Reviewerly/exHarmony huggingface dataset:
Benchmarking embedded chain breaking in quantum annealing <sup>*</sup>

Abstract Quantum annealing solves combinatorial optimization problems by finding the energetic ground states of an embedded Hamiltonian. However, quantum annealing dynamics under the embedded Hamiltonian may violate the principles of adiabatic evolution and generate excitations that correspond to errors in the computed solution. Here we empirically benchmark the probability of chain breaks and identify sweet spots for solving a suite of embedded Hamiltonians. We further correlate the physical location of chain breaks in the quantum annealing hardware with the underlying embedding technique and use these localized rates in a tailored post-processing strategies. Our results demonstrate how to use characterization of the quantum annealing hardware to tune the embedded Hamiltonian and remove computational errors.
Modeling of Multiple Share Creation with Optimal Signcryption Technique for Digital Image Security

Digital image security plays an essential role in the shared communication model. Encryption and decryption process is commonly applied to securely transmit the images in various real-time applications. In addition, the generation of encryption/decryption keys is also essential to achieve enhanced image security. This study presents a multiple share creation scheme with an optimal signcryption (MSS-OSC) technique for digital image security. The MSS-OSC technique primarily generates a set of various shares for every digital image that needs to be transmitted. In addition, the encryption of generated shares takes place via the optimal signcryption (OSC) technique. Moreover, genetic programming (GP) is employed to optimally choose the keys involved in the encryption and decryption process. The detailed experimental validation of the MSS-OSC technique is investigated using a set of benchmark test images. The results analysis demonstrated that the MSS-OSC technique had a superior performance by accomplishing maximum digital image security.
Image Encryption Algorithm Based on a Novel 4D Chaotic System

In this paper, a novel four-dimensional chaotic system has been created, which has characteristics such as high sensitivity to the initial conditions and parameters. It also has two a positive Lyapunov exponents. This means the system is hyper chaotic. In addition, a new algorithm was suggested based on which they constructed an image cryptosystem. In the permutation stage, the pixel positions are scrambled via a chaotic sequence sorting. In the substitution stage, pixel values are mixed with a pseudorandom sequence generated from the 4D chaotic system using XOR operation. A simulation has been conducted to evaluate the algorithm, using the standardized tests such as information entropy, histogram, number of pixel change rate, unified average change intensity, and key space. Experimental results and performance analyses demonstrate that the proposed encryption algorithm achieves high security and efficiency.
Causal Support: Modeling Causal Inferences with Visualizations

Analysts often make visual causal inferences about possible data-generating models. However, visual analytics (VA) software tends to leave these models implicit in the mind of the analyst, which casts doubt on the statistical validity of informal visual “insights”. We formally evaluate the quality of causal inferences from visualizations by adopting <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">causal support</i> —a Bayesian cognition model that learns the probability of alternative causal explanations given some data—as a normative benchmark for causal inferences. We contribute two experiments assessing how well crowdworkers can detect (1) a treatment effect and (2) a confounding relationship. We find that chart users' causal inferences tend to be insensitive to sample size such that they deviate from our normative benchmark. While interactively cross-filtering data in visualizations can improve sensitivity, on average users do not perform reliably better with common visualizations than they do with textual contingency tables. These experiments demonstrate the utility of causal support as an evaluation framework for inferences in VA and point to opportunities to make analysts' mental models more explicit in VA software.
Enhancement of Human Complex Activity Recognition using Wearable Sensors Data with InceptionTime Network

Effective human activity recognition can be incredibly beneficial in big data applications like ambient healthcare-supported living. Deep learning (DL) techniques have considerably advanced research in human activity recognition (HAR). These deep learning algorithms outperform conventional machine learning methods in terms of automatic feature extraction. Many deep learning models have recently been demonstrated as state-of-the-art approaches to efficiently classify simple and complex human behaviors to address the HAR. This work proposes a new sensor-based HAR to classify complex activities with high performance using a DL model called InceptTime network. The proposed architecture is evaluated on a public benchmark complex activity dataset named PAMAP2. The experimental outcomes show that the proposed InceptTime model is significantly better than other baseline D L models on the same dataset with the highest accuracy of 88 %.
Augmented Reality in STEM Education: Bibliometric Analysis

Augmented reality applications in STEM education have increasing importance in recent years and it draws attention that scientific studies on this subject have gained momentum in the literature. The purpose of this research is to conduct a bibliometric analysis of studies on the use of augmented reality applications in STEM education in the literature. The Web of Science database has been used to collect the data. A total of 741 studies were accessed by going through various screening processes for the research. Content analysis and bibliometric analysis have been used in the analysis of the data. In the research, the distribution of publications by years and countries and the most published authors, journals, and countries were accessed. As a result of the research, in terms of the institutions with which the authors work, "National Taiwan University of Science Technology" ranked near the top for the number of citations and "National Taiwan Normal University" ranked near the top for the number of publications as the most productive institutions. It has been detected that "Wu, H. –K." and "Chang, H. –Y" are the most effective and productive researchers. According to the analysis conducted in the context of journals, "Computers Education" and "Interactive Learning Environments" have been the journals that contributed the most to this subject. As a result of the analysis, it was found that the co-authorship network structure is predominant in England and Spain. Concepts that become apparent in clusters in co-occurrences analysis are "augmented reality", "virtual reality", "mobile learning", "science education" and "mixed reality".
DynaComm: Accelerating Distributed CNN Training Between Edges and Clouds Through Dynamic Communication Scheduling

To reduce uploading bandwidth and address privacy concerns, deep learning at the network edge has been an emerging topic. Typically, edge devices collaboratively train a shared model using real-time generated data through the Parameter Server framework. Although all the edge devices can share the computing workloads, the distributed training processes over edge networks are still time-consuming due to the parameters and gradients transmission procedures between parameter servers and edge devices. Focusing on accelerating distributed Convolutional Neural Networks (CNNs) training at the network edge, we present DynaComm, a novel scheduler that dynamically decomposes each transmission procedure into several segments to achieve optimal layer-wise communications and computations overlapping during run-time. Through experiments, we verify that DynaComm manages to achieve optimal layer-wise scheduling for all cases compared to competing strategies while the model accuracy remains untouched.
Mitigating Hot Spot Problem in Wireless Sensor Networks using Political Optimizer Based Unequal Clustering Technique

Wireless Sensor Network (WSN) encompasses a set of wirelessly connected sensor nodes in the network for tracking and data gathering applications. The sensors in WSN are constrained in energy, memory, and processing capabilities. Despite the benefits of WSN, the sensors closer to the base station (BS) expels their energy faster. It suffers from hot spot issues and can be resolved by the use of unequal clustering techniques. In this aspect, this paper presents a political optimizer-based unequal clustering scheme (POUCS) for mitigating hot spot problems in WSN. The goal of the POUCS technique is to choose cluster heads (CHs) and determine unequal cluster sizes. The POUCS technique derives a fitness function involving different input parameters to minimize energy consumption and maximize the lifetime of the network. To showcase the enhanced performance of the POUCS technique, a comprehensive experimental analysis takes place, and the detailed comparison study reported the better performance of the POUCS technique over the recent techniques.
Evolution of the Koma Bangou Gold Panning Site (Niger) From 1984 to 2020 Using Landsat Imagery

Abstract The severe drought of 1983–1984 in the Sahel region, and its socio‐economic impacts for people relying on farming had for consequence the first major gold rush at Koma Bangou in the southwestern part of Niger. Initiated in 1984, the gold panning activities were interrupted from 1989 to 1999 with exploration permits assigned to the mining industry. The site was reclassified at the year‐end 1999 as a gold panning site and artisanal mining resumed until present‐day. Gold panning activities such as ore extraction and cyanide processing produced mining waste including rocks, mine tailings, and treatment residues. Mining waste is a serious environmental, health and safety problem. Multispectral Landsat images (TM4‐5, ETM7+, OLI/TIRS) acquired between 1984 and 2020 were used to map the spatial evolution of waste generated by gold panning activities at Koma Bangou. Different processing methods were tested, including Minimum Noise Fraction (MNF) transform, Band Ratio (BR), and Feature Oriented Principal Component Selection (FPCS). The FPCS applied to hydroxyl‐bearing minerals appears to be most efficient to map gold extraction and cyanidation waste areas. The waste surface associated with ore extraction has increased from 9.43 ha in 1984 to 234.20 ha in 2020, with continuous expansion during the period of clandestine activity (1989–1999). The waste surface associated with cyanidation has increased from 5.56 ha in 2009 (the year of cyanide treatment introduction) to 99.53 ha in 2020. Landsat multispectral imagery proved a suitable data source for monitoring the evolution of gold mining waste and consequences of public policies at Koma Bangou.
The Influence of Robot-Assisted Learning System on Health Literacy and Learning Perception

Healthy aging is a new challenge for the world. Therefore, health literacy education is a key issue in the current health care field. This research has developed a robot-assisted learning system to explore the possibility of significantly improving health literacy and learning perception through interaction with robots. In particular, this study adopted an experimental design, in which the experiment lasted for 90 min. A total of 60 participants over the age of 50 were randomly assigned to different learning modes. The RobotLS group learned by interacting with robots, while the VideoLS group watched health education videos on a tablet computer. The content dealt with hypertension related issues. This study used the European Health Literacy Survey Questionnaire (HLS-EU-Q16), Health Knowledge Questionnaire, Reduced Instructional Materials Motivation Survey (RIMMS), and Flow Scale as evaluation tools. The result shows no significant difference in the pre-test scores between the two groups. Compared with the video-assisted learning system, the robot-assisted learning system can significantly improve health knowledge, health literacy, learning motivation, and flow perception. According to the findings of this study, a robot-assisted learning system can be introduced in the future into homes and care institutions to enhance the health literacy of the elderly.
Effect of ultraviolet light on the degradation of Low-Density and High-Density Polyethylene characterized by the weight loss and FTIR

In this study, the research was made to understand the knowledge widely related to the degradation process of polyethylene polymer. The mode of treatment that involves in the degradation process of polyethylene is physical treatment while the method of degradation used is photo-degradation of UV light. By using the physical treatment of UV irradiation light, it helps by affecting the bonding that holds the polymer together to break and weakens the plastic. From the result obtained in FTIR and SEM analysis, in FTIR spectrum of LDPE shows higher transmittance compared to FTIR spectrum of HDPE both UV-treated for 30 days. This indicates the high transmittance have few bonds to absorb light in the LDPE sample, low transmittance in HDPE sample means has high population of bonds which have vibrational energies corresponding to the incident light. For SEM result, the polyethylene for LDPE plastic sheet shows the best results for degradation and managed to reduce the weight loss at 87.5% compared to HDPE plastic which at 21.6%.
Equinox: Training (for Free) on a Custom Inference Accelerator

DNN inference accelerators executing online services exhibit low average loads because of service demand variability, leading to poor resource utilization. Unfortunately, reclaiming idle inference cycles is difficult as other workloads can not execute on a custom accelerator. With recent proposals for the use of fixed-point arithmetic in training, there are opportunities for training services to piggyback on inference accelerators. We make the observation that a key challenge in doing so is maintaining service-level latency constraints for inference. We show that relaxing latency constraints in an inference accelerator with ALU arrays that are batching-optimized achieves near-optimal throughput for a given area and power envelope while maintaining inference services’ tail latency goals.
Property-Directed Verification and Robustness Certification of Recurrent Neural Networks

This paper presents a property-directed approach to verifying recurrent neural networks (RNNs). To this end, we learn a deterministic finite automaton as a surrogate model from a given RNN using active automata learning. This model may then be analyzed using model checking as a verification technique. The term property-directed reflects the idea that our procedure is guided and controlled by the given property rather than performing the two steps separately. We show that this not only allows us to discover small counterexamples fast, but also to generalize them by pumping towards faulty flows hinting at the underlying error in the RNN. We also show that our method can be efficiently used for adversarial robustness certification of RNNs.
Modality-Guided Subnetwork for Salient Object Detection

Recent RGBD-based models for saliency detection have attracted research attention. The depth clues such as boundary clues, surface normal, shape attribute, etc., contribute to the identification of salient objects with complicated scenarios. However, most RGBD networks require multi-modalities from the input side and feed them separately through a two-stream design, which inevitably results in extra costs on depth sensors and computation. To tackle these inconveniences, we present in this paper a novel fusion design named modality-guided subnetwork (MGSnet). It has the following superior designs: 1) Our model works for both RGB and RGBD data, and dynamically estimating depth if not available. Taking the inner workings of depth-prediction networks into account, we propose to estimate the pseudo-geometry maps from RGB input - essentially mimicking the multi-modality input. 2) Our MGSnet for RGB SOD results in real-time inference but achieves state-of-the-art performance compared to other RGB models. 3) The flexible and lightweight design of MGS facilitates the integration into RGBD two-streaming models. The introduced fusion design enables a cross-modality interaction to enable further progress but with a minimal cost.
Adaptive estimation of quantum observables

The accurate estimation of quantum observables is a critical task in science. With progress on the hardware, measuring a quantum system will become increasingly demanding, particularly for variational protocols that require extensive sampling. Here, we introduce a measurement scheme that adaptively modifies the estimator based on previously obtained data. Our algorithm, which we call AEQuO, continuously monitors both the estimated average and the associated error of the considered observable, and determines the next measurement step based on this information. We allow both for overlap and non-bitwise commutation relations in the subsets of Pauli operators that are simultaneously probed, thereby maximizing the amount of gathered information. AEQuO comes in two variants: a greedy bucket-filling algorithm with good performance for small problem instances, and a machine learning-based algorithm with more favorable scaling for larger instances. The measurement configuration determined by these subroutines is further post-processed in order to lower the error on the estimator. We test our protocol on chemistry Hamiltonians, for which AEQuO provides error estimates that improve on all state-of-the-art methods based on various grouping techniques or randomized measurements, thus greatly lowering the toll of measurements in current and future quantum applications.
Reciprocal Scoring: A Method for Forecasting Unanswerable Questions

We propose an elicitation method, Reciprocal Scoring (RS), that challenges forecasters to predict the forecasts of other forecasters. Two studies show how RS can generate accurate forecasts of otherwise unanswerable questions. Study 1 establishes the epistemic credibility of RS: forecasters randomly assigned to use RS were as accurate as forecasters predicting objectively resolvable outcomes using a proper scoring rule—and both groups were more accurate than a control group that felt accountable to neither intersubjective RS metrics nor objective metrics. Study 2 establishes the practical value of RS. We ask highly accurate forecasters to predict each other’s forecasts of the effect of government policies on COVID-19 mortality, yielding a real-time ranking of the expected effectiveness of pandemic-containment policies. As in Study 1, RS forecasters converged but in this case on policy recommendations that stand up to scrutiny, even with the benefit of hindsight. The core contribution of RS is its power to create accountability for accuracy in policy debates that have long been stalemated by the absence of accountability.
Extracting Cancer Treatments from Clinical Text written in Spanish: A Deep Learning Approach

Extracting accurate information about cancer patients&#x0027; treatments is crucial to support clinical research, treatment planning, and to improve clinical care outcomes. However, treatment information resides in unstructured clinical text, making the task of data structuring especially challenging. Although several approaches have been proposed to extract treatments from clinical text, most of these proposals have focused on the English language. In this paper, we propose a deep learning-based approach to extract cancer treatments from clinical text written in Spanish. This approach uses a Bidirectional Long Short Memory (BiLSTM) neural net with a CRF layer to perform Named Entity Recognition. An annotated corpus from clinical text written about lung cancer patients is used to train the BiLSTM-based model. Performed tests have shown a performance of 90&#x0025; in the F1-score, suggesting the feasibility of our approach to extract cancer treatments from clinical narratives.
Robust Neural Language Translation Model Formulation using Seq2seq approach

In this work, the approach used is to sequence powerful models that have achieved excellent performance on language translation encoding-decoding tasks. A language transformer model is used in this work based on the sequence to sequence approach, which uses a Long Short-Term Memory (LSTM) to map the input sequence to a vector of fixed dimensionality. Then another deep LSTM decodes the target sequence from the vector. Evaluated the model efficiency through BLEU score and LSTM's BLEU score was penalized on out-of-vocabulary words. Additionally, the LSTM did not have difficulty with long-short of sentences. This work performed the deep LSTM setup English-Japanese translation accuracy at an order of magnitude faster speed, both on GPU and CPU. The variety of the data is introduced into it to evaluate the robustness using the BLEU score. Finally, a better result is achieved on merging the two different types of datasets and got the highest BLEU score of 40.1 at the end.
Distributed File Storage Model using IPFS and Blockchain

In current scenario the patients’ medical report is stored digitally in medical industry. The report consists of patients’ details such as patients’ private details, medical reports, and doctor prescriptions. This sensitive information is stored in centralized storage model. The disadvantage of this system is that there is problem in safeguarding user’s security. The problems such as illegal access of private data such as identity data and illness of the patient, and their medical reports. To address this issue, distributed file system of medical data using Interplanetary File System and blockchain technology is proposed. Where hash value of the report is stored in blockchain thus reducing the size of blockchain and the file is stored in IPFS. In IPFS the file is stored as hash value of the file. This framework preserves patient privacy and facilitates easy access of details of patients by authorized users such as doctors and patients. From this framework availability, integrity and consistency was achieved.
A qualitative mapping of Darkweb marketplaces

Darkweb marketplaces have evolved greatly since the rise of the Silk Road in 2011, the first platform of its kind, and have become a highly profitable underground trading ecosystem, which provides anonymity for both buyers and sellers. Law enforcement along with researchers, have been successful in taking down marketplaces over the years. However, the combination of mechanisms implemented by these platforms (e.g. payment mechanisms, cryptocurrencies, trust systems), along with the success of the Tor network&#x2019;s anonymity properties, have made marketplaces much more enticing to users, while providing ease of access and use, as well as resilience against hostile actions. Through qualitative methods, this paper presents a mapping of darkweb marketplaces. We systematically investigate the operation of 41 marketplaces, along with 35 vendor shops, and gather information about the mechanisms and features implemented. Additionally, to acquire real world information, we explore the marketplaces&#x2019; integrated forums, as well as 3 popular independent ones, focusing on discussions between vendors, buyers and marketplace owners, on topics related to illegal trading. We believe that gaining an up-to-date and deep understanding of the framework that marketplaces are built upon, is the first step towards discovering weak spots in the cyber security product and service market, with the disruption of its operation being the ultimate goal.