Reviewerly/exHarmony huggingface dataset:
Sensing Fine-Grained Hand Activity with Smartwatches

Capturing fine-grained hand activity could make computational experiences more powerful and contextually aware. Indeed, philosopher Immanuel Kant argued, "the hand is the visible part of the brain." However, most prior work has focused on detecting whole-body activities, such as walking, running and bicycling. In this work, we explore the feasibility of sensing hand activities from commodity smartwatches, which are the most practical vehicle for achieving this vision. Our investigations started with a 50 participant, in-the-wild study, which captured hand activity labels over nearly 1000 worn hours. We then studied this data to scope our research goals and inform our technical approach. We conclude with a second, in-lab study that evaluates our classification stack, demonstrating 95.2% accuracy across 25 hand activities. Our work highlights an underutilized, yet highly complementary contextual channel that could unlock a wide range of promising applications.
Semantic Cues Enhanced Multimodality Multistream CNN for Action Recognition

This paper addresses the issue of video-based action recognition by exploiting an advanced multistream convolutional neural network (CNN) to fully use semantics-derived multiple modalities in both spatial (appearance) and temporal (motion) domains, since the performance of the CNN-based action recognition methods heavily relates to two factors: semantic visual cues and the network architecture. Our work consists of two major parts. First, to extract useful human-related semantics accurately, we propose a novel spatiotemporal saliency-based video object segmentation (STS) model. By fusing different distinctive saliency maps, which are computed according to object signatures of complementary object detection approaches, a refined STS maps can be obtained. In this way, various challenges in the realistic video can be handled jointly. Based on the estimated saliency maps, an energy function is constructed to segment two semantic cues: the actor and one distinctive acting part of the actor. Second, we modify the architecture of the two-stream network (TS-Net) to design a multistream network that consists of three TS-Nets with respect to the extracted semantics, which is able to use deeper abstract visual features of multimodalities in multi-scale spatiotemporally. Importantly, the performance of action recognition is significantly boosted when integrating the captured human-related semantics into our framework. Experiments on four public benchmarks-JHMDB, HMDB51, UCF-Sports, and UCF101-demonstrate that the proposed method outperforms the state-of-the-art algorithms.
Predicting Motor Insurance Claims Using Telematics Data—XGBoost versus Logistic Regression

XGBoost is recognized as an algorithm with exceptional predictive capacity. Models for a binary response indicating the existence of accident claims versus no claims can be used to identify the determinants of traffic accidents. This study compared the relative performances of logistic regression and XGBoost approaches for predicting the existence of accident claims using telematics data. The dataset contained information from an insurance company about the individuals’ driving patterns—including total annual distance driven and percentage of total distance driven in urban areas. Our findings showed that logistic regression is a suitable model given its interpretability and good predictive capacity. XGBoost requires numerous model-tuning procedures to match the predictive performance of the logistic regression model and greater effort as regards to interpretation.
ACVAE-VC: Non-Parallel Voice Conversion With Auxiliary Classifier Variational Autoencoder

This paper proposes a non-parallel voice conversion (VC) method using a variant of the conditional variational autoencoder (VAE) called an auxiliary classifier VAE. The proposed method has two key features. First, it adopts fully convolutional architectures to construct the encoder and decoder networks so that the networks can learn conversion rules that capture the time dependencies in the acoustic feature sequences of source and target speech. Second, it uses information-theoretic regularization for the model training to ensure that the information in the attribute class label will not be lost in the conversion process. With regular conditional VAEs, the encoder and decoder are free to ignore the attribute class label input. This can be problematic since in such a situation, the attribute class label will have little effect on controlling the voice characteristics of input speech at test time. Such situations can be avoided by introducing an auxiliary classifier and training the encoder and decoder so that the attribute classes of the decoder outputs are correctly predicted by the classifier. We also present several ways to convert the feature sequence of input speech using the trained encoder and decoder and compare them in terms of audio quality through objective and subjective evaluations. We confirmed experimentally that the proposed method outperformed baseline non-parallel VC systems and performed comparably to an open-source parallel VC system trained using a parallel corpus in a speaker identity conversion task.
Single Image Reflection Removal Beyond Linearity

Due to the lack of paired data, the training of image reflection removal relies heavily on synthesizing reflection images. However, existing methods model reflection as a linear combination model, which cannot fully simulate the real-world scenarios. In this paper, we inject non-linearity into reflection removal from two aspects. First, instead of synthesizing reflection with a fixed combination factor or kernel, we propose to synthesize reflection images by predicting a non-linear alpha blending mask. This enables a free combination of different blurry kernels, leading to a controllable and diverse reflection synthesis. Second, we design a cascaded network for reflection removal with three tasks: predicting the transmission layer, reflection layer, and the non-linear alpha blending mask. The former two tasks are the fundamental outputs, while the latter one being the side output of the network. This side output, on the other hand, making the training a closed loop, so that the separated transmission and reflection layers can be recombined together for training with a reconstruction loss. Extensive quantitative and qualitative experiments demonstrate the proposed synthesis and removal approaches outperforms state-of-the-art methods on two standard benchmarks, as well as in real-world scenarios.
The State of the Art in Visualizing Multivariate Networks

Abstract Multivariate networks are made up of nodes and their relationships (links), but also data about those nodes and links as attributes. Most real‐world networks are associated with several attributes, and many analysis tasks depend on analyzing both, relationships and attributes. Visualization of multivariate networks, however, is challenging, especially when both the topology of the network and the attributes need to be considered concurrently. In this state‐of‐the‐art report, we analyze current practices and classify techniques along four axes: layouts, view operations, layout operations, and data operations. We also provide an analysis of tasks specific to multivariate networks and give recommendations for which technique to use in which scenario. Finally, we survey application areas and evaluation methodologies.
The application of virtual reality technology in architectural pedagogy for building constructions

The recent development in information technology has huge opportunities to improve the architectural education in terms of methodologies, strategies and tools. Building construction courses taught in the College of Architecture and Design at Jordan University of Science and Technology mainly depend on the traditional_Teacher–centered_method of teaching. This research suggests a virtual environment technology as a tool to develop new educational approach for these courses. This study developed computer software for this purpose to deal with building construction using virtual reality technology (BC\VR software). This software is designed by the authors for research purpose and presents 4D model (3D model and time dimension) for certain building construction phases using VR technology to do immersive and non-immersive virtual reality experience for the users. This research aims at evaluating the (BC\VR Software) in architectural education of building construction courses as a case study at Jordan University of Science and Technology (JUST) in terms of three axes: providing students with the building construction information, achieving enjoyment, and the integrating with other courses. The study sample was selected from the population of building construction students at Jordan University of Science and Technology (JUST). A structured questionnaire was designed and distributed to the students of the abovementioned classes. The results show that the VR software has the ability to achieve the three axes better than those of the traditional teaching method. As a conclusion, using the BC\VR software as a tool in building construction courses is very useful and effective for the students. The VR technology is also applicable on other architectural courses.
CapsuleGAN: Generative Adversarial Capsule Network

We present Generative Adversarial Capsule Network (CapsuleGAN), a framework that uses capsule networks (CapsNets) instead of the standard convolutional neural networks (CNNs) as discriminators within the generative adversarial network (GAN) setting, while modeling image data. We provide guidelines for designing CapsNet discriminators and the updated GAN objective function, which incorporates the CapsNet margin loss, for training CapsuleGAN models. We show that CapsuleGAN outperforms convolutional-GAN at modeling image data distribution on MNIST and CIFAR-10 datasets, evaluated on the generative adversarial metric and at semi-supervised image classification.
Detection of malicious and low throughput data exfiltration over the DNS protocol

In the presence of security countermeasures, a malware designed for data exfiltration must use a covert channel to achieve its goal. The Domain Name System (DNS) protocol is a covert channel commonly used by malware developers today for this purpose. Although the detection of covert channels using the DNS has been studied for the past decade, prior research has largely dealt with a specific subclass of covert channels, namely DNS tunneling. While the importance of tunneling detection should not be minimized, an entire class of low throughput DNS exfiltration malware has been overlooked. In this study, we propose a method for detecting both tunneling and low throughput data exfiltration over the DNS. After determining that previously detected malware used Internet domains that were registered for a cyber-campaign rather than compromising existing legitimate domains, we focus on detecting and denying requests to these domains as an effective data leakage shutdown. Therefore, our proposed solution handles streaming DNS traffic in order to detect and automatically deny requests to domains that are used for data exchange. The initial data collection phase collects DNS logs per domain in a manner that permits scanning for long periods of time, and is thus capable of dealing with "low and slow" attacks. In the second phase features are extracted based on the querying behavior of each domain, and in the last phase an anomaly detection model is used to classify domains based on their use for data exfiltration. With regard to detection, DNS requests to domains that were classified as being used for data exfiltration will be denied indefinitely. Our method was evaluated on a large-scale recursive DNS server's logs with a peaking high of 47 million requests per hour. Within these DNS logs, we injected data exfiltration traffic from DNS tunneling tools as well as two real-life malware: FrameworkPOS, previously used for the theft of 56M credit cards from Home Depot in 2014, and Backdoor.Win32.Denis, which was active in the Cobalt Kitty APT in 2016. Even when restricting our method to an extremely low false positive rate (i.e., one in fifty thousand domains), it detected all of the above. In addition, the logs are used to compare our system with two recently published methods that focus on detecting DNS tunneling in order to stress the novelty of detecting low throughput exfiltration malware.
Curriculum Model Adaptation with Synthetic and Real Data for Semantic Foggy Scene Understanding

This work addresses the problem of semantic scene understanding under fog. Although marked progress has been made in semantic scene understanding, it is mainly concentrated on clear-weather scenes. Extending semantic segmentation methods to adverse weather conditions such as fog is crucial for outdoor applications. In this paper, we propose a novel method, named Curriculum Model Adaptation (CMAda), which gradually adapts a semantic segmentation model from light synthetic fog to dense real fog in multiple steps, using both labeled synthetic foggy data and unlabeled real foggy data. The method is based on the fact that the results of semantic segmentation in moderately adverse conditions (light fog) can be bootstrapped to solve the same problem in highly adverse conditions (dense fog). CMAda is extensible to other adverse conditions and provides a new paradigm for learning with synthetic data and unlabeled real data. In addition, we present four other main stand-alone contributions: (1) a novel method to add synthetic fog to real, clear-weather scenes using semantic input; (2) a new fog density estimator; (3) a novel fog densification method for real foggy scenes without known depth; and (4) the Foggy Zurich dataset comprising 3808 real foggy images, with pixel-level semantic annotations for 40 images with dense fog. Our experiments show that (1) our fog simulation and fog density estimator outperform their state-of-the-art counterparts with respect to the task of semantic foggy scene understanding (SFSU); (2) CMAda improves the performance of state-of-the-art models for SFSU significantly, benefiting both from our synthetic and real foggy data. The foggy datasets and code are publicly available.
The linkage between knowledge risk management and organizational performance

The purpose of this study is to examine the effect of knowledge risk management (KRM) on organizational performance, with measures considered as “softer” measures of performance, i.e. innovativeness, responsiveness, sustainability, and agility. Data were collected using an online questionnaire sent to private and public organizations located all over the world. The analysis and hypotheses testing were performed using structural equation modeling. The results showed that KRM positively affects organizational success, sustainability, growth, innovativeness, and agility, however, KRM does not show any positive effect on the responsiveness of organizations. The results can help managers and owners to a better understanding of the linkage between KRM and organizational performance. They could use the results to design their KRM practices accordingly. To the authors' best knowledge, this paper is the first empirical study that has investigated the relationship between KRM and organizational performance with a broad organization sample.
Text mining in education

The explosive growth of online education environments is generating a massive volume of data, specially in text format from forums, chats, social networks, assessments, essays, among others. It produces exciting challenges on how to mine text data in order to find useful knowledge for educational stakeholders. Despite the increasing number of educational applications of text mining published recently, we have not found any paper surveying them. In this line, this work presents a systematic overview of the current status of the Educational Text Mining field. Our final goal is to answer three main research questions: Which are the text mining techniques most used in educational environments? Which are the most used educational resources? And which are the main applications or educational goals? Finally, we outline the conclusions and the more interesting future trends.
Variational Autoencoders Pursue PCA Directions (by Accident)

The Variational Autoencoder (VAE) is a powerful architecture capable of representation learning and generative modeling. When it comes to learning interpretable (disentangled) representations, VAE and its variants show unparalleled performance. However, the reasons for this are unclear, since a very particular alignment of the latent embedding is needed but the design of the VAE does not encourage it in any explicit way. We address this matter and offer the following explanation: the diagonal approximation in the encoder together with the inherent stochasticity force local orthogonality of the decoder. The local behavior of promoting both reconstruction and orthogonality matches closely how the PCA embedding is chosen. Alongside providing an intuitive understanding, we justify the statement with full theoretical analysis as well as with experiments.
The impact of ICT on financial development: Empirical evidence from the Gulf Cooperation Council countries

The globalization revolution has led to many countries considering advancing technology, which has led to electronic finance becoming an important aspect in all economic and financial sectors. This study aims to investigate the impact of information and communication technology (ICT) on the financial development index of six Gulf Cooperation Council (GCC) countries from the period 2000 to 2016. The results are reported in terms of two main ICT variables: fixed broadband and Internet users as a proxy of ICT and domestic credit to private sector as a percentage of gross domestic product (GDP) and broad money supply/GDP as two proxies of the financial development index. This methodology used fixed effects (FEs) estimations, and the results show that an increase in fixed broadband has a statistically significant and positive effect on both proxies of financial development. In terms of domestic credit as a percentage of the GDP proxy, the positive effects of ICT (broadband) are greater than the one from Internet users. A 1% increase in fixed broadband leads to approximately 2% increase in financial development, but the Internet user variable resulted in about a 0.09% increase. The other money supply proxy increased by 0.40% when ICT increased by 1%. Additionally, money supply increased by 0.11% when the Internet user ratio increased by 1% .To control for the endogeneity problem, the study used a generalized method of moments estimator, and the results confirm the previous results of the FE. Moreover, the negative impact of economic growth and natural resources was found to be valid and significant, while urbanization and trade openness were found to significantly and positively affect both financial development proxies. The main conclusion of the study is that GCC countries should take action in building an effective joint information system to help construct efficient economic sectors.
Privacy-Preserving Deep Neural Networks with Pixel-Based Image Encryption Considering Data Augmentation in the Encrypted Domain

We present a novel privacy-preserving scheme for deep neural networks (DNNs) that enables us not to only apply images without visual information to DNNs for both training and testing but to also consider data augmentation in the encrypted domain for the first time. In this paper, a novel pixel-based image encryption method is first proposed for privacy-preserving DNNs. In addition, a novel adaptation network is considered that reduces the influence of image encryption. In an experiment, the proposed method is applied to a well-known network, ResNet-18, for image classification. The experimental results demonstrate that conventional privacy-preserving machine learning methods including the state-of-the-arts cannot be applied to data augmentation in the encrypted domain and that the proposed method outperforms them in terms of classification accuracy.
Using text mining to extract depressive symptoms and to validate the diagnosis of major depressive disorder from electronic health records

Many studies have used Taiwan's National Health Insurance Research database (NHIRD) to conduct psychiatric research. However, the accuracy of the diagnostic codes for psychiatric disorders in NHIRD is not validated, and the symptom profiles are not available either. This study aimed to evaluate the accuracy of diagnostic codes and use text mining to extract symptom profile and functional impairment from electronic health records (EHRs) to overcome the above research limitations.A total of 500 discharge notes were randomly selected from a medical center's database. Three annotators reviewed the notes to establish gold standards. The accuracy of diagnostic codes for major psychiatric illness was evaluated. Text mining approaches were applied to extract depressive symptoms and function profiles and to identify patients with major depressive disorder.The accuracy of the diagnostic code for major depressive disorder, schizophrenia, and dementia was acceptable but that of bipolar disorder and minor depression was less satisfactory. The performance of text mining approach to recognize depressive symptoms is satisfactory; however, the recall for functional impairment is lower resulting in lower F-scores of 0.774-0.753. Using the text mining approach to identify major depressive disorder, the recall was 0.85 but precision was only 0.69.The accuracy of the diagnostic code for major depressive disorder in discharge notes was generally acceptable. This finding supports the utilization of psychiatric diagnoses in claims databases. The application of text mining to EHRs might help in overcoming current limitations in research using claims databases.
Multi-task Attention-Based Semi-supervised Learning for Medical Image Segmentation

We propose a novel semi-supervised image segmentation method that simultaneously optimizes a supervised segmentation and an unsupervised reconstruction objectives. The reconstruction objective uses an attention mechanism that separates the reconstruction of image areas corresponding to different classes. The proposed approach was evaluated on two applications: brain tumor and white matter hyperintensities segmentation. Our method, trained on unlabeled and a small number of labeled images, outperformed supervised CNNs trained with the same number of images and CNNs pre-trained on unlabeled data. In ablation experiments, we observed that the proposed attention mechanism substantially improves segmentation performance. We explore two multi-task training strategies: joint training and alternating training. Alternating training requires fewer hyperparameters and achieves a better, more stable performance than joint training. Finally, we analyze the features learned by different methods and find that the attention mechanism helps to learn more discriminative features in the deeper layers of encoders.