Reviewerly/exHarmony huggingface dataset:
Comparing Logistic Regression Models with Alternative Machine Learning Methods to Predict the Risk of Drug Intoxication Mortality

(1) Medical research has shown an increasing interest in machine learning, permitting massive multivariate data analysis. Thus, we developed drug intoxication mortality prediction models, and compared machine learning models and traditional logistic regression. (2) Categorized as drug intoxication, 8,937 samples were extracted from the Korea Centers for Disease Control and Prevention (2008-2017). We trained, validated, and tested each model through data and compared their performance using three measures: Brier score, calibration slope, and calibration-in-the-large. (3) A chi-square test demonstrated that mortality risk statistically significantly differed according to severity, intent, toxic substance, age, and sex. The multilayer perceptron model (MLP) had the highest area under the curve (AUC), and lowest Brier score in training and validation phases, while the logistic regression model (LR) showed the highest AUC (0.827) and lowest Brier score (0.0307) in the testing phase. MLP also had the second-highest AUC (0.816) and second-lowest Brier score (0.003258) in the testing phase, demonstrating better performance than the decision-making tree model. (4) Given the complexity of choosing tuning parameters, LR proved competitive when using medical datasets, which require strict accuracy.
A Release-Aware Bug Triaging Method Considering Developers' Bug-Fixing Loads

This paper proposes a release-aware bug triaging method that aims to increase the number of bugs that developers can fix by the next release date during open-source software development. A variety of methods have been proposed for recommending appropriate developers for particular bug-fixing tasks, but since these approaches only consider the developers' ability to fix the bug, they tend to assign many of the bugs to a small number of the project's developers. Since projects generally have a release schedule, even excellent developers cannot fix all the bugs that are assigned to them by the existing methods. The proposed method places an upper limit on the number of tasks which are assigned to each developer during a given period, in addition to considering the ability of developers. Our method regards the bug assignment problem as a multiple knapsack problem, finding the best combination of bugs and developers. The best combination is one that maximizes the efficiency of the project, while meeting the constraint where it can only assign as many bugs as the developers can fix during a given period. We conduct the case study, applying our method to bug reports from Mozilla Firefox, Eclipse Platform and GNU compiler collection (GCC). We find that our method has the following properties: (1) it can prevent the bug-fixing load from being concentrated on a small number of developers; (2) compared with the existing methods, the proposed method can assign a more appropriate amount of bugs that each developer can fix by the next release date; (3) it can reduce the time taken to fix bugs by 35%-41%, compared with manual bug triaging
Multi-Agent Image Classification via Reinforcement Learning

We investigate a classification problem using multiple mobile agents capable of collecting (partial) pose-dependent observations of an unknown environment. The objective is to classify an image over a finite time horizon. We propose a network architecture on how agents should form a local belief, take local actions, and extract relevant features from their raw partial observations. Agents are allowed to exchange information with their neighboring agents to update their own beliefs. It is shown how reinforcement learning techniques can be utilized to achieve decentralized implementation of the classification problem by running a decentralized consensus protocol. Our experimental results on the MNIST handwritten digit dataset demonstrates the effectiveness of our proposed framework.
Impact of Landslides Induced by the 2018 Palu Earthquake on Flash Flood in Bangga River Basin, Sulawesi, Indonesia

High magnitude flash flood has occurred several times in some areas in Central Sulawesi Province after the 2018 Palu Earthquake, one of them is in the Bangga River, Sigi Regency, Indonesia.It has caused massive impacts such as damaging agricultural and plantation areas and submerging public facilities and infrastructure and even causing fatalities.The flood carries a variety of materials, especially high concentration sediments which are thought to originate from eroded soils due to landslides induced by a 7.5 magnitude earthquake.These materials are eroded and transported by the flow at the upstream watershed due to heavy rainfall.This study intends to investigate the potential of landslides, factors that trigger floods and increased flooding after the earthquake.This research was conducted by investigating the landslides potency based on field surveys and interpretation of the latest satellite imagery, analyzing the characteristics of rainfall as a trigger for flooding, and predicting the flood potency as the primary impact of these two factors.Rainfall-flood transformation was simulated with the HEC-HMS Model, one of the freeware semi-distributed models commonly used in hydrological analysis.The model input is the configuration of river networks generated from the National DEM (DEMNAS), hourly rainfall during floods and other watershed parameters such as land cover, soil types and river slope.The similar simulation was also carried out on the condition of the watershed before the earthquake.Based on the results of the analysis, It can be inferred that flash floods in the Bangga River are mainly caused by heavy rainfall with long duration and landslide areas in the upper watershed triggered by the 2018 Palu Earthquake with an area of approximately 10.8 km 2 .The greatest depth of rainfall as a trigger for flooding is 30.4 mm with a duration of 8 hours.The results of the study also showed that landslides in the upper watershed could increase the peak flood by 33.33% from 118.56 m 3 /s to 158.08 m 3 /s for conditions before and after the earthquake.
Mirrored entanglement witnesses

Abstract Entanglement detection, which signifies the task of distinguishing entangled states from separable states, can be generally performed by realizing entanglement witnesses via local measurements on a single-copy level and classical communication, and are known to be experimenter friendly. We introduce a framework of constructing mirrored entanglement witnesses by showing that an experimental observable is twice as effective since it generally provides bounds from above and below for separable states. Differently stated, a pair of witnesses, mirrored witnesses, exists for the characterization of the bounds, which are two faces of one observable. We show how to generally construct those witnesses and provide examples for bipartite and multipartite systems. We also show that both mirrored witnesses can be improved by introducing nonlinearities, by which a larger set of entangled states can be certified.
HUAD: Hierarchical Urban Anomaly Detection Based on Spatio-Temporal Data

Due to the rapid development of communication and sensing technology, a large amount of mobile data is collected so that we can infer the complex movement laws of humans.For cities, some unusual events may endanger public safety.If the early warning of an abnormal event can be issued, it is of great application value to urban construction services.To detect urban anomalies, this paper proposes the Hierarchical Urban Anomaly Detection (HUAD) framework.The first step in this framework is to build rough anomaly characteristics that need to be calculated by some traffic flow consisted of subway and taxi data.In the second step, the alternative abnormal regions were obtained.Then, the long short-term memory (LSTM) network is used to predict the traffic to get the historical anomaly scores.Following that, the refined anomaly characteristics are generated from adjacent regions, adjacent periods and historical anomalies.The final abnormal regions were detected by One-Class Support Vector Machine (OC-SVM).At last, based on real data sets, we analyzes the traffic flow of the target region and adjacent regions from multiple perspectives in view of the large crowd gathering activities, and the effectiveness of the method is verified.
TwinBERT: Distilling Knowledge to Twin-Structured BERT Models for Efficient Retrieval

Pre-trained language models like BERT have achieved great success in a wide variety of NLP tasks, while the superior performance comes with high demand in computational resources, which hinders the application in low-latency IR systems. We present TwinBERT model for effective and efficient retrieval, which has twin-structured BERT-like encoders to represent query and document respectively and a crossing layer to combine the embeddings and produce a similarity score. Different from BERT, where the two input sentences are concatenated and encoded together, TwinBERT decouples them during encoding and produces the embeddings for query and document independently, which allows document embeddings to be pre-computed offline and cached in memory. Thereupon, the computation left for run-time is from the query encoding and query-document crossing only. This single change can save large amount of computation time and resources, and therefore significantly improve serving efficiency. Moreover, a few well-designed network layers and training strategies are proposed to further reduce computational cost while at the same time keep the performance as remarkable as BERT model. Lastly, we develop two versions of TwinBERT for retrieval and relevance tasks correspondingly, and both of them achieve close or on-par performance to BERT-Base model. The model was trained following the teacher-student framework and evaluated with data from one of the major search engines. Experimental results showed that the inference time was significantly reduced and was firstly controlled around 20ms on CPUs while at the same time the performance gain from fine-tuned BERT-Base model was mostly retained. Integration of the models into production systems also demonstrated remarkable improvements on relevance metrics with negligible influence on latency.
GAN-Based Rain Noise Removal From Single-Image Considering Rain Composite Models

Under severe weather conditions, outdoor images or videos captured by cameras can be affected by heavy rain and fog. For example, on a rainy day, autonomous vehicles have difficulty determining how to navigate due to the degraded visual quality of images. In this paper, we address a single-image rain removal problem (de-raining). As compared to video-based methods, single-image based methods are challenging because of the lack of temporal information. Although many existing methods have tackled these challenges, they suffer from overfitting, over-smoothing, and unnatural hue change. To solve these problems, we propose a GAN-based de-raining method. The optimal generator is determined by experimental comparisons. To train the generator, we learn the mapping between rainy and residual images from the training dataset. Besides, we synthesize a variety of rainy images to train our network. In particular, we focus on not only the orientations and scales of rain streaks but also the rainy image composite models. Our experimental results show that our method is suitable for a wide range of rainy images. Our method also achieves better performance on both synthetic and real-world images than state-of-the-art methods in terms of quantitative and visual performances.
Reinforcement Learning through Active Inference

The central tenet of reinforcement learning (RL) is that agents seek to maximize the sum of cumulative rewards. In contrast, active inference, an emerging framework within cognitive and computational neuroscience, proposes that agents act to maximize the evidence for a biased generative model. Here, we illustrate how ideas from active inference can augment traditional RL approaches by (i) furnishing an inherent balance of exploration and exploitation, and (ii) providing a more flexible conceptualization of reward. Inspired by active inference, we develop and implement a novel objective for decision making, which we term the free energy of the expected future. We demonstrate that the resulting algorithm successfully balances exploration and exploitation, simultaneously achieving robust performance on several challenging RL benchmarks with sparse, well-shaped, and no rewards.
Improvement of Collaborative Filtering Recommendation Algorithm Based on Intuitionistic Fuzzy Reasoning Under Missing Data

Commodity recommendation plays an essential role in the marketing field in the Internet era, and collaborative filtering, as a powerful technique of commodity recommendation, has been widely concerned in both academic studies and practical applications.Existing research on collaborative filtering often uses methods such as genetic algorithm and neural network to solve the sparsity and cold start problems while ignoring the fuzziness of users' ratings on goods or services.To solve the problems, we propose a recommendation algorithm (IFR-CF) based on intuitionistic fuzzy reasoning and collaborative filtering.In this algorithm, the characteristic coefficient in intuitionistic fuzzy reasoning is used to replace the traditional similarity coefficient to determine neighbor set, and the finite prior ordering method is used to replace traditional algorithm to recommend commodity.Two groups of data are extracted from Movielens and Jester datasets for experiments, and the MAE value generated by the recommended items is taken as the metric to verify the algorithm performance.Experimental results show that compared with the traditional algorithms, our algorithm achieves lower MAE value and higher recommendation accuracy.Meanwhile, the intuitionistic index of fuzzy set is taken into account in the calculation of the hesitation coefficient, which provides a novel solution to the problem of missing scoring data of users.
Modern Russian Universities: Positioning, Development Trends, Potential to Enhance Competitive Advantages

The article focuses on the main trends in the development of modern Russian education. The authors reveal some of its sore points, show the possibilities to overcome them. Some positive examples of the leading universities of the country confirm the high importance of modern University centers of learning, science and culture in the formation of a competence-oriented national educational cluster of innovative type. The authors come to a conclusion that Russian universities, taking into account the existing motivation and the targeted state support provided for them, are able to become quite competitive, to join the ranks of the world top universities, to be attractive not only for Russian but also foreign students. The authors make the case for the the competitiveness growth of the leading universities, argue that the increase of their competitive advantages can become one of the drivers of the development of Russian higher education and the country’s economy as a whole.
Safety in Industry 4.0: The Multi-Purpose Applications of Augmented Reality in Digital Factories

The digital factory is a concept signing a new era for industry, the so-called Industry 4.0, in which ordinary production systems like manufacturing plants, machineries and utilities are being equipped with diagnostic and sensitive instrumentation to be connected to digital shared networks.This data-sharing, trough the big data management techniques, is a fully integrated base on which digital tools can be installed and exchange information.Data stored from physical world of machines can be redirected to simulation environments like Digital Twins and can be inspected, for several purposes like predictive analysis, by humans too.On top of these scopes, within the realization of an Internet of Things, there is safety, especially in industrial sites where injury risk for operators and workers is a key factor for production sustainability.This paper, starting from a tested case study in mechanical stressed monitoring, focuses on the application of upgraded augmented reality systems for safety practices, expanding the area of interest to electric and overheating risk mitigation in electrical equipment and to emergency management.
A Baseline for Attribute Disclosure Risk in Synthetic Data

The generation of synthetic data is widely considered as viable method for alleviating privacy concerns and for reducing identification and attribute disclosure risk in micro-data. The records in a synthetic dataset are artificially created and thus do not directly relate to individuals in the original data in terms of a 1-to-1 correspondence. As a result, inferences about said individuals appear to be infeasible and, simultaneously, the utility of the data may be kept at a high level. In this paper, we challenge this belief by interpreting the standard attacker model for attribute disclosure as classification problem. We show how disclosure risk measures presented in recent publications may be compared to or even be reformulated as machine learning classification models. Our overall goal is to empirically analyze attribute disclosure risk in synthetic data and to discuss its close relationship to data utility. Moreover, we improve the baseline for attribute disclosure risk from the attacker's perspective by applying variants of the RadiusNearestNeighbor and the EnsembleVote classifier.
Swarm and Multi-agent Time-based A* Path Planning for Lighter-Than-Air Systems

This work develops and implements a multi-agent time-based path-planning method using A*. The purpose of this work is to create methods in which multi-agent systems can coordinate actions and complete them at the same time. We utilized A* with constraints defined by a dynamic model of each agent. The model for each agent is updated during each time step and the resulting control is determined. This results in a translational path that each of the agents is physically capable of completing in synchrony. The resulting path is given to the agents as a sequence of waypoints. Periodic updates of the path are calculated, utilizing real-world position and velocity information, as the agents complete the task to account for external disturbances. Our methodology is tested in a dynamic simulation environment as well as on real-world lighter-than-air robotic agents.
A Life of Data

As self-tracking has evolved from a niche movement to a mass-market phenomenon, it has become possible for people to track a broad range of activities and vital parameters over years, even decades. The associated opportunities, as well as the challenges, have had very little research attention so far. With the phenomenon of long-term tracking becoming widespread and important, we have identified its key characteristics by drawing on work from UbiComp, HCI, and health informatics. We identify important differences between long- and short-term tracking, and discuss consequences for the tracking process. Going beyond previous models for short-term tracking, we now present a model for long-term tracking, integrating its distinctive characteristics in purposeful and incidental tracking. Finally, we present major topics for future research.
Geometry of Energy Landscapes and the Optimizability of Deep Neural Networks

Deep neural networks are workhorse models in machine learning with multiple layers of nonlinear functions composed in series. Their loss function is highly nonconvex, yet empirically even gradient descent minimization is sufficient to arrive at accurate and predictive models. It is hitherto unknown why deep neural networks are easily optimizable. We analyze the energy landscape of a spin glass model of deep neural networks using random matrix theory and algebraic geometry. We analytically show that the multilayered structure holds the key to optimizability: Fixing the number of parameters and increasing network depth, the number of stationary points in the loss function decreases, minima become more clustered in parameter space, and the trade-off between the depth and width of minima becomes less severe. Our analytical results are numerically verified through comparison with neural networks trained on a set of classical benchmark datasets. Our model uncovers generic design principles of machine learning models.
A hybrid convolutional neural network for super‐resolution reconstruction of MR images

Purpose Spatial resolution is an important parameter for magnetic resonance imaging (MRI). High‐resolution MR images provide detailed information and benefit subsequent image analysis. However, higher resolution MR images come at the expense of longer scanning time and lower signal‐to‐noise ratios (SNRs). Using algorithms to improve image resolution can mitigate these limitations. Recently, some convolutional neural network (CNN)‐based super‐resolution (SR) algorithms have flourished on MR image reconstruction. However, most algorithms usually adopt deeper network structures to improve the performance. Methods In this study, we propose a novel hybrid network (named HybridNet) to improve the quality of SR images by increasing the width of the network. Specifically, the proposed hybrid block combines a multipath structure and variant dense blocks to extract abundant features from low‐resolution images. Furthermore, we fully exploit the hierarchical features from different hybrid blocks to reconstruct high‐quality images. Results All SR algorithms are evaluated using three MR image datasets and the proposed HybridNet outperformed the comparative methods with peak a signal‐to‐noise ratio (PSNR) of 42.12 ± 0.92 dB, 38.60 ± 2.46 dB, 35.17 ± 2.96 dB and a structural similarity index (SSIM) of 0.9949 ± 0.0015, 0.9892 ± 0.0034, 0.9740 ± 0.0064, respectively. Besides, our proposed network can reconstruct high‐quality images on an unseen MR dataset with PSNR of 33.27 ± 1.56 and SSIM of 0.9581 ± 0.0068. Conclusions The results demonstrate that HybridNet can reconstruct high‐quality SR images from degraded MR images and has good generalization ability. It also can be leveraged to assist the task of image analysis or processing.
Gaze Tracking and Point Estimation Using Low-Cost Head-Mounted Devices

In this study, a head-mounted device was developed to track the gaze of the eyes and estimate the gaze point on the user’s visual plane. To provide a cost-effective vision tracking solution, this head-mounted device is combined with a sized endoscope camera, infrared light, and mobile phone; the devices are also implemented via 3D printing to reduce costs. Based on the proposed image pre-processing techniques, the system can efficiently extract and estimate the pupil ellipse from the camera module. A 3D eye model was also developed to effectively locate eye gaze points from extracted eye images. In the experimental results, average accuracy, precision, and recall rates of the proposed system can achieve an average of over 97%, which can demonstrate the efficiency of the proposed system. This study can be widely used in the Internet of Things, virtual reality, assistive devices, and human-computer interaction applications.