Reviewerly/exHarmony huggingface dataset:
Single Sample Face Recognition Under Varying Illumination via QRCP Decomposition

In this paper, we present a novel high-frequency facial feature and a high-frequency based sparse representation classification to tackle single sample face recognition (SSFR) under varying illumination. Firstly, we propose the assumption that QRCP bases can represent intrinsic face surface features with different frequencies, and their corresponding energy coefficients describe illumination intensities. Based on this assumption, we take QRCP bases with corresponding weighting coefficients (i.e. the major components of energy coefficients) to develop the high-frequency facial feature of the face image, which is named as QRCP-face. The normalized QRCP-face (NQRCPface) is constructed to further constraint illumination effects by normalizing the weighting coefficients of QRCP-face. Moreover, we propose the adaptive QRCP-face (AQRCP-face) that assigns a special parameter to NQRCP-face via the illumination level estimated by the weighting coefficients. Secondly, we consider that the differences of pixel images cannot model the intraclass variations of generic faces with illumination variations, and the specific identification information of the generic face is redundant for the current SSFR with generic learning. To tackle above two issues, we develop a general high-frequency based sparse representation (GHSP) model. Two practical approaches separated high-frequency based sparse representation (SHSP) and unified high-frequency based sparse representation (UHSP) are developed. Finally, the performances of the proposed methods are verified on the Extended Yale B, CMU PIE, AR, LFW and our self-built Driver face databases. The experimental results indicate that the proposed methods outperform previous approaches for SSFR under varying illumination.
Visual tracking with online structural similarity-based weighted multiple instance learning

This paper presents an online adaptive tracker, which employs a novel weighted multiple instance learning (WMIL) approach.In the proposed tracker, both positive and negative sample importances are integrated into an online learning mechanism for improving tracking performance in challenging environments. The sample importance is computed based on a new measure, i.e., structural similarity (SSIM), instead of using the Euclidean distance. Moreover, a novel bag probability function, which adopts both positive and negative weighted instance probabilities, is designed. Furthermore, a novel efficient weak classifier selection solution is developed for the proposed tracker. Qualitative and quantitative experiments on 30 challenging image sequences show that the novel tracking algorithm, i.e., SSIM-WMIL tracker, performs favorably against the MIL and WMIL counterparts as well as other 13 recently-proposed state-of-the-art trackers in terms of accuracy, robustness and efficiency. In addition, the negative sample importance can be used to enhance the multiple instance learning, and the SSIM-based approach is capable of improving the multiple instance learning performance for object tracking when compared to the Euclidean distance-based method.
Adaptive event-triggered anomaly detection in compressed vibration data

Anomaly detection is a crucial task in Prognostics and Condition Monitoring (PCM) of machinery. In modern remote PCM systems, data compression techniques are regularly used to reduce the need for bandwidth and storage. In these systems the challenge arises of how the compressed (distorted) vibration data affects the condition monitoring algorithms. This paper introduces a novel algorithm that can adaptively establish normal bounds of operation from continuous noisy vibration profiles working with compressed vibration data. The proposed technique is based on four modules, including feature extraction, feature fusion, extreme value vibration modeling and adaptive thresholding for anomaly detection. The proposed method has been validated with experiments using three time-series datasets. The experimental results indicate that the proposed algorithm is able to perform detection of malfunctions in rotating machines effectively without faulty reference data. Moreover, the proposed method is able to produce accurate early warning and alarm indications from both the raw and compressed (distorted) datasets with equal veracity.
Autosubst 2: reasoning with multi-sorted de Bruijn terms and vector substitutions

Formalising metatheory in the Coq proof assistant is tedious as reasoning with binders without native support requires a lot of uninteresting technicalities. To relieve users from so-produced boilerplate, the Autosubst framework automates working with de Bruijn terms: For each annotated inductive type, Autosubst generates a corresponding instantiation operation for parallel substitutions and a decision procedure for assumption-free substitution lemmas. However, Autosubst is implemented in Ltac, Coq's tactic language, and thus suffers from Ltac's limitations. In particular, Autosubst is restricted to Coq and unscoped, non-mutual inductive types with a single sort of variables. In this paper, we present a new version of Autosubst that overcomes these restrictions. Autosubst 2 is an external code generator, which translates second-order HOAS specifications into potentially mutual inductive term sorts. We extend the equational theory of Autosubst to the case of mutual inductive sorts by combining the application of multiple parallel substitutions into exactly one instantiation operation for each sort, i.e. we parallelise substitutions to vector substitutions. The resulting equational theory is both simpler and more expressive than that of the original Autosubst framework and allows us to present an even more elegant proof of part A of the POPLMark challenge.
Deeply Supervised Face Completion With Multi-Context Generative Adversarial Network

Recent face completion works have achieved significant improvement using generative adversarial networks (GANs). There are still two important issues in this challenging task: first, semantic understanding; and second, high-frequency details prediction. In this letter, we propose a unified model by introducing multi-context structures within GANs. Our model, named multi-context generative adversarial networks (MCGAN), automatically learns the hierarchical appearances of a corrupted image and predicted the missing regions from different perspectives. In this model, semantic understanding and high-frequency details are both taken into account and modeled with two parallel networks, respectively. While one learns the semantic understanding of the input face image at a high level, the other extracts low-level features for high-frequency details prediction. Our MCGAN takes full advantage of multi-scale features learned from two complementary networks and generates semantically new pixels for the missing region with fine details. Extensive quantitative and qualitative experiments on benchmark datasets show that the proposed model outperforms several state-of-the-art models.
A FULLY CLASSICAL TRUTH THEORY CHARACTERIZED BY SUBSTRUCTURAL MEANS

Abstract We will present a three-valued consequence relation for metainferences , called CM , defined through ST and TS , two well known substructural consequence relations for inferences. While ST recovers every classically valid inference, it invalidates some classically valid metainferences. While CM works as ST at the inferential level, it also recovers every classically valid metainference. Moreover, CM can be safely expanded with a transparent truth predicate. Nevertheless, CM cannot recapture every classically valid meta-metainference. We will afterwards develop a hierarchy of consequence relations CM n for metainferences of level n (for 1 ≤ n &lt; ω ). Each CM n recovers every metainference of level n or less, and can be nontrivially expanded with a transparent truth predicate, but cannot recapture every classically valid metainferences of higher levels. Finally, we will present a logic CM ω , based on the hierarchy of logics CM n , that is fully classical , in the sense that every classically valid metainference of any level is valid in it. Moreover, CM ω can be nontrivially expanded with a transparent truth predicate.
Generation of telecom-band correlated photon pairs in different spatial modes using few-mode fibers

We experimentally demonstrate the production of photon pairs at 1555 nm in LP01 mode and at 1566.8 nm in LP11 mode, respectively, via the spontaneous intermodal four-wave mixing in a 100-m-long few-mode fiber. We achieve a coincidence to accidental-coincidence ratio of 28, which clearly indicates the existence of quantum correlation. We also discuss the contribution of spontaneous Raman scattering to the background noise of photon pairs. Our source of photon pairs, compatible with the low-loss multiplexing and de-multiplexing components for space division multiplexing optical fiber communication, can be used for generating higher-dimensional entanglement.
Recurrent Models of Visual Co-Attention for Person Re-Identification

Person re-identification (re-id) refers to matching people across disjoint camera views.Most of person re-id methods extract discriminative features from the whole images or fixed regions and develop their metrics.However, these methods ignore that the attention regions with temporal cues in the pedestrian image pair hold discriminative information.In this paper, we propose the recurrent models of visual co-attention that aim to simulate human eye movement, focusing on the sequential concurrent attention (co-attention) regions of the same locations when comparing image pairs.Since reinforcement learning provides a flexible learning strategy for sequential decision-making, it is naturally applied to perform the temporal re-id co-attention learning task.The reward functions are designed to recursively optimize the prediction by rewarding or punishing the learning process.The recurrent models are used to extract information from a sequence of attention regions.Finally, person re-id is performed based on the whole image feature and the features from the recurrent models.Our contributions are: 1) the visual mechanism, which can dynamically locate the optimal co-attention regions to simulate the human re-id process; 2) the design of reward functions in reinforcement learning, which aims to recursively optimize the prediction process; and 3) experimental results, which demonstrate the advantages of our method compared with the state-of-the-art methods.
A 1920 $\times$ 1080 25-Frames/s 2.4-TOPS/W Low-Power 6-D Vision Processor for Unified Optical Flow and Stereo Depth With Semi-Global Matching

This paper presents a unified 6-D vision processor that enables dense real-time 3-D depth and 3-D motion perception at full-high-definition (1920 × 1080, FHD) resolution. The proposed design implements a neighbor-guided semi-global matching (NG-SGM) algorithm to unify the stereo depth and optical flow matching problem and to reduce computation by 98% compared with the original SGM. We introduce a new custom-designed, high-bandwidth coalescing crossbar circuit that automatically coalesces redundant memory accesses to mitigate the highly irregular memory accesses observed in NG-SGM. The proposed 6-D vision processor also maximizes on-chip memory reuse by using 64 on-chip rotating image buffers that cover a wide optical flow and depth disparity search range of 176 pixels per dimension. The processor implements massive parallel processing with 576 compute units that are deeply pipelined with a dependency-resolving skewed-diagonal scan to hide the dynamic and variable dependency in the pipeline. The fabricated processor performs dense NG-SGM at 25 frames/s for optical flow or 30 frames/s for stereo depth at FHD resolution while consuming only 760 mW in 28-nm CMOS.
Dimensions in programming multi-agent systems

Abstract Research on Multi-Agent Systems (MAS) has led to the development of several models, languages, and technologies for programming not only agents, but also their interaction, the application environment where they are situated, as well as the organization in which they participate. Research on those topics moved from agent-oriented programming towards multi-agent-oriented programming (MAOP). A MAS program is then designed and developed using a structured set of concepts and associated first-class design and programming abstractions that go beyond the concepts normally associated with agents. They include those related to environment, interaction, and organization. JaCaMo is a platform for MAOP built on top of three seamlessly integrated dimensions (i.e. structured sets of concepts and associated execution platforms): for programming belief desire intention (BDI) agents, their artefact-based environments, and their normative organizations. The key purpose of our work on JaCaMo is to support programmers in exploring the synergy between these dimensions, providing a comprehensive programming model, as well as a corresponding platform for developing and running MAS. This paper provides a practical overview of MAOP using JaCaMo. We show how emphasizing one particular dimension leads to different solutions to the same problem, and discuss the issues of each of those solutions.
An adaptive clinical decision support system for serving the elderly with chronic diseases in healthcare industry

Abstract With the increasing ageing population worldwide, providing effective nursing care planning in nursing homes is important in meeting the expectations of elderly patients and in streamlining the healthcare information process, hence maintaining high‐quality services. Instead of the traditional manual nursing care planning formulation based on expert experience and subjective judgement, this paper describes an adaptive decision support system, namely, the cloud‐based nursing care planning system, to enable decision making in formulating nursing care strategies. By integrating cloud computing technology and the case‐based reasoning (CBR) technique, medical records and documents pertaining to the elderly can be captured in real time, whereas appropriate treatment plans based on past similar treatment records can be formulated. However, the current case adaptation processes in CBR rely on domain experts to modify retrieved cases, which may not satisfy the needs of the elderly. Therefore, text mining is integrated in the case adaptation process of CBR for extracting up‐to‐date medical information from the Internet so that its efficiency can be improved. By conducting a pilot study in a nursing home, it was shown that the time for formulating applicable treatment plans for elderly patients can be reduced, and the service satisfaction level can be enhanced.
TOSCA-Based and Federation-Aware Cloud Orchestration for Kubernetes Container Platform

Kubernetes, a container orchestration tool for automatically installing and managing Docker containers, has recently begun to support a federation function of multiple Docker container clusters. This technology, called Kubernetes Federation, allows developers to increase the responsiveness and reliability of their applications by distributing and federating container clusters to multiple service areas of cloud service providers. However, it is still a daunting task to manually manage federated container clusters across all the service areas or to maintain the entire topology of cloud applications at a glance. This research work proposes a method to automatically form and monitor Kubernetes Federation, given application topology descriptions in TOSCA (Topology and Orchestration Specification for Cloud Applications), by extending the orchestration tool that automatizes the modeling and instantiation of cloud applications. It also demonstrates the successful federation of the clusters according to the TOSCA specifications and verifies the auto-scaling capability of the configured system through a scenario in which the servers of a sample application are deployed and federated.
Transfer to Rank for Heterogeneous One-Class Collaborative Filtering

Heterogeneous one-class collaborative filtering is an emerging and important problem in recommender systems, where two different types of one-class feedback, i.e., purchases and browses, are available as input data. The associated challenges include ambiguity of browses, scarcity of purchases, and heterogeneity arising from different feedback. In this article, we propose to model purchases and browses from a new perspective, i.e., users’ roles of mixer, browser and purchaser. Specifically, we design a novel transfer learning solution termed role-based transfer to rank (RoToR), which contains two variants, i.e., integrative RoToR and sequential RoToR. In integrative RoToR, we leverage browses into the preference learning task of purchases, in which we take each user as a sophisticated customer (i.e., mixer ) that is able to take different types of feedback into consideration. In sequential RoToR, we aim to simplify the integrative one by decomposing it into two dependent phases according to a typical shopping process. Furthermore, we instantiate both variants using different preference learning paradigms such as pointwise preference learning and pairwise preference learning. Finally, we conduct extensive empirical studies with various baseline methods on three large public datasets and find that our RoToR can perform significantly more accurate than the state-of-the-art methods.
Sentiment Analysis of Iraqi Arabic Dialect on Facebook Based on Distributed Representations of Documents

Nowadays, social media is used by many people to express their opinions about a variety of topics. Opinion Mining or Sentiment Analysis techniques extract opinions from user generated contents. Over the years, a multitude of Sentiment Analysis studies has been done about the English language with deficiencies of research in all other languages. Unfortunately, Arabic is one of the languages that seems to lack substantial research, despite the rapid growth of its use on social media outlets. Furthermore, specific Arabic dialects should be studied, not just Modern Standard Arabic. In this paper, we experiment sentiments analysis of Iraqi Arabic dialect using word embedding. First, we made a large corpus from previous works to learn word representations. Second, we generated word embedding model by training corpus using Doc2Vec representations based on Paragraph and Distributed Memory Model of Paragraph Vectors (DM-PV) architecture. Lastly, the represented feature used for training four binary classifiers (Logistic Regression, Decision Tree, Support Vector Machine and Naive Bayes) to detect sentiment. We also experimented different values of parameters (window size, dimension and negative samples). In the light of the experiments, it can be concluded that our approach achieves a better performance for Logistic Regression and Support Vector Machine than the other classifiers.
Towards a Blockchain based Fall Prediction Model for Aged Care

Falls are one of the major health concerns for the elderly people. These falls often result in severe injuries which lead into huge medical expenses. Over the recent years, many ICT based fall detection and fall prevention solutions emerged to address the risk factors associated with falls. However, despite of these research studies, predicting the likelihood of falls still remains as a huge challenge in both medical and IT research domains. Data related to these risk factors being scattered among different healthcare providers can be attributed as a main reason for this challenge. This is further amplified by healthcare providers being reluctant to disseminate the data beyond their entities due to the security and privacy concerns. However, in recent years, blockchain has been proven as a promising technology to address the security and privacy challenges in healthcare data exchange as it provides a shared, immutable, and transparent audit trail for accessing data. Therefore, in this paper, we are going to propose a conceptual blockchain based fall prediction model leveraging smart contracts and FHIR (Fast Healthcare Interoperability Resources) standard to identify the elderly people who are at a higher risk of falling.
iMCRec: A multi-criteria framework for personalized point-of-interest recommendations

User preferences on geographical positions and categories of points-of-interest (POIs) have been exploited for POI recommendations. However, in reality, the users' visiting behaviors are also affected by the attributes of POIs, which reflect the important features of the POIs. Integrating the geographical, category and attribute criteria for POI recommendations suffers challenges of (1) modeling user preferences on multiple attributes with different values, (2) integrating conflicting multiple criteria for POI recommendations, and (3) learning personalized weights from one's check-in histories with heterogeneous data types. To address these challenges, we propose a new personalized POI recommendation framework, called iMCRec, which recommends POIs by integrating user preferences on geographical, category and attribute criteria with personalized weights. In iMCRec, preference models are first built for individual user's geographical, category, and attribute preferences. Especially, we propose an attribute preference model by considering both preferences on values of each attribute and importance of different attributes. A sophisticated collaborative filtering method is also developed to fuse the opinion of similar users under the three criteria separately. To learn the personalized weights on the three criteria, a weight learning strategy is proposed. We then develop a fast Multi-Criteria Decision Making (MCDM) algorithm, called FastMCDM, to integrate the three conflicting criteria and efficiently generate top-N POIs as recommendations. Finally, we evaluate the performance of our iMCRec through extensive experiments using two real-world datasets collected from Yelp. Experimental results show that iMCRec not only performs better than the state-of-the-art POI recommendation techniques, but it is also more flexible in dealing with the scale problem and more effective in learning personalized weights than other multi-criteria-based techniques.
Facility Location Problem Approach for Distributed Drones

Currently, industry and academia are undergoing an evolution in developing the next generation of drone applications. Including the development of autonomous drones that can carry out tasks without the assistance of a human operator. In spite of this, there are still problems left unanswered related to the placement of drone take-off, landing and charging areas. Future policies by governments and aviation agencies are inevitably going to restrict the operational area where drones can take-off and land. Hence, there is a need to develop a system to manage landing and take-off areas for drones. Additionally, we proposed this approach due to the lack of justification for the initial location of drones in current research. Therefore, to provide a foundation for future research, we give a justified reason that allows predetermined location of drones with the use of drone ports. Furthermore, we propose an algorithm to optimally place these drone ports to minimize the average distance drones must travel based on a set of potential drone port locations and tasks generated in a given area. Our approach is derived from the Facility Location problem which produces an efficient near optimal solution to place drone ports that reduces the overall drone energy consumption. Secondly, we apply various traveling salesman algorithms to determine the shortest route the drone must travel to visit all the tasks.