Reviewerly/exHarmony huggingface dataset:
On the Trustworthiness of Tree Ensemble Explainability Methods

The recent increase in the deployment of machine learning models in critical domains such as healthcare, criminal justice, and finance has highlighted the need for trustworthy methods that can explain these models to stakeholders. Feature importance methods (e.g. gain and SHAP) are among the most popular explainability methods used to address this need. For any explainability technique to be trustworthy and meaningful, it has to provide an explanation that is accurate and stable. Although the stability of local feature importance methods (explaining individual predictions) has been studied before, there is yet a knowledge gap about the stability of global features importance methods (explanations for the whole model). Additionally, there is no study that evaluates and compares the accuracy of global feature importance methods with respect to feature ordering. In this paper, we evaluate the accuracy and stability of global feature importance methods through comprehensive experiments done on simulations as well as four real-world datasets. We focus on tree-based ensemble methods as they are used widely in industry and measure the accuracy and stability of explanations under two scenarios: 1) when inputs are perturbed 2) when models are perturbed. Our findings provide a comparison of these methods under a variety of settings and shed light on the limitations of global feature importance methods by indicating their lack of accuracy with and without noisy inputs, as well as their lack of stability with respect to: 1) increase in input dimension or noise in the data; 2) perturbations in models initialized by different random seeds or hyperparameter settings.
Comparative Analysis of Deep Neural Networks for the Detection and Decoding of Data Matrix Landmarks in Cluttered Indoor Environments

Abstract Data Matrix patterns imprinted as passive visual landmarks have shown to be a valid solution for the self-localization of Automated Guided Vehicles (AGVs) in shop floors. However, existing Data Matrix decoding applications take a long time to detect and segment the markers in the input image. Therefore, this paper proposes a pipeline where the detector is based on a real-time Deep Learning network and the decoder is a conventional method, i.e. the implementation in libdmtx . To do so, several types of Deep Neural Networks (DNNs) for object detection were studied, trained, compared, and assessed. The architectures range from region proposals (Faster R-CNN) to single-shot methods (SSD and YOLO). This study focused on performance and processing time to select the best Deep Learning (DL) model to carry out the detection of the visual markers. Additionally, a specific data set was created to evaluate those networks. This test set includes demanding situations, such as high illumination gradients in the same scene and Data Matrix markers positioned in skewed planes. The proposed approach outperformed the best known and most used Data Matrix decoder available in libraries like libdmtx .
Restoring Raindrops Using Attentive Generative Adversarial Networks

Artificial intelligence technologies and vision systems are used in various devices, such as automotive navigation systems, object-tracking systems, and intelligent closed-circuit televisions. In particular, outdoor vision systems have been applied across numerous fields of analysis. Despite their widespread use, current systems work well under good weather conditions. They cannot account for inclement conditions, such as rain, fog, mist, and snow. Images captured under inclement conditions degrade the performance of vision systems. Vision systems need to detect, recognize, and remove noise because of rain, snow, and mist to boost the performance of the algorithms employed in image processing. Several studies have targeted the removal of noise resulting from inclement conditions. We focused on eliminating the effects of raindrops on images captured with outdoor vision systems in which the camera was exposed to rain. An attentive generative adversarial network (ATTGAN) was used to remove raindrops from the images. This network was composed of two parts: an attentive-recurrent network and a contextual autoencoder. The ATTGAN generated an attention map to detect rain droplets. A de-rained image was generated by increasing the number of attentive-recurrent network layers. We increased the number of visual attentive-recurrent network layers in order to prevent gradient sparsity so that the entire generation was more stable against the network without preventing the network from converging. The experimental results confirmed that the extended ATTGAN could effectively remove various types of raindrops from images.
Design of Mobile Learning Resources Based on New Blended Learning: A Case Study of Superstar Learning APP

With the rapid development of information technology, the integration of systematic learning of online education platform and fragmented learning of mobile terminal has given birth to a new hybrid learning mode. The key to the integration of the two platforms is the effective integration of learning resources, so that all kinds of resources complement each other. In order to adapt to the change of learners' learning style, and promote the learning effect of learners. To integrate the resources of the two platforms, we should follow the principles of systematization, miniaturization of knowledge points, simplicity of presentation and through use. This paper analyzes the structure and content of online education platform learning resources, establishes curriculum learning resource database, selects the appropriate learning resources of mobile learning platform (Superstar Learning APP as an example), selects the resource presentation mode suitable for mobile terminal, and designs mobile learning resources based on new hybrid learning.
Accuracy and precision of visual and auditory stimulus presentation in virtual reality in Python 2 and 3 environments for human behavior research

Virtual reality (VR) is a new methodology for behavioral studies. In such studies, the millisecond accuracy and precision of stimulus presentation are critical for data replicability. Recently, Python, which is a widely used programming language for scientific research, has contributed to reliable accuracy and precision in experimental control. However, little is known about whether modern VR environments have millisecond accuracy and precision for stimulus presentation, since most standard methods in laboratory studies are not optimized for VR environments. The purpose of this study was to systematically evaluate the accuracy and precision of visual and auditory stimuli generated in modern VR head-mounted displays (HMDs) from HTC and Oculus using Python 2 and 3. We used the newest Python tools for VR and Black Box Toolkit to measure the actual time lag and jitter. The results showed that there was an 18-ms time lag for visual stimulus in both HMDs. For the auditory stimulus, the time lag varied between 40 and 60 ms, depending on the HMD. The jitters of those time lags were 1 ms for visual stimulus and 4 ms for auditory stimulus, which are sufficiently low for general experiments. These time lags were robustly equal, even when auditory and visual stimuli were presented simultaneously. Interestingly, all results were perfectly consistent in both Python 2 and 3 environments. Thus, the present study will help establish a more reliable stimulus control for psychological and neuroscientific research controlled by Python environments.
Multi-Perspective Anomaly Detection

Anomaly detection is a critical problem in the manufacturing industry. In many applications, images of objects to be analyzed are captured from multiple perspectives which can be exploited to improve the robustness of anomaly detection. In this work, we build upon the deep support vector data description algorithm and address multi-perspective anomaly detection using three different fusion techniques, i.e., early fusion, late fusion, and late fusion with multiple decoders. We employ different augmentation techniques with a denoising process to deal with scarce one-class data, which further improves the performance (ROC AUC =80%). Furthermore, we introduce the dices dataset, which consists of over 2000 grayscale images of falling dices from multiple perspectives, with 5% of the images containing rare anomalies (e.g., drill holes, sawing, or scratches). We evaluate our approach on the new dices dataset using images from two different perspectives and also benchmark on the standard MNIST dataset. Extensive experiments demonstrate that our proposed multi-perspective approach exceeds the state-of-the-art single-perspective anomaly detection on both the MNIST and dices datasets. To the best of our knowledge, this is the first work that focuses on addressing multi-perspective anomaly detection in images by jointly using different perspectives together with one single objective function for anomaly detection.
Minimal constraints in the parity formulation of optimization problems

As a means to solve optimization problems using quantum computers, the problem is typically recast into a Ising spin model whose ground-state is the solution of the optimization problem. An alternative to the Ising formulation is the Lechner-Hauke-Zoller model, which has the form of a lattice gauge model with nearest neighbor 4-body constraints. Here we introduce a method to find the minimal strength of the constraints which are required to conserve the correct ground-state. Based on this, we derive upper and lower bounds for the minimal constraints strengths. We find that depending on the problem class, the exponent ranges from linear $\alpha \propto 1$ to quadratic $\alpha \propto 2$ scaling with the number of logical qubits.
A Simple Soft Computing Structure for Modeling and Control

Using the interpolation/extrapolation skills of the core function of an iterative adaptive controller, a structurally simple single essential layer neural network-based topological structure is suggested with fast and explicit single-step teaching and data-retrieving abilities. Its operation does not assume massive parallelism, therefore it easily can be simulated by simple sequential program codes not needing sophisticated data synchronization mechanisms. It seems to be advantageous in approximate model-based common, robust, or adaptive controllers that can compensate for the effects of minor modeling imprecisions. In this structure a neuron can be in either a firing or a passive (i.e., producing zero output) state. In firing state its activation function realizes an abstract rotation that maps the desired kinematic data into the space of the necessary control forces. The activation function allows the use of a simple and fast incremental model modification for slowly varying dynamic models. Its operation is exemplified by numerical simulations for a van der Pol oscillator in free motion, and within a Computed Torque type control. To reveal the possibility for efficient model correction, a robust Variable Structure/Sliding Mode Controller is applied, too. The novel structure can be obtained by approximate experimental observations as e.g., the fuzzy models.
Dimension-Free Entanglement Detection in Multipartite Werner States

Werner states are multipartite quantum states that are invariant under the diagonal conjugate action of the unitary group. This paper gives a complete characterization of their entanglement that is independent of the underlying local Hilbert space: for every entangled Werner state there exists a dimension-free entanglement witness. The construction of such a witness is formulated as an optimization problem. To solve it, two semidefinite programming hierarchies are introduced. The first one is derived using real algebraic geometry applied to positive polynomials in the entries of a Gram matrix, and is complete in the sense that for every entangled Werner state it converges to a witness. The second one is based on a sum-of-squares certificate for the positivity of trace polynomials in noncommuting variables, and is a relaxation that involves smaller semidefinite constraints.
Are We Ready for Accurate and Unbiased Fine-Grained Vehicle Classification in Realistic Environments?

Fine-grained vehicle classification from images, also known as Vehicle Make and Model Recognition (VMMR), has become an important research topic in the last years, with a growing number of scientific contributions in multiple application areas, such as autonomous vehicles, surveillance systems, traffic monitoring and management, among others.Recent techniques based on deep learning have proven to be very effective in addressing this problem.So effective that, based on the state-of-the-art results (above 95% accuracy), it would seem that the problem is practically solved.However, our main hypothesis is that the existing datasets to date have limited variability, which precludes good and unbiased generalisation of the models trained with them.In particular, it is observed that the test datasets are very similar in nature to those used for training and validation which makes these benchmarks prone to dataset bias and to overfitting.When these systems are tested with more challenging data or data from different datasets performance degrades considerably.In this paper, on the one hand, we evaluate state-of-the-art deep learning models to perform fine-grained vehicle classification and explore multiple training techniques, such as curriculum learning or weighted losses, to mitigate the bias between different makes and models and to assess the limits of current approaches.On the other hand, we analyse the existing datasets, present an additional dataset from a challenging scenario, and merge all the data into a cross-dataset that includes common samples and classes from the existing datasets.In this way, we can evaluate geographical, make and model biases, and performance and generalisation capabilities from a more realistic perspective.The obtained results suggest that we are still far from accurate and unbiased vehicle make and model recognition in realistic traffic and driving scenarios.
What do class comments tell us? An investigation of comment evolution and practices in Pharo Smalltalk

Abstract Context Previous studies have characterized code comments in various programming languages, showing how high quality of code comments is crucial to support program comprehension activities, and to improve the effectiveness of maintenance tasks. However, very few studies have focused on understanding developer practices to write comments. None of them has compared such developer practices to the standard comment guidelines to study the extent to which developers follow the guidelines. Objective Therefore, our goal is to investigate developer commenting practices and compare them to the comment guidelines. Method This paper reports the first empirical study investigating commenting practices in Pharo Smalltalk. First, we analyze class comment evolution over seven Pharo versions. Then, we quantitatively and qualitatively investigate the information types embedded in class comments. Finally, we study the adherence of developer commenting practices to the official class comment template over Pharo versions. Results Our results show that there is a rapid increase in class comments in the initial three Pharo versions, while in subsequent versions developers added comments to both new and old classes, thus maintaining a similar code to comment ratio. We furthermore found three times as many information types in class comments as those suggested by the template. However, the information types suggested by the template tend to be present more often than other types of information. Additionally, we find that a substantial proportion of comments follow the writing style of the template in writing these information types, but they are written and formatted in a non-uniform way. Conclusion The results suggest the need to standardize the commenting guidelines for formatting the text, and to provide headers for the different information types to ensure a consistent style and to identify the information easily. Given the importance of high-quality code comments, we draw numerous implications for developers and researchers to improve the support for comment quality assessment tools.
Integration of Wearable Smart Devices and Internet of Things Technology into Public Physical Education

With the development of the Internet, virtual reality technology is manifested in various products through a more intuitive visual experience. As the carrier of virtual reality technology, according to many predictions, smart wearable devices will be the main development direction in the next few years; the era of intelligence also provides new requirements and new challenges for the education and teaching mode of ordinary colleges and universities. This paper aims to integrate wearable smart devices into public physical education, adopt comparative experiment method and data statistics method, and design and conduct teaching experiments with wearable smart devices based on the Internet of Things technology. The experimental class students’ shooting percentage score increased from 12.80 points to 21.43 points, calculated by SPSS. SPSS is “Statistical Products and Service Solutions” software. Initially, the full name of the software was “Statistical Software Package for Social Sciences.” However, with the expansion of the SPSS product service field and the increase of service depth, SPSS officially changed the English full name to “Statistical Products and Service Solutions” in 2000, which marked the strategic direction of SPSS which is making major adjustments. SPSS is the general term for a series of software products and related services for statistical analysis operations, data mining, predictive analysis, and decision support tasks launched by IBM. There are Windows and Mac OS X versions. <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mi>P</mi> <mo>=</mo> <mn>0.003</mn> <mo>&lt;</mo> <mn>0.01</mn> </math> , which showed very obvious difference. At the same time, after the experiment, the comparison of the two groups of students’ skill scores showed that there is also a significant difference ( <math xmlns="http://www.w3.org/1998/Math/MathML" id="M2"> <mi>P</mi> <mo>=</mo> <mn>0.003</mn> <mo>&lt;</mo> <mn>0.05</mn> </math> ). A modern leap-forward teaching model that organically integrates wearable smart devices and public physical education is realized.
Gradient-Based Training of Gaussian Mixture Models for High-Dimensional Streaming Data

Abstract We present an approach for efficiently training Gaussian Mixture Model (GMM) by Stochastic Gradient Descent (SGD) with non-stationary, high-dimensional streaming data. Our training scheme does not require data-driven parameter initialization (e.g., k-means) and can thus be trained based on a random initial state. Furthermore, the approach allows mini-batch sizes as low as 1, which are typical for streaming-data settings. Major problems in such settings are undesirable local optima during early training phases and numerical instabilities due to high data dimensionalities. We introduce an adaptive annealing procedure to address the first problem, whereas numerical instabilities are eliminated by an exponential-free approximation to the standard GMM log-likelihood. Experiments on a variety of visual and non-visual benchmarks show that our SGD approach can be trained completely without, for instance, k-means based centroid initialization. It also compares favorably to an online variant of Expectation-Maximization (EM)—stochastic EM (sEM), which it outperforms by a large margin for very high-dimensional data.
QoE-Aware Video Streaming over Integrated Space and Terrestrial 5G Networks

The integration of the space information network (SIN) with terrestrial infrastructures has been attracting significant attention in the context of 5G, where satellite communications can be leveraged as additional capabilities such as backhauling between the core network and remote mobile edge sites. However, simple addition of SIN capabilities to terrestrial 5G does not automatically lead to enhanced service performance without systematic scheduling of coexisting resources. In this article, we focus on the scenario of multi-link video streaming over both parallel geostationary Earth orbit (GEO) satellite and terrestrial 5G backhaul links for enhancing user quality of experience and network efficiency. The distinct challenge is the complex optimization of scheduling video segment delivery via two parallel channels with very different characteristics while striving to enhance the video quality and resource optimality. We carried out systematic experiments based on a real-life 5G testing framework with integrated GEO satellite and terrestrial backhaul links. The experimental results demonstrate the effectiveness of our proposed 5G edge-computing-based solution for holistically achieving assured user experiences and optimized network resource efficiency in terms of video traffic offloading.
CALTEC: Content-Adaptive Linear Tensor Completion For Collaborative Intelligence

In collaborative intelligence, an artificial intelligence (AI) model is typically split between an edge device and the cloud. Feature tensors produced by the edge sub-model are sent to the cloud via an imperfect communication channel. At the cloud side, parts of the feature tensor may be missing due to packet loss. In this paper we propose a method called Content-Adaptive Linear Tensor Completion (CALTeC) to recover the missing feature data. The proposed method is fast, data-adaptive, does not require pre-training, and produces better results than existing methods for tensor data recovery in collaborative intelligence.
E-Scooter Sustainability – A Clash of Needs, Perspectives, and Experiences

Electric stand-up scooters (e-scooters) are introduced in several cities worldwide, providing new means for people to travel around the city. While praised for their flexibility, e-scooters are also met with negative sentiments due to fatal accidents and chaotic parking. In this paper, we seek to understand the mobility of shared e-scooters and point to gaps in the user interaction between the digital and physical world. We carried out three data collections, including interviews, in situ observation, analysis of news media coverage. Our findings illustrate integration with alternate modes of transportation in urban context, and how technologies facilitate or hinder (micro-) mobility. We found that users of e-scooters primarily view these devices as an alternative to walking rather than other transportation forms. Additionally, we found that users’ and non-users’ needs, perspectives and experiences of e-scooters clash, in particular with regard to perceptions of sustainability. Based on these findings, we present three relevant perspectives of sustainability, extending the ongoing debate of sustainable HCI research. We contribute with an empirically supported understanding of the perception of mobility and sustainability for e-scooters in a Scandinavian urban context.