Reviewerly/exHarmony huggingface dataset:
Optimized Multi-Spectral Filter Arrays for Spectral Reconstruction

Multispectral filter array (MSFA)-based imaging is a compact, practical technique for snapshot spectral image capturing and reconstruction. The imaging and reconstruction quality is highly influenced by the spectral sensitivities and spatial arrangement of channels on MSFAs, and the used reconstruction method. In order to design a MSFA with high imaging capacity, we propose a sparse representation based approach to optimize spectral sensitivities and spatial arrangement of MSFAs. The proposed approach first overall models the various errors associated with spectral reconstruction, and then uses a global heuristic searching method to optimize MSFAs via minimizing the estimated error of MSFAs. Our MSFA optimization method can select filters from off-the-shelf candidate filter sets while assigning the selected filters to the designed MSFA. Experimental results on three datasets show that the proposed method is more efficient, flexible, and can design MSFAs with lower spectral construction errors when compared with existing state-of-the-art methods. The MSFAs designed by our method show better performance than others even using different spectral reconstruction methods.
Automating Chaos Experiments in Production

Distributed systems often face transient errors and localized component degradation and failure. Verifying that the overall system remains healthy in the face of such failures is challenging. At Netflix, we have built a platform for automatically generating and executing chaos experiments, which check how well the production system can handle component failures and slowdowns. This paper describes the platform and our experiences operating it.
A Stand-Alone Intraoral Tongue-Controlled Computer Interface for People With Tetraplegia

The intraoral Tongue Drive System (iTDS) is an embedded wireless tongue-operated assistive technology developed for people with tetraplegia to provide them a higher level of independence in performing daily living tasks, such as accessing computers, smartphones, and driving wheelchairs. The iTDS was built as an arch-shaped dental retainer hermetically sealed and placed in the buccal shelf area of the mouth, completely hidden from sight. To provide high level of comfort, the iTDS is customized based on the users' oral anatomy to stably fix onto the lower teeth. We have presented a standalone version of the iTDS, capable of recognizing tongue gestures/commands by processing raw magnetic sensor data with a built-in pattern recognition algorithm in real time. The iTDS then sends the commands out in 10-b packets through a custom-designed high-gain intraoral antenna at 2.4 GHz to an external receiver. To evaluate the standalone iTDS performance, four subjects performed a computer access task by issuing random tongue commands over five sessions. Subjects completed 99.2% of the commands, and achieved an information transfer rate of 150.1 b/min. Moreover, a new typing method, designed specifically for the iTDS, resulted in typing at a rate of 3.76 words/min and error rate of 2.23%.
Quantum chemistry on quantum computers: quantum simulations of the time evolution of wave functions under the<b>S</b><sup>2</sup>operator and determination of the spin quantum number<i>S</i>

Quantum computers have an enormous impact on quantum chemical calculations. Approaches to calculate the energies of atoms and molecules on quantum computers by utilizing quantum phase estimation (QPE) and the variational quantum eigensolver (VQE) have been well documented, and dozens of methodological improvements to decrease computational costs and to mitigate errors have been reported until recently. However, the possible methodological implementation of observables on quantum computers such as calculating the spin quantum numbers of arbitrary wave functions, which is a crucial issue in quantum chemistry, has been discussed less. Here, we propose a quantum circuit to simulate the time evolution of wave functions under an S2 operator, exp(-iS2t)|Ψ, and integrate it into the QPE circuit enabling us to determine the spin quantum number of the arbitrary wave functions. We demonstrate that the spin quantum numbers of up to three spins can be determined by only one qubit measurement in QPE.
Automated Combinatorial Testing for Detecting SQL Vulnerabilities in Web Applications

In this paper, we present a combinatorial testing methodology for testing web applications in regards to SQL injection vulnerabilities. We describe three attack grammars that were developed and used to generate concrete attack vectors. Furthermore, we present and evaluate two different oracles used to observe the application's behavior when subjected to such attack vectors. We also present a prototype tool called SQLInjector capable of automated SQL injection vulnerability testing for web applications. The developed methodology can be applied to any web application that uses server side scripting and HTML for handling user input and has a SQL database backend. Our approach relies on the use of a database proxy, making this a gray-box testing method. We establish the effectiveness of the proposed tool with the WAVSEP verification framework and conduct a case study on real-world web applications, where we are able to discover both known vulnerabilities and additional previously undiscovered flaws.
Industry-Academy Collaboration in Teaching DevOps and Continuous Delivery to Software Engineering Students: Towards Improved Industrial Relevance in Higher Education

Global industrial demand for highly skilled professional software engineers is increasing. Many countries already experience shortage of developer workforce and it is predicted that the industrial need for software engineers will grow on a higher rate than educational institutes are able to train new workforce. The main reasons for this deficit are in the education system's inability to adapt to current market needs and in difficulties in matching available skills with existing jobs. Therefore, increasing the industrial and market relevance of the education can be a key solution. Another significant contributor is teaching more efficient working methods such as automating repetitive parts of developer work to help to concentrate on tasks that directly create customer and business value. This paper presents the design and execution of a Continuous Delivery and DevOps course organized in company-university collaboration. The objective is to investigate how university courses requiring multidisciplinary lecturer skills and complex execution architectures can be organized in industry-academia collaboration to improve the industrial relevance of higher education.
Blockchain Approach to Solve Collective Decision Making Problems for Swarm Robotics

Recently, there are significantly increasing number of applications of swarm robotics for example targeted material delivery, precision farming, surveillance, defense and many other areas. Some qualities such as robot autonomy, decentralized control, collective decision-making ability, high fault tolerance etc. make swarm robotics suitable for solving these real-world problems. Blockchain, a decentralized ledger which is managed by a peer-to-peer network with cryptographic algorithms, provides a platform to perform different transactions in a secure way. The decentralized nature of swarm robotics makes it compatible to combine with blockchain technology and allows it to implement different decentralized decision making, behavior differentiation and other business models. This paper proposed a new distributed collective decision-making algorithm (best-of-n) for swarm robotics, where robots form a peer-to-peer network and perform different transactions using blockchain technology. For performance comparison, both collective decision-making algorithm with and without using blockchain have been implemented and their results have also been compared with respect to different metrics such as consensus time and exit probability. The performance shows that our proposed method gives a lower consensus time and higher exit probability value and therefore outperforms classical methods.
Machine Learning Methods for Ranking

Learning-to-rank is one of the learning frameworks in machine learning and it aims to organize the objects in a particular order according to their preference, relevance or ranking. In this paper, we give a comprehensive survey for learning-to-rank. First, we discuss the different approaches along with different machine learning methods such as regression, SVM, neural network-based, evolutionary, boosting method. In order to compare different approaches: we discuss the characteristics of each approach. In addition to that, learning-to-rank algorithms combine with other machine learning paradigms such as semi-supervised learning, active learning, reinforcement learning and deep learning. The learning-to-rank models employ with parallel or big data analytics to review computational and storage advantage. Many real-time applications use learning-to-rank for preference learning. In regard to this, we introduce some representative works. Finally, we highlighted future directions to investigate learning-to-rank methods.
Reducing Forks in the Blockchain via Probabilistic Verification

Blockchain is a disruptive technique that finds many applications in FinTech, IoT, and token economy. Because of the asynchrony of network, the competition of mining, and the nondeterministic block propagation delay, forks in the blockchain occur frequently which not only waste a lot of computing resources but also result in potential security issues. This paper introduces PvScheme, a probabilistic verification scheme that can effectively reduce the block propagation delay and hence reduce the occurrence of blockchain forks. We further enhance the security of PvScheme to provide reliable block delivery. We also analyze the resistance of PvScheme to fake blocks and double spending attacks. The results of several comparative experiments show that our scheme can indeed reduce forks and improve the blockchain performance.
Multilabel Annotation of Multispectral Remote Sensing Images using Error-Correcting Output Codes and Most Ambiguous Examples

This paper presents a novel framework for multilabel classification of multispectral remote sensing images using error-correcting output codes. Starting with a set of primary class labels, the proposed framework consists in transforming the multiclass problem into multiple binary learning subtasks. The distributed output representations of these binary learners are then transformed into primary class labels. In order to train robust binary classifiers on a reduced annotated dataset, the learning process is iterative and involves determining most ambiguous examples, which are included in the training set at each iteration. As part of the semantic image recognition process, two categories of high-level image representations are proposed for the feature extraction part. First, deep convolutional neural networks are used to form high-level representations of the images. Second, we test our classification framework with a bag-of-visual words model based on the scale invariant feature transform, used in combination with color descriptors. In the first case, we propose the usage of pretrained state-of-the-art deep learning models that cancel the need to estimate model parameters of complex architectures, whereas, in the second case, a dictionary of visual words must be determined from the training set. Experiments are conducted on GeoEye-1 and Sentinel-2 images and the results show the effectiveness of the proposed approach toward a multilabel classification, when compared to other methods.
Three Key Checklists and Remedies for Trustworthy Analysis of Online Controlled Experiments at Scale

Online Controlled Experiments (OCEs) are transforming the decision-making process of data-driven companies into an experimental laboratory. Despite their great power in identifying what customers actually value, experimentation is very sensitive to data loss, skipped checks, wrong designs, and many other 'hiccups' in the analysis process. For this purpose, experiment analysis has traditionally been done by experienced data analysts and scientists that closely monitored experiments throughout their lifecycle. Depending solely on scarce experts, however, is neither scalable nor bulletproof. To democratize experimentation, analysis should be streamlined and meticulously performed by engineers, managers, or others responsible for the development of a product. In this paper, based on synthesized experience of companies that run thousands of OCEs per year, we examined how experts inspect online experiments. We reveal that most of the experiment analysis happens before OCEs are even started, and we summarize the key analysis steps in three checklists. The value of the checklists is threefold. First, they can increase the accuracy of experiment set-up and decision-making process. Second, checklists can enable novice data scientists and software engineers to become more autonomous in setting-up and analyzing experiments. Finally, they can serve as a base to develop trustworthy platforms and tools for OCE set-up and analysis.
Isoentangled Mutually Unbiased Bases, Symmetric Quantum Measurements, and Mixed-State Designs

Discrete structures in Hilbert space play a crucial role in finding optimal schemes for quantum measurements. We solve the problem of whether a complete set of five isoentangled mutually unbiased bases exists in dimension four, providing an explicit analytical construction. The reduced density matrices of these 20 pure states forming this generalized quantum measurement form a regular dodecahedron inscribed in a sphere of radius $\sqrt{3/20}$ located inside the Bloch ball of radius $1/2$. Such a set forms a mixed-state 2-design---a discrete set of quantum states with the property that the mean value of any quadratic function of density matrices is equal to the integral over the entire set of mixed states with respect to the flat Hilbert-Schmidt measure. We establish necessary and sufficient conditions mixed-state designs need to satisfy and present general methods to construct them. Furthermore, it is shown that partial traces of a projective design in a composite Hilbert space form a mixed-state design, while decoherence of elements of a projective design yields a design in the classical probability simplex. We identify a distinguished two-qubit orthogonal basis such that four reduced states are evenly distributed inside the Bloch ball and form a mixed-state 2-design.
VF3-Light: A lightweight subgraph isomorphism algorithm and its experimental evaluation

In this paper we introduce VF3-Light, a simplification of VF3, a recently introduced, general-purpose subgraph isomorphism algorithm. While VF3 has demonstrated to be very effective on several datasets, especially on very large and very dense graphs, we will show that on some classes of graphs, the full power of VF3 may become an overkill; indeed, by removing some of the heuristics used in it, and as a consequence also some of the data structures that are required by them, we obtain an algorithm (VF3-Light) that is actually faster. In order to provide a characterization of this modified algorithm, we have performed an evaluation using several publicly available graph datasets. Besides comparing VF3-Light with VF3, we have also included in the comparison other recent algorithms that are rated among the fastest in the state of the art.
Hybrid data‐model method to improve generation estimation and performance assessment of grid‐tied PV: a case study

Increased installed capacity of distributed photovoltaic (PV) systems has necessitated accurate measurement and tracking of PV performance under locality-specific conditions of irradiance, temperature, and derate factors. Existing PV generation estimation methods are strictly model based and not responsive to changes in weather and system losses. Metrics computed using these methods, therefore, do not capture the real PV behaviour well. This study proposes a hybrid data-model method (HDMM) that uses historical PV data in addition to model information to improve the accuracy of generation estimation. The generation estimated by HDMM is used to compute performance metrics - performance ratio, yield, capacity factor, energy performance index, and power performance index - for two real-world PV systems at Miami (ℳ, 1.4 MW) and Daytona (D, 1.28 MW) for 2017. The significance of these metrics is then evaluated, and a preliminary analysis of inverter efficiencies is provided. Results from this study show that when compared with the existing estimation method, HDMM performs better on an average by 75% for D and 10% for ℳ. Further, at a given point in time, system ℳ is likely to perform better than D. The study gives system installers and other stakeholders better PV system visibility, enabling aggregation and transactive energy.
Replicating Novices' Struggles with Coding Style

Good style makes code easier for others to read and modify. Control flow is one element of style where experts expect particular structure, such as conjoining conditions rather than nesting if statements. Empirical work is necessary to understand why novices use poor style, so they can be taught to use good style. Previous work shows that many students know what control flows experts prefer, but may say that novice-styled code is more readable. Yet, these same students showed similarly high comprehension across both expert-and novice-styled code. We propose a replication of that work that more fully assesses students' code comprehension and code writing. Our replication focuses on students who are earlier in their computer science courses and are less likely to be majors, to determine whether the pattern of results is particular to students who are relatively attuned to style concerns. Our pilot of the proposed replication finds that: students in this new population are less able to identify expert code; expert style may reduce comprehension for some control flows; and writing with good style does not always predict a preference for reading code with good style.
Socio-Technical Work-Rate Increase Associates With Changes in Work Patterns in Online Projects

Software developers work on a variety of tasks ranging from the technical, e.g., writing code, to the social, e.g., participating in issue resolution discussions.The amount of work developers perform per week (their work-rate) also varies and depends on project needs and developer schedules.Prior work has shown that while moderate levels of increased technical work and multitasking lead to higher productivity, beyond a certain threshold, they can lead to lowered performance.Here, we study how increases in the short-term work-rate along both the technical and social dimensions are associated with changes in developers' work patterns, in particular communication sentiment, technical productivity, and social productivity.We surveyed active and prolific developers on GitHub to understand the causes and impacts of increased work-rates.Guided by the responses, we developed regression models to study how communication and committing patterns change with increased work-rates and fit those models to large-scale data gathered from traces left by thousands of GitHub developers.From our survey and models, we find that most developers do experience work-rate-increase-related changes in behavior.Most notably, our models show that there is a sizable effect when developers comment much more than their average: the negative sentiment in their comments increases, suggesting an increased level of stress.Our models also show that committing patterns do not change with increased commenting, and vice versa, suggesting that technical and social activities tend not to be multitasked.
Towards Quality-of-Experience Models for Watching 360° Videos in Head-Mounted Virtual Reality

Although watching 360° videos with Head-Mounted Displays (HMDs) is getting increasingly more popular, our understanding of its Quality-of-Experience (QoE) is rather limited. This paper develops models to predict the QoE levels of watching 360° videos with HMDs. We implement a 360° video player for diverse projection schemes and conduct user studies to explore the implications of different factors affecting the QoE levels of 360° videos. Multiple QoE models are constructed based on these factors. Evaluation results on the performance of our QoE models are quite positive. Furthermore, we offer our recommendation on model selections under different circumstances. To the best of our knowledge, this paper is among the few attempts to build QoE models for watching 360° videos with HMDs.
HDRNET

As opposed to the low dynamic range (LDR) image, high dynamic range (HDR) image can represent a greater dynamic range of luminosity that can be perceived by human visual system. As under-/over-exposure and color quantization will cause information loss, inferring a HDR image from a single LDR input is an ill-posed problem. To tackle this, we present an end-to-end convolutional neural network (CNN) termed HDRNET to directly reconstruct HDR image given only a single 8-bit LDR image, which does not require any human expertise. Information from different scales should be considered, so our architecture is multiscale. To enhance the representational ability of CNN, we propose a hybird loss and use channel attention mechanism to adaptively rescale channel-wise features. To train the HDRNET, we build a large dataset of LDR-HDR image pairs. Comparative experiments demonstrate the superiority of the proposed algorithm both qualitatively and quantitatively.
Flexible fault tolerance in cloud through replicated cooperative resource group

Fault tolerance (FT) is a highly focused issue in cloud due to high reliability requirements especially for delay sensitive tasks. Numerous FT frameworks in cloud have been proposed in literature. By and large, proactive and reactive FT approaches are followed in the existing frameworks. Irrespective of any of the applied approaches, FT implementation remains service provider centric in the existing frameworks. Cloud users are not given any control over deciding the level of FT. However, cloud follows the service orientation model with greater flexibility in using the computing services on demand basis and in pay-per-use manner. The user's control in deciding the level of FT while executing their tasks will further reinforce the model of service orientation in cloud. Therefore, this paper proposes a flexible fault tolerance framework (FFTF) in cloud. FFTF provision the users to categorize their tasks as premium/standard/economy to implement the corresponding level of fault tolerance (FT). To implement a FT level, user task is executed on a cooperative resource group in cloud. Flexibility of FFTF is endorsed by maintaining the performance parameters of deadline guarantee ratio and average task delay for different task categories. The performance of FFTF is analyzed and compared through extensive simulation experiments on artificial and real workload in term of its FT capability, resource consumption and utilization with an existing FT framework.
UC-Secure CRS Generation for SNARKs

Zero-knowledge SNARKs (zk-SNARKs) have recently found various applications in verifiable computation and blockchain applications (Zerocash), but unfortunately they rely on a common reference string (CRS) that has to be generated by a trusted party. A standard suggestion, pursued by Ben Sasson et al. [IEEE S&P, 2015], is to generate CRS via a multi-party protocol. We enhance their CRS-generation protocol to achieve UC-security. This allows to safely compose the CRS-generation protocol with the zk-SNARK in a black-box manner with the insurance that the security of the zk-SNARK is not influenced. Differently from the previous work, the new CRS-generation protocol also avoids the random oracle model which is typically not required by zk-SNARKs themselves. As a case study, we apply the protocol to the state-of-the-art zk-SNARK by Groth [EUROCRYPT, 2016].