Reviewerly/exHarmony huggingface dataset:
On Bayesian Posterior Mean Estimators in Imaging Sciences and Hamilton–Jacobi Partial Differential Equations

Variational and Bayesian methods are two widely used set of approaches to solve image denoising problems. In a Bayesian setting, these approaches correspond, respectively, to using maximum a posteriori estimators and posterior mean estimators for reconstructing images. In this paper, we propose novel theoretical connections between Hamilton–Jacobi partial differential equations (HJ PDEs) and a broad class of posterior mean estimators with quadratic data fidelity term and log-concave prior. Where solutions to some first-order HJ PDEs with initial data describe maximum a posteriori estimators, here we show that solutions to some viscous HJ PDEs with initial data describe a broad class of posterior mean estimators. We use these connections to establish representation formulas and various properties of posterior mean estimators. In particular, we use these connections to show that some Bayesian posterior mean estimators can be expressed as proximal mappings of smooth functions and derive representation formulas for these functions.
MEG: Multi-Evidence GNN for Multimodal Semantic Forensics

Fake news often involves semantic manipulations across modalities such as image, text, location etc and requires the development of multimodal semantic forensics for its detection. Recent research has centered the problem around images, calling it image repurposing - where a digitally unmanipulated image is semantically misrepresented by means of its accompanying multimodal metadata such as captions, location, etc. The image and metadata together comprise a multimedia package. The problem setup requires algorithms to perform multimodal semantic forensics to authenticate a query multimedia package using a reference dataset of potentially related packages as evidences. Existing methods are limited to using a single evidence (retrieved package), which ignores potential performance improvement from the use of multiple evidences. In this work, we introduce a novel graph neural network based model for multimodal semantic forensics, which effectively utilizes multiple retrieved packages as evidences and is scalable with the number of evidences. We compare the scalability and performance of our model against existing methods. Experimental results show that the proposed model outperforms existing state-of-the-art algorithms with an error reduction of up to 25 %.
Using Social Intelligence to Overcome Agile Adoption Challenges

Agile adoption is gaining popularity in many organizations, even those steeped in traditional software development. However, project managers often cannot implement agile to its full potential given the numerous human- and process-related challenges. This research focuses on how project managers can tailor agile to overcome human-related challenges. Using a grounded theory approach, we examine how managers' social intelligence competencies play a pivotal role in overcoming the human-related challenges emanating from stakeholders rooted in the traditional waterfall development approach. An understanding of this area informs practitioners on the interventions that can be taken and competencies that can be cultivated to maximize the success of agile adoption despite human-related challenges.
Attention-based networks for analyzing inappropriate speech in Arabic text

Analyzing social media posts and comments has become a critical task to prevent cyberbullying and hate speech. In this work we present a classification models based on the attention mechanism to analyze Arabic posts and filter out all kinds of inappropriate speech including Religious based hate speech, offensive and abusive content in different Arabic dialects. The attention-based models show promising results for four Arabic datasets. The results are presented and compared in terms of accuracy and training time.
CAggNet: Crossing Aggregation Network for Medical Image Segmentation

In this paper, we present Crossing Aggregation Network (CAggNet), a novel densely connected semantic segmentation approach for medical image analysis. The crossing aggregation network improves the idea from deep layer aggregation and makes significant innovations in semantic and spatial information fusion. In CAggNet, the simple skip connection structure of general U-Net is replaced by aggregations of multilevel down-sampling and up-sampling layers, which is a new form of nested skip connection. This aggregation architecture enables the network to fuse both coarse and fine features interactively in semantic segmentation. It also introduces weighted aggregation module to up-sample multi-scale output at the end of the network. We have evaluated and compared our CAggNet with several advanced U-Net based methods in two public medical image datasets, including the 2018 Data Science Bowl nuclei detection dataset and the 2015 MICCAI gland segmentation competition dataset. Experimental results indicate that CAggNet improves medical object recognition and achieves a more accurate and efficient segmentation compared to existing improved U-Net and UNet++ structure.
Black-Box IoT

We propose Black-Box IoT (BBox-IoT), a new ultra-lightweight black-box system for authenticating and storing IoT data. BBox-IoT is tailored for deployment on IoT devices (including low-Size Weight and Power sensors) which are extremely constrained in terms of computation, storage, and power. By utilizing core Blockchain principles, we ensure that the collected data is immutable and tamper-proof while preserving data provenance and non-repudiation. To realize BBox-IoT, we designed and implemented a novel chain-based hash signature scheme which only requires hashing operations and removes all synchronicity dependencies between signer and verifier. Our approach enables low-SWaP devices to authenticate removing reliance on clock synchronization. Our evaluation results show that BBox-IoT is practical in Industrial Internet of Things (IIoT) environments: even devices equipped with 16MHz microcontrollers and 2KB memory can broadcast their collected data without requiring heavy cryptographic operations or synchronicity assumptions. Finally, when compared to industry standard ECDSA, our approach is two and three orders of magnitude faster for signing and verification operations respectively. Thus, we are able to increase the total number of signing operations by more than 5000% for the same amount of power.
The Application of Acceleration-Level Quaternion Interpolation in the Visual-Servoing Process

Visual servoing process is widely applied in robot control. In the visual servoing process, an interpolation of the robot end pose is necessary. In the interpolation, quaternion suffers a lot for the discontinuity in trajectory velocity and acceleration. In this paper, we propose an acceleration-level continuous interpolation method and apply it to visual servoing process. In the method, we make up use of the conversion between different rotation descriptors and achieve a smooth enough trajectory. Simulations and experiments are carried out to validate the method and the results show considerable performance.
Sketch-to-Color Image with GANs

Unsupervised Learning is a trending research field of artificial intelligence, which aims to interpret and understand the hidden structure of the data. However, the development of deep learning with Generative Adversarial Networks (GANs) creates more possibilities for unsupervised learning. GAN is a category of Neural Networks, which are mostly applied to generating images. In this paper, how GAN was implemented to help with sketch-to-color translation is illustrated. In order to achieve this goal, data-preprocessing is implemented first. Then, the model is trained for 65 epochs, and the performance of the model is improved by virtue of loss functions and optimizers. In the end, a proper User Interface (GUI) is designed to have a full application, and people could turn any sketch picture they want into a colored image.
LociMotion: Towards Learning a Strong Authentication Secret in a Single Session

In this work, we design and evaluate LociMotion, a training interface to learn a strong authentication secret in a single session. LociMotion automatically takes a random password with twelve lowercase letters (56-bit entropy) to generate the training interface. It first leverages users’ spatial and visual (declarative) memory by showing them a video clip based on the method of loci, and then consolidates the learning process by having them play a computer game that leverages their motor (procedural) memory. The results of a memorability study with 300 participants showed that LociMotion had a significantly higher recall success rate than a control condition. A second study with 200 participants demonstrated the effectiveness of LociMotion over a period of time (99%, 96%, and 81% recall success rates after 1, 4, and 18 days, respectively). LociMotion offers an alternative to the spaced repetition technique, as it does not require dozens of training sessions.
Comparing Correction Methods to Reduce Misclassification Bias

When applying supervised machine learning algorithms to classification, the classical goal is to reconstruct the true labels as accurately as possible. However, if the predictions of an accurate algorithm are aggregated, for example by counting the predictions of a single class label, the result is often still statistically biased. Implementing machine learning algorithms in the context of official statistics is therefore impeded. The statistical bias that occurs when aggregating the predictions of a machine learning algorithm is referred to as misclassification bias. In this paper, we focus on reducing the misclassification bias of binary classification algorithms by employing five existing estimation techniques, or estimators. As reducing bias might increase variance, the estimators are evaluated by their mean squared error (MSE). For three of the estimators, we are the first to derive an expression for the MSE in finite samples, complementing the existing asymptotic results in the literature. The expressions are then used to compute decision boundaries numerically, indicating under which conditions each of the estimators is optimal, i.e., has the lowest MSE. Our main conclusion is that the calibration estimator performs best in most applications. Moreover, the calibration estimator is unbiased and it significantly reduces the MSE compared to that of the uncorrected aggregated predictions, supporting the use of machine learning in the context of official statistics.
Keyword Searchable Encryption Scheme based on Blockchain in Cloud Environment

With the development of cloud computing, a large amount of sensitive data information is often stored on cloud servers in the form of ciphertext, thereby alleviating local storage pressure while ensuring data privacy. Although encryption helps protect the confidentiality of the data, a secure and efficient search of ciphertext data is a challenging issue. Aiming at the problems of searchable encryption schemes in the existing cloud environment, such as low search efficiency and accuracy, and poor security, this paper proposes a traceable, keyword searchable encryption scheme based on blockchain technology. This solution makes use of the advantages of the non-tampering and non-repudiation of the blockchain to store query records on the blockchain, thereby achieving traceability of user queries and preventing illegal users from leaking sensitive data. Finally, under the random oracle model, the scheme is proved to be adaptive and indistinguishable. Through comparative analysis, it is shown that the scheme has high search accuracy and security.
Multiple Human Tracking in Non-Specific Coverage with Wearable Cameras

Compared to fixed cameras, wearable cameras have time-varying non-specific view coverage and can be used to alternately observe people at different sites by varying the camera views. However, such view change of wearable cameras may introduce intervals of transitional frames without useful information, which brings new challenge for the important multiple object tracking (MOT) task – existing MOT methods can not handle well frequent disappearing/reappearing targets in the field of view, especially in the presence of informationless transitional sequences of frames. To address this problem, in this paper we propose a Markov Decision Process with jump state (JMDP) to model the target’s lifetime in tracking, and use optical flow of the camera motion and the statistical information of the targets to model the camera state transition. We further develop a frame-level classification algorithm to locate the transitional sequence. By combining all of them, we formulate the proposed non-specific-coverage MOT problem as a joint state transition problem, which can be solved by the state transfer mechanism of the targets and the camera. We collect a new dataset for performance evaluation and the experimental results show the effectiveness of the proposed method.
Incorporating contextual information into personalized mobile applications recommendation

With the rise of the mobile Internet, the number of mobile applications (apps) has shown explosive growth, which directly leads to the apps data overload. Currently, the recommender system has become the most effective method to solve the app data overload. However, most of the existing recommended methods for apps ignore the app functional exclusiveness features, which makes it difficult to further improve the app recommendation performance. To solve this problem, we propose a personalized context-aware mobile app recommendation approach, called PCMARA. Specifically, (1) PCMARA explores the relationship between contextual information and function of apps and constructs the app contextual factors for app which represent the function of app. (2) PCMARA leverages the app contextual factor to design a novel app similarity model, which enable to effectively eliminate the adverse effects that ignore the app functional exclusiveness. (3) PCMARA comprehensively considers the contextual information of users and apps to generate a recommendation list for users based on the target users’ current time and location. We applied the PCMARA to a real-world dataset and conducted a large-scale recommendation effect experiment. The experimental results demonstrate the superiority of PMARA over the benchmark methods.
Energy Efficiency and Timeliness in Model Training for Internet-of-Things Applications

Neural network model training is indispensable for domain-specific Artificial Intelligent Internet-of-Things (AIoT) applications. Typically, a GPU graphics card may take several hundreds watts in average during model training, while an embedded GPU device may take only couple watts for the same purpose at the cost of a longer training time. In this paper, we report our empirical study on the model training using NVIDIA RTX 2080 Ti graphics card and NVIDIA Jetson Nano embedded device. We show that, surprisingly, while the training time using the Jetson Nano is 30 times slower than that using the graphics card, the total energy consumption by Jetson Nano is actually only half. The result suggests that when the response time is less critical, one may choose to do model training on GPU embedded devices instead.
Fear and Volatility in Digital Assets

We show Bitcoin implied volatility on a 5 minute time horizon is modestly predictable from price, volatility momentum and alternative data including sentiment and engagement. Lagged Bitcoin index price and volatility movements contribute to the model alongside Google Trends with markets responding often several hours later. The code and datasets used in this paper can be found at https://github.com/Globe-Research/bitfear.
Characterizing Growth and Decline in Online UX Communities

UX practitioners increasingly rely on online communities to collaborate on and discuss complex design problems. Understanding how these platforms flourish is thus of interest to both HCI academia and the broader UX discipline. In this study, we comparatively investigate the longevity of two such groups: the r/userexperience community on Reddit and the UX subforum on Stack Exchange. By quantifying how users post online on aggregate and what users discuss in their individual posts, we find that Reddit has grown consistently as a digital forum for UX practice. In contrast, Stack Exchange has contracted despite being more responsive and being as capable of addressing mainstream UX concepts as Reddit. Discussions of niche, higher-level UX concepts on Stack Exchange also declined disproportionately, leading to less conceptual diversity. Our results therefore contribute an initial comparative understanding of community longevity between online UX platforms.
A Novel Adaptive Minority Oversampling Technique for Improved Classification in Data Imbalanced Scenarios

Imbalance in the proportion of training samples belonging to different classes often poses performance degradation of conventional classifiers. This is primarily due to the tendency of the classifier to be biased towards the majority classes in the imbalanced dataset. In this paper, we propose a novel three step technique to address imbalanced data. As a first step we significantly oversample the minority class distribution by employing the traditional Synthetic Minority Oversampling Technique (SMOTE) algorithm using the neighborhood of the minority class samples and in the next step we partition the generated samples using a Gaussian-Mixture Model based clustering algorithm. In the final step synthetic data samples are chosen based on the weight associated with the cluster, the weight itself being determined by the distribution of the majority class samples. Extensive experiments on several standard datasets from diverse domains show the usefulness of the proposed technique in comparison with the original SMOTE and its state-of-the-art variants algorithms.
Speaker Recognition with ResNet and VGG Networks

Various deep neural network (DNN) topologies have been recently introduced for automatic speaker recognition (SR) tasks, which tend to have ever deeper architectures. Several specific architectures have been proposed to tackle the well-known vanishing gradient issue in DNN training. We present results of experiments on text-independent SR in the wild with two deep Convolutional DNN architectures: ResNet and VGG. The results on the VoxCeleb1 benchmark database demonstrate the superiority of the ResNet solutions, in comparison with VGG and standard i-vector approaches, for speaker identification and verification in realistic acoustic conditions.
Question Answering System with Indic multilingual-BERT

Question answering system is a natural language processing (NLP) task that is very important when it comes to search useful information on websites or large documents. Many language models have been evolved to provide language processing capabilities in various languages. The mBERT is the popular model used to deal with multilingual datasets throughout the world. The research focus was centric to the high resource languages such as English, Hindi, etc. due to the availability of the dataset and many other local languages are overlooked for sentiment analysis. In the present research, a question answering system is focused with Indic Bidirectional Encoder Representations from Transformers (BERT). The performance of Indic BERT with mBERT was analyzed along with the equivalent models.
Few-Shot Learning Based on Metric Learning Using Class Augmentation

Few-shot learning is a machine learning problem in which new categories are learned from only a few samples. One approach for few-shot learning is metric learning, which learns an embedding space in which learning is efficient for few-shot samples. In this paper, we focus on metric learning and demonstrate that the number of classes in the training data used for metric learning has a greater impact on the accuracy of few-shot learning than the number of samples per class. We propose a few-shot learning approach based on metric learning in which the number of classes in the training data for performing metric learning is increased. The number of classes is augmented by synthesizing samples of imaginary classes at a feature level from the original training data. The proposed method is evaluated on the miniImageNet dataset using the nearest neighbor method or a support vector machine as the classifier, and the effectiveness of the approach is demonstrated.
Face recognition and tracking for security surveillance

According to the National Crime Records Bureau, 63,407 children have gone missing in the year 2016, which makes almost 174 children go missing in India every day, out of which only 50% are ever found again. This brings up a need for an efficient solution to trace missing children. The proposed solution uses machine assistance during these search activities with face recognition technologies and can be used for essential development of applications which use CCTV footage across a camera network to identify the person lost. In our solution we use One Shot learning for face recognition to identify stranded people in places such as mass gatherings. The same technology can be used for identification of criminals across the city. The paper also talks about the tracking of people across a network of multiple non-overlapping cameras, with a feature of shifting the target tovehicle, if the target gets into one. The experimentation is performed using mobile cameras and thus, helps in monitoring actions of criminals and finding their hideout.
Integrating Adaptive Mechanisms into Mobile Applications Exploiting User Feedback

Mobile applications have become a commodity in multiple daily scenarios. Their increasing complexity has led mobile software ecosystems to become heterogeneous in terms of hardware specifications, features and context of use, among others. For their users, fully exploiting their potential has become challenging. While enacting software systems with adaptation mechanisms has proven to ease this burden from users, mobile devices present specific challenges related to privacy and security concerns. Nevertheless, rather than being a limitation, users can play a proactive role in the adaptation loop by providing valuable feedback for runtime adaptation. To this end, we propose the use of chatbots to interact with users through a human-like smart conversational process. We depict a work-in-progress proposal of an end-to-end framework to integrate semi-automatic adaptation mechanisms for mobile applications. These mechanisms include the integration of both implicit and explicit user feedback for autonomous user categorization and execution of enactment action plans. We illustrate the applicability of such techniques through a set of scenarios from the Mozilla mobile applications suite. We envisage that our proposal will improve user experience by bridging the gap between users' needs and the capabilities of their mobile devices through an intuitive and minimally invasive conversational mechanism.