Reviewerly/exHarmony huggingface dataset:
Backtracking search algorithm with specular reflection learning for global optimization

Benefiting from population, randomness and simple structures, metaheuristic methods show excellent performance for solving global optimization problems. However, in some cases, in order to get promising solutions, the existing metaheuristic methods usually need to be modified. This work reports a new technique, called specular reflection learning, for improving the optimization performance of metaheuristic methods. Specular reflection learning is motivated by specular reflection phenomenon in physics. Note that, there is a close relationship between opposition-based learning and specular reflection learning. Opposition-based learning can be seen as a special case of specular reflection learning. In order to investigate the effectiveness of specular reflection learning, specular reflection learning is employed to improve backtracking search algorithm (BSA). The performance of the proposed backtracking search algorithm with specular reflection learning is evaluated by 88 test functions extracted from the well-known CEC 2013, CEC 2014 and CEC 2017 test suites, and two constrained engineering design problems. Experimental results confirm that specular reflection learning is a more effective technique for improving BSA compared with opposition-based learning, which establishes the foundation for the applications of specular reflection learning on other metaheuristics. In addition, the source code of this work can be found from https://www.mathworks.com/matlabcentral/fileexchange/79030-bsa_srl.
An Adaptive Chaotic Sine Cosine Algorithm for Constrained and Unconstrained Optimization

Sine cosine algorithm (SCA) is a new meta-heuristic approach suggested in recent years, which repeats some random steps by choosing the sine or cosine functions to find the global optimum. SCA has shown strong patterns of randomness in its searching styles. At the later stage of the algorithm, the drop of diversity of the population leads to locally oriented optimization and lazy convergence when dealing with complex problems. Therefore, this paper proposes an enriched SCA (ASCA) based on the adaptive parameters and chaotic exploitative strategy to alleviate these shortcomings. Two mechanisms are introduced into the original SCA. First, an adaptive transformation parameter is proposed to make transformation more flexible between global search and local exploitation. Then, the chaotic local search is added to augment the local searching patterns of the algorithm. The effectiveness of the ASCA is validated on a set of benchmark functions, including unimodal, multimodal, and composition functions by comparing it with several well-known and advanced meta-heuristics. Simulation results have demonstrated the significant superiority of the ASCA over other peers. Moreover, three engineering design cases are employed to study the advantage of ASCA when solving constrained optimization tasks. The experimental results have shown that the improvement of ASCA is beneficial and performs better than other methods in solving these types of problems.
Solar Panel Detection within Complex Backgrounds Using Thermal Images Acquired by UAVs

The installation of solar plants everywhere in the world increases year by year. Automated diagnostic methods are needed to inspect the solar plants and to identify anomalies within these photovoltaic panels. The inspection is usually carried out by unmanned aerial vehicles (UAVs) using thermal imaging sensors. The first step in the whole process is to detect the solar panels in those images. However, standard image processing techniques fail in case of low-contrast images or images with complex backgrounds. Moreover, the shades of power lines or structures similar to solar panels impede the automated detection process. In this research, two self-developed methods are compared for the detection of panels in this context, one based on classical techniques and another one based on deep learning, both with a common post-processing step. The first method is based on edge detection and classification, in contrast to the second method is based on training a region based convolutional neural networks to identify a panel. The first method corrects for the low contrast of the thermal image using several preprocessing techniques. Subsequently, edge detection, segmentation and segment classification are applied. The latter is done using a support vector machine trained with an optimized texture descriptor vector. The second method is based on deep learning trained with images that have been subjected to three different pre-processing operations. The postprocessing use the detected panels to infer the location of panels that were not detected. This step selects contours from detected panels based on the panel area and the angle of rotation. Then new panels are determined by the extrapolation of these contours. The panels in 100 random images taken from eleven UAV flights over three solar plants are labeled and used to evaluate the detection methods. The metrics for the new method based on classical techniques reaches a precision of 0.997, a recall of 0.970 and a F1 score of 0.983. The metrics for the method of deep learning reaches a precision of 0.996, a recall of 0.981 and a F1 score of 0.989. The two panel detection methods are highly effective in the presence of complex backgrounds.
Photons

Serverless computing allows users to create short, stateless functions and invoke hundreds of them concurrently to tackle massively parallel workloads. We observe that even though most of the footprint of a serverless function is fixed across its invocations --- language runtime, libraries, and other application state --- today's serverless platforms do not exploit this redundancy. Such an inefficiency has cascading negative impacts: longer startup times, lower throughput, higher latency, and higher cost. To mitigate these problems, we have built Photons, a framework leveraging workload parallelism to co-locate multiple instances of the same function within the same runtime. Concurrent invocations can then share the runtime and application state transparently, without compromising execution safety. Photons reduce function's memory consumption by 25% to 98% per invocation, with no performance degradation compared to today's serverless platforms. We also show that our approach can reduce the overall memory utilization by 30%, and the total number of cold starts by 52%.
A User Guide to Low-Pass Graph Signal Processing and Its Applications: Tools and Applications

The notion of graph filters can be used to define generative models for graph data. In fact, the data obtained from many examples of network dynamics may be viewed as the output of a graph filter. With this interpretation, classical signal processing tools, such as frequency analysis, have been successfully applied with analogous interpretation to graph data, generating new insights for data science. What follows is a user guide on a specific class of graph data, where the generating graph filters are low pass; i.e., the filter attenuates contents in the higher graph frequencies while retaining contents in the lower frequencies. Our choice is motivated by the prevalence of low-pass models in application domains such as social networks, financial markets, and power systems. We illustrate how to leverage properties of low-pass graph filters to learn the graph topology and identify its community structure; efficiently represent graph data through sampling; recover missing measurements; and denoise graph data. The low-pass property is also used as the baseline to detect anomalies.
Minimum description length revisited

This is an up-to-date introduction to and overview of the Minimum Description Length (MDL) Principle, a theory of inductive inference that can be applied to general problems in statistics, machine learning and pattern recognition. While MDL was originally based on data compression ideas, this introduction can be read without any knowledge thereof. It takes into account all major developments since 2007, the last time an extensive overview was written. These include new methods for model selection and averaging and hypothesis testing, as well as the first completely general definition of MDL estimators. Incorporating these developments, MDL can be seen as a powerful extension of both penalized likelihood and Bayesian approaches, in which penalization functions and prior distributions are replaced by more general luckiness functions, average-case methodology is replaced by a more robust worst-case approach, and in which methods classically viewed as highly distinct, such as AIC versus BIC and cross-validation versus Bayes can, to a large extent, be viewed from a unified perspective.
Designing spontaneous behavioral switching via chaotic itinerancy

Chaotic itinerancy is a frequently observed phenomenon in high-dimensional and nonlinear dynamical systems, and it is characterized by the random transitions among multiple quasi-attractors. Several studies have revealed that chaotic itinerancy has been observed in brain activity, and it is considered to play a critical role in the spontaneous, stable behavior generation of animals. Thus, chaotic itinerancy is a topic of great interest, particularly for neurorobotics researchers who wish to understand and implement autonomous behavioral controls for agents. However, it is generally difficult to gain control over high-dimensional nonlinear dynamical systems. Hence, the implementation of chaotic itinerancy has mainly been accomplished heuristically. In this study, we propose a novel way of implementing chaotic itinerancy reproducibly and at will in a generic high-dimensional chaotic system. In particular, we demonstrate that our method enables us to easily design both the trajectories of quasi-attractors and the transition rules among them simply by adjusting the limited number of system parameters and by utilizing the intrinsic high-dimensional chaos. Finally, we quantitatively discuss the validity and scope of application through the results of several numerical experiments.
Biomedical Event Extraction as Sequence Labeling

We introduce Biomedical Event Extraction as Sequence Labeling (BEESL), a joint endto-end neural information extraction model.BEESL recasts the task as sequence labeling, taking advantage of a multi-label aware encoding strategy and jointly modeling the intermediate tasks via multi-task learning.BEESL is fast, accurate, end-to-end, and unlike current methods does not require any external knowledge base or preprocessing tools.BEESL outperforms the current best system (Li et al., 2019) on the Genia 2011 benchmark by 1.57% absolute F1 score reaching 60.22% F1, establishing a new state of the art for the task.Importantly, we also provide first results on biomedical event extraction without gold entity information.Empirical results show that BEESL's speed and accuracy makes it a viable approach for large-scale real-world scenarios. 1
Physical-Layer Supervised Learning Assisted by an Entangled Sensor Network

Many existing quantum supervised learning (SL) schemes consider data given a priori in a classical description. With only noisy intermediate-scale quantum (NISQ) devices available in the near future, their quantum speedup awaits the development of quantum random access memories (qRAMs) and fault-tolerant quantum computing. There, however, also exist a multitude of SL tasks whose data are acquired by sensors, e.g., pattern classification based on data produced by imaging sensors. Solving such SL tasks naturally requires an integrated approach harnessing tools from both quantum sensing and quantum computing. We introduce supervised learning assisted by an entangled sensor network (SLAEN) as a means to carry out SL tasks at the physical layer. The entanglement shared by the sensors in SLAEN boosts the performance of extracting global features of the object under investigation. We leverage SLAEN to construct an entanglement-assisted support-vector machine for data classification and entanglement-assisted principal component analyzer for data compression. In both schemes, variational circuits are employed to seek the optimum entangled probe states and measurement settings to maximize the entanglement-enabled {enhancement}. We observe that SLAEN enjoys an appreciable entanglement-enabled performance gain, even in the presence of loss, over conventional strategies in which classical data are acquired by separable sensors and subsequently processed by classical SL algorithms. SLAEN is realizable with available technology, opening a viable route toward building NISQ devices that offer unmatched performance beyond what the optimum classical device is able to afford.
Quantum Hamiltonian Identifiability via a Similarity Transformation Approach and Beyond

The identifiability of a system is concerned with whether the unknown parameters in the system can be uniquely determined with all the possible data generated by a certain experimental setting. A test of quantum Hamiltonian identifiability is an important tool to save time and cost when exploring the identification capability of quantum probes and experimentally implementing quantum identification schemes. In this article, we generalize the identifiability test based on the similarity transformation approach (STA) in classical control theory and extend it to the domain of quantum Hamiltonian identification. We employ the STA to prove the identifiability of spin-1/2 chain systems with arbitrary dimension assisted by single-qubit probes. We further extend the traditional STA method by proposing a structure preserving transformation (SPT) method for nonminimal systems. We use the SPT method to introduce an indicator for the existence of economic quantum Hamiltonian identification algorithms, whose computational complexity directly depends on the number of unknown parameters (which could be much smaller than the system dimension). Finally, we give an example of such an economic Hamiltonian identification algorithm and perform simulations to demonstrate its effectiveness.
Evaluating Usability of Mobile-Based Augmented Reality Learning Environments for Early Childhood

Adults and kids struggle equally, if they have to focus on a topic at hand for longer durations. However, teaching the latter can be very challenging as they get easily distracted too. Particularly, while using mobile-based Augmented Reality applications for teaching kids, there are associated problems of small screen size, complex functionality and inconsistency. As per the existing literature, there are no clear design principles or guidelines that can be considered while designing and developing mobile AR applications, particularly for kids. This paper presents 23 identified usability principles that are further used to develop an interactive mobile-based AR application for learning basic English. The usability and suitability of the proposed application from users’ perception was established using qualitative and quantitative analysis. Analysis of the students’ behavior and teachers’ interviews substantiates the use of proposed application in classrooms. Thirty pre-schoolers and 12 kindergarten teachers participated in this study. The results showed that the developed ARLE is efficient, effective, and highly usable for the kids. Further, the same identified usability principles can be effectively referred by researchers and developers while developing AR applications for kindergarten kids.
HexagDLy—Processing hexagonally sampled data with CNNs in PyTorch

<h2>Abstract</h2> HexagDLy is a Python-library extending the PyTorch deep learning framework with convolution and pooling operations on hexagonal grids. It aims to ease the access to convolutional neural networks for applications that rely on hexagonally sampled data as, for example, commonly found in ground-based astroparticle physics experiments.
Mining input grammars from dynamic control flow

One of the key properties of a program is its input specification. Having a formal input specification can be critical in fields such as vulnerability analysis, reverse engineering, software testing, clone detection, or refactoring. Unfortunately, accurate input specifications for typical programs are often unavailable or out of date.
Improving Constituency Parsing with Span Attention

Constituency parsing is a fundamental and important task for natural language understanding, where a good representation of contextual information can help this task.N-grams, which is a conventional type of feature for contextual information, have been demonstrated to be useful in many tasks, and thus could also be beneficial for constituency parsing if they are appropriately modeled.In this paper, we propose span attention for neural chartbased constituency parsing to leverage n-gram information.Considering that current chartbased parsers with Transformer-based encoder represent spans by subtraction of the hidden states at the span boundaries, which may cause information loss especially for long spans, we incorporate n-grams into span representations by weighting them according to their contributions to the parsing process.Moreover, we propose categorical span attention to further enhance the model by weighting ngrams within different length categories, and thus benefit long-sentence parsing.Experimental results on three widely used benchmark datasets demonstrate the effectiveness of our approach in parsing Arabic, Chinese, and English, where state-of-the-art performance is obtained by our approach on all of them.
THÖR: Human-Robot Navigation Data Collection and Accurate Motion Trajectories Dataset

Understanding human behavior is key for robots and intelligent systems that share a space with people. Accordingly, research that enables such systems to perceive, track, learn and predict human behavior as well as to plan and interact with humans has received increasing attention over the last years. The availability of large human motion datasets that contain relevant levels of difficulty is fundamental to this research. Existing datasets are often limited in terms of information content, annotation quality or variability of human behavior. In this paper, we present TH\"OR, a new dataset with human motion trajectory and eye gaze data collected in an indoor environment with accurate ground truth for position, head orientation, gaze direction, social grouping, obstacles map and goal coordinates. TH\"OR also contains sensor data collected by a 3D lidar and involves a mobile robot navigating the space. We propose a set of metrics to quantitatively analyze motion trajectory datasets such as the average tracking duration, ground truth noise, curvature and speed variation of the trajectories. In comparison to prior art, our dataset has a larger variety in human motion behavior, is less noisy, and contains annotations at higher frequencies.
Approximating smooth functions by deep neural networks with sigmoid activation function

We study the power of deep neural networks (DNNs) with sigmoid activation function. Recently, it was shown that DNNs approximate any d-dimensional, smooth function on a compact set with a rate of order W−p∕d, where W is the number of nonzero weights in the network and p is the smoothness of the function. Unfortunately, these rates only hold for a special class of sparsely connected DNNs. We ask ourselves if we can show the same approximation rate for a simpler and more general class, i.e., DNNs which are only defined by its width and depth. In this article we show that DNNs with fixed depth and a width of order Md achieve an approximation rate of M−2p. As a conclusion we quantitatively characterize the approximation power of DNNs in terms of the overall weights W0 in the network and show an approximation rate of W0−p∕d. This more general result finally helps us to understand which network topology guarantees a special target accuracy.
Why and how to balance alignment and diversity of requirements engineering practices in automotive

In large-scale automotive companies, various requirements engineering (RE) practices are used across teams. RE practices manifest in Requirements Information Models (RIM) that define what concepts and information should be captured for requirements. Collaboration of practitioners from different parts of an organization is required to define a suitable RIM that balances support for diverse practices in individual teams with the alignment needed for a shared view and team support on system level. There exists no guidance for this challenging task. This paper presents a mixed methods study to examine the role of RIMs in balancing alignment and diversity of RE practices in four automotive companies. Our analysis is based on data from systems engineering tools, 11 semi-structured interviews, and a survey to validate findings and suggestions. We found that balancing alignment and diversity of RE practices is important to consider when defining RIMs. We further investigated enablers for this balance and actions that practitioners take to achieve it. From these factors, we derived and evaluated recommendations for managing RIMs in practice that take into account the lifecycle of requirements and allow for diverse practices across sub-disciplines in early development, while enforcing alignment of requirements that are close to release.
Analyzing host security using D‐S evidence theory and multisource information fusion

Security monitoring and analysis can help users to timely perceive threats faced by the host, thereby protecting and backup data and improving the host's security status. In the research domain of host security analysis, many feasible solutions have been proposed. However, real-time performance and accuracy still need improvement. This paper proposes a host security analysis method based on Dempster–Shafer (D-S) evidence theory. It adopts three models of support vector regression, logistic regression, and K-nearest neighbor regression, as sensors for multisource information fusion. Multiple sensors perform security analysis on the host, respectively, and use the analysis results as evidence of D-S evidence theory. Experiments show that the proposed method provides effective security protection for the host in terms of absolute error, root mean square error, and the average absolute percentage error.
Operational definition of a quantum speed limit

This work provides an operational definition of the quantum speed limit. The authors show that the true quantum speed limit should be independent of the time for non-controlled Hamiltonians and uncover that a lousy purity can benefit the reduction of the quantum speed limit.
Multimodal trajectory optimization for motion planning

Existing motion planning methods often have two drawbacks: 1) goal configurations need to be specified by a user, and 2) only a single solution is generated under a given condition. In practice, multiple possible goal configurations exist to achieve a task. Although the choice of the goal configuration significantly affects the quality of the resulting trajectory, it is not trivial for a user to specify the optimal goal configuration. In addition, the objective function used in the trajectory optimization is often non-convex, and it can have multiple solutions that achieve comparable costs. In this study, we propose a framework that determines multiple trajectories that correspond to the different modes of the cost function. We reduce the problem of identifying the modes of the cost function to that of estimating the density induced by a distribution based on the cost function. The proposed framework enables users to select a preferable solution from multiple candidate trajectories, thereby making it easier to tune the cost function and obtain a satisfactory solution. We evaluated our proposed method with motion planning tasks in 2D and 3D space. Our experiments show that the proposed algorithm is capable of determining multiple solutions for those tasks.