Reviewerly/exHarmony huggingface dataset:
Learning the Implicit Semantic Representation on Graph-Structured Data

Existing representation learning methods in graph convolutional networks are mainly designed by describing the neighborhood of each node as a perceptual whole, while the implicit semantic associations behind highly complex interactions of graphs are largely unexploited. In this paper, we propose a Semantic Graph Convolutional Networks (SGCN) that explores the implicit semantics by learning latent semantic-paths in graphs. In previous work, there are explorations of graph semantics via meta-paths. However, these methods mainly rely on explicit heterogeneous information that is hard to be obtained in a large amount of graph-structured data. SGCN first breaks through this restriction via leveraging the semantic-paths dynamically and automatically during the node aggregating process. To evaluate our idea, we conduct sufficient experiments on several standard datasets, and the empirical results show the superior performance of our model (Our code is available online at https://github.com/WLiK/SGCN_SemanticGCN ).
SPESC-Translator: Towards Automatically Smart Legal Contract Conversion for Blockchain-Based Auction Services

In recent years, advanced smart contract languages (ASCLs) have been proposed to solve the problem of difficult reading, comprehension, and collaboration when writing smart legal contracts among people in different fields. However, this kind of languages are still hard to put into practice due to the lack of an effective conversion method from the ASCLs to executable smart contract programs. Aiming at this problem, we take SPESC as example to explore how to design conversion rules from the contract in it to the target programming language in Solidity, and to propose a three-layer smart contract framework, including advanced smart-contract layer, general smart-contract layer, and executable machine-code layer. These rules provide an approach to convert the definition of SPESC contracting parties into party-contracts on target language, as well as to produce SPESC contract terms into main-contract on target language. Moreover, the proposed framework specifies not only program architecture and storage structure on general smart-contract layer, but also important mechanisms, including personnel management, timing control, exception handling, etc., which can assist programmers to write smart contract programs. Furthermore, taking four SPESC contracts as testing objects, we provide the whole process of converting from SPESC contracts to Solidity programs by the SPESC-Translator, and verify the efficiency and security of the conversion process, including coding, deploying, running, and testing through Ethereum. The instance results show that the conversion rules and the three-layer framework can simplify the writing of smart contracts, standardize the program structure, and help programmers to verify the correctness of the contract programs.
Makeup Style Transfer on Low-quality Images with Weighted Multi-scale Attention

Facial makeup style transfer is an extremely challenging sub-field of image-to-image-translation.Due to this difficulty, state-of-the-art results are mostly reliant on the Face Parsing Algorithm, which segments a face into parts in order to easily extract makeup features.However, this algorithm can only work well on high-definition images where facial features can be accurately extracted.Faces in many real-world photos, such as those including a large background or multiple people, are typically of low-resolution, which considerably hinders stateof-the-art algorithms.In this paper, we propose an end-to-end holistic approach to effectively transfer makeup styles between two low-resolution images.The idea is built upon a novel weighted multi-scale spatial attention module, which identifies salient pixel regions on low-resolution images in multiple scales, and uses channel attention to determine the most effective attention map.This design provides two benefits: low-resolution images are usually blurry to different extents, so a multi-scale architecture can select the most effective convolution kernel size to implement spatial attention; makeup is applied on both a macro-level (foundation, fake tan) and a micro-level (eyeliner, lipstick) so different scales can excel in extracting different makeup features.We develop an Augmented CycleGAN network that embeds our attention modules at selected layers to most effectively transfer makeup.Our system is tested with the FBD data set, which consists of many low-resolution facial images, and demonstrate that it outperforms state-of-the-art methods, particularly in transferring makeup for blurry images and partially occluded images.
A Flatter Loss for Bias Mitigation in Cross-dataset Facial Age Estimation

Existing studies in facial age estimation have mostly focused on intra-dataset protocols that assume training and test images captured under similar conditions.However, this is rarely valid in practical applications, where training and test sets usually have different characteristics.In this paper, we advocate a cross-dataset protocol for age estimation benchmarking.In order to improve the cross-dataset age estimation performance, we mitigate the inherent bias caused by the learning algorithm itself.To this end, we propose a novel loss function that is more effective for neural network training.The relative smoothness of the proposed loss function is its advantage with regards to the optimisation process performed by stochastic gradient descent.Its lower gradient, compared with existing loss functions, facilitates the discovery of and convergence to a better optimum, and consequently a better generalisation.The crossdataset experimental results demonstrate the superiority of the proposed method over the state-of-the-art algorithms in terms of accuracy and generalisation capability.
Efficient Anomaly Detection for High-Dimensional Sensing Data With One-Class Support Vector Machine

This paper addresses the problem of anomaly detection for high-dimensional sensing data. The one-class support vector machine (OCSVM) is one of the most popular unsupervised methods for anomaly detection. When data are high dimensional and large scale, however, the efficiency of OCSVM-based methods in anomaly detection suffers. Although dimensionality-reduction tools, such as deep belief networks, can be applied to compress the high-dimensional data to alleviate the problem, the accuracy and timely detection are still hard to improve due to the inherent features of OCSVM. In this paper, we propose a new form of OCSVM model based on the structure of the compressed data and the characteristics of OCSVM. Based on the new model, we design both optimal and approximate methods for model training and testing. We evaluate the performance of our methods with extensive experiments on four real-world datasets. The experimental results demonstrate that our new methods, both optimal and approximate ones, not only significantly outperform the state-of-the-art in accuracy and efficiency, but also achieve the good performance without the need of manual parameter tuning. In addition, our approximate training and testing mechanism can reduce the computing time by three orders of magnitude with a negligible loss in accuracy.
High-Speed and Scalable FPGA Implementation of the Key Generation for the Leighton-Micali Signature Protocol

Due to the rapid progress made in quantum computers, modern cryptography faces great challenges. Many new digital signature schemes that have resistance to quantum computing are being presented for Post-Quantum Cryptography (PQC) standardization. The Leighton-Micali signature (LMS), a kind of hash-based signature scheme, is selected as a promising candidate for the PQC signature protocols by the Internet Engineering Task Force (IETF) because of its small private and public key sizes. However, the low-efficiency in key generation forms the bottleneck in practical applications. In this paper, we propose a high-speed architecture for the key generation to accelerate the LMS for the first time. The architecture is delicately devised to be scalable, supporting all the parameter sets for the LMS. The degree of parallelism is carefully designed to achieve low latency and high hardware utilization efficiency. Moreover, the control flow is well managed to accommodate different parameter sets with constant power for the consideration of anti-power analysis attacks. We code our design with Verilog language and implement it on the Xilinx Zynq UltraScale+ FPGA. The experimental results show that, compared with the optimal software implementation running on an Intel(R) Core(TM) i7-6850K 3.60GHz CPU with threading enabled, the new design achieves 55x to 2091x speedup in different parameter configurations.
Control power of a high-dimensional controlled nonlocal quantum computation

We propose a scheme for a high-dimensional nonlocal controlled quantum computation network. A client with limited quantum technology can control a distributed computation between two quantum servers in a secure manner, such that the client's computational information remains private. We define a measure to quantify the client's control power when performing nonlocal quantum gates. We find that, given the same channel resources, the client's control power over nonlocal quantum gate operations between quantum servers is much greater than that of quantum state transfer between quantum servers. Our scheme prevents quantum servers in the network from stealing each other's information or jointly stealing the client's information. Our protocol provides new avenues for building quantum computing networks and methods to develop the resource theory of quantum channels.
A universal method for the calibration of swing-scanning line structured light measurement system

As a non-contact, low cost and efficient method for three dimensional (3D) measurements, the swing-scanning line structured light measurement system has a more compact structure than the linear scanning system. In measuring process, the laser line projector is driven by a step motor with the laser plane intersecting the object sequentially. To obtain the intersection profiles, the corresponding laser plane equations need to be real-time calculated. Moreover, the laser plane can be an arbitrary relative position with the swing axis due to the mount error of laser projector. These would make the calibration model and the solution rather difficult. To address these problems, a universal calibration method is proposed which bridges the swing angle and the coefficients of laser plane with a constant mapping matrix directly. This matrix is determined by use of several (three at least) specific laser planes. The whole calibration process can be achieved by one freely moved checkerboard and is very convenient to use. Several experiments are carried out to validate the proposed method including the measurement of the planar surface, the steps and the sculptured surface.
Caring and Commoning as Cooperative Work

This paper offers the related concepts of 'caring' and 'commoning', borrowed from feminist technoscience and feminist political economy, as both values for design and analytical lenses to scrutinize computer-cooperative infrastructures for activist interventions. The paper draws on the empirical work conducted in a European participatory design project confronting risks of social exclusion that result from precarious labor conditions increasingly affecting the European populations. We discuss how the combined concepts of caring and commoning present alternatives to capitalist accumulation, supporting activist and emancipatory aims by shedding light on dimensions related to work, affects, and politics.
Short Message Service (SMS) Spam Filtering using Machine Learning in Bahasa Indonesia

Short Message Service (SMS) is an essential communication tool in Indonesian society. Companies use SMS as a promotion tool but unfortunately some individuals use SMS to send spam messages. A smartphone user in Indonesia has had an experience with these spam and promotional messages. This study presents a model to classify spam, promotion and ham messages based on Indonesian text messages. The model was trained with 4,125 text messages, tested with 1,260 text messages. A 10-fold cross validation method was used to evaluate the classifiers and the results show that Random Forest (94.62%), Multinomial Logistic Regression (94.57%), Support Vector Machine (94.38%), and XGBoost (94.52%) are among the best models to be used for a multiclass SMS classification.
Identifying Missing Children: Face Age-Progression via Deep Feature Aging

Given a face image of a recovered child at age ageprobe, we search a gallery of missing children with known identities and age agegallery at which they were either lost or stolen in an attempt to unite the recovered child with his family. We propose a feature aging module that can age-progress deep face features output by a face matcher to improve the recognition accuracy of age-separated child face images. In addition, the feature aging module guides age-progression in the image space such that synthesized aged gallery faces can be utilized to further enhance cross-age face matching accuracy of any commodity face matcher. For time lapses larger than 10 years (the missing child is recovered after 10 or more years), the proposed age-progression module improves the rank-1 open-set identification accuracy of CosFace from 22.91 % to 25.04% on a child celebrity dataset, namely ITWCC. The proposed method also outperforms state-of-the-art approaches with a rank-1 identification rate of 95.91 %, compared to 94.91 %, on a public aging dataset, FG-NET, and 99.58%, compared to 99.50%, on CACD-VS. These results suggest that aging face features enhances the ability to identify young children who are possible victims of child trafficking or abduction.
ANN Back Propagation in forecasting and policy analysis on family planning programs: A case study in NTB Province

Abstract Various government programs and policies are aimed at improving the welfare of the community. One of them is implementing a family planning program to suppress high population growth. The research aims to implement the artificial neural network architecture of the Back Propagation method of two hidden layers to see an increase in active users of the family planning program on three types of programs namely implants, injections, and pills. The data used include family planning data of ten districts in NTB province consisting of 165 input data. Predicted results showed that the best architecture with an accuracy rate of 99.21%, obtained by the value of learning rate of 0.8, the activation function of each layer is logsig, and the training method is TRAINRP. Besides, obtained information that the average decline in the user type of pills in each district/city. While in other types there is an increase of 8% on Lombok Island, while on Sumbawa Island amounted to 50%.
Exploring Text Revision with Backspace and Caret in Virtual Reality

Current VR systems provide various text input methods that enable users to enter text efficiently with virtual keyboards. However, little attention has been paid to facilitate text revision during the VR text input process. We first summarized existing text revision solutions in current VR text input research and found that backspace is the only tool available for text revision with virtual keyboards with few mentioning designs for caret control. To systematically explore VR text revision designs, we presented a design space for VR text revision based on backspace and caret. With the proposed design space, we further analyzed the feasibility of the combined usage of backspace and caret by proposing and evaluating four VR text revision techniques. Outcomes of this research can provide a fundamental understanding of VR text revision solutions (with backspace and caret) and a comparable basis for evaluating future VR text revision techniques.
Intimate touch

opinion Share on Intimate touch Authors: Madeline Balaam KTH Royal Institute of Technology KTH Royal Institute of TechnologyView Profile , Nadia Campo Woytuk KTH Royal Institute of Technology KTH Royal Institute of TechnologyView Profile , Marianela Ciolfi Felice KTH Royal Institute of Technology KTH Royal Institute of TechnologyView Profile , Ozgun Kilic Afsar KTH Royal Institute of Technology KTH Royal Institute of TechnologyView Profile , Anna Ståhl RISE (Research Institutes of Sweden) RISE (Research Institutes of Sweden)View Profile , Marie Louise Juul Søndergaard KTH Royal Institute of Technology KTH Royal Institute of TechnologyView Profile Authors Info & Claims InteractionsVolume 27Issue 6November - December 2020 pp 14–17https://doi.org/10.1145/3427781Published:02 November 2020Publication History 6citation2,966DownloadsMetricsTotal Citations6Total Downloads2,966Last 12 Months187Last 6 weeks20 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Interactive Search vs. Automatic Search

This article conducts user evaluation to study the performance difference between interactive and automatic search. Particularly, the study aims to provide empirical insights of how the performance landscape of video search changes, with tens of thousands of concept detectors freely available to exploit for query formulation. We compare three types of search modes: free-to-play (i.e., search from scratch), non-free-to-play (i.e., search by inspecting results provided by automatic search), and automatic search including concept-free and concept-based retrieval paradigms. The study involves a total of 40 participants; each performs interactive search over 15 queries of various difficulty levels using two search modes on the IACC.3 dataset provided by TRECVid organizers. The study suggests that the performance of automatic search is still far behind interactive search. Furthermore, providing users with the result of automatic search for exploration does not show obvious advantage over asking users to search from scratch. The study also analyzes user behavior to reveal insights of how users compose queries, browse results, and discover new query terms for search, which can serve as guideline for future research of both interactive and automatic search.
Extracting Rationale for Open Source Software Development Decisions — A Study of Python Email Archives

A sound Decision-Making (DM) process is key to the successful governance of software projects. In many Open Source Software Development (OSSD) communities, DM processes lie buried amongst vast amounts of publicly available data. Hidden within this data lie the rationale for decisions that led to the evolution and maintenance of software products. While there have been some efforts to extract DM processes from publicly available data, the rationale behind 'how' the decisions are made have seldom been explored. Extracting the rationale for these decisions can facilitate transparency (by making them known), and also promote accountability on the part of decision-makers. This work bridges this gap by means of a large-scale study that unearths the rationale behind decisions from Python development email archives comprising about 1.5 million emails. This paper makes two main contributions. First, it makes a knowledge contribution by unearthing and presenting the rationale behind decisions made. Second, it makes a methodological contribution by presenting a heuristics-based rationale extraction system called Rationale Miner that employs multiple heuristics, and follows a data-driven, bottom-up approach to infer the rationale behind specific decisions (e.g., whether a new module is implemented based on core developer consensus or benevolent dictator's pronouncement). Our approach can be applied to extract rationale in other OSSD communities that have similar governance structures.
Raising Sustainability Awareness in Agile Blockchain-Oriented Software Engineering

This paper presents a first investigation to join agile blockchain-oriented software development principles with sustainability software design principles. The development of blockchain-oriented software should always be performed in the awareness of the potential effects generated from its use, especially in a long-term life cycle perspective. In other terms in the awareness of its present and future sustainability. By using the principles of sustainability software design and recognized the role of blockchain-oriented Agile methodologies to manage changes in technology and requirements, we present a new Agile method for the development of blockchain-oriented systems that includes sustainability awareness practices within the development phases, in particular in the requirements and the acceptance tests. This allows to deal with blockchain-oriented systems sustainability immediately and during the incremental and iterative development process. The paper describes the process in its phases and provides an example of an application to the supply chain sector.
Image-Guided Human Reconstruction via Multi-Scale Graph Transformation Networks

3D human reconstruction from a single image is a challenging problem. Existing methods have difficulties to infer 3D clothed human models with consistent topologies for various poses. In this paper, we propose an efficient and effective method using a hierarchical graph transformation network. To deal with large deformations and avoid distorted geometries, rather than using Euclidean coordinates directly, 3D human shapes are represented by a vertex-based deformation representation that effectively encodes the deformation and copes well with large deformations. To infer a 3D human mesh consistent with the input real image, we also use a perspective projection layer to incorporate perceptual image features into the deformation representation. Our model is easy to train and fast to converge with short test time. Besides, we present the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$D^{2}Human$ </tex-math></inline-formula> (Dynamic Detailed Human) dataset, including variously posed 3D human meshes with consistent topologies and rich geometry details, together with the captured color images and SMPL models, which is useful for training and evaluation of deep frameworks, particularly for graph neural networks. Experimental results demonstrate that our method achieves more plausible and complete 3D human reconstruction from a single image, compared with several state-of-the-art methods. The code and dataset are available for research purposes at <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><uri>http://cic.tju.edu.cn/faculty/likun/projects/MGTnet</uri></i> .