Reviewerly/exHarmony huggingface dataset:
Methodology, Legend, and Rhetoric: The Constructions of AI by Academia, Industry, and Policy Groups for Lifelong Learning

Artificial intelligence (AI) is again attracting significant attention across all areas of social life. One important sphere of focus is education; many policy makers across the globe view lifelong learning as an essential means to prepare society for an “AI future” and look to AI as a way to “deliver” learning opportunities to meet these needs. AI is a complex social, cultural, and material artifact that is understood and constructed by different stakeholders in varied ways, and these differences have significant social and educational implications that need to be explored. Through analysis of thirty-four in-depth interviews with stakeholders from academia, commerce, and policy, alongside document analysis, we draw on the social construction of technology (SCOT) to illuminate the diverse understandings, perceptions of, and practices around AI. We find three different technological frames emerging from the three social groups and argue that commercial sector practices wield most power. We propose that greater awareness of the differing technical frames, more interactions among a wider set of relevant social groups, and a stronger focus on the kinds of educational outcomes society seeks are needed in order to design AI for learning in ways that facilitate a democratic education for all.
Dynamic Hand Gesture Recognition Based on Micro-Doppler Radar Signatures Using Hidden Gauss–Markov Models

Dynamic hand gesture recognition using the microwave or millimeter-wave radar sensors has become a typical technology for many human-computer interaction (HCI) applications. In this letter, a novel method is proposed for dynamic hand gesture recognition based on micro-Doppler radar signatures. The short-time Fourier transform is carried out on the raw data to obtain the time-frequency spectrogram. The time-frequency spectrograms associated with the same dynamic hand gesture are modeled by a hidden Gauss-Markov model (HGMM), and the testing gesture is recognized by the maximum likelihood criterion. Experimental results with real radar data demonstrate that the proposed method has a strong generalization ability for radar gesture recognition in the cases of low signal-to-noise ratio (SNR) and unknown users.
Content-aware specular reflection suppression based on adaptive image inpainting and neural network for endoscopic images

In this article, a content-aware specular reflection suppression scheme was developed based on adaptive image inpainting and neural network for endoscopic images. To decrease the impact of specular reflection on visual quality, the proposed scheme consists of three parts: reflection detection, reflection region classification, and reflection concealment. To automatically locate specular reflection regions, a thresholding algorithm with a morphological dilation operation is employed. To reduce the effect of specular reflection, an adaptive image inpainting algorithm is devised to deal with different reflection regions. To achieve content-aware image inpainting, a reflection region classification algorithm is designed by analyzing the local image content to adjust the parameters in the proposed image inpainting algorithm. The experimental results demonstrate that the proposed scheme can automatically and correctly not only locate but also conceal specular reflection regions in endoscopic images. Furthermore, since the average SSIM (structural similarity index) value of the proposed scheme is higher than those of the existing methods, our specular reflection suppression scheme is superior to the existing methods.
Dover: A Method for Combining Diarization Outputs

Speech recognition and other natural language tasks have long benefited from voting-based algorithms as a method to aggregate outputs from several systems to achieve a higher accuracy than any of the individual systems. Diarization, the task of segmenting an audio stream into speaker-homogeneous and co-indexed regions, has so far not seen the benefit of this strategy because the structure of the task does not lend itself to a simple voting approach. This paper presents DOVER (diarization output voting error reduction), an algorithm for weighted voting among diarization hypotheses, in the spirit of the ROVER algorithm for combining speech recognition hypotheses. We evaluate the algorithm for diarization of meeting recordings with multiple microphones, and find that it consistently reduces diarization error rate over the average of results from individual channels, and often improves on the single best channel chosen by an oracle.
Utility and Privacy Assessments of Synthetic Data for Regression Tasks

With ever increasing capacity for collecting, storing, and processing of data, there is also a high demand for intelligent data analysis methods. While there have been impressive advances in machine learning and similar domains in recent years, this also gives rise to concerns regarding the protection of personal and otherwise sensitive data, especially if it is to be analysed by third parties. Besides anonymisation, which becomes challenging with high dimensional data, one approach for privacy-preserving data mining lies in the usage of synthetic data, which comes with the promise of protecting the users' data and producing analysis results close to those achieved by using real data. In this paper, we analyse a number of different approaches for creating synthetic data, and study the utility of the created datasets for regression tasks, i.e. the prediction of a numeric value. We further investigate the similarity of real and synthetic data samples. Finally, we contribute to privacy assessments and measurements of the risk of attribute disclosure on synthetic data by extending an approach developed for categorical data.
Collaborative Trajectory Mining in Smart-Homes to Support Early Diagnosis of Cognitive Decline

Our ageing world population claims for innovative tools to support healthcare and independent living. In this article, we address this challenge by introducing a novel system to recognize symptoms of cognitive decline by exploiting modern smart-home sensors. Since several studies indicate that cognitive issues are frequently associated to locomotion anomalies, our work relies on clinical models of wandering behavior. Previous works tried to recognize wandering of elderly people in outdoor environments, using GPS data and location trace analysis. However, the recognition of wandering indoors poses additional challenges. On the one hand, when moving in a restricted indoor environment, a system for wandering recognition may produce a large number of false positive, since a person's movements are frequently more intricate indoors than outdoors. On the other hand, several indoor movements resembling wandering may be actually due to the normal execution of daily living activities, or to the particular shape of the home. To address these challenges, we adopt a collaborative learning approach, using a training set of trajectories shared by individuals living in smart-homes. New wandering episodes are classified using a personalized model, built considering the homes’ shape and the individuals’ profiles. We apply a long-term analysis of classified wandering episodes to provide a hypothesis of diagnosis to be communicated to a medical center for further inspection. We implemented our algorithms and evaluated the system with a large dataset of real-world subjects, including people with dementia, MCI persons, and cognitively healthy people. The results indicate the potential utility of this system to support the early diagnosis of cognitive impairment.
Certificateless public key encryption with cryptographic reverse firewalls

The Snowden incident shows that powerful attackers can compromise users' machines to steal users' private information. Currently, many encryption schemes that have proven to be secure cannot detect vulnerabilities. These vulnerabilities may reveal users' secrets, e.g., a machine hides some backdoors without a user's awareness, and an attacker can steal the user's private information through these backdoors. In 2015, Mironov and Stephens-Davidowitz proposed a new framework for solving this problem: cryptographic reverse firewall (CRF). However, their protocols can't protect certificateless public key encryption (CL-PKE). In this paper, we design a CRF protocol for CL-PKE, which is a one-round CL-PKE with CRFs (CL-PKE-CRF) deployed on a receiver and a key generating centre (KGC). This protocol is proved to achieve indistinguishability against chosen plaintext attack (IND-CPA). Compared with existing protocol with CRFs and two CL-PKE schemes, our protocol has the least communication cost. Meanwhile, we use JPBC library to implement our one-round CL-PKE-CRF. The experimental results indicate that under high security levels, our protocol has a advantage in the percentage of the running time. Since our protocol can resist exfiltration attacks from internal attackers, it is efficient and practical. Finally, we apply the same method to design a secure and effective one-round certificateless public key signature with a CRF (CL-PKS-CRF). This means that our protocol is not only targeted at a single CL-PKE scheme, but also can inspire the design of other certificateless public key cryptography schemes with CRFs.
Deeply Supervised Discriminative Learning for Adversarial Defense

Deep neural networks can easily be fooled by an adversary with minuscule perturbations added to an input image. The existing defense techniques suffer greatly under white-box attack settings, where an adversary has full knowledge of the network and can iterate several times to find strong perturbations. We observe that the main reason for the existence of such vulnerabilities is the close proximity of different class samples in the learned feature space of deep models. This allows the model decisions to be completely changed by adding an imperceptible perturbation to the inputs. To counter this, we propose to class-wise disentangle the intermediate feature representations of deep networks, specifically forcing the features for each class to lie inside a convex polytope that is maximally separated from the polytopes of other classes. In this manner, the network is forced to learn distinct and distant decision regions for each class. We observe that this simple constraint on the features greatly enhances the robustness of learned models, even against the strongest white-box attacks, without degrading the classification performance on clean images. We report extensive evaluations in both black-box and white-box attack scenarios and show significant gains in comparison to state-of-the-art defenses.
Alexa, What Should I Eat? : A Personalized Virtual Nutrition Coach for Native American Diabetes Patients Using Amazon’s Smart Speaker Technology

Native Americans are disproportionately affected by diabetes and diabetes complications. To control this disease, self-management, especially diet management is very important. There have appeared many electronic tools to help diabetic patients to manage their diet and control their blood glucose level. However, due to their lack of consideration of the special requirement of this ethical group, these tools are not well-accepted by Native American communities. In this paper, we propose a culturally appropriate tool to help this population to manage their disease. Specifically, we propose a voice-based Artificial Intelligence-powered virtual assistant to help Native American diabetic patients to manage their daily diet, and to learn food and nutrition-related knowledge. Voice is the most natural communication modality and it is easy to use without any technical background. In addition, the communication and recommendation provided by the system are personalized based on each user's physical, social, and cultural profile. Therefore, it would be easy to be accepted by the target audience. The proposed virtual assistant has been implemented on the Amazon Alexa platform. Preliminary experiments have demonstrated the usefulness of the virtual assistant.
Prediction modeling—part 1: regression modeling

Risk prediction models are statistical models that estimate the probability of individuals having a certain disease or clinical outcome based on a range of characteristics, and they can be used in clinical practice to stratify disease severity and characterize the risk of disease or disease prognosis. With technological advancements and the proliferation of clinical and biological data, prediction models are increasingly being developed in many areas of nephrology practice. This article guides the reader through the process of creating a prediction model, including (i) defining the clinical question and type of model, (ii) data collection and data cleaning, (iii) model building and variable selection, (iv) model performance, (v) model validation, (vi) model presentation and reporting, and (vii) impact evaluation. An example of developing a prediction model to predict mortality after intensive care unit admission for patients with end-stage kidney disease is also provided to illustrate the model development process.
Improving FastText with inverse document frequency of subwords

Word embedding is important in natural language processing, and word2vec is known as a representative algorithm. However, word2vec and many other dictionary-based word embedding algorithms create word vectors only for words that appear in the training data, ignoring morphological features of these words. The FastText algorithm was previously proposed to solve this problem: it creates a word vector from subword vectors, making it possible to create word embeddings even for words never seen during the training. Because of morphological features, FastText is strong in syntactic tasks but weak in semantic tasks, compared with word2vec. In this paper, we propose a method of improving FastText by using the inverse document frequency of subwords. Our approach is intended to overcome the weakness of FastText in semantic tasks. According to our experiments, the proposed method shows improved results in semantic tests with a little loss in syntactic tests. Our method can be applied to any word embedding algorithm that uses subwords. We additionally tested probabilistic FastText, an algorithm designed to distinguish multiple-meaning words, by adding the inverse document frequency, and the results confirmed an improved performance.
EPASS360: QoE-Aware 360-Degree Video Streaming Over Mobile Devices

The 360-degree video streaming system delivers a monocular panoramic video surrounding the user, and the user can change the viewing direction of mobile devices to see different parts of the video through the “viewport”. Due to the limited network bandwidth, playbacks of high-resolution 360-degree videos often suffer from rebuffering, while too much bandwidth is wasted in delivering those out-of-viewport parts that the user never watches. In this article, we present an Ensemble Prediction and Allocation based Streaming System, named as EPASS360, for delivering high Quality of Experience (QoE) 360-degree videos. The prediction model takes advantages of ensemble learning, providing high accuracy on the prediction of viewports. The allocation model divides a video into tiles, and allocates high resolution to tiles where a user's viewpoint may appear in the future by solving the QoE-aware optimization problem. Trace-driven emulation on real-world datasets shows that EPASS360 enhances the QoE in various scenarios compared to state-of-the-art streaming approaches. Experiments on the head-mounted device and the hand-held device over real-world Internet confirm the high user experience of EPASS360.
Harnessing Multi-View Perspective of Light Fields for Low-Light Imaging

Light Field (LF) offers unique advantages such as post-capture refocusing and depth estimation, but low-light conditions severely limit these capabilities. To restore low-light LFs we should harness the geometric cues present in different LF views, which is not possible using single-frame low-light enhancement techniques. We propose a deep neural network L3Fnet for Low-Light Light Field (L3F) restoration, which not only performs visual enhancement of each LF view but also preserves the epipolar geometry across views. We achieve this by adopting a two-stage architecture for L3Fnet. Stage-I looks at all the LF views to encode the LF geometry. This encoded information is then used in Stage-II to reconstruct each LF view. To facilitate learning-based techniques for low-light LF imaging, we collected a comprehensive LF dataset of various scenes. For each scene, we captured four LFs, one with near-optimal exposure and ISO settings and the others at different levels of low-light conditions varying from low to extreme low-light settings. The effectiveness of the proposed L3Fnet is supported by both visual and numerical comparisons on this dataset. To further analyze the performance of low-light restoration methods, we also propose the L3F-wild dataset that contains LF captured late at night with almost zero lux values. No ground truth is available in this dataset. To perform well on the L3F-wild dataset, any method must adapt to the light level of the captured scene. To do this we use a pre-processing block that makes L3Fnet robust to various degrees of low-light conditions. Lastly, we show that L3Fnet can also be used for low-light enhancement of single-frame images, despite it being engineered for LF data. We do so by converting the single-frame DSLR image into a form suitable to L3Fnet, which we call as pseudo-LF. Our code and dataset is available for download at https://mohitlamba94.github.io/L3Fnet/.
DIPNet: Dynamic Identity Propagation Network for Video Object Segmentation

Many recent methods for semi-supervised Video Object Segmentation (VOS) have achieved good performance by exploiting the annotated first frame via one-shot fine-tuning or mask propagation. However, heavily relying on the first frame may weaken the robustness for VOS, since video objects can show large variations through time. In this work, we propose a Dynamic Identity Propagation Network (DIPNet) that adaptively propagates and accurately segments the video objects over time. To achieve this, DIPNet factors the VOS task at each time step into a dynamic propagation phase and a spatial segmentation phase. The former utilizes a novel identity representation to adaptively propagate objects’ reference information over time, which enhances the robustness to videos’ temporal variations. The segmentation phase uses the propagated information to tackle the object segmentation as an easier static image problem that can be optimized via light-weight fine-tuning on the first frame, thus reducing the computational cost. As a result, by optimizing these two components to complement each other, we can achieve a robust system for VOS. Evaluations on four benchmark datasets show that DIPNet provides state-of-the-art performance with time efficiency.
Quantum-enhanced interferometry with asymmetric beam splitters

In this paper, we investigate the phase sensitivities in two-path optical interferometry with asymmetric beam splitters. Here, we present the optimal conditions for the transmission ratio and the phase of the beam splitter to gain the highest sensitivities for a general class of non-classical states with parity symmetry. Additionally, we address the controversial question of whether the scheme with a combination of coherent state and photon-added or photon-subtracted squeezed vacuum state is better or worse than the most celebrated one using a combination of coherent state and squeezed vacuum state.
Parallel embeddings

We introduce "Parallel Embeddings", a new technique that generalizes the classical Parallel Coordinates visualization technique to sequences of learned representations. This visualization technique is designed for concept-oriented "model comparison" tasks, allowing data scientists to understand qualitative differences in how models interpret input data. We compare user performance with our tool against Tensor Board Embedding Projector for understanding model accuracy and qualitative model differences. With our tool, users were more accurate and learned strategies for the tasks more quickly. Furthermore, users' analytical process in the comparison condition was positively influenced by using our tool beforehand.
Insights for Curriculum Development

Updating curricula in new computer science domains is a critical challenge faced by many instructors and programs. In this paper we present an approach for identifying emerging topics and issues in Data Science by using Question and Answer (Q&A) sites as a resource. Q&A sites provide a useful online platform for discussion of topics and through the sharing of information they become a valuable corpus of knowledge. We applied latent Dirichlet allocation (LDA), a statistical topic modeling technique, to analyze data science related threads from from two popular Q&A communities "Stack Exchange and Reddit". We uncovered both important topics as well as useful examples that can be incorporated into teaching. In addition to technical topics, our analysis also identified topics related to professional development. We believe that approaches such as these are critical in order to update curriculum and bridge the workplace-school divide in teaching of newer topics such as data science. Given the pace of technical development and frequent changes in the field, this is an inventive and effective method to keep teaching up to date. We also discuss the limitations of this approach whereby topics of importance such as data ethics are largely missing from online discussions.
Ontology‐driven perspective of CFRaaS

Abstract A Cloud Forensic Readiness as a Service (CFRaaS) model allows an environment to preemptively accumulate relevant potential digital evidence (PDE) which may be needed during a post‐event response process. The benefit of applying a CFRaaS model in a cloud environment, is that, it is designed to prevent the modification/tampering of the cloud architectures or the infrastructure during the reactive process, which if it could, may end up having far‐reaching implications. The authors of this article present the reactive process as a very costly exercise when the infrastructure must be reprogrammed every time the process is conducted. This may hamper successful investigation from the forensic experts and law enforcement agencies perspectives. The CFRaaS model, in its current state, has not been presented in a way that can help to classify or visualize the different types of potential evidence in all the cloud deployable models, and this may limit the expectations of what or how the required PDE may be collected. To address this problem, the article presents the CFRaaS from a holistic ontology‐driven perspective, which allows the forensic experts to be able to apply the CFRaaS based on its simplicity of the concepts, relationship or semantics between different form of potential evidence, as well as how the security of a digital environment being investigated could be upheld. The CFRaaS in this context follows a fundamental ontology engineering approach that is based on the classical Resource Description Framework. The proposed ontology‐driven approach to CFRaaS is, therefore, a knowledge‐base that uses layer‐dependencies, which could be an essential toolkit for digital forensic examiners and other stakeholders in cloud‐security. The implementation of this approach could further provide a platform to develop other knowledge base components for cloud forensics and security. This article is categorized under: Digital and Multimedia Science &gt; Cloud Forensics Digital and Multimedia Science &gt; Cyber Threat Intelligence Digital and Multimedia Science &gt; Multimedia Forensics