Reviewerly/exHarmony huggingface dataset:
Pensieve: Skewness-Aware Version Switching for Efficient Graph Processing

Multi-version graph processing has recently attracted much research efforts. Existing multi-version graph storage designs use either copy-based schemes or delta-based schemes. A copy-based scheme stores every version separately and may lead to expensive space cost due to high storage redundancy. On the contrary, a delta based scheme only stores incremental deltas between different versions and relies on delta computation for version switching. In this work, we observe: 1) high degree vertices incur much more significant storage overheads during graph version evolving compared to low degree vertices; 2) the skewed access frequency among graph versions greatly influences the system performance for version reproducing. Based on the observations, we propose Pensieve, a skewness-aware multi-version graph processing system. Two factors contribute to the efficiency of Pensieve. First, Pensieve leverages a differentiated graph storage strategy that stores low degree vertices using copy-based scheme while stores high degree ones using delta-based scheme. Such a design achieves a good trade-off between storage cost and version switching time for multi-version graph processing. Second, the Pensieve graph storage exploits the time locality of graph version access and designs a novel last-root version switching scheme, which significantly improves the access efficiency for recent versions. We implement Pensieve on top of Ligra, and conduct comprehensive experiments to evaluate the performance of this design using large-scale datasets collected from real world systems. The results show that Pensieve substantially outperforms state-of-the-art designs in terms of memory consumption and version switching time.
Chronological independently verifiable electronic chain of custody ledger using blockchain technology

As evidence in digital form becomes more prevalent in all kinds of investigations, there is a pressing need for a trustworthy and transparent way to maintain electronic chain of custody (e-CoC) and integrity information that is independently verifiable. Generally, a hash value of digital evidence is calculated and documented with the acquired data to prove that it has not been altered. However, the hash value alone does not prove that digital evidence is the same as when it was obtained, only that the contents has not been modified since the time when the hash was calculated. This work responds to the need for a chronological independently verifiable e-CoC ledger using blockchain technology. Employing this approach, each e-CoC record is stored in a block, each block being connected to the previous one with the hash value of the block. This e-CoC ledger, the blockchain, can be hosted by a trusted entity and accessed by any party to verify e-CoC details. For privacy reasons, sensitive information is not stored inside the e-CoC record in the blockchain. Moreover, to prove that the e-CoC ledger itself has not been modified, information is periodically sent to a public blockchain, where the integrity is guaranteed by its decentralization and the structure of such a secure ledger. Not all of the blocks are sent into a public blockchain which allows different levels of verification. Proof-of-work examples are provided using AFF4 and dc3dd to demonstrate how this e-CoC ledger can be used by different parties in a legal context, including digital forensic practitioners, attorneys and judges.
Outdated Fact Detection in Knowledge Bases

Knowledge bases (KBs), which store high-quality information, are crucial for many applications, such as enhancing search results and serving as external sources for data cleaning. Not surprisingly, there exist outdated facts in most KBs due to the rapid change of information. Naturally, it is important to keep KBs up-to-date. Traditional wisdom has investigated the problem of using reference data (such as new facts extracted from the news) to detect outdated facts in KBs. However, existing approaches can only cover a small percentage of facts in KBs. In this paper, we propose a novel human-in-the-loop approach for outdated fact detection in KBs. It trains a binary classifier using features such as historical update frequency and existence time of a fact to compute the likelihood of a fact in a KB to be outdated. Then, it interacts with humans to verify whether a fact with high likelihood is indeed outdated. In addition, it also uses logical rules to detect more outdated facts based on human feedback. The outdated facts detected by the logical rules will also be fed back to train the ML model further for data augmentation. Extensive experiments on real-world KBs, such as Yago and DBpedia, show the effectiveness of our solution.
FoveaCam: A MEMS Mirror-Enabled Foveating Camera

Most cameras today photograph their entire visual field. In contrast, decades of active vision research have proposed foveating camera designs, which allow for selective scene viewing. However, active vision's impact is limited by slow options for mechanical camera movement. We propose a new design, called FoveaCam, and which works by capturing reflections off a tiny, fast moving mirror. FoveaCams can obtain high resolution imagery on multiple regions of interest, even if these are at different depths and viewing directions. We first discuss our prototype and optical calibration strategies. We then outline a control algorithm for the mirror to track target pairs. Finally, we demonstrate a practical application of the full system to enable eye tracking at a distance for frontal faces.
Enforcing Affinity Feature Learning through Self-attention for Person Re-identification

Person re-identification is the task of recognizing an individual across heterogeneous non-overlapping camera views. It has become a crucial capability needed by many applications in public space video surveillance. However, it remains a challenging task due to the subtle inter-class similarity and large intra-class variation found in person images. Current CNN-based approaches have focused and investigated traditional identification or verification frameworks. Such approaches typically use the whole input image including the background and fail to pay attention to specific body parts, deviating the feature representation learning from informative parts. In this article, we introduce a self-attention mechanism coupled with cross-resolution to improve the feature representation learning of person re-identification task. The proposed self-attention module reinforces the most informative parts from a high-resolution image using its internal representation at the low-resolution. In particular, the model is fed with a pair of images on a different scale and consists of two branches. The upper branch processes the high-resolution image and learns high dimensional feature representation while the lower branch processes the low-resolution image and learns a filtering attention heatmap. The feature maps on the lower branch are subsequently weighted to reflect the importance of each patch of the input image using a softmax operation; whereas, on the upper branch, we apply a max pooling operation to downsample the high-resolution feature map before element-wise multiplied with the attention heatmap. Our attention module helps the network learn the most discriminative visual features of multiple regions of the image and is specifically optimized to attend and enforce feature representation at different scales. Extensive experiments on three large-scale datasets show that network architectures augmented with our self-attention module systematically improve their accuracy and outperform various state-of-the-art models by a large margin.
A hybrid Type-2 Fuzzy Logic System and Extreme Learning Machine for low-cost INS/GPS in high-speed vehicular navigation system

Due to the combined navigation system consisting of both Inertial Navigation System (INS) and Global Positioning System (GPS) in a complementary mode which assure a reliable, accurate, and continuous navigation system, we use a GPS/INS navigation system in our research. Because of the conditions of navigation system such as low-cost MEMS-based inertial sensors with considerable uncertainty in INS sensors, a highly noisy real data, and a long term outage of GPS signals during our flight tests, we enhance the positioning speed and accuracy by an Extreme Learning Machine (ELM) with the features of excellent generalization performance and fast learning speed. However, the generalization capability of ELM usually destabilizes with uncertainty existing in the dataset. In order to fix this limitation, first, a Type-2 Fuzzy Logic System (T2-FLS) handles the uncertainties in GPS/INS data, and then the final output ends up to the ELM to train and predict INS positioning error. We verify the efficiency of the suggested method in the estimation of speed and accuracy in INS sensors error during GPS satellites outage, particularly in real-time applications with a high-speed vehicle. Then, to evaluate the overall performance of the proposed method, the achieved results are discussed and compared to other methods like Extended Kalman Filter (EKF), wavelet-ELM, and Adaptive Neuro-Fuzzy Inference System (ANFIS). The results present considerable achievement and open the door to the application of T2-FLS and ELM in GPS/INS integration even in severe conditions.
EEG Compression Using Motion Compensated Temporal Filtering and Wavelet Based Subband Coding

Electroencephalography (EEG) signals are commonly used in medical applications for prevention, diagnosis, and detection of neurological diseases.These EEG signals have also been used in designing brain computer interfaces for assistive technologies.For densely placed electrodes and long EEG recordings, a large amount of data needs to be stored, preferably in compressed form.This EEG signal compression is particularly required in an out-of-the-lab environment so that these signals are efficiently transmitted over wired/wireless communication channels.To this end, we propose a novel compression scheme for EEG signals, which exploits the intra-channel redundancy using motion compensated temporal filtering (MCTF) and discrete wavelet transform (DWT) based sub-band coding.In the pre-processing stage, multi-frame data is constructed such that each group of picture (GOP) contains information from a single channel of EEG data.This helps in removing the intra-channel redundancy.We apply MCTF on each GOP to exploit temporal redundancy, following which the DWT is applied on temporally decomposed frames to exploit spatial redundancy.Each spatio-temporal decomposed frame is assigned a bit budget for minimum distortion.For this purpose, we assign more bit budget to temporally decomposed low pass frames as compared to high pass frames.Spatio-temporal frames are then encoded at the assigned bit rate by using set partitioning in hierarchical tree (SPIHT) algorithm to create the bit stream.Our experimental results showed 4.5% and 2.4% reduction in distortion at the same data rate for BCI-3 and BCI-4 datasets, respectively.These results improve upon the reduction in data size achieved using state-of-the-art compression methods such as SPIHT and SPIHT with independent component analysis.
An Unsupervised Hierarchical Rule Based Model for Aspect Term Extraction Augmented with Pruning Strategies

Sentiment Analysis is an interesting field in text analytics which has emerged from an individual’s curiosity to know what others think or feel about an entity or experience and hence take a decision. Aspect level sentiment analysis is a sub-field which operates at a more fine grained level to meet the demand of the end users who are no longer satisfied with the overall sentiment about the entity but are keen to know what features of the entity are matters of concern and what inturn is the sentiment reflected on each of these features/aspects. Aspect term extraction is the first and the most difficult step in aspect level sentiment analysis which attempts to discover the features of the product that are discussed about in the reviews. The proposed approach presents an unsupervised hierarchical rule based approach for aspect term extraction oriented towards a high recall and augmented by pruning strategies for filtering the false positives. The proposed model has reported an appreciable recall of 81.9 and 68.7 on Restaurant and Laptop domains respectively on SemEval 2014 dataset and has also been compared with the state of art models.
Supporting the Analysis of Safety Critical User Interfaces

Use error due to user interface design defects is a major concern in many safety critical domains, for example avionics and health care. Early detection of latent user interface problems can be facilitated by user-centered design methods that integrate formal verification technologies. This article considers the role that formal verification technologies can play in the context of user-centered design by considering the following three existing tools: CIRCUS, PVSio-web, and IVY. These tools have been developed to support the model based analysis of critical user interfaces. They have their foundations in existing formal verification technologies, but each of them is focused towards particular issues relating to user interface design. The article explores the different phases of the user-centered design process and the extent to which each of these tools supports these phases. Criteria are developed for assessing their role at each stage of the design process. The results of the evaluation provide guidance to developers to help choose the most appropriate tool based on their analysis needs while at the same time setting challenges for future developments.
On Data Protection Using Multi-Factor Authentication

Multi-Factor Authentication (MFA) has been around for three decades and now authentication and security has taken priority in securing data. Cyber-attacks have become widely popular and successful because organizations are poorly prepared to handle cyber security operations. These organizations hold critical information about employees, customers and patients. Adding additional security methods like MFA makes it more difficult for attackers to successfully exfiltrate data and cause additional damage. The methods of MFA can be used in combination to successfully secure data and provide adequate authentication practices. In this paper, we discuss the implementations of MFA, privacy behind using more than one authentication method and also the security concerns related to MFA. We also reveal the flaws of MFA systems and discuss how these flaws can result in a security breach.
High Resolution Light Field Recovery with Fourier Disparity Layer Completion, Demosaicing, and Super-Resolution

In this paper, we present a novel approach for recovering high resolution light fields from input data with many types of degradation and challenges typically found in lenslet based plenoptic cameras. Those include the low spatial resolution, but also the irregular spatio-angular sampling and color sampling, the depth-dependent blur, and even axial chromatic aberrations. Our approach, based on the recent Fourier Disparity Layer representation of the light field, allows the construction of high resolution layers directly from the low resolution input views. High resolution light field views are then simply reconstructed by shifting and summing the layers. We show that when the spatial sampling is regular, the layer construction can be decomposed into linear optimization problems formulated in the Fourier domain for small groups of frequency components. We additionally propose a new preconditioning approach ensuring spatial consistency, and a color regularization term to simultaneously perform color demosaicing. For the general case of light field completion from an irregular sampling, we define a simple iterative version of the algorithm. Both approaches are then combined for an efficient super-resolution of the irregularly sampled data of plenoptic cameras. Finally, the Fourier Disparity Layer model naturally extends to take into account a depth-dependent blur and axial chromatic aberrations without requiring an estimation of depth or disparity maps.
Issues and Concepts of Graph Database and a Comparative Analysis on list of Graph Database tools

The work is review in nature and focuses on basic concepts and example on Graph Database with a special focus on list of standard computerized tools available for handling the queries using graph database structure. The implementation benefits of each tool and a comparative analysis on various functionalities has been presented in this work. This work also elaborates on popular Graph Databases tool that includes Allegro Graph, ArangoDB, OrientDB, Infinite Graph, Neo4j, Titan, FlockDB, Bitsy, StarDog, MongoDB and investigate their acceptance for solving day to day problems.
Towards Predicting Reading Comprehension From Gaze Behavior

As readers of a language, we all agree to move our eyes in roughly the same way. Yet might there be hidden within this self-similar behavior subtle clues as to how a reader is understanding the material being read? Here we attempt to decode a reader's eye movements to predict their level of text comprehension and related states. Eye movements were recorded from 95 people reading 4 published SAT passages, each followed by corresponding SAT questions and self-evaluation questionnaires. A sequence of 21 fixation-location (x,y), fixation-duration, and pupil-size features were extracted from the reading behavior and input to two deep networks (CNN/RNN), which were used to predict the reader's comprehension level and other comprehension-related variables. The best overall comprehension prediction accuracy was 65% (cf. null accuracy = 54%) obtained by CNN. This prediction generalized well to fixations on new passages (64%) from the same readers, but did not generalize to fixations from new readers (41%), implying substantial individual differences in reading behavior. Our work is the first attempt to predict comprehension from fixations using deep networks, where we hope that our large reading dataset and our protocol for evaluation will benefit the development of new methods for predicting reading comprehension by decoding gaze behavior.
A Semantics-Guided Graph Convolutional Network for Skeleton-Based Action Recognition

Action recognition with skeleton data is a challenging task in computer vision. Graph convolutional networks (GCNs), which directly model the human body skeletons as the graph structure, have achieved remarkable performance. However, current architectures of GCNs are limited to the small receptive field of convolution filters, only capturing local physical dependencies among joints and using all skeleton data indiscriminately. To address these limitations and to achieve a flexible graph representation of the skeleton features, we propose a novel semantics-guided graph convolutional network (Sem-GCN) for skeleton-based action recognition. Three types of semantic graph modules (structural graph extraction module, actional graph inference module and attention graph iteration module) are employed in Sem-GCN to aggregate L-hop joint neighbors' information, to capture action-specific latent dependencies and to distribute importance level. Combing these semantic graphs into a generalized skeleton graph, we further propose the semantics-guided graph convolution block, which stacks semantic graph convolution and temporal convolution, to learn both semantic and temporal features for action recognition. Experimental results demonstrate the effectiveness of our proposed model on the widely used NTU and Kinetics datasets.
A structure optimization method for extended belief-rule-based classification system

The widely applied belief-rule-based(BRB) system has demonstrated its advantages in handling both qualitative and quantitative information. As an extension of BRB system, the extended belief-rule-based(EBRB) system bridges the rule-based methods and data-driven methods by efficiently transforming data into extended belief rules(EBRs). Many works have been done to apply EBRB system in addressing classification problems. However, the problems of making use of all attributes indiscriminately and activating almost all EBRs still affect the accuracy and computational efficiency of EBRB system. In this paper, a structure optimization method for EBRB(SO-EBRB) system, including attribute optimization and rule activation, is proposed to address aforementioned problems. In the attribute optimization, a weighted minimum redundancy maximum relevance(MRMR) method is proposed, where the relevance between attributes and label as well as the redundancy among attributes are used to evaluate attributes. Afterwards, the proposed attribute weight calculation method is utilized to assign attribute weights for the EBRB system. In rule activation, an improved minimum centre distance rule activation(MCDRA) method, which considering the weights of attributes in distance calculation, is used to activate customized EBRs for input query data. 15 benchmark classification data sets are utilized to verify the effectiveness of the proposed SO-EBRB method. The results show that, compared with conventional EBRB system, the SO-EBRB system achieves higher classification accuracy, lower rule activation ratio and less response time. Additionally, comparison between the proposed method and some state-of-art machine learning algorithms demonstrates that the SO-EBRB system achieves prominent performance in addressing classification problems.
Seamless group target tracking using random finite sets

In many surveillance applications, target tracking algorithms have to deal with dense group targets in contrast to independently moving and well-separated targets as assumed in most scenarios. An effective strategy to handle such a group of targets is to first track the overall group and then attempt to extract the states of individual targets. Based on the Random Finite Set theory, the τ-Cardinalized Probability Hypothesis Density (τ-CPHD) filter is proposed in this paper as an effective method for group target tracking. This filter accurately extracts target states and represents the distribution of the legacy PHD. Association and track extraction are done in the post-processing step, without affecting the filtering process or overloading the computing resources. Furthermore, the group motion is modeled in conjunction with individual target motion models, with model proportion being updated by the proposed filter. Initialization process is carried out adaptively using the group motion trend through group state fitting. All these characteristics make the new filter flexible and seamlessly applicable to real-world tracking problems with group targets. Simulations demonstrate the superior performance of the proposed filter with individual target and group motion models being considered in combination or separately.