Reviewerly/exHarmony huggingface dataset:
Block trees

Let string S[1..n] be parsed into z phrases by the Lempel-Ziv algorithm. The corresponding compression algorithm encodes S in O(z) space, but it does not support random access to S. We introduce a data structure, the block tree, that represents S in O(zlog⁡(n/z)) space and extracts any symbol of S in time O(log⁡(n/z)), among other space-time tradeoffs. The structure also supports other queries that are useful for building compressed data structures on top of S. Further, block trees can be built in linear time and in a scalable manner. Our experiments show that block trees offer relevant space-time tradeoffs compared to other compressed string representations for highly repetitive strings.
Nonlinear Model Predictive Control of Electrical Submersible Pumps based on Echo State Networks

Employed for artificial lifting in oil well production, Electrical Submersible Pumps (ESP) can be operated with Model Predictive Control (MPC) to drive an optimal production, while ensuring a safe operation and respecting system constraints. Due to the nonlinear dynamics of ESPs, Echo State Networks (ESNs), a recurrent neural network with fast training, are employed for efficient system identification of unknown dynamic systems. Besides the synthesis of highly accurate prediction models, this work contributes by designing two Nonlinear MPC (NMPC) strategies for the control of an ESP-lifted oil well: a standard Single-Shooting NMPC that embeds the ESN model completely, and the Practical Nonlinear Model Predictive Controller (PNMPC) that approximates the NMPC through fast trajectory-linearization of the ESN model. Another contribution is the implementation of an error correction filter to reject disturbances and counter modeling errors in both NMPC strategies. Finally, in computational experiments, both ESN-based NMPC strategies performed well in controlling simulated ESP-lifted oil wells when the model of the plant is unknown. However, PNMPC was more efficient and induced a similar performance to standard NMPC.
A novel searchable encryption scheme to reduce the access pattern leakage

Searchable symmetric encryption (SSE) is a technology that enables clients to outsource sensitive data in the cloud. The vast usage of cloud and rapid growth in file sharing over the cloud has forced researchers into devising newer searchable encryption (SE) systems that are more secure and robust. This paper explores the effects of leakage of access patterns to an adversary. It is observed that in most of the existing systems, a successful statistical attack can help the adversary learn the access pattern, provide him with the required document and reveal the underlying data. This paper addresses these issues by presenting a novel SE system which hides the search pattern and access pattern. In this paper, we propose a SSE system that uses forward index while lifting the one-time search limitation of the previous solutions. Our system uses a trapdoor obfuscation technique to deceive the 3rd party adversary who is considered as an active entity other than client and server, observing the information being exchanged during the active session of client and the cloud service provider. Compared with the existing techniques, this solution uses keyword analysis to extract limited words from the documents based on the frequency of occurrence. To provide stronger privacy, an encoding technique is used which aims to hide the access pattern from the adversary. In order to eliminate trapdoor linkability, a probabilistic trapdoor generation process is presented. The test results demonstrate that our system is suitable for practical usage with minimal overhead and is more efficient as compared to the state of the art.
Application and performance of artificial intelligence technology in cytopathology

Deep learning algorithms and artificial intelligence (AI) are making great progress in their capacity to evaluate and interpret image data recent advancements in computer vision and machine learning. The first use of AI in a pathology lab was in cytopathology, when a computer-assisted Pap test screening was created. Initially designed to diagnose rather than screen, there was a lot of disagreement concerning their wide use to clinical specimens. However, whole-slide imaging of both gynaecological and non-gynaecological histopathology have been the subject of recent AI work. An overview of the literature on AI in cytopathology is provided in this brief review. To be more precise, it intends to emphasize the relevance of applications of AI algorithms to gynaecological and non-gynaecologic cytology. Between January 2000 and December 2021, a search on artificial intelligence in cytopathology was conducted in several well-known databases, including PubMed, Web of Science, Scopus, Embase, and Google Scholar. Only full-text papers that could be accessed online were evaluated.
Mask R-CNN with New Data Augmentation Features for Smart Detection of Retail Products

Human–computer interactions (HCIs) use computer technology to manage the interfaces between users and computers. Object detection systems that use convolutional neural networks (CNNs) have been repeatedly improved. Computer vision is also widely applied to multiple specialties. However, self-checkouts operating with a faster region-based convolutional neural network (faster R-CNN) image detection system still feature overlapping and cannot distinguish between the color of objects, so detection is inhibited. This study uses a mask R-CNN with data augmentation (DA) and a discrete wavelet transform (DWT) in lieu of a faster R-CNN to prevent trivial details in images from hindering feature extraction and detection for deep learning (DL). The experiment results show that the proposed algorithm allows more accurate and efficient detection of overlapping and similarly colored objects than a faster R-CNN with ResNet 101, but allows excellent resolution and real-time processing for smart retail stores.
qopt: An Experiment-Oriented Software Package for Qubit Simulation and Quantum Optimal Control

Realistic modeling of qubit systems including noise and constraints imposed by control hardware is required for performance prediction and control optimization of quantum processors. We introduce qopt, a software framework for simulating qubit dynamics and robust quantum optimal control considering common experimental situations. To this end, we model open and closed qubit systems with a focus on the simulation of realistic noise characteristics and experimental constraints. Specifically, the influence of noise can be calculated using Monte Carlo methods, effective master equations or with the efficient filter function formalism, which enables the investigation and mitigation of autocorrelated noise. In addition, limitations of control electronics, including finite bandwidth effects as well as nonlinear transfer functions and drive-dependent noise, can be considered. The calculation of gradients based on analytic results is implemented to facilitate the efficient optimization of control pulses. The software easily interfaces with QuTiP, is published under an open-source license, is well tested, and features a detailed documentation.
Prognosticating anthropomorphic chatbots' usage intention as an e-banking customer service gateway: cogitations from Zimbabwe

Purpose The purpose of this article is to investigate the factors that explain the reasons why customers may be willing to use chatbots in Zimbabwe as an e-banking customer service gateway, an area that remains under researched. Design/methodology/approach The research study applied a cross-sectional survey of 430 customers from five selected commercial banks conducted in Harare, the capital city of Zimbabwe. Hypotheses were tested using structural equation modelling. Findings The research study showed that a counterintuitive intention to use chatbots is directly affected by chatbots' expected performance, the habit of using them and other factors. Research limitations/implications To better appreciate the current research concept, there is a need to replicate the same study in other contexts to enhance generalisability. Practical implications Chatbots are a trending new technology and are starting to be increasingly adopted by banks and they have to consider that customers need to get used to them. Originality/value This study contributes to bridging the knowledge gap as it investigates the factors that explain why bank customers may be willing to use chatbots in five selected commercial Zimbabwean banks. This is a pioneering study in the context of a developing economy such as Zimbabwe.
Mining sequential patterns with flexible constraints from MOOC data

Online learning is playing an increasingly important role in education. Massive open online course (MOOC) platforms are among the most important tools in online learning, and record historical learning data from an extremely large number of learners. To enhance the learning experience, a promising approach is to apply sequential pattern mining (SPM) to discover useful knowledge in these data. In this paper, mining sequential patterns (SPs) with flexible constraints in MOOC enrollment data is proposed, which follows that research approach. Three constraints are proposed: the length constraint, discreteness constraint, and validity constraint. They are used to describe the effect of the length of enrollment sequences, variance of enrollment dates, and enrollment moments, respectively. To improve the mining efficiency, the three constraints are pushed into the support, which is the most typical parameter in SPM, to form a new parameter called support with flexible constraints (SFC). SFC is proved to satisfy the downward closure property, and two algorithms are proposed to discover SPs with flexible constraints. They traverse the search space in a breadth-first and depth-first manner. The experimental results demonstrate that the proposed algorithms effectively reduce the number of patterns, with comparable performance to classical SPM algorithms.
Recent innovations in solar energy education and research towards sustainable energy development

The essential requirements of our everyday lives are fresh air, pure water, nourishing food, and clean energy in a most sustainable manner. The present review article concisely discusses recent innovations in solar energy education, research, and development toward providing clean and affordable energy and clean water to some extent. This article primarily addresses the Sustainable Development Goal 7 of the United Nations (SDG 7: Affordable and Clean Energy). Over the past few decades, many research activities have been carried out on solar energy conversion and utilization. The deployment of solar energy technologies has been witnessed to combat global warming and the betterment of the planet. Drivers and barriers to implementing solar energy systems from school to master's level through real-time deployments are discussed for further development and innovations. Mainly, expedited solar energy education and research are essential to improve solar energy utilization. The advancements in solar energy education and research towards sustainable energy development and circular economy are highlighted along with further directions required.
Edge-enhanced instance segmentation by grid regions of interest

This paper focuses on the instance segmentation task. The purpose of instance segmentation is to jointly detect, classify and segment individual instances in images, so it is used to solve a large number of industrial tasks such as novel coronavirus diagnosis and autonomous driving. However, it is not easy for instance models to achieve good results in terms of both efficiency of prediction classes and segmentation results of instance edges. We propose a single-stage instance segmentation model EEMask (edge-enhanced mask), which generates grid ROIs (regions of interest) instead of proposal boxes. EEMask divides the image uniformly according to the grid and then calculates the relevance between the grids based on the distance and grayscale values. Finally, EEMask uses the grid relevance to generate grid ROIs and grid classes. In addition, we design an edge-enhanced layer, which enhances the model's ability to perceive instance edges by increasing the number of channels with higher contrast at the instance edges. There is not any additional convolutional layer overhead, so the whole process is efficient. We evaluate EEMask on a public benchmark. On average, EEMask is 17.8% faster than BlendMask with the same training schedule. EEMask achieves a mask AP score of 39.9 on the MS COCO dataset, which outperforms Mask RCNN by 7.5% and BlendMask by 3.9%.
Optimized User-Friendly Transaction Time Management in the Blockchain Distributed Energy Market

The blockchain nexus with energy transactions in the distributed energy-trading arena successfully achieved a decentralized transaction and increased security.With the elimination of a third-party middle man, an electronic app is introduced to achieve decentralization and aid transactive communications.Observing the internet-of-things (IoT) intense protocols in the transactive communications amongst blockchain participants, however, leads to transaction time delay with associated uncertainty.This paper integrates the practical byzantine fault tolerance (pBFT) algorithm with the private Hyperledger Sawtooth blockchain network (P) to achieve a P-pBFT algorithm.The P-pBFT achieved a combined two-step transaction latency optimization (minimization) amongst the blockchain participants in the distributed energy generation (DEG) ecosystem.Through their combined feature extraction, further minimization is achieved by simulating the resulting transaction model derived from the integration.Subsequently, an optimization method is proposed to achieve the shortest transaction time given transaction constraints based on participants' comfort.Thus, the ratio of node population to the transaction size and the choice of constraints can be regulated at the participants' convenience to achieve minimum transaction time.Hence, the benefit of deciding the transaction time is achieved thereby eliminating the undesired characteristic uncertainty.
OJXPerf

Memory bloat is an important source of inefficiency in complex production software, especially in software written in managed languages such as Java. Prior approaches to this problem have focused on identifying objects that outlive their life span. Few studies have, however, looked into whether and to what extent myriad objects of the same type are identical. A quantitative assessment of identical objects with code-level attribution can assist developers in refactoring code to eliminate object bloat, and favor reuse of existing object(s). The result is reduced memory pressure, reduced allocation and garbage collection, enhanced data locality, and reduced re-computation, all of which result in superior performance.
Artificial Intelligence and Software Engineering: Are We Ready?

Artificial intelligence and software engineering complement each other in various ways. This special issue highlights how this relationship is developing over time to address the challenges faced in modern-day computing.
GraphWorld

Despite advances in the field of Graph Neural Networks (GNNs), only a small number (~5) of datasets are currently used to evaluate new models. This continued reliance on a handful of datasets provides minimal insight into the performance differences between models, and is especially challenging for industrial practitioners who are likely to have datasets which are very different from academic benchmarks. In the course of our work on GNN infrastructure and open-source software at Google, we have sought to develop benchmarks that are robust, tunable, scalable, and generalizable.
Pair-Level Supervised Contrastive Learning for Natural Language Inference

Natural language inference (NLI) is an increasingly important task for natural language understanding, which requires one to infer the relationship between the sentence pair (premise and hypothesis). Many recent works have used contrastive learning by incorporating the relationship of the sentence pair from NLI datasets to learn sentence representation. However, these methods only focus on comparisons with sentence-level representations. In this paper, we propose a Pair-level Supervised Contrastive Learning approach (PairSCL). We adopt a cross attention module to learn the joint representations of the sentence pairs. A contrastive learning objective is designed to distinguish the varied classes of sentence pairs by pulling those in one class together and pushing apart the pairs in other classes. We evaluate PairSCL on two public datasets of NLI where the accuracy of PairSCL outperforms other methods by 2.1% on average. Furthermore, our method outperforms the previous state-of-the-art method on seven transfer tasks of text classification.
Putting plain language summaries into perspective

Publication-associated plain language summaries are brief, jargon-free summaries of scientific publications. They are intended for a broad, non-expert audience to help maximize the accessibility of the publication. Plain language summaries are typically found alongside peer-reviewed publications or in supplementary materials and can be indexed in PubMed. In this narrative commentary, we present the perspectives of five day-to-day users of plain language summaries from different stakeholder groups, gained through semi-structured qualitative interviews about the users' experiences with and opinions of plain language summaries.Plain language summaries such as this one are an increasingly popular tool for helping readers cut though jargon and understand the key points of a research article faster. To get a better idea of how plain language summaries are being used, we spoke to five people from different backgrounds who read, write and review plain language summaries in their personal and professional lives.
An Ensemble-Learning Approach To Predict the Coke Yield of Commercial FCC Unit

This work proposes an ensemble learning-based catalytic cracking coke yield prediction model called the harmonic-ensembled extreme learning machine (HEELM). The model integrates extreme learning machine (ELM) base learners with different activation functions to improve the overall prediction effect. An overfitting index is proposed, and the optimal number of hidden layer nodes of ELM-base learners is determined with it. By examining the influence of different activation functions on the prediction results, the best activation function structure of the ELM-base learner has been determined. Besides, a harmonic layer is established to determine the weight of each base learner in real-time. The proposed model is validated using 1.5 years of historical data from China’s commercial fluidized catalytic cracking (FCC) plant. The results show that the proposed model has outperformed most other ELM-base learners. The relative prediction error is further reduced by 10.97% after introducing the harmonic layer. The proposed model exhibits stable performance with good generalization in three segments of industrial data, and it has guiding significance for stable operation and CO2 emission reduction of the FCC plant.
The price and cost of bitcoin

Explaining changes in bitcoin’s price and predicting its future have been the foci of many research studies. In contrast, far less attention has been paid to the relationship between bitcoin’s mining costs and its price. One popular notion is the cost of bitcoin creation provides a support level below which this cryptocurrency’s price should never fall because if it did, mining would become unprofitable and threaten the maintenance of bitcoin’s public ledger. Other research has used mining costs to explain or forecast bitcoin’s price movements. Competing econometric analyses have debunked this idea, showing that changes in mining costs follow changes in bitcoin’s price rather than preceding them, but the reason for this behavior remains unexplained in these analyses. This research aims to employ economic theory to explain why econometric studies have failed to predict bitcoin prices and why mining costs follow movements in bitcoin prices rather than precede them. We do so by explaining the chain of causality connecting a bitcoin’s price to its mining costs.
ripple2vec: Node Embedding with Ripple Distance of Structures

Abstract Graph is a generic model of various networks in real-world applications. And, graph embedding aims to represent nodes (edges or graphs) as low-dimensional vectors which can be fed into machine learning algorithms for downstream graph analysis tasks. However, existing random walk-based node embedding methods often map some nodes with (dis)similar local structures to (near) far vectors. To overcome this issue, this paper proposes to implement node embedding by constructing a context graph via a new defined ripple distance over ripple vectors, whose components are the hitting times of fully condensed neighborhoods and thus characterize their structures as pure quantities. The distance is able to capture the (dis)similarities of nodes’ local neighborhood structures and satisfies the triangular inequality. The neighbors of each node in the context graph are defined via the ripple distance, which makes the short random walks from a given node over the context graph only visit its similar nodes in the original graph. This property guarantees that the proposed method, named as $$\mathsf {ripple2vec}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mi>ripple</mml:mi><mml:mn>2</mml:mn><mml:mi>vec</mml:mi></mml:mrow></mml:math> , is able to map (dis)similar nodes to (far) near vectors. Experimental results on real datasets, where labels are mainly related to nodes’ local structures, show that the results of $$\mathsf {ripple2vec}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mi>ripple</mml:mi><mml:mn>2</mml:mn><mml:mi>vec</mml:mi></mml:mrow></mml:math> behave better than those of state-of-the-art methods, in node clustering and node classification, and are competitive to other methods in link prediction.
Motion-compensated online object tracking for activity detection and crowd behavior analysis

It is a nontrivial task to manage crowds in public places and recognize unacceptable behavior (such as violating social distancing norms during the COVID-19 pandemic). In such situations, people should avoid loitering (unnecessary moving out in public places without apparent purpose) and maintain a sufficient physical distance. In this study, a multi-object tracking algorithm has been introduced to improve short-term object occlusion, detection errors, and identity switches. The objects are tracked through bounding box detection and with linear velocity estimation of the object using the Kalman filter frame by frame. The predicted tracks are kept alive for some time, handling the missing detections and short-term object occlusion. ID switches (mainly due to crossing trajectories) are managed by explicitly considering the motion direction of the objects in real time. Furthermore, a novel approach to detect unusual behavior of loitering with a severity level is proposed based on the tracking information. An adaptive algorithm is also proposed to detect physical distance violation based on the object dimensions for the entire length of the track. At last, a mathematical approach to calculate actual physical distance is proposed by using the height of a human as a reference object which adheres more specific distancing norms. The proposed approach is evaluated in traffic and pedestrian movement scenarios. The experimental results demonstrate a significant improvement in the results.