Reviewerly/exHarmony huggingface dataset:
Saliency Prediction Network for $360^\circ$ Videos

Panoramic videos are becoming more and more easily obtained for common users. Although these videos have 360° field of view, they are usually displayed with perspective views, which needs the saliency informations for viewing angle selection. In this paper, we propose a saliency prediction network for 360° videos. Our network takes video frames and optical flows in cube map format as input, thus it does not suffer from image distorations of panoramic frames. The network is composed of feature encoding module and saliency prediction module. The feature encoding module extracts spatial and temporal features. Then these features are processed by a decoder and bidirectional convolutional LSTM for saliency prediction. To more thoroughly mine the motion information, the temporal stream of feature encoding module accepts optical flows before and after current frame. We also incorporate the global feature of video frames, residual attention and Gaussian priors into the network by considering the viewing behavior of 360° videos, which is useful for performance improvement. To evaluate the performance of our method, we compare it with three state-of-the-art saliency prediction algorithms on two publicly available datasets. The experimental result has shown the effectiveness of our method, which gets the best performance.
An Efficient Passive-to-Active Compiler for Honest-Majority MPC over Rings

Multiparty computation (MPC) over rings such as \(\mathbb {Z}_{2^{32}}\) or \(\mathbb {Z}_{2^{64}}\) has received a great deal of attention recently due to its ease of implementation and attractive performance. Several actively secure protocols over these rings have been implemented, for both the dishonest majority setting and the setting of three parties with one corruption. However, in the honest majority setting, no concretely efficient protocol for arithmetic computation over rings has yet been proposed that allows for an arbitrary number of parties.We present a novel compiler for MPC over the ring \(\mathbb {Z}_{2^{k}}\) in the honest majority setting that turns a semi-honest protocol into an actively secure protocol with very little overhead. The communication cost per multiplication is only twice that of the semi-honest protocol, making the resultant actively secure protocol almost as fast.To demonstrate the efficiency of our compiler, we implement both an optimized 3-party variant (based on replicated secret-sharing), as well as a protocol for n parties (based on a recent protocol from TCC 2019). For the 3-party variant, we obtain a protocol which outperforms the previous state of the art that we can experimentally compare against. Our n-party variant is the first implementation for this particular setting, and we show that it performs comparably to the current state of the art over fields.
Simultaneous convolutional neural network for highly efficient image steganography

Over the past decade, steganography has attracted a large number of researchers' attention because of its practical value to information security. Unlike cryptography which aims to protect the content of the message, steganography involves hiding the message within a transport layer. Most traditional methods directly hide information in one or two least significant bits. Recently, miscellaneous effort with deep learning approach has been devoted to hiding more information without losing the integrity of the transportation layer. In this paper, our work focuses on image steganography which means hiding an image (secret image) inside another image of the same size (cover image). A deep convolutional neural network with similar architecture to U-Net is employed as the hiding network, and a new training scheme is proposed to speed up the training phase. Through extensive experiments, it has been verified that the new network architecture, combined with the new training strategy, can result in lower mean square error of pixel difference whereas the training time is reduced by half.
Multiview Scene Image Inpainting Based on Conditional Generative Adversarial Networks

With the help of a multiview system, an unmanned vehicle system can better understand the surrounding environment and choose a more accurate and safer path to avoid obstacles. However, due to the interference of the signal or the loss of part of the signal during the acquisition, processing, compression, transmission and decompression of the video image signal, the local area of the image is abnormal, which affects the perception and decision of the system. This article addresses the problems of inaccurate restored images and noise in the restored images by proposing an image restoration method that is applied to a multicamera system. We utilize different perspective images captured by different cameras to assist and constrain the restoration of the damaged image. This method restores the image by combining sample representations and sample distribution models which respectively based on self-encoder reconstruction loss learning and generative adversarial networks. In this method, the infrastructure is a conditional generative adversarial network, the condition is the images that are from the other perspectives, and the generator is a self-encoder structure with cross-layer connection, group convolution and feature map channel exchanged. This method was carried out on a dataset recorded in Zurich using a pair of cameras mounted on a mobile platform. The experimental results demonstrate that the proposed method is superior to the existing methods in terms of mean L1 Loss, mean L2 Loss and the peak signal to noise ratio (PSNR).
Development and Evaluation of a New Platform for Accelerating Cross-Domain Data Exchange and Cooperation

Abstract The technologies for collecting and analyzing data are developing significantly, enabling us to gain new knowledge from various types of data. Recently, the considerably increasing expectation for cross-domain data exchange and cooperation has attracted attention from data markets. However, as we lack data-utilization knowledge, an accurate evaluation of data is difficult; a non-price indicator for deciding the exchange of data is needed. Thus, in this study, we propose a new online platform for stakeholders to communicate regarding data utilization. Our online platform, called Web Innovators Marketplace on Data Jackets (Web IMDJ), is built with reference to the process of IMDJ workshops. Web IMDJ is superior to the conventional paper-based IMDJ (hereafter, Table IMDJ) in terms of reducing the burden on conducting workshops. Notably, Table IMDJ and Web IMDJ have different communication media, and this may affect the data-utilization knowledge proposed in the workshop. Therefore, we conducted workshops on both platforms under a controlled experimental environment to compare the proposed data-utilization knowledge. Consequently, the knowledge proposed in Web IMDJ gained equal or higher ratings by third-party evaluators (those who did not join in the experimental workshop). By contrast, subjects themselves evaluated the knowledge proposed in Table IMDJ as superior to Web IMDJ. These results revealed that both workshops have advantages as data-utilization platforms. Furthermore, we derived the best practices to utilize data effectively from a detailed analysis of the data obtained from the experimental workshops.
Locating nearby physical objects in augmented reality

Locating objects in physical environments can be an exhausting and frustrating task, particularly when these objects are out of the user's view or occluded by other objects. With recent advances in Augmented Reality (AR), these environments can be augmented to visualize objects for which the user searches. However, it is currently unclear which visualization strategy can best support users in locating these objects. In this paper, we compare a printed map to three different AR visualization strategies: (1) in-view visualization, (2) out-of-view visualization, and (3) the combination of in-view and out-of-view visualizations. Our results show that in-view visualization reduces error rates for object selection accuracy, while additional out-of-view object visualization improves users' search time performance. However, combining in-view and out-of-view visualizations leads to visual clutter, which distracts users.
Multi-copy nested entanglement purification for quantum repeaters

Photon decoherence is one of the main obstacles in global-scale quantum communication. Entanglement purification is a powerful approach to distill high quality entanglement ensembles from low quality entanglement ensembles. In this paper, we propose a multi-copy nested entanglement purification protocol (EPP). After performing the entanglement purification, we can distill a high fidelity entangled photon pair from multiple copies of low fidelity photon pairs. This protocol has several advantages. First, the double-pair noise components generated from the spontaneous parametric down conversion (SPDC) source can be automatically eliminated. Second, with the help of entanglement swapping, the distance of purified entangled state can be extended. Third, this EPP is based on current available technology. Fourth, we can obtain high fidelity by performing this EPP only once. This EPP may have potential application in future long-distance quantum communication.
Emotion analysis from faces for social robotics

A social robot is able to perceive the information about the environment (both in terms of persons and objects populating the scene), to reason about the acquired information and to interact with the human in a proper way. Among the information required for the interaction with a human, the capability of analysing the emotion is surely among the most important ones. Another relevant feature of social robots is the possibility to interact with the human in real time, without any latency that could introduce a delay in the talk between the human and the robot. It means that all the processing of the information (for instance the analysis of the sequence of images for the detection of the persons and the consequent emotion analysis) needs to be performed directly on board of the robot, without any possibility to use high performance servers (for instance services on the cloud) but only small devices that can be installed directly on board of the robotic platform. In this paper we propose MIVIAbot, a robotic platform based on the Pepper humanoid robot and equipped with a small, low cost and low energy consumption embedded device installed directly on board of Pepper, without any requirements for a Wi-Fi connection that could introduce any latency for the transmission of the data. Furthermore, we also propose a method for the analysis of the emotion of the persons by face analysis, able to run on the considered hardware platform in real time without paying in terms of accuracy. The experimentation conducted over various widely adopted datasets of images and videos for emotion analysis confirms both the efficiency and the effectiveness of the proposed approach.
Importance-Aware Semantic Segmentation with Efficient Pyramidal Context Network for Navigational Assistant Systems

Semantic Segmentation (SS) is a task to assign semantic label to each pixel of the images, which is of immense significance for autonomous vehicles, robotics and assisted navigation of vulnerable road users. It is obvious that in different application scenarios, different objects possess hierarchical importance and safety-relevance, but conventional loss functions like cross entropy have not taken the different levels of importance of diverse traffic elements into consideration. To address this dilemma, we leverage and re-design an importance-aware loss function, throwing insightful hints on how importance of semantics are assigned for real-world applications. To customize semantic segmentation networks for different navigational tasks, we extend ERF-PSPNet, a realtime segmenter designed for wearable device aiding visually impaired pedestrians, and propose BiERF-PSPNet, which can yield high-quality segmentation maps with finer spatial details exceptionally suitable for autonomous vehicles. A comprehensive variety of experiments with these efficient pyramidal context networks on CamVid and Cityscapes datasets demonstrates the effectiveness of our proposal to support diverse navigational assistant systems.
Real-Time and Low-Memory Multi-Faces Detection System Design With Naive Bayes Classifier Implemented on FPGA

In recent years, face detection has been widely applied to a variety of fields, such as face recognition, image focusing, and surveillance systems. This study proposes a real-time multi-faces detection system based on naive Bayesian classifier using Field Programmable Gate Array (FPGA). The system includes three main parts, feature extraction, candidate face detection, and false elimination. First, downscale the image to the image pyramid and extract local binary image features from each downscaling image. With the bit-plane slicing for Local Binary Pattern (LBP) can save the memory consumption and speed up the computation. Then, adopt the naive Bayesian classifier to identify candidate faces. Finally use skin color filter and face overlapping elimination to remove false positives. The experimental results show that the accuracy rate is up to 96.14% in face detection, which demonstrates the proposed real-time multi-faces detection system is indeed effective and efficient.
Search Strategy to Update Systematic Literature Reviews in Software Engineering

[Context] Systematic Literature Reviews (SLRs) have been adopted within the Software Engineering (SE) domain for more than a decade to provide meaningful summaries of evidence on several topics. Many of these SLRs are now outdated, and there are no standard proposals on how to update SLRs in SE. [Objective] The goal of this paper is to provide recommendations on how to best to search for evidence when updating SLRs in SE. [Method] To achieve our goal, we compare and discuss outcomes from applying different search strategies to identifying primary studies in a previously published SLR update on effort estimation. [Results] The use of a single iteration forward snowballing with Google Scholar, and employing the original SLR and its primary studies as a seed set seems to be the most cost-effective way to search for new evidence when updating SLRs. [Conclusions] The recommendations can be used to support decisions on how to update SLRs in SE.
Valiant’s Universal Circuits Revisited: An Overall Improvement and a Lower Bound

A universal circuit (UC) is a general-purpose circuit that can simulate arbitrary circuits (up to a certain size n). At STOC 1976 Valiant presented a graph theoretic approach to the construction of UCs, where a UC is represented by an edge universal graph (EUG) and is recursively constructed using a dedicated graph object (referred to as supernode). As a main end result, Valiant constructed a 4-way supernode of size 19 and an EUG of size $$4.75n\log n$$ (omitting smaller terms), which remained the most size-efficient even to this day (after more than 4 decades). Motivated by the emerging applications of UCs in various privacy preserving computation scenarios, we revisit Valiant's universal circuits, and propose a 4-way supernode of size 18, and an EUG of size $$4.5n\log n$$. As confirmed by our implementations, we reduce the size of universal circuits (and the number of AND gates) by more than 5% in general, and thus improve upon the efficiency of UC-based cryptographic applications accordingly. Our approach to the design of optimal supernodes is computer aided (rather than by hand as in previous works), which might be of independent interest. As a complement, we give lower bounds on the size of EUGs and UCs in Valiant's framework, which significantly improves upon the generic lower bound on UC size and therefore reduces the gap between theory and practice of universal circuits.
Interoperability and Considerations for Standards-Based Exchange of Medical Images: HIMSS-SIIM Collaborative White Paper

Abstract This white paper explores the considerations of standards-based interoperability of medical images between organizations, patients, and providers. In this paper, we will look at three different standards-based image exchange implementations that have been deployed to facilitate exchange of images between provider organizations. The paper will describe how each implementation uses applicable technology and standards; the image types that are included; and the governance policies that define participation, access, and trust. Limitations of the solution or non-standard approaches to solve challenges will also be identified. Much can be learned from successes elsewhere, and those learnings will point to recommendations of best practices to facilitate the adoption of image exchange.
A novel approach for the development of the Twofish algorithm based on multi-level key space

This paper presents a new modification of the Twofish algorithm for strengthening security. This is done by using two additional control keys during 16 rounds of the standard Feistel Encryption algorithm. Our proposed algorithm uses three keys in the encryption and decryption processes instead of only one key for controlling variable bit sizes of the blocks (either 1or 2, or 4 or 8 bits), which determine the state table numbers. These tables are constructed from the addition in a Galois field (GF(2n)) based on the block bit size to increase the complexity of the proposed algorithm. Results are evaluated based on the criteria of complexity, time encryption, throughput, National Institute of Standards and Technology (NIST), histogram analysis, Number of Pixel of Change Rate (NPCR) and Unified Average Changing Intensity (UACI). The simulation results show that our proposed algorithm exceeds the original algorithm in complexity, where it obtained (24,722,900) probabilities for 16 rounds. In addition, NIST tests and histogram analysis provide better results for our proposed algorithm compared with the original. Also, NPCR of our proposed Twofish is close to the ideal value of this standard. All these results prove that our modification of the Twofish algorithm makes it stronger against attacks by increasing its complexity.
Estimation of construction project building cost by back-propagation neural network

Purpose Building cost is an important part of construction projects, and its correct estimation has important guiding significance for the follow-up decision-making of construction units. Design/methodology/approach This study focused on the application of back-propagation (BP) neural network in the estimation of building cost. First, the influencing factors of building cost were analyzed. Six factors were selected as input of the estimation model. Then, a BP neural network estimation model was established and trained by ten samples. Findings According to the experimental results, it was found that the estimation model converged at about 85 times; compared with radial basis function (RBF), the estimation accuracy of the model was higher, and the average error was 5.54 per cent, showing a good reliability in cost estimation. Originality/value The results of this study provide a reliable basis for investment decision-making in the construction industry and also contribute to the further application of BP neural network in cost estimation.
Scale-invariant batch-adaptive residual learning for person re-identification

The problem of person re-identification (re-ID) deals with matching two similar persons in probe and gallery sets. The underlying pattern matching task can become more complex as similar persons can appear in different scales in the two sets. In this paper, we address this challenging problem of scale-invariant person re-ID. As a solution, we propose two scale-invariant residual networks with a new loss function for deep metric learning. The first network, termed as Scale Invariant Triplet Network (SI-TriNet), is deeper and is trained from the pre-trained weights. In contrast, the second network, named Scale-Invariant Siamese Resnet-32 (SISR-32), is shallower and uses training from the scratch. Deep metric learning for both the networks are realized through a batch adaptive triplet loss function. Extensive comparisons and ablation studies on the benchmark Market-1501 and CUHK03 datasets clearly demonstrate the effectiveness of the proposed formulation.
Adaptive Activation Thresholding: Dynamic Routing Type Behavior for Interpretability in Convolutional Neural Networks

There is a growing interest in strategies that can help us understand or interpret neural networks -- that is, not merely provide a prediction, but also offer additional context explaining why and how. While many current methods offer tools to perform this analysis for a given (trained) network post-hoc, recent results (especially on capsule networks) suggest that when classes map to a few high level ``concepts'' in the preceding layers of the network, the behavior of the network is easier to interpret or explain. Such training may be accomplished via dynamic/EM routing where the network ``routes'' for individual classes (or subsets of images) are dynamic and involve few nodes even if the full network may not be sparse. In this paper, we show how a simple modification of the SGD scheme can help provide dynamic/EM routing type behavior in convolutional neural networks. Through extensive experiments, we evaluate the effect of this idea for interpretability where we obtain promising results, while also showing that no compromise in attainable accuracy is involved. Further, we show that the minor modification is seemingly ad-hoc, the new algorithm can be analyzed by an approximate method which provably matches known rates for SGD.
An area-efficient bit-serial sequential polynomial basis finite field GF(2 ) multiplier

Many cryptographic and error control coding algorithms rely on finite field arithmetic. Hardware implementation of these algorithms requires an efficient realization of finite field GF(2m) arithmetic operations. Finite field multiplication is complex among the basic arithmetic operations, and it is employed in field exponentiation and inversion operations. Various algorithms and architectures are proposed in the literature for hardware implementation of finite field multiplication to achieve a reduction in area and delay. In this paper, a modified interleaved modular reduction multiplication algorithm and its bit-serial sequential architecture are proposed. It is observed from the comparison of analytical results that the proposed architecture achieves the reduction in area and area-delay product compared to the existing multipliers. The proposed multiplier achieves an improvement of 39% in area and 17% in area-delay product estimations for field order of 409 when compared with the best sequential multiplier available in the literature. Application specific integrated circuit (ASIC) implementation of the proposed multiplier together with the two most comparable multipliers confirms that the proposed multiplier outperforms in terms of area and area-delay product. The proposed multiplier is suitable for implementation of security in Internet of Things (IoT) gateways and edge-devices.