Reviewerly/exHarmony huggingface dataset:
UAFA: Unsupervised Attribute-Friendship Attention Framework for User Representation

The problem of user representation has received considerable attention in recent years. A variety of social networks include not only network structures (friendships) but also information about users’ attributes. Previous studies have explored the integration of the two information to encode users. However, these methods focus on how to fuse the target user’s friendships as a whole with its attribute information to get its representation vector, without considering the inside information of friendships, that is the influence of intimacy difference between the target user and its each friend on its representation vector. In addition, most of the above methods are supervised, which can only be applied to limited social networks analysis tasks. In this paper, we investigate a novel unsupervised method for learning the user representation by considering the influence of intimacy difference. The proposed methods take both the users’ attributes and their friendships into consideration with attribute-friendship attention network. Experimental results demonstrate that the user vectors generated by the proposed methods significantly outperform state-of-the-art user representation methods on two different scale real-world networks.
Designing Zero-Energy Buildings Using Advanced Fuzzy Cognitive Maps

Energy efficient buildings are able to provide effective solutions to reduce energy consumption and carbon emissions, support environmental-friendly energy management and facilitate significant energy savings. The concept of Zero-Energy Buildings is gaining a constant increasing focus. The use of Advanced Fuzzy Cognitive Maps (AFCMs) as a new modelling methodology to provide energy performance indicators in a quantified manner that will drive the appropriate integration of required renewable energy generation, in order to design a (nearly) Zero-Energy Building (nZEB) is considered. The new approach is used to calculate the energy balance of buildings in alternative climate contexts and thus to explore their energy efficiency in six use cases. Simulation results and observations show that AFCMs could provide valuable insight to design and development issues of nZEBs.
Joint end-to-end learning for scale-adaptive person super-resolution and re-identification

Persons captured in real-life scenarios are generally in non-uniform scales. However, most generally acknowledged person re-identification (Re-ID) methods lay emphasis on matching normal-scale high-resolution person images. To address this problem, the ideas of existing image reconstruction techniques are incorporated which are expected contribute to recover accurate appearance information for low-resolution person Re-ID. In specific, this paper proposes a joint deep learning approach for Scale-Adaptive person Super-Resolution and Re-identification (SASR<sup>2</sup> ). It is for the first time that scale-adaptive learning is jointly implemented for super-resolution and re-identification without any extra post-processing process. With the super-resolution module, the high-resolution appearance information can be automatically reconstructed from scales of low-resolution person images, bringing a direct beneficial impact on the subsequent Re-ID thanks to the joint learning nature of the proposed approach. It deserves noting that SASR<sup>2</sup> is not only simple but also flexible, since it can be adaptable to person Re-ID on both multi-scale LR and normal-scale HR datasets. A large amount of experimental analysis demonstrates that SASR<sup>2</sup> achieves competitive performance compared with previous low-resolution Re-ID methods especially on the realistic CAVIAR dataset.
Combination of 1D CNN and 2D CNN to Evaluate the Attractiveness of Display Image Advertisement and CTR Prediction

With the explosion ofJhinn, Wee Lorn digital data nowadays, it has catapulted the usage of data analytic in the emergence of digital advertising space. One of theHoong, Poo Kuan digital advertising giants, Facebook has accelerated the growth of this digital data volume asChua, Hiang-Kwang they are the most common used platforms for advertisers to advertise and deliver advertising messages to the mass audience online. However, this phenomenon has increased the challenges faced by advertisers to further attract audiences attentions to look at the digital advertisements when they are shown advertisements in Facebook platforms. Hence, in this paper, we proposed a method to evaluate and analyze the elements of attractiveness within the display advertisement in Facebook Advertisement platform by applying the 2D CNN on the display advertisement images while 1D CNN on the click metric data respectively. Based on our experiment results, we are able to predict the display CTR with a reasonable margin of error.
A survey of data hiding schemes based on AMBTC

Absolute moment block truncation coding (AMBTC) is an effective compression algorithm that has been widely used for signal compression due to its simple calculation and reasonable compression ratio. It is an improved version of Block Truncation Coding (BTC), obtained by preserving absolute moments rather than a standard moment. In recent years, due to the rapid development of the Internet and the focus of information security, people began to study how to hide data in the compressed domain. Many AMBTC-based data hiding schemes have been proposed to address the problem of message hiding. In this paper, various AMBTC-based data hiding methods are studied and analyzed, and the characteristics, image quality and embedding capacity of each method are compared and discussed.
DRAM: A Deep Reinforced Intra-attentive Model for Event Prediction

We address the problem of event prediction which aims to predict next probable event given a sequence of previous historical events. Event prediction is meaningful and important for the government, agencies and companies to take proactive actions to avoid damages. By acquiring knowledge from large-scale news series which record sequences of real-world events, we are expected to learn from the past and see into the future. Most existing works focus on predicting known events from a given candidate set, instead of devoting to more realistic unknown event prediction. In this paper, we propose a novel deep reinforced intra-attentive model, named DRAM, for unknown event prediction, by automatically generating the text description of the next probable unknown event. Specifically, DRAM designs a novel hierarchical intra-attention mechanism to take care not only the previous events but also those words describing the events. In addition, DRAM combines standard supervised word prediction and reinforcement learning in model training, allowing it to directly optimize the non-differentiable BLEU score tracking human evaluation and generate higher quality of events. Extensive experiments on real-world datasets demonstrate that our model significantly outperforms state-of-the-art methods.
Multimodal Measurement Systems for Health and Behavior Analysis in Living Environment

This review reveals and briefly discusses problems of capturing, archiving and analyzing the human behavior in a natural dwelling environment. Ambient assisted living systems are defined and justified with current social needs. Sensing paradigms and various examples of sensors are presented with a focus to their imperceptibility and data safety. Two paradigms of behavioral data storage are presented and examples of behavior predictive methods conclude the paper.
Mathematical Comics on Class X Trigonometry Learning

Learning with the trigonometric material is considered difficult by students, so they feel less interested in learning and also affect learning outcomes. By this, math comics are used to help students quickly learn trigonometry. This study aims to 1) describe the application of mathematical comics in learning trigonometry, 2) find out the results of trigonometry learning after the use of mathematical comics, and 3) find out the students' responses to mathematical comics. This type of research is a development study that uses Borg and Gall development models consisting of research and data collection stages, planning, product development, field implementation testing and refinement of final products. The test of field implementation located in Wachid Hasyim 2 Vocational School 2 Surabaya by using students of class X-AK 3 as the subject of research. Observation of student activities in the implementation of learning in Wachid Hasyim 2 Vocational School in Surabaya analysed descriptively. The results of the feasibility test by material experts, media experts and practitioners (teachers), the quality of mathematical comics that have developed categorised as very feasible with an average score of 4.67. Student responses to comics were positive
Research Process Traceability Based the RD Relationship

It is not easy to define the well-defined process for software research project but it is important to keep the tracking the progress of the projects. Without the process, it is not easy to measure the progress since we cannot define the unit of measure. In this paper we suggested RD(research descriptor) concept to define the unit of measure. Also we suggested RD relation to trace the progress the research workflow. With suggested mechanism, we can monitor the research project process and visualization the progress.
Named Entity Recognition with Homophones-Noisy Data

General named entity recognition systems exclusively focus on higher accuracy regardless of dirty data. However, raw source data face serious challenges specially that are originated from automated speech recognition systems’ results. In this paper, we propose Pinyin (Pinyin is the official romanization system for Standard Chinese, each Chinese character has its own pinyin sequence which is composed of Latin alphabet) Hierarchical Attention Encoder-Decoder network and Character Alternate Network to overcome Chinese homophones’ problems which frequently frustrate researchers in consecutive Natural Language Understanding (NLU). Our models present a none word segmentation structure to effectively avoid secondary data corruption and adequately extract words’ internal features. Besides, corrupted sequences can be revised by character-level network. Evaluation demonstrates that our proposed method achieves 93.73% F1 scores which are higher than 90.97% F1 scores using baseline models in homophone-noisy dataset. Additional experiments are conducted to show equivalent results in the universal dataset.
Propositional Deductive Inference by Semantic Vectors

Representing symbols by high-dimensional vectors makes it easier to perform analogical and associational reasoning, but performing multi-step deductive reasoning typically requires a discrete knowledge base. In this paper, we show a method by which deductive inference can be performed directly on high-dimensional semantic vectors, and characterize some limitations and advantages of this approach. We provide a method for taking a set of semantic vectors representing propositions and encoding a knowledge base telling how those propositions are logically related.
A hybridized method for clustering datasets using principal components, selection and rejection methods

A novel clustering method based on a k-means algorithm to address the complexity for clustering big data has been shown to be fast, scalable and with high accuracy. The method does so by computing only over those attributes of the datasets that are of interest to the analyst. In this study, selection and rejection methods are performed after Principal Component Analysis (PCA) on the dataset to identify the relevant features and their order of significance for clustering. This hybridization process allows identification of the order of relevant features from the Principal Components of the dataset prior to clustering using the novel method. The method was implemented to cluster the Iris dataset and a dataset of Conus shell samples. Results show that the clustering precision using the hybridized method was comparable to the results of the existing novel algorithm yet it remains to be higher compared to using the k-means clustering algorithm.
Epistemic Argumentation Framework

The paper introduces the notion of an epistemic argumentation framework (EAF) as a means to integrate the beliefs of a reasoner with argumentation. Intuitively, an EAF encodes the beliefs of an agent who reasons about arguments. Formally, an EAF is a pair of an argumentation framework and an epistemic constraint. The semantics of the EAF is defined by the notion of an $$\omega $$ -epistemic labelling set, where $$\omega $$ is complete, stable, grounded, or preferred, which is a set of $$\omega $$ -labellings that collectively satisfies the epistemic constraint of the EAF. The paper shows how EAF can represent different views of reasoners on the same argumentation framework. It also includes representing preferences in EAF and multi-agent argumentation. Finally, the paper discusses the complexity of the problem of determining whether or not an $$\omega $$ -epistemic labelling set exists.
User Profile Construction Method for Personalized Access to Data Sources Using Multivariate Conjoint Analysis and Collaborating Filtering

Current information systems provide access to multiple, distributed, autonomous and potentially redundant data sources. Their users may not know the sources they questioned, nor their description and content. Consequently, their queries reflect no more a need that must be satisfied but an intention that must be refined. The purpose of personalization is to facilitate the expression of users’ needs. It allows them to obtain relevant information by maximizing the exploitation of their preferences grouped in their respective profile. In this work, we present a collaborative filtering method based on a Multivariate Conjoint Analysis approach to get these profiles. The proposed strategy provides a representation of the users and of the items, according to their characteristics, on factorial plans; whereas, the collaborative approach predicts the missing preferences.
Constructing Dataset Based on Concept Hierarchy for Evaluating Word Vectors Learned from Multisense Words

Recently, word embedding techniques that assign a multidimensional vector to each word in a given corpus are often used in various tasks in Natural Language Processing. Although most of existing methods such as word2vec assign a single vector to each word, some advanced ones assign a multisense word with multiple vectors corresponding to individual meanings it has. However, unfortunately, it is difficult to properly evaluate those word vectors assigned to multisense words by using publicly available word similarity datasets. Thus, in this paper, we propose a novel dataset and a corresponding evaluation metric that enable us to evaluate such word vectors learned considering multisense words. The proposed dataset consists of synsets in WordNet and BabelNet that are well-known lexical databases, instead of using individual words, and incorporates the distance between synsets in the concept hierarchies of WordNet and BabelNet to evaluate the similarity between word vectors. We empirically show that the proposed dataset and evaluation metric allow us to evaluate word vectors for multisense words more properly than metrics for an existing dataset.
Geometrical and Statistical Properties of the Rational Order Pyramid Transform and Dilation Filtering

The pyramid transform of rational orders is described using matrix transform. This matrix expression of the rational order pyramid transform clarifies the eigenspace properties of the transform. The matrix-based expression, however, derives orthogonal base in each resolution. This orthogonal property of base of signals derive a unified computation of linear transformation to images any rational resolutions. Furthermore, the eigenspace property allows us to define the rational pyramid transform families using the discrete cosine transform. Numerical evaluation of the transform clarifies that rational order pyramid transform preserves the normalised distribution of grey-scale in images.
Research on Recommendation Method Based on Multi-Convolution Neural Network

Traditional recommendation methods mainly use the relational data of users and projects to extract characteristics of users, which is highly dependent on the relationship data and ignores the feature learning of the data of users and projects resulting in low recommendation accuracy. Moreover, the feature extraction of traditional recommendation algorithms principally rely to manual design causing the high time complexity of constructing feature model . In view of the above problems, this paper proposes a recommendation method based on multi-convolution neural network, which takes relational data as the basis of regression training. In addition to extracting the deep features of projects and users data, convolution neural network extracts the features of text data of projects or users, and constructs a recommendation model that can mine the deep features of users and projects. With Movielens movie data set as data source, this experiment is based on collaborative filtering, full-connected neural network, title text convolution neural network and the proposed recommendation method. The results show that MAE and RMSE have been optimized with multi-convolution neural network.
Lessons from Hands-Free Data Entry in Flexible Cystoscopy with Glass for Future Smart Assistance

We explore how Google Glass can be used to annotate cystoscopy findings in a hands-free and reproducible manner by surgeons during operations in the sterile environment inspired by the current practice of hand-drawn sketches. We present three data entry variants involving head movements and speech input. In an experiment with eight surgeons and foundation doctors having up to 30 years’ of cystoscopy experience at a UK hospital, we assessed the feasibility, benefits and drawbacks of the system. We report data entry speed and error rate of input modalities and contrast it with the participants’ feedback on their perception of usability, acceptance and suitability for deployment. These results offer an expanded analysis of the participants’ feedback compared to previous analysis. The results highlight the potential of new data entry technologies and point out directions for future improvement of eyewear computers. The findings can be generalised to other endoscopic procedures (e.g. OGD/laryngoscopy) and could be included within hospital IT in future. The source code of the Glass application is available at https://github.com/sussexwearlab/GlassMedicalDataEntry.
A pessimistic method in web service composition by view on execution states

Summary The application of web services in implementing enterprise workflows has recently been prevalent. In this regard, one of the important challenges is to create web service compositions that satisfy quality properties. One of the properties is rollback cost that it must be paid for resolving effects of a web service that its execution is completed. In the previous methods, a web service was evaluated based on whether failure or successful execution state and other two states (in the middle of execution and waiting for execution) were neglected. By considering these two states, it is possible to prevent imposition in a web service rollback cost or reduce it if needed to finally provide the possibility to decrease a web service average rollback cost. Two mentioned states are related to execution time, and it is clear that the proposed solution should be related to execution time. According to implemented tests, the method not only reduces rollback cost but also causes the execution time to reduce. The reduction in execution time parameter is due to necessary waiting for a web service completion before providing the ability to rollback it in conventional methods, but in the method, it is possible to rollback web service before execution completion and waiting for completion is not mandatory.
Resultant Analysis Method for Supportability Evaluation of Radar Equipment Based on Drift Degree

In view of the factors affecting the supportability of radar equipment are numerous and hierarchical, according to the requirements of supportability of radar equipment, the evaluation index system for supportability of radar equipment is constructed. In order to avoid the limitations of the single evaluation methods and improve the objectivity and accuracy of supportability evaluation of radar equipment, the concept of drift degree is introduced and the resultant analysis model for supportability evaluation of radar equipment based on drift degree is designed in this paper. The results for supportability evaluation of radar equipment based on cloud model method, the improved catastrophe progression method, the extension method and the grey method are analyzed to provide reference for the combination of evaluation results. The validity of the model is verified by an example.
A Study of Persona Research on Domestic Music Applications

If you look at the download ranking of users in the domestic Google Play music and audio parts, you can see that the streaming app is popular. This is because you can easily listen to the music you want and the recommended music for each category. In this study, we survey Melon, Genie, and Bugs which are the most users through the questionnaires of college students. In this study, firstly literature study was conducted to understand overall understanding of mobile music service, and completed persona through survey methods such as surveys and in-depth interviews. The user’s goal was simple, easy to operate the app easily, and wanted to listen to music stably and seamlessly. Needs wanted diversity of content and low price plan. I would also like to make it easier to find non-mainstream music, and I wanted to decide whether to use the additional services in the unnecessary apps by selecting ON/OFF. The interface design aspect required a simple yet intuitive design. Since most of the users are students, they were sensitive to the fare. Nevertheless, I wanted to enjoy a lot of music, and I wanted to use less data when streaming. I wanted to concentrate more on music than on additional services, and all these needs in one way was that users wanted a simple, lightweight music application.
Skeleton based action recognition using pose change map and convolutional neural networks

Recent skeleton-based action recognition approaches have achieved significant improvement by using convolutional neural networks. These methods usually map skeleton sequences into images, and an end-to-end CNN is adopted for label prediction. In this paper, a novel image mapping method is proposed, named pose change map (PCM), which provides a visual indication of how the similarity between human pose and atoms of pose dictionary changes over time. Then, PCM as well as raw skeleton coordinates are fed into CNN to extract robust and discriminative features for action recognition. Experiments on two challenging datasets NTU RGB+D and UTKinect-Action consistently demonstrate the superiority of our method.