Reviewerly/exHarmony huggingface dataset:
Influence of air pressure patterns over Europe on solar radiation variability over Poland (1986–2015)

Abstract The key factor that affects the inflow of radiant energy to the Earth's surface is the circulation of the atmosphere (caused by uneven distribution of air pressure on the globe) and the related changes in the amount of aerosols and cloudiness. This paper identified the areas in the Euro‐Atlantic region where the air pressure change had a significant impact on global solar radiation (GSR) changes over Poland, during the period 1986–2015. In general, growing GSR sums over Poland are to some extent related to the positive phase of the NAO (simultaneous pressure growth in the area of the Azores High and the pressure decrease in the area of the Icelandic Low). Correlation coefficient between the NAO index and GSR over Poland equals 0.18 (statistically significant at α = 0.05). In turn, in summer and autumn the pressure growth in southern Scandinavia results in a significant increase in the amount of GSR over Poland. Days with extremely large GSR sums (above the 95th percentile) are also prompted by the Azores High ridge which covers Central and Southern Europe, and by air mass inflow from the Atlantic.
Multidimensional Extra Evidence Mining for Image Sentiment Analysis

Image sentiment analysis is a hot research topic in the field of computer vision. However, two key issues need to be addressed. First, high-quality training samples are scarce. There are numerous ambiguous images in the original datasets owing to diverse subjective cognitions from different annotators. Second, the cross-modal sentimental semantics among heterogeneous image features has not been fully explored. To alleviate these problems, we propose a novel model called multidimensional extra evidence mining (ME <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> M) for image sentiment analysis, it involves sample-refinement and cross-modal sentimental semantics mining. A new soft voting-based sample-refinement strategy is designed to address the former problem, whereas the state-of-the-art discriminant correlation analysis (DCA) model is used to completely mine the cross-modal sentimental semantics among diverse image features. Image sentiment analysis is conducted based on the cross-modal sentimental semantics and a general classifier. The experimental results verify that the ME <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> M model is effective and robust and that it outperforms the most competitive baselines on two well-known datasets. Furthermore, it is versatile owing to its flexible structure.
An innovative integrated modelling of safety data using multiple correspondence analysis and fuzzy discretization techniques

In this study, we have proposed an innovative integrated methodology to handle a mix of categorical and numeric safety data. We have augmented the traditional multiple correspondence analysis (MCA) through the use of fuzzy discretization approach, t-SNE technique and fuzzy c-means clustering. The fuzzy discretization approach transforms the continuous variables to categorical variables to make them analyzable using MCA. R2-profile is adopted to obtain the best number of hidden dimensions representing the maximum categorical information. Then, t-SNE technique is used to represent the high dimensional categorical information in a 2D map to visualize the significant categorical associations. Then, fuzzy c-means clustering (FCM) is used to group the categories in different clusters based on their membership degree. To determine the optimal number of clusters, cluster validity indices are used. Davies-Bouldin (DB) Index, Dunn's (DU) Index and Silhouette (SW) coefficients are used to determine the quality of clustering solutions. The proposed methodology is tested using electric overhead traveling (EOT) crane related near-miss incidents and found that our approach is effective. From managerial implication point of view, several safety rules are generated and subsequent safety countermeasures are proposed. Further, the results obtained through FCM is compared with K-means (KM) algorithm and unsupervised fuzzy c-means clustering (UPFCM). FCM outperforms KM and UPFCM on the basis of quality of solutions.
AI Chips on Things for Sustainable Society: A 28-nm CMOS, Fully Spin-to-spin Connected 512-Spin, Multi-Spin-Thread, Folded Halved-Interaction Circuits Method, Annealing Processing Chip

For sustainable society, next-generation Internet of Things (IoT) requires ultra-low-power information processing that is useful for both sensor area expansion and high-speed low-power feature extraction using a new signal processing artificial intelligence (AI) large-scale integration (LSI) chip developed for not only the cloud side but also the “things” side (edge) with an attached sensor. For this purpose, a fully spin-to-spin connected Ising model (annealing processing) AI LSI chip was successfully demonstrated for the first time. Its specifications are as follows: 521 spins, 262,144 interactions (with a halved-interaction circuit method), and 4-bit interaction accuracy. The chip was designed and fabricated using a 28-nm CMOS process. The new circuit technologies confirmed in actual operation of the chip are a block configuration realizing all spin-to-spin interactions, 8-spin-threads (core) method, and folded halved-interaction circuit method.
Volunteer Down: How COVID-19 Created the Largest Idling Supercomputer on Earth

From close to scratch, the COVID-19 pandemic created the largest volunteer supercomputer on earth. Sadly, processing resources assigned to the corresponding Folding@home project cannot be shared with other volunteer computing projects efficiently. Consequently, the largest supercomputer had significant idle times. This perspective paper investigates how the resource sharing of future volunteer computing projects could be improved. Notably, efficient resource sharing has been optimized throughout the last ten years in cloud computing. Therefore, this perspective paper reviews the current state of volunteer and cloud computing to analyze what both domains could learn from each other. It turns out that the disclosed resource sharing shortcomings of volunteer computing could be addressed by technologies that have been invented, optimized, and adapted for entirely different purposes by cloud-native companies like Uber, Airbnb, Google, or Facebook. Promising technologies might be containers, serverless architectures, image registries, distributed service registries, and all have one thing in common: They already exist and are all tried and tested in large web-scale deployments.
An Efficient Pairing-Free Certificateless Signcryption Without Secure Channel Communication During Secret Key Issuance ☆

Signcryption is one of the recent public key paradigms that satisfies both the requirement of authenticity and confidentiality of messages between parties. However, most of all the existing schemes use secure channel communication while distributing the partial-private-keys to the users in the network. However, it is not always efficient to establish a secure channel during the interaction between entities. To address this issue, this paper aims to present a new certificateless signcryption scheme that does not consider secure channel communication. The proposed scheme is designed without considering high computation bilinear pairing and map-to-point (MTP) hash function. The scheme can be shown secured in the random oracle model based on the infeasibility elliptic curve discrete logarithm problem (ECDLP). Performance analysis demands better acceptability than other existing ones.
Performance Evaluation of Normalization Techniques in Adverse Conditions

This paper explores the behavior of different normalization techniques viz. cepstral mean normalization, cepstral variance normalization, cepstral mean subtraction. cepstral mean and variance normalization, wiener filter, and spectral subtraction in noisy conditions. The performance parameters viz. EER (Equal Error Rate) and DCF (Detection Cost Function) has been calculated using NIST 2003 SRE and Aurora 2 with the help of various normalization techniques considered in this paper for different noisy backgrounds at 0, 5 and 10 dB signal-to-noise ratio. The experimental results obtained from these techniques reveal that cepstral mean normalization (CVN) normalization method is found to be better when compared to other normalization techniques used in this paper.
KissGlass

Cheek kissing is a common greeting in many countries around the world. Many parameters are involved when performing the kiss, such as which side to begin the kiss on and how many times the kiss is performed. These parameters can be used to infer one's social and physical context. In this paper, we present KissGlass, a system that leverages off-the-shelf smart glasses to recognize different kinds of cheek kissing gestures. Using a dataset we collected with 5 participants performing 10 gestures, our system obtains 83.0% accuracy in 10-fold cross validation and 74.33% accuracy in a leave-one-user-out user independent evaluation.
Sensei: Enforcing secure coding guidelines in the integrated development environment

Summary We discuss the potential benefits, requirements, and implementation challenges of a security‐by‐design approach in which an integrated development environment plugin assists software developers to write code that complies with secure coding guidelines. We discuss how such a plugin can enable a company's policy‐setting security experts and developers to pass their knowledge on to each other more efficiently, and to let developers more effectively put that knowledge into practice. This is achieved by letting the team members develop customized rule sets that formalize coding guidelines and by letting the plugin check the compliance of code being written to those rule sets in real time, similar to an as‐you‐type spell checker. Upon detected violations, the plugin suggests options to quickly fix them and offers additional information for the developer. We share our experience with proof‐of‐concept designs and implementations rolled out in multiple companies, and present some future research and development directions.
MAENet: Boosting Feature Representation for Cross-Modal Person Re-Identification with Pairwise Supervision

Person re-identification aims at successfully retrieving the images of a specific person in the gallery dataset given a probe image. Among all the existing research areas related to person re-identification, visible to thermal person re-identification (VT-REID) has gained proliferating momentum. VT-REID is deemed to be a rather challenging task owing to the large cross-modality gap [25], cross-modality variation and intra-modality variation. Existing techniques generally tackle this problem by embedding cross-modality data with convolutional neural networks into shared feature space to bridge the cross-modality discrepancy, and subsequently, devise hinge losses on similarity learning to alleviate the variation. However, feature extraction methods based simply on convolutional neural networks may fail to capture the distinctive and modality-invariant features, resulting in noises for further re-identification techniques. In this work, we present a novel modality and appearance invariant embedding learning framework equipped with maximum likelihood learning to perform cross-modal person re-identification. Extensive and comprehensive experiments are conducted to test the effectiveness of our framework. Results demonstrated that the proposed framework yields state-of-the-art Re-ID accuracy on RegDB and SYSU-MM01 datasets.
Survey on techniques for Authorized Deduplication of Encrypted data in Cloud

Cloud computing technology is on-demand nowadays. Number of files created, stored and processed in cloud is increasing per day. The amount of space, processing power, bandwidth and cost requirement is quiet high. It is been observed that more than half of the data generated and stored on the cloud is duplicate. Management and processing of this duplicate data requires huge expenditure and storage space. So, it is a critical problem of the cloud service provider to be solved. Data deduplication is a way to remove duplicate data and reduce management expenditure as well as improve storage efficiency.Data Deduplication technique is one of the compression methodologies which eliminates multiple instances of data on the cloud and maintain unique data. This technique is used in order to reduce bandwidth utilization and enhance storage proficiency. Data privacy preserving is an important aspect while performing data deduplication in the cloud. Therefore, it is important to secure data while performing data deduplication. The data deduplication of encrypted data can be achieved by implementing different methods. This paper describes and compare and different techniques carried out by the cloud service provider to achieve data deduplication in the cloud.
Actor-Critic Sequence Generation for Relative Difference Captioning

This paper investigates a new task named relative difference caption which aims to generate a sentence to tell the difference between the given image pair. Difference description is a crucial task for developing intelligent machines that can understand and handle changeable visual scenes and applications. Towards that end, we propose a reinforcement learning-based model, which utilizes a policy network and a value network in a decision procedure to collaboratively produce a difference caption. Specifically, the policy network works as an actor to estimate the probability of next word based on the current state and the value network serves as a critic to predict all possible extension values according to current action and state. To encourage generating correct and meaningful descriptions, we leverage a visual-linguistic similarity-based reward function as feedback. Empirical results on the recently released dataset demonstrate the effectiveness of our method in comparison with various baselines and model variants.
Automatic body segmentation for accelerated rendering of digitally reconstructed radiograph images

The rendering of digitally reconstructed radiograph (DRR) images involves creating a digital reconstruction of an image made by a three-dimensional (3D) imaging system, such as a computed tomography (CT) scan, to produce a new two-dimensional (2D) image that emulates a medical X-ray scan. Acceleration of DRR generation has been the focus of much research in recent years, especially for medical applications that are designed to work on different imaging systems such as 2D/3D medical image registration. In this study, we succeeded in reducing the DRR generation time by using an automatic body segmentation approach on the CT volume. The proposed automatic body segmentation methodology consists of several steps that are carried out sequentially, starting with the use of fractional order Darwinian particle swarm optimization to generate a binary mask for all CT volume slices and ending with the use of a watershed transform to obtain the parameters required to identify the human body or part thereof as a confirmed region of interest (ROI). In our experiment, nearly half of the CT volume was identified as a non-ROI by the proposed approach. The approach also achieved an acceleration in the DRR generation process because the time complexity rate is reduced from O(N3) to O(M3), where M≈12N. That is to say, the DRR generation process was accelerated from 9.3 s when using the original (512 × 512 × 350) CT volume to 4.9 s when using the automated segmented CT volume, which involved the applications of the traditional ray-casting projection technique. The proposed methodology was applied by using a computer with an Intel® Core™ i7-7500 U processor, 8 GB RAM and 2.70 GHz processing speed. We used the two most commonly employed measurements, i.e., the dice similarity coefficient (DSC) and the Tanimoto coefficient (TC) to evaluate the segmentation results for the tested datasets. The results were also compared with ground truth images drawn by a specialist at the King Hussein Cancer Center in Jordan. The comparison of the automated segmented volume and its ground truth showed that the proposed methodology obtained 99.50% accuracy based on the DSC and 99.03% based on TC.
Privacy-Preserving Outsourced Inner Product Computation on Encrypted Database

We consider an outsourced computation model in the selective data sharing setting.Specifically, one of the data owners outsources the encrypted data to an untrusted cloud server, and wants to share the specific function of these data with a group of data users.A data user can perform the specific computation on the data that it is authorized to access.We propose a construction under this model for the inner product computation by using the Inner Product Functional Encryption (IPFE) as a building block.A standard IPFE used on this model has two privacy weaknesses regarding the master secret key and the encrypted vector.We propose a strengthened IPFE that revises these weaknesses.We construct a new IPFE scheme and use it to construct an efficient outsourced inner product computation scheme.In our outsourced computation scheme, the storage overhead and the computation cost for a data user are independent of the vector size.The result privacy and the outsourced data privacy are well preserved against the untrusted cloud server.The experimental results show that our schemes are efficient and practical.
Opportunities and Challenges of Mobile Learning Implementation in Schools in Oman

Mobile learning research has been flourishing to keep pace with the rapid technological advancement that has been invading all parts of modern life. There is a shortage of such research in Oman, especially in schools. The aim of this study is to uncover mLearning employment in schools in Oman by exploring two cases. The study investigates the perceptions of headteachers, teachers, technicians and students about the opportunities and challenges of mLearning. It involved two headteachers, 16 teachers, two technicians and 237 students in the questionnaires, and involved two headteachers, seven teachers in the interviews and 13 students in three focus groups. The results revealed that mobile learning enhances students' learning and engagement, enhances communication between participants, and provides authentic and situated learning. The results also revealed that teachers and students are faced with four challenges. These challenges include cognitively demanding environment, technical problems, disruption and distraction, and teachers' confidence.
An Efficient Implementation of the ALS-WR Algorithm on x86 CPUs

With the continuous development of computers and big data technology, more recommendation systems are applied in the fields of online music, online movies, games, online shopping, and so on, to solve information redundancy and effectively to recommend interesting products for users. In this paper, we implement and accelerate the Alternating-Least-Squares with Weighted-$$\lambda $$-Regularization (ALS-WR) by adopting a two-level parallel strategies on the x86-64 Zen-based CPUs. As one of the most widely used recommendation algorithms, the ALS-WR algorithm is based on matrix factorization. In the mathematical discipline of linear algebra, a matrix decomposition or matrix factorization is a dimensionality reduction technique that factorizes a matrix into a product of matrices. Therefore, vector and matrix operations are the computational core of the ALS-WR algorithm, accelerating these computational kernels can effectively improve the overall performance of the ALS-WR algorithm. The experimental results show that our high-performance ALS-WR implementation can achieve 185.09 s (with 100 features and 30 iterations) on the MovieLens 20 M dataset.
Customizable GAN: A Method for Image Synthesis of Human Controllable

In the research of computer vision, artificial controllability of image synthesis is a significant and challenging task. At present, there are two available methods. One is to utilize a simple contour to determine the shape of the synthetic object. This method has a promising effect, but it can only control the shape information of the synthetic object, but not the specific content. The other is to employ the text description to synthesize the corresponding image, which effectively controls the specific content of the synthesis, but it cannot do anything for the synthesized shape. In this paper, we propose a highly flexible and human customizable image synthesis model based on simple contour and natural language description, in which the specific content of contour and text description can be determined artificially. The contour determines basic synthetic object shape, and the natural language describes specific object content. Based on these, highly authentic and customizable images can be synthesized. The experiments are executed in the Caltech-UCSD Birds (CUB) and Oxford-102 flower datasets, and the experimental results demonstrate the effectiveness and superiority of our method. The results not only maintain the contour but also conform to the natural language description. Simultaneously, the high-quality image synthesis results, based on artificial hand-drawing contour and text description, are displayed to illustrate the high flexibility and customizability of our model.
Juniper: A Jnd-Based Perceptual Video Coding Framework to Jointly Utilize Saliency and JND

The characteristics of human perception are not fully exploited by the conventional quantization control methods. Just Noticeable Distortion (JND)-methods facilitate this by finding perceivable distortion levels. However, these methods fail to use human attention to further reduce bitrate. To address this issue, this paper proposes a Just Noticeable distortion-based Perceptual (JUNIPER) framework for video coding, which combines saliency and JND concepts. Firstly, a JND predictor is trained using Support Vector Machines (SVM) and various quality metrics. Secondly, an efficient saliency model is used to measure the visual importance of each Coding Tree Unit (CTU) in HEVC. Finally, a quantization control algorithm is proposed that jointly considers the visual importance, and the perceivable quality levels, to assign a quantization parameter (QP) to each CTU. Experimental results demonstrate that JUNIPER saves 29.57% bitrate on average, with similar visual quality, compared with baseline HEVC coding.
The Rise of NoSQL Systems

Transaction processing systems are primarily based on the relational model of data and offer the advantages of decades of research and experience in enforcing data quality through integrity constraints, allowing concurrent access and supporting recoverability. From a performance standpoint, they offer joins-based query optimization and data structures to promote fast reads and writes, but are usually vertically scalable from a hardware standpoint. NoSQL (Not Only SQL) systems follow different data representation formats than relations, such as key-value pairs, graphs, documents or column-families. They offer a flexible data representation format as well as horizontal hardware scalability so that Big Data can be processed in real time. In this review article, we review recent research on each type of system, and then discuss how teaching of NoSQL may be incorporated into traditional undergraduate database courses in information systems curricula.