nils-herrmann/clean_scirepeval huggingface dataset:
Adaptive Region Embedding for Text Classification
Venue: AAAI
Year: 2019

Deep learning models such as convolutional neural networks and recurrent networks are widely applied in text classification. In spite of their great success, most deep learning models neglect the importance of modeling context information, which is crucial to understanding texts. In this work, we propose the Adaptive Region Embedding to learn context representation to improve text classification. Specifically, a metanetwork is learned to generate a context matrix for each region, and each word interacts with its corresponding context matrix to produce the regional representation for further classification. Compared to previous models that are designed to capture context information, our model contains less parameters and is more flexible. We extensively evaluate our method on 8 benchmark datasets for text classification. The experimental results prove that our method achieves state of the art performances and effectively avoids word ambiguity.
UnDeepVO: Monocular Visual Odometry Through Unsupervised Deep Learning
Venue: 2018 IEEE International Conference on Robotics and Automation (ICRA)
Year: 2018

We propose a novel monocular visual odometry (VO) system called UnDeepVO in this paper. UnDeepVO is able to estimate the 6 DoF pose of a monocular camera and the depth of its view by using deep neural networks. There are two salient features of the proposed UnDeepVo:one is the unsupervised deep learning scheme, and the other is the absolute scale recovery. Specifically, we train UnDeepVoby using stereo image pairs to recover the scale but test it by using consecutive monocular images. Thus, UnDeepVO is a monocular system. The loss function defined for training the networks is based on spatial and temporal dense information. A system overview is shown in Fig. 1. The experiments on KITTI dataset show our UnDeepVO achieves good performance in terms of pose accuracy.
Improving face representation learning with center invariant loss
Venue: Image Vis. Comput.
Year: 2018

In this paper, we address on the deep face representation learning with imbalanced data. With a large number of available face images of different people for training, Convolutional Neural Networks could learn deep face representation through classifying these people. However, uniformed distributed data for all people are hard to get. Some people come with more images but some come with less. In learning the deep face representation, the imbalanced images between people introduce the bias towards these people that have more images. Existing methods focus on the intra class and inter class variations but not well address the imbalanced data problem. To generate a robust and discriminative face representation for all people, we propose a center invariant loss which adds penalty to the differences between each center of classes. The center invariant loss could align the center of each person to the mean of all centers, which could force the deeply learned face features to have a good representation for all people with better generalization ability. Extensive experiments well demonstrate the effectiveness of the proposed approach. Many existing methods in learning deep face representation are further improved after adding the proposed center invariant loss.
Learning with Pseudo Ensembles
Venue: NIPS
Year: 2014

We formalize the notion of a pseudo ensemble, a (possibly infinite) collection of child models spawned from a parent model by perturbing it according to some noise process. E.g. dropout [9] in a deep neural network trains a pseudo ensemble of child subnetworks generated by randomly masking nodes in the parent network. We examine the relationship of pseudo ensembles, which involve perturbation in model space, to standard ensemble methods and existing notions of robustness, which focus on perturbation in observation space. We present a novel regularizer based on making the behavior of a pseudo ensemble robust with respect to the noise process generating it. In the fully supervised setting, our regularizer matches the performance of dropout. But, unlike dropout, our regularizer naturally extends to the semi supervised setting, where it produces state of the art results. We provide a case study in which we transform the Recursive Neural Tensor Network of [19] into a pseudo ensemble, which significantly improves its performance on a real world sentiment analysis benchmark.
A Knowledge Aware Sequence to Tree Network for Math Word Problem Solving
Venue: EMNLP
Year: 2020

With the advancements in natural language processing tasks, math word problem solving has received increasing attention. Previous methods have achieved promising results but ignore background common sense knowledge not directly provided by the problem. In addition, during generation, they focus on local features while neglecting global information. To incorporate external knowledge and global expression information, we propose a novel knowledge aware sequence to tree (KA S2T) network in which the entities in the problem sequences and their categories are modeled as an entity graph. Based on this entity graph, a graph attention network is used to capture knowledge aware problem representations. Further, we use a tree structured decoder with a state aggregation mechanism to capture the long distance dependency and global expression information. Experimental results on the Math23K dataset revealed that the KA S2T model can achieve better performance than previously reported best results.
Mining Public Opinion on Transportation Systems Based on Social Media Data
Venue: Sustainability
Year: 2019

Public participation plays an important role of traffic planning and management, but it is a great challenge to collect and analyze public opinions for traffic problems on a large scale under traditional methods. Traffic management departments should appropriately adopt public opinions in order to formulate scientific and reasonable regulations and policies. At present, while increasing degree of public participation, data collection and processing should be accelerated to make up for the shortcomings of traditional planning. This paper focuses on text analysis using large data with temporal and spatial attributes of social network platform. Web crawler technology is used to obtain traffic related text in mainstream social platforms. After basic treatment, the emotional tendency of the text is analyzed. Then, based on the probabilistic topic modeling (latent Dirichlet allocation model) the main opinions of the public are extracted, and the spatial and temporal characteristics of the data are summarized. Taking Nanjing Metro as an example, the existing problems are summarized from the public opinions and improvement measures are put forward, which proves the feasibility of providing technical support for public participation in public transport with social media big data.
Overview of O RAN Fronthaul Specifications
Year: 2019

Standard specifications for fronthaul interfaces with a view to the next generation radio access network were released by the xRAN Forum in April 2018. Then, with the integration of the xRAN Forum into the O RAN Alliance in March 2019, these specifications continued on as O RAN fronthaul specifications. This article describes the O RAN fronthaul specifications that are expected to be the first standard to enable interoperability between different vendors.
Optimal Design of Controller Parameters for Improving the Stability of MMC HVDC for Wind Farm Integration
Venue: IEEE Journal of Emerging and Selected Topics in Power Electronics
Year: 2018

A subsynchronous oscillation (SSO) phenomenon has been observed in a modular multilevel converter based high voltage dc (MMC HVDC) transmission system for wind farm integration in the real world, which is independent of the type of wind turbine generator. This kind of oscillation appears different from those in doubly fed induction generator based wind farm with series compensation line or wind farm integration through two level voltage source converter HVDC transmission system, because the internal dynamics of the MMC may have significant impact on the oscillation. By far, however, very few papers have reported it. In this paper, the generation mechanism of the SSO phenomenon in an MMC HVDC transmission system for wind farm integration is revealed from an impedance point of view. The harmonic state space modeling method is applied to model the multifrequency behavior of the MMC, based on which, the ac side small signal impedance of the MMC is analytically derived according to harmonic linearization theory. As a general rule, the controller parameters of the wind power inverter and the HVDC converter are designed separately, to meet the performance requirements of the single converter under ideal conditions, but this practice does not guarantee the stability of the interconnected system. Therefore, an optimal design method for controller parameters is proposed in this paper in order to guarantee the small signal stability of the interconnected system from a system point of view. Finally, time domain simulations validate the effectiveness of the theoretical analysis and the proposed optimal design method.
Multilevel converter topology for solar PV based grid tie inverters
Venue: 2010 IEEE International Energy Conference
Year: 2010

A solar PV based grid tie inverters are used for dc ac conversion. The conventional line commutated ac to dc inverters have square shaped line current which contains higher order harmonics. The line current with the high harmonic contents generates EMI and moreover it causes more heating of the core of distribution/ power transformers. Alternatively, PWM based inverters using MOSFET/IGBT switches are also used for the same purpose. However, apart from higher switching losses, the power handling capability and reliability of these devices are quite low in comparison to thyristors/ SCR. Nevertheless, the conventional thyristor based forced commutated inverters are not suitable for PWM applications due to the problems of commutation circuits. A pure sinusoidal line current or waveform with low harmonic contents is the most desirable for grid tie inverters. In the present work, various multilevel line commutated inverter topologies has been proposed and analyzed which improves the wave shape and hence reduces the total harmonic distortion (THD) of the line current in a grid tie line commutated inverter. The scheme has successfully been implemented and tested. Moreover, the performance of the proposed topology is far better than the conventional line commutated inverter. It reduces THD, losses, switching stress and EMI.
Exploiting Temporal Relationships in Video Moment Localization with Natural Language
Venue: ACM Multimedia
Year: 2019

We address the problem of video moment localization with natural language, i.e. localizing a video segment described by a natural language sentence. While most prior work focuses on grounding the query as a whole, temporal dependencies and reasoning between events within the text are not fully considered. In this paper, we propose a novel Temporal Compositional Modular Network (TCMN) where a tree attention network first automatically decomposes a sentence into three descriptions with respect to the main event, context event and temporal signal. Two modules are then utilized to measure the visual similarity and location similarity between each segment and the decomposed descriptions. Moreover, since the main event and context event may rely on different modalities (RGB or optical flow) we use late fusion to form an ensemble of four models, where each model is independently trained by one combination of the visual input. Experiments show that our model outperforms the state of the art methods on the TEMPO dataset.
Financial Synergy On Mergers And Acquisition Of Commercial Banks
Year: 2019

The study focused on the financial synergy on mergers and acquisition of commercial banks in CALABARZON, Philippines. Descriptive method using questionnaire was treated with frequency and percentage, weighted mean, t test and Pearson's r correlation. Based on the study, commercial banks were above 21 years, 100 199 employees, more than Php10, 001, 000.00 capital, engaged in horizontal merger, and acquired financing decision. Respondents assessed that profitability, leverage, and liquidity were financially synergized to a moderate extent. Moreover, respondents have fairly serious problems and a characteristic of commercial banks is not significantly different to the factors of financial synergy. Thus, lessen risk, maximization of shareholders' return, increase dynamic buyouts, invest and maintain advances in technology, focus on affiliate companies and analysis of inflation and foreign exchange were proposed strategies on financial synergy. In connection, commercial banks should promote quality of services and innovation, avail the ability of maximizing their assets, and valuate/assess plan comprehensively.
Seismic Loss Estimation for an Old Masonry Building in Italy
Year: 2019

The last seismic events in Central Italy (L'Aquila 2009, Amatrice 2016) have demonstrated that the economic loss from physical damage strongly influence the community's recovery capability, especially when a relevant portion of the building stock is represented by unreinforced masonry (URM) constructions. As a matter of fact, URMs are recognized as the most vulnerable structures with respect to seismic forces. Furthermore, damage on masonry usually involves expensive and time consuming repairing activities that can be carried out only by expert builders. Although these considerations are widely known among the Italian technical scientific community, nowadays the social and political awareness about the problem is still quite low. One of the key aspects for an effective seismic risk mitigation is the analytical quantification of decision variables (e.g. monetary loss) to be shared with different stakeholders such as building owners, policy makers, insurance companies, etc. In response of this need, new assessment methodologies have been included in technical guidelines. In the present paper, the facade of a historic URM building located in the city center of L'Aquila is adopted as a case study for the quantification of its seismic induced economic average annual loss. In detail the Performance Based Earthquake Engineering (PBEE) approach proposed by the US FEMA P 58 and the simplified economic assessment methodology included in the Italian Sisma Bonus act are herein discussed and compared, pointing out the salient aspects of their application to URMs.
A Prototype Student Advising Expert System Supported with an Object Oriented Database
Year: 2011

Using intelligent computer systems technology to support the academic advising process offers many advantages over the traditional student advising. The objective of this research is to develop a prototype student advising expert system that assists the students of Information Systems (IS) major in selecting their courses for each semester towards the academic degree. The system can also be used by academic advisors in their academic planning for students. The expert system is capable of advising students using prescriptive advising model and developmental advising model. The system is supported with an object oriented database and provides a friendly graphical user interface. Academic advising cases tested using the system showed high matching (93% between the automated advising provided by the expert system and the advising performed by human advisors. This proves that the developed prototype expert system is successful and promising.
Hidden Trigger Backdoor Attacks
Venue: AAAI
Year: 2020

With the success of deep learning algorithms in various domains, studying adversarial attacks to secure deep models in real world applications has become an important research topic. Backdoor attacks are a form of adversarial attacks on deep networks where the attacker provides poisoned data to the victim to train the model with, and then activates the attack by showing a specific small trigger pattern at the test time. Most state of the art backdoor attacks either provide mislabeled poisoning data that is possible to identify by visual inspection, reveal the trigger in the poisoned data, or use noise to hide the trigger. We propose a novel form of backdoor attack where poisoned data look natural with correct labels and also more importantly, the attacker hides the trigger in the poisoned data and keeps the trigger secret until the test time. We perform an extensive study on various image classification settings and show that our attack can fool the model by pasting the trigger at random locations on unseen images although the model performs well on clean data. We also show that our proposed attack cannot be easily defended using a state of the art defense algorithm for backdoor attacks.
UZH@CRAFT ST: a Sequence labeling Approach to Concept Recognition
Venue: BioNLP OST@EMNLP IJNCLP
Year: 2019

As our submission to the CRAFT shared task 2019, we present two neural approaches to concept recognition. We propose two different systems for joint named entity recognition (NER) and normalization (NEN) both of which model the task as a sequence labeling problem. Our first system is a BiLSTM network with two separate outputs for NER and NEN trained from scratch, whereas the second system is an instance of BioBERT fine tuned on the concept recognition task. We exploit two strategies for extending concept coverage, ontology pretraining and backoff with a dictionary lookup. Our results show that the backoff strategy effectively tackles the problem of unseen concepts, addressing a major limitation of the chosen design. In the cross system comparison, BioBERT proves to be a strong basis for creating a concept recognition system, although some entity types are predicted more accurately by the BiLSTM based system.
Exploring the links between students' engagement in cooperative learning, their goal preferences and appraisals of instructional conditions in the classroom
Year: 2007

Previous research suggested that social support, belongingness and mastery goals were related to the quality of cooperative learning (CL) In this in depth study we explored how to differentiate between four effective CL teams and four ineffective CL teams, in terms of students' goals and perceptions of instructional conditions. Apart from the earlier mentioned goals we found students' preference for social responsibility, learning for a certificate and entertainment goals to be salient in the CL setting. Mastery and social responsibility goals were prevalent in effective teams, while learning for a certificate and entertainment goals were prevalent in ineffective teams. Moreover, the type of task, group composition and teacher support were mentioned as reasons for effective or ineffective CL.
Emotion recognition method based on normalization of prosodic features
Venue: 2013 Asia Pacific Signal and Information Processing Association Annual Summit and Conference
Year: 2013

Emotion recognition from speech signals is one of the most important technologies for natural conversation between humans and robots. Most emotion recognizers extract prosodic features from an input speech in order to use emotion recognition. However, prosodic features changes drastically depending on the uttered text. In order to solve this problem, we have proposed the normalization method of prosodic features by using the synthesized speech, which has the same word sequence but uttered with a "neutral" emotion. In this method, all prosodic features (pitch, power, etc. are normalized. However, nobody knows which kind of prosodic features should be normalized. In this paper, all combinations of with/without normalization were examined, and the most appropriate normalization method was found. When both "RMS Energy" (root mean square frame energy) and "VoiceProb" (power of harmonics divided by the total power) were normalized, emotion recognition accuracy became 5.98% higher than the recognition accuracy without normalization.
CACAT YANG TERJADI PADA PROSES CASTINGCOVER R KWCA 2 (ASTRA HONDA MOTOR)DI PT. ASTRA Otoparts Divisi NUSAMETAL
Year: 2011

Dalam proses Casting Teknik Pengecoran Logam) banyak hal hal yang mempengaruhi di antaranya, jenis material, temperatur, kapasitas Furnace (tungku) dan bahan bakar. Di area Melting proses peleburan Ingot (alumunium cair) ADC 12 dilakukan pada Mesin Melting Striko 1, dimana Furnace tungku) tersebut berkapasitas 2500 kg, dengan bahan bakar Liquid Propane Gas LPG dengan temperatur 760 degC. Pada proses Casting, menggunakan mesin Ube dengan kapasitas 650 ton dan pada proses pembuatan Part R KWCA 2 banyak sekali ditemukan Cacat proses dalam Area produksi Casting, di antaranya: Stained surface,Holl, Flow line, Bocor,  Soldering, Shinkage crack, Triming (dentet,gate) Sink mark. Dalam hal ini Casting sangat memegang peranan penting dalam pembuatan produk produk, khususnya yang berhubungan dengan Alumunium pada umumnya.Karena banyaknya cacat yang sering terjadi pada proses Casting, maka dibutukan kerjasama dalam menjaga mutu barang dan koordinasi diantara divisi sangat diperlukan. Oleh karena itu pentingnya menjaga mutu barang, agar konsumen tidak merasa dirugikan. Dengan kemajuan yang sangat pesat maka para kompotitor saling berlomba lomba membuat produk yang berkualitas, oleh sebab itu Divisi Casting sangat memegang peran penting dalam menjaga mutu produk sebelum dilempar ke pasar.
M0043: Use of Magnetic Resonance Imaging (MRI) and Low Resolution NMR Relaxometry to Follow Physicochemical Changes in Foods, Detect Quality and Asses Stability of Colloidal Systems
Year: 2017

Drying of foods is an ancient practice that has been adopted to preserve foods beyond their natural shelf life. Among the numerous methods used for food conservation, drying is unquestionably the most ancient but still very much used nowadays. The process started with the exposure of foods to the sun, to extract from them a great proportion of the water, thus contributing for their conservation. The traditional solar dying with direct exposure to the sun had many disadvantages and presently more modern methods are used, such as hot air drying, spray drying, lyophilization, infrared, microwave or radiofrequency drying, osmotic dehydration or many combined processes. In drying water is removed from the food, thus reducing its availability for degradation reactions of chemical, enzymatic or microbial nature. The main objectives of drying include to preserve foods and increase their shelf life; avoid the need for use of refrigeration systems for transport and storage (expensive) reduce space requirements for storage and transport; diversify the supply of foods with different flavours and textures, thus offering the consumers a great choice when buying foods. Many foods can be preserved through drying, but their organoleptic and nutritional properties are greatly altered as compared to the fresh counterparts. Thus, during drying it is of interest to minimize chemical changes, such as enzymatic and non enzymatic browning, and to maximize nutrient retention, such as macronutrients (proteins, sugars, fibres, micronutrients (vitamins, minerals, or bioactive compounds (phenolic compounds, carotenoids, isoflavones, One effect frequently observed when drying foods is shrinkage, which considerably affects their structure and texture. This work describes the most relevant drying methods and advances in drying technology and the effects of this operation on the attributes of the dried foods, including physical characteristics, chemical components and nutritional value or sensory properties like aroma, texture and colour.