bluuebunny/crossref_metadata_2025 huggingface dataset:
Pengaruh Pemahaman Akuntansi, Kompetensi Sumber Daya Manusia, Pengalaman Kerja, dan Peran Internal Audit Terhadap Kualitas Laporan Keuangan pada SKPD Kabupaten Bangli
Authors: Wirawan Dewa Gede Candra
Published: July 2018

Tujuan dari penelitian ini adalah untuk membuktikan secara empiris pengaruh pemahaman akuntansi, kompetensi sumber daya manusia, pengalaman kerja, dan peran internal audit terhadap kualitas laporan keuangan. Penelitian ini merupakan penelitian kuantitatif dengan menggunakan data primer yang diperoleh dari kuesioner dan diukur dengan menggunakan skala likert. Teknik pengambilan sampel dalam penelitian ini menggunakan judgement sampling. Sampel yang digunakan dalam penelitian ini adalah Kasubag keuangan dan staf akuntansi pada masing-masing SKPD Kabupaten Bangli, sehingga jumlah responden sebanyak 64 orang dari 32 SKPD yang terdapat di Kabupaten Bangli. Setiap SKPD terdiri dari dua responden. Kuesioner yang di sebar sebanyak 64 kuesioner dan yang kembali sebanyak 64 kuesioner. Teknik analisis data yang digunakan yaitu uji regresi linier berganda. Data dianalisis dengan menggunakan software SPSS versi 19.Hasil dari penelitian ini menunjukan bahwa (1) terdapat pengaruh yang positif dan signifikan antara pemahaman akuntansi terhadap kualitas laporan keuangan, (2) terdapat pengaruh yang positif dan signifikan antara kompetensi sumber daya manusia terhadap kualitas laporan keuangan, (3) terdapat pengaruh yang positif dan signifikan antara pengalaman kerja terhadap kualitas laporan keuangan, dan (4) terdapat pengaruh yang positif dan signifikan antara peran internal audit terhadap kualitas laporan keuangan. Kata Kunci: pemahaman akuntansi, sistem informasi akuntansi keuangan daerah, pengalaman kerja, internal audit, kualitas laporan keuangan
Menguji Konsep Diri Dibidang Akademik Sebagai Variabel Mediator dari Pengaruh Persepsi Atas Keterlibatan Orang Tua Terhadap Hasil Belajar Ekonomi Siswa Kelas XI IPS SMAN Se Kota Malang
Authors: Rafsanjani Arief
Published: July 2018

Tujuan dari penelitian ini adalah untuk menguji pengaruh persepsi atas keterlibatan orangtua terhadap hasil belajar ekonomi pada siswa SMA, dan untuk menguji apakah konsep diri dibidang akademik berfungsi secara signifikan sebagai variabel mediator pengaruh persepsi keterlibatan orangtua terhadap hasil belajar ekonomi pada siswa SMA. Penelitian ini termasuk jenis penelitian eksplanasi (explanatory research). Penelitian ini merupakan penelitian cross sectional, dimana penelitian ini untuk memperoleh data dari responden pada periode waktu tertentu (data are gathered just once). Populasi dalam penelitian ini adalah seluruh siswa kelas XI Jurusan IPS Tahun Pelajaran 2012/2013 di SMA Negeri di Kota Malang. Penetapan jumlah sampel untuk menjadi responden dalam penelitian ini dilakukan berdasarkan kriteria bahwa sampel memiliki katakteristik estimator yang baik (characteristics of good estimator) dan bermanfaat sebagai penaksir yang baik terhadap populasi Pengambilan sampel sekolah juga memperhatikan sebaran semua SMA Negeri di tiap kecamatan di Kota Malang, yaitu 5 sekolah (50%) berada di Kec. Klojen, 3 sekolah (30%) berada di Kec. Lowokwaru, dan 2 sekolah (20%) berada di Kec. Kedungkandang.Berdasarkan hasil penelitian yang telah dilakukan diperoleh kesimpulan yaitu terdapat pengaruh positif signifikan persepsi atas keterlibatan terhadap hasil belajar ekonomi pada siswa SMAN se Kota Malang, demikain halnya variabel konsep diri dibidang akademik berfungsi secara signifikan sebagai  variabel mediator dari pengaruh persepsi keterlibatan orangtua terhadap hasil belajar ekonomi pada siswa SMA. Namun peran variabel konsep diri dibidang akademik dalam model penelitian ini merupakan partial mediation variable atau merupakan variabel yang memediasi sebagian dari pengaruh persepsi atas keterlibatan orang tua terhadap hasil belajar sehingga ada variable lain yang juga turut memediasi hubungan kausal tersebut yang tidak diteliti dalam model iniKata Kunci : Konsep diri, keterlibatan orang tua, hasil belajar,
Jalur Penerimaan Mahasiswa Baru Bukan Penentu Prestasi Belajar Mahasiswa
Authors: Suwena Kadek Rai
Published: July 2018

Penelitian ini bertujuan untuk mengetahui (1) Prestasi belajar mahasiswa Jurusan Pendidikan Ekonomi yang diterima melalui jalur SNMPTN Undangan, (2) Prestasi belajar mahasiswa Jurusan Pendidikan Ekonomi yang diterima melalui jalur SBMPTN, (3) Prestasi belajar mahasiswa Jurusan Pendidikan Ekonomi yang diterima melalui Jalur Mandiri, dan (4) Perbedaan prestasi belajar mahasiswa Jurusan Pendidikan Ekonomi ditinjau dari jalur penerimaan mahasiswa baru. Penelitian ini adalah penelitian deskriptif komparatif dengan pendekatan kuantitatif. Hasil penelitian menunjukkan bahwa (1) prestasi belajar mahasiswa yang diterima melalui Jalur SNMPTN mengalami peningkatan, (2) prestasi belajar mahasiswa diterima melalui Jalur SBMPTN Tulis mengalami peningkatan, (3) prestasi belajar mahasiswa diterima jalur Mandiri mengalami peningkatan, dan (4) ada perbedaan prestasi belajar mahasiswa Jurusan Pendidikan Ekonomi Undiksha ditinjau dari Jalur Penerimaan SNMPTN Undangan dengan perolehan  Mean sebesar 2,8895, Jalur SBMPTN dengan Mean 2,9800, dan Jalur mandiri dengan perolehan  Mean 2,8268 dengan tingkat signifikansi 0,001%.Kata kunci: Perbedaan Prestasi Belajar, Jalur Penerimaan Mahasiswa
PENGARUH KARAKTERISTIK ORGANISASI DAN PERUBAHAN LINGKUNGAN TERHADAP PENCIPTAAN LANDASAN KEWIRAUSAHAAN MELALUI VARIABEL KARAKTERISTIK INDIVIDUAL (STUDI KASUS PADA UKM DI KOTA PALEMBANG)
Authors: Rasyid Nirwan
Published: July 2018

Tujuan penelitian ini adalah untuk mengetahui pengaruh karakteristik organisasi dan perubahan lingkungan terhadap penciptaan landasan kewirausahaan  melalui variabel karakteristik individual (studi kasus pada UKM di Kota Palembang). Desain penelitian merupakan penelitian hubungan sebab-akibat (causal). Jenis data yang digunakan  berupa data primer, yakni kuisioner. Teknik pengambilan sampel yang digunakan yakni purposive sampling. Berdasarkan kriteria didapatkan jumlah sampel pelaku UKM sebanyak 101 orang. Teknik analisis data yang digunakan adalah metode analisis jalur. Dengan melakukan analisis jalur  untuk direct effect, indirect effect, dan total effect. Hasil studi menyatakan: 1. Karakteristik organisasi berpengaruh signifikan positif terhadap karakteristik individu (hipotesis pertama diterima), 2. Perubahan lingkungan usaha berpengaruh signifikan positif terhadap karakteristik individu (hipotesis kedua diterima), 3. Karakteristik organisasi berpengaruh signifikan positif  terhadap penciptaan organizational entrepreneurs UKM (hipotesis ketiga diterima), 4. Perubahan lingkungan usaha berpengaruh signifikan positif terhadap penciptaan organizational entrepreneurs UKM (hipotesis keempat diterima), 5. Karakteristik individu berpengaruh signifikan positif terhadap penciptaan organizational entrepreneurs UKM (hipotesis kelima diterima), 6. Karakteristik organisasi, karakteristik individu, dan perubahan lingkungan berpengaruh signifikan positif terhadap penciptaan organizational entrepreneurs UKM (hipotesis keenam diterima), 7. Karakteristik organisasi dan perubahan lingkungan berpengaruh signifikan positif terhadap penciptaan organizational entrepreneurs UKM  melalui karakteristik individu sebagai variabel intervening (hipotesis ketujuh diterima).
IMPROVING STUDENTS’ READING COMPREHENSION OF DESCRIPTIVE TEXTS THROUGH COGNITIVE STRATEGY AT GRADE VII-2 OF SMPN 1 INDRA PRAJA TEMBILAHAN
Authors: Etfita Fauzul
Published: July 2018

This study aims to determine whether cognitive strategy can improve students' skills in writing descriptive text, and what factors affect the improvement of writing skills. This research is a classroom action research conducted in two cycles. Each cycle consists of four meetings. Each consists of plan, action, observation, and reflection. Participants in this study is as straight as the students of class VII-2 SMPN 1 Indra Praja Tembilahan totaling 22 students. The data of this study were collected through: (1) observation sheet; students, teachers, teaching steps (2) field notes (3) interviews, and (4) writing tests. This study found that the use of cognitive strategy can improve students' skills in writing descriptive text. The findings of this study indicate that there is an increase in the average score of students above the completeness criteria of a minimum of English subjects is 75. The factors that influence the skills of students in writing descriptive text are (a) reading material given to the students, (b) motivation students, and (c) teacher's approach to students. So it can be concluded that the application of cognitive strategy on the teaching of reading descriptive text can give positive results with the increase of students' skills in reading descriptive text.
Staðsetning háskóla og menntabil í háskólamenntun
Authors: Bjarnason Þóroddur
Published: July 2018

Umtalsverður munur er á hlutfalli háskólamenntaðs fólks á höfuðborgarsvæðinu og utan þess. Þetta menntabil skýrist að hluta til af fjölbreyttari atvinnumöguleikum háskólamenntaðs fólks á höfuðborgarsvæðinu en ýmsir aðrir efnahagslegir, félagslegir, menningarlegir og landfræðilegir þættir skipta þar einnig máli. Þannig hafa rannsóknir sýnt að staðsetning háskóla getur haft veruleg áhrif á búsetu brautskráðra háskólanema. Í þessari rannsókn eru áhrif íslenskra háskóla á menntunarstig og menntabil einstakra landsvæða metin á grundvelli manntals og spurningakannana meðal 25–64 ára íbúa landsins. Niðurstöður sýna að 28% íbúa höfuðborgarsvæðisins en 12–14% íbúa annarra landshluta hafa lokið prófi frá háskólum í Reykjavík. Mikill meirihluti háskólamenntaðra landsmanna hefur lokið prófi frá Háskóla Íslands. Hlutfallið er hæst á höfuðborgarsvæðinu en lægst á Akureyri, þar sem nánast jafn margir hafa lokið prófi frá Háskóla Íslands og Háskólanum á Akureyri. Á höfuðborgarsvæðinu og í öðrum þéttbýliskjörnum í seilingarfjarlægð á Háskólinn í Reykjavík næstmesta hlutdeild í háskólamenntuðum íbúum en á norðursvæði landsins er hlutdeild Háskólans á Akureyri næstmest. Flestir brautskráðir nemendur háskólanna í Reykjavík og Háskólans á Bifröst eru búsettir á höfuðborgarsvæðinu en búseta nemenda annarra háskóla er dreifðari um landið. Brautskráðir nemendur Háskólans á Akureyri skiptast nokkuð jafnt milli höfuðborgarsvæðisins, Akureyrar og annarra landsvæða en rúmlega helmingur brautskráðra nemenda Landbúnaðarháskóla Íslands er að vonum búsettur utan þéttbýlustu svæða landsins. Landsbyggðaháskólarnir útskrifa þannig nemendur sem eru mun líklegri til að búa utan höfuðborgarsvæðisins en áhrif þeirra á menntunarstig einstakra landsvæða eru einkum bundin við áhrif Háskólans á Akureyri á Akureyri og annars staðar á norðanverðu landinu. Eigi að hækka menntunarstig í öllum landshlutum og draga úr menntabili milli Reykjavíkur og annarra landshluta þarf að skilgreina slík markmið með skýrum hætti og ákveða hvort það skuli vera hlutverk allra háskóla eða sérstakt verkefni þeirra sem eru utan Reykjavíkur.
A diversity-sensitive long-term care for gay and lesbian elders in need of care – Results of the research project GLESA / Eine diversitätssensible Pflege für schwule und lesbische Pflegebedürftige – Ergebnisse des Forschungsprojekts GLESA
Authors: Lottmann Ralf , Kollak Ingrid
Published: July 2018

This paper presents results of the research project „Gleichgeschlechtliche Lebensweisen und Selbstbestimmung im Alter“ (GLESA) by the Alice Salomon University of Applied Sciences, Berlin, and the Berlin School of Economics and Law concerning the needs of gay and lesbian elders regarding long-term care. The main focus is on the expectations and perceptions of long-term care (facilities) of the interviewees living in a gay housing project in Berlin, Germany. The study is based on 26 interviews: with eleven gay, one lesbian and three heterosexual tenants – two of them lived in a shared community with long-term care services. Another eleven interviews were conducted with experts (five cis-female, four cis-male and two transgender) working in social and health services (social workers, carers and psychologists). The data was gathered via problem-centered interviews (Witzel 2000) and analysed with Mayrings‘ (2007) qualitative content analysis. Long-term care aspects were one out of five dimensions of the analysis. The study illustrates the discomfort of LGB elders regarding regular care services. The interviewees prefer LGB(T*I)-friendly facilities, in part because they demonstrate overt signs of diversity and promise a high competence of LGB(T*I) personnel in terms of self-determination, awareness, visibility and knowledge about LGBT*I communities. Finally, the authors advocate enhancing the concept of culture-sensitive long-term care according to diversity-sensitive aspects. A better understanding of diversity will help to better consider individuality and biographies in long-term care (facilities) and to support the social inclusion of LGB(T*I) elders in need of care.
Live Face Verification with Multiple Instantialized Local Homographic Parameterization
Authors: Lin Chen , Liao Zhouyingcheng , Zhou Peng , Hu Jianguo , Ni Bingbing
Published: July 2018

State-of-the-art live face verification methods would easily be attacked by recorded facial expression sequence. This work directly addresses this issue via proposing a patch-wise motion parameterization based verification network infrastructure. This method directly explores the underlying subtle motion difference between the facial movements re-captured from a planer screen (e.g., a pad) and those from a real face; therefore interactive facial expression is no longer required. Furthermore, inspired by the fact that ?a fake facial movement sequence MUST contains many patch-wise fake sequences?, we embed our network into a multiple instance learning framework, which further enhance the recall rate of the proposed technique. Extensive experimental results on several face benchmarks well demonstrate the superior performance of our method.
Progressive Generative Hashing for Image Retrieval
Authors: Ma Yuqing , He Yue , Ding Fan , Hu Sheng , Li Jun , Liu Xianglong
Published: July 2018

Recent years have witnessed the success of the emerging hashing techniques in large-scale image retrieval. Owing to the great learning capacity, deep hashing has become one of the most promising solutions, and achieved attractive performance in practice. However, without semantic label information, the unsupervised deep hashing still remains an open question. In this paper, we propose a novel progressive generative hashing (PGH) framework to help learn a discriminative hashing network in an unsupervised way. Very different from existing studies, it first treats the hash codes as a kind of semantic condition for the similar image generation, and simultaneously feeds the original image and its codes into the generative adversarial networks (GANs). The real images together with the synthetic ones can further help train a discriminative hashing network based on a triplet loss. By iteratively inputting the learnt codes into the hash conditioned GANs, we can progressively enable the hashing network to discover the semantic relations. Extensive experiments on the widely-used image datasets demonstrate that PGH can significantly outperforms state-of-the-art unsupervised hashing methods.
CR-GAN: Learning Complete Representations for Multi-view Generation
Authors: Tian Yu , Peng Xi , Zhao Long , Zhang Shaoting , Metaxas Dimitris N.
Published: July 2018

Generating multi-view images from a single-view input is an important yet challenging problem. It has broad applications in vision, graphics, and robotics. Our study indicates that the widely-used generative adversarial network (GAN) may learn ?incomplete? representations due to the single-pathway framework: an encoder-decoder network followed by a discriminator network.We propose CR-GAN to address this problem. In addition to the single reconstruction path, we introduce a generation sideway to maintain the completeness of the learned embedding space. The two learning paths collaborate and compete in a parameter-sharing manner, yielding largely improved generality to ?unseen? dataset. More importantly, the two-pathway framework makes it possible to combine both labeled and unlabeled data for self-supervised learning, which further enriches the embedding space for realistic generations. We evaluate our approach on a wide range of datasets. The results prove that CR-GAN significantly outperforms state-of-the-art methods, especially when generating from ?unseen? inputs in wild conditions.
Do not Lose the Details: Reinforced Representation Learning for High Performance Visual Tracking
Authors: Wang Qiang , Zhang Mengdan , Xing Junliang , Gao Jin , Hu Weiming , Maybank Steve
Published: July 2018

This work presents a novel end-to-end trainable CNN model for high performance visual object tracking. It learns both low-level fine-grained representations and a high-level semantic embedding space in a mutual reinforced way, and a multi-task learning strategy is proposed to perform the correlation analysis on representations from both levels. In particular, a fully convolutional encoder-decoder network is designed to reconstruct the original visual features from the semantic projections to preserve all the geometric information. Moreover, the correlation filter layer working on the fine-grained representations leverages a global context constraint for accurate object appearance modeling. The correlation filter in this layer is updated online efficiently without network fine-tuning. Therefore, the proposed tracker benefits from two complementary effects: the adaptability of the fine-grained correlation analysis and the generalization capability of the semantic embedding. Extensive experimental evaluations on four popular benchmarks demonstrate its state-of-the-art performance.
SSR-Net: A Compact Soft Stagewise Regression Network for Age Estimation
Authors: Yang Tsun-Yi , Huang Yi-Hsuan , Lin Yen-Yu , Hsiu Pi-Cheng , Chuang Yung-Yu
Published: July 2018

This paper presents a novel CNN model called Soft Stagewise Regression Network (SSR-Net) for age estimation from a single image with a compact model size. Inspired by DEX, we address age estimation by performing multi-class classification and then turning classification results into regression by calculating the expected values. SSR-Net takes a coarse-to-fine strategy and performs multi-class classification with multiple stages. Each stage is only responsible for refining the decision of its previous stage for more accurate age estimation. Thus, each stage performs a task with few classes and requires few neurons, greatly reducing the model size. For addressing the quantization issue introduced by grouping ages into classes, SSR-Net assigns a dynamic range to each age class by allowing it to be shifted and scaled according to the input face image. Both the multi-stage strategy and the dynamic range are incorporated into the formulation of soft stagewise regression. A novel network architecture is proposed for carrying out soft stagewise regression. The resultant SSR-Net model is very compact and takes only 0.32 MB. Despite its compact size, SSR-Net’s performance approaches those of the state-of-the-art methods whose model sizes are often more than 1500× larger.
Extracting Privileged Information from Untagged Corpora for Classifier Learning
Authors: Yao Yazhou , Zhang Jian , Shen Fumin , Yang Wankou , Hua Xian-Sheng , Tang Zhenmin
Published: July 2018

The performance of data-driven learning approaches is often unsatisfactory when the training data is inadequate either in quantity or quality. Manually labeled privileged information (PI), \eg attributes, tags or properties, is usually incorporated to improve classifier learning. However, the process of manually labeling is time-consuming and labor-intensive. To address this issue, we propose to enhance classifier learning by extracting PI from untagged corpora, which can effectively eliminate the dependency on manually labeled data. In detail, we treat each selected PI as a subcategory and learn one classifier for per subcategory independently. The classifiers for all subcategories are then integrated together to form a more powerful category classifier. Particularly, we propose a new instance-level multi-instance learning (MIL) model to simultaneously select a subset of training images from each subcategory and learn the optimal classifiers based on the selected images. Extensive experiments demonstrate the superiority of our approach.
DehazeGAN: When Image Dehazing Meets Differential Programming
Authors: Zhu Hongyuan , Peng Xi , Chandrasekhar Vijay , Li Liyuan , Lim Joo-Hwee
Published: July 2018

Single image dehazing has been a classic topic in computer vision for years. Motivated by the atmospheric scattering model, the key to satisfactory single image dehazing relies on an estimation of two physical parameters, i.e., the global atmospheric light and the transmission coefficient. Most existing methods employ a two-step pipeline to estimate these two parameters with heuristics which accumulate errors and compromise dehazing quality. Inspired by differentiable programming, we re-formulate the atmospheric scattering model into a novel generative adversarial network (DehazeGAN). Such a reformulation and adversarial learning allow the two parameters to be learned simultaneously and automatically from data by optimizing the final dehazing performance so that clean images with faithful color and structures are directly produced. Moreover, our reformulation also greatly improves the GAN’s interpretability and quality for single image dehazing. To the best of our knowledge, our method is one of the first works to explore the connection among generative adversarial models, image dehazing, and differentiable programming, which advance the theories and application of these areas. Extensive experiments on synthetic and realistic data show that our method outperforms state-of-the-art methods in terms of PSNR, SSIM, and subjective visual quality.
A General Approach to Running Time Analysis of Multi-objective Evolutionary Algorithms
Authors: Bian Chao , Qian Chao , Tang Ke
Published: July 2018

Evolutionary algorithms (EAs) have been widely applied to solve multi-objective optimization problems. In contrast to great practical successes, their theoretical foundations are much less developed, even for the essential theoretical aspect, i.e., running time analysis. In this paper, we propose a general approach to estimating upper bounds on the expected running time of multi-objective EAs (MOEAs), and then apply it to diverse situations, including bi-objective and many-objective optimization as well as exact and approximate analysis. For some known asymptotic bounds, our analysis not only provides their leading constants, but also improves them asymptotically. Moreover, our results provide some theoretical justification for the good empirical performance of MOEAs in solving multi-objective combinatorial problems.
Multiwinner Voting with Fairness Constraints
Authors: Celis L. Elisa , Huang Lingxiao , Vishnoi Nisheeth K.
Published: July 2018

Multiwinner voting rules are used to select a small representative subset of candidates or items from a larger set given the preferences of voters. However, if candidates have sensitive attributes such as gender or ethnicity (when selecting a committee), or specified types such as political leaning (when selecting a subset of news items), an algorithm that chooses a subset by optimizing a multiwinner voting rule may be unbalanced in its selection -- it may under or over represent a particular gender or political orientation in the examples above. We introduce an algorithmic framework for multiwinner voting problems when there is an additional requirement that the selected subset should be ``fair'' with respect to a given set of attributes. Our framework provides the flexibility to (1) specify fairness with respect to multiple, non-disjoint attributes (e.g., ethnicity and gender) and (2) specify a score function. We study the computational complexity of this constrained multiwinner voting problem for monotone and submodular score functions and present several approximation algorithms and matching hardness of approximation results for various attribute group structure and types of score functions. We also present simulations that suggest that adding fairness constraints may not affect the scores significantly when compared to the unconstrained case.
A Simple Convolutional Neural Network for Accurate P300 Detection and Character Spelling in Brain Computer Interface
Authors: Shan Hongchang , Liu Yu , Stefanov Todor
Published: July 2018

A Brain Computer Interface (BCI) character speller allows human-beings to directly spell characters using eye-gazes, thereby building communication between the human brain and a computer. Convolutional Neural Networks (CNNs) have shown better performance than traditional machine learning methods for BCI signal recognition and its application to the character speller. However, current CNN architectures limit further accuracy improvements of signal detection and character spelling and also need high complexity to achieve competitive accuracy, thereby preventing the use of CNNs in portable BCIs. To address these issues, we propose a novel and simple CNN which effectively learns feature representations from both raw temporal information and raw spatial information. The complexity of the proposed CNN is significantly reduced compared with state-of-the-art CNNs for BCI signal detection. We perform experiments on three benchmark datasets and compare our results with those in previous research works which report the best results. The comparison shows that our proposed CNN can increase the signal detection accuracy by up to 15.61% and the character spelling accuracy by up to 19.35%.
Cross-Domain Depression Detection via Harvesting Social Media
Authors: Shen Tiancheng , Jia Jia , Shen Guangyao , Feng Fuli , He Xiangnan , Luan Huanbo , Tang Jie , Tiropanis Thanassis , Chua Tat-Seng , Hall Wendy
Published: July 2018

Depression detection is a significant issue for human well-being. In previous studies, online detection has proven effective in Twitter, enabling proactive care for depressed users. Owing to cultural differences, replicating the method to other social media platforms, such as Chinese Weibo, however, might lead to poor performance because of insufficient available labeled (self-reported depression) data for model training. In this paper, we study an interesting but challenging problem of enhancing detection in a certain target domain (e.g. Weibo) with ample Twitter data as the source domain. We first systematically analyze the depression-related feature patterns across domains and summarize two major detection challenges, namely isomerism and divergency. We further propose a cross-domain Deep Neural Network model with Feature Adaptive Transformation & Combination strategy (DNN-FATC) that transfers the relevant information across heterogeneous domains. Experiments demonstrate improved performance compared to existing heterogeneous transfer methods or training directly in the target domain (over 3.4% improvement in F1), indicating the potential of our model to enable depression detection via social media for more countries with different cultural settings.
Neural Framework for Joint Evolution Modeling of User Feedback and Social Links in Dynamic Social Networks
Authors: Wu Peizhi , Tu Yi , Yuan Xiaojie , Jatowt Adam , Yang Zhenglu
Published: July 2018

Modeling the evolution of user feedback and social links in dynamic social networks is of considerable significance, because it is the basis of many applications, including recommendation systems and user behavior analyses. Most of the existing methods in this area model user behaviors separately and consider only certain aspects of this problem, such as dynamic preferences of users, dynamic attributes of items, evolutions of social networks, and their partial integration. This work proposes a comprehensive general neural framework with several optimal strategies to jointly model the evolution of user feedback and social links. The framework considers the dynamic user preferences, dynamic item attributes, and time-dependent social links in time evolving social networks. Experimental results conducted on two real-world datasets demonstrate that our proposed model performs remarkably better than state-of-the-art methods.
Adaptive Collaborative Similarity Learning for Unsupervised Multi-view Feature Selection
Authors: Dong Xiao , Zhu Lei , Song Xuemeng , Li Jingjing , Cheng Zhiyong
Published: July 2018

In this paper, we investigate the research problem of unsupervised multi-view feature selection. Conventional solutions first simply combine multiple pre-constructed view-specific similarity structures into a collaborative similarity structure, and then perform the subsequent feature selection. These two processes are separate and independent. The collaborative similarity structure remains fixed during feature selection. Further, the simple undirected view combination may adversely reduce the reliability of the ultimate similarity structure for feature selection, as the view-specific similarity structures generally involve noises and outlying entries. To alleviate these problems, we propose an adaptive collaborative similarity learning (ACSL) for multi-view feature selection. We propose to dynamically learn the collaborative similarity structure, and further integrate it with the ultimate feature selection into a unified framework. Moreover, a reasonable rank constraint is devised to adaptively learn an ideal collaborative similarity structure with proper similarity combination weights and desirable neighbor assignment, both of which could positively facilitate the feature selection. An effective solution guaranteed with the proved convergence is derived to iteratively tackle the formulated optimization problem. Experiments demonstrate the superiority of the proposed approach.
Active Discriminative Network Representation Learning
Authors: Gao Li , Yang Hong , Zhou Chuan , Wu Jia , Pan Shirui , Hu Yue
Published: July 2018

Most of current network representation models are learned in unsupervised fashions, which usually lack the capability of discrimination when applied to network analysis tasks, such as node classification. It is worth noting that label information is valuable for learning the discriminative network representations. However, labels of all training nodes are always difficult or expensive to obtain and manually labeling all nodes for training is inapplicable. Different sets of labeled nodes for model learning lead to different network representation results. In this paper, we propose a novel method, termed as ANRMAB, to learn the active discriminative network representations with a multi-armed bandit mechanism in active learning setting. Specifically, based on the networking data and the learned network representations, we design three active learning query strategies. By deriving an effective reward scheme that is closely related to the estimated performance measure of interest, ANRMAB uses a multi-armed bandit mechanism for adaptive decision making to select the most informative nodes for labeling. The updated labeled nodes are then used for further discriminative network representation learning. Experiments are conducted on three public data sets to verify the effectiveness of ANRMAB.
Interactive, Collaborative Robots: Challenges and Opportunities
Authors: Kragic Danica , Gustafson Joakim , Karaoguz Hakan , Jensfelt Patric , Krug Robert
Published: July 2018

Robotic technology has transformed manufacturing

industry ever since the first industrial robot was put

in use in the beginning of the 60s. The challenge

of developing flexible solutions where production

lines can be quickly re-planned, adapted and structured

for new or slightly changed products is still

an important open problem. Industrial robots today

are still largely preprogrammed for their tasks,

not able to detect errors in their own performance

or to robustly interact with a complex environment

and a human worker. The challenges are even more

serious when it comes to various types of service

robots. Full robot autonomy, including natural interaction,

learning from and with human, safe and

flexible performance for challenging tasks in unstructured

environments will remain out of reach

for the foreseeable future. In the envisioned future

factory setups, home and office environments,

humans and robots will share the same workspace

and perform different object manipulation tasks in

a collaborative manner. We discuss some of the

major challenges of developing such systems and

provide examples of the current state of the art.
Outer Product-based Neural Collaborative Filtering
Authors: He Xiangnan , Du Xiaoyu , Wang Xiang , Tian Feng , Tang Jinhui , Chua Tat-Seng
Published: July 2018

In this work, we contribute a new multi-layer neural network architecture named ONCF to perform collaborative filtering. The idea is to use an outer product to explicitly model the pairwise correlations between the dimensions of the embedding space. In contrast to existing neural recommender models that combine user embedding and item embedding via a simple concatenation or element-wise product, our proposal of using outer product above the embedding layer results in a two-dimensional interaction map that is more expressive and semantically plausible. Above the interaction map obtained by outer product, we propose to employ a convolutional neural network to learn high-order correlations among embedding dimensions. Extensive experiments on two public implicit feedback data demonstrate the effectiveness of our proposed ONCF framework, in particular, the positive effect of using outer product to model the correlations between embedding dimensions in the low level of multi-layer neural recommender model.
Efficient DNN Neuron Pruning by Minimizing Layer-wise Nonlinear Reconstruction Error
Authors: Jiang Chunhui , Li Guiying , Qian Chao , Tang Ke
Published: July 2018

Deep neural networks (DNNs) have achieved great success, but the applications to mobile devices are limited due to their huge model size and low inference speed. Much effort thus has been devoted to pruning DNNs. Layer-wise neuron pruning methods have shown their effectiveness, which minimize the reconstruction error of linear response with a limited number of neurons in each single layer pruning. In this paper, we propose a new layer-wise neuron pruning approach by minimizing the reconstruction error of nonlinear units, which might be more reasonable since the error before and after activation can change significantly. An iterative optimization procedure combining greedy selection with gradient decent is proposed for single layer pruning. Experimental results on benchmark DNN models show the superiority of the proposed approach. Particularly, for VGGNet, the proposed approach can compress its disk space by 13.6× and bring a speedup of 3.7×; for AlexNet, it can achieve a compression rate of 4.1× and a speedup of 2.2×, respectively.
A Degeneracy Framework for Graph Similarity
Authors: Nikolentzos Giannis , Meladianos Polykarpos , Limnios Stratis , Vazirgiannis Michalis
Published: July 2018

The problem of accurately measuring the similarity between graphs is at the core of many applications in a variety of disciplines. Most existing methods for graph similarity focus either on local or on global properties of graphs. However, even if graphs seem very similar from a local or a global perspective, they may exhibit different structure at different scales. In this paper, we present a general framework for graph similarity which takes into account structure at multiple different scales. The proposed framework capitalizes on the well-known k-core decomposition of graphs in order to build a hierarchy of nested subgraphs. We apply the framework to derive variants of four graph kernels, namely graphlet kernel, shortest-path kernel, Weisfeiler-Lehman subtree kernel, and pyramid match graph kernel. The framework is not limited to graph kernels, but can be applied to any graph comparison algorithm. The proposed framework is evaluated on several benchmark datasets for graph classification. In most cases, the core-based kernels achieve significant improvements in terms of classification accuracy over the base kernels, while their time complexity remains very attractive.
Progressive Blockwise Knowledge Distillation for Neural Network Acceleration
Authors: Wang Hui , Zhao Hanbin , Li Xi , Tan Xu
Published: July 2018

As an important and challenging problem in machine learning and computer vision, neural network acceleration essentially aims to enhance the computational efficiency without sacrificing the model accuracy too much. In this paper, we propose a progressive blockwise learning scheme for teacher-student model distillation at the subnetwork block level. The proposed scheme is able to distill the knowledge of the entire teacher network by locally extracting the knowledge of each block in terms of progressive blockwise function approximation. Furthermore, we propose a structure design criterion for the student subnetwork block, which is able to effectively preserve the original receptive field from the teacher network. Experimental results demonstrate the effectiveness of the proposed scheme against the state-of-the-art approaches.
Iterative Metric Learning for Imbalance Data Classification
Authors: Wang Nan , Zhao Xibin , Jiang Yu , Gao Yue
Published: July 2018

In many classification applications, the amount of data from different categories usually vary significantly, such as software defect predication and medical diagnosis. Under such circumstances, it is essential to propose a proper method to solve the imbalance issue among the data. However, most of the existing methods mainly focus on improving the performance of classifiers rather than searching for an appropriate way to find an effective data space for classification. In this paper, we propose a method named Iterative Metric Learning (IML) to explore the correlations among imbalance data and construct an effective data space for classification. Given the imbalance training data, it is important to select a subset of training samples for each testing data. Thus, we aim to find a more stable neighborhood for testing data using the iterative metric learning strategy. To evaluate the effectiveness of the proposed method, we have conducted experiments on two groups of dataset, i.e., the NASA Metrics Data Program (NASA) dataset and UCI Machine Learning Repository (UCI) dataset. Experimental results and comparisons with state-of-the-art methods have exhibited better performance of our proposed method.
Positive and Unlabeled Learning for Detecting Software Functional Clones with Adversarial Training
Authors: Wei Hui-Hui , Li Ming
Published: July 2018

Software clone detection is an important problem for software maintenance and evolution and it has attracted lots of attentions. However, existing approaches ignore a fact that people would label the pairs of code fragments as \emph{clone} only if they happen to discover the clones while a huge number of undiscovered clone pairs and non-clone pairs are left unlabeled. In this paper, we argue that the clone detection task in the real-world should be formalized as a Positive-Unlabeled (PU) learning problem, and address this problem by proposing a novel positive and unlabeled learning approach, namely CDPU, to effectively detect software functional clones, i.e., pieces of codes with similar functionality but differing in both syntactical and lexical level, where adversarial training is employed to improve the robustness of the learned model to those non-clone pairs that look extremely similar but behave differently. Experiments on software clone detection benchmarks indicate that the proposed approach together with adversarial training outperforms the state-of-the-art approaches for software functional clone detection.
Efficient Attributed Network Embedding via Recursive Randomized Hashing
Authors: Wu Wei , Li Bin , Chen Ling , Zhang Chengqi
Published: July 2018

Attributed network embedding aims to learn a low-dimensional representation for each node of a network, considering both attributes and structure information of the node. However, the learning based methods usually involve substantial cost in time, which makes them impractical without the help of a powerful workhorse. In this paper, we propose a simple yet effective algorithm, named NetHash, to solve this problem only with moderate computing capacity. NetHash employs the randomized hashing technique to encode shallow trees, each of which is rooted at a node of the network. The main idea is to efficiently encode both attributes and structure information of each node by recursively sketching the corresponding rooted tree from bottom (i.e., the predefined highest-order neighboring nodes) to top (i.e., the root node), and particularly, to preserve as much information closer to the root node as possible. Our extensive experimental results show that the proposed algorithm, which does not need learning, runs significantly faster than the state-of-the-art learning-based network embedding methods while achieving competitive or even better performance in accuracy.
Towards Enabling Binary Decomposition for Partial Label Learning
Authors: Wu Xuan , Zhang Min-Ling
Published: July 2018

The task of partial label (PL) learning is to learn a multi-class classifier from training examples each associated with a set of candidate labels, among which only one corresponds to the ground-truth label. It is well known that for inducing multi-class predictive model, the most straightforward solution is binary decomposition which works by either one-vs-rest or one-vs-one strategy. Nonetheless, the ground-truth label for each PL training example is concealed in its candidate label set and thus not accessible to the learning algorithm, binary decomposition cannot be directly applied under partial label learning scenario. In this paper, a novel approach is proposed to solving partial label learning problem by adapting the popular one-vs-one decomposition strategy. Specifically, one binary classifier is derived for each pair of class labels, where PL training examples with distinct relevancy to the label pair are used to generate the corresponding binary training set. After that, one binary classifier is further derived for each class label by stacking over predictions of existing binary classifiers to improve generalization. Experimental studies on both artificial and real-world PL data sets clearly validate the effectiveness of the proposed binary decomposition approach w.r.t state-of-the-art partial label learning techniques.
Cutting the Software Building Efforts in Continuous Integration by Semi-Supervised Online AUC Optimization
Authors: Xie Zheng , Li Ming
Published: July 2018

Continuous Integration (CI) systems aim to provide quick feedback on the success of the code changes by keeping on building the entire systems upon code changes are committed. However, building the entire software system is usually resource and time consuming. Thus, build outcome prediction is usually employed to distinguish the successful builds from the failed ones to cut the building efforts on those successful builds that do not result in any immediate action of the developer. Nevertheless, build outcome prediction in CI is challenging since the learner should be able to learn from a stream of build events with and without the build outcome labels and provide immediate prediction on the next build event. Also, the distribution of the successful and the failed builds are often highly imbalanced. Unfortunately, the existing methods fail to address these challenges well. In this paper, we address these challenges by proposing a semi-supervised online AUC optimization method for CI build outcome prediction. Experiments indicate that our method is able to cut the software building efforts by effectively identify the successful builds, and it outperforms the existing methods that elaborate to address part of these challenges.
Fostering Cooperation in Structured Populations Through Local and Global Interference Strategies
Authors: Han The Anh , Lynch Simon , Tran-Thanh Long , Santos Francisco C.
Published: July 2018

We study the situation of an exogenous decision-maker aiming to encourage a population of autonomous, self-regarding agents to follow a desired behaviour at a minimal cost. The primary goal is therefore to reach an efficient trade-off between pushing the agents to achieve the desired configuration while minimising the total investment. To this end, we test several interference paradigms resorting to simulations of agents facing a cooperative dilemma in a spatial arrangement. We systematically analyse and compare interference strategies rewarding local or global behavioural patterns.  Our results show that taking into account the neighbourhood's local properties, such as its level of cooperativeness, can lead to a significant improvement regarding cost efficiency while guaranteeing high levels of cooperation. As such, we argue that local interference strategies are more efficient than global ones in fostering cooperation in a population of autonomous agents.
Multi-Label Co-Training
Authors: Xing Yuying , Yu Guoxian , Domeniconi Carlotta , Wang Jun , Zhang Zili
Published: July 2018

Multi-label learning aims at assigning a set of appropriate labels to multi-label samples.  Although it has been successfully applied in various domains in recent years, most multi-label learning methods require sufficient labeled training samples, because of the large number of possible label sets.  Co-training, as an important branch of semi-supervised learning, can leverage unlabeled samples, along with scarce labeled ones, and can potentially help with the large labeled data requirement. However, it is a difficult challenge to combine multi-label learning with co-training. Two distinct issues are associated with the challenge: (i) how to solve the widely-witnessed class-imbalance problem in multi-label learning; and (ii) how to select samples with confidence, and  communicate their predicted labels among  classifiers for model refinement. To address these issues, we introduce an approach called Multi-Label Co-Training (MLCT). MLCT leverages information concerning the co-occurrence  of pairwise labels to address the class-imbalance challenge; it introduces a predictive reliability measure to select samples, and applies label-wise filtering to confidently communicate labels of selected samples among co-training classifiers.  MLCT performs favorably against related competitive multi-label learning methods on benchmark datasets and it is also robust to the input parameters.
Semi-Supervised Optimal Transport for Heterogeneous Domain Adaptation
Authors: Yan Yuguang , Li Wen , Wu Hanrui , Min Huaqing , Tan Mingkui , Wu Qingyao
Published: July 2018

Heterogeneous domain adaptation (HDA) aims to exploit knowledge from a heterogeneous source domain to improve the learning performance in a target domain. Since the feature spaces of the source and target domains are different, the transferring of knowledge is extremely difficult. In this paper, we propose a novel semi-supervised algorithm for HDA by exploiting the theory of optimal transport (OT), a powerful tool originally designed for aligning two different distributions. To match the samples between heterogeneous domains, we propose to preserve the semantic consistency between heterogeneous domains by incorporating label information into the entropic Gromov-Wasserstein discrepancy, which is a metric in OT for different metric spaces, resulting in a new semi-supervised scheme. Via the new scheme, the target and transported source samples with the same label are enforced to follow similar distributions. Lastly, based on the Kullback-Leibler metric, we develop an efficient algorithm to optimize the resultant problem. Comprehensive experiments on both synthetic and real-world datasets demonstrate the effectiveness of our proposed method.