Reviewerly/exHarmony huggingface dataset:
Load Balancing With Deadline-Driven Parallel Data Transmission in Data Center Networks

With the explosive growth of the Internet of Things (IoT), an increasing amount of sensor data generated by soft real-time IoT applications has been moved to data centers for storage and data analysis. Large amounts of these data are required to be processed within a given deadline to ensure application performance. Therefore, meeting the transmission deadlines of data flows for soft real-time applications has always been crucial yet challenging to current data centers. Recent progress has demonstrated that adopting parallel data transmission over multipath data center network combining with effective load balancing can achieve a high bisection network bandwidth, thus speeding up the network transfer of data flows. Nevertheless, the deadline miss ratios (DMRs) of these flows are not lowered as expected since the existing load balancing schemes are naturally agnostic to the deadline requirement. They are either unable to reroute traffic flexibly or aimlessly reroute these deadline-restrained flows, regardless of their urgent levels and path conditions. To address these inefficiencies, we propose a deadline-aware load-balancing scheme, namely, DLB, which perceives the deadline requirements and helps the urgent flows to timely switch to those faster transmission paths to complete quickly. Specifically, DLB computes the urgent level for each flow in real time to judge if the switch needs to make proactive rerouting. When a flow is nonurgent, DLB does not proactively change its transmission path, leaving more available paths to those flows with higher urgent levels. When a flow becomes extremely urgent, it immediately switches to those light-loaded paths to finish its data transmission before its deadline as far as possible. Experimental results of NS2 simulations and real testbed implementations show that DLB reduces the DMRs by up to 50% compared to the state-of-the-art data center load-balancing schemes, while only induces trivial overhead during deployment.
A Privacy-Preserving Architecture and Data-Sharing Model for Cloud-IoT Applications

Many service providers offer their services in exchange for users’ private data. Despite new regulations created to protect users privacy, users are often given little choice over the way their data is collected and used. To address privacy concerns in cloud-IoT applications, we propose to use an architecture, called Data Bank, which gives users fine-grained control over their data. Data Bank uses a category-based data access (CBDA) model which covers the whole data life-cycle, from data collection from IoT devices to data sharing with services. We show how dynamic policies can be specified using a new attribute-based instance of CBDA, and describe the use of policy graphs to visualise and analyse policies.
Massive Data Generation for Deep Learning-Aided Wireless Systems Using Meta Learning and Generative Adversarial Network

As an entirely-new paradigm to design the communication systems, deep learning (DL), an approach that the machine learns the desired wireless function, has received much attention recently. In order to fully realize the benefit of DL-aided wireless system, we need to collect a large number of training samples. Unfortunately, collecting massive samples in the real environments is very challenging since it requires significant signal transmission overhead. In this paper, we propose a new type of data acquisition framework for DL-aided wireless systems. In our work, generative adversarial network (GAN) is used to generate samples approximating the real samples. To reduce the amount of training samples required for the wireless data generation, we train GAN with the help of the meta learning. From numerical experiments, we show that the DL model trained by the GAN generated samples performs close to that trained by the real samples.
Deep Supervised Dual Cycle Adversarial Network for Cross-Modal Retrieval

Cross-modal retrieval tasks, which are more natural and challenging than traditional retrieval tasks, have attracted increasing interest from researchers in recent years. Although different modalities with the same semantics have some potential relevance, the feature space heterogeneity still seriously weakens the performance of cross-modal retrieval models. To solve this problem, common space-based methods in which multimodal data is projected into a learned common space for similarity measurement have become the mainstream approach for cross-modal retrieval tasks. However, current methods entangle the modality style and semantic content in the common space and neglect to fully explore the semantic and discriminative representation/reconstruction of the semantic content. This often results in an unsatisfactory retrieval performance. To solve these issues, this paper proposes a new Deep Supervised Dual Cycle Adversarial Network (DSDCAN) model based on common space learning. It is composed of two cross-modal cycle GANs, one for the image and one for the text. The proposed cycle GAN model disentangles the semantic content and modality style features by making the data of one modality well reconstructed from the extracted modal style feature and the content feature of the other modality. Then, a discriminative semantic and label loss is proposed by fully considering the category, sample contrast, and label supervision to enhance the semantic discrimination of the common space representation. Besides this, to make the data distribution between two modalities similar, a second-order similarity is presented as a distance measurement of the cross-modal representation in the common space. Extensive experiments have been conducted on the Wikipedia, Pascal Sentence, NUS-WIDE-10k, PKU XMedia, MSCOCO, NUS-WIDE, Flickr30k and MIRFlickr datasets. The results demonstrate that the proposed method can achieve a higher performance than the state-of-the-art methods.
BodyTrak

In this paper, we present BodyTrak, an intelligent sensing technology that can estimate full body poses on a wristband. It only requires one miniature RGB camera to capture the body silhouettes, which are learned by a customized deep learning model to estimate the 3D positions of 14 joints on arms, legs, torso, and head. We conducted a user study with 9 participants in which each participant performed 12 daily activities such as walking, sitting, or exercising, in varying scenarios (wearing different clothes, outdoors/indoors) with a different number of camera settings on the wrist. The results show that our system can infer the full body pose (3D positions of 14 joints) with an average error of 6.9 cm using only one miniature RGB camera (11.5mm x 9.5mm) on the wrist pointing towards the body. Based on the results, we disscuss the possible application, challenges, and limitations to deploy our system in real-world scenarios.
Vehicle Detection Based on Improved Yolov5s Algorithm

Vehicle detection technology has been widely used in the field of intelligent transportation, and the performance of existing vehicle detection technology in both detection accuracy and detection speed has been continuously improved. However, when encountering complex road environments, problems such as low vehicle detection rate and poor real-time performance can occur. To address these problems, an improved YOLOv5s vehicle detection algorithm is proposed. Firstly, in the feature fusion module of neck part, a new detection scale is added and the original FPN+PAN structure is replaced with an improved Bi-directional Feature Pyramid Network (BiFPN). Secondly, the Triplet Attention (TA) module l is added to the backbone part and the improved neck part to enhance the feature extraction capability. Finally, the improved algorithm is tested on the MS COCO 2017 dataset, and the experimental results show that the algorithm improves the mean average precision (mAP) by 1.34% to 67.64% compared with the original YOLOv5s algorithm. The detection effect of small-scale vehicle targets is better than the original YOLOv5s algorithm, and the detection accuracy is higher.
Assessment of IT Maturity Level and Roadmap Preparation for Improving Governance Based on COBIT 5 (Case Study: XYZ Company)

Based on the results of the assessment conducted at XYZ Company, XYZ Company has implemented information technology to help run its business processes. However, the use of this technology has not yet identified the maturity level. For this reason, it is necessary to conduct an assessment and then design recommendations and a roadmap for improvement based on ideal IT governance based on the COBIT Framework. The formulation of recommendations considers the results of the assessment at the assessment stage which consists of3 main steps, namely Analysis of organizational Context and IT Governance Needs, Assessment of Existing Conditions, and Preparation of Recommendations for Strengthening Existing Governance. The purpose of implementing IT Maturity Level assessment is to find out how optimally IT in various aspects such as aspects of people, processes, and tools has been managed by the Company, and this must be done because it is a mandate from regulatory regulations. Companies must carry out IT maturity level assessments to find out how far/well they have managed IT in various aspects, and they must identify gaps between what they have implemented and what is ideal according to best practice/framework to be used as a reference, then after that, develop a roadmap to find out what improvement initiatives/projects should take precedence and which are correlated, so that implementation of the initiatives/projects is neater and more focused. It should be underlined that this research is limited to identifying existing conditions, gaps, and compiling a recommendation roadmap.
Classification of uranium ore concentrates applying support vector machine to spectrophotometric and textural features

Uranium ore concentrates (UOCs) are produced in the early stages of the nuclear fuel cycle, prior to conversion to uranium hexafluoride. Because of their high uranium content and the large-scale production, UOCs diversion from civilian use and proliferation are potential risks. This implies the necessity to develop methods able to recognise characteristic parameters correlating each UOC powder to its history and origin. Here, a novel methodology is proposed: first the reflectance spectra of 79 commercial UOCs are acquired and clustered by means of Ward's clustering analysis, then classified by Support Vector Machine (SVM). Second, SVM classification is applied to the image textural features extracted with the Grey Level Co-occurrence Matrix (GLCM) and the Angle Measure Technique (AMT) algorithms for powders in two different colour groups. The developed SVM models present good classification quality: a Matthews correlation coefficient (MCC) of 0.95 is obtained for the classification based on colours while macro-F1 is generally greater than 0.81 (MCC larger than 0.75) for the texture-based classification. These results reveal the potentiality of the present automated classification for the scopes of nuclear forensics in the identification of an unknown uranium ore concentrate sample.
Frequency estimation under multiparty differential privacy

We study the fundamental problem of frequency estimation under both privacy and communication constraints, where the data is distributed among k parties. We consider two application scenarios: (1) one-shot, where the data is static and the aggregator conducts a one-time computation; and (2) streaming, where each party receives a stream of items over time and the aggregator continuously monitors the frequencies. We adopt the model of multiparty differential privacy (MDP), which is more general than local differential privacy (LDP) and (centralized) differential privacy. Our protocols achieve optimality (up to logarithmic factors) permissible by the more stringent of the two constraints. In particular, when specialized to the ε-LDP model, our protocol achieves an error of √ k /(ε Θ(ε) − 1) using O ( k max{ε, log 1/ε}) bits of communication and O ( k log u ) bits of public randomness, where u is the size of the domain.
Multi-scale dense and attention mechanism for image semantic segmentation based on improved DeepLabv3+

Semantic scene segmentation has become an important application in computer vision and is an essential part of intelligent transportation systems for complete scene understanding of the surrounding environment. Several methods based on convolutional neural networks have emerged, but they have some problems, including small-scale target loss, inaccurate detailed region segmentation, and boundary category confusion. Using shallow features, we exploit the capabilities of global context information according to the theory of pyramids. A weighted pyramid feature fusion module is constructed to fuse the feature maps of different scales generated by the backbone network, and the proportion of feature fusion is dynamically updated by trainable parameters. After that, a self-attention mechanism is introduced to discover information about spatial channel interdependencies. Finally, the atrous spatial pyramid pooling module of the DeepLabv3+ network is improved by connecting the atrous convolution with different dilation rates at the receptive field. The experimental results show 4.1% mean pixel accuracy and 3.92% mean intersection over union improvements in the proposed method compared with the DeepLabv3+, and the result of semantic segmentation is more accurate.
Quantum Error Correction: Noise-Adapted Techniques and Applications

The quantum computing devices of today have tens to hundreds of qubits that are highly susceptible to noise due to unwanted interactions with their environment. The theory of quantum error correction provides a scheme by which the effects of such noise on quantum states can be mitigated, paving the way for realising robust, scalable quantum computers. In this article we survey the current landscape of quantum error correcting (QEC) codes, focusing on recent theoretical advances in the domain of noise-adapted QEC, and highlighting some key open questions. We also discuss the interesting connections that have emerged between such adaptive QEC techniques and fundamental physics, especially in the areas of many-body physics and cosmology. We conclude with a brief review of the theory of quantum fault tolerance which gives a quantitative estimate of the physical noise threshold below which error-resilient quantum computation is possible.
On the reliability of deep learning-based classification for Alzheimer’s disease: Multi-cohorts, multi-vendors, multi-protocols, and head-to-head validation

Structural changes in the brain due to Alzheimer’s disease dementia (ADD) can be observed through brain T1-weighted magnetic resonance imaging (MRI) images. Many ADD diagnostic studies using brain MRI images have been conducted with machine-learning and deep-learning models. Although reliability is a key in clinical application and applicability of low-resolution MRI (LRMRI) is a key to broad clinical application, both are not sufficiently studied in the deep-learning area. In this study, we developed a 2-dimensional convolutional neural network-based classification model by adopting several methods, such as using instance normalization layer, Mixup, and sharpness aware minimization. To train the model, MRI images from 2,765 cognitively normal individuals and 1,192 patients with ADD from the Samsung medical center cohort were exploited. To assess the reliability of our classification model, we designed external validation in multiple scenarios: (1) multi-cohort validation using four additional cohort datasets including more than 30 different centers in multiple countries, (2) multi-vendor validation using three different MRI vendor subgroups, (3) LRMRI image validation, and finally, (4) head-to-head validation using ten pairs of MRI images from ten individual subjects scanned in two different centers. For multi-cohort validation, we used the MRI images from 739 subjects from the Alzheimer’s Disease Neuroimaging Initiative cohort, 125 subjects from the Dementia Platform of Korea cohort, 234 subjects from the Premier cohort, and 139 subjects from the Gachon University Gil Medical Center. We further assessed classification performance across different vendors and protocols for each dataset. We achieved a mean AUC and classification accuracy of 0.9868 and 0.9482 in 5-fold cross-validation. In external validation, we obtained a comparable AUC of 0.9396 and classification accuracy of 0.8757 to other cross-validation studies in the ADNI cohorts. Furthermore, we observed the possibility of broad clinical application through LRMRI image validation by achieving a mean AUC and classification accuracy of 0.9404 and 0.8765 at cross-validation and AUC and classification accuracy of 0.8749 and 0.8281 at the ADNI cohort external validation.
A comprehensive bibliometric analysis on opinion mining and sentiment analysis global research output

The rise of the Internet and social media (i.e. reviews, forum discussions, blogs and social networks) constituted an interesting source to detect user opinion trends. This study examines the global publication output on opinion mining and sentiment analysis from documents published in 2000 to 2020. Bibliometric indicators on the trends, most cited papers, authors, institutions, countries, funding agencies and research subject areas were independently screened and analysed using bibliometrix package in R. A total of 7603 eligible documents were identified from 2000 to 2020. The total number of citations for all publications was 129,251, with an average of 17.0 citations per publication. About 14,629 authors wrote those documents with 1.93 authors per document and a collaboration index of 1.98. The most prolific author was Cambria Erik, with 47 publications and h-index of 42. The leading countries for research were China with n = 824, India with n = 576 and the United States with n = 244 publications. Lecture Notes in Computer Science proceedings was the top-ranked venue for publications with n = 434, h-index of 32 and 4598 total citation scores. National Natural Science Foundation of China was the top-ranked funding agency for research, and most of the publications were computer science ( n = 6320) documents. The study provides an in-depth assessment of the landmark of the hot research topic and acknowledges the contribution of the most productive and active authors across different countries in the world. In addition, the findings could support the younger scholars in their future research direction and improve the efficiency in potential future research collaborations and projects.
An Anonymous Authentication and Key Update Mechanism for IoT Devices Based on EnOcean Protocol

EnOcean, a commonly used control protocol in smart lighting systems, provides authentication, as well as message integrity verification services, and can resist replay attack and tamper attack. However, since the device identity information transmitted between sensors in smart lighting control systems is easily accessible by malicious attackers, attackers can analyze users' habits based on the intercepted information. This paper analyzed the security of the EnOcean protocol using a formal analysis method based on the colored Petri net (CPN) theory and the Dolev-Yao attacker model and found that the protocol did not anonymize the device identity information and did not have a communication key update mechanism, so an attacker could easily initiate a key compromise impersonation attack (KCIA) after breaking the pre-shared communication key. To address the above security issues, this paper proposed an EnOcean-A protocol with higher security based on the EnOcean protocol. The EnOcean-A protocol introduced a trusted third-party server to send communication keys to communication devices because devices must obtain different communication keys from the trusted third-party server each time they communicated. Thus, this protocol could resist a KCIA and achieve forward security. Meanwhile, the device identity information was anonymized using a homomorphic hash function in the EnOcean-A protocol, and the dynamic update mechanism of the device identity information was added so that an attacker could not obtain the real identity information of the device. Finally, the formal analysis of the EnOcean-A protocol showed that the new protocol could resist a KCIA and ensure the anonymity and untraceability of the communication device, which had higher security compared with the EnOcean protocol.
A Study on the Calibrated Confidence of Text Classification Using a Variational Bayes

Recently, predictions based on big data have become more successful. In fact, research using images or text can make a long-imagined future come true. However, the data often contain a lot of noise, or the model does not account for the data, which increases uncertainty. Moreover, the gap between accuracy and likelihood is widening in modern predictive models. This gap may increase the uncertainty of predictions. In particular, applications such as self-driving cars and healthcare have problems that can be directly threatened by these uncertainties. Previous studies have proposed methods for reducing uncertainty in applications using images or signals. However, although studies that use natural language processing are being actively conducted, there remains insufficient discussion about uncertainty in text classification. Therefore, we propose a method that uses Variational Bayes to reduce the difference between accuracy and likelihood in text classification. This paper conducts an experiment using patent data in the field of technology management to confirm the proposed method’s practical applicability. As a result of the experiment, the calibrated confidence in the model was very small, from a minimum of 0.02 to a maximum of 0.04. Furthermore, through statistical tests, we proved that the proposed method within the significance level of 0.05 was more effective at calibrating the confidence than before.
Research on Convergence Media Consensus Mechanism Based on Blockchain

In recent years, the media industry has achieved rapid development and experienced three development stages from traditional media to new media and then to the current convergence media. Convergence media has brought about great changes in content production, communication mechanism, operation and maintenance management, but also brought about problems such as declining credibility of the industry, difficulty in confirming content rights, difficulty in protecting user privacy, etc. Research on convergence media based on blockchain can make use of the characteristics of blockchain to design or optimize the media industry. In this paper, we introduced the development of convergence media, blockchain and consensus mechanism, then we described a sustainable convergence media ecology based on blockchain. Furthermore, we designed and implemented a consensus mechanism named proof of efficiency (PoE). After analysis, PoE can provide high security and resist 51% resource attack, sybil attack, etc. The experimental results show that PoE has the characteristics of decentralization, strong consistency, low energy consumption, short average block generation time, high throughput and short block confirmation time; the consensus results of PoE can reflect the node’s ecological characteristics in convergence media which can stimulate the activity of nodes and better solve the generation of the Matthew effect.