Reviewerly/exHarmony huggingface dataset:
An Inverted Teaching Model for a Mechanics of Materials Course

Instructors at Missouri University of Science and Technology have been offering certain sections of a mechanics of materials course in an inverted format for the past two years.In this format, students learn the concepts outside of class, using a textbook, animations and videos developed by the authors, and work on homework either individually or in groups during the optional class time.Students take eight multiple-choice exams and a final exam that is common to both the inverted sections and the more traditional lecture-based sections.Homework in the inverted sections is assigned but not graded.The in-class exams are given in a computer lab, and each student receives an individualized set of questions.Over 1200 students in 18 course sections have participated in either the inverted sections themselves or the other non-traditional sections that preceded the particular format used today.A subset of this group was compared to students in the traditional sections.No statistically significant difference between the two groups was found based on (1) performance on the common final exams or (2) course grade in a structural analysis course.The animations and videos used by students in the inverted sections are available on a class web site.There are 167 animation modules and 230 videos.The animations contain example problems and exercises.The videos are, on average, six minutes in length and cover concepts, demonstrations, problem strategies, problem solutions, and experiments.The authors use Google Analytics to track how much each piece of content is utilized.The website was accessed 46,500 times, and the content, excluding the animations, was used for a total of 12,700 hours during the past 16 months.By tracking how students perform on each multiple-choice question, the authors have developed a concept inventory with numerical rankings from the best to worst understood concepts.Combining this with how much each online resource is utilized, the authors can now target development of future course materials on the least-understood concepts and in the format most preferred by the students.An inverted teaching format would not be appropriate for every college course, but it has helped the authors begin looking at their mechanics of materials course in a more scientific, data-driven manner.
General framework for constructing fast and near-optimal machine-learning-based decoder of the topological stabilizer codes

Quantum error correction is an essential technique for constructing a scalable quantum computer. In order to implement quantum error correction with near-term quantum devices, a fast and near-optimal decoding method is demanded. A decoder based on machine learning is considered as one of the most viable solutions for this purpose, since its prediction is fast once training has been done, and it is applicable to any quantum error correcting code and any noise model. So far, various formulations of the decoding problem as the task of machine learning have been proposed. Here, we discuss general constructions of machine-learning-based decoders. We found several conditions to achieve near-optimal performance, and proposed a criterion which should be optimized when a size of training data set is limited. We also discuss preferable constructions of neural networks, and proposed a decoder using spatial structures of topological codes using a convolutional neural network. We numerically show that our method can improve the performance of machine-learning-based decoders in various topological codes and noise models.
Real-Time and Proactive SLA Renegotiation for a Cloud-Based System

Cloud-based systems involve the management and control on the level of delivered service, which is crucial in order to maintain service continuity and customers' trust for the service provided. Predefined service level agreement (SLA) is normally used as the main element in managing the cloud-based system. However, such SLA basically cannot be adjusted during operations. Current studies on SLA adjustment are mainly focused on modifying the agreement before commencing operations. The cloud environment is, however, dynamic and requires occasional review of the SLA parameters to manage expectations. Thus, to maintain service continuity that is acceptable to all parties, SLA adjustment is necessary during service operations. This paper presents a real-time and proactive SLA renegotiation model to support the dynamic nature of cloud-based systems. Renegotiation is shown for four different scenarios based on parameter weightage. In order to achieve real-time decision, a multi-offer generation approach is used. A novel mechanism to detect and predict service violation is proposed to ensure proactive renegotiation. Simulation is performed to verify the model. In the simulation, the model is found to be effective as it is able to generate multi-offers in a single renegotiation round and reduce the impact of service violations during service operations.
A Searchable and Verifiable Data Protection Scheme for Scholarly Big Data

Scientific research achievements play a positive role in the promotion of social development. Scholarly big data include scholars' scientific research, experimental data, and their own identity information. The security of scholarly big data relates to the authors' reputation and the copyright of their works. This paper proposes a trusted third-party-aided searchable and verifiable data protection scheme that utilizes cloud computing technology. For a better description of the the protocol, we first present a user-differentiated system model and a cube data storage structure. On the basis of the novel system model and data structure, the scheme helps the users review the integrity of their uploaded or downloaded data at any time and search the online scholarly data with encrypted keywords. The security analysis and performance simulation demonstrate that the novel scheme is a secure and efficient scheme for scholarly big data applications.
A Treasury System for Cryptocurrencies: Enabling Better Collaborative Intelligence

A treasury system is a community-controlled and decentralized collaborative decision-making mechanism for sustainable funding of blockchain development and maintenance.During each treasury period, project proposals are submitted, discussed, and voted for; top-ranked projects are funded from the treasury.The Dash governance system is a real-world example of such kind of systems.In this work, we, for the first time, provide a rigorous study of the treasury system.We modelled, designed, and implemented a provably secure treasury system that is compatible with most existing blockchain infrastructures, such as Bitcoin, Ethereum, etc.More specifically, the proposed treasury system supports liquid democracy/delegative voting for better collaborative intelligence.Namely, the stake holders can either vote directly on the proposed projects or delegate their votes to experts.Its core component is a distributed universally composable secure end-to-end verifiable voting protocol.The integrity of the treasury voting decisions is guaranteed even when all the voting committee members are corrupted.To further improve efficiency, we proposed the world's first honest verifier zero-knowledge proof for unit vector encryption with logarithmic size communication.This partial result may be of independent interest to other cryptographic protocols.A pilot system is implemented in Scala over the Scorex 2.0 framework, and its benchmark results indicate that the proposed system can support tens of thousands of treasury participants with high efficiency.
Rejecting the death of passwords: Advice for the future

Passwords have been a recurring subject of research ever since Morris and Thompson first pointed out their disadvantages in 1979. Several decades later, textual passwords remain to be the most used authentication method, despite the growing number of security breaches. In this article, we highlight technological advances that have the potential to ease brute-force attacks on longer passwords. We point out users? persistently bad password creation and management practices, arguing that the users will be unable to keep up with the increasingly demanding security requirements in the future. We examine a set of real, user-generated passwords, and compare them to the passwords collected by Morris and Thompson. The results show that today?s passwords remain as weak as they were nearly four decades ago. We provide insight on how the current password security could be improved by giving recommendations to users, administrators, and researchers. We dispute the reiterated claim that passwords should be replaced, by exposing the alternatives? weaknesses. Finally, we argue passwords will remain widespread until two conditions are met: First, a Pareto-improving authentication method is discovered, and second, the users are motivated to replace textual passwords.
A Novel Extension to Fuzzy Connectivity for Body Composition Analysis: Applications in Thigh, Brain, and Whole Body Tissue Segmentation

Magnetic resonance imaging (MRI) is the non-invasive modality of choice for body tissue composition analysis due to its excellent soft-tissue contrast and lack of ionizing radiation. However, quantification of body composition requires an accurate segmentation of fat, muscle, and other tissues from MR images, which remains a challenging goal due to the intensity overlap between them. In this study, we propose a fully automated, data-driven image segmentation platform that addresses multiple difficulties in segmenting MR images such as varying inhomogeneity, non-standardness, and noise, while producing a high-quality definition of different tissues. In contrast to most approaches in the literature, we perform segmentation operation by combining three different MRI contrasts and a novel segmentation tool, which takes into account variability in the data. The proposed system, based on a novel affinity definition within the fuzzy connectivity image segmentation family, prevents the need for user intervention and reparametrization of the segmentation algorithms. In order to make the whole system fully automated, we adapt an affinity propagation clustering algorithm to roughly identify tissue regions and image background. We perform a thorough evaluation of the proposed algorithm's individual steps as well as comparison with several approaches from the literature for the main application of muscle/fat separation. Furthermore, whole-body tissue composition and brain tissue delineation were conducted to show the generalization ability of the proposed system. This new automated platform outperforms other state-of-the-art segmentation approaches both in accuracy and efficiency.
On semantic detection of cloud API (anti)patterns

Context: Open standards are urgently needed for enabling software interoperability in Cloud Computing.Open Cloud Computing Interface (OCCI) provides a set of best design principles to create interoperable REST management APIs.Although OCCI is the only standard addressing the management of any kind of cloud resources, it does not support a range of best principles related to REST design.This often worsens REST API quality by decreasing their understandability and reusability.Objective: We aim at assisting cloud developers to enhance their REST management APIs by providing a compliance evaluation of OCCI and REST best principles and a recommendation support to comply with these principles.Method: First, we leverage patterns and anti-patterns to drive respectively the good and poor practices of OCCI and REST best principles.Then, we propose a semantic-based approach for defining and detecting REST and OCCI (anti)patterns and providing a set of correction recommendations to comply with both REST and OCCI best principles.We validated this approach by applying it on cloud REST APIs and evaluating its accuracy, usefulness and extensibility.Results: We found that our approach accurately detects OCCI and REST(anti)patterns and provides useful recommendations.According to the compliance results, we reveal that there is no widespread adoption of OCCI principles in existing APIs.In contrast, these APIs have reached an acceptable level of maturity regarding REST principles.Conclusion: Our approach provides an effective and extensible technique for defining and detecting OCCI and REST (anti)patterns in Cloud REST APIs.Cloud software developers can benefit from our approach and defined principles to accurately evaluate their APIs from OCCI and REST perspectives.This contributes in designing interoperable, understandable, and reusable Cloud management APIs.Thank to the compliance analysis and the recommendation support, we also contribute to improving these APIs, which make them more straightforward.
A Recursive Algorithm for Projected Model Counting

We present a recursive algorithm for projected model counting, i.e., the problem consisting in determining the number of models k∃X.Σk of a propositional formula Σ after eliminating from it a given set X of variables. Based on a ”standard” model counter, our algorithm projMC takes advantage of a disjunctive decomposition scheme of ∃X.Σ for computing k∃X.Σk. It also looks for disjoint components in its input for improving the computation. Our experiments show that in many cases projMC is significantly more efficient than the previous algorithms for projected model counting from the literature.
New Approach for Conversational Agent Definition by Non-Programmers: A Visual Domain-Specific Language

Intelligent tutors and conversational agents (CAs) have proven to be useful learning tools. They have potential not only as stand-alone devices but also as integrable components to enrich and complement other educational resources. For this, new authoring approaches and platforms are required. They should be accessible to non-programmers (such as most teachers) and they should be integrable into current web-based educational platforms. This paper proposes a new approach to define such agents through a visual domain-specific language based on Google Blockly (a scratch-like language). It also develops a web-based integrable authoring platform to serve as a prototype, describing the requirements and architecture. To evaluate whether this novel approach is effective, a multi-stage experiment was conducted. First, participants learned to use the prototype authoring platform through an interactive tutorial. Second, they created a CA with a specific domain model. Times and performance were measured. Finally, they answered a standardized usability questionnaire (UMUX) and a purpose-specific survey. Results show that participants were able to learn to use the domain-specific language in a short time. Moreover, the purpose-specific survey indicates that their perception of the approach (and its potential) is positive. The standardized questionnaire indicates that even in its prototype stage, its usability is satisfactory.
Adaptive Differential Evolution with Elite Opposition-Based Learning and its Application to Training Artificial Neural Networks

Differential Evolution (DE) algorithm is one of the popular evolutionary algorithms that is designed to find a global optimum on multi-dimensional continuous problems. In this paper, we propose a new variant of DE algorithm by combining a self-adaptive DE algorithm called dynNP-DE with Elite Opposi tion-Based Learning (EOBL) scheme. Since dynNP-DE algorithm uses a small number of population size in the later of the search process, the population diversity becomes low, and therefore premature convergence may occur. We have therefore extended an OBL scheme to dynNP-DE algorithm to overcome this shortcoming and improve the optimization performance. By combining EOBL scheme to dynNP-DE algorithm, the population diversity can be supplemented because not only the information of individuals but also their opposition information can be utilized. We measured the optimization performance of the proposed algorithm on CEC 2005 benchmark problems and breast cancer detection, which is a research field that has recently attracted a lot of attention. It was verified that the proposed algorithm could find better solutions than five state-of-the-art DE algorithms.
The Investors Reaction to the Disclosure of Key Audit Matters: Empirical Evidence from Jordan

This study aims to examine the investors reaction to the disclosure of key audit matters (KAMs) as mandated by ISA701, the study&amp;rsquo;s sample consisted of all the (195) public shareholding companies listed in Amman Stock Exchange (ASE) as at the end of 2017, and through which we have conducted a manual content analysis to tracing the auditor practices in reporting KAMs, the final sample consisted of (128) public shareholding companies and for which we used the event study test to examine the study hypothesis, results revealed that the disclosure of KAMs has significantly affected the investors decisions measured by the abnormal trading volume, Our findings suggest that the mandating of KAM&amp;rsquo;s disclosure has informational value to the investors.
High-Quality 3D Reconstruction With Depth Super-Resolution and Completion

The 3D reconstruction is an important topic in computer vision with many applications, such as robotics and augmented reality. Since the raw depth images captured by consumer RGB-D cameras are often low resolution (LR), noisy, and incomplete. How to obtain high-quality 3D models with a consumer RGB-D camera is still a challenge for the existing systems. In this paper, we propose a new depth super-resolution and completion method implemented in a deep learning framework and build a high-quality 3D reconstruction system. We first improve the resolution of LR depth image with a depth super-resolution network and remove the outliers in high-resolution (HR) depth image based on gradient saliency. To further enhance the quality of HR depth image with the guide of HR color image, we learn surface normal and occlusion boundary images from the corresponding HR color image through two deep fully convolutional networks. In particular, the blurriness of HR color image is also detected and pixel-wise quantized. Finally, we obtain a completed HR depth image by optimizing the HR depth image with the surface normal, occlusion boundary, and color image blurriness. We have carried out qualitative and quantitative evaluations with baseline methods on public datasets. The experimental results demonstrate that our method has better performance both on single depth image enhancement and 3D reconstruction.
A Three-Dimensional Hough Transform-Based Track-Before-Detect Technique for Detecting Extended Targets in Strong Clutter Backgrounds

Hough Transform (HT), which has a low sensitivity to local faults and good ability in suppressing noise and clutters, usually applies to trajectory detection in a cluttered environment. This paper describes its application for detecting the trajectories of extended targets in three-dimensional measurements, i.e., a two-dimensional positional information and its measuring time. For taking the full merits of a multi-scan, the measuring time is regarded as a variable for the time axis. This correspondence extends the HT to 3-dimensional data. Meanwhile, a three-dimensional accumulator matrix is built for the purpose of voting. The voting process is done in an iterative way by selecting the 3D-line with the most votes and removing the corresponding measurements in each step. The three dimensional Hough Transform-based extended target track-before-detect technique (3DHT-ET-TBD), proposed here, is suitable to track the extended target and non-extended target simultaneously and few false alarm trajectories arise. Both the real data and simulated data are exploited to evaluate its performance. Compared with the Gaussian Mixture Probability Hypothesis Density (GM-PHD) filter based methods and a 4DHT-TBD algorithm, the 3DHT-ET-TBD is a more promising approach for multi-extended target tracking problems due to its high efficiency and low computation, especially in situations where the noise and false alarms are considerably high but few measurements are generated by the extended targets.
How Can Despeckling and Structural Features Benefit to Change Detection on Bitemporal SAR Images?

Change detection on bitemporal synthetic aperture radar (SAR) images is a key branch of SAR image interpretation. However, it is challenging due to speckle and unavoidable registration errors within bitemporal SAR images. A key issue is whether and how despeckling and structural features can improve accuracy. In this paper, we investigate how despeckling and structural features can benefit change detection for SAR images. Several change detection methods were performed on both input images and the corresponding despeckled images, where despeckling was achieved by different methods. The comparisons demonstrate that despeckling methods that preserve the structures can suppress noise in difference images and can improve the accuracy of change detection. We also developed a sparse model to exploit structural features from the difference images while reducing the influence of misalignment between bitemporal SAR images. The comparisons were performed on five datasets of bitemporal SAR images, and the experimental results show that our proposed sparse model outperforms other traditional methods, demonstrating the advantages of change detection.
Compressed CNN Training with FPGA-based Accelerator

Training convolutional neural network (CNN) usually requires large amount of computation resource, time and power. Researchers and cloud service providers in this region needs fast and efficient training system. GPU is currently the best candidate for CNN training. But FPGAs have already shown good performance and energy efficiency as CNN inference accelerators. In this work, we design a compressed training process together with an FPGA-based accelerator for energy efficient CNN training. We adopt two of the widely used model compression methods, quantization and pruning, to accelerate CNN training process. The difference between inference and training brought challenges to apply the two methods in training. First, training requires higher data precision. We use the gradient accumulation buffer to achieve low operation complexity while keeping gradient descent precision. Second, sparse network results in different types of functions in forward and back-propagation phases. We design a novel architecture to utilize both inference and back-propagation sparsity. Experimental results show that the proposed training process achieves similar accuracy compared with traditional training process with floating point data. The proposed accelerator achieves 641GOP/s equivalent performance and 2.86x better energy efficiency compared with GPU.
Confusion Matrix-Based Building of Hierarchical Classification

Present an evaluation of methods for automatically building hierarchical classifiers from the analysis on the confusion matrix generated with flat classification. By defining a basic framework for that, we investigate the effects of different methods for transforming the confusion matrix, for computing the similarity between classes, and the choice of base classifier. The experimental evaluation, conducted on three datasets from EMNIST with varied number of classes and samples, has shown that the choice of method can highly affect not only the overall accuracy of the system, but also the underlying hierarchical structure that is created. Among such methods, we demonstrate that the proposed penalty matrix with Pearson correlation as similarity metric might be the best option for finding confusion between the classes.