Reviewerly/exHarmony huggingface dataset:
Context-Aware Affective Graph Reasoning for Emotion Recognition

Affective computing has attracted researchers' attention in recent years. Emotion recognition is part of affective computing, which aims to recognize how the person feels, such as happy, sad, anger, disgust, fear, surprise. Traditional works about emotion recognition mainly focus on the characteristic of the person itself, such as audio, text, facial expression, body posture. However, the feelings of people can easily be affected by the context information. In this paper, we utilize the context to construct an affective graph to reason the emotional states. In detail, we detect the context using Region Proposal Network (RPN) to extract nodes as the input of the Graph Convolution Network (GCN), which transfers the convolution operation from Euclidean data structure to nonEuclidean data structure. The GCN learns the affective relationship during the back-propagation process. Moreover, the body feature is extracted by Convolution Neural Network (CNN). The output of GCN and CNN are combined finally to infer the discrete emotion categories and the continuous dimensions for VAD (Valence, Arouse, Dominance) measurement. Our method achieves higher performance than the baseline based on the EXOTIC dataset.
An Attribute-Based Access Control Model in RFID Systems Based on Blockchain Decentralized Applications for Healthcare Environments

The growing adoption of Radio-frequency Identification (RFID) systems, particularly in the healthcare field, demonstrates that RFID is a positive asset for healthcare institutions. RFID offers the ability to save organizations time and costs by enabling data of traceability, identification, communication, temperature and location in real time for both people and resources. However, the RFID systems challenges are financial, technical, organizational and above all privacy and security. For this reason, recent works focus on attribute-based access control (ABAC) schemes. Currently, ABAC are based on mostly centralized models, which in environments such as the supply chain can present problems of scalability, synchronization and trust between the parties. In this manuscript, we implement an ABAC model in RFID systems based on a decentralized model such as blockchain. Common criteria for the selection of the appropriate blockchain are detailed. Our access control policies are executed through the decentralized application (DApp), which interfaces with the blockchain through the smart contract. Smart contracts and blockchain technology, on the one hand, solve current centralized systems issues as well as being flexible infrastructures that represent the relationship of trust and support essential in the ABAC model in order to provide the security of RFID systems. Our system has been designed for a supply chain environment with an use case suitable for healthcare systems, so that assets such as surgical instruments containing an associated RFID tag can only access to specific areas. Our system is deployed in both a local and Testnet environment in order to stablish a deep comparison and determining the technical feasibility.
Fast and effective Big Data exploration by clustering

The rise of Big Data era calls for more efficient and effective Data Exploration and analysis tools. In this respect, the need to support advanced analytics on Big Data is driving data scientist’ interest toward massively parallel distributed systems and software platforms, such as Map-Reduce and Spark, that make possible their scalable utilization. However, when complex data mining algorithms are required, their fully scalable deployment on such platforms faces a number of technical challenges that grow with the complexity of the algorithms involved. Thus algorithms, that were originally designed for a sequential nature, must often be redesigned in order to effectively use the distributed computational resources. In this paper, we explore these problems, and then propose a solution which has proven to be very effective on the complex hierarchical clustering algorithm CLUBS+. By using four stages of successive refinements, CLUBS+ delivers high-quality clusters of data grouped around their centroids, working in a totally unsupervised fashion. Experimental results confirm the accuracy and scalability of CLUBS+ on platforms tailored for Big Data management.
Accurate occupancy estimation with WiFi and bluetooth/BLE packet capture

The capillary spread of personal devices equipped with wireless communication capabilities has enabled a series of high level services which build on capturing and processing the data packets emitted by such devices. In this paper we tackle the problem of exploiting such a methodology to perform occupancy estimation, i.e., understanding how many people are present in a specific place, an information which is valuable in many scenarios (HVAC and lighting system control, building energy optimisation, allocation and reservation of spaces, etc.). Traditional systems for occupancy estimation rely either on environmental sensors (CO2, temperature, humidity) or video cameras, which both have drawbacks: the former have generally low accuracy, while the latter require high setup and maintenance costs. In this paper we depart from such traditional approaches and propose a cheap and accurate occupancy estimation system based on the capture of both Wi-Fi and Bluetooth or Bluetooth Low Energy management frames transmitted from users’ devices. The system, implemented on low-cost hardware, leverages a supervised learning model to adapt to different spaces and transmits estimated occupancy information to a web-based dashboard. Experimental results in both indoor and outdoor uncontrolled scenarios demonstrate the validity of the proposed solution.
Creating a Stressful Decision Making Environment for Aerial Firefighter Training in Virtual Reality

The decisions made by an Air Attack Supervisor (AAS) helicopter co-pilots in aerial firefighting have critical and immediate impacts. It is difficult to always make fast, high quality decisions due to the mental and physical stress being experienced. Real world training exercises have limitations such as safety, cost, time and difficulty in reproducing events, making frequent training infeasible. Virtual Reality (VR) offers new training opportunities, but it is challenging to create a virtual environment with the analogous level of stress experienced in the real-world. In this paper, we investigate the use of a multi-user, collaborative, multi-sensory (vision, audio, tactile) VR system to produce a realistic training environment for practising aerial firefighting training scenarios. We focus on a comparison between our VR training system, an equivalent real-world field training and an existing radio-only exercise currently in use, where we compare Heart-Rate Variability (HRV) and self reported stress using the Short Stress State Questionnaire (SSSQ). We conducted the study with real trainee AAS firefighters to determine the effectiveness of the system. Our results show that there were no significant differences between the VR training exercise and the real-world exercise in terms of the level of stress, measured by HRV, and no significant difference between VR and radio-only exercises, as reported by the SSSQ.
Aggregating Deep Pyramidal Representations for Person Re-Identification

Learning discriminative, view-invariant and multi-scale representations of person appearance with different semantic levels is of paramount importance for person Re-Identification (Re-ID).A surge of effort has been spent by the community to learn deep Re-ID models capturing a holistic single semantic level feature representation.To improve the achieved results, additional visual attributes and body part-driven models have been considered.However, these require extensive human annotation labor or demand additional computational efforts.We argue that a pyramid-inspired method capturing multi-scale information may overcome such requirements.Precisely, multi-scale stripes that represent visual information of a person can be used by a novel architecture factorizing them into latent discriminative factors at multiple semantic levels.A multitask loss is combined with a curriculum learning strategy to learn a discriminative and invariant person representation which is exploited for triplet-similarity learning.Results on three benchmark Re-ID datasets demonstrate that better performance than existing methods are achieved (e.g., more than 90% accuracy on the Duke-MTMC dataset).
Virtual Hand Realism Affects Object Size Perception in Body-Based Scaling

How does the representation of an embodied avatar influence the way in which one perceives the scale of a virtual environment? In virtual reality, it is common to embody avatars of various appearances, from abstract to realistic. It is known that changes in the realism of virtual hands affect the self-body perception, including body ownership. However, the influence of self-avatar realism on the perception of non-body objects has not been investigated. Considering the theory that the scale of the external environment is perceived relative to the size of one's body (body-based scaling), it can be hypothesized that the realism of an avatar affects not only body ownership but also the fidelity of the avatar with respect to our own body as a metric. Therefore, this study examines how avatar realism affects perceived object sizes as the size of the virtual hand changes. In the experiment, we manipulate the level of realism (realistic, iconic, and abstract) and size (veridical and enlarged) of the virtual hand and measure the perceived size of a cube. The results show that the size of the cube is perceived to be smaller when the virtual hand is enlarged compared to when it is veridical, indicating that the participants perceive the sizes of objects based on the size of the avatar, only in the case of a highly realistic hand. Our findings indicate that the more realistic the avatar, the stronger is the sense of embodiment including body ownership, which fosters scaling the size of objects using the size of the body as a fundamental metric. This provides evidence that self-avatar appearances affect how we perceive not only virtual bodies but also virtual spaces.
Exploiting evolving micro-clusters for data stream classification with emerging class detection

Learning non-stationary data streams is challenging due to the unique characteristics of infinite length and evolving property. Current existing works often concentrate on the concept-drift problem in data streams. Concept evolution, indicating novel classes are emerged in data streams, has gained growing attention recently due to its practical values in many real-world applications. Thereby, how to design a new robust learning model on data streams to handle concept drift, concept evolution and outliers simultaneously, is of significant importance. To this end, we propose a new data stream classification approach, called EMC, which dynamically learns the Evolving Micro-Clusters to examine both concept drift and evolution. Specifically, to capture time-changing concept, EMC dynamically maintains a set of online micro-clusters and learns their importance with error-based representative learning. Building upon the evolving micro-clusters, the novel class detector is introduced based on a local density perspective, which allows handling the data streams with complex class distribution. Beyond, EMC allows distinguishing concept drift and evolution from noisy instances. Extensive experiments on both synthetic and real-world data sets show that our method has good classification and novel class detection performance compared to state-of-the-art algorithms.
Arx

In recent years, encrypted databases have emerged as a promising direction that provides data confidentiality without sacrificing functionality: queries are executed on encrypted data. However, many practical proposals rely on a set of weak encryption schemes that have been shown to leak sensitive data. In this paper, we propose Arx, a practical and functionally rich database system that encrypts the data only with semantically secure encryption schemes. We show that Arx supports real applications such as ShareLaTeX with a modest performance overhead.
Neural Compatibility Modeling With Probabilistic Knowledge Distillation

In modern society, clothing matching plays a pivotal role in people's daily life, as suitable outfits can beautify their appearance directly. Nevertheless, how to make a suitable outfit has become a daily headache for many people, especially those who do not have much sense of aesthetics. In the light of this, many research efforts have been dedicated to the task of complementary clothing matching and have achieved great success relying on the advanced data-driven neural networks. However, most existing methods overlook the rich valuable knowledge accumulated by our human beings in the fashion domain, especially the rules regarding clothing matching, like "coats go with dresses" and "silk tops cannot go with chiffon bottoms". Towards this end, in this work, we propose a knowledge-guided neural compatibility modeling scheme, which is able to incorporate the rich fashion domain knowledge to enhance the performance of the compatibility modeling in the context of clothing matching. To better integrate the huge and implicit fashion domain knowledge into the data-driven neural networks, we present a probabilistic knowledge distillation (PKD) method, which is able to encode vast knowledge rules in a probabilistic manner. Extensive experiments on two real-world datasets have verified the guidance of rules from different sources and demonstrated the effectiveness and portability of our model. As a byproduct, we released the codes and involved parameters to benefit the research community.
A Survey on Security and Privacy Issues of Blockchain Technology

Blockchain technology offers great benefits to the development of information technology. However, in order to use blockchain effectively, it is necessary to consider security and privacy aspects. This paper presents a survey of different security and privacy aspects of blockchain. In particular, we present the common types of security attacks on blockchain and security enhancement solutions. We also focus on the existing solutions to protect privacy for blockchain, and on the directions for future research using group signature and zero-knowledge schemes. Group signature scheme can be used to validate transactions in a blockchain network, and zero-knowledge to guarantee that transactions are valid despite the fact that the detailed information of transactions remains obscure. The combination of group signature and zero-knowledge could be feasible solutions for improving security and privacy for blockchain. This solution could also be deployed for IoT networks.
Syntactic complexity development in the writings of EFL learners: Insights from a dependency syntactically-annotated corpus

This study investigates the syntactic complexity, as measured by both large-grained and fine-grained measures, of 410 narrative writings across four writing proficiency levels written by beginner and intermediate L2 English learners. By exploring the differences in syntactic complexity between writings at different proficiency levels, the study is purposed to find out the measures that can best discriminate and predict writing proficiency. The L2 Syntactic Complexity Analyzer and the dependency syntactically-annotated corpus are used respectively to collect the data for large-grained and fine-grained measures. With regard to large-grained measures, it is found that students with higher writing proficiency tend to produce longer language units, more subordinate clauses, more coordinate clauses, and more noun phrases in their writings; mean length of T-unit, mean length of sentence, and dependent clauses per clause can better predict writing proficiency than other traditional large-grained measures. As for fine-grained measures, it is found that three types of subordinate clauses, that is, adverbial clauses, complement clauses and relative clauses, and two types of noun modifiers, that is, prepositional phrases and adjectival relative clauses, occur more frequently in the writings of more proficient learners; the frequency of compound nouns correlates negatively with writing proficiency.
Moving beyond classic readability formulas: new methods and new models

Background Advances in natural language processing (NLP) and computational linguistics have facilitated major improvements on traditional readability formulas that aim at predicting the overall difficulty of a text. Recent studies have identified several types of linguistic features that are theoretically motivated and predictive of human judgments of text readability, which outperform predictions made by traditional readability formulas, such as Flesch–Kincaid. The purpose of this study is to develop new readability models using advanced NLP tools to measure both text comprehension and reading speed. Methods This study used crowdsourcing techniques to collect human judgments of text comprehension and reading speed across a diverse variety of topic domains (science, technology and history). Linguistic features taken from state‐of‐the‐art NLP tools were used to develop models explaining human judgments of text comprehension and reading speed. The accuracy of these models was then compared with classic readability formulas. Results The results indicated that models employing linguistic features more theoretically related to text comprehension and reading speed outperform classic readability models. Conclusions This study developed new readability formulas based on advanced NLP tools for both text comprehension and reading speed. These formulas, based on linguistic features that better represent theoretical and behavioural accounts of the reading process, significantly outperformed classic readability formulas.
Accurate and Robust Video Saliency Detection via Self-Paced Diffusion

Conventional video saliency detection methods frequently follow the common bottom-up thread to estimate video saliency within the short-term fashion. As a result, such methods can not avoid the obstinate accumulation of errors when the collected low-level clues are constantly ill-detected. Also, being noticed that a portion of video frames, which are not nearby the current video frame over the time axis, may potentially benefit the saliency detection in the current video frame. Thus, we propose to solve the aforementioned problem using our newly-designed key frame strategy (KFS), whose core rationale is to utilize both the spatial-temporal coherency of the salient foregrounds and the objectness prior (i.e., how likely it is for an object proposal to contain an object of any class) to reveal the valuable long-term information. We could utilize all this newly-revealed long-term information to guide our subsequent “self-paced” saliency diffusion, which enables each key frame itself to determine its diffusion range and diffusion strength to correct those ill-detected video frames. At the algorithmic level, we first divide a video sequence into short-term frame batches, and the object proposals are obtained in a frame-wise manner. Then, for each object proposal, we utilize a pre-trained deep saliency model to obtain high-dimensional features in order to represent the spatial contrast. Since the contrast computation within multiple neighbored video frames (i.e., the non-local manner) is relatively insensitive to the appearance variation, those object proposals with high-quality low-level saliency estimation frequently exhibit strong similarity over the temporal scale. Next, the long-term common consistency (e.g., appearance models/movement patterns) of the salient foregrounds could be explicitly revealed via similarity analysis accordingly. We further boost the detection accuracy via long-term information guided saliency diffusion in a self-paced manner. We have conducted extensive experiments to compare our method with 16 state-of-the-art methods over 4 largest public available benchmarks, and all results demonstrate the superiority of our method in terms of both accuracy and robustness.
lpRide: Lightweight and Privacy-Preserving Ride Matching Over Road Networks in Online Ride Hailing Systems

An Online Ride Hailing (ORH) system enables a rider to request a taxi through a smartphone app on a short notice. To enjoy ORH services, users have to submit their location information to ORH servers. There are serious privacy concerns for users to reveal location information to ORH servers. In this paper, we propose a lightweight and privacy-preserving ride matching scheme for ORH systems, named lpRide, which can find the taxi with the minimum road distance to serve an incoming rider, while protecting the location privacy of both riders and taxis against the ORH server. In lpRide, we propose an efficient shortest road distance computation approach over encrypted data, which computes approximate road distance by using road network embedding and the modified Paillier cryptosystem. Moreover, we design a secure comparison method to compare two distances over the corresponding blinded ciphertexts, without revealing the actual distances. Theoretical analysis and experimental evaluations show that lpRide is private, accurate and efficient.
Stop the Presses? Moving From Strategic Silence to Strategic Amplification in a Networked Media Ecosystem

In a media ecosystem besieged with misinformation and polarizing rhetoric, what the news media chooses not to cover can be as significant as what they do cover. In this article, we examine the historical production of silence in journalism to better understand the role amplification plays in the editorial and content moderation practices of current news media and social media platforms. Through the lens of strategic silence (i.e., the use of editorial discretion for the public good), we examine two U.S.-based case studies where media coverage produces public harms if not handled strategically: White violence and suicide. We analyze the history of journalistic choices to illustrate how professional and ethical codes for best practices played a key role in producing a more responsible field of journalism. As news media turned to online distribution, much has changed for better and worse. Platform companies now curate news media alongside user generated content; these corporations are largely responsible for content moderation on an enormous scale. The transformation of gatekeepers has led an evolution in disinformation and misinformation, where the creation and distribution of false and hateful content, as well as the mistrust of social institutions, have become significant public issues. Yet it is not just the lack of editorial standards and ethical codes within and across platforms that pose a challenge for stabilizing media ecosystems; the manipulation of search engines and recommendation algorithms also compromises the ability for lay publics to ascertain the veracity of claims to truth. Drawing on the history of strategic silence, we argue for a new editorial approach—“strategic amplification”—which requires both news media organizations and platform companies to develop and employ best practices for ensuring responsibility and accountability when producing news content and the algorithmic systems that help spread it.