Reviewerly/exHarmony huggingface dataset:
TDFI: Two-stage Deep Learning Framework for Friendship Inference via Multi-source Information

Due to the explosive growth of social network services, friendship inference has been widely adopted by Online Social Service Providers (OSSPs) for friend recommendation. The conventional techniques, however, have limitations in accuracy or scalability to handle such a large yet sparse multi-source data. For example, the OSSPs will be required to manually give the order in which the various information is applied. This unavoidably reduces the applicability of existing friend recommendation systems. To address this issue, we propose a Two-stage Deep learning framework for Friendship Inference (TDFI). This approach can utilize multi-source information simultaneously with low complexity. In particular, we apply an Extended Adjacency Matrix (EAM) to represent the multi-source information. We then adopt an improved Deep AutoEncoder Network (iDAEN) to extract the fused feature vector for each user. The TDFI framework also provides an improved Deep Siamese Network (iDSN) to measure user similarity from iDAEN. Finally, we evaluate the effectiveness and robustness of TDFI on three large-scale real-world datasets. It shows that TDFI can effectively handle the sparse multi-source data while providing better accuracy for friend recommendation.
Grapheme-to-Phoneme Conversion with Convolutional Neural Networks

Grapheme-to-phoneme (G2P) conversion is the process of generating pronunciation for words based on their written form. It has a highly essential role for natural language processing, text-to-speech synthesis and automatic speech recognition systems. In this paper, we investigate convolutional neural networks (CNN) for G2P conversion. We propose a novel CNN-based sequence-to-sequence (seq2seq) architecture for G2P conversion. Our approach includes an end-to-end CNN G2P conversion with residual connections and, furthermore, a model that utilizes a convolutional neural network (with and without residual connections) as encoder and Bi-LSTM as a decoder. We compare our approach with state-of-the-art methods, including Encoder-Decoder LSTM and Encoder-Decoder Bi-LSTM. Training and inference times, phoneme and word error rates were evaluated on the public CMUDict dataset for US English, and the best performing convolutional neural network-based architecture was also evaluated on the NetTalk dataset. Our method approaches the accuracy of previous state-of-the-art results in terms of phoneme error rate.
i'sFree

Entering text without having to pay attention to the keyboard is compelling but challenging due to the lack of visual guidance. We propose i'sFree to enable eyes-free gesture typing on a distant display from a touch-enabled remote control. i'sFree does not display the keyboard or gesture trace but decodes gestures drawn on the remote control into text according to an invisible and shifting Qwerty layout. i'sFree decodes gestures similar to a general gesture typing decoder, but learns from the instantaneous and historical input gestures to dynamically adjust the keyboard location. We designed it based on the understanding of how users perform eyes-free gesture typing. Our evaluation shows eyes-free gesture typing is feasible: reducing visual guidance on the distant display hardly affects the typing speed. Results also show that the i'sFree gesture decoding algorithm is effective, enabling an input speed of 23 WPM, 46% faster than the baseline eyes-free condition built on a general gesture decoder. Finally, i'sFree is easy to learn: participants reached 22 WPM in the first ten minutes, even though 40% of them were first-time gesture typing users.
Trust-Based Security Mechanism for Detecting Clusters of Fake Users in Social Networks

Inspired by the ability of the trust mechanism which can capture human trust using measurement theory we have designed trust-based security mechanism to find clusters of fake users in social networks. Proposed framework enables us to take human and community knowledge into a loop by using a variation of trust to detect fake users. This framework will increase the accuracy of detecting fake users by using both human and machine knowledge. Experiments are performed on randomly generated social networks to validate the potential of this framework. Results show that the variation of trust over time can able to differentiate between clusters of fake users from clusters of real users.
AR-PED: A framework of augmented reality enabled pedestrian-in-the-loop simulation

Pedestrian simulators are in high demand for many research and industry applications, such as evaluating pedestrian safety and testing of autonomous vehicles. A recent trend on the development of pedestrian simulators is to use virtual reality (VR) head-mounted display (HMD) devices, since they offer a highly immersive and realistic view of the virtual environment (VE). However, existing pedestrian simulators that make use of this technology have a set of limitations that make them unsuitable for the simulation of a large virtual scenario. Specifically, the boundaries of the virtual scene set by these simulators encompass a relatively small area (e.g., a single crosswalk). In light of these issues, this paper aims to develop an augmented reality (AR) enabled pedestrian-in-the-loop simulation (AR-PED) framework that enables the simulation of pedestrians in a large virtual scenario (e.g., a city) with an actual scale. The proposed AR-PED simulation framework is empowered by the latest advances in cloud computing and AR to allow multiple users to access the simulation at the same time. A client-server architecture allows users to act as pedestrians, drive a car or simply visualize the scene simultaneously. Additionally, other vehicles can be artificially generated by a microscopic traffic simulation (MTS) package that acts as the server. A prototype of the proposed framework is implemented and demonstrated by simulating a model of a city. The promising capabilities and performance of the AR-PED framework are attested with the deployable experiments and potential improvements over the present study to further excel the current framework are also discussed.
Large-scale and rotation-invariant template matching using adaptive radial ring code histograms

Although template matching has been widely studied in the fields of image processing and computer vision, current template matching methods still cannot address large-scale changes and rotation changes simultaneously. In this study, we propose a novel adaptive radial ring code histograms (ARRCH) image descriptor for large-scale and rotation-invariant template matching. The image descriptor is constructed by (1) identifying, inside the template, a set of concentric ring regions around a reference point, (2) detecting "stable" pixels based on the ASGO, which is tolerant with respect to large scale change, (3) extracting a rotation-invariant feature for each "stable" pixel, and (4) discretizing the features in a separate histogram for each concentric ring region in the scale space. Finally, an ARRCH image descriptor is obtained by chaining the histograms of all concentric ring regions for each scale. In matching mode, a sliding window approach is used to extract descriptors, which are compared with the template one, and a coarse-to-fine search strategy is employed to detect the scale of the target image. To demonstrate the performance of the ARRCH, several experiments are carried out, including a parameter experiment and a large-scale and rotation change matching experiment, and some applications are presented. The experimental results demonstrate that the proposed method is more resistant to large-scale and rotation differences than previous state-of-the-art matching methods.
Tangible Objects for Reminiscing in Dementia Care

Reminiscence in dementia care often does not make use of interactive technology. In this work we present a study conducted in two dementia care facilities aimed at developing prototypes for reminiscence. We conducted contextual inquiries over a week to learn how 80 people with varying stages of dementia reminisce throughout the day. We present resulting needs and three tangible prototypes designed to facilitate reminiscence. These prototypes - the pyramid, the set of drawers and the jukebox - were tested in three exploratory field studies. We elaborate on the features of the prototypes that facilitated communication and reminiscence and share insights from failures that need to be considered when designing tangibles in the dementia context. To visualize both positive and negative aspects we introduce a model of successful interaction in the dementia context.
Conditional Generative Adversarial Refinement Networks for Unbalanced Medical Image Semantic Segmentation

We propose a new generative adversarial architecture to mitigate imbalance data problem in medical image semantic segmentation where the majority of pixels belongs to a healthy region and few belong to lesion or non-health region. A model trained with imbalanced data tends to bias towards healthy data which is not desired in clinical applications and predicted outputs by these networks have high precision and low sensitivity. We propose a new conditional generative refinement network with three components: a generative, a discriminative, and a refinement networks to mitigate imbalanced data problem through ensemble learning. The generative network learns to the segment at the pixel level by getting feedback from the discriminative network according to the true positive and true negative maps. On the other hand, the refinement network learns to predict the false positive and the false negative masks produced by the generative network that has significant value, especially in medical application. The final semantic segmentation masks are then composed by the output of the three networks. The proposed architecture shows state-of-the-art results on LiTS-2017 for simultaneous liver and lesion segmentation, and MDA231 for microscopic cell segmentation. We have achieved competitive results on BraTS-2017 for brain tumor segmentation.
Platinum, Pd, Mo, Au and Re deportment in hyper-enriched black shale Ni-Zn-Mo-PGE mineralization, Peel River, Yukon, Canada

Middle Devonian Ni-Mo-Zn-Pt-Pd-Au-Re hyper-enriched black shales (HEBS) from Peel River in north Yukon have high abundances of platinum group elements (PGE; ca. 500 ppb), Au (40–130 ppb) and Re (8–55 ppm). Mineralization occurs in up to three thin (0.5–10 cm thick) layers that include the regionally extensive HEBS at the Road River Group-Canol Formation stratigraphic contact (e.g., correlative with the Nick Ni-Mo-Zn-PGE-Au-Re prospect, northwestern Yukon); two other previously undocumented HEBS layers stratigraphic layers underlie the upper layer. Although the three different layers have minor textural differences, they display marked mineralogical and geochemical similarities to each other. Millerite is the predominant Ni sulfide, and it postdates several textural varieties of pyrite. A longstanding, unresolved question regarding this mineralization in Yukon is the mineralogical host of PGE, Mo, Au and Re. We analyzed the sulfide mineral assemblages using laser ablation-inductively coupled plasma-mass spectrometry (LA-ICP-MS). LA-ICP-MS analyses reveal that pyrite within all of the layers is the principal host of PGE, Re, Au and Mo, and that paragenetically late pyrite (spatially associated with millerite) contains the highest abundances, with up to 4 ppm Pd, 8 ppm Pt, 500 ppm Re, 1.5 ppm Au and 7000 ppm Mo. Rare, late pyrite-marcasite veins in the upper HEBS layer cross-cut all previous generations of sulfides and also postdate quartz cement. The late sulfide veins are devoid of Mo-Pd-Re-Pt-Au, indicating that mineralization coincided with early to late diagenesis and predates lithification. The high abundances of these elements in diagenetic varieties of pyrite suggests that hyper-enrichment occurred near the seafloor, and that this suite of elements was likely derived from ambient seawater.
Persistent identifiers: the building blocks of the research information infrastructure

Persistent identifiers (PIDs) – for people (researchers), places (their organizations) and things (their research outputs and other contributions) – are foundational elements in the overall research information infrastructure. They enable these entities to be uniquely identified and connected, to create reliable links between them. In this paper we describe what PIDs are and how they work. We demonstrate how, if widely adopted, the connections they enable will result in improved access to information, opportunitiesfor collaboration, reduced administrative overhead and, ultimately, increased trust in scholarship and research. To ensure they are fit for purpose, we propose that PID metadata should meet FAIR principles, and that the source of information they contain should be clear and transparent. We also recognize existing usage of persistent identifiers, and invite community support for wider adoption of PIDs in future.
Bilateral Dependency Neural Networks for Cross-Language Algorithm Classification

Algorithm classification is to automatically identify the classes of a program based on the algorithm(s) and/or data structure(s) implemented in the program. It can be useful for various tasks, such as code reuse, code theft detection, and malware detection. Code similarity metrics, on the basis of features extracted from syntax and semantics, have been used to classify programs. Such features, however, often need manual selection effort and are specific to individual programming languages, limiting the classifiers to programs in the same language.To recognize the similarities and differences among algorithms implemented in different languages, this paper describes a framework of Bilateral Neural Networks (Bi-NN) that builds a neural network on top of two underlying sub-networks, each of which encodes syntax and semantics of code in one language. A whole Bi-NN can be trained with bilateral programs that implement the same algorithms and/or data structures in different languages and then be applied to recognize algorithm classes across languages.We have instantiated the framework with several kinds of token-, tree- and graph-based neural networks that encode and learn various kinds of information in code. We have applied the instances of the framework to a code corpus collected from GitHub containing thousands of Java and C++ programs implementing 50 different algorithms and data structures. Our evaluation results show that the use of Bi-NN indeed produces promising algorithm classification results both within one language and across languages, and the encoding of dependencies from code into the underlying neural networks helps improve algorithm classification accuracy further. In particular, our custom-built dependency trees with tree-based convolutional neural networks achieve the highest classification accuracy among the different instances of the framework that we have evaluated. Our study points to a possible future research direction to tailor bilateral and multilateral neural networks that encode more relevant semantics for code learning, mining and analysis tasks.
A Movement-Tremors Recorder for Patients of Neurodegenerative Diseases

Neurodegenerative diseases such as Alzheimer, Parkinson, motor neuron, and Chorea affect millions of people today. Their effect on the central nervous system causes the loss of brain functions as well as motor disturbances and sometimes cognitive deficits. In such a scenario, the monitoring and evaluation of early symptoms are mandatory for the improvement of the patient's quality of life. Here, the authors describe the development, the laboratory calibration, and the “in-field validation” under the medical supervision of a movement tremors recorder for subjects affected by neurodegenerative diseases. The developed device is based on an array of four accelerometers connected to an embedded development board. This system is able to monitor tremor/movement, accidental falls, and, moreover, it can track the Alzheimer subjects' geographical position. A remote supervisor can collect data from the system through Bluetooth, Wi-Fi, or GSM connections. A data compression algorithm was developed directly on board in order to increase the efficiency of data transmission and reduce power consumptions.
Blockchain: A Research Framework for Data Security and Privacy

As an emerging field of research, Blockchain is currently experiencing a lack of research frameworks to guide studies in the field. Emerging technologies are usually triggered first, and then the academic world will attempt to develop discipline for these technologies. Our current exploratory work-in-progress is an initial attempt in developing a research framework for investigating the security of data through Blockchain. This study is the first phase of a more extensive study that aims to provide a conceptual framework for blockchain research in general. In its current form, the initial proposed framework of this study can be used for both scoping as well as evaluating existing research approaches in the field of data security through blockchain technology. It is also expected to facilitate scoping and categorizing future studies by providing a set of defined set research categories in the field.
Composite Feature Extraction and Selection for Text Classification

Although words are basic semantic units in text, phrases, and expressions contain additional information, which is important for text classification. To capture this information, traditional algorithms extract composite features via word sequences or co-occurrences, such as bigrams and termsets, but ignore the influence of stop words and punctuation, which results in huge amounts of weak features. In this paper, we propose a text structure-based algorithm to extract composite features. Termsets that cross punctuation marks or stop words in the text are excluded. To eliminate redundancy, a novel discriminative measure containing two factors is suggested. One is employed to measure the relevancy, while the other is incorporated to increase the values of composite features, whose class frequencies are much smaller than those of their sub-features. The experiments on three benchmark datasets with both a support vector machine and a naive Bayes classifier illustrate the effectiveness of the approach.
An Overview of Multi-cloud Computing

The purpose of this paper is to provide a brief overview of cloud computing technologies, particularly with respect to multi-cloud networks. First, the basics of cloud computing concepts are discussed. Next we outline some challenges facing cloud computing, and discuss how multi-cloud systems including multi-clouds, hybrid clouds, federated clouds, and cross-clouds may be used to deal with some of these issues. Finally, multi-cloud systems may also be used in conjunction with new developing technologies such as Big Data and Machine Learning, leading to exciting innovations. These are reviewed in brief. Our goal is to provide a modern look at the state of the art in multi-cloud computing and review open issues in the field. The goal is that this paper will help the reader to understand challenges facing cloud computing, how multi-cloud computing addresses some of these issues, and inspire community excitement at the future integration of multi-cloud platforms with other novel technologies.
A product and process analysis of post-editor corrections on neural, statistical and rule-based machine translation output

This paper presents a comparison of post-editing (PE) changes performed on English-to-Finnish neural (NMT), rule-based (RBMT) and statistical machine translation (SMT) output, combining a product-based and a process-based approach. A total of 33 translation students acted as participants in a PE experiment providing both post-edited texts and edit process data. Our product-based analysis of the post-edited texts shows statistically significant differences in the distribution of edit types between machine translation systems. Deletions were the most common edit type for the RBMT, insertions for the SMT, and word form changes as well as word substitutions for the NMT system. The results also show significant differences in the correctness and necessity of the edits, particularly in the form of a large number of unnecessary edits in the RBMT output. Problems related to certain verb forms and ambiguity were observed for NMT and SMT, while RBMT was more likely to handle them correctly. Process-based comparison of effort indicators shows a slight increase of keystrokes per word for NMT output, and a slight decrease in average pause length for NMT compared to RBMT and SMT in specific text blocks. A statistically significant difference was observed in the number of visits per sub-segment, which is lower for NMT than for RBMT and SMT. The results suggest that although different types of edits were needed to outputs from NMT, RBMT and SMT systems, the difference is not necessarily reflected in process-based effort indicators.
Elemental patterns of verification strategies

Abstract This paper proposes a set of seven elemental patterns of verification strategies. These patterns can be useful in modeling verification strategies in a wide range of engineered systems. They form the building blocks under which any verification strategy can be modeled. The patterns lead to a fundamental understanding of the interplay between system parameters and verification activities, as well as an understanding of the mechanisms by which verification evidence builds up. For each pattern, we provide a description and a few examples of its application. A few important theoretical properties of the corresponding set of patterns are discussed, such as belief update, inferential properties, and graph disconnection, as well as some practical guidance to be taken into account when applying them to authentic verification problems. These patterns are intended to be a useful tool for researchers, practitioners, and educators, by formalizing the application of Bayesian networks to verification problems, hence facilitating instruction and communication among verification engineers and with researchers from other domains, particularly statisticians and Bayesian analysts.
Experimental study of four-state reference-frame-independent quantum key distribution with source flaws

Reference-frame-independent quantum key distribution (RFI-QKD) has been proposed as a method to avoid the requirement of real-time alignment of reference frames. However, the source flaw has not been considered in most of the previously reported works. In this paper, based on the analysis of Wang et al. [Phys. Rev. A 92, 042319 (2015)], we perform a proof-of-concept experiment to demonstrate the loss-tolerant RFI-QKD by constructing a time-bin phase-encoded system and quantifying its source flaws. Moreover, the finite-key analysis is taken into account while estimating the secure key rate. Our experimental demonstration makes a positive contribution to the realization of quantum cryptography in a complex and imperfect real-world environment.