Reviewerly/exHarmony huggingface dataset:
Heterogeneous Face Recognition Using Domain Specific Units

The task of Heterogeneous Face Recognition consists in matching face images that are sensed in different domains, such as sketches to photographs (visual spectra images), and thermal images to photographs or near-infrared images to photographs. In this paper, we suggest that the high-level features of Deep Convolutional Neural Networks trained in visual spectra images are potentially domain independent and can be used to encode faces sensed in different image domains. A generic framework for Heterogeneous Face Recognition is proposed by adapting Deep Convolutional Neural Networks low-level features in, so-called, Domain Specific Units. The adaptation using the Domain Specific Units allows the learning of shallow feature detectors specific for each new image domain. Furthermore, it handles its transformation to a generic face space shared between all image domains. Experiments carried out with four different face databases covering three different image domains show substantial improvements, in terms of recognition rate, surpassing the state-of-the-art for most of them. This work is made reproducible: all the source code, scores, and trained models of this approach are made publicly available.
Multiple Object Tracking via Feature Pyramid Siamese Networks

When multiple object tracking (MOT) based on the tracking-by-detection paradigm is implemented, the similarity metric between the current detections and existing tracks plays an essential role. Most of the MOT schemes based on a deep neural network learn the similarity metric using a Siamese architecture, but the plain Siamese architecture might not be enough owing to its structural simplicity and lack of motion information. This paper aims to propose a new MOT scheme to overcome the existing problems in the conventional MOTs. Feature pyramid Siamese network (FPSN) is proposed to address the structural simplicity. The FPSN is inspired by a feature pyramid network (FPN) and it extends the Siamese network by applying FPN to the plain Siamese architecture and by developing a new multi-level discriminative feature. A spatiotemporal motion feature is added to the FPSN to overcome the lack of motion information and to enhance the performance in MOT. Thus, FPSN-MOT considers not only the appearance feature but also motion information. Finally, FPSN-MOT is applied to the public MOT challenge benchmark problems and its performance is compared to that of the other state-of-the-art MOT methods.
RecWalk

Random walks can provide a powerful tool for harvesting the rich network of interactions captured within item-based models for top-n recommendation. They can exploit indirect relations between the items, mitigate the effects of sparsity, ensure wider itemspace coverage, as well as increase the diversity of recommendation lists. Their potential however, is hindered by the tendency of the walks to rapidly concentrate towards the central nodes of the graph, thereby significantly restricting the range of K-step distributions that can be exploited for personalized recommendations. In this work we introduce RecWalk; a novel random walk-based method that leverages the spectral properties of nearly uncoupled Markov chains to provably lift this limitation and prolong the influence of users' past preferences on the successive steps of the walk--allowing the walker to explore the underlying network more fruitfully. A comprehensive set of experiments on real-world datasets verify the theoretically predicted properties of the proposed approach and indicate that they are directly linked to significant improvements in top-n recommendation accuracy. They also highlight RecWalk's potential in providing a framework for boosting the performance of item-based models. RecWalk achieves state-of-the-art top-n recommendation quality outperforming several competing approaches, including recently proposed methods that rely on deep neural networks.
An optical asymmetric encryption scheme with biometric keys

In this paper, an asymmetric cryptosystem for optical image encryption using biometric keys is proposed based on the phase retrieval algorithm and the phase-truncated Fourier transforms (PTFT) scheme which addresses the issue of key distribution with enhanced security in the optical encryption process. In the proposed system, the encryption keys include optically generated biometric keys and the random phase mask keys of the PTFT scheme, while the decryption keys are kept as the private key in terms of the binary key and the generated phase-only mask of the PTFT scheme. If a person is authorized to retrieve the biometric keys by providing the fingerprint hologram and reconstruction parameters at the receiver stage, then the proposed system satisfies the criteria of asymmetric cryptography with information authentication approach. The biometric key features, by using the digital holography are kept secure and is utilized in both the encryption and decryption operations so that the authenticity of the ciphertext by using the biometric keys can be verified during the decryption process. Numerical simulations are carried out to test the effectiveness and validity of the proposed system.
Comment Sections as Targets of Dark Participation? Journalists’ Evaluation and Moderation of Deviant User Comments

User comments, as the most prominent form of participation in online journalism, offer multiple options for so-called dark participation, comments that transgress norms of politeness or honesty with partially sinister motives. Strategic forms of dark participation such as political trolling in user comments have both raised attention and global concern. Community managers are expected to guard the gates of their comment sections, carefully disentangling valid opinions from manipulated statements—with the obvious danger of either censoring genuine speech or letting potentially damaging content slip through. So far, empirical studies on how media actors perceive this challenge and how they respond to it, especially in light of strategic attempts at manipulation, are scarce. The current study aims to fill this gap. Based on a series of guided interviews (N = 25), we explored community managers’ experiences with dark participation in German newspaper sites. The qualitative analysis of the content of the interviews identified four types of comment section managers, ranging from unconcerned gatekeepers to relaxed gate-watchers, alarmed guards, and struggling fighters.
Cervical cancer classification from Pap-smears using an enhanced fuzzy C-means algorithm

Globally, cervical cancer ranks as the fourth most prevalent cancer affecting women. However, it can be successfully treated if detected at an early stage. The Pap smear is a good tool for initial screening of cervical cancer, but there is the possibility of error due to human mistake. Moreover, the process is tedious and time-consuming. The objective of this study was to mitigate the risk of mistake by automating the process of cervical cancer classification from Pap smear images. In this research, contrast local adaptive histogram equalization was used for image enhancement. Cell segmentation was achieved through a Trainable Weka Segmentation classifier, and a sequential elimination approach was used for debris rejection. Feature selection was achieved using simulated annealing integrated with a wrapper filter, while classification was achieved using a fuzzy c-means algorithm. The evaluation of the classifier was carried out on three different datasets (single cell images, multiple cell images and Pap smear slide images from a pathology unit). An overall classification accuracy, sensitivity and specificity of ‘98.88%, 99.28% and 97.47%‘, ‘97.64%, 98.08% and 97.16%’ and ‘96.80%, 98.40% and 95.20%’ were obtained for each dataset respectively. The higher accuracy and sensitivity of the classifier was attributed to the robustness of the feature selection method that was utilized to select cell features that would improve the classification performance, and the number of clusters used during defuzzification and classification. The evaluation and testing conducted confirmed the rationale of the approach taken, which is based on the premise that the selection of salient features embeds sufficient discriminatory information that leads to an increase in the accuracy of cervical cancer classification. Results show that the method outperforms many of the existing algorithms in terms of the false negative rate (0.72%), false positive rate (2.53%), and classification error (1.12%), when applied to the DTU/Herlev benchmark Pap smear dataset. The approach articulated in this paper is applicable to many Pap smear analysis systems, but is particularly pertinent to low-cost systems that should be of significant benefit to developing economies.
An efficient social-like semantic-aware service discovery mechanism for large-scale Internet of Things

Due to the enormous search space, dynamic availability, and restrictions on geographic positions, achieving a scalable and efficient service discovery mechanism for large-scale Internet of Things (i.e., IoT) is a challenging job. Owing to the similarity between social networks and IoT, social strategies can be integrated to improve the performance of IoT solutions. In this paper, we propose an efficient social-like semantic-aware service discovery mechanism named SLSA by mimicking human-like social behaviors and exploring cooperative intelligence. Our mechanism can discover desired services in a fast and scalable manner. The update process of knowledge index adopts a dual-modular-ordering stack strategy that makes search more efficient. Considering the semantic similarity and semantic relativity of two concepts in the domain ontology, we introduce the fuzzy logic method to calculate their correlation degree for device ranking. The SLSA implements an adaptive forwarding strategy, where the service query is forwarded to a selected subset of neighboring devices in a preferred order. We conduct comprehensive experiments to evaluate four mechanisms by establishing dynamic environments. The simulation results show that the SLSA achieves better performance than the other relevant mechanisms with three aspects. Furthermore, confirmative tests are carried out on the characteristics of small-world networks.
Cross-Network Embedding for Multi-Network Alignment

Recently, data mining through analyzing the complex structure and diverse relationships on multi-network has attracted much attention in both academia and industry. One crucial prerequisite for this kind of multi-network mining is to map the nodes across different networks, i.e., so-called network alignment. In this paper, we propose a cross-network embedding method CrossMNA for multi-network alignment problem through investigating structural information only. Unlike previous methods focusing on pair-wise learning and holding the topology consistent assumption, our proposed CrossMNA considers the multi-network scenarios which involve at least two types of networks with diverse network structures. CrossMNA leverages the cross-network information to refine two types of node embedding vectors, i.e., inter-vector for network alignment and intra-vector for other downstream network analysis tasks. Finally, we verify the effectiveness and efficiency of our proposed method using several real-world datasets. The extensive experiments show that our CrossMNA can significantly outperform the existing baseline methods on multi-network alignment task, and also achieve better performance for link prediction task with less memory usage.
Chaos-based firefly algorithms for optimization of cyclically large-size braced steel domes with multiple frequency constraints

The success of the search in the firefly algorithm is highly dependent on a good balance between exploration and exploitation. Although, the basic firefly algorithm is relatively efficient; two chaotic firefly algorithms based on Logistic map (CLFA) and Gaussian map (CGFA) have been presented to improve basic FA. As numerical examples, some large-scale domes are designed for multiple frequency constraints optimization. Despite the fact that these problems are highly nonlinear, results illustrate a desirable performance of the algorithms in both obtaining lower weight and having a higher convergence rate.
A classification method based on optical flow for violence detection

Violence detection is one of the substantial and challenging topics in intelligent video surveillance systems. As there is a growing demand on video surveillance systems with the capability of automatic violence detection, we focus on existing violence detection methods to improve them. In this paper, we introduce a new feature descriptor named Histogram of Optical flow Magnitude and Orientation (HOMO). First, the proposed method converts input frames to the grayscale format. Next, it computes the optical flow between two consequence frames. Then, the optical flow magnitude and orientation of each pixel in each frame are compared separately with its predecessor frame to obtain meaningful changes of magnitude and orientation. Subsequently, different threshold values are applied to the magnitude and orientation changes for obtaining six binary indicators. Finally, these binary indicators are analyzed to get the HOMO descriptor which is used to train a SVM classifier. The system has been implemented using MATLAB. To evaluate the proposed method, two benchmark datasets have been used. The comparison of HOMO and other descriptors on benchmark datasets demonstrates satisfactory performance.
Breaking an Image Encryption Algorithm Based on DNA Encoding and Spatiotemporal Chaos

Recently, an image encryption algorithm based on DNA encoding and spatiotemporal chaos (IEA-DESC) was proposed. In IEA-DESC, pixel diffusion, DNA encoding, DNA-base permutation and DNA decoding are performed successively to generate cipher-images from the plain-images. Some security analyses and simulation results are given to prove that it can withstand various common attacks. However, in this paper, it is found that IEA-DESC has some inherent security defects as follows: (1) the pixel diffusion is invalid for attackers from the perspective of cryptanalysis; (2) the combination of DNA encoding and DNA decoding is equivalent to bitwise complement; (3) the DNA-base permutation is actually a fixed position shuffling operation for quaternary elements, which has been proved to be insecure. In summary, IEA-DESC is essentially a combination of a fixed DNA-base position permutation and bitwise complement. Therefore, IEA-DESC can be equivalently represented as simplified form, and its security solely depends on the equivalent secret key. So the equivalent secret key of IEA-DESC can be recovered using chosen-plaintext attack and chosen-ciphertext attack, respectively. Theoretical analysis and experimental results show that the two attack methods are both effective and efficient.
Digital numerical map-oriented estimation of solar energy potential for site selection of photovoltaic solar panels on national highway slopes

Photovoltaic systems are promising replacements for fossil fuels at places where high solar energy is available. The estimation of available solar energy is the key to maximizing energy generation because sites with high available solar energy must be selected. Previous approaches to site selection required several experts to avoid subjective biases, often relying on rough estimations where the topography was not fully considered. Therefore, this study proposes a computational method that estimates the potential of solar energy for prioritizing and selecting sites for photovoltaic solar panels using publicly available digital numerical maps. These maps provide various kinds of spatial data to support proper land utilization. We focus on the use of national highway slopes as potential installation sites because they are typically unused public areas with high accessibility and few restrictions. For the estimation of annual solar irradiation, elevation contours and highway networks are first separated from the digital numerical maps. The extracted contours are subsequently used to generate a set of topographic data for the estimation. The extracted highway network is used to produce a binary mask representing fill slopes within which grid cells of high solar energy are identified and clustered to locate suitable sites. The proposed method is applied to a test site near Gongju, South Korea, where top 10 potential installation sites are clustered and ranked based on their estimated annual solar irradiation. The most suitable site is identified in eastern Gongju with an annual solar energy of approximately 49,623.7 MWh.
Deep Learning With Sampling in Colon Cancer Histology

This study applied a deep-learning cell identification algorithm to diagnostic images from the colon cancer repository at The Cancer Genome Atlas (TCGA). Within-image sampling improved performance without loss of accuracy. The features thus derived were associated with various clinical variables including metastasis, residual tumor, venous invasion, and lymphatic invasion. The deep-learning algorithm was trained using images from a locally available data set, then applied to the TCGA images by tiling them, and identifying cells in each patch defined by the tiling. In this application the average number of patches containing tissue in an image was ~900. Processing a random sample of patches greatly reduced computation costs. The cell identification algorithm was applied directly to each sampled patch, resulting in a list of cells. Each cell was labeled with its location and classification ("epithelial," "inflammatory," "fibroblast," or "other"). The number of cells of a given type in the patch was calculated, resulting in a patch profile containing four features. A morphological profile that applied to the entire image was obtained by averaging profiles over all patches. Two sampling policies were examined. The first policy was random sampling which samples patches with uniform weighting. The second policy was systematic random sampling which takes spatial dependencies into account. Compared with the processing of complete whole slide images there was a seven-fold improvement in performance when systematic random spatial sampling was used to select 100 tiles from the whole-slide image for processing, with very little loss of accuracy (~4% on average). We found links between the predicted features and clinical variables in the TCGA colon cancer data set. Several significant associations were found: increased fibroblast numbers were associated with the presence of metastasis, venous invasion, lymphatic invasion and residual tumor while decreased numbers of inflammatory cells were associated with mucinous carcinomas. Regarding the four different types of cell, deep learning has generated morphological features that are indicators of cell density. The features are related to cellularity, the numbers, degree, or quality of cells present in a tumor. Cellularity has been reported to be related to patient survival and other diagnostic and prognostic indicators, indicating that the features calculated here may be of general usefulness.
Pedestrian Detection for Autonomous Vehicle Using Multi-Spectral Cameras

Pedestrian detection is a critical feature of autonomous vehicle or advanced driver assistance system. This paper presents a novel instrument for pedestrian detection by combining stereo vision cameras with a thermal camera. A new dataset for vehicle applications is built from the test vehicle recorded data when driving on city roads. Data received from multiple cameras are aligned using trifocal tensor with pre-calibrated parameters. Candidates are generated from each image frame using sliding windows across multiple scales. A reconfigurable detector framework is proposed, in which feature extraction and classification are two separate stages. The input to the detector can be the color image, disparity map, thermal data, or any of their combinations. When applying to convolutional channel features, feature extraction utilizes the first three convolutional layers of a pre-trained convolutional neural network cascaded with an AdaBoost classifier. The evaluation results show that it significantly outperforms the traditional histogram of oriented gradients features. The proposed pedestrian detector with multi-spectral cameras can achieve 9% log-average miss rate. The experimental dataset is made available at http://computing.wpi.edu/dataset.html.
Identifying and Planning for Individualized Change

Identifying and planning strategies that support a healthy lifestyle or manage a chronic disease often require patient-provider collaboration. For example, people with healthy eating goals often share everyday food, exercise, or sleep data with health coaches or nutritionists to find opportunities for change, and patients with irritable bowel syndrome (IBS) often gather food and symptom data as part of working with providers to diagnose and manage symptoms. However, a lack of effective support often prevents health experts from reviewing large amounts of data in time-constrained visits, prevents focusing on individual goals, and prevents generating correct, individualized, and actionable recommendations. To examine how to design photo-based diaries to help people and health experts exchange knowledge and focus on collaboration goals when reviewing the data together, we designed and developed Foodprint, a photo-based food diary. Foodprint includes three components: (1) A mobile app supporting lightweight data collection, (2) a web app with photo-based visualization and quantitative visualizations supporting collaborative reflection, and (3) a pre-visit note communicating an individual's expectations and questions to experts. We deployed Foodprint in two studies: (1) with 17 people with healthy eating goals and 7 health experts, and (2) with 16 IBS patients and 8 health experts. Building upon the lens of boundary negotiating artifacts and findings from two field studies, our research contributes design principles to (1) prepare individuals to collect data relevant to their health goals and for collaboration, (2) help health experts focus on an individual's eating context, experiences, and goals in collaborative review, and (3) support individuals and experts to develop individualized, actionable plans and strategies.
HILATSA: A hybrid Incremental learning approach for Arabic tweets sentiment analysis

A huge amount of data is generated since the evolution in technology and the tremendous growth of social networks. In spite of the availability of data, there is a lack of tools and resources for analysis. Though Arabic is a popular language, there are too few dialectal Arabic analysis tools. This is because of the many challenges in Arabic due to its morphological complexity and dynamic nature. Sentiment Analysis (SA) is used by different organizations for many reasons as developing product quality, adjusting market strategy and improving customer services. This paper introduces a semi- automatic learning system for sentiment analysis that is capable of updating the lexicon in order to be up to date with language changes. HILATSA is a hybrid approach which combines both lexicon based and machine learning approaches in order to identify the tweets sentiments polarities. The proposed approach has been tested using different datasets. It achieved an accuracy of 73.67% for 3-class classification problem and 83.73% for 2-class classification problem. The semi-automatic learning component proved to be effective as it improved the accuracy by 17.55%.