Reviewerly/exHarmony huggingface dataset:
Geometrically invariant color, shape and texture features for object recognition using multiple kernel learning classification approach

The geometrically invariant image descriptors are very important for recognizing objects of arbitrary shapes and orientations. We propose a framework for the fusion of the geometrically invariant descriptors representing color, shape, and texture for the recognition of color objects using multiple kernel learning (MKL) approach. To describe texture of color images, we propose an effective rotation invariant texture descriptor which is based on the Zernike moments (ZMs) of the gradient of the color images, referred to as the GZMs. For the shape features, we use the ZMs of the intensity component of a color image and also use multi-channel ZMs (MZMs) which have proven to be superior in performance than the quaternion ZMs (QZMs). For comparative performance analysis, rotation invariants of the QZMs (RQZMs) are also considered. Since the color histograms (CH) are known to be very effective color descriptors, we consider them for representing color. The five sets of features – CH, ZMs, GZMs, MZMs, and RQZMs are invariant to translation, rotation, and scale. The fusion of color, shape and texture features in different combinations using the MKL approach is shown to provide very high recognition rates on PASCAL VOC 2005, Soccer, SIMPLIcity, Flower, and Caltech-101 datasets.
Context Hiding Multi-key Linearly Homomorphic Authenticators

Demanding computations are increasingly outsourced to cloud platforms. For such outsourced computations, the efficient verifiability of results is a crucial requirement. When sensitive data is involved, the verification of a computation should preserve the privacy of the input values: it should be context hiding. Context hiding verifiability is enabled by existing homomorphic authenticator schemes. However, until now, no context hiding homomorphic authenticator scheme supports multiple independent clients, e.g. multiple keys. Multi-key support is necessary for datasets involving input authenticated by different clients, e.g. multiple hospitals in e-health scenarios. In this paper, we propose the first perfectly context hiding, publicly verifiable multi-key homomorphic authenticator scheme supporting linear functions. Our scheme is provably unforgeable in the standard model, and succinct. Verification time depends only linearly on the number of clients, in an amortized sense.
On Decoding and Applications of Interleaved Goppa Codes

Goppa Codes are a well-known class of codes with, among others, applications in code-based cryptography. In this paper, we present a collaborative decoding algorithm for interleaved Goppa codes (IGC). Collaborative decoding increases the decoding radius beyond half of the designed minimum distance. We consider wild Goppa codes and show that we can collaboratively correct more errors for binary Goppa codes than the Patterson decoder. We propose a modified version of the McEliece cryptosystem using wild IGC based on a recently proposed system by Elleuch et al., analyze attacks on the system and present some parameters with the corresponding key sizes.
Multi-person Pose Estimation for Pose Tracking with Enhanced Cascaded Pyramid Network

Multi-person pose estimation is a fundamental yet challenging task in machine learning. In parallel, recent development of pose estimation has increased interests on pose tracking in recent years. In this work, we propose an efficient and powerful method to locate and track human pose. Our proposed method builds upon the state-of-the-art single person pose estimation system (Cascaded Pyramid Network), and adopts the IOU-tracker module to identify the people in the wild. We conduct experiments on the released multi-person video pose estimation benchmark (PoseTrack2018) to validate the effectiveness of our network. Our model achieves an accuracy of 80.9% on the validation and 77.1% on the test set using the Mean Average Precision (MAP) metric, an accuracy of 64.0% on the validation and 57.4% on the test set using the Multi-Object Tracking Accuracy (MOTA) metric.
Learning Semantic Models of Data Sources Using Probabilistic Graphical Models

A semantic model of a data source is a representation of the concepts and relationships contained in the data. Building semantic models is a prerequisite to automatically publishing data to a knowledge graph. However, creating these semantic models is a complex process requiring considerable manual effort and can be error-prone. In this paper, we present a novel approach that efficiently searches over the combinatorial space of possible semantic models, and applies a probabilistic graphical model to identify the most probable semantic model for a data source. Probabilistic graphical models offer many advantages over existing methods: they are robust to noisy inputs and provide a straightforward approach for exploiting relationships within the data. Our solution uses a conditional random field (CRF) to encode structural patterns and enforce conceptual consistency within the semantic model. In an empirical evaluation, our approach outperforms state of the art systems by an average 8.4% of F1 score, even with noisy input data.
Crowd-Mapping Urban Objects from Street-Level Imagery

Knowledge about the organization of the main physical elements (e.g. streets) and objects (e.g. trees) that structure cities is important in the maintenance of city infrastructure and the planning of future urban interventions. In this paper, a novel approach to crowd-mapping urban objects is proposed. Our method capitalizes on strategies for generating crowdsourced object annotations from street-level imagery, in combination with object density and geo-location estimation techniques to enable the enumeration and geo-tagging of urban objects. To address both the coverage and precision of the mapped objects within budget constraints, we design a scheduling strategy for micro-task prioritization, aggregation, and assignment to crowd workers. We experimentally demonstrate the feasibility of our approach through a use case pertaining to the mapping of street trees in New York City and Amsterdam. We show that anonymous crowds can achieve high recall (up to 80%) and precision (up to 68%), with geo-location precision of approximately 3m. We also show that similar performance could be achieved at city scale, possibly with stringent budget constraints.
Classifying Extremely Short Texts by Exploiting Semantic Centroids in Word Mover's Distance Space

Automatically classifying extremely short texts, such as social media posts and web page titles, plays an important role in a wide range of content analysis applications. However, traditional classifiers based on bag-of-words (BoW) representations often fail in this task. The underlying reason is that the document similarity can not be accurately measured under BoW representations due to the extreme sparseness of short texts. This results in significant difficulty to capture the generality of short texts. To address this problem, we use a better regularized word mover's distance (RWMD), which can measure distances among short texts at the semantic level. We then propose a RWMD-based centroid classifier for short texts, named RWMD-CC. Basically, RWMD-CC computes a representative semantic centroid for each category under the RWMD measure, and predicts test documents by finding the closest semantic centroid. The testing is much more efficient than the prior art of K nearest neighbor classifier based on WMD. Experimental results indicate that our RWMD-CC can achieve very competitive classification performance on extremely short texts.
Deep Attention Models for Human Tracking Using RGBD

Visual tracking performance has long been limited by the lack of better appearance models. These models fail either where they tend to change rapidly, like in motion-based tracking, or where accurate information of the object may not be available, like in color camouflage (where background and foreground colors are similar). This paper proposes a robust, adaptive appearance model which works accurately in situations of color camouflage, even in the presence of complex natural objects. The proposed model includes depth as an additional feature in a hierarchical modular neural framework for online object tracking. The model adapts to the confusing appearance by identifying the stable property of depth between the target and the surrounding object(s). The depth complements the existing RGB features in scenarios when RGB features fail to adapt, hence becoming unstable over a long duration of time. The parameters of the model are learned efficiently in the Deep network, which consists of three modules: (1) The spatial attention layer, which discards the majority of the background by selecting a region containing the object of interest; (2) the appearance attention layer, which extracts appearance and spatial information about the tracked object; and (3) the state estimation layer, which enables the framework to predict future object appearance and location. Three different models were trained and tested to analyze the effect of depth along with RGB information. Also, a model is proposed to utilize only depth as a standalone input for tracking purposes. The proposed models were also evaluated in real-time using KinectV2 and showed very promising results. The results of our proposed network structures and their comparison with the state-of-the-art RGB tracking model demonstrate that adding depth significantly improves the accuracy of tracking in a more challenging environment (i.e., cluttered and camouflaged environments). Furthermore, the results of depth-based models showed that depth data can provide enough information for accurate tracking, even without RGB information.
Implicit dimension identification in user-generated text with LSTM networks

In the process of online storytelling, individual users create and consume highly diverse content that contains a great deal of implicit beliefs and not plainly expressed narrative. It is hard to manually detect these implicit beliefs, intentions and moral foundations of the writers. We study and investigate two different tasks, each of which reflect the difficulty of detecting an implicit user's knowledge, intent or belief that may be based on writer's moral foundation: 1) political perspective detection in news articles 2) identification of informational vs. conversational questions in community question answering (CQA) archives and. In both tasks we first describe new interesting annotated datasets and make the datasets publicly available. Second, we compare various classification algorithms, and show the differences in their performance on both tasks. Third, in political perspective detection task we utilize a narrative representation language of local press to identify perspective differences between presumably neutral American and British press.
Efficient Chroma Sub-Sampling and Luma Modification for Color Image Compression

In color image compression, the chroma components are often sub-sampled before compression and up-sampled after compression. Although sub-sampling the chroma components saves the bit-cost for compression, it often induces extra color distortions in the compressed images. In this paper, we propose two approaches to tackle this problem. First, we propose a sub-sampling method in the transform domain and apply it to both chroma components. Then, based on this sub-sampling, we propose a novel method to modify the luma component. In our proposed luma modification algorithm, the distortions that occurred in the two chroma components can be coupled together and utilized to modify the luma component. With our proposed chroma sub-sampling and luma modification algorithms, we can achieve a low RGB distortion in practical image coding. The experimental results demonstrate that our proposed methods offer more significant coding gains compared with the state-of-the-art methods for the compression of color images.
Linking text readability and learner proficiency using linguistic complexity feature vector distance

How can we identify authentic reading material that matches the learner's proficiency and fosters their language development? Traditionally, this involves assigning a one-dimensional label to the text that identifies the grade or proficiency level of the learners that the text is intended for. Such an approach is inadequate given that both the text complexity and proficiency constructs are multi-dimensional in nature. We propose to instead link readers and texts through multidimensional vectors characterizing the linguistic complexity of the reading material and that of texts written by the learners as proxy of their proficiency level. We first validate the approach using a leveled reading corpus by showing that vector distances computed on the complexity representations can serve the function of the traditional labels. We then highlight the advantage of the multi-dimensional approach using data from a continuation writing task, showing that it makes it possible to study individual complexity dimensions and to explore different degrees of challenge for different dimensions. Our approach essentially makes it possible to empirically investigate the +1 of Krashen's i+1, the challenge that best fosters development given the learner's interlanguage. On the practical side, we discuss an ICALL system demonstrating the viabilityof the approach in real-life..
An analytical system for user emotion extraction, mental state modeling, and rating

Emotion is a crucial indicator of the mental state influenced by environmental cognition, health, and intention. Therefore, modeling emotional patterns from daily communications is useful and vital for behavioral studies. This paper presents an analytical system that combines psycho-linguistics, statistical modeling, and computational algorithms to extract, analyze, and rank users’ emotions from their emails. A new category of twelve emotions is proposed to identify a balanced set of six positive emotions and their negative counterparts, respectively. A lexicon-based algorithm is proposed to extract emotions from individual emails at the sentence level by combining thesaurus and parts-of-speech. A Markov chain model is then proposed for the time-dependent emotion stochastic process. Statistical characterization of the Markov chain model provides insights from four aspects: repetition, revisits, steady states, and emotional stability. Finally, an algorithm is discussed to compute the relative ratings of user emotions and subsequently ranking them from the most emotional to the least. As the verification that the proposed system is able to distinguish detailed emotional behaviors caused by mental activities, the Enron email dataset is used to model emotional patterns from 22 executives who were deemed guilty in the court of law vs. 21 non-guilty. Our analysis demonstrates that the proposed system uncovers emotional patterns that are more distinct compared to the traditional emotion categorizations. It is also observed that the guilty subjects express more positive emotions in their emails compared to their non-guilty counterparts, while they are not more joyous than the non-guilty. Such insights have implications for employee risk assessment in organizations. The proposed approach can also be applied to other related areas such as mental health.
An Adaptive Optimization Algorithm Based on Hybrid Power and Multidimensional Update Strategy

Recently, the adaptive learning rate optimization algorithm has shown excellent performances in the field of deep learning.However, the exponential moving average method can lead to convergence problem in some cases, such as it only converges to the sub-optimal minimum.Although AMSGrad algorithm provides solutions for convergence problems, the actual performance is close to or even weaker than that of Adam.In this paper, a new updating rule is proposed based on mixed high power historical and current squared gradients to construct a targeted first-order optimization algorithm for the adaptive learning rate.This algorithm not only overcomes the convergence problems encountered in most current optimization algorithms but also has a quick convergence.It outperforms the state-of-the-art algorithms on various realworld datasets, i.e., the forecast root-mean-square error performance is improved by about 20% on average than that of Adam and AMSGrad algorithms in time series prediction tasks.
ASCENT: Active Supervision for Semi-Supervised Learning

Active learning algorithms attempt to overcome the labeling bottleneck by asking queries from large collection of unlabeled examples. Existing batch mode active learning algorithms sufferfrom three limitations: (1) The methods that are based on similarityfunction or optimizing certain diversity measurement, in which may lead to suboptimal performance and produce the selected set with redundant examples. (2) The models with assumption on data are hard in finding images that are both informative and representative. (3) The problem of noise labels has been an obstacle for algorithms. In this paper, we propose a novel active learning method that makes embeddings of labeled examples to those of unlabeled ones and back via deep neural networks. The active scheme makes correct association cycles that end up at the same class from that the association was started, which considers both the informativeness and representativeness of examples, as well as being robust to the noise labels. We apply our active learning method to semi-supervised classification and clustering. The submodular function is designed to reduce the redundancy of the selected examples. Specifically, we incorporate our batch mode active scheme into the classification approaches, in which the generalization ability is improved. For semi-supervised clustering, we try to use our active scheme for constraints to make fast convergence and perform better than unsupervised clustering. Finally, we apply our active learning method to data filtering. To validate the effectiveness of the proposed algorithms, extensive experiments are conducted on diversity benchmark datasets for different tasks, i.e., classification, clustering, and data filtering, and the experimental results demonstrate consistent and substantial improvements over the state-of-the-art approaches.
Multiscale Object Detection in Infrared Streetscape Images Based on Deep Learning and Instance Level Data Augmentation

The development of object detection in infrared images has attracted more attention in recent years. However, there are few studies on multi-scale object detection in infrared street scene images. Additionally, the lack of high-quality infrared datasets hinders research into such algorithms. In order to solve these issues, we firstly make a series of modifications based on Faster Region-Convolutional Neural Network (R-CNN). In this paper, a double-layer region proposal network (RPN) is proposed to predict proposals of different scales on both fine and coarse feature maps. Secondly, a multi-scale pooling module is introduced into the backbone of the network to explore the response of objects on different scales. Furthermore, the inception4 module and the position sensitive region of interest (ROI) align (PSalign) pooling layer are utilized to explore richer features of the objects. Thirdly, this paper proposes instance level data augmentation, which takes into account the imbalance between categories while enlarging dataset. In the training stage, the online hard example mining method is utilized to further improve the robustness of the algorithm in complex environments. The experimental results show that, compared with baseline, our detection method has state-of-the-art performance.
Efficient deadline-aware scheduling for the analysis of Big Data streams in public Cloud

The emergence of Big Data has had a profound impact on how data are analyzed. Open source distributed stream processing platforms have gained popularity for analyzing streaming Big Data as they provide low latency required for streaming Big Data applications using Cloud resources. However, existing resource schedulers are still lacking the efficiency and deadline meeting that Big Data analytical applications require. Recent works have already considered streaming Big Data characteristics to improve the efficiency and the likelihood of deadline meeting for scheduling in the platforms. Nevertheless, they have not taken into account the specific attributes of analytical application, public Cloud utilization cost and delays caused by performance degradation of leasing public Cloud resources. This study, therefore, presents BCframework, an efficient deadline-aware scheduling framework used by streaming Big Data analysis applications based on public Cloud resources. BCframework proposes a scheduling model which considers public Cloud utilization cost, performance variation, deadline meeting and latency reduction requirements of streaming Big Data analytical applications. Furthermore, it introduces two operator scheduling algorithms based on both a novel partitioning algorithm and an operator replication method. BCframework is highly adaptable to the fluctuation of streaming Big Data and the performance degradation of public Cloud resources. Experiments with the benchmark and real-world queries show that BCframework can significantly reduce the latency and utilization cost and also minimize deadline violations and provisioned virtual machine instances.
DTrace: fine-grained and efficient data integrity checking with hardware instruction tracing

Recently released Intel processors have been equipped with hardware instruction tracing facilities to securely and efficiently record the program execution path. In this paper, we study a case for data integrity checking based on Intel Processor Trace (Intel PT), the instruction tracing facility on x86 processors. We incorporate software instrumentation and hardware instruction tracing to guarantee fine-grained data integrity without frequently switching the processor mode. We incorporate the idea in a system named DTrace which provides primitives to instruct Intel PT to capture the data load and store events, even current Intel PT implementations only record control transfers. The trace is analyzed before the program makes security-sensitive operations. We apply DTrace in several case studies to show that the primitives that DTrace provides are easy to use and help to enhance data integrity in applications. We further evaluate DTrace with several microbenchmarks to show the time cost that DTrace's data tracing operation incurs. We also evaluate DTrace on Nginx to show the performance impact when Nginx is enhanced in security to provide the integrity during the runtime execution for programmer-defined security sensitive data. We find the performance overhead that DTrace incurs for the data tracing is moderate.