Reviewerly/exHarmony huggingface dataset:
Checking for Prior-Data Conflict Using Prior-to-Posterior Divergences

When using complex Bayesian models to combine information, checking consistency of the information contributed by different components of the model for inference is good statistical practice.Here a new method is developed for detecting prior-data conflicts in Bayesian models based on comparing the observed value of a prior-to-posterior divergence to its distribution under the prior predictive distribution for the data.The divergence measure used in our model check is a measure of how much beliefs have changed from prior to posterior, and can be thought of as a measure of the overall size of a relative belief function.It is shown that the proposed method is intuitive, has desirable properties, can be extended to hierarchical settings, and is related asymptotically to Jeffreys' and reference prior distributions.In the case where calculations are difficult, the use of variational approximations as a way of relieving the computational burden is suggested.The methods are compared in a number of examples with an alternative but closely related approach in the literature based on the prior predictive distribution of a minimal sufficient statistic.
FIRST - Flexible Interactive Retrieval SysTem for Visual Lifelog Exploration at LSC 2020

Lifelog can provide useful insights of our daily activities. It is essential to provide a flexible way for users to retrieve certain events or moments of interest, corresponding to a wide variation of query types. This motivates us to develop FIRST, a Flexible Interactive Retrieval SysTem, to help users to combine or integrate various query components in a flexible manner to handle different query scenarios, such as visual clustering data based on color histogram, visual similarity, GPS location, or scene attributes. We also employ personalized concept detection and image captioning to enhance image understanding from visual lifelog data, and develop an autoencoder-like approach for query text and image feature mapping. Furthermore, we refine the user interface of the retrieval system to better assist users in query expansion and verifying sequential events in a flexible temporal resolution to control the navigation speed through sequences of images.
Deep learning on information retrieval and its applications

In the domain of information retrieval (IR), the matching of query and document relies on ranking models to calculate the degree of their relevance. Therefore, ranking models remain as the central component of the research. During the past decades, there has been a trend moving from traditional approaches to IR toward deep learning approaches to IR. Traditional IR models include basic handcrafted retrieval models, semantic-based models, term dependency-based models, and learning to rank models. The deep learning approaches, on the other hand, involve methods of representation learning, methods of matching function learning, and methods of relevance learning. Recently, we have seen a growing number of publications in both conferences and journals using deep learning techniques to solve the IR problems. The capability of neural ranking models to extract features directly from raw text inputs overcomes many limitations of traditional IR models that rely on handcrafted features. Moreover, the deep learning methods manage to capture complicated matching patterns for document ranking. In this chapter, we introduce a novel way of classifying these existing IR models, along with their recent improvements and developments. To the best of our knowledge, our approach is the first one to classify the existing work according to how they generate the features and the ranking functions. Moreover, we provide a review of these proposed models to discuss different dimensions and to make empirical comparisons, followed by a conclusion with possible directions of future work.
User Behavioral Biometrics Identification on Mobile Platform using Multimodal Fusion of Keystroke and Swipe Dynamics and Recurrent Neural Network

Concerns of mobile technology security has become an important issue in this era because of the popularity of mobile devices. Private and sensitive data such as photos, contacts, and information for e-banking services are being stored in users' mobile devices. In tradition, single-factor authentication, such as password-based authentication, is employed for identifying who is accessing the device. However, problem happened if the password is stolen. Therefore, numerous researchers have spent efforts in developing and investigating various approaches related to mobile technology security by incorporating of biometrics information. Soft biometrics, such as keystroke and swipe dynamics, and fusion of their features, have been proposed as an alternative solution to traditional biometrics, such as face, iris and voice, in authentication. This paper presents a multi-stream recurrent neural network (RNN) for user identification. Modals to temporal feature, spatial feature and swipe dynamic feature are fused for achieving improved performance with accuracy of 94.26% and F1 score of 93.19%.
Multi-head enhanced self-attention network for novelty detection

One-class classification (OCC) is a classical problem in computer vision that can be described as the task of classifying outlier class samples (OC samples) from the OCC model trained on inlier class samples (IC samples) when datasets are highly biased toward one class due to the insufficient sample size of the other class. Currently, the adversarial learning OCC (ALOCC) method has been proven to significantly improve OCC performance. However, its drawbacks include instability issues and non-evident reconstruction between the IC and OC samples. Therefore, we propose multihead enhanced self-attention in the ALOCC network, thereby increasing the difference between the IC and OC samples and significantly increasing OCC accuracy compared with ALOCC accuracy. For training, we propose a new loss, called adversarial-balance loss, that effectively solves the training instability problem, further increasing OCC accuracy. The experiments show the effectiveness of the proposed method compared with state-of-art methods.
A Novel Approach for Character Segmentation of Offline Handwritten Marathi Documents written in MODI Script

One of the most challenging module in any handwritten character recognition system is separating individual characters from the document. Better character recognition rate is highly influenced by how much accuracy, character segmentation phase, has achieved. As the input data set is contains documents written in MODI script, and since MODI script characters are written in cursive manner, obtaining individual characters from the text becomes extremely complicated. Moreover, there is no separation between two adjacent words. Although now-a-days, Marathi language uses Devnagari script, several years back MODI Script was used for writing documents in Marathi language. This work presented in this paper tackles segmentation of individual characters from ancient handwritten MODI Script documents. Vertical Projection Profile (VPP) method can segment characters from a given line accurately only when two adjacent characters are separated by a zero-pixel column. Since characters of one line in this script are written without lifting hand, output of VPP does not suffice. This paper presents a novel approach for isolating individual characters from the line obtained using authors’ previous research work which uses dual thresholding criterion to minimize the character segmentation error. The techniques used in this paper are straightforward and reasonably economical in terms of execution time efficiency. The results obtained are quite satisfactory.
Breast density classification in mammograms: An investigation of encoding techniques in binary-based local patterns

We investigate various channel encoding techniques applied to breast density classification in mammograms; specifically, local binary, ternary, and quinary encoding approaches are considered. Subsequently, we propose a new encoding approach based on a seven-encoding technique, yielding a new local pattern operator called a local septenary pattern operator. Experimental results suggest that the proposed local pattern operator is robust and outperforms the other encoding techniques when evaluated on the Mammographic Image Analysis Society (MIAS) and InBreast datasets. The local septenary pattern operator achieved a maximum classification accuracy of 83.3% and 80.5% on the MIAS and InBreast datasets, respectively. The closest comparison achieved by the other local pattern operators is the local quinary operator, with maximum accuracies of 82.1% (MIAS) and 80.1% (InBreast), respectively.
A Novel Multi-Scale Adversarial Networks for Precise Segmentation of X-Ray Breast Mass

With the constant changes of people's lifestyle and living environment, the morbidity of breast cancer is increasing year by year. It is highly imperative to develop an effective breast mass segmentation method for early breast cancer diagnosis. However, segmenting breast masses in mammograms is still one hot issue with enormous challenges because of masses' irregular shapes and various sizes. In this study, we propose multi-adversarial learning to capture multi-scale image information for accurate breast mass segmentation. To effectively reinforce higher-order consistency in the segmentation results, the proposed network introduces the idea of adversarial networks, mainly consisting of a segmentation network and a discrimination network. An improved U-Net is introduced as the segmentation network to generate masks of the suspicious regions, while the discrimination network combines three convolutional critic networks that operate at different scales to discriminate the input masks. To weaken the unbalanced class problem and produce fine-grained segmentation results, weighted cross entropy loss and Earth-Mover distance are jointly used as an integrated loss function to guide the optimization process. Furthermore, the spectral normalization is adopted to the critics to alleviate the instability of training. The effectiveness of the proposed method is evaluated on two public datasets (INbreast and CBIS-DDSM). Experimental results empirically demonstrate that our method outperforms FCNs-based methods and the state-of-the-art method with dice of 81.64% for INbreast and 82.16% for CBIS-DDSM.
Pixels-off: Data-augmentation Complementary Solution for Deep-learning Steganalysis

After 2015, CNN-based steganalysis approaches have started replacing the two-step machine-learning-based steganalysis approaches (feature extraction and classification), mainly due to the fact that they offer better performance.
Energy-efficient and Dynamic Consolidation of Virtual Machines in OpenStack-based Private Cloud

Dynamic Virtual Machines (VMs) consolidation is an efficient technique for enhancing resource utilization and reducing energy consumption by the physical servers. Over-utilized host affects the degradation of resource utilization and VM performance. Determining when it is best to migrate VMs from an over-utilized host to an under-utilized host is the aim of dynamic Live VM Migration (LVM). It influences the resource utilization and Quality of Services (QoS) offered by cloud providers. In the OpenStack cloud, LVM is done manually, and it uses first-fit as a default migration technique, which is not efficient for resource utilization. The paper proposes a novel approach for the dynamic consolidation of VMs in the OpenStack cloud. The CPU utilization, RAM utilization, and the number of instances of each host are monitored over a period of time, and overload detection of a host is carried out using the SVM classification model. The Load balancing consolidation is performed based on the SVM classification result. Further, an energy-efficient consolidation achieved to optimize the energy of physical servers. We carry out the performance analysis of the proposed work in the OpenStack-based real-time testbed of five-node. The result reveals the fair distribution of load within the servers and the significant benefits of energy-efficient VM consolidation resulting up to 15% energy savings.
Improved k-means and spectral matching for hyperspectral mineral mapping

Mineral mapping is an important step for the development and utilization of mineral resources. The emergence of remote sensing technology, especially hyperspectral imagery, has paved a new approach to geological mapping. The k-means clustering algorithm is a classical approach to classifying hyperspectral imagery, but the influence of mixed pixels and noise mean that it usually has poor mineral mapping accuracy. In this study, the mapping accuracy of the k-means algorithm was improved in three ways: similarity measurement methods that are insensitive to dimensions are used instead of the Euclidean distance for clustering; the spectral absorption features of minerals are enhanced; and the mineral mapping results are combined as the number of cluster centers (K) is incremented from 1. The improved algorithm is used with combined spectral matching to match the clustering results with a spectral library. A case study on Cuprite, Nevada, demonstrated that the improved k-means algorithm can identify most minerals with the kappa value of over 0.8, which is 46% and 15% higher than the traditional k-means and spectral matching technology. New mineral types are more likely to be found with increasing K. When K is much greater than the number of mineral types, the accuracy is improved, and the mineral mapping results are independent of the similarity measurement method. The improved k-means algorithm can also effectively remove speckle noise from the mineral mapping results and be used to identify other objects.
Deep Learning Based Sentiment Analysis in a Code-Mixed English-Hindi and English-Bengali Social Media Corpus

Sentiment analysis is a circumstantial analysis of text, identifying the social sentiment to better understand the source material. The article addresses sentiment analysis of an English-Hindi and English-Bengali code-mixed textual corpus collected from social media. Code-mixing is an amalgamation of multiple languages, which previously mainly was associated with spoken language. However, social media users also deploy it to communicate in ways that tend to be somewhat casual. The coarse nature of social media text poses challenges for many language processing applications. Here, the focus is on the low predictive nature of traditional machine learners when compared to Deep Learning counterparts, including the contextual language representation model BERT (Bidirectional Encoder Representations from Transformers), on the task of extracting user sentiment from code-mixed texts. Three deep learners (a BiLSTM CNN, a Double BiLSTM and an Attention-based model) attained accuracy 20–60% greater than traditional approaches on code-mixed data, and were for comparison also tested on monolingual English data.
DAVD-Net: Deep Audio-Aided Video Decompression of Talking Heads

Close-up talking heads are among the most common and salient object in video contents, such as face-to-face conversations in social media, teleconferences, news broadcasting, talk shows, etc. Due to the high sensitivity of human visual system to faces, compression distortions in talking heads videos are highly visible and annoying. To address this problem, we present a novel deep convolutional neural network (DCNN) method for very low bit rate video reconstruction of talking heads. The key innovation is a new DCNN architecture that can exploit the audio-video correlations to repair compression defects in the face region. We further improve reconstruction quality by embedding into our DCNN the encoder information of the video compression standards and introducing a constraining projection module in the network. Extensive experiments demonstrate that the proposed DCNN method outperforms the existing state-of-the-art methods on videos of talking heads.
Co-occurrence networks of Twitter content after manual or automatic processing. A case-study on “gluten-free”

Gathering information from social networks such as Twitter has emerged to obtain spontaneous and direct opinions of users about a topic. This study focuses on using co-occurrence networks to analyse Twitter information. The objectives were to study the impact of text pre-treatment (codification based in qualitative analysis or just pre-cleaning) and to apply co-occurrence networks for analysing what is said on Twitter about specific topics like "gluten-free". As such, 16,386 tweets in Spanish containing terms "sin-gluten" and "gluten-free" were collected. A subset of 3000 tweets was used to make co-occurrence networks two ways: i) from the manually coded text and ii) from pre-cleaned text. Results indicate that the co-occurrence network from pre-cleaned text provides meaningful information showing structure and relevance for terms like the network from coded text. The whole set of tweets was used to explore Twitter information on gluten-free, showing users share information about products, occasions, social situations, and places but also product characteristics, sensations, and diet or health issues related to the products. Five product categories, critical for the lack of gluten (bread, cake, cookie, beer, and pizza), occupied most tweets, and according to the related terms, were intended to recommend how to get (buying or cooking) these gluten-free products and to exhibit what (how, when, and where) they prepare and eat. These aspects were different among products, and separated co-occurrence networks allowed better identification.
Adaptive Discretization for Episodic Reinforcement Learning in Metric Spaces

We present an efficient algorithm for model-free episodic reinforcement learning on large (potentially continuous) state-action spaces. Our algorithm is based on a novel Q-learning policy with adaptive data-driven discretization. The central idea is to maintain a finer partition of the state-action space in regions which are frequently visited in historical trajectories, and have higher payoff estimates. We demonstrate how our adaptive partitions take advantage of the shape of the optimal Q-function and the joint space, without sacrificing the worst-case performance. In particular, we recover the regret guarantees of prior algorithms for continuous state-action spaces, which additionally require either an optimal discretization as input, and/or access to a simulation oracle. Moreover, experiments demonstrate how our algorithm automatically adapts to the underlying structure of the problem, resulting in much better performance compared both to heuristics and Q-learning with uniform discretization.
Local Context Normalization: Revisiting Local Normalization

Normalization layers have been shown to improve convergence in deep neural networks, and even add useful inductive biases. In many vision applications the local spatial context of the features is important, but most common normalization schemes including Group Normalization (GN), Instance Normalization (IN), and Layer Normalization (LN) normalize over the entire spatial dimension of a feature. This can wash out important signals and degrade performance. For example, in applications that use satellite imagery, input images can be arbitrarily large; consequently, it is nonsensical to normalize over the entire area. Positional Normalization (PN), on the other hand, only normalizes over a single spatial position at a time. A natural compromise is to normalize features by local context, while also taking into account group level information. In this pa- per, we propose Local Context Normalization (LCN): a normalization layer where every feature is normalized based on a window around it and the filters in its group. We propose an algorithmic solution to make LCN efficient for arbitrary window sizes, even if every point in the image has a unique window. LCN outperforms its Batch Normalization (BN), GN, IN, and LN counterparts for object detection, semantic segmentation, and instance segmentation applications in several benchmark datasets, while keeping performance in- dependent of the batch size and facilitating transfer learning.
Supreme: Fine-grained Radio Map Reconstruction via Spatial-Temporal Fusion Network

Radio map, serving as an eﬃcient indicator of wireless environments, has been widely used in smart-city applications, including network monitoring/planning, anomaly signal detection, and indoor/outdoor localization. It is hard to maintain an update-to-date fine-grained radio map within a large area, since the radio map changes rapidly due to the internal and external factors. Previous studies usually relied on time-consuming site surveys at densely predefined reference points, leading to either coarse-grained or out-of-date radio maps. In this paper, we propose a fine-grained radio map reconstruction framework, called Supreme, based on crowd-sourced data in an image super-resolution manner. Specifically, Supreme explores spatial-temporal relationships in historical coarse-grained radio maps and builds a real-time fine-grained radio map using deep spatial-temporal reconstruction networks. Furthermore, a heterogeneous data fusion module is devised to make full use of external information. To evaluate the performance of Supreme, we conduct extensive experiments and ablation studies on a large-scale dataset with a total of six-month data collected from two university campuses. Besides, we investigate the transferability of Supreme in different locations and service networks, showing that the fine-tuned model can largely reduce the training time and achieve better performance. Experimental results demonstrate that our model outperforms state-of-the-art baselines and a case study on the localization is enhanced with marginal improvements on accuracy.
DNDNet: Reconfiguring CNN for Adversarial Robustness

Several successful adversarial attacks have demonstrated the vulnerabilities of deep learning algorithms. These attacks are detrimental in building deep learning based dependable AI applications. Therefore, it is imperative to build a defense mechanism to protect the integrity of deep learning models. In this paper, we present a novel "defense layer" in a network which aims to block the generation of adversarial noise and prevents an adversarial attack in black-box and gray-box settings. The parameter-free defense layer, when applied to any convolutional network, helps in achieving protection against attacks such as FGSM, L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> , Elastic-Net, and DeepFool. Experiments are performed with different CNN architectures, including VGG, ResNet, and DenseNet, on three databases, namely, MNIST, CIFAR-10, and PaSC. The results showcase the efficacy of the proposed defense layer without adding any computational overhead. For example, on the CIFAR-10 database, while the attack can reduce the accuracy of the ResNet-50 model to as low as 6.3%, the proposed "defense layer" retains the original accuracy of 81.32%.