Reviewerly/exHarmony huggingface dataset:
Blockchain-Based Trusted Cross-organizational Deliveries of Sensor-Equipped Parcels

Today’s cross-organizational deliveries of high value and perishable goods are difficult to monitor in a reliable and trustful way. Every logistics organization operates its own track and trace system, usually in an isolated manner and with most being incompatible to each other. In order to provide better end-to-end insights and to speed-up conflict management processes, we propose to let all involved parties mutually confirm cross-organizational handovers of a parcel and to log the event immutably within a common distributed ledger. Smart sensors within or attached to parcels will in addition act as independent oracles to monitor environmental variables with respect to parcel-specific service level agreements. Violations of service level agreements will be trustfully detected and logged by the smart sensors directly on the distributed ledger, without potentially compromising legacy systems being involved. The proposed concept will serve as the base for future implementations and opens up new ways to analyze and optimize inter-organizational logistics.
Measurement of 1–10 Hz 3D vibration modes with a CT-scanner

Abstract A new in situ vibration mode measurement method within a tomograph is proposed based on Projection-based Digital Volume Correlation techniques. Several projection angles are selected and a large number of radiographs of the vibrating sample are acquired at random instants with a small exposure time in order to ‘freeze out’ the displacement and avoid motion blurring. Based on an initial reconstruction acquired in a static configuration, the displacement field measurement is performed using a Proper Generalized Decomposition technique. All projections are analyzed as being due to a few vibration modes deforming the known reference volume. The different projection directions are related to each other assuming that the modal amplitude probability distribution functions are statistically similar. A synthetic test case, mock-up of a liver, is used to illustrate and validate the approach. In this case, 5 projection angles were chosen, 300 radiographs per angle, and the first three vibration modes could be recovered with a good accuracy.
Performance Evaluation of Feature Encoding Methods in Network Traffic Prediction Using Recurrent Neural Networks

Recurrent neural network method considering traffic volume, timestamp and day of the week (RNN-VTD method) is a promising network traffic prediction method because of its high prediction accuracy. The RNN-VTD method encodes timestamp and day of the week, which are categorical data, to numerical data using label encoding. The label encoding, however, gives magnitude to the encoded values, which may cause misunderstanding of recurrent neural network models, and consequently, the prediction accuracy of the RNN-VTD method may be degraded. In this paper, we investigate the effect of using one-hot encoding instead of label encoding for a feature encoding method in the RNN-VTD method. In the one-hot encoding, each input data is encoded to an k-dimensional 0--1 vector where k is the number of category types. Because the encoded data do not have magnitude, it is expected that the prediction accuracy of the RNN-VTD method is improved.
On Refining Design Patterns for Smart Contracts

The need for a Blockchain Oriented Software Engineering (BOSE) has been recognized in several research papers. Design Patterns are considered among the main and compelling areas to be developed in BOSE. Anyway, design patterns need to be enhanced with some additional fields to better support the specific needs of Blockchain development. In this paper, we discuss the use of Solidity design patterns applied to a water management use case and we introduce specific fields in their description, aiming at offering to Blockchain developers more support in the critical decisions to build efficient decentralized applications.
Optimising Network Architectures for Provable Adversarial Robustness

Existing Lipschitz-based provable defences to adversarial examples only cover the L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> threat model. We introduce the first bound that makes use of Lipschitz continuity to provide a more general guarantee for threat models based on any L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</sub> norm. Additionally, a new strategy is proposed for designing network architectures that exhibit superior provable adversarial robustness over conventional convolutional neural networks. Experiments are conducted to validate our theoretical contributions, show that the assumptions made during the design of our novel architecture hold in practice, and quantify the empirical robustness of several Lipschitz-based adversarial defence methods.
Semi-supervised Mixed Sparse Representation based Classification for Face Recognition

The purpose of this paper is to solve the problem that the local optimum is prone to arise in Semi-Supervised Sparse Representation based Classification (S3RC), so as to improve the classification effect. Although S3RC can solve the issue of face recognition in the case of insufficient samples and corruption (including linear and non-linear) by using a Gaussian Mixture Model, due to the defect of Expectation-Maximization (EM) algorithm, which can only get local extremum but not global extremum, prototype dictionary (which contains only class-specific information) has a great contribution to the final recognition effect. This paper introduces a Semi-supervised Mixed Sparse Representation (SMSR) method based on dictionary decomposition to construct a prototype dictionary to solve this problem. We decompose the training image multiple times to ensure a more suitable prototype dictionary on the basis of reducing the corruption of the training data. The experiments results on AR and FERET databases demonstrate the effectiveness that, the proposed approach yields improved results compared to S3RC and other state-of-the-art algorithms.
Multi-Level Context Aggregation Network With Channel-Wise Attention for Salient Object Detection

Fully convolutional neural networks (FCNs) have shown their advantages in the salient object detection task. However, the prediction results do not perform well in most existing FCN-based methods, such as coarse object boundaries or even getting wrong predictions, which resulted from ignoring the difference between multi-level features during feature aggregation or underutilizing the spatial details suitable for locating boundaries. In this paper, we propose a novel end-to-end multi-level context aggregation network (MLCANet) to solve the problem mentioned-above, in which both bottom-up and top-down message passing can cooperate in a joint manner.The bottom-up process that aggregates low-level fine details features into high-level semantically-richer features would enhance high-level features, and in turn the top-down process that passes refined features from deeper layers to the shallower ones could benefit from the enhanced high-level features. Also by considering that the features from different layers may not be equally important, a multi-level feature aggregation mechanism with channel-wise attention is proposed to aggregate multi-level features by flexibly adjusting their contributions and absorbing useful information to refine themselves. The features after message passing which simultaneously encode semantic information and spatial details are used to predict saliency maps in our network. Extensive experiments demonstrate that our method can obtain high quality saliency maps with clear boundaries, and perform favorably against the state-of-the-art methods without any pre-processing and post-processing.
Temporal Multiple-convolutional Network for Commodity Classification of Online Retail Platform Data

With the development and popularization of online shopping, a large number of commodities on the online retail platform should be managed through the multilevel category system, in which there are thousands of categories. Automatic commodity classification has become an issue. Classifying commodities according to their text titles can improve the efficiency of online retail platforms. The text titles of commodities contain the information of commodities, but the text titles seldom follow the grammar rules and the text length varies greatly. We propose a classification model, Temporal Multiple-Convolutional Network (TMN), which combines Temporal Convolutional Network(TCN) model and Multiple-Convolutional Neural Network(MCNN) model. The TCN model is firstly used to model sequence data based on both word embedding and character embedding respectively, and generate two output vectors. Then, the MCNN models are used to extract features from the abstract vectors. Finally, output layer employs softmax function to classify commodities. We conduct experiment on the online retail platform dataset provided by Inspur Group Co., Ltd. The results show that the TMN has a commodity classification accuracy of 84.2%, which is superior to that of state-of-the-art models.
CM-supplement network model for reducing the memory consumption during multilabel image annotation

With the rapid development of the Internet and the increasing popularity of mobile devices, the availability of digital image resources is increasing exponentially. How to rapidly and effectively retrieve and organize image information has been a hot issue that urgently must be solved. In the field of image retrieval, image auto-annotation remains a basic and challenging task. Targeting the drawbacks of the low accuracy rate and high memory resource consumption of current multilabel annotation methods, this study proposed a CM-supplement network model. This model combines the merits of cavity convolutions, Inception modules and a supplement network. The replacement of common convolutions with cavity convolutions enlarged the receptive field without increasing the number of parameters. The incorporation of Inception modules enables the model to extract image features at different scales with less memory consumption than before. The adoption of the supplement network enables the model to obtain the negative features of images. After 100 training iterations on the PASCAL VOC 2012 dataset, the proposed model achieved an overall annotation accuracy rate of 94.5%, which increased by 10.0 and 1.1 percentage points compared with the traditional convolution neural network (CNN) and double-channel CNN (DCCNN). After stabilization, this model achieved an accuracy of up to 96.4%. Moreover, the number of parameters in the DCCNN was more than 1.5 times that of the CM-supplement network. Without increasing the amount of memory resources consumed, the proposed CM-supplement network can achieve comparable or even better annotation effects than a DCCNN.
Deep Learning Algorithm for Minimum Constraint Removal (MCR) Problem

This paper investigated the path planning problem and the feasibility of a deep learning algorithm to search a solution. In this paper, the structural model and mathematical model of the robotic path problem were constructed, and a radial basis function (RBF) neural network, which is based on the deep learning algorithm, was proposed. This algorithm extends three sets of neural networks in the direction of deep learning; the deep learning process was progressively advanced using a "reachable" network, a "least barrier" network, and a "shortest path" network. The proposed deep learning algorithm was experimentally compared with the ACO algorithm. The results indicated that the proposed algorithm can plan a path with fewer barriers and shorter distances and is suitable for solving path planning problems.
Segmentation-based compression techniques for medical images

With the increased use of medical imagery, a medical image is required to be compressed before storage and transmission. However, compression leads to a reduction in image fidelity. Therefore medical imaging causes a big challenge for developing compression techniques that yield high fidelity as much as possible so as to reduce the diagnostic errors and yet achieving high compression rates. To meet this challenge, several segmentation-based compression schemes have been presented in recent years exclusively for medical images. This chapter presents a critical appraisal of the recent status of segmentation-based coding techniques used for the medical images. All the existing methods are analyzed with an importance placed on revealing the segmentation methods used for extraction of the region of interest. Moreover, it accentuates some questions found in the literature that entail the interest of future researchers to investigate new prospects in this realm. Finally, the conclusions and guidelines for the potential work in the area of segmentation-based image coding techniques are discussed.
Recognition of dot-matrix character-degraded based on affine registration

Dot-matrix characters are widely used for steel slab labeling. Different from the universal stroke characters, the shape and quality of dot-matrix characters are prone to being degraded by various factors, which makes it difficult to recognize them by conventional methods. In this paper, a new dot-matrix characters-degraded recognition method is proposed. Firstly, this method adopts the affine transform model to guide the transformation of degraded characters with their references. Then, an objective function is designed to iteratively optimize the desired correspondence. Finally, according to the alignment performance, a scoring mechanism is proposed to evaluate the recognition results. Various experiments were conducted and the results verify the suitability of the proposed recognition method for dot-matrix characters with degradation like distortion, tilt and dot-missing.
L-Visor: Visor Input Device for Labo Head-Mounted Display

Among VR devices on the market, 'Nintendo Labo VR Kit (Labo VR)' has various pieces of DIY (Do-It-Yourself) hardware which are constructed from corrugated cardboard blueprints. While using the Labo VR, the user must hold the Labo HMD with both hands and this prevents the simultaneous use of other input devices. To overcome this limitation, we developed L-Visor which mimics Labo VR HMD but frees the hands to widen the virtual reality experience. In addition, we developed four prototype games for use with L-Visor. The results of our pilot user study showed that participants had positive impressions of L-Visor, because they felt L-Visor was intuitively consistent with the game content. This paper shows how to expand DIY's value in VR by creating a new cardboard visor input device for the Labo head-mounted display and by customizing game contents which allow users to interact with virtual environments in their own way.
Adaptive entropy coding method for stream-based lossless data compression

High performance lossless data compression is an emerged technology that enhances fast and effective data processing such as for communication and for storage applications. The recent devices generate continuous data flow at high rate, that is very fast stream data. Therefore, an elegant hardware-based compression technology is demanded. This paper proposes a lossless data compression called ASE coding. It encodes stream data by applying entropy coding approach. The encoding mechanism works as a lossless data compression. ASE coding instantly assigns the fewest bits to the corresponding compressed data according to the number of occupied entries in a look-up table. This paper shows the performance evaluations to promise ASE coding adaptively shrinks stream data and works on small hardware resources without stalling or buffering.
Sketch-aae

Sketch plays an important role in human nonverbal communication, which is a superior way to describe specific objects visually. Generating human free-hand sketches has become topical in computer graphics and vision, inspired by various applications related to sketches such as sketch object recognition. Existing methods on sketch generation failed to utilize stroke sequence information of human free-hand sketches. Especially, a recent study proposed an end-to-end variational autoencoder (VAE) model called sketch-rnn which learned to sketch with human input. However, the performance of sketch-rnn is affected by the original input seriously hence decreased its robustness. In this paper, we proposed a sequence-to-sequence model called sketch-aae to generate multiple categories of humanlike sketches of higher quality than sketch-rnn. We achieve this by introducing an adversarial autoencoder (AAE) model, which uses generative adversarial networks (GAN) to improve the robustness of VAE. To our best knowledge, for the first time, the AAE model is used to synthesize sketches. A VGGNet classification model is then formulated to prove the similarity between our generated sketches and human free-hand sketches. Extensive experiments both qualitatively and quantitatively demonstrate that the proposed model is superiority over the state-of-the-art for sketch generation and multi-class sketch classification.
Research on Feature Extraction and Multimodal Fusion of Video Caption Based on Deep Learning

Video Caption shows the objects, attributes and their relationship in natural language. It has been a very challenging research topic in the field of computer and multimedia. In this paper, the method of deep learning is used to extract the video frame feature, motion information, video sequence feature. And the multi-modal feature fusion method: feature cascade, model weighted average fusion are studied, and then the valuation is also studied. The experimental results show that the score of each evaluation in the model of weighted average fusion method is higher than that of the feature cascade method. The feature extraction methods and multimodal fusion methods in this paper have certain value for the application of video caption.
Two theorems on “Polar coordinates and generalized h-type indices”

With the expansion of electronic publishing, a new dynamics of scientific articles dissemination was initiated. Still substantially important, citations became a longer term effect. Nowadays, many works are widely disseminated even before publication, in the form of preprints. Another important new element concerns the views of published articles. Thanks to the availability of respective data by some journals, such as PLoS ONE, it became possible to develop investigations on how scientific works are viewed along time, often before the first citations appear. This provides the main theme of the present work. More specifically, our research was motivated by preliminary observations that the view profiles along time tend to present a piecewise linear nature. A methodology was then delineated in order to identify the main segments in the view profiles, which allowed several related measurements to be derived. In particular, we focused on the inclination and length of each subsequent segment. Basic statistics indicated that the inclination can vary substantially along subsequent segments, while the segment lengths resulted more stable. Complementary joint statistics analysis, considering pairwise correlations, provided further information about the properties of the views. In order to better understand the view profiles, we performed respective multivariate statistical analysis, including principal component analysis and hierarchical clustering. The results suggest that a portion of the polygonal views are organized into clusters or groups. These groups were characterized in terms of prototypes indicating the relative increase or decrease along subsequent segments. Four respective distinct models were then developed for representing the observed segments. It was found that models incorporating joint dependencies between the properties of the segments provided the most accurate results among the considered alternatives.
Meta-Reasoning about Decisions in Autonomous Semi-Intelligent Systems

For intelligent systems to become autonomous in any real sense, they need an ability to make decisions on situations that were not entirely conceived of at compile-time. Machine learning algorithms are excellent in mimicking the behaviour of some gold standard role model, and this can include decision making by the role model. But once out of familiar contexts, the decision making becomes harder and needs an element of more independent probabilistic reasoning and decision making. This paper presents such a method based on a belief mass interpretation of the decision information, where the components are imprecise and thus uncertain by means of intervals.
Differential Privacy Algorithm Based on Personalized Anonymity

The existing anonymized differential privacy model adopts a unified anonymity method, ignoring the difference of personal privacy, which may lead to the problem of excessive or insufficient protection of the original data [1]. Therefore, this paper proposes a personalized k-anonymity model for tuples (PKA) and proposes a differential privacy data publishing algorithm (DPPA) based on personalized anonymity, firstly based on the tuple personality factor set by the user in the original data set. The values are classified and the corresponding privacy protection relevance is calculated. Then according to the tuple personality factor classification value, the data set is clustered by clustering method with different anonymity, and the quasi-identifier attribute of each cluster is aggregated and noise-added to realize anonymized differential privacy; finally merge the subset to get the data set that meets the release requirements. In this paper, the correctness of the algorithm is analyzed theoretically, and the feasibility and effectiveness of the proposed algorithm are verified by comparison with similar algorithms.
"I just leaned on it!" Exploring Opportunistic Social Discovery of a Technologically Augmented Cushion

While personal devices are often used to connect online with others far away, public media rarely offers opportunities to connect with collocated individuals. We explore novel interaction strategies to enhance opportunistic collocated sociality through technologically augmented daily objects. ThinkCushion is an augmented cushion allowing users to record and playback audio messages either explicitly or implicitly by leaning on it. We deployed ThinkCushion in an open coworking space and gathered quantitative and qualitative data over one month to unveil how individuals discovered it and interacted. We individuate three modes of discovery (serendipitous, spectated and facilitated) and their relations with situated socio-spatial aspects. We discuss the interplay of active and passive interaction modalities for locally accessing and creating content, and how verbal content can be used in either performative or informative ways. We suggest future research on how to design public technologies supporting collocated sociality already from the phase of technological discovery.