Reviewerly/exHarmony huggingface dataset:
Estimate Sentiment of Crowds from Social Media during City Events

City events are being organized more frequently, and with larger crowds, in urban areas. There is an increased need for novel methods and tools that can provide information on the sentiments of crowds as an input for crowd management. Previous work has explored sentiment analysis and a large number of methods have been proposed relating to various contexts. None of them, however, aimed at deriving the sentiments of crowds using social media in city events, and no existing event-based dataset is available for such studies. This paper investigates how social media can be used to estimate the sentiments of crowds in city events. First, some lexicon-based and machine learning-based methods were selected to perform sentiment analyses, then an event-based sentiment annotated dataset was constructed. The performance of the selected methods was trained and tested in an experiment using common and event-based datasets. Results show that the machine learning method LinearSVC achieves the lowest estimation error for sentiment analysis on social media in city events. The proposed event-based dataset is essential for training methods to reduce estimation error in such contexts.
Analysis of Gaussian &amp; Cauchy Mutations in Modified Particle Swarm Optimization Algorithm

This paper provides an analysis of the application of Cauchy mutation and Gaussian mutation in one of the modified version of the popular and well known particle swarm optimization technique. The main objective is to provide better convergence as well finest results in the solutions of various real world applications. The particle swarm optimization is generally acted as a foundation in the area of swarm intelligence. The modified PSO used a improved weight factor to provide a better convergence than the conventional PSO. But many real world problems need a further refinement in the solution. Therefore, in this paper two types of well known mutations are used with the modified PSO for betterment in the refining process. The proposed mutations are Gaussian mutation and Cauchy mutation. The proposed mutated modified PSO algorithms are simulated by the use of standard benchmarking functions to establish their dominance over the already modified PSO. Between Cauchy MPSO and Gaussian MPSO, the overall performance of Cauchy MPSO is better. The use of Cauchy mutation as well as Gaussian mutation in the modified PSO helps in the improvement of the convergence of the modified PSO for approaching much better solutions.
Cross-Domain Recommendation for Enhancing Cultural Heritage Experience

In this paper, we describe our research activities for integrating the recommendation process of nearby points of artistic and cultural interest (POIs) with related multimedia content. The recommendation engine exploits the potential offered by linked open data (LOD), by following semantic links in the LOD graph to identify movies, books, and music artists/songs related to that specific POI. This content is subsequently reranked based on the activity of the user and her friends on social media (i.e., Facebook), in order to provide personalized suggestions.
Sonar Image Translation Using Generative Adversarial Network for Underwater Object Recognition

Sonar sensor is widely used for underwater object recognition. However, acquiring reference sonar images for each target object is high-cost and time-consuming. Sonar image simulators can generate reference sonar images with small computation, but the simulated images are different with actual sonar images captured in the field. This paper proposes a method to translate actual sonar images to simulated-like images using a generative adversarial network. We trained the network with images captured by the indoor water tank test. The trained neural network can generate simulator-like images from given actual sonar images. Further, we can recognize the target object using template matching between the translated image and the reference images simulating the target object.
RADAR: Robust Algorithm for Depth Image Super Resolution Based on FRI Theory and Multimodal Dictionary Learning

Depth image super-resolution is a challenging problem, since normally high upscaling factors are required (e.g., 16×), and depth images are often noisy. In order to achieve large upscaling factors and resilience to noise, we propose a Robust Algorithm for Depth imAge super Resolution (RADAR) that combines the power of finite rate of innovation (FRI) theory with multimodal dictionary learning. Given a low-resolution (LR) depth image, we first model its rows and columns as piece-wise polynomials and propose an FRI-based depth upscaling (FDU) algorithm to super-resolve the image. Then, the upscaled moderate quality (MQ) depth image is further enhanced with the guidance of a registered high-resolution (HR) intensity image. This is achieved by learning multimodal mappings from the joint MQ depth and HR intensity pairs to the HR depth, through a recently proposed triple dictionary learning (TDL) algorithm. Moreover, to speed up the super-resolution process, we introduce a new projection-based rapid upscaling (PRU) technique that pre-calculates the projections from the joint MQ depth and HR intensity pairs to the HR depth. Compared with the state-of-the-art deep learning-based methods, our approach has two distinct advantages: we need a fraction of training data but can achieve the best performance, and we are resilient to mismatches between training and testing datasets. The extensive numerical results show that the proposed method outperforms other state-of-the-art methods on either noise-free or noisy datasets with large upscaling factors up to 16× and can handle unknown blurring kernels well.
New Low-Area Designs for the AES Forward, Inverse and Combined S-Boxes

The implementation of AES S-boxes is one of the most extensively studied areas of cryptography. In this paper, we propose three new hardware designs for the AES S-box that can serve in the forward, inverse and combined data paths. Each of these designs represents the smallest AES S-box ever proposed in its respective category. We achieve this goal by using new tower field representation over normal bases and optimizing each and every block inside the three proposed architectures. Our complexity analysis and ASIC synthesis results in the CMOS STM 65 nm, as well as the NanGate 15 nm technologies, show that our designs outperform their counterparts in terms of area and power.
The utilisation of social media among users with hearing loss: an analysis of Facebook communities

Due to the ubiquitous use of social media, our aim was to examine how social media communities are being accessed, created and maintained by D/deaf and hard of hearing users. We examined 133 social media communities of the D/deaf and hard of hearing from three main aspects: relational, substantive and technical aspects. The analysis included the size, strength, organisation and content nature of communities, members’ communication and accessibility of the platform, as well as its availability, attention and response indicators. Firstly, most communities were found to be small, containing less than 500 members, being determined regionally and led mainly by organisations with an aim to create awareness among members. Secondly, members communicate mostly in written language and use sign language to a lesser extent. Thirdly, Facebook provides advantageous opportunities for D/deaf and hard of hearing users in terms of availability of captions/subtitles in videos on demand and live videos. The findings may serve educators, social practitioners and representatives of deaf clubs to get acknowledged with the functioning of social media communities, allowing the D/deaf and hard of hearing to be connected to each other in a more efficient way beyond Facebook as well.
Deep Learning Underwater Image Color Correction and Contrast Enhancement Based on Hue Preservation

Underwater Image suffers from serious color distortion and low contrast problems because of complex light propagation in the ocean. In view of computing constraints of underwater vehicles, we propose a high-efficiency deep-learning based framework based on hue preservation. The framework contains three convolutional neural networks for underwater image color restoration. At first, we use the first CNN to convert the input underwater image into the grayscale image. Next, we enhanced the grayscale underwater image by the second CNN. And then, we perform the color correction to the input underwater image by the third CNN. At last, we can obtain the color-corrected image by integrating the outputs of three CNNs based on the hue preservation. In our framework, that CNNs specialize on each work can be able to simplify each architecture of CNNs at most and improve the regression quality to achieve the low computing cost and high effeciency. However, the problem of the underwater CNNs is that the underwater training data is too few and without the corresponding ground truth. Thus, we use the unsupervised learning method CycleGAN to train the underwater CNNs. We design a training method as the combination of three CycleGANs that can train the three CNNs at the same time to share the regression status. This training method may let the three CNNs of our proposed framework support each other to avoid the training overfitting and without constraint. By the proposed framework and training method, our method can process the underwater images with high quality and low computing cost. The experimental results have demonstrated the correct colors and high image quality of the proposed method's results, compared with other related approaches.
Discovery Strategy and Method for Remanufacturing Service Demand Using Situational Semantic Network

Due to customer individual difference, limitation of cognitive process and insufficient real-time response of cloud-based remanufacturing service platform, the problems such as disordered demand expression, difficulty in extracting implicit customer demand, and insufficient real-time performance of demand acquisition may be encountered. To this end, this paper presents an edge computing-based dynamic demand discovery and acquisition strategy. On the basis of existing methods and experimental results of implicit demand acquisition, a potential demand discovery method based on situational semantic network is proposed in this study. Firstly, the semantic similarity of ontology concept is used to calculate the correlation strength of registered keywords, and then registration keyword semantic network is constructed accordingly within the edge computing server. Afterwards, the keywords matrix of all web pages within single search behavior is obtained by data aggregation, the core attribute keywords of single search behavior are procured by the Kmeans algorithm and the retrieval keyword semantic network is constructed. After aggregating the two types of keywords semantic networks, the core semantics of aggregated semantic network are extracted by the pangrank method and customer situation semantic network reflecting current potential requirements is formed. Finally, an application example was demonstrated to verify the correctness and practicability of the remanufacturing service demand discovery strategy. This method has the potential to be applied in the intelligent management demand acquisition system of enterprises and urban communities, which provides reference for realization of intelligence technology in digital cities.
Detection and Prevention of Data Fraud in a Study of Community College Career Technical Education Students

Online surveys are an efficient and relatively affordable way to collect data. However, there are issues associated with it, including potential fraudulent data due to accidental or deliberate duplicate entries. In this brief we describe a case study of deliberate duplicate entries in an ongoing study of community college students. Students from career technical education (CTE) courses at community colleges across California were invited to participate in a study of persistence in CTE. Shortly after data collection started, we noticed irregularities in survey entries which were subsequently investigated for fraud. Steps were taken to confirm the validity of survey entries and prevent future fraudulent entries. This paper is the first, to our knowledge, to document the process of identifying, rectifying, and reducing the potential for future fraudulent entries in a community college student sample. Our case study can serve as an important lesson for researchers utilizing online survey data collection methods in longitudinal studies.
MSURU: Large Scale E-commerce Image Classification with Weakly Supervised Search Data

In this paper we present a deployed image recognition system used in a large scale commerce search engine, which we call MSURU. It is designed to process product images uploaded daily to Facebook Marketplace. Social commerce is a growing area within Facebook and understanding visual representations of product content is important for search and recommendation applications on Marketplace. In this paper, we present techniques we used to develop efficient large-scale image classifiers using weakly supervised search log data. We perform extensive evaluation of presented techniques, explain practical experience of developing large-scale classification systems and discuss challenges we faced. Our system, MSURU out-performed current state of the art system developed at Facebook [23] by 16% in e-commerce domain. MSURU is deployed to production with significant improvements in search success rate and active interactions on Facebook Marketplace.
HEXO

OS-capable embedded systems exhibiting a very low power consumption are available at an extremely low price point. It makes them highly compelling in a datacenter context. In this paper we show that sharing long-running, compute-intensive datacenter HPC workloads between a server machine and one or a few connected embedded boards of negligible cost and power consumption can bring significant benefits in terms of consolidation. Our approach, named Heterogeneous EXecution Offloading (HEXO), selectively offloads Virtual Machines (VMs) from server class machines to embedded boards. Our design tackles several challenges. We address the Instruction Set Architecture (ISA) difference between typical servers (x86) and embedded systems (ARM) through hypervisor and guest OS-level support for heterogeneous-ISA runtime VM migration. We cope with the low amount of resources in embedded systems by using lightweight VMs: unikernels. VMs are offloaded based on an estimation of the slowdown expected from running on a given board. We build a prototype of HEXO and demonstrate significant increase in throughput (up to 67%) and energy efficiency (up to 56%) over a set of macro-benchmarks running datacenter compute-intensive jobs.
A vision from a physical point of view and the information theory on the image segmentation

Entropy has been used in many fields of computer vision, like image restoration, edge detection, pattern recognition, and as an evaluation method for image segmentation. The mean shift iterative algorithm (MSHi) was proposed in 2006, where the Shannon entropy was used as a stopping criterion. Late r, it was introduced a theorem where this ensures, with a new stopping criterion, the convergence of the MSHi and determines what happens with the entropy at the limit of the segmentation process. The goal of this paper is carry out an analysis of the implications of this theorem and highlight the relation that were found from a physical point of view with image segmentation and the information theory. This last aspect being the novel part of this work.
DAVEE

The post-graduate enrollment statistic for deaf and hard of hearing (DHH) in Science, Technology, Math and Engineering (STEM) fields is very low compared to the hearing population. This drastically reduces DHH representation in the Information Technology (IT) workforce or academic research. DHH students generally use sign language interpreters to understand lecture materials but technically qualified interpreters are rare. These days, traditional in-person classes are being replaced with Massive Open Online Courses (MOOC). MOOCs improve access to materials, but hinder opportunities for collaboration which is vital for the DHH population. In this work, we propose DAVEE, a Virtual Reality (VR) classroom experience that facilitates live interpretation. During live sessions, DHH students can ask questions, receive feedback and have interactions with other students. The lectures and the interpretations can also be recorded for offline viewing.
Clusters of Features Using Complementary Information Applied to Gender Classification From Face Images

Face recognition performance by computers has been shown to be more accurate than that of humans.However, a bias with soft-biometrics features has been detected.This bias reduces recognition performance when gender is used.Feature selection for gender classification from face images is a difficult problem since faces contain symmetrical and redundant features.We argue that traditional methods, based on mutual information using pairs of features to estimate the relevance and redundancy among features, fail to select the right set of features in cases where there are strong spatial correlations among features, which is the case with facial images.In this paper, a new method is proposed fusing a filter and a wrapper to measure the relationships among image features, and to select feature clusters based on mutual information for gender classification.We applied this method on nine face datasets using an SVM classifier.We were able to achieve 98.2% correct gender classification in the testing partition using the UND, 95.56% with the Morph II, 98.33% on the LFW, and 98.66% on celebA databases.We validated the results using a cross-test with three different datasets: COFW, Adience, and Image of Groups, that were not used to define the parameters of our method.Additionally, the method was tested with a Random Forest.All the results achieved are better than those previously published on the same databases, and with a significantly smaller number of total features.
Performance by Design

Poor indoor air quality (IAQ) can affect health and cognitive performance prior to users becoming aware of the declining air quality. Yet office occupants rarely have access to IAQ information upon which to base their ventilation decisions. This paper details the design and deployment of a situated IAQ display as a probe to explore ventilation and building operation practices when IAQ information is made available. Based on deployments in 11 naturally ventilated offices, we present an analysis of how reflection and sense making around IAQ can inform interactions with buildings. We suggest displays that are locally situated, non-disruptive and visualise the effects of poor IAQ with human analogies may hold potential for engaging office occupants with IAQ. We highlight how ambient displays represent a stepping-stone towards more informed interactions which can improve air quality and cognitive performance, and how IAQ feedback may usefully contribute to alternative HCI research agendas such as Human-Building Interaction.
A Fair Share for All: TCP-Inspired Adaptation Logic for QoE Fairness Among Heterogeneous HTTP Adaptive Video Streaming Clients

This paper presents a novel adaptation logic for HTTP adaptive streaming (HAS), which achieves not only a high quality of experience (QoE) but also high QoE fairness among independent and heterogeneous clients. The algorithm forces video clients to adapt the requested quality level based on the current network conditions and their individual bit rate requirements, such that the overall quality levels selected by all currently active streaming clients are fairly distributed, i.e., they do not diverge too much. The design of the algorithm is inspired by the well-known transmission control protocol (TCP) congestion control, and drives heterogeneous clients to independently converge on similar quality levels without the need for communicating with each other and/or with a centralized controller in the network. By defining quality levels with equal visual quality, and preparing video representations accordingly, the quality level fairness is extended to QoE fairness. In this paper, the design of the TCP-inspired adaptation logic (TCPAL) is described and a simulative performance evaluation is conducted to compare the QoE and QoE fairness of the proposed algorithm with other HAS adaptation logics. TCPAL is evaluated both in scenarios with stable and fluctuating streaming capacity, and the impact of its parameters is explored. The results suggest that TCPAL performs on par with other HAS adaptation logics in terms of QoE and QoE fairness for low link capacities, but significantly improves the QoE fairness for increased link capacity. Moreover, the fairness achieved by TCPAL does not degrade in situations with fluctuating streaming capacity.
Anomaly Detection Algorithm Based on Subspace Local Density Estimation

In this article, the authors propose a novel anomaly detection algorithm based on subspace local density estimation. The key insight of the proposed algorithm is to build multiple trident trees, which can implement the process of building subspace and local density estimation. Each trident tree (T-tree) is constructed recursively by splitting the data outside of 3 sigma into the left or right subtree and splitting the remaining data into the middle subtree. Each node in trident tree records the number of instances that falls on this node, so each trident tree can be used as a local density estimator. The density of each instance is the average of all trident tree evaluation instance densities, and it can be used as the anomaly score of instances. Since each trident tree is constructed according to 3 sigma principle, it can obtain good anomaly detection results without a large tree height. Theoretical analysis and experimental results show that the proposed algorithm is effective and efficient.
Attention in Character-Based BiLSTM-CRF for Chinese Named Entity Recognition

Named Entity Recognition (NER) is a crucial step in natural language processing (NLP). Recently, some researches work on enhancing the word representations by character-level extensions in English and have achieve excellent performance. The same method applies to Chinese as well. In this paper, we introduce this idea into character-based Chinese NER. The method uses the information from character-level and glyph-level representations at the same time to achieve high accuracy. In addition, we propose an attentional model which is able to decide how much information to use from a character-level or glyph-level component dynamically. We validate the proposed method on the third SIGHAN Bakeoff MSRA datasets. Experimental results show that the proposed approaches are effective in improving the performance of Chinese NER and achieve 91.11% F1 without any additional handmade features.
New Construction Solutions for Geoenvironment Protection of Transport Infrastructure

Abstract Heavy metals are one of the most dangerous types of pollution for human, animal and plant life. The problem of pollution of geoenvironment by heavy metals during the operation of the railways is considered. To solve this problem the authors propose a new construction and technological solutions that can minimize geoenvironment pollution of transport infrastructure. These solutions are based on the use of mineral geoantidotes. Mineral geoantidotes are substances that have detoxification properties in relation to heavy metal ions. Qualitative chemical and potentiometric analyzes were used to study mineral geoantidotes. Schemes of technological operations for the creation of geoecoprotective constructions and objects are presented. New construction solutions will protect the geoenvironment from heavy metals contamination by combining geoenvironmental technology with the railway track repair or reconstruction.