Reviewerly/exHarmony huggingface dataset:
Valence/Arousal Estimation of Occluded Faces from VR Headsets

Emotion recognition from facial visual signals is a challenge which has attracted enormous interest over the past two decades. Researchers are attempting to teach computers to better understand a person's emotional state. Providing emotion recognition can massively enrich experiences. The benefits of this research for human-computer interactions are limitless. Emotions are intricate, and so we need a representative model of the full spectrum displayed by humans. A multi-dimensional emotion representation, which includes valence (how positive an emotion) and arousal (how calming or exciting an emotion), is a good fit. Virtual Reality (VR), a fully immersive computer-generated world, has witnessed significant growth over the past years. It has a wide range of applications including in mental health, such as exposure therapy and the self-attachment technique. In this paper, we address the problem of emotion recognition when the user is immersed in VR. Understanding emotions from facial cues is in itself a demanding task. It is made even harder when a head-mounted VR headset is worn, as now an occlusion blocks the upper half of the face. We attempt to overcome this issue by introducing EmoFAN-Vr,a deep neural network architecture, to analyse facial affect in the presence of a severe occlusion from a VR headset with a high level of accuracy. We simulate an occlusion representing a VR headset and apply it to all datasets in this work. EmoFAN-Vrpredicts both discrete and continuous emotions in one step, meaning it can be used in real-time deployment. We fine-tune our network on the AffectNet dataset under VR occlusion and test it on the AFEW-VA dataset, setting a new baseline for this dataset whilst under VR occlusion.
Privacy Pretexts

Data privacy’s ethos lies in protecting the individual from institutions. Increasingly, however, institutions are deploying privacy arguments in ways that harm individuals. Platforms like Amazon, Facebook, and Google wall off information from competitors in the name of privacy. Financial institutions under investigation justify withholding files from the Consumer Financial Protection Bureau by saying they must protect sensitive customer data. In these and other ways, the private sector is exploiting privacy to avoid competition and accountability. This Article highlights the breadth of privacy pretexts and uncovers their moral structure. Like most pretexts, there is an element of truth to the claims. But left unchallenged, they will pave a path contrary to privacy’s ethos by blocking individuals’ data allies—the digital helpers, competitors, and regulators who need access to personal data to advance people’s interests. Addressing this move requires recognizing and overcoming deep tensions in the field of privacy. Although data privacy’s roots are in guarding against access, its future depends on promoting allied access.
Nonlinear Traffic Data Reconstruction in Large-Scale Internet of Vehicle Systems: A Neural Network Approach

Multi-Access Edge Computing (MEC) can reduce transmission costs and provide faster interactive responses in cloud server-centric Internet of Vehicle (IoV) scenarios. However, the resource space of MEC server is limited, and the large amount of data uploaded by RSU may limit the further development of MEC network. Compressed Sensing (CS) proves that for the data with certain internal structure, a complete data set can be reconstructed even if the sampling frequency is lower than the requirements of the sampling theorem. In the current works, traffic data compression is accomplished by directly reducing the sampling frequency in the process of collecting traffic data. However, CS reconstruction only utilizes the linear characteristics of data. Aiming to fully utilize the nonlinear characteristics of data, we propose a neural network based traffic data reconstruction model framework, called NRTD. NRTD instantiates the process of generalized matrix factorization and combines the potential features of multilayer perceptrons to improve the fitting degree of the model to the traffic flow data change curve. Parameter optimization and model comparison experiments are carried out with different missing rates and missing scenes. The experimental results show that the proposed method is superior to the conventional compressed sensing reconstruction model before improvement under different conditions.
Deep asymmetric hashing with dual semantic regression and class structure quantization

Recently, deep hashing methods have been widely used in image retrieval task. Most existing deep hashing approaches adopt one-to-one quantization to reduce information loss. However, such class-unrelated quantization cannot give discriminative feedback for network training. In addition, these methods only utilize single label to integrate supervision information of data for hashing function learning, which may result in inferior network generalization performance and relatively low-quality hash codes since the inter-class information of data is totally ignored. In this paper, we propose a dual semantic asymmetric hashing (DSAH) method, which generates discriminative hash codes under threefold constraints. Firstly, DSAH utilizes class prior to conduct class structure quantization so as to transmit class information during the quantization process. Secondly, a simple yet effective label mechanism is designed to characterize both the intra-class compactness and inter-class separability of data, thereby achieving semantic-sensitive binary code learning. Finally, a meaningful pairwise similarity preserving loss is devised to minimize the distances between class-related network outputs based on an affinity graph. With these three main components, high-quality hash codes can be generated through network. Extensive experiments conducted on various data sets demonstrate the superiority of DSAH in comparison with state-of-the-art deep hashing methods.
Creating a Post-sedentary Work Context for Software Engineering

Software engineers are sedentary and need technological help for a more healthy life. Current software engineering tasks are mostly confined to the standard sedentary desktop user interface. We believe that software engineering should be restructured so that it offers a non-sedentary alternative. In this paper, we describe a new research approach, called Post-sedentary Software Engineering. Our ambition with this approach is to provide an alternative, healthier work context without decreasing productivity. We take a spatial approach to post-sedentary tool design, starting from the assumption an interactive 3D environment with appropriate metaphors is necessary for full body movement. We discuss available technologies for achieving this goal and outline four studies that incorporate the software engineering phases of code comprehension, code creation and debugging in a non-sedentary context.
Architecture of Knowledge Extraction System based on NLP

Knowledge extraction is to extract useful structured text information from messy free text. Under the current massive information background, it has attracted extensive attention. This paper analyzes the concept of NLP and the application process of NLP algorithm, discusses web information retrieval system, information extraction based on natural language processing and text relationship extraction, and tests the pipeline performance. The results show that the pipeline time is not linearly correlated with the size of the novel, but positively correlated.
Finding Access Points for Data Literacy: The Example of the ERASMUS+ Project DaLiCo (Data Literacy in Context)

The paper addresses the increased need for data literacy education in higher education. The background for this is provided by the international Erasmus+ project DaLiCo, in which four universities of applied sciences are developing concepts for the fostering of data literacy. Research results and lessons learned from project activities such as the joint summer school will be presented.
A high‐performance processor for optimal ate pairing computation over Barreto–Naehrig curves

This paper presents a high-performance processor for optimal ate pairing on Barreto–Naehrig curves over 256-bit prime field at the 128-bit security level. The proposed design exploits parallelism and pipeline at different levels of the pairing algorithm, including the prime field operation, the second extension of the prime field F p 2 $\left({F}_{{p}^{2}}\right)$ operation, and operations based on F p 2 ${F}_{{p}^{2}}$ . The proposed design needs 37,271 cycles to compute optimal ate pairings. The results of implementation on a 90 nm standard cell library show that the proposed design consumes 751k gates and can compute the respective pairings in 0.10 ms. This result is at least 60 percent better than related reports in terms of normalised area-time on ASIC. Moreover, the design is also implemented on Xilinx Virtex-6 platform, which consumes 25K Slices and 240 DSPs and takes 0.52 ms to calculate one optimal ate pairing operation.
Positive Sparse Signal Denoising: What Does a CNN Learn?

Convolutional neural networks (CNNs) provide impressive empirical success in various tasks; however, their inner workings generally lack interpretability. In this paper, we interpret shallow CNNs that we have trained for the task of positive sparse signal denoising. We identify and analyze common structures among the trained CNNs. We show that the learned CNN denoisers can be interpreted as a nonlinear locally-adaptive thresholding procedure, which is an empirical approximation of the minimum mean square error estimator. Based on our interpretation, we train constrained CNN denoisers and demonstrate no loss in performance despite having fewer trainable parameters. The interpreted CNN denoiser is an instance of a multivariate spline regression model, and a generalization of classical proximal thresholding operators.
Quantifying Urban Safety Perception on Street View Images

In the last 40 years, Urban perception has become an important research area covering several fields, such as criminology, psychology, urban planning, Broken windows theory. It aims to analyze and interpret the behavior of the perception in cities. Urban perception focuses on understanding urban environments based on the characteristics of the city. With the rapidly increasing data availability and highly scalable data collection methods powered by modern web services, new techniques from other domains enabled the exploration of solutions to estimate urban perception (i.e., quantify urban perception autonomously). This work presents a methodology to explore the urban perception analysis task. The work relies on the benchmark dataset, Place Pulse. This dataset is used to perform our classification tasks concerning the category of safety in urban perception problems.
Combined weight and density bounds on the polynomial threshold function representation of Boolean functions

In an earlier report it was shown that an arbitrary n-variable Boolean function f can be represented as a polynomial threshold function (PTF) with 0.75×2n or less number of monomials. In this report, we derive an upper bound on the absolute value of the (integer) weights of a PTF that represents f and still obeys the aforementioned density bound. To our knowledge this provides the best combined bound on the PTF density (number of monomials) and PTF weight (sum of the coefficient magnitudes) of general Boolean functions. For the special case of bent functions, it is found that any n-variable bent function can be represented with integer coefficients less than or equal to 2n with density no more than 0.75×2n, and for the case of m-sparse Boolean functions that are almost constant except for small (m≪2n) number of variable assignments, it is shown that they can be represented with small weight PTFs with density at most m+2n−1. In addition, tight PTF weight bounds with conformance to the density bound of 0.75×2n are numerically obtained for the general Boolean functions up to 6 variables.
qTReX : A semi-autonomous continuous-variable quantum key distribution system

Continuous-variable quantum cryptography can leverage existing telecommunication technology for solving the cryptographic task of secure key distribution. We present qTReX: A low-noise, highly stable, semi-autonomous prototype using optical coherent states for quantum key distribution.
An Elderly Indoor Behavior Recognition Method Based on Improved SlowFast Network*

Abstract Due to the limited resources of home-based elderly care monitoring equipment, how to accurately identify the elderly behavior is a hot issue concerned by many equipment manufacturers. A behavior recognition method suitable for embedded equipment is proposed. This method first introduces the TPN module in the SlowFast network to capture the visual action tempo of the elderly and uses Faster-CNN to detect the elderly and objects in the video frames. Then in the AIA module, the accuracy of the elderly indoor behavior recognition is improved by strengthening the correlation between the elderly and the objects. Experiments show that the improved network can run stably at 18FPS on Jetson Xavier NX. The indoor behavior recognition accuracy for the elderly reaches 90.61%, which is 8.25% higher than the original SlowFast network.
Time Controlled Expressive Predicate Query With Accountable Anonymity

Many existing searchable encryption schemes are inflexible in retrieval patterns. The data usage authorization is almost permanent valid as long as the user is not revoked. This “all-or-nothing” authorization mode is not compatible with the “pay-as-you-use” commercial billing model. In this article, we propose a new notion called time controlled expressive predicate query with accountable anonymity. It realizes time controlled data query, where a time server issues time token to authorize search privilege in designated time period. The data users can anonymously query on encrypted data and the anonymity is accountable in a way that the trusted authority is able to deanonymize data users if they misbehave in the system. The underlying techniques are anonymous credential, Pederson commitment and non-interactive zero-knowledge proof. We firstly design an efficient expressive predicate query (EPQ) scheme, which is proved secure to protect the privacy of expressive search predicate. Based on EPQ, we present a concrete system instantiation, which realizes key-escrow free and time token nontransferability. The formal definition and security models are given out. The system is formally proved indistinguishable against chosen keyword-set attacks, unforgeable of time tokens and accountable of anonymous users. The comparison and experiment results demonstrate its scalability and efficiency.
Digital Violence Against Women: A Time Series Analysis

Violence Against Women is a phenomenon that has grown in the last decades. In this research we address this phenomenon in Digital Space as Digital Violence Against Women. We trained a Support Vector Machine Classifier that predicted a 20.16% presence of Digital Violence Against Women in a Self-constructed Tweet dataset containing over 10 Million Spanish-language tweets in Mexico. A time series was constructed on the percentage of Digital Violence Against Women in tweets per day. Findings indicate increasing presence of violence on specific dates of fight for Women’s Rights, such as 25th of November and 8th of March. Finally, forecasts under an ARIMA model gave a Root Mean Squared Error of 0.0062.
Cybersecurity Risk Management Frameworks in the Oil and Gas Sector: A Systematic Literature Review

Oil and gas industry is an important part of the country's critical infrastructure that employs sophisticated equipment, tools, assets and specialized interdisciplinary knowledge. In order to improve productivity and efficiency, there is an increasing trend in the industry towards digital transformation. This, as a result has increased cyber vulnerabilities and threats in the oil and gas industry. Fear of cyberattacks is particularly high in oil and gas organization as they are part of critical infrastructure. They face a wide spectrum of threats due to the complex nature of their value chain. Different frameworks have been developed in different sector to address the cyber risk management. But unfortunately, enough research hasn't been done in the oil and gas sector. To address this gap, a rigorous and methodical approach was applied to conduct a systematic literature review of original frameworks, peer reviewed articles, book chapters and conference papers related to 25 most commonly used frameworks, after performing a thorough search in the four major databases such as AAPG, Web of Science, SCOPUS, and ProQuest. The main aim of this paper is to conduct systematic literature review on the cybersecurity risk management frameworks available in literature that guides the practice of cyber risk management in the oil and gas projects. The findings of this paper illuminates key research gaps, strengths, and limitations of these frameworks, and help to identify directions for further research.
TextFace: Text-to-Style Mapping Based Face Generation and Manipulation

As a subtopic of text-to-image synthesis, text-to-face generation has great potential in face-related applications. In this paper, we propose a generic text-to-face framework, namely, TextFace, to achieve diverse and high-quality face image generation from text descriptions. We introduce text-to-style mapping, a novel method where the text description can be directly encoded into the latent space of a pretrained StyleGAN. Guided by our text-image similarity matching and face captioning-based text alignment, the textual latent code can be fed into the generator of a well-trained StyleGAN to produce diverse face images with high resolution (1024×1024). Furthermore, our model inherently supports semantic face editing using text descriptions. Finally, experimental results quantitatively and qualitatively demonstrate the superior performance of our model.
Rotate or Wrap? Interactive Visualisations of Cyclical Data on Cylindrical or Toroidal Topologies

In this paper, we report on a study of visual representations for cyclical data and the effect of interactively wrapping a bar chart 'around its boundaries'. Compared to linear bar chart, polar (or radial) visualisations have the advantage that cyclical data can be presented continuously without mentally bridging the visual 'cut' across the left-and-right boundaries. To investigate this hypothesis and to assess the effect the cut has on analysis performance, this paper presents results from a crowdsourced, controlled experiment with 72 participants comparing new continuous panning technique to linear bar charts (interactive wrapping). Our results show that bar charts with interactive wrapping lead to less errors compared to standard bar charts or polar charts. Inspired by these results, we generalise the concept of interactive wrapping to other visualisations for cyclical or relational data. We describe a design space based on the concept of one-dimensional wrapping and two-dimensional wrapping, linked to two common 3D topologies; cylinder and torus that can be used to metaphorically explain one- and two-dimensional wrapping. This design space suggests that interactive wrapping is widely applicable to many different data types.
Motion State Estimation Based on Multi-sensor Fusion and Noise Covariance Estimation

Detecting or estimating the state of the moving target accurately on the road helps predict the behavior of the moving target, and this is beneficial for self-vehicles’ trajectory planning and safe navigation. The difficulty of state estimation lies in how to estimate the state of the target in real-time and accurately, and it will not be divergence when the sensor has missed detection, false detection and time-varying measurement noise. In response to this difficulty, it is proposed to use multi-source information fusion and Kalman filter to make up for the missed detection of the sensor, use the nearest neighbor correlation algorithm to match the detection and prediction to reduce the impact of false detection, and use the adaptive measurement noise estimation method to estimate the current value of the measurement noise in real-time to improve the stability and accuracy of the filter when the measurement noise is time-varying. The proposed method relies on the smart car platform for experiments. The experimental results show that the position and velocity estimation accuracy of the proposed algorithm are all better than the comparison method.
Information-Theoretic Bias Assessment Of Learned Representations Of Pretrained Face Recognition

As equality issues in the use of face recognition have garnered a lot of attention lately, greater efforts have been made to debiased deep learning models to improve fairness to minorities. However, there is still no clear definition nor sufficient analysis for bias assessment metrics. We propose an information-theoretic, independent bias assessment metric to identify degree of bias against protected demographic attributes from learned representations of pretrained facial recognition systems. Our metric differs from other methods that rely on classification accuracy or examine the differences between ground truth and predicted labels of protected attributes predicted using a shallow network. Also, we argue, theoretically and experimentally, that logits-level loss is not adequate to explain bias since predictors based on neural networks will always find correlations. Further, we present a synthetic dataset that mitigates the issue of insufficient samples in certain cohorts. Lastly, we establish a benchmark metric by presenting advantages in clear discrimination and small variation comparing with other metrics, and evaluate the performance of different debiased models with the proposed metric.
Hate Speech Detection Using Multi-Channel Convolutional Neural Network

As we are used to seeing the Internet as a mode of availability that is available at our doorstep, it is no wonder that we have access to a number of online stages. Increasing their use brings both advantages and disadvantages. One of such disadvantages is hate speech. Hate speech is a subject of worry for online media stages. With powerfully expanding datasets manual mediation of posts is very inconceivable or will be tedious. Hate speech detection should be an automated task to distinguish hate speech from the provided input. In this work, we have implemented a deep learning model multi-channel convolutional neural network (MCCNN). The model consists of 3 channels of Convolutional Neural Network. Each channel is merged and connected to a fully connected layer from where the final output is obtained. We have compared our model with a single-channel convolution neural network and results have shown that MCCNN outperformed simple CNN. The accuracy and F1-score achieved by our model are 95.49 and 93.93 for dataset D1 and for dataset D2 97.85% and 95.74% respectively.