Reviewerly/exHarmony huggingface dataset:
Computationally efficient inference in large Bayesian mixed frequency VARs

Mixed frequency Vector Autoregressions (MF-VARs) can be used to provide timely and high frequency estimates or nowcasts of variables for which data is available at a low frequency. Bayesian methods are commonly used with MF-VARs to overcome over-parameterization concerns. But Bayesian methods typically rely on computationally demanding Markov Chain Monte Carlo (MCMC) methods. In this paper, we develop Variational Bayes (VB) methods for use with MF-VARs using Dirichlet–Laplace global–local shrinkage priors. We show that these methods are accurate and computationally much more efficient than MCMC in two empirical applications involving large MF-VARs.
Generalized Term Similarity for Feature Selection in Text Classification Using Quadratic Programming

The rapid growth of Internet technologies has led to an enormous increase in the number of electronic documents used worldwide. To organize and manage big data for unstructured documents effectively and efficiently, text categorization has been employed in recent decades. To conduct text categorization tasks, documents are usually represented using the bag-of-words model, owing to its simplicity. In this representation for text classification, feature selection becomes an essential method because all terms in the vocabulary induce enormous feature space corresponding to the documents. In this paper, we propose a new feature selection method that considers term similarity to avoid the selection of redundant terms. Term similarity is measured using a general method such as mutual information, and serves as a second measure in feature selection in addition to term ranking. To consider balance of term ranking and term similarity for feature selection, we use a quadratic programming-based numerical optimization approach. Experimental results demonstrate that considering term similarity is effective and has higher accuracy than conventional methods.
Key-Homomorphic Pseudorandom Functions from LWE with Small Modulus

Pseudorandom functions (PRFs) are fundamental objects in cryptography that play a central role in symmetric-key cryptography. Although PRFs can be constructed from one-way functions generically, these black-box constructions are usually inefficient and require deep circuits to evaluate compared to direct PRF constructions that rely on specific algebraic assumptions. From lattices, one can directly construct PRFs from the Learning with Errors (LWE) assumption (or its ring variant) using the result of Banerjee, Peikert, and Rosen (Eurocrypt 2012) and its subsequent works. However, all existing PRFs in this line of work rely on the hardness of the LWE problem where the associated modulus is super-polynomial in the security parameter. In this work, we provide two new PRF constructions from the LWE problem. In each of these constructions, each focuses on either minimizing the depth of its evaluation circuit or providing key-homomorphism while relying on the hardness of the LWE problem with either a polynomial modulus or nearly polynomial modulus. Along the way, we introduce a new variant of the LWE problem called the Learning with Rounding and Errors (LWRE) problem. We show that for certain settings of parameters, the LWRE problem is as hard as the LWE problem. We then show that the hardness of the LWRE problem naturally induces a pseudorandom synthesizer that can be used to construct a low-depth PRF. The techniques that we introduce to study the LWRE problem can then be used to derive variants of existing key-homomorphic PRFs whose security can be reduced from the hardness of the LWE problem with a much smaller modulus.
Dynamic Joint Variational Graph Autoencoders

Learning network representations is a fundamental task for many graph applications such as link prediction, node classification, graph clustering, and graph visualization. Many real-world networks are interpreted as dynamic networks and evolve over time. Most existing graph embedding algorithms were developed for static graphs mainly and cannot capture the evolution of a large dynamic network. In this paper, we propose Dynamic joint Variational Graph Autoencoders (Dyn-VGAE) that can learn both local structures and temporal evolutionary patterns in a dynamic network. Dyn-VGAE provides a joint learning framework for computing temporal representations of all graph snapshots simultaneously. Each auto-encoder embeds a graph snapshot based on its local structure and can also learn temporal dependencies by collaborating with other autoencoders. We conduct experimental studies on dynamic real-world graph datasets and the results demonstrate the effectiveness of the proposed method.
Two-Stage Learning for Uplink Channel Estimation in One-Bit Massive MIMO

We develop a two-stage deep learning pipeline architecture to estimate the uplink massive MIMO channel with one-bit ADCs. This deep learning pipeline is composed of two separate generative deep learning models. The first one is a supervised learning model and designed to compensate for the quantization loss. The second one is an unsupervised learning model and optimized for denoising. Our results show that the proposed deep learning-based channel estimator can significantly outperform other state-of-the-art channel estimators for one-bit quantized massive MIMO systems. In particular, our design provides 5-10 dB gain in channel estimation error. Furthermore, it requires a reasonable amount of pilots, on the order of 20 per coherence time interval.
A Blockchain-Based Secure Data Storage and Trading Model for Wireless Sensor Networks

Data storage on local devices provides fast, secure and complete access to the users. However, it needs sufficient storage, which is not feasible for light-weight clients’ environments. In such scenarios, the usage of external devices makes the system vulnerable to data tampering, privacy leakage, and other data security issues. Wireless Sensor Networks (WSNs) consist of resource constrained devices, where external storage is preferred due to the lack of storage capacity in local devices. Therefore, using a centralized storage mechanism in WSNs causes slow data retrieval, which affects further operations on data. Relying on trusted parties also cause certain privacy and trust issues. Various data trading mechanisms are introduced to efficiently utilize the stored data. However, they still lack in certain aspects as discussed above. Therefore, we propose and implement a Secure Incentive-based Data Storage and Trading Model (SIDSTM), which provides a secure and efficient distributed storage mechanism. The proposed scheme uses AES-128 encryption scheme to encrypt the data for privacy purposes. An elliptic curve Diffie-Hellman key exchange is used for securely exchanging the private keys among the network peers. In WSNs, there are fewer data trading models so far. Anyone can access the data if he has access to the hash provided by the IPFS in the InterPlanetary File System (IPFS). Thus, IPFS provides efficient data access and storage mechanism. However, it deletes the data after a certain time interval due to the limited storage. Therefore, the proposed model provides incentives to motivate IPFS’s peers for data storage. It also eliminates the need for the third-party involvement in data trading. The simulations are conducted to prove the effectiveness of the proposed model by evaluating its efficiency and scalability.
Imagining Data-Objects for Reflective Self-Tracking

While self-tracking data is typically captured real-time in a lived experience, the data is often stored in a manner detached from the context where it belongs. Research has shown that there is a potential to enhance people's lived experiences with data-objects (artifacts representing contextually relevant data), for individual and collective reflections through a physical portrayal of data. This paper expands that research by studying how to design contextually relevant data-objects based on people's needs. We conducted a participatory research project with five households using object theater as a core method to encourage participants to speculate upon combinations of meaningful objects and personal data archives. In this paper, we detail three aspects that seem relevant for designing data-objects: social sharing, contextual ambiguity and interaction with the body. We show how an experience-centric view on data-objects can contribute with the contextual, social and bodily interplay between people, data and objects.
Multimedia and the Tactile Internet

With the rapid development in the areas of multisensory hard- and software and the emergence of Tactile Internet, new media such as haptics, smell, olfaction, etc., nowadays, play a prominent role in making virtual objects physically tangible in a collaborative and/or networked virtual environment. By allowing users to feel each other's presence and physically manipulate objects from their interacted environments within 1 ms. The Tactile Internet facilitates fast multimodal interactions with multisensory information over the 5G network. 1 ms is a critical threshold in human perception of tactile response. For auditory response, this threshold is 100 ms and for visual response, it is 10 ms, which means delays above these thresholds are within the latency limit sensed by the human brain. In 4G, the round trip latency is 25 ms for an ideal environment. Clearly, that indicates 4G is not able to meet the requirements of tactile response. For this reason, the efforts to reduce latency in 5G are critical for Tactile Internet. Low-latency communications will also enable other digital twins’ applications such as real-time control of smart grid, self-driving car, and so on.
Smart glove-based gestures recognition system for Arabic sign language

Sign language is a communication way that enables deaf and dump people to communicate with others. Different communication systems are developed to cover various languages including Arabic language. The goal of this study was to design a low cost smart glove system that recognizes hands gestures in Arabic sign language. Through a set of flex sensors and a tilting sensing module for right and left hands, gestures were converted into a vocalized speech. Further, by developing Android application “Smart Gloves” a text speech was appeared. It facilitates user interface with the proposed system. Indeed, Bluetooth module was used for wireless communication between the system and a mobile phone. The glove system has been designed for words level and sentences level as well. Results point to 90% of recognition rate. The system has proven its ability to translate hands gestures into Arabic Sign Language in spoken and written ways.
Learning Effective Skeletal Representations on RGB Video for Fine-Grained Human Action Quality Assessment

In this paper, we propose an integrated action classification and regression learning framework for the fine-grained human action quality assessment of RGB videos. On the basis of 2D skeleton data obtained per frame of RGB video sequences, we present an effective representation of joint trajectories to train action classifiers and a class-specific regression model for a fine-grained assessment of the quality of human actions. To manage the challenge of view changes due to camera motion, we develop a self-similarity feature descriptor extracted from joint trajectories and a joint displacement sequence to represent dynamic patterns of the movement and posture of the human body. To weigh the impact of joints for different action categories, a class-specific regression model is developed to obtain effective fine-grained assessment functions. In the testing stage, with the supervision of the action classifier’s output, the regression model of a specific action category is selected to assess the quality of skeleton motion extracted from the action video. We take advantage of the discrimination of the action classifier and the viewpoint invariance of the self-similarity feature to boost the performance of the learning-based quality assessment method in a realistic scene. We evaluate our proposed method using diving and figure skating videos of the publicly available MIT Olympic Scoring dataset, and gymnastic vaulting videos of the recent benchmark University of Nevada Las Vegas (UNLV) Olympic Scoring dataset. The experimental results show that the proposed method achieved an improved performance, which is measured by the mean rank correlation coefficient between the predicted regression scores and the ground truths.
A critical review of graphics for subgroup analyses in clinical trials

Subgroup analyses are a routine part of clinical trials to investigate whether treatment effects are homogeneous across the study population. Graphical approaches play a key role in subgroup analyses to visualise effect sizes of subgroups, to aid the identification of groups that respond differentially, and to communicate the results to a wider audience. Many existing approaches do not capture the core information and are prone to lead to a misinterpretation of the subgroup effects. In this work, we critically appraise existing visualisation techniques, propose useful extensions to increase their utility and attempt to develop an effective visualisation approach. We focus on forest plots, UpSet plots, Galbraith plots, subpopulation treatment effect pattern plot, and contour plots, and comment on other approaches whose utility is more limited. We illustrate the methods using data from a prostate cancer study.
Fused behavior recognition model based on attention mechanism

With the rapid development of deep learning technology, behavior recognition based on video streams has made great progress in recent years. However, there are also some problems that must be solved: (1) In order to improve behavior recognition performance, the models have tended to become deeper, wider, and more complex. However, some new problems have been introduced also, such as that their real-time performance decreases; (2) Some actions in existing datasets are so similar that they are difficult to distinguish. To solve these problems, the ResNet34-3DRes18 model, which is a lightweight and efficient two-dimensional (2D) and three-dimensional (3D) fused model, is constructed in this study. The model used 2D convolutional neural network (2DCNN) to obtain the feature maps of input images and 3D convolutional neural network (3DCNN) to process the temporal relationships between frames, which made the model not only make use of 3DCNN's advantages on video temporal modeling but reduced model complexity. Compared with state-of-the-art models, this method has shown excellent performance at a faster speed. Furthermore, to distinguish between similar motions in the datasets, an attention gate mechanism is added, and a Res34-SE-IM-Net attention recognition model is constructed. The Res34-SE-IM-Net achieved 71.85%, 92.196%, and 36.5% top-1 accuracy (The predicting label obtained from model is the largest one in the output probability vector. If the label is the same as the target label of the motion, the classification is correct.) respectively on the test sets of the HMDB51, UCF101, and Something-Something v1 datasets.
Fuzzy-rough assisted refinement of image processing procedure for mammographic risk assessment

The use of computer aided diagnosis (CAD) systems, which are computer based tools for the automatic analysis of medical images such as mammogram and prostate MRI, can assist in the early detection and diagnosis of developing cancer. In the process of CAD for mammogram, the task of image processing (IP) plays a fundamental role in providing promising diagnostic results, by exploiting high-quality features extracted from the mammographic images. Normally, an IP procedure for mammographic images involves three mechanisms: region of interest (ROI) extraction, image enhancement (IE) and feature extraction (FE). However, an improper utilisation of IE may lead to an inferior composition of the features due to unexpected enhancement of any irrelevant or useless information in ROI. In order to overcome this problem, a fuzzy-rough refined IP (FRIP) framework is presented in this paper to improve the quality of mammographic image features hierarchically. Following the proposed framework, the ROI of each mammographic image is segmented and enhanced locally in the area of the block which is of the highest value of fuzzy positive region (FPR). Here, FPR implies a positive dependency relationship between the block and the decision with regard to the given feature set. The higher a block's FPR value the more certain its underlying image category. To attain a high quality of the image enhancement procedure, the winner block will be further improved by a multi-round strategy to create a pool of IE results. As such, for a mammographic image, after embedding the candidate enhanced blocks into the original ROI, the respectively extracted features from the locally enhanced ROI are compared against each other on the basis of the value of FPR. A given image is therefore represented by a set of features which are supported by the premier FPR among all of the resulting extracted features. The quality of the extracted features by FRIP is compared against that of those directly extracted from the original images, from the globally enhanced images or from the randomly locally enhanced images in performing classification tasks. The experimental results demonstrate that the mammographic risk assessment results based on the features achieved by the proposed framework are much improved over those by the alternatives.
Multi-scale generative adversarial inpainting network based on cross-layer attention transfer mechanism

Deep learning-based methods have recently shown promising results in image inpainting. These methods generate patches with visually plausible image structures and textures, which are semantically coherent with the context of surrounding regions. However, existing methods tend to generate artifacts which are inconsistent with surrounding regions, especially when dealing with complex images. Aiming at the limitations current in deep learning-based methods, this paper proposes a multi-scale generative adversarial network model based on cross-layer attention transfer mechanism. Cross-Layer Attention Transfer Module (CL-ATM) is presented to guide the filling of the corresponding low-level semantic feature map by using the high-level semantic feature map, so as to ensure visual and semantic consistency of inpainting. Meanwhile, a multi-scale generator and the multi-scale discriminators are added into the network structure. Different scales of discriminators have different receptive fields, which enable the generator to produce images with better global consistency and more details. Qualitative and quantitative experiments show that our method has superior performance against state-of-art inpainting models.
Sentiment Analysis for Arabic Reviews using Machine Learning Classification Algorithms

Sentiment Analysis (SA) is an increasingly important field of study, also known as opinion mining, as it has the ability to identify the emotional tone of a source material as being either positive, negative, or neutral. Simply, SA digests natural languages and extracts insights, making it extremely useful to gain an overview of the public opinion in a certain issue, topic, or even a product. With the vast amount of information over social network platforms, shared views, website reviews, and blogs comes the importance of SA. Arabic is a rich language with extremely complex inflectional and derivational morphology making sentiment analysis in Arabic text more challenging. In this paper, we propose a novel approach to enhance the accuracy of Arabic Sentiment Analysis (ASA). In this aspect, nine supervised machine learning algorithms have been implemented for ASA. Three of these classifiers have never been used before in ASA classification, namely, they are Ridge, Gradient Boosting, and Multi-layer Perceptron. The performance of all nine classifiers is tested using our constructed dataset. The dataset contains 6318 reviews written in different forms of Arabic language and prepared manually by gathering hotel reviews from Booking.com website.
Data science and machine learning in anesthesiology

Machine learning (ML) is revolutionizing anesthesiology research. Unlike classical research methods that are largely inference-based, ML is geared more towards making accurate predictions. ML is a field of artificial intelligence concerned with developing algorithms and models to perform prediction tasks in the absence of explicit instructions. Most ML applications, despite being highly variable in the topics that they deal with, generally follow a common workflow. For classification tasks, a researcher typically tests various ML models and compares the predictive performance with the reference logistic regression model. The main advantage of ML lies in its ability to deal with many features with complex interactions and its specific focus on maximizing predictive performance. However, emphasis on data-driven prediction can sometimes neglect mechanistic understanding. This article mainly focuses on the application of supervised ML to electronic health record (EHR) data. The main limitation of EHR-based studies is in the difficulty of establishing causal relationships. However, the associated low cost and rich information content provide great potential to uncover hitherto unknown correlations. In this review, the basic concepts of ML are introduced along with important terms that any ML researcher should know. Practical tips regarding the choice of software and computing devices are also provided. Towards the end, several examples of successful ML applications in anesthesiology are discussed. The goal of this article is to provide a basic roadmap to novice ML researchers working in the field of anesthesiology.
Spatial distribution of iron forms and features in the dried lake bed of Urmia Lake of Iran

Urmia Lake, located in Iran, has a unique pedological feature as fresh soil materials are being exposed as the lake is shrinking. Overuse of its water resources has caused its water level to drop, particularly in the east shore of that lake. This consequence has caused a serious environmental risk, but created a rare opportunity to investigate the spatial distribution of six iron forms and features, i.e., total iron (Fet); dithionite-extractable iron (Fed); oxalate-extractable iron (Feo); crystalline iron (Fed-Feo); activity ratio (Feo/Fed) and crystallinity ratio (Fed-Feo/Fet). In total, 130 soil samples were taken from three strata based on their proximity to the lake. Spectral indices derived from Landsat-8 OLI imagery acquired in July 2017 were used as environmental covariates in spatial models using Random Forests (RF) technique. The calibrated RF model for 2017 allowed us to backcast data for 2013 and 2015. The results showed that the iron forms and features varied primarily as a function of distance from the shore. Soil ripening process was also demonstrated in the east shore of Urmia Lake. Overall, this study showed the possibility of a combination of spectral imageries and spatial models to predict the dynamic environment of iron forms.
Bandwidth Slicing to Boost Federated Learning Over Passive Optical Networks

During federated learning (FL) process, each client needs to periodically upload local model parameters and download global model parameters to/from the central server, thus requires efficient communications. Meanwhile, passive optical network (PON) is promising to support fog computing where FL tasks can be executed and the traffic generated by FL needs to be transmitted together with other types of traffic for broadband access. In this letter, a bandwidth slicing algorithm in PONs is introduced for efficient FL, in which bandwidth is reserved for the involved ONUs collaboratively and mapped into each polling cycle. Results reveal that the proposed bandwidth slicing significantly improves training efficiency while achieving good learning accuracy for the FL task running over the PON.