Reviewerly/exHarmony huggingface dataset:
ISKE: An unsupervised automatic keyphrase extraction approach using the iterated sentences based on graph method

The automatic extraction of key information is necessary for knowledge discovery in this era of rapid knowledge growth. The extraction of key information can also help researchers quickly obtain the information they want instead of reading through all potential documents. Recently, researchers have refocused their attention from words to sentences because utilizing sentences outperforms with respect to illustrating semantics and reduces the calculation complexity. We present a novel and lightweight automatic keyphrase extraction algorithm that does not depend on any external resources, including an external dictionary or corpus. Unlike traditional graph-based algorithms that iterate words to generate keyphrase lists, our proposal uses iterated sentences to rank words and generate keyphrase lists for the semantic information of sentences that are more complete than the word. We initialize the values of words with weighted information and generate a sentence score using these values. Then, we integrate sentences to update their values; hence, the values of the words are updated with the sentence information. We iterate this process until the values of the sentences and words converge. The proposed method is based on a measurement of the relations between sentences and an evaluation of the flow of these relations in an easily understood manner. These relationships are based on the hypothesis that the causality between adjacent sentences is semantically stronger than the causality between words. We not only increase the extraction accuracy, but also reduce the number of iterations of the algorithm. We compare our proposed method with five strong, popular baseline algorithms on four datasets. The results show that our proposed method performs better than the other algorithms on three evaluation metrics.
On visual masking estimation for adaptive quantization using steerable filters

A fast and accurate assessment of visual masking effects is desirable while encoding in order to utilize such effects to improve the quality of compressed videos through an adaptive quantization (AQ) scheme. Here, we propose a method of estimating the contrast masking threshold on natural scene patches, using texture cues imparted by steerable filter responses. We then employ the estimated thresholds to perform AQ for AV1 encoding. Our experimental results establish that the proposed method is able to outperform existing visual masking models in terms of estimation performance while being relatively computationally inexpensive than these models, and is also able to improve the variance based AQ algorithm that is currently deployed in the SVT-AV1 codec. Using the multi-scale structural similarity index measure (MS-SSIM) as the quality model, our approach achieves an average BD-rate of -1.82% using the uniform quantization scheme as anchor as compared to 5.83% obtained with the variance based method. We note that the proposed approach produces less visible compression artifacts than the variance based AQ approach at lower bitrates, while maintaining similar encoding complexity.
RENOIR: Accelerating Blockchain Validation using State Caching

A Blockchain system such as Ethereum is a peer to peer network where each node works in three phases: creation, mining, and validation phases. In the creation phase, it executes a subset of locally cached transactions to form a new block. In the mining phase, the node solves a cryptographic puzzle (Proof of Work-PoW) on the block it forms. On receiving a block from another peer, it starts the validation phase, where it executes the transactions in the received block in order to ensure all transactions are valid. This execution also updates the blockchain state, which must be completed before creating the next block. A long block validation time lowers the system's overall throughput and brings the well known Verifier's dilemma into play. Additionally, this leads to wasted mining power utilization (MPU).
Formal Verification of a Hybrid IoT Operating System Model

The Internet of Things (IoT) is becoming an increasingly common paradigm. As IoT usage scenarios have increased, many challenges in IoT operating systems’ safety and adaptability have remained. According to the programming model, IoT operating systems can be categorized into three types: multithreading, event-driven, and hybrid. Different operating system models are applied in different scenarios depending on the real-time requirements or resource richness. The safety of IoT operating systems is critical; hence, formal verification is an important method of detecting potential vulnerabilities and providing safety guarantees. This paper proposes a hybrid model for an IoT operating system and employs the Event-B method for modeling and verification. We rewrite the requirements and divide the Event-Bus hybrid operating system model into eight levels for refinement. The safety and liveness properties of Event-Bus are guaranteed by generating and proving the proof obligations at each model level. A large proportion of the proof obligations (91%) are automatically proven on the Rodin platform to simplify the development process.
True Bit Generation by Using Two Different Noise Sources

Chaos-based random bit generators (RBGs) are used in encryption and in chaotic communication systems applications, such as synchronization and secure communication. Chaotic maps and circuits that display chaotic behavior as entropy sources are used to implement the random number generators (RNGs). In this study, a hybrid true random bit generator is proposed using Chua and memristor structures that display chaotic behavior as entropy sources. Continuous Chua and memristor outputs were sampled from the proposed hybrid structure. The samples obtained were converted into bit sequences by using modular arithmetic. Finally, both bit sequences were given as input to a summation generator. The output of the summation generator produces the desired bit sequences. The NIST Test Suite and the Scale Index tests were used on the resulting bit sequences to indicate their randomness. Also, correlation, complexity and key sensitivity analyses were performed for analyzing the security of the generated numbers.
Assessing the variability of crypto collateral assets in secured lending on the blockchain

The objective is to assess the variability of collateral crypto-assets used in secured lending on the blockchain. Using the coefficient of variation, the study estimates volatility of selected assets and implied risk intensity for both borrowers and lenders on blockchain-based lending. The coefficient of variation model was adopted by testing volatility. The model produced a number of key empirical observations from January 2017 to December 2018 reflecting market swings resulting in volatility, despite its simplicity, using Bitcoin, Ethereum and Ripple crypto-assets. The results of the study provide clarity on the crypto-assets bullish and bearish markets and whether there is a correlation between crypto-assets and Bitcoin market dominance. The recommendations include that financial institutions should de-risk their investment, participate in the crypto-currency domain and ensure financial inclusion; concepts of traditional asset-backed lending could be implemented by looking at 50 percent loan-to-value ratio.
A cluster medoid approach for cloud task scheduling

One of the familiar distributed technologies for sharing computing resources through internet is a cloud computing technology. One need not setup all computing resources on their own to design their applications. They can own as much they want by requesting computing resources through net. These resources are shared between users upon request by properly scheduling tasks in cloud. The process of scheduling tasks is to be optimized to share the resources very fast. The paper proposes a cluster medoid based task scheduling technique KMPS (K-medoid particle swarm approach) for minimizing the makespan. KMPS uses the merits of both Particle Swarm Optimization (PSO) and k-medoid approaches with added weights concept. Experimental results have shown that KMPS has optimized the results of make span and it is most suitable one for cloud computing.
RST Discourse Parser for Russian: An Experimental Study of Deep Learning Models

This work presents the first fully-fledged discourse parser for Russian based on the Rhetorical Structure Theory of Mann and Thompson (1988). For the segmentation, discourse tree construction, and discourse relation classification we employ deep learning models. With the help of multiple word embedding techniques, the new state of the art for discourse segmentation of Russian texts is achieved. We found that the neural classifiers using contextual word representations outperform previously proposed feature-based models for discourse relation classification. By ensembling both methods, we are able to further improve the performance of the discourse relation classification achieving the new state of the art for Russian.
A RNN Based Parallel Deep Learning Framework for Detecting Sentiment Polarity from Twitter Derived Textual Data

Social media platforms have become one of the primary mediums of communication nowadays. Along with communication, they are currently being utilized in a wide range of activities like digital marketing, customer care, e-learning etc. The unceasing use of social media is generating gigantic amount of textual data everyday. It is essential to properly analyze these data with the consideration of underlying human traits sentiments for exploring the full potential of these platforms. However, sentiment analysis from text has been considered as a challenging task because of the rapid use of informal and noisy words. Updated and powerful word embeddings are being invented almost every two years so that the machines could understand the underlying features of linguistics. Each of these embedding techniques excel in different aspects. In this paper, we present a novel RNN based sentiment polarity detection framework which feeds the power of three different word embeddings: Word2Vec, GloVe and SSWE into a single powerful network with three parallel branches. The proposed network effectively utilizes the semantic, syntactic and sentiment polarity wise embeddings in word vectors encoding three major aspect of language from the viewpoint of extracting sentiment information from text. Posts collected from twitter was used to train and validate the proposed network. The results demonstrate that the proposed network containing parallelly configured multiple word embeddings outperforms the single word vectorization techniques. Additionally, it shows comparable or better evaluation scores when compared to several contemporary state-of-the-art models.
Controlled Abstention Neural Networks for Identifying Skillful Predictions for Classification Problems

The earth system is exceedingly complex and often chaotic in nature, making prediction incredibly challenging: we cannot expect to make perfect predictions all of the time. Instead, we look for specific states of the system that lead to more predictable behavior than others, often termed "forecasts of opportunity." When these opportunities are not present, scientists need prediction systems that are capable of saying "I don't know." We introduce a novel loss function, termed the "NotWrong loss", that allows neural networks to identify forecasts of opportunity for classification problems. The NotWrong loss introduces an abstention class that allows the network to identify the more confident samples and abstain (say "I don't know") on the less confident samples. The abstention loss is designed to abstain on a user-defined fraction of the samples via a PID controller. Unlike many machine learning methods used to reject samples post-training, the NotWrong loss is applied during training to preferentially learn from the more confident samples. We show that the NotWrong loss outperforms other existing loss functions for multiple climate use cases. The implementation of the proposed loss function is straightforward in most network architectures designed for classification as it only requires the addition of an abstention class to the output layer and modification of the loss function.
Proposed New AV-Type Test-Bed for Accurate and Reliable Fish-Eye Lens Camera Self-Calibration

The fish-eye lens camera has a wide field of view that makes it effective for various applications and sensor systems. However, it incurs strong geometric distortion in the image due to compressive recording of the outer part of the image. Such distortion must be interpreted accurately through a self-calibration procedure. This paper proposes a new type of test-bed (the AV-type test-bed) that can effect a balanced distribution of image points and a low level of correlation between orientation parameters. The effectiveness of the proposed test-bed in the process of camera self-calibration was verified through the analysis of experimental results from both a simulation and real datasets. In the simulation experiments, the self-calibration procedures were performed using the proposed test-bed, four different projection models, and five different datasets. For all of the cases, the Root Mean Square residuals (RMS-residuals) of the experiments were lower than one-half pixel. The real experiments, meanwhile, were carried out using two different cameras and five different datasets. These results showed high levels of calibration accuracy (i.e., lower than the minimum value of RMS-residuals: 0.39 pixels). Based on the above analyses, we were able to verify the effectiveness of the proposed AV-type test-bed in the process of camera self-calibration.
Large scale multi-label learning using Gaussian processes

Abstract We introduce a Gaussian process latent factor model for multi-label classification that can capture correlations among class labels by using a small set of latent Gaussian process functions. To address computational challenges, when the number of training instances is very large, we introduce several techniques based on variational sparse Gaussian process approximations and stochastic optimization. Specifically, we apply doubly stochastic variational inference that sub-samples data instances and classes which allows us to cope with Big Data. Furthermore, we show it is possible and beneficial to optimize over inducing points, using gradient-based methods, even in very high dimensional input spaces involving up to hundreds of thousands of dimensions. We demonstrate the usefulness of our approach on several real-world large-scale multi-label learning problems.
Changes in iron mineralogy and magnetic susceptibility during crude oil incubation in four textural soils in Central Iran

We explore changes in magnetic susceptibility (χlf), and various forms of Fe and ferrimagnetic minerals after incubation by crude oil in four different textural soils. Four soil classes: clay, sandy, silt loam and sandy loam, were collected in central Iran and incubated with 0, 2.5, 5.0, 7.5 and 10% w/w of crude oil at 80% moisture of field capacity for 1, 3 and 6 months. After each period of incubation, various forms of Fe using extraction techniques, χlf and total petroleum hydrocarbon (TPH) were measured. In addition selected samples from the four soil types were analyzed by XRD for characterization of ferrimagnetic minerals. The results showed that all forms of iron and magnetic susceptibility increased significantly with increasing concentration of crude oil and incubation time. The highest dithionite extractable iron (Fed), χlf, as well as the ratio of Fed to oxalate extractable iron (Feo) were obtained for 10% w/w crude oil after 6 months incubation due to formation of ferrimagnetic minerals. The increase in the concertation of ferrimagnetic minerals (magnetite/maghemite) was verified by XRD. Significant positive correlations were obtained among χlf, Fed and TPH after 6 months of incubation. The results from this study confirm that magnetic methods can be used as a quick and cost-effective means for evaluation of TPH in polluted soils during the remediation, as compared to the chemical analyses.
Utility-based Frequent Itemsets in Data Streams using Sliding Window

There is a need for increased use of Market Basket Analysis in Economics to estimate consumer behaviour and demand function more realistic especially in a data streaming environment, which is a challenging task. A sliding window contains the latest fixed number of elements of the data stream. The algorithm FIMIU, proposed in this paper, replaces the itemsets in the sliding window by pointers to a single copy of the itemset, thereby creating more space for new itemsets in it which allows the user to analyze a bigger part of the data stream at a time. Experiments have shown that the proposed algorithm is memory efficient, however requires a bit extra time.
Efficient search over incomplete knowledge graphs in binarized embedding space

Knowledge graph (KG) embedding techniques represent entities and relations as low-dimensional and continuous vectors. This enables KG machine learning models to be easily adapted for KG reasoning, completion, and querying tasks. However, learned dense vectors are inefficient for large-scale similarity computations. Learning-to-hash is to a method that learns compact binary codes from high-dimensional input data and provides a promising way to accelerate efficiency by measuring the Hamming distance instead of Euclidean distance. Alternatively, a dot-product is used in a continuous vector space. Unfortunately, most learning-to-hash methods cannot be directly applied to KG structure encoding because they focus on similarity preservation between images. In this paper, we introduce a novel end-to-end learning-to-hash framework for encoding incomplete KGs and graph queries in a Hamming space. To preserve KG structure information, from embeddings to hash codes, and address the ill-posed gradient issue in the optimization, we utilize a continuation method (with convergence guarantees) to jointly encode queries and KG entities using geometric operations. The hashed embedding of a query can be utilized to discover target entities from incomplete KGs whilst the efficiency has been greatly improved. To evaluate the proposed framework, we have compared our model to state-of-the-art methods commonly used in real-world KGs. Extensive experimental results show that our framework not only significantly speeds up the search process, but also provides good results when unanswerable queries are caused by incomplete information.1
Operationalizing Behavior Change Theory as Part of Persuasive Technology: A Scoping Review on Social Comparison

Theories from psychology or the social sciences are commonly used as a starting point when designing persuasive technologies that aim to evoke a specific behavior change. Ideally, using these theories would (1) help guide the design of the persuasive technology and (2) help evaluate and inform the theory. In this paper, we focused on the first aspect and looked at how papers report on how a theory guided the design of persuasive technology. We performed a scoping review focused on the operationalization of social comparison theory as part of persuasive design. We chose social comparison due to its ubiquitous use in persuasive design as well as its potential positive or negative influence on the user. The former requires careful consideration in a persuasive design prototype. We focused on the proceedings of the Persuasive Technology conference from 2006 to 2020 to gain an understanding of the use of social comparison theory as part of persuasive design. Twelve studies met our inclusion criteria. Explanations of how the theory guides design decisions leading to the final operationalization were sparse. We suggest that conducting manipulation checks and using a systematic approach to reporting design decisions including the potential grounding of design elements in theory could highlight and clarify theoretical insights, and could increase our understanding of how social comparison—and behavior change theory in general—could be efficiently operationalized in persuasive technologies.
virtio-mem: paravirtualized memory hot(un)plug

The ability to dynamically increase or reduce the amount of memory available to a virtual machine is getting increasingly important: as one example, cloud users want to dynamically adjust the memory assigned to their virtual machines to optimize costs. Traditional memory hot(un)plug, such as hot(un)plugging emulated DIMMs, and memory ballooning can dynamically resize virtual machine memory. However, existing approaches provide limited flexibility, are incompatible with important technologies like vNUMA and fast operating system reboots, or are unsuitable when hosting untrusted virtual machines.
Automatic control: the natural approach for a quantitative-based personalized education

This paper proposes an engineering-oriented framework that casts the problem of learning as an automatic control problem, and that can ultimately be used to design education activities that autonomously adapt to individual students’ abilities, prerequisites, learning goals and other restrictions. The framework leverages on quantitative descriptions of knowledge flows within university programs in terms of Knowledge Components Matrices (KCMs) and Knowledge Flow Graphs (KFGs), that serve as the basis for developing the aforementioned automated approach to personalized education. Essentially, the manuscript proposes to: 1) combine these descriptions with results from exams and assessments to statistically estimate the learning status of a student; 2) combine these descriptions with data-driven approaches to derive models of how knowledge ladders logically and in time; 3) use these two ingredients to automatically design suitable and personalized study activities for a student, given his/her current knowledge status and desired learning outcome. We describe all steps (modelling of the knowledge flows, estimating the current learning status, and derivation of suitable learning activities to close the loop) with formal and control-oriented notation. The paper serves thus the purpose of showing how methods from the field of system theory and control engineering are naturally useful for the implementation of quantitative-based personalized education.
Tumor detection from breast ultrasound images using mammary gland attentive U-Net

Computer-aided diagnosis (CAD) has gained considerable attention for breast cancer screening owing to its high diagnostic efficiency and satisfactory accuracy. However, it has been revealed that traditional CAD systems for mammography are vulnerable to dense breast tissue, which could hide underlying tumors. To resolve this issue, we devised a learning scheme that equips the U-Net backbone with a well-designed attention mechanism to suppress the over-detection rate for nongland mammary regions in dense breast tissue and applied to the CAD for breast ultrasound (BUS) images. The proposed method has two stages: initial mammary gland segmentation, which involves the selection of a region in the mammary gland where a tumor may occur; then tumor region segmentation, wherein the attention U-Net detects tumor regions by characterizing the selected mammary gland probability map as a spatial attention map, drawing selective attention to mammary gland tissues. We evaluated the proposed tumor detection scheme on several public BUS image datasets. Comparative results demonstrate that the proposed approach achieves the best performance in most conditions. Notably, when considering the percentage of all actual tumors that were correctly segmented, the proposed method showed a tumorwise accuracy performance of 92.7%.