Reviewerly/exHarmony huggingface dataset:
How the Accuracy and Confidence of Sensitivity Classification Affects Digital Sensitivity Review

Government documents must be manually reviewed to identify any sensitive information, e.g., confidential information, before being publicly archived. However, human-only sensitivity review is not practical for born-digital documents due to, for example, the volume of documents that are to be reviewed. In this work, we conduct a user study to evaluate the effectiveness of sensitivity classification for assisting human sensitivity reviewers. We evaluate how the accuracy and confidence levels of sensitivity classification affects the number of documents that are correctly judged as being sensitive (reviewer accuracy) and the time that it takes to sensitivity review a document (reviewing speed). In our within-subject study, the participants review government documents to identify real sensitivities while being assisted by three sensitivity classification treatments , namely None (no classification predictions), Medium (sensitivity predictions from a simulated classifier with a balanced accuracy (BAC) of 0.7), and Perfect (sensitivity predictions from a classifier with an accuracy of 1.0). Our results show that sensitivity classification leads to significant improvements (ANOVA, p &lt; 0.05) in reviewer accuracy in terms of BAC (+37.9% Medium , +60.0% Perfect ) and also in terms of F 2 (+40.8% Medium , +44.9% Perfect ). Moreover, we show that assisting reviewers with sensitivity classification predictions leads to significantly increased (ANOVA, p &lt; 0.05) mean reviewing speeds (+72.2% Medium , +61.6% Perfect ). We find that reviewers do not agree with the classifier significantly more as the classifier’s confidence increases. However, reviewing speed is significantly increased when the reviewers agree with the classifier (ANOVA, p &lt; 0.05). Our in-depth analysis shows that when the reviewers are not assisted with sensitivity predictions, mean reviewing speeds are 40.5% slower for sensitive judgements compared to not-sensitive judgements. However, when the reviewers are assisted with sensitivity predictions, the difference in reviewing speeds between sensitive and not-sensitive judgements is reduced by ˜10%, from 40.5% to 30.8%. We also find that, for sensitive judgements, sensitivity classification predictions significantly increase mean reviewing speeds by 37.7% when the reviewers agree with the classifier’s predictions ( t -test, p &lt; 0.05). Overall, our findings demonstrate that sensitivity classification is a viable technology for assisting human reviewers with the sensitivity review of digital documents.
Interpretable Multivariate Time Series Forecasting with Temporal Attention Convolutional Neural Networks

Data in time series format, such as biological signals from medical sensors or machine signals from sensors in industrial environments are rich sources of information that can give crucial insights on the present and future condition of a person or machine. The task of predicting future values of time series has been initially approached with simple machine learning methods, and lately with deep learning. Two models that have shown good performance in this task are the temporal convolutional network and the attention module. However, despite the promising results of deep learning methods, their black-box nature makes them unsuitable for real-world applications where the predictions need to be explainable in order to be trusted. In this paper we propose an architecture comprised of a temporal convolutional network with an attention mechanism that makes predictions while presenting the timesteps of the input that were most influential for future outputs. We apply it on two datasets and we show that we gain interpretability without degrading the accuracy compared to the original temporal convolutional models. We then go one step further and we combine our configuration with various machine learning methods on top, creating a pipeline that achieves interpretability both across timesteps and input features. We use it to forecast a different variable from one of the above datasets and we study how the accuracy is affected compared to the original black-box approach.
Automotive Velocity Sensing Using Millimeter-Wave Interferometric Radar

In this article, we demonstrate the use of interferometric radar for high-accuracy measurement of the ground speed of a moving vehicle. Recent work has shown the capability of radar systems using interferometric antenna apertures to directly measure the angular velocity of objects. In this work, we apply this new measurement to the automotive application of velocity estimation. We start by formulating the interferometric response from a ground surface, which is modeled as a summation of independent point scatterers. Next, we present two velocity estimation methods: a Bayesian method that uses a signal model and a neural network-based approach. We then describe a 40-GHz interferometric radar system that we have developed and present on-vehicle measurements. Finally, we present the results of the two estimators and compare these results with the theoretical accuracy of the system. We show that the Bayesian and neural net estimators achieved a root-mean-square velocity error of 0.403 and 0.183 m/s, respectively.
Manga filling style conversion with screentone variational autoencoder

Western color comics and Japanese-style screened manga are two popular comic styles. They mainly differ in the style of region-filling. However, the conversion between the two region-filling styles is very challenging, and manually done currently. In this paper, we identify that the major obstacle in the conversion between the two filling styles stems from the difference between the fundamental properties of screened region-filling and colored region-filling. To resolve this obstacle, we propose a screentone variational autoencoder, ScreenVAE, to map the screened manga to an intermediate domain. This intermediate domain can summarize local texture characteristics and is interpolative. With this domain, we effectively unify the properties of screening and color-filling, and ease the learning for bidirectional translation between screened manga and color comics. To carry out the bidirectional translation, we further propose a network to learn the translation between the intermediate domain and color comics. Our model can generate quality screened manga given a color comic, and generate color comic that retains the original screening intention by the bitonal manga artist. Several results are shown to demonstrate the effectiveness and convenience of the proposed method. We also demonstrate how the intermediate domain can assist other applications such as manga inpainting and photo-to-comic conversion.
A Lattice-Based Provably Secure Multisignature Scheme in Quantum Random Oracle Model

The multisignature schemes are attracted to utilize in some cryptographic applications such as the blockchain. Though the lattice-based constructions of multisignature schemes exist as quantum-secure multisignature, a multisignature scheme whose security is proven in the quantum random oracle model (QROM), rather than the classical random oracle model (CROM), is not known. In this paper, we propose a first lattice-based multisignature scheme whose security is proven in QROM. The difficultly of proving the security in QROM than CROM is how to program the random oracle in the security proof. Although our proposed scheme is based on the Dilithium-QROM signature whose security is proven in QROM, their proof technique cannot be directly applied to the multisignature setting. To solve the problems in the security proof, we develop several proof techniques in QROM. First, we employ the searching query technique by Targi and Unruh to convert the Dilithium-QROM into the multisignature setting. For the second, we develop a new programming technique in QROM, since the conventional programming techniques seem not to work in the multisignature setting of QROM. We combine the programming technique by Unruh with the one by Liu and Zhandry. The new technique enables us to program the random oracle in QROM and to construct the signing oracle in the security proof.
Monadic Pavlovian associative learning in a backpropagation-free photonic network

Over a century ago, Ivan P. Pavlov, in a classic experiment, demonstrated how dogs can learn to associate a ringing bell with food, thereby causing a ring to result in salivation. Today, it is rare to find the use of Pavlovian type associative learning for artificial intelligence (AI) applications even though other learning concepts, in particular backpropagation on artificial neural networks (ANNs) have flourished. However, training using the backpropagation method on 'conventional' ANNs, especially in the form of modern deep neural networks (DNNs), is computationally and energy intensive. Here we experimentally demonstrate a form of backpropagation-free learning using a single (or monadic) associative hardware element. We realize this on an integrated photonic platform using phase-change materials combined with on-chip cascaded directional couplers. We then develop a scaled-up circuit network using our monadic Pavlovian photonic hardware that delivers a distinct machine-learning framework based on single-element associations and, importantly, using backpropagation-free architectures to address general learning tasks. Our approach reduces the computational burden imposed by learning in conventional neural network approaches, thereby increasing speed, whilst also offering higher bandwidth inherent to our photonic implementation.
On Structural Properties of <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mi>ξ</mi> </math>-Complex Fuzzy Sets and Their Applications

Complex fuzzy sets are the novel extension of Zadeh’s fuzzy sets. In this paper, we comprise the introduction to the concept of <math xmlns="http://www.w3.org/1998/Math/MathML" id="M2"> <mi>ξ</mi> </math> -complex fuzzy sets and proofs of their various set theoretical properties. We define the notion of <math xmlns="http://www.w3.org/1998/Math/MathML" id="M3"> <mfenced open="(" close=")" separators="|"> <mrow> <mi>α</mi> <mo>,</mo> <mi>δ</mi> </mrow> </mfenced> </math> -cut sets of <math xmlns="http://www.w3.org/1998/Math/MathML" id="M4"> <mi>ξ</mi> </math> -complex fuzzy sets and justify the representation of an <math xmlns="http://www.w3.org/1998/Math/MathML" id="M5"> <mi>ξ</mi> </math> -complex fuzzy set as a union of nested intervals of these cut sets. We also apply this newly defined concept to a physical situation in which one may judge the performance of the participants in a given task. In addition, we innovate the phenomena of <math xmlns="http://www.w3.org/1998/Math/MathML" id="M6"> <mi>ξ</mi> </math> -complex fuzzy subgroups and investigate some of their fundamental algebraic attributes. Moreover, we utilize this notion to define level subgroups of these groups and prove the necessary and sufficient condition under which an <math xmlns="http://www.w3.org/1998/Math/MathML" id="M7"> <mi>ξ</mi> </math> -complex fuzzy set is <math xmlns="http://www.w3.org/1998/Math/MathML" id="M8"> <mi>ξ</mi> </math> -complex fuzzy subgroup. Furthermore, we extend the idea of <math xmlns="http://www.w3.org/1998/Math/MathML" id="M9"> <mi>ξ</mi> </math> -complex fuzzy normal subgroup to define the quotient group of a group <math xmlns="http://www.w3.org/1998/Math/MathML" id="M10"> <mi>G</mi> </math> by this particular <math xmlns="http://www.w3.org/1998/Math/MathML" id="M11"> <mi>ξ</mi> </math> -complex fuzzy normal subgroup and establish an isomorphism between this quotient group and a quotient group of <math xmlns="http://www.w3.org/1998/Math/MathML" id="M12"> <mi>G</mi> </math> by a specific normal subgroup <math xmlns="http://www.w3.org/1998/Math/MathML" id="M13"> <msub> <mrow> <mi>G</mi> </mrow> <mrow> <msup> <mrow> <mi>A</mi> </mrow> <mrow> <mi>ξ</mi> </mrow> </msup> </mrow> </msub> </math> .
Efficient secure surveillance on smart healthcare IoT system through cosine-transform encryption

The emerging technologies with IoT (Internet of Things) systems are elevated as a prototype and combination of the smart connectivity ecosystem. These ecosystems are appropriately connected in a smart healthcare system which are generating finest monitoring activities among the patients, well-organized diagnosis process, intensive support and care against the traditional healthcare operations. But facilitating these highly technological adaptations, the preserving personal information of the patients are on the risk with data leakage and privacy theft in the current revolution. Concerning secure protection and privacy theft of the patient’s information. We emphasized this paper on secure monitoring with the help of intelligently recorded summary’s keyframe extraction and applied two rounds lightweight cosine-transform encryption. This article includes firstly, a regimented process of keyframe extraction which is employed to retrieve meaningful frames of image through visual sensor with sending alert (quick notice) to authority. Secondly, employed two rounds of lightweight cosine-transform encryption operation of agreed (detected) keyframes to endure security and safety for the further any kinds of attacks from the adversary. The combined methodology corroborates highly usefulness with engendering appropriate results, little execution of encryption time (0.2277-0.2607), information entropy (7.9996), correlation coefficient (0.0010), robustness (NPCR 99.6383, UACI 33.3516), uniform histogram deviation (R 0.0359, G 0.0492, B 0.0582) and other well adopted secure ideology than any other keyframe or image encryption approaches. Furthermore, this incorporating method can effectively reduce vital communication cost, bandwidth issues, storage, data transmission cost and effective timely judicious analysis over the occurred activities and keep protection by using effective encryption methodology to remain attack free from any attacker or adversary, and provide confidentiality about patient’s privacy in the smart healthcare system.
Laser Ablation Manipulator Coverage Path Planning Method Based on an Improved Ant Colony Algorithm

Coverage path planning on a complex free-form surface is a representative problem that has been steadily investigated in path planning and automatic control. However, most methods do not consider many optimisation conditions and cannot deal with complex surfaces, closed surfaces, and the intersection of multiple surfaces. In this study, a novel and efficient coverage path-planning method is proposed that considers trajectory optimisation information and uses point cloud data for environmental modelling. First, the point cloud data are denoised and simplified. Then, the path points are converted into the rotation angle of each joint of the manipulator. A mathematical model dedicated to energy consumption, processing time, and path smoothness as optimisation objectives is developed, and an improved ant colony algorithm is used to solve this problem. Two measures are proposed to prevent the algorithm from being trapped in a local optimum, thereby improving the global search ability of the algorithm. The standard test results indicate that the improved algorithm performs better than the ant colony algorithm and the max–min ant system. The numerical simulation results reveal that compared with the point cloud slicing technique, the proposed method can obtain a more efficient path. The laser ablation de-rusting experiment results specify the utility of the proposed approach.
ScribbleBox: Interactive Annotation Framework for Video Object Segmentation

Manually labeling video datasets for segmentation tasks is extremely time consuming. We introduce ScribbleBox, an interactive framework for annotating object instances with masks in videos with a significant boost in efficiency. In particular, we split annotation into two steps: annotating objects with tracked boxes, and labeling masks inside these tracks. We introduce automation and interaction in both steps. Box tracks are annotated efficiently by approximating the trajectory using a parametric curve with a small number of control points which the annotator can interactively correct. Our approach tolerates a modest amount of noise in box placements, thus typically requiring only a few clicks to annotate a track to a sufficient accuracy. Segmentation masks are corrected via scribbles which are propagated through time. We show significant performance gains in annotation efficiency over past work. We show that our ScribbleBox approach reaches 88.92% J&F on DAVIS2017 with an average of 9.14 clicks per box track, and only 4 frames requiring scribble annotation in a video of 65.3 frames on average.
Low-Profile Induced-Voltage Distance Ranger for Smart Contact Lenses

<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Objective:</i> In this paper, we present a novel, low-profile, scleral-coil based, distance ranging system which is suitable for smart, accommodating contact lenses. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Methods:</i> We measure the induced emf between a set of four thin semi-circular coils patterned on flexible Kapton substrates that conform to the eyes’ sclera. This induced emf is a function of eye gaze angles. The system then determines the distance from the eyes to the desired object via the triangulation of these eye gaze angles <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Results</i> : Experiments on eyeball simulated tissue gels indicate an accurate prediction of object distance in the 0.1-15 D range with a 0.15 D RMS error and object direction in the −15 to 15-degree arc with 0.4-degree RMS error, respectively. The energy required was determined to be as low as 20 μJ per range reading. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Conclusion:</i> Experimental data shows that our proposed new method of eye-tracking and distance ranging system can accurately predict eye-gaze angles and object-distance, whilst using only 20 μJ per range reading. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Significance:</i> The high-accuracy, low-profile and reduced energy requirements of the proposed eye-tracking technique, make it suitable for applications in the vast field of adaptive optics such as smart contact lenses and other low-power vision corrective applications.
Selection of Strategy for Large Group Emergency Decision-making based on Risk Measurement.

Different strategies can be applied in large group emergency decision-making, including the voting strategy, the delegation strategy, and the averaging strategy, etc. The choice of decision-making strategy not only affects the efficiency but also the accuracy of decision-making. Based on signal detection theory, a preference representation method for large group emergency decision-making is proposed. Combining the characteristics of the strategies, rules for the fusion of group preferences are presented, and a risk measurement model for large group emergency decision-making is constructed. By comparing the decision-making risk degree of each decision-making strategy under different conditions of group perception information breadth and external environment, the universal rules of large group emergency decision-making strategy selection are obtained. The results revealed that there are comparative advantages among decision-making strategies in specific situations, and that reasonable decision-making strategy selection can effectively control decision-making risk. The results provide a scientific reference and basis for guiding strategy selection in large group emergency decision-making.
Criminals And Missing Children Identification Using Face Recognition And Web Scrapping

Face recognition is a biometric based technology that maps an individual's facial features mathematically and stores the data as a face print. It employs Machine Learning on the image and generates a feature vector which maps an object with array of numbers. This technology is used by organizations such as Google and Facebook to create a digital profile for its users. This project proposes to use this technology for identifying criminals who are on the run from their previous records. An NCRB (National Crime Records Bureau) report shows that 70% of crimes are repeatedly committed by the same criminals. These criminals can be identified by the face recognition from an image or video frame which is captured by the cameras which are installed in various locations and it can also be used for identifying missing children. The disadvantage posed is that the images are usually blurred, have less clarity and not recognizable to the human eye. The proposed system can successfully recognize more than one face which is useful for quickly searching suspected persons as the computation time is very low. It creates a unique template for each face and compare them with other images available in dataset. If the match is found for the input face, then the details associated with the related image will be displayed. This system will decrease the crimes and ensure the security in our society.
Multitask Representation Learning With Multiview Graph Convolutional Networks

Link prediction and node classification are two important downstream tasks of network representation learning. Existing methods have achieved acceptable results but they perform these two tasks separately, which requires a lot of duplication of work and ignores the correlations between tasks. Besides, conventional models suffer from the identical treatment of information of multiple views, thus they fail to learn robust representation for downstream tasks. To this end, we tackle link prediction and node classification problems simultaneously via multitask multiview learning in this article. We first explain the feasibility and advantages of multitask multiview learning for these two tasks. Then we propose a novel model named MT-MVGCN to perform link prediction and node classification tasks simultaneously. More specifically, we design a multiview graph convolutional network to extract abundant information of multiple views in a network, which is shared by different tasks. We further apply two attention mechanisms: view the attention mechanism and task attention mechanism to make views and tasks adjust the view fusion process. Moreover, view reconstruction can be introduced as an auxiliary task to boost the performance of the proposed model. Experiments on real-world network data sets demonstrate that our model is efficient yet effective, and outperforms advanced baselines in these two tasks.
A Binarized Segmented ResNet Based on Edge Computing for Re-Identification

With the advent of the Internet of Everything, more and more devices are connected to the Internet every year. In major cities, in order to maintain normal social order, the demand for deployed cameras is also increasing. In terms of public safety, person Re-Identification (ReID) can play a big role. However, the current methods of ReID are to transfer the collected pedestrian images to the cloud for processing, which will bring huge communication costs. In order to solve this problem, we combine the recently emerging edge computing and use the edge to combine the end devices and the cloud to implement our proposed binarized segmented ResNet. Our method is mainly to divide a complete ResNet into three parts, corresponding to the end devices, the edge, and the cloud. After joint training, the corresponding segmented sub-network is deployed to the corresponding side, and inference is performed to realize ReID. In our experiments, we compared some traditional ReID methods in terms of accuracy and communication overhead. It can be found that our method can greatly reduce the communication cost on the basis of basically not reducing the recognition accuracy of ReID. In general, the communication cost can be reduced by four to eight times.