bluuebunny/crossref_metadata_2025 huggingface dataset:
Tecnología y educación en América Latina
Authors: Peirone Fernando
Published: July 2020

En 1992, la CEPAL publicó la propuesta “Transformación productiva con equidad” como parte de un plan estratégico regional. El eje “Educación y conocimiento” definió los códigos de la modernidad, como el conjunto de conocimientos y destrezas necesarios para participar en la vida pública y desenvolverse productivamente en la sociedad moderna. Hoy, en el contexto de una reconfiguración cultural y de avances informacionales sin precedentes, no contamos con una codificación equivalente que dialogue con nuestra época y que pueda ser incorporada al sistema educativo, al orden laboral y a la organización de las instituciones. El presente trabajo propone una revisión de la vigencia que tienen aquellos códigos de la modernidad frente a la emergencia del extenso cambio que transita el mundo, cambio que todavía dista de ser comprendido y asimilado en su dimensión y alcance verdaderos, aunque ya ha comenzado a establecer nuevas prácticas sociales y culturales vinculadas a la tecnosociabilidad.
Una aproximación a la brecha digital de las personas con discapacidad en los espacios educativos del noroeste argentino
Authors: Soto Mauro Alejandro
Published: July 2020

El acceso a las tecnologías digitales es un derecho humano ampliamente garantizado para las personas con discapacidad, en tanto estas aportan a la inclusión social, a la autonomía y vida independiente. Asimismo, las mismas, usadas y desarrolladas bajo los principios de accesibilidad y criterios de diseño universal, pueden contribuir significativamente a los procesos de educación inclusiva del colectivo con discapacidad.

Partiendo de este reconocimiento, se exponen los primeros avances de una investigación que indaga en la brecha digital de las personas con discapacidad en los espacios educativos del noroeste argentino. Por medio de un recorrido bibliográfico se explicitarán los principios epistemológicos que la estructuran, los conceptos teóricos que delimitan el objeto de estudio y los aportes de otras investigaciones conexas que brindan información para su contextualización.
Tatakelola Anestesi untuk Dekompresi Kraniektomi pada Cedera Otak Traumatik Berat dengan Penyulit Obesitas Morbid
Authors: Sumardi Fitri Sepviyanti , Rachman Iwan Abdul , Oetoro Bambang J.
Published: July 2020

Anestesi dan pembedahan mungkin meyebabkan risiko yang cukup besar untuk pasien obesitas, apalagi obesitas morbid. Populasi orang gemuk meningkat, baik di negara maju dan berkembang, sehingga para ahli anestesi lebih sering menghadapi tantangan dalam mengelola pasien obesitas. Trauma multipel bertanggung jawab atas 5 juta kematian per tahun di seluruh dunia dan merupakan penyebab kematian utama bagi orang-orang muda di bawah 40 tahun, mewakili peristiwa akut dan tak terduga. Kami akan melaporkan seorang lelaki 36 tahun dengan obesitas morbid, BMI 48,97 kg/m2, yang mengalami multipel trauma akibat kecelakaan lalulintas, yang akan menjalani operasi evakuasi perdarahan subdural dan dekompresi kraniektomi. Pemilihan obat dan dosis aman sangat sulit pada pasien dengan multipel trauma, karena mungkin status volumenya tidak diketahui secara akurat. Rencana anestesi harus mempertimbangkan status resusitasi dan riwayat penyakit penyerta lain. Peran penting lainnya dari anestesiologis adalah pencegahan cedera sekunder yang disebabkan oleh syok berulang atau resusitasi tidak tepat. Anesthesia Management for Craniectomy Decompression on Severe Brain Traumatic Injury with Comorbid Morbid Obesity AbstractAnesthesia and surgery may cause considerable risk for obese patients, especially morbid obesity. Obese populations increase, both in developed and developing countries, so anesthesiologists more often face challenges in managing obese patients. Multiple traumas is responsible for 5 million deaths per year worldwide and is the leading cause of death for young people under 40, representing acute and unexpected events. We will report a 36-year-old man with morbid obesity, a BMI of 48.97 kg/m2, who experienced multiple traumas due to a traffic accident, who will undergo an evacuation operation for subdural hemorrhage and craniectomy decompression. The selection of drugs and safe doses is very difficult in patients with multiple traumas, because their volume status may not be accurately known. Anesthetic plan must consider resuscitation status and history of other comorbidities. Another important role of anesthesiologist is the prevention of secondary injury caused by recurrent shock or improper resuscitation.
Peranan Hiperventilasi terhadap Penurunan Tekanan Intrakranial dalam Kasus Bedah Saraf
Authors: Wibowo Irwan , Harahap M Sofyan
Published: July 2020

AbstrakHiperventilasi telah ditemukan sebagai salah satu cara untuk menurunkan aliran darah otak (cerebral blood flow) (CBF) sejak tahun 1920-an. Pada saat itu telah dilaporkan bahwa penggunaan hiperventilasi dapat mengurangi peningkatan tekanan intrakranial (intracranial pressure/ICP) dengan vasokonstriksi serebral sehingga mampu menurunkan volume darah di daerah serebral. Secara teoritis, manfaat hiperventilasi mungkin lebih khusus diharapkan pada pasien di mana peningkatan ICP terjadi terutama karena peningkatan volume darah otak akibat mekanisme vasodilatasi. Efek vasokonstriksi tersebut akan menghilang setelah pH pada ruang perivaskular kembali normal setelah 24 jam. Yang menjadi perhatian utama dalam metode ini adalah tindakan tersebut mampu menginduksi terjadinya iskemia serebral baik secara regional maupun global. Risiko kerusakan iskemik tersebut bergantung pada sejauh mana dan seberapa lama otak mengalami aliran darah yang rendah. Masih terdapat data yang kontroversial antara yang mendukung ataupun menentang penggunaan terapi hiperventilasi, namun menurut penelitian yang telah dilakukan, tindakan ini mampu menurunkan ICP jika dilakukan dalam jangka pendek. Pemantauan multimodalitas terhadap pasien tetap diperlukan untuk memantau keberhasilan dalam tindakan ini. Hyperventilation Management for Decrease Intracranial Pressure in Neurosurgery CasesAbstractHyperventilation has been found as a way to reduce cerebral blood flow (CBF) since 1920s. At that time it was reported that the use of hyperventilation can reduce the increase in intracranial pressure (ICP) by causing cerebral vasoconstriction and decreasing cerebral blood volume. Theoretically, the benefits of hyperventilation may be more specifically expected in patients which has increasing ICP because of an increasing in blood volume and vasodilation mechanism. The vasoconstriction effect disappears after the pH in the perivascular space returns to normal after 24 hours. The main concern in treating patients with increased ICP using hyperventilation is to induce cerebral ischemia both regionally and globally. As with a stroke, the risk of ischemic damage depends on the extent and how long the brain experiences low blood flow. Controversial data still exists between those that support or oppose the use of hyperventilation therapy, but if hypocapnia monitoring is done to control the increase in ICP in the short term, hyperventilation therapy remains beneficial. Multimodality monitoring is needed so that hyperventilation therapy can be used safely in certain patients who may need this therapy.
Anestesi Scalp Block dan Occipital Block pada Tindakan External Ventricular Drainage (EVD) pada Kehamilan 32 Minggu
Authors: Arshad Muhammad , Bisri Dewi Yulianti , Rahardjo Sri
Published: July 2020

Hidrosefalus adalah pembesaran ventrikel otak sebagai akibat peningkatan jumlah cairan serebrospinal (CSS) yang disebabkan oleh ketidakseimbangan antara produksi, sirkulasi dan absorbsinya. Perkiraan wanita hamil yang dilakukan operasi non obstetric sekitar 1–2%. Pasien obstetri dengan penyakit bedah saraf selalu dirawat di ICU. Insidensi operasi selama kehamilan dilaporkan 42% prosedur pembedahan terjadi pada trimester pertama, 35% pada trimester kedua, dan 23% pada trimester ketiga. Seorang wanita 35 tahun dengan usia kehamilan 32–33 minggu datang ke IGD dengan penurunan kesadaran tanpa riwayat trauma, tidak dijumpai tekanan darah tinggi. Dari pemeriksaan CT-Scan ditemukan adanya hidrosefalus, sehingga dilakukan tindakan external ventricular drainage (EVD). Anestesi untuk pasien dalam keadaan hamil harus mempertimbangkan bayi dalam kandungan sehingga dilakukan pembiusan scalp block dan occipital block. Obat yang digunakan ropivakain HCl 0,375 % (sebanyak 10cc). Setelah operasi pasien dirawat di ruang pemulihan, 2 jam kemudian dirawat di ruang high care unit (HCU). Pencegahan terhadap hipoksia dan hipotensi adalah keharusan, walaupun periode hipoksemia yang pendek masih bisa ditoleransi. Hipoksemia maternal yang berkepanjangan akan menyebabkan vasokonstriksi sirkulasi uteroplasental yang pada akhirnya menurunkan perfusi sehingga menyebabkan hipoksemia, asidosis dan kematian janin. Scalp Block and Occipital Block Anesthesia for External Ventricular Drainage (EVD) in 32-Week PregnancyAbstractHydrocephalus is enlargement of the brain ventricles as a result of an increase in the amount of cerebrospinal fluid (CSS) caused by an imbalance between production, circulation and absorption. Estimates of pregnant women in non-obstetric surgery are around 1–2%. Obstetric patients with neurosurgery disorders are always treated in the ICU. The incidence of surgery during pregnancy was reported in 42% of surgical procedures occurred in the first trimester, 35% in the second trimester, and 23% in the third trimester. A 35-year-old woman with 32-33 weeks' gestation came to the emergency room with decreased consciousness yet no history of trauma and high blood pressure. Hydrocephalus were found in CT scan, and the external ventricular drainage (EVD) is taken. The patient has intrauterine pregnancy so the anesthesia choice is scalp block and occipital block. Ropivacaine HCl 0.375% (as much 10cc) was used. After surgery the patient was treated in the recovery room for 2 hours, and discharged to the high care unit (HCU) afterwards. Hypoxia and hypotension is a challenge in short periods of hypoxemia that can still be tolerated. Prolonged maternal hypoxemia prevention will cause uteroplacental circulation vasoconstriction which ultimately reduces perfusion, causing foetal hypoxemia, acidosis and death.
Ventilasi Mekanik yang Memanjang pada Pasien Cedera Otak Traumatik Berat dengan Subdural Hematoma
Authors: Putri Dini Handayani , Rachman Iwan Abdul , Rahardjo Sri
Published: July 2020

Cedera otak traumatik (COT) adalah suatu proses patologis pada otak yang berasal dari luar tubuh, yang dapat menyebabkan kerusakan permanen atau sementara dari fungsi otak. Salah satu perdarahan otak yang sering menyertai terjadinya COT berat adalah subdural hematoma (SDH). Pasien laki-laki 41 tahun, datang ke rumah sakit dengan penurunan kesadaran GCS E2M2V2 akibat kecelakaan. Pasien dilakukan tatalaksana dan diintubasi di IGD, hasil CT-Scan memperlihatkan SDH, dan edema serebri. Pasien dilakukan evakuasi hematom dengan anestesi umum, diinduksi dengan fentanil 100 μgr, propofol 20 mg dan atracurium 20 mg intravena. Pemeliharaan dengan sevofluran, propofol kontinyu, fentanil dan atracurium intermiten. Pascaoperasi pasien mengalami ventilator associated pneumonia (VAP), diberikan antibiotik sprektum luas untuk pemulihan paru, pada hari ke 10 pasien dilakukan trakeostomi. Pasien dapat disapih dari ventilator hari ke 21 dengan GCS E4M4Vtrach tanpa kontak, dengan skor GOS (Glasgow Outcome Scale) 3, direncanakan untuk homecare. COT berat membutuhkan tatalaksana pascaoperasi yang lebih kompleks, antisipasi penggunaan ventilasi mekanik yang memanjang, risiko VAP serta pertimbangan pemasangan trakeostomi secara cepat hingga pemberian antibiotik yang adekuat untuk mendapatkan hasil luaran terbaik. Prolonged Mechanical Ventilation in Severe Traumatic Brain Injury Patients with Subdural HematomasAbstractTraumatic brain injury (TBI) is a pathological process in the brain that originates from outside the body, which can lead to permanent or temporary damage to brain function. One of the brain hemorrhages that often accompanies severe TBI is subdural hematoma (SDH). Male patient 41 years old, was admitted to our hospital with decreased of consciusness with GCS E2M2V2 due to a motorcycle accident. The patient was intubated in the emergency room and then performed head CT scan examination which showed SDH and cerebral edema as the result. Patients then underwent hematoma evacuation under general anesthesia, induced with fentanyl 100 g, propofol 20 mg and atracurium 20 mg intravenously. Maintenance of anesthesia with sevoflurane, continuous propofol, fentanyl and intermittent atracurium. Postoperatively the patient experienced ventilator associated pneumonia (VAP) and given broad-spectrum antibiotics for lung recovery, then on the 10th day tracheostomy was performed. Patients can be weaned from the ventilator on day 21st with GCS E4M4Vtrach without contact, with a GOS (Glasgow Outcome Scale) score of 3, and was planned for homecare. Severe TBI requires more complex postoperative management, anticipation of prolonged use of mechanical ventilation, risk of VAP and consideration of rapid tracheostomy installation to adequate antibiotic administration to obtain the best outcome.
THE INFLUENCE OF SHARIA COMPLIANCE AND ISLAMIC CORPORATE GOVERNANCE ON FRAUD IN ISLAMIC BANKS IN INDONESIA
Authors: Putri Rahmawati , Mutia Evi
Published: July 2020

This study aims to examine the Influence of Sharia Compliance and Islamic Corporate Governance on fraud on Islamic banks in Indonesia. The independent variable that was mummified was sharia compliance with Islamic Income Ratio (IsIR), Profit Sharing Ratio (PSR), Islamic Investment Ratio (IIR), and Islamic Corporate Governance. The dependent variable used is fraud that occurs in Islamic commercial banks in Indonesia. The research method used in this study is the method of library research. The type of data used is secondary data in the form of financial statement data and annual GCG implementation reports for the period of 2014 to 2018. The population in this study were all Sharia Commercial Banks (BUS) registered at Bank Indonesia in the period 2014 to 2018. The sample was selected using the purposive sampling method. The total sample used in this study amounted to 11 Islamic Commercial Banks with a study period of 5 years. The analytical method used in this study is multiple regression processed using SPSS version 23. The results of this study indicate that the variable sharia compliance as a proxy Islamic Income Ratio (ISIR), Profit Sharing Ratio (PSR), Islamic Investment Ratio (IIR), have a negative effect on Islamic bank’s fraud while Islamic corporate governance had no effect on fraud in Islamic banks in Indonesia.
Exhibition Сzech and Кoman King Wenceslas IV: «Beautiful Style» of Gothic Art. On the 600th Anniversary of the Death of the Czech King
Authors: Lanceva A. M.
Published: July 2020

The article is devoted to the historical and cultural aspects of the development of Czech art in the late Middle Ages on the example of an exhibition held from August 16 to November 3 at Prague Castle, which was dedicated to the 600th anniversary of the death of the Czech and Roman King Wenceslas IV. The author of the article considers the significance of the Czech culture and sacred art in the context of the political and historical specifics of the development of medieval Bohemia and the features of the reign of Vaclav IV, who wasthe son of the Holy Roman Emperor and the Czech King Charles IV . Wenceslas IV is a complex and controversial figure in Czech history, who stood at the «crossroads» of epochs and cultures, around him various disputes persist in historiography up to our time. This article provides an overview of the nature of the sacred artifacts of culture and art presented at the exhibition «Czech and Roman King Wenceslas IV: «beautiful style» of Gothic art», as well as the characteristics of the artistic style , defined in terms of historical and cultural, internal and external political development of the Czech Republic, crosscultural dialogue of the Czech Republic with European countries on the background of the emerging religious controversy in the country. The work takes into account the features of the Late Gothic style in the Central Europe. On the example of the remarkable works of painting, sculpture, fragments of architectural monuments, decorative and applied art and manuscripts, first of all the monumental Wenceslas Bible, many of which were brought to Prague from various European Galleries and Castles of Poland, Germany, France, New York, as well as from private collections, can demonstrate the rise of Czech culture and art in the late XIV-early XV centuries, which was presented the process of cultural accumulation of the European style of the late Gothic, received Czech national artificial identity.
XXIV International Scientific and Practical Conference Linguistics for All
Authors: Afanasieva N. D.
Published: July 2020

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Fine-Grained View on Bribery for Group Identification
Authors: Boehmer Niclas , Bredereck Robert , Knop Dušan , Luo Junjie
Published: July 2020

Given a set of individuals qualifying or disqualifying each other, group identification is the task of identifying a socially qualified subgroup of individuals. Social qualification depends on the specific rule used to aggregate individual qualifications. The bribery problem in this context asks how many agents need to change their qualifications in order to change the outcome.

Complementing previous results showing polynomial-time solvability or NP-hardness of bribery for various social rules in the constructive (aiming at making specific individuals socially qualified) or destructive (aiming at making specific individuals socially disqualified) setting, we provide a comprehensive picture of the parameterized computational complexity landscape. Conceptually, we also consider a more fine-grained concept of bribery cost, where we ask how many single qualifications need to be changed, and a more general bribery goal that combines the constructive and destructive setting.
Learning Task-aware Local Representations for Few-shot Learning
Authors: Dong Chuanqi , Li Wenbin , Huo Jing , Gu Zheng , Gao Yang
Published: July 2020

Few-shot learning for visual recognition aims to adapt to novel unseen classes with only a few images. Recent work, especially the work based on low-level information, has achieved great progress. In these work, local representations (LRs) are typically employed, because LRs are more consistent among the seen and unseen classes. However, most of them are limited to an individual image-to-image or image-to-class measure manner, which cannot fully exploit the capabilities of LRs, especially in the context of a certain task. This paper proposes an Adaptive Task-aware Local Representations Network (ATL-Net) to address this limitation by introducing episodic attention, which can adaptively select the important local patches among the entire task, as the process of human recognition. We achieve much superior results on multiple benchmarks. On the miniImagenet, ATL-Net gains 0.93% and 0.88% improvements over the compared methods under the 5-way 1-shot and 5-shot settings. Moreover, ATL-Net can naturally tackle the problem that how to adaptively identify and weight the importance of different key local parts, which is the major concern of fine-grained recognition. Specifically, on the fine-grained dataset Stanford Dogs, ATL-Net outperforms the second best method with 5.39% and 9.69% gains under the 5-way 1-shot and 5-shot settings.
Latent Regularized Generative Dual Adversarial Network For Abnormal Detection
Authors: Chen Chengwei , Liu Jing , Xie Yuan , Ban Yin Xiao , Wu Chunyun , Tao Yiqing , Song Haichuan
Published: July 2020

With the development of adversarial attack in deep learning, it is critical for abnormal detector to not only discover the out-of-distribution samples but also provide defence against the adversarial attacker. Since few previous universal detector is known to work well on both tasks, we consider against both scenarios by constructing a robust and effective technique, where one sample could be regarded as the abnormal sample if it exhibits a higher image reconstruction error. Due to the training instability issues existed in previous generative adversarial networks (GANs) based methods, in this paper we propose a dual auxiliary autoencoder to make a tradeoff between the capability of generator and discriminator, leading to a more stable training process and high-quality image reconstruction. Moreover, to generate discriminative and robust latent representations, the mutual information estimator regarded as latent regularizer is adopted to extract the most unique information of target class. Overall, our generative dual adversarial network simultaneously optimizes the image reconstruction space and latent space to improve the performance. Experiments show that our model has the clear superiority over cutting edge semi-supervised abnormal detectors and achieves the state-of-the-art results on the datasets.
k-SDPP: Fixed-Size Video Summarization via Sequential Determinantal Point Processes
Authors: Zheng Jiping , Lu Ganfeng
Published: July 2020

With the explosive growth of video data, video summarization which converts long-time videos to key frame sequences has become an important task in information retrieval and machine learning. Determinantal point processes (DPPs) which are elegant probabilistic models have been successfully applied to video summarization. However, existing DPP-based video summarization methods suffer from poor efficiency of outputting a specified size summary or neglecting inherent sequential nature of videos. In this paper, we propose a new model in the DPP lineage named k-SDPP in vein of sequential determinantal point processes but with fixed user specified size k. Our k-SDPP partitions sampled frames of a video into segments where each segment is with constant number of video frames. Moreover, an efficient branch and bound method (BB) considering sequential nature of the frames is provided to optimally select k frames delegating the summary from the divided segments. Experimental results show that our proposed BB method outperforms not only k-DPP and sequential DPP (seqDPP) but also the partition and Markovian assumption based methods.
AttAN: Attention Adversarial Networks for 3D Point Cloud Semantic Segmentation
Authors: Zhang Gege , Ma Qinghua , Jiao Licheng , Liu Fang , Sun Qigong
Published: July 2020

3D point cloud semantic segmentation has attracted wide attention with its extensive applications in autonomous driving, AR/VR, and robot sensing fields. However, in existing methods, each point in the segmentation results is predicted independently from each other. This property causes the non-contiguity of label sets in three-dimensional space and produces many noisy label points, which hinders the improvement of segmentation accuracy. To address this problem, we first extend adversarial learning to this task and propose a novel framework Attention Adversarial Networks (AttAN). With high-order correlations in label sets learned from the adversarial learning, segmentation network can predict labels closer to the real ones and correct noisy results. Moreover, we design an additive attention block for the segmentation network, which is used to automatically focus on regions critical to the segmentation task by learning the correlation between multi-scale features. Adversarial learning, which explores the underlying relationship between labels in high-dimensional space, opens up a new way in 3D point cloud semantic segmentation. Experimental results on ScanNet and S3DIS datasets show that this framework effectively improves the segmentation quality and outperforms other state-of-the-art methods.
Dynamic Language Binding in Relational Visual Reasoning
Authors: Minh Le Thao , Le Vuong , Venkatesh Svetha , Tran Truyen
Published: July 2020

We present Language-binding Object Graph Network, the first neural reasoning method with dynamic relational structures across both visual and textual domains with applications in visual question answering. Relaxing the common assumption made by current models that the object predicates pre-exist and stay static, passive to the reasoning process, we propose that these dynamic predicates expand across the domain borders to include pair-wise visual-linguistic object binding. In our method, these contextualized object links are actively found within each recurrent reasoning step without relying on external predicative priors. These dynamic structures reflect the conditional dual-domain object dependency given the evolving context of the reasoning through co-attention. Such discovered dynamic graphs facilitate multi-step knowledge combination and refinements that iteratively deduce the compact representation of the final answer. The effectiveness of this model is demonstrated on image question answering demonstrating favorable performance on major VQA datasets. Our method outperforms other methods in sophisticated question-answering tasks wherein multiple object relations are involved. The graph structure effectively assists the progress of training, and therefore the network learns efficiently compared to other reasoning models.
Few-shot Human Motion Prediction via Learning Novel Motion Dynamics
Authors: Zang Chuanqi , Pei Mingtao , Kong Yu
Published: July 2020

Human motion prediction is a task where we anticipate future motion based on past observation. Previous approaches rely on the access to large datasets of skeleton data, and thus are difficult to be generalized to novel motion dynamics with limited training data. In our work, we propose a novel approach named Motion Prediction Network (MoPredNet) for few-short human motion prediction. MoPredNet can be adapted to predicting new motion dynamics using limited data, and it elegantly captures long-term dependency in motion dynamics. Specifically, MoPredNet dynamically selects the most informative poses in the streaming motion data as masked poses. In addition, MoPredNet improves its encoding capability of motion dynamics by adaptively learning spatio-temporal structure from the observed poses and masked poses. We also propose to adapt MoPredNet to novel motion dynamics based on accumulated motion experiences and limited novel motion dynamics data. Experimental results show that our method achieves better performance over state-of-the-art methods in motion prediction.
Set and Rebase: Determining the Semantic Graph Connectivity for Unsupervised Cross-Modal Hashing
Authors: Wang Weiwei , Shen Yuming , Zhang Haofeng , Yao Yazhou , Liu Li
Published: July 2020

The label-free nature of unsupervised cross-modal hashing hinders models from exploiting the exact semantic data similarity. Existing research typically simulates the semantics by a heuristic geometric prior in the original feature space. However, this introduces heavy bias into the model as the original features are not fully representing the underlying multi-view data relations. To address the problem above, in this paper, we propose a novel unsupervised hashing method called Semantic-Rebased Cross-modal Hashing (SRCH). A novel ‘Set-and-Rebase’ process is defined to initialize and update the cross-modal similarity graph of training data. In particular, we set the graph according to the intra-modal feature geometric basis and then alternately rebase it to update the edges within according to the hashing results. We develop an alternating optimization routine to rebase the graph and train the hashing auto-encoders with closed-form solutions so that the overall framework is efficiently trained. Our experimental results on benchmarked datasets demonstrate the superiority of our model against state-of-the-art algorithms.
Detecting Adversarial Attacks via Subset Scanning of Autoencoder Activations and Reconstruction Error
Authors: Cintas Celia , Speakman Skyler , Akinwande Victor , Ogallo William , Weldemariam Komminist , Sridharan Srihari , McFowland Edward
Published: July 2020

Reliably detecting attacks in a given set of inputs is of high practical relevance because of the vulnerability of neural networks to adversarial examples.

These altered inputs create a security risk in applications with real-world consequences, such as self-driving cars, robotics and financial services.

We propose an unsupervised method for detecting adversarial attacks in inner layers of autoencoder (AE) networks by maximizing a non-parametric measure of anomalous node activations.

Previous work in this space has shown AE networks can detect anomalous images by thresholding the reconstruction error produced by the final layer. Furthermore, other detection methods rely on data augmentation or specialized training techniques which must be asserted before training time. In contrast, we use subset scanning methods from the anomalous pattern detection domain to enhance detection power without labeled examples of the noise, retraining or data augmentation methods. In addition to an anomalous “score” our proposed method also returns the subset of nodes within the AE network that contributed to that score. This will allow future work to pivot from detection to visualisation and explainability. Our scanning approach shows consistently higher detection power than existing detection methods across several adversarial noise models and a wide range of perturbation strengths.
Consistent Domain Structure Learning and Domain Alignment for 2D Image-Based 3D Objects Retrieval
Authors: Su Yuting , Li Yuqian , Song Dan , Nie Weizhi , Li Wenhui , Liu An-An
Published: July 2020

2D image-based 3D objects retrieval is a new topic for 3D objects retrieval which can be used to manage 3D data with 2D images. The goal is to search some related 3D objects when given a 2D image. The task is challenging due to the large domain gap between 2D images and 3D objects. Therefore, it is essential to consider domain adaptation problems to reduce domain discrepancy. However, most of the existing domain adaptation methods only utilize the semantic information from the source domain to predict labels in the target domain and neglect the intrinsic structure of the target domain. In this paper, we propose a domain alignment framework with consistent domain structure learning to reduce the large gap between 2D images and 3D objects. The domain structure learning module makes use of both the semantic information from the source domain and the intrinsic structure of the target domain, which provides more reliable predicted labels to the domain alignment module to better align the conditional distribution. We conducted experiments on two public datasets, MI3DOR and MI3DOR-2, and the experimental results demonstrate the proposed method outperforms the state-of-the-art methods.
On the Complexity of Winner Verification and Candidate Winner for Multiwinner Voting Rules
Authors: Sonar Chinmay , Dey Palash , Misra Neeldhara
Published: July 2020

The Chamberlin-Courant and Monroe rules are fundamental and well-studied rules in the literature of multi-winner elections. The problem of determining if there exists a committee of size k that has a Chamberlin-Courant (respectively, Monroe) dissatisfaction score of at most r is known to be NP-complete. We consider the following natural problems in this setting: a) given a committee S of size k as input, is it an optimal k-sized committee?, and b) given a candidate c and a committee size k, does there exist an optimal k-sized committee that contains c? In this work, we resolve the complexity of both problems for the Chamberlin-Courant and Monroe voting rules in the settings of rankings as well as approval ballots. We show that verifying if a given committee is optimal is coNP-complete whilst the latter problem is complete for Theta_2^P. Our contribution fills an essential gap in the literature for these important multi-winner rules.
Self-supervised Monocular Depth and Visual Odometry Learning with Scale-consistent Geometric Constraints
Authors: Xiong Mingkang , Zhang Zhenghong , Zhong Weilin , Ji Jinsheng , Liu Jiyuan , Xiong Huilin
Published: July 2020

The self-supervised learning-based depth and visual odometry (VO) estimators trained on monocular videos without ground truth have drawn significant attention recently. Prior works use photometric consistency as supervision, which is fragile under complex realistic environments due to illumination variations. More importantly, it suffers from scale inconsistency in the depth and pose estimation results. In this paper, robust geometric losses are proposed to deal with this problem. Specifically, we first align the scales of two reconstructed depth maps estimated from the adjacent image frames, and then enforce forward-backward relative pose consistency to formulate scale-consistent geometric constraints. Finally, a novel training framework is constructed to implement the proposed losses. Extensive evaluations on KITTI and Make3D datasets demonstrate that, i) by incorporating the proposed constraints as supervision, the depth estimation model can achieve state-of-the-art (SOTA) performance among the self-supervised methods, and ii) it is effective to use the proposed training framework to obtain a uniform global scale VO model.
Progressive Domain-Independent Feature Decomposition Network for Zero-Shot Sketch-Based Image Retrieval
Authors: Xu Xinxun , Yang Muli , Yang Yanhua , Wang Hao
Published: July 2020

Zero-Shot Sketch-Based Image Retrieval (ZS-SBIR) is a specific cross-modal retrieval task for searching natural images given free-hand sketches under the zero-shot scenario. Most existing methods solve this problem by simultaneously projecting visual features and semantic supervision into a low-dimensional common space for efficient retrieval. However, such low-dimensional projection destroys the completeness of semantic knowledge in original semantic space, so that it is unable to transfer useful knowledge well when learning semantic features from different modalities. Moreover, the domain information and semantic information are entangled in visual features, which is not conducive for cross-modal matching since it will hinder the reduction of domain gap between sketch and image. In this paper, we propose a Progressive Domain-independent Feature Decomposition (PDFD) network for ZS-SBIR. Specifically, with the supervision of original semantic knowledge, PDFD decomposes visual features into domain features and semantic ones, and then the semantic features are projected into common space as retrieval features for ZS-SBIR. The progressive projection strategy maintains strong semantic supervision. Besides, to guarantee the retrieval features to capture clean and complete semantic information, the cross-reconstruction loss is introduced to encourage that any combinations of retrieval features and domain features can reconstruct the visual features. Extensive experiments demonstrate the superiority of our PDFD over state-of-the-art competitors.
Co-Saliency Spatio-Temporal Interaction Network for Person Re-Identification in Videos
Authors: Liu Jiawei , Zha Zheng-Jun , Zhu Xierong , Jiang Na
Published: July 2020

Person re-identification aims at identifying a certain pedestrian across non-overlapping camera networks. Video-based person re-identification approaches have gained significant attention recently, expanding image-based approaches by learning features from multiple frames. In this work, we propose a novel Co-Saliency Spatio-Temporal Interaction Network (CSTNet) for person re-identification in videos. It captures the common salient foreground regions among video frames and explores the spatial-temporal long-range context interdependency from such regions, towards learning discriminative pedestrian representation. Specifically, multiple co-saliency learning modules within CSTNet are designed to utilize the correlated information across video frames to extract the salient features from the task-relevant regions and suppress background interference. Moreover, multiple spatial-temporal interaction modules within CSTNet are proposed, which exploit the spatial and temporal long-range context interdependencies on such features and spatial-temporal information correlation, to enhance feature representation. Extensive experiments on two benchmarks have demonstrated the effectiveness of the proposed method.
CP-NAS: Child-Parent Neural Architecture Search for 1-bit CNNs
Authors: Zhuo Li'an , Zhang Baochang , Chen Hanlin , Yang Linlin , Chen Chen , Zhu Yanjun , Doermann David
Published: July 2020

Neural architecture search (NAS) proves to be among the best approaches for many tasks by generating an application-adaptive neural architectures, which are still challenged by high computational cost and memory consumption. At the same time, 1-bit convolutional neural networks (CNNs) with binarized weights and activations show their potential for resource-limited embedded devices. One natural approach is to use 1-bit CNNs to reduce the computation and memory cost of NAS by taking advantage of the strengths of each in a uniﬁed framework. To this end, a Child-Parent model is introduced to a differentiable NAS to search the binarized architecture(Child) under the supervision of a full-precision model (Parent). In the search stage, the Child-Parent model uses an indicator generated by the parent and child model accuracy to evaluate the performance and abandon operations with less potential. In the training stage, a kernel level CP loss is introduced to optimize the binarized network. Extensive experiments demonstrate that the proposed CP-NAS achieves a comparable accuracy with traditional NAS on both the CIFAR and ImageNet databases. It achieves an accuracy of 95.27% on CIFAR-10, 64.3% on ImageNet with binarized weights and activations, and a 30% faster search than prior arts.
Strategic Campaign Management in Apportionment Elections
Authors: Bredereck Robert , Faliszewski Piotr , Furdyna Michal , Kaczmarczyk Andrzej , Lackner Martin
Published: July 2020

In parliamentary elections, parties compete for a limited, typically fixed number of seats. We study the complexity of the following bribery-style problem: Given the distribution of votes among the parties, what is the smallest number of voters that need to be convinced to vote for our party, so that it gets a desired number of seats. We also run extensive experiments on real-world election data and measure the effectiveness of our method.
Overcoming Language Priors with Self-supervised Learning for Visual Question Answering
Authors: Zhu Xi , Mao Zhendong , Liu Chunxiao , Zhang Peng , Wang Bin , Zhang Yongdong
Published: July 2020

Most Visual Question Answering (VQA) models suffer from the language prior problem, which is caused by inherent data biases. Specifically, VQA models tend to answer questions (e.g., what color is the banana?) based on the high-frequency answers (e.g., yellow) ignoring image contents. Existing approaches tackle this problem by creating delicate models or introducing additional visual annotations to reduce question dependency and strengthen image dependency. However, they are still subject to the language prior problem since the data biases have not been fundamentally addressed. In this paper, we introduce a self-supervised learning framework to solve this problem. Concretely, we first automatically generate labeled data to balance the biased data, and then propose a self-supervised auxiliary task to utilize the balanced data to assist the VQA model to overcome language priors. Our method can compensate for the data biases by generating balanced data without introducing external annotations. Experimental results show that our method achieves state-of-the-art performance, improving the overall accuracy from 49.50% to 57.59% on the most commonly used benchmark VQA-CP v2. In other words, we can increase the performance of annotation-based methods by 16% without using external annotations. Our code is available on GitHub.
Diversity of Solutions: An Exploration Through the Lens of Fixed-Parameter Tractability Theory
Authors: Baste Julien , Fellows Michael R. , Jaffke Lars , Masařík Tomáš , de Oliveira Oliveira Mateus , Philip Geevarghese , Rosamond Frances A.
Published: July 2020

When modeling an application of practical relevance as an instance of a combinatorial problem X, we are often interested not merely in finding one optimal solution for that instance, but in finding a sufficiently diverse collection of good solutions. In this work we initiate a systematic study of diversity from the point of view of fixed-parameter tractability theory. We consider an intuitive notion of diversity of a collection of solutions which suits a large variety of combinatorial problems of practical interest. Our main contribution is an algorithmic framework which --automatically-- converts a tree-decomposition-based dynamic programming algorithm for a given combinatorial problem X into a dynamic programming algorithm for the diverse version of X. Surprisingly, our algorithm has a polynomial dependence on the diversity parameter.
Subgraph Isomorphism Meets Cutting Planes: Solving With Certified Solutions
Authors: Gocht Stephan , McCreesh Ciaran , Nordström Jakob
Published: July 2020

Modern subgraph isomorphism solvers carry out sophisticated reasoning using graph invariants such as degree sequences and path counts. We show that all of this reasoning can be justified compactly using the cutting planes proofs studied in complexity theory. This allows us to extend a state of the art subgraph isomorphism enumeration solver with proof logging support, so that the solutions it outputs may be audited and verified for correctness and completeness by a simple third party tool which knows nothing about graph theory.
Inductive Link Prediction for Nodes Having Only Attribute Information
Authors: Hao Yu , Cao Xin , Fang Yixiang , Xie Xike , Wang Sibo
Published: July 2020

Predicting the link between two nodes is a fundamental problem for graph data analytics. In attributed graphs, both the structure and attribute information can be utilized for link prediction. Most existing studies focus on transductive link prediction where both nodes are already in the graph. However, many real-world applications require inductive prediction for new nodes having only attribute information. It is more challenging since the new nodes do not have structure information and cannot be seen during the model training. To solve this problem, we propose a model called DEAL, which consists of three components: two node embedding encoders and one alignment mechanism. The two encoders aim to output the attribute-oriented node embedding and the structure-oriented node embedding, and the alignment mechanism aligns the two types of embeddings to build the connections between the attributes and links. Our model DEAL is versatile in the sense that it works for both inductive and transductive link prediction. Extensive experiments on several benchmark datasets show that our proposed model significantly outperforms existing inductive link prediction methods, and also outperforms the state-of-the-art methods on transductive link prediction.
On the Enumeration of Association Rules: A Decomposition-based Approach
Authors: Izza Yacine , Jabbour Said , Raddaoui Badran , Boudane Abdelahmid
Published: July 2020

While traditional data mining techniques have been used extensively for finding patterns in databases, they are not always suitable for incorporating user-specified constraints. To overcome this issue, CP and SAT based frameworks for modeling and solving pattern mining tasks have gained a considerable audience in recent years. However, a bottleneck for all these CP and SAT-based approaches is the encoding size which makes these algorithms inefficient for large databases. This paper introduces a practical SAT-based approach to discover efficiently (minimal non-redundant) association rules. First, we present a decomposition-based paradigm that splits the original transaction database into smaller and independent subsets. Then, we show that without producing too large formulas, our decomposition method allows independent mining evaluation on a multi-core machine, improving performance. Finally, an experimental evaluation shows that our method is fast and scale well compared with the existing CP approach even in the sequential case, while significantly reducing the gap with the best state-of-the-art specialized algorithm.
Enhancing Urban Flow Maps via Neural ODEs
Authors: Zhou Fan , Li Liang , Zhong Ting , Trajcevski Goce , Zhang Kunpeng , Wang Jiahao
Published: July 2020

Flow super-resolution (FSR) enables inferring fine-grained urban flows with coarse-grained observations and plays an important role in traffic monitoring and prediction. The existing FSR solutions rely on deep CNN models (e.g., ResNet) for learning spatial correlation, incurring excessive memory cost and numerous parameter updates. We propose to tackle the urban flows inference using dynamic systems paradigm and present a new method FODE -- FSR with Ordinary Differential Equations (ODEs). FODE extends neural ODEs by introducing an affine coupling layer to overcome the problem of numerically unstable gradient computation, which allows more accurate and efficient spatial correlation estimation, without extra memory cost. In addition, FODE provides a flexible balance between flow inference accuracy and computational efficiency. A FODE-based augmented normalization mechanism is further introduced to constrain the flow distribution with the influence of external factors. Experimental evaluations on two real-world datasets demonstrate that FODE significantly outperforms several baseline approaches.
Understanding the Success of Graph-based Semi-Supervised Learning using Partially Labelled Stochastic Block Model
Authors: Saha Avirup , Sheshadri Shreyas , Datta Samik , Ganguly Niloy , Makhija Disha , Patel Priyank
Published: July 2020

With the proliferation of learning scenarios with an abundance of instances, but limited amount of high-quality labels, semi-supervised learning algorithms came to prominence. Graph-based semi-supervised learning (G-SSL) algorithms, of which Label Propagation (LP) is a prominent example, are particularly well-suited for these problems. The premise of LP is the existence of homophily in the graph, but beyond that nothing is known about the efficacy of LP. In particular, there is no characterisation that connects the structural constraints, volume and quality of the labels to the accuracy of LP. In this work, we draw upon the notion of recovery from the literature on community detection, and provide guarantees on accuracy for partially-labelled graphs generated from the Partially-Labelled Stochastic Block Model (PLSBM). Extensive experiments performed on synthetic data verify the theoretical findings.
Recovering Accurate Labeling Information from Partially Valid Data for Effective Multi-Label Learning
Authors: Li Ximing , Wang Yang
Published: July 2020

Partial Multi-label Learning (PML) aims to induce the multi-label predictor from datasets with noisy supervision, where each training instance is associated with several candidate labels but only partially valid. To address the noisy issue, the existing PML methods basically recover the ground-truth labels by leveraging the ground-truth confidence of the candidate label, i.e., the likelihood of a candidate label being a ground-truth one. However, they neglect the information from non-candidate labels, which potentially contributes to the ground-truth label recovery. In this paper, we propose to recover the ground-truth labels, i.e., estimating the ground-truth confidences, from the label enrichment, composed of the relevance degrees of candidate labels and irrelevance degrees of non-candidate labels. Upon this observation, we further develop a novel two-stage PML method, namely Partial Multi-Label Learning with Label Enrichment-Recovery (PML3ER), where in the first stage, it estimates the label enrichment with unconstrained label propagation, then jointly learns the ground-truth confidence and multi-label predictor given the label enrichment. Experimental results validate that PML3ER outperforms the state-of-the-art PML methods.