Reviewerly/exHarmony huggingface dataset:
Interactive Decision Tree Learning and Decision Rule Extraction Based on the ImbTreeEntropy and ImbTreeAUC Packages

This paper presents two new R packages ImbTreeEntropy and ImbTreeAUC for building decision trees, including their interactive construction and analysis, which is a highly regarded feature for field experts who want to be involved in the learning process. ImbTreeEntropy functionality includes the application of generalized entropy functions, such as Renyi, Tsallis, Sharma-Mittal, Sharma-Taneja and Kapur, to measure the impurity of a node. ImbTreeAUC provides non-standard measures to choose an optimal split point for an attribute (as well the optimal attribute for splitting) by employing local, semi-global and global AUC measures. The contribution of both packages is that thanks to interactive learning, the user is able to construct a new tree from scratch or, if required, the learning phase enables making a decision regarding the optimal split in ambiguous situations, taking into account each attribute and its cut-off. The main difference with existing solutions is that our packages provide mechanisms that allow for analyzing the trees’ structures (several trees simultaneously) that are built after growing and/or pruning. Both packages support cost-sensitive learning by defining a misclassification cost matrix, as well as weight-sensitive learning. Additionally, the tree structure of the model can be represented as a rule-based model, along with the various quality measures, such as support, confidence, lift, conviction, addedValue, cosine, Jaccard and Laplace.
Enhancing Scientific Papers Summarization with Citation Graph

Previous work for text summarization in scientific domain mainly focused on the content of the input document, but seldom considering its citation network. However, scientific papers are full of uncommon domain-specific terms, making it almost impossible for the model to understand its true meaning without the help of the relevant research community. In this paper, we redefine the task of scientific papers summarization by utilizing their citation graph and propose a citation graph-based summarization model CGSum which can incorporate the information of both the source paper and its references. In addition, we construct a novel scientific papers summarization dataset Semantic Scholar Network (SSN) which contains 141K research papers in different domains and 661K citation relationships. The entire dataset constitutes a large connected citation graph. Extensive experiments show that our model can achieve competitive performance when compared with the pretrained models even with a simple architecture. The results also indicates the citation graph is crucial to better understand the content of papers and generate high-quality summaries.
Scheduling of Time-Varying Workloads Using Reinforcement Learning

Resource usage of production workloads running on shared compute clusters often fluctuate significantly across time. While simultaneous spike in the resource usage between two workloads running on the same machine can create performance degradation, unused resources in a machine results in wastage and undesirable operational characteristics for a compute cluster. Prior works did not consider such temporal resource fluctuations or their alignment for scheduling decisions. Due to the variety of time-varying workloads, their complex resource usage characteristics, it is challenging to design well-defined heuristics for scheduling them optimally across different machines in a cluster. In this paper, we propose a Deep Reinforcement Learning (DRL) based approach to exploit various temporal resource usage patterns of time varying workloads as well as a technique for creating equivalence classes among a large number of production workloads to improve scalability of our method. Validations with real production traces from Google and Alibaba show that our technique can significantly improve metrics for operational excellence (e.g. utilization, fragmentation, resource exhaustion etc.) for a cluster, compared to the baselines.
A Novel Interval Type-2 Fuzzy System Identification Method Based on the Modified Fuzzy <i>C</i>-Regression Model

In this article, a novel interval type-2 Takagi–Sugeno fuzzy <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$c$ </tex-math></inline-formula> -regression modeling method with a modified distance definition is proposed. The modified distance definition is developed to describe the distance between each data point and the local type-2 fuzzy model. To improve the robustness of the proposed identification method, a modified objective function is presented. In addition, different from most previous studies that require numerous free parameters to be determined, an interval type-2 fuzzy <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$c$ </tex-math></inline-formula> -regression model is developed to reduce the number of such free parameters. Furthermore, an improved ratio between the upper and lower weights is proposed based on the upper and lower membership function with each input data, and the ordinary least-squares method is adopted to establish the type-2 fuzzy model. The Box–Jenkins model and two numerical models are given to illustrate the effectiveness and robustness of the proposed results.
EDEN

Crowd sensing applications have demonstrated their usefulness in many real-life scenarios (e.g., air quality monitoring, traffic and noise monitoring). Preserving the privacy of crowd sensing app users is becoming increasingly important as the collected geo-located data may reveal sensitive information about these users (e.g., home, work places, political, religious, sexual preferences). In this context, a large variety of Location Privacy Protection Mechanisms (LPPMs) have been proposed. However, each LPPM comes with a given set of configuration parameters. The value of these parameters impacts not only the privacy level but also the utility of the resulting data. Choosing the right LPPM and the right configuration for reaching a satisfactory privacy vs. utility tradeoff is generally a difficult problem mobile app developers have to face. Solving this problem is commonly done by relying on a trusted proxy server to which raw geo-located traces are sent and privacy vs. utility assessment is performed enabling the selection of the best LPPM for each trace. In this paper we present EDEN, the first solution that selects automatically the best LPPM and its corresponding configuration without sending raw geo-located traces outside the user's device. We reach this objective by relying on a federated learning approach. The evaluation of EDEN on five real-world mobility datasets shows that EDEN outperforms state-of-the-art LPPMs reaching a better privacy vs. utility tradeoff.
Effectiveness of the Wearable Sensor-based Ambient Intelligent Geriatric Management (AmbIGeM) System in Preventing Falls in Older People in Hospitals

Abstract Background The Ambient Intelligent Geriatric Management (AmbIGeM) system augments best practice and involves a novel wearable sensor (accelerometer and gyroscope) worn by patients where the data captured by the sensor are interpreted by algorithms to trigger alerts on clinician handheld mobile devices when risk movements are detected. Methods A 3-cluster stepped-wedge pragmatic trial investigating the effect on the primary outcome of falls rate and secondary outcome of injurious fall and proportion of fallers. Three wards across 2 states were included. Patients aged ≥65 years were eligible. Patients requiring palliative care were excluded. The trial was registered with the Australia and New Zealand Clinical Trials registry, number 12617000981325. Results A total of 4924 older patients were admitted to the study wards with 1076 excluded and 3240 (1995 control, 1245 intervention) enrolled. The median proportion of study duration with valid readings per patient was 49% ((interquartile range [IQR] 25%-67%)). There was no significant difference between intervention and control relating to the falls rate (adjusted rate ratio = 1.41, 95% confidence interval [0.85, 2.34]; p = .192), proportion of fallers (odds ratio = 1.54, 95% confidence interval [0.91, 2.61]; p = .105), and injurious falls rate (adjusted rate ratio = 0.90, 95% confidence interval [0.38, 2.14]; p = .807). In a post hoc analysis, falls and injurious falls rate were reduced in the Geriatric Evaluation and Management Unit wards when the intervention period was compared to the control period. Conclusions The AmbIGeM system did not reduce the rate of falls, rate of injurious falls, or proportion of fallers. There remains a case for further exploration and refinement of this technology given the post hoc analysis findings with the Geriatric Evaluation and Management Unit wards. Clinical Trials Registration Number: 12617000981325
Performance of ARCH and GARCH Models in Forecasting Cryptocurrency Market Volatility

The cryptocurrency market is highly volatile; this can be attributed to several factors such as being an emerging market that is purely digital and still evolving with many speculations taking place aligning with behavioural finance factors such as media and investors profile. This study aims to investigate the Autoregressive Conditional Heteroskedasticity (ARCH) and the Generalized Autoregressive Conditional Heteroskedasticity (GARCH) in forecasting selected 9 cryptocurrencies that represent over 80% of the total market capitalization. This study carries a time-series of daily data ranges from 2010 to 2020 base on each cryptocurrency starting date. The results show that the ARCH and GARCH have a significant effect in forecasting cryptocurrency market volatility which means that the past volatility of cryptocurrencies affects the current volatility of it. It also shows that bad and good news can significantly affect the conditional volatility of all cryptocurrencies returns. This study contributes to the investors’ understanding of the dynamics of the cryptocurrency market which enhances the ability to make informed decisions based on a scientific approach.
Cross-Cultural Design and Evaluation of Robot Prototypes Based on Kawaii (Cute) Attributes

We report on a cross-cultural collaborative project between students and faculty at DePauw University in the United States and Shibaura Institute of Technology in Japan that used cross-cultural teams to design and evaluate robotic gadgets to gain a deeper understanding of the role that kawaii (Japanese cuteness) plays in fostering positive human response to, and acceptance of, these devices across cultures. Two cross-cultural design teams used Unity and C# to design and implement prototypes of virtual robotic gadgets as well as virtual environments for the robots to interact in. One team designed a virtual train station as well as robotic gadgets to operate in the station. The other team designed a virtual university campus as well as robotic gadgets that operated in that environment. Two versions of each robotic gadget were designed, such that the two versions differed with respect to one kawaii attribute (shape, size, etc.) Using these robots, we conducted a formal study that compared perceptions of kawaii robots between American college students and Japanese college students, as well as across genders. The findings revealed that there was not much difference in perception of kawaii across cultures and genders. Furthermore, the study shows that designing a robot to be more kawaii/cute appears to positively influence human preference for being around the robot. This study will inform our long-term goal of designing robots that are appealing across gender and culture.
Generating 3D digital material twins for woven ceramic‐matrix composites from μCT images

Abstract This paper proposes a novel method for generating 3D digital material twins (DMTs) from μCT images for woven ceramic‐matrix composites. The key points to generating DMTs are the efficient and high‐precision identification and segmentation of fiber yarns, matrix, and defects in μCT images. Due to the low gray contrast among fiber yarns, traditional threshold segmentation methods cannot effectively obtain the corresponding woven structures. Therefore, a novel deep convolution neural network (DCNN) model with the U‐Net architecture is developed to overcome these difficulties. Based on this approach, cross sections and centerlines of fiber yarns are identified and used to reconstruct their 3D architectures. Defects are introduced through the spatial mapping between their locations in μCT images and the 3D meshes of the matrix. The DMTs of a C/SiC woven composite with quantitative matrix porosity defects are established, and a simulation of the composite under tension is presented. The DMTs approach provides new insights into damage and failure analyses of ceramic‐matrix composites.
Composing Pick-and-Place Tasks by Grounding Language

Controlling robots to perform tasks via natural language is one of the most challenging topics in human-robot interaction. In this work, we present a robot system that follows unconstrained language instructions to pick and place arbitrary objects and effectively resolves ambiguities through dialogues. Our approach infers objects and their relationships from input images and language expressions and can place objects in accordance with the spatial relations expressed by the user. Unlike previous approaches, we consider grounding not only for the picking but also for the placement of everyday objects from language. Specifically, by grounding objects and their spatial relations, we allow specification of complex placement instructions, e.g. “place it behind the middle red bowl”. Our results obtained using a real-world PR2 robot demonstrate the effectiveness of our method in understanding pick-and-place language instructions and sequentially composing them to solve tabletop manipulation tasks. Videos are available at http://speechrobot.cs.uni-freiburg.de .
A Dataset of Photos and Videos for Digital Forensics Analysis Using Machine Learning Processing

Deepfake and manipulated digital photos and videos are being increasingly used in a myriad of cybercrimes. Ransomware, the dissemination of fake news, and digital kidnapping-related crimes are the most recurrent, in which tampered multimedia content has been the primordial disseminating vehicle. Digital forensic analysis tools are being widely used by criminal investigations to automate the identification of digital evidence in seized electronic equipment. The number of files to be processed and the complexity of the crimes under analysis have highlighted the need to employ efficient digital forensics techniques grounded on state-of-the-art technologies. Machine Learning (ML) researchers have been challenged to apply techniques and methods to improve the automatic detection of manipulated multimedia content. However, the implementation of such methods have not yet been massively incorporated into digital forensic tools, mostly due to the lack of realistic and well-structured datasets of photos and videos. The diversity and richness of the datasets are crucial to benchmark the ML models and to evaluate their appropriateness to be applied in real-world digital forensics applications. An example is the development of third-party modules for the widely used Autopsy digital forensic application. This paper presents a dataset obtained by extracting a set of simple features from genuine and manipulated photos and videos, which are part of state-of-the-art existing datasets. The resulting dataset is balanced, and each entry comprises a label and a vector of numeric values corresponding to the features extracted through a Discrete Fourier Transform (DFT). The dataset is available in a GitHub repository, and the total amount of photos and video frames is 40,588 and 12,400, respectively. The dataset was validated and benchmarked with deep learning Convolutional Neural Networks (CNN) and Support Vector Machines (SVM) methods; however, a plethora of other existing ones can be applied. Generically, the results show a better F1-score for CNN when comparing with SVM, both for photos and videos processing. CNN achieved an F1-score of 0.9968 and 0.8415 for photos and videos, respectively. Regarding SVM, the results obtained with 5-fold cross-validation are 0.9953 and 0.7955, respectively, for photos and videos processing. A set of methods written in Python is available for the researchers, namely to preprocess and extract the features from the original photos and videos files and to build the training and testing sets. Additional methods are also available to convert the original PKL files into CSV and TXT, which gives more flexibility for the ML researchers to use the dataset on existing ML frameworks and tools.
An ARCore-Based Augmented Reality Campus Navigation System

Currently, the route planning functions in 2D/3D campus navigation systems in the market are unable to process indoor and outdoor localization information simultaneously, and the UI experiences are not optimal because they are limited by the service platforms. An ARCore-based augmented reality campus navigation system is designed in this paper in order to solve the relevant problems. Firstly, the proposed campus navigation system uses ARCore to enhance reality by presenting 3D information in real scenes. Secondly, a visual inertial ranging algorithm is proposed for real-time locating and map generating in mobile devices. Finally, rich Unity3D scripts are designed in order to enhance users’ autonomy and enjoyment during navigation experience. In this paper, indoor navigation and outdoor navigation experiments are carried out at the Lingang campus of Shanghai University of Electric Power. Compared with the AR outdoor navigation system of Gaode, the proposed AR system can achieve increased precise outdoor localization by deploying the visual inertia odometer on the mobile phone and realizes the augmented reality function of 3D information and real scene, thus enriching the user’s interactive experience. Furthermore, four groups of students have been selected for system testing and evaluation. Compared with traditional systems, such as Gaode map or Internet media, experimental results show that our system could facilitate the effectiveness and usability of learning on campus.
StyleGANs and Transfer Learning for Generating Synthetic Images in Industrial Applications

Deep learning applications on computer vision involve the use of large-volume and representative data to obtain state-of-the-art results due to the massive number of parameters to optimise in deep models. However, data are limited with asymmetric distributions in industrial applications due to rare cases, legal restrictions, and high image-acquisition costs. Data augmentation based on deep learning generative adversarial networks, such as StyleGAN, has arisen as a way to create training data with symmetric distributions that may improve the generalisation capability of built models. StyleGAN generates highly realistic images in a variety of domains as a data augmentation strategy but requires a large amount of data to build image generators. Thus, transfer learning in conjunction with generative models are used to build models with small datasets. However, there are no reports on the impact of pre-trained generative models, using transfer learning. In this paper, we evaluate a StyleGAN generative model with transfer learning on different application domains—training with paintings, portraits, Pokémon, bedrooms, and cats—to generate target images with different levels of content variability: bean seeds (low variability), faces of subjects between 5 and 19 years old (medium variability), and charcoal (high variability). We used the first version of StyleGAN due to the large number of publicly available pre-trained models. The Fréchet Inception Distance was used for evaluating the quality of synthetic images. We found that StyleGAN with transfer learning produced good quality images, being an alternative for generating realistic synthetic images in the evaluated domains.
End-to-End Speaker-Attributed ASR with Transformer

This paper presents our recent effort on end-to-end speaker-attributed automatic speech recognition, which jointly performs speaker counting, speech recognition and speaker identification for monaural multi-talker audio. Firstly, we thoroughly update the model architecture that was previously designed based on a long short-term memory (LSTM)-based attention encoder decoder by applying transformer architectures. Secondly, we propose a speaker deduplication mechanism to reduce speaker identification errors in highly overlapped regions. Experimental results on the LibriSpeechMix dataset shows that the transformer-based architecture is especially good at counting the speakers and that the proposed model reduces the speaker-attributed word error rate by 47% over the LSTM-based baseline. Furthermore, for the LibriCSS dataset, which consists of real recordings of overlapped speech, the proposed model achieves concatenated minimum-permutation word error rates of 11.9% and 16.3% with and without target speaker profiles, respectively, both of which are the state-of-the-art results for LibriCSS with the monaural setting.
DPGS: Degree-Preserving Graph Summarization

Given a large graph, how can we summarize it with fewer nodes and edges while maintaining its key properties, e.g.node degrees and graph spectrum?As a solution, graph summarization, which aims to find the compact representation for optimally describing and reconstructing a given graph, has received much attention, and numerous methods have been developed for it.However, many existing methods adopt the uniform reconstruction scheme, which is an unrealistic assumption as most real-world graphs have highly skewed node degrees, even within communities.Therefore we propose a degree-preserving graph summarization model, DPGS, with a novel reconstruction scheme based on the configuration model.To optimize the Minimum Description Length of our model, we deisgn a linearly scalable algorithm using hashing techniques.We theoretically show that the minimized reconstruction error bounds the perturbation of graph spectral information.Extensive experiments on realworld datasets show that DPGS yields more accurate summary graphs than several well-known baselines.Moreover, our reduced summary graphs can effectively train graph neural networks (GNNs) while saving computational cost.
Research on Underwater Target Detection Method Based on Improved MSRCP and YOLOv3

With the increasing demand for marine resources, the development of underwater visual robot technology has become more and more important as the main tool for developing marine resources. Aiming at the problem of insufficient illumination in the underwater environment and slow detection speed in the detection of marine biological targets, a rapid marine organisms detection scheme based on the improved MSRCP image enhancement algorithm and YOLOv3 is designed. First, in order to solve the problem of fuzzy and low contrast of underwater images, an improved MSRCP image enhancement algorithm based on a self-adaptive algorithm is proposed to clarify the collected underwater degraded images. We chose YOLOv3 as our detection method,which has high processing speed and high detection accuracy. A large-scale underwater biological data set is established,and we use the Darknet-53 network to train the data set. Finally, we use the trained network to detect the enhanced image and compare it with other target detection algorithms. Experimental results show that the proposed scheme can quickly and accurately detect underwater targets.