Reviewerly/exHarmony huggingface dataset:
The postdigital challenge of redefining academic publishing from the margins

This paper explores relationships between knowledge production and academic publication and shows that the current political economy of mainstream academic publishing has resulted from a complex interplay between large academic publishers, academics, and hacker-activists. The process of publishing is a form of ‘social production’ that takes place across the economy, politics and culture, all of which are in turn accommodating both old and new technology in our postdigital age. Technologies such as software cannot be separated from human labour, academic centres cannot be looked at in isolation from their margins, and the necessity of transdisciplinary approaches does not imply the disappearance of traditional disciplines. In the postdigital age, the concept of the margins has not disappeared, but it has become somewhat marginal in its own right. We need to develop a new language of describing what we mean by ‘marginal voices’ in the social relations between knowledge production and academic publication. Universities require new strategies for cohabitation of, and collaboration between, various socio-technological actors, and new postdigital politics and practice of knowledge production and academic publishing.
Image denoising via a new anisotropic total-variation-based model

To keep local structures when denoising the degraded image, we propose a new anisotropic total variation (TV)-based restored model based on the combination of the gradient operator ∇ and the adaptive weighted matrix ,T into the ℓ1-norm regularized term. The weighted matrix ,T depends on the edge indicator function along the x and y-axis directions, so this matrix can rotate the direction of the gradient operator tending to bigger weight and the proposed model can thus describe the local features in image. In order to cope with this nonsmooth model, we employ the alternating direction method of multipliers (ADMM) to solve it. Relying on the convexity, the convergence of the proposed numerical algorithm is provided as well. Denoising experiments on the artificial images and benchmark images show the effectiveness of the proposed model by comparing it to other well-known total-variation-based models in terms of the restored quality.
CSIDH on the Surface

For primes $$p \equiv 3 \bmod 4$$, we show that setting up CSIDH on the surface, i.e., using supersingular elliptic curves with endomorphism ring $$\mathbf {Z}[(1 + \sqrt{-p})/2]$$, amounts to just a few sign switches in the underlying arithmetic. If $$p \equiv 7 \bmod 8$$ then horizontal 2-isogenies can be used to help compute the class group action. The formulas we derive for these 2-isogenies are very efficient (they basically amount to a single exponentiation in $$\mathbf {F}_p$$) and allow for a noticeable speed-up, e.g., our resulting CSURF-512 protocol runs about $$5.68\%$$ faster than CSIDH-512. This improvement is completely orthogonal to all previous speed-ups, constant-time measures and construction of cryptographic primitives that have appeared in the literature so far. At the same time, moving to the surface gets rid of the redundant factor $$\mathbf {Z}_3$$ of the acting ideal-class group, which is present in the case of CSIDH and offers no extra security.
How do users experience the interaction with an immersive screen?

Ultra-high definition television (UHDTV) is designed using a variety of immersive technologies that are believed to promote user engagement. This study examines the immersion feature of UHDTV by focusing on user experience (UX) to determine whether there is a favorable response by users and whether immersion improves their viewing experience. Further, it examines the relationship between UX and the quality perception of UHDTV to develop a conceptual model for users' quality of experience (QoE) with respect to UHDTV. It uses multiple methods combining experiment (to measure the effect of the immersive experience) and survey (to model the UHDTV UX). The results imply an optimum level of immersion as well as the need for user-based quality measurement. The results confirm the existing models of predicting user intentions with digital technologies and have potential to provide new insights to the research of immersive technologies.
TwinLeak: RFID-based Liquid Leakage Detection in Industrial Environments

Liquid leakage detection is a crucial issue in modern industry, which concerns industrial safety. Traditional solutions, which generally rely on specialized sensors, suffer from intrusive deployment, high cost, and high power consumption. Such problems prohibit applying those solutions for large-scale and continuously industrial monitoring. In this work, we present a RFID-based solution, TwinLeak, to detect liquid leakage using COTS RFID devices. Detecting the leakage accurately with coarse-grained RSSI and phase readings of tags has been a daunting task, which is especially challenging when low detection delay is required. Our system achieves these goals based on the fact that the inductive coupling between two adjacent tags is highly sensitive to the liquid leaked between them. Therefore, instead of judging according to the signals of each individual tag, TwinLeak utilizes the relationship between the signals of two tags as an effective feature for leakage detection. Specifically, Twin-Leak extracts discriminative signal features from short segments of signals and instantly identifies leakage using a light-weight classifier. A model-guided method for leakage progress tracking is further devised to simultaneously estimate the leakage volume and rate. We implement TwinLeak, evaluate its performance across various scenarios, and deploy it in a real-world industrial IoT system. In average, TwinLeak achieves a TPR higher than 97.2%, a FPR lower than 0.5%, and a relative property estimation error around 10%, while triggering early alarms after only about 4.6mL liquid leaks.
A Low-Cost Closed-Loop Solar Tracking System Based on the Sun Position Algorithm

Sun position and the optimum inclination of a solar panel to the sun vary over time throughout the day. A simple but accurate solar position measurement system is essential for maximizing the output power from a solar panel in order to increase the panel efficiency while minimizing the system cost. Solar position can be measured either by a sensor (active/passive) or through the sun position monitoring algorithm. Sensor-based sun position measuring systems fail to measure the solar position in a cloudy or intermittent day, and they require precise installation and periodic calibrations. In contrast, the sun position algorithms use mathematical formula or astronomical data to obtain the station of the sun at a particular geographical location and time. A standalone low-cost but high-precision dual-axis closed-loop sun-tracking system using the sun position algorithm was implemented in an 8-bit microcontroller platform. The Astronomical Almanac ’s (AA) algorithm was used for its simplicity, reliability, and fast computation capability of the solar position. Results revealed that incorporation of the sun position algorithm into a solar tracking system helps in outperforming the fixed system and optical tracking system by 13.9% and 2.1%, respectively. In summary, even for a small-scale solar tracking system, the algorithm-based closed-loop dual-axis tracking system can increase overall system efficiency.
Measuring SMEs’ Propensity for Open Innovation Using Cognitive Mapping and MCDA

Open innovation (OI) has captured increasing interest over recent decades as this approach is linked to higher level organizational ambidextrous strategy, and has become a prerequisite for achieving competitive advantages. Assessing firms' propensity for OI has, however, turned out to be an increasingly challenging endeavor. This is particularly true for small and medium-sized enterprises (SMEs) due to the myriad of factors that influence these companies' capability for innovation. To overcome this challenge, this paper sought to integrate two operational research/management science techniques-cognitive mapping and the Choquet integral (CI) (a nonadditive measure and information aggregator)-to identify and prioritize relevant criteria for evaluating SMEs' propensity for OI, and improving their organizational ambidexterity. To facilitate a real-world application, information was first collected from SME managers and entrepreneurs who agreed to participate in face-to-face group meetings, allowing realism to be incorporated in the decision-making process. The results were validated by both the panel members and project director of COTEC Portugal-a leading think-and-action network for advancing technology diffusion and business innovation cooperation. The findings include that cognitive mapping facilitates the identification and understanding of cause-and-effect relationships between the determinants of OI in SMEs. The CI, in turn, introduces realism into the construction of value functions and the respective assessments of SMEs. The limitations and implications of the proposed system are also discussed.
A Fuzzy Deep Model Based on Fuzzy Restricted Boltzmann Machines for High-dimensional Data Classification

We establish a fuzzy deep model called the fuzzy deep belief net (FDBN) based on fuzzy restricted Boltzmann machines (FRBMs) due to their excellent generative and discriminative properties. The learning procedure of an FDBN is divided into a pretraining phase and a subsequent fine-tuning phase. In the pretraining phase, a group of FRBMs is trained in a greedy layerwise way: the first FRBM is trained by original samples, and the average values of the left and right probabilities produced by its hidden units are treated as the training data for subsequent FRBMs. The resulting FDBN is either a generative or a discriminative model depending on the choice of training a generative or a discriminative type of FRBM on top. Then, a hybrid learning approach is proposed to fine-tune this novel fuzzy deep model: the well pretrained fuzzy parameters are first defuzzified, and the FDBN with defuzzified parameters is fine-tuned by the wake-sleep or stochastic gradient descent algorithm. This hybrid strategy not only avoids learning an intractable fuzzy neural network, but also greatly improves the classification capability of the FDBN. The experimental results on MNIST, NORB, and 15 Scene databases indicate that the FDBN with the hybrid learning approach can handle high-dimensional raw images directly. It inherits the fine nature of the FRBM and outperforms some state-of-the-art discriminative models in classification accuracy. Moreover, it shows better capability of robustness than a deep belief net when encountering noisy data.
Dominance as a New Trusted Computing Primitive for the Internet of Things

The Internet of Things (IoT) is rapidly emerging as one of the dominant computing paradigms of this decade.Applications range from in-home entertainment to large-scale industrial deployments such as controlling assembly lines and monitoring traffic.While IoT devices are in many respects similar to traditional computers, user expectations and deployment scenarios as well as cost and hardware constraints are sufficiently different to create new security challenges as well as new opportunities.This is especially true for large-scale IoT deployments in which a central entity deploys and controls a large number of IoT devices with minimal human interaction.Like traditional computers, IoT devices are subject to attack and compromise.Large IoT deployments consisting of many nearly identical devices are especially attractive targets.At the same time, recovery from root compromise by conventional means becomes costly and slow, even more so if the devices are dispersed over a large geographical area.In the worst case, technicians have to travel to all devices and manually recover them.Data center solutions such as the Intelligent Platform Management Interface (IPMI) which rely on separate service processors and network connections are not only not supported by existing IoT hardware, but are unlikely to be in the foreseeable future due to the cost constraints of mainstream IoT devices.This paper presents CIDER, a system that can recover IoT devices within a short amount of time, even if attackers have taken root control of every device in a large deployment.The recovery requires minimal manual intervention.After the administrator has identified the compromise and produced an updated firmware image, he/she can instruct CIDER to force the devices to reset and to install the patched firmware on the devices.We demonstrate the universality and practicality of CIDER by implementing it on three popular IoT platforms (HummingBoard Edge, Raspberry Pi Compute Module 3 and Nucleo-L476RG) spanning the range from high to low end.Our evaluation shows that the performance overhead of CIDER is generally negligible.
Practical Multi-Keyword and Boolean Search Over Encrypted E-mail in Cloud Server

With the outbreak of e-mail message leakage events, such as the Hillary Clinton’s Email Controversy, privacy and security of sensitive e-mail information have become users’ primary concern. Encrypted email seems to be a viable solution for providing security, but it will greatly limit their operations. Public encryption with keyword search (PEKS) scheme is a popular technology to incorporate security protection and favorable operability functions together, which can play an important role in searching over encrypted email in a cloud server. In this paper, we propose a practical PEKS scheme named as public-key multi-keyword searchable encryption with hidden structures (PMSEHS). It could enable e-mail receivers to do the multi-keyword and boolean search in the large encrypted email database as fast as possible, without revealing more information to the cloud server. We also give comparative experiments, which demonstrate that our scheme has a higher efficiency in multi-keyword search for encrypted emails.
Fast Cylinder Shape Matching Using Random Sample Consensus in Large Scale Point Cloud

In this paper, an algorithm is proposed that can perform cylinder type matching faster than the existing method in point clouds that represent space. The existing matching method uses Hough transform and completes the matching through preprocessing such as noise filtering, normal estimation, and segmentation. The proposed method completes the matching through the methodology of random sample consensus (RANSAC) and principal component analysis (PCA). Cylindrical pipe estimation is based on two mathematical models that compute the parameters and combine the results to predict spheres and lines. RANSAC fitting computes the center and radius of the sphere, which can be the radius of the cylinder axis and finds straight and curved areas through PCA. This allows fast matching without normal estimation and segmentation. Linear and curved regions are distinguished by a discriminant using eigenvalues. The linear region is the sum of the vectors of linear candidates, and the curved region is matched by a Catmull–Rom spline. The proposed method is expected to improve the work efficiency of the reverse design by matching linear and curved cylinder estimation without vertical/horizontal constraint and segmentation. It is also more than 10 times faster while maintaining the accuracy of the conventional method.
Shake to Communicate: Secure Handshake Acceleration-Based Pairing Mechanism for Wrist Worn Devices

With the booming penetration of wrist worn smart devices in daily lives, a wide range of applications have been enabled, such as exchanging social information, sharing sports data, and sending messages. Securing data exchange between these devices has become a challenging issue, considering the high security requirements and low computation capabilities of these wrist worn devices. In this paper, we propose a secure wrist worn smart device pairing scheme by exploiting the motion signal of the devices generated by the handshake to negotiate a reliable key between users. To ensure the security of key negotiation, a novel fuzzy cryptography algorithm is further developed. Compared with existing algorithms, the proposed algorithm avoids complicated error correction algorithms and has low requirements for data coincidence on the premise of individual differentiation. At the same time, the security is guaranteed by feature reordering and protection of auxiliary data. Extensive experimental results are provided, which demonstrate that the proposed handshake acceleration-based pairing scheme is robust, secure, and efficient.
A Single Shot Framework with Multi-Scale Feature Fusion for Geospatial Object Detection

With the rapid advances in remote-sensing technologies and the larger number of satellite images, fast and effective object detection plays an important role in understanding and analyzing image information, which could be further applied to civilian and military fields. Recently object detection methods with region-based convolutional neural network have shown excellent performance. However, these two-stage methods contain region proposal generation and object detection procedures, resulting in low computation speed. Because of the expensive manual costs, the quantity of well-annotated aerial images is scarce, which also limits the progress of geospatial object detection in remote sensing. In this paper, on the one hand, we construct and release a large-scale remote-sensing dataset for geospatial object detection (RSD-GOD) that consists of 5 different categories with 18,187 annotated images and 40,990 instances. On the other hand, we design a single shot detection framework with multi-scale feature fusion. The feature maps from different layers are fused together through the up-sampling and concatenation blocks to predict the detection results. High-level features with semantic information and low-level features with fine details are fully explored for detection tasks, especially for small objects. Meanwhile, a soft non-maximum suppression strategy is put into practice to select the final detection results. Extensive experiments have been conducted on two datasets to evaluate the designed network. Results show that the proposed approach achieves a good detection performance and obtains the mean average precision value of 89.0% on a newly constructed RSD-GOD dataset and 83.8% on the Northwestern Polytechnical University very high spatial resolution-10 (NWPU VHR-10) dataset at 18 frames per second (FPS) on a NVIDIA GTX-1080Ti GPU.
TensorClog: An Imperceptible Poisoning Attack on Deep Neural Network Applications

Internet application providers now have more incentive than ever to collect user data, which greatly increases the risk of user privacy violations due to the emerging of deep neural networks. In this paper, we propose TensorClog—a poisoning attack technique that is designed for privacy protection against deep neural networks. TensorClog has three properties with each of them serving a privacy protection purpose: 1) training on TensorClog poisoned data results in lower inference accuracy, reducing the incentive of abusive data collection; 2) training on TensorClog poisoned data converges to a larger loss, which prevents the neural network from learning the privacy; and 3) TensorClog regularizes the perturbation to remain a high structure similarity, so that the poisoning does not affect the actual content in the data. Applying our TensorClog poisoning technique to CIFAR-10 dataset results in an increase in both converged training loss and test error by 300% and 272%, respectively. It manages to maintain data’s human perception with a high SSIM index of 0.9905. More experiments including different limited information attack scenarios and a real-world application transferred from pre-trained ImageNet models are presented to further evaluate TensorClog’s effectiveness in more complex situations.
Composite Event Recognition for Maritime Monitoring

Maritime monitoring systems support safe shipping as they allow for the real-time detection of dangerous, suspicious and illegal vessel activities. We present such a system using the Run-Time Event Calculus, a composite event recognition system with formal, declarative semantics. For effective recognition, we developed a library of maritime patterns in close collaboration with domain experts. We present a thorough evaluation of the system and the patterns both in terms of predictive accuracy and computational efficiency, using real-world datasets of vessel position streams and contextual geographical information.
Omnidirectional Pedestrian Detection by Rotation Invariant Training

Recently much progress has been made in pedestrian detection by utilizing the learning ability of convolutional neural networks (CNNs). However, due to the lack of omnidirectional images to train CNNs, few CNN-based detectors have been proposed for omnidirectional pedestrian detection. One significant difference between omnidirectional images and perspective images is that the appearance of pedestrians is rotated in omnidirectional images. A previous method has dealt with this by transforming omnidirectional images into perspective images in the test phase. However, this method has significant drawbacks, namely, the computational cost and the performance degradation caused by the transformation. To address this issue, we propose a rotation invariant training method, which only uses randomly rotated perspective images without any additional annotation. By this method, existing large-scale datasets can be utilized. In test phase, omnidirectional images can be used without the transformation. To group predicted bounding boxes, we also develop a bounding box refinement, which works better for our detector than non-maximum suppression. The proposed detector achieved a state-of-the-art performance on four public benchmarks.
Fashion recommendations through cross-media information retrieval

Fashion recommendation has attracted much attention given its ready applications to e-commerce. Traditional methods usually recommend clothing products to users on the basis of their textual descriptions. Product images, although covering a large resource of information, are often ignored in the recommendation processes. In this study, we propose a novel fashion product recommendation method based on both text and image mining techniques. Our model facilitates two kinds of fashion recommendation, namely, similar product and mix-and-match, by leveraging text-based product attributes and image features. To suggest similar products, we construct a new similarity measure to compare the image colour and texture descriptors. For mix-and-match recommendation, we firstly adopt convolutional neural network (CNN) to classify fine-grained clothing categories and fine-grained clothing attributes from product images. Algorithm is developed to make mix-and-match recommendations by integrating the image extracted categories and attributes information are with text-based product attributes. Our comprehensive experimental work on a real-life online dataset has demonstrated the effectiveness of the proposed method.