that provided a malignancy score (range 0–100; &gt; 10 considered as positive). The hot spots in the AI report were compared with the US and MRI findings to determine if the cancers were correctly marked by the AI software. The clinicopathologic characteristics of the AI-detected cancers were analyzed and compared with those of undetected cancers. Results Among the 1890 breast cancers, 6.8% (128/1890) were mammographically occult, among which 38.3% (49/128) had positive results in the AI analysis. Of them, 81.6% (40/49) were correctly marked by the AI software and determined as “AI-detected cancers.” As such, 31.3% (40/128) of mammographically occult breast cancers could be identified by the AI software. Of the AI-detected cancers, 97.5% were found in heterogeneously or extremely dense breasts, 52.5% were asymptomatic, 86.5% were invasive, and 29.7% had axillary lymph node metastasis. Compared with undetected cancers, the AI-detected cancers were more likely to be found in younger patients ( p &lt; 0.001), undergo neoadjuvant chemotherapy as well as mastectomy rather than breast-conserving operation (both p &lt; 0.001), and accompany axillary lymph node metastasis ( p = 0.003). Conclusions AI conferred an added value in the detection of mammographically occult breast cancers.
Computational pathology for musculoskeletal conditions using machine learning: advances, trends, and challenges

Histopathology is widely used to analyze clinical biopsy specimens and tissues from pre-clinical models of a variety of musculoskeletal conditions. Histological assessment relies on scoring systems that require expertise, time, and resources, which can lead to an analysis bottleneck. Recent advancements in digital imaging and image processing provide an opportunity to automate histological analyses by implementing advanced statistical models such as machine learning and deep learning, which would greatly benefit the musculoskeletal field. This review provides a high-level overview of machine learning applications, a general pipeline of tissue collection to model selection, and highlights the development of image analysis methods, including some machine learning applications, to solve musculoskeletal problems. We discuss the optimization steps for tissue processing, sectioning, staining, and imaging that are critical for the successful generalizability of an automated image analysis model. We also commenting on the considerations that should be taken into account during model selection and the considerable advances in the field of computer vision outside of histopathology, which can be leveraged for image analysis. Finally, we provide a historic perspective of the previously used histopathological image analysis applications for musculoskeletal diseases, and we contrast it with the advantages of implementing state-of-the-art computational pathology approaches. While some deep learning approaches have been used, there is a significant opportunity to expand the use of such approaches to solve musculoskeletal problems.
New incremental SVM algorithms for human activity recognition in smart homes

Abstract Smart homes are equipped with several sensor networks to keep an eye on both residents and their environment, to interpret the current situation and to react immediately. Handling large scale dataset of sensory events on real time to enable efficient interventions is challenging and very difficult. To deal with these data flows and challenges, traditional streaming data classification approaches can be boosted by use of incremental learning. In this paper, we presented two new Incremental SVM methods to improve the performance of SVM classification in the context of human activity recognition tasks. Two feature extraction methods elaborated by refining dependency sensor extraction feature and focusing on the last sensor event only have been suggested. On the other hand, a clustering based approach and a similarity based approach have been suggested to boost learning performance of the incremental SVM algorithms capitalizing on the relationship between data chunk and support vectors of previous chunk. We demonstrate through several simulations on two major publicly available data sets (Aruba and Tulum), the feasibility and improvements in learning and classification performances in real time achieved by our proposed methods over the state-of-the-art. For instance, we have shown that the introduced similarity-based incremental learning is 5 to 9 times faster than other methods in terms of training performances. Similarly, the introduced Last-state sensor feature method induces at least 5% improvement in terms of F1-score when using baseline SVM classifier.