Miracle-dz/UniFAR-FTUs huggingface dataset:
Preliminary Application of the Abbreviated PAD Emotion Scale to Chinese Undergraduates

Objective:To explore the applicability of the Abbreviated PAD Emotion Scale for Chinese undergraduates. Methods: 1000 undergraduates in Beijing were examined with the Abbreviated PAD Emotion Scales, and the reliability and construct validity were evaluated by inter-item consistency analysis, item analysis and confirmatory factor analysis.Results: The inter-item consistency (Cronbach's α) for P (pleasure), A (arousal), and D (dominance) were 0.85, 0.58 and 0.72 respectively; the correlations between items and the dimensions they belong to ranged between 0.64 and 0.87 (P0.01); the fit indices of the three-factor model by confirmatory factor analysis were all above 0.90. Conclusions: The inter-item consistency (Cronbach's α) for P and D were good enough; the scales had good construct validity for Chinese undergraduates.
Block-Based Spectral Processing of Static and Dynamic 3D Meshes using Orthogonal Iterations.

Spectral methods are widely used in geometry processing of 3D models. They rely on the projection of the mesh geometry on the basis defined by the eigenvectors of the graph Laplacian operator, becoming computationally prohibitive as the density of the models increases. In this paper, we propose a novel approach for supporting fast and efficient spectral processing of dense 3D meshes, ideally suited for real-time compression and denoising scenarios. To achieve that, we apply the problem of tracking graph Laplacian eigenspaces via orthogonal iterations, exploiting potential spectral coherence between adjacent parts. To avoid perceptual distortions when a fixed number of eigenvectors is used for all the individual parts, we propose a flexible solution that automatically identifies the optimal subspace size for satisfying a given reconstruction quality constraint. Extensive simulations carried out with different 3D meshes in compression and denoising setups, showed that the proposed schemes are very fast alternatives of SVD based spectral processing while achieving at the same time similar or even better reconstruction quality. More importantly, the proposed approach can be employed by several other state-of-the-art denoising methods as a preprocessing step, optimizing both their reconstruction quality and their computational complexity.
Lithium-Ion Battery Health Prognosis Based on a Real Battery Management System Used in Electric Vehicles

This paper developed an effective health indicator to indicate lithium-ion battery state of health and moving-window-based method to predict battery remaining useful life. The health indicator was extracted based on the partial charge voltage curve of cells. Battery remaining useful life was predicted using a linear aging model constructed based on the capacity data within a moving window, combined with Monte Carlo simulation to generate prediction uncertainties. Both the developed capacity estimation and remaining useful life prediction methods were implemented based on a real battery management system used in electric vehicles. Experimental data for cells tested at different current rates, including 1 and 2 C, and different temperatures, including 25 and 40 °C, were collected and used. The implementation results show that the capacity estimation errors were within 1.5%. During the last 20% of battery lifetime, the root-mean-square errors of remaining useful life predictions were within 20 cycles, and the 95% confidence intervals mainly cover about 20 cycles.
A Data-Driven Unified Framework for Predicting Citation Dynamics

With the rising interest in predicting the scientific output, various efforts have been made to predict a scientist's <inline-formula><tex-math notation="LaTeX">$h$</tex-math><alternatives><mml:math><mml:mi>h</mml:mi></mml:math><inline-graphic xlink:href="gogoglou-ieq1-2884505.gif"/></alternatives></inline-formula>-index or the citation trajectory of a publication. In this work, we employ a dynamic categorization for scientists to ensure at each stage of their careers a comparison amongst their peers and combine this grouping with predictive models to estimate a scientist's future impact, as expressed by citation counts. Moreover, we investigate a wide range of factors identifying their importance in determining the future of science for different performance and academic levels with particular emphasis on features describing a scholar's position in multi-layered collaboration and citation networks. The robustness of the approach is examined on a longitudinal dataset centered around 700,302 data points representing Computer Scientists in various time periods with their complete networks of over 18 million collaboration links and 36 million citations. Our results indicate up to 30 percent improvement in prediction performance compared to baseline methods along with an average <inline-formula><tex-math notation="LaTeX">$R^2$</tex-math><alternatives><mml:math><mml:msup><mml:mi>R</mml:mi><mml:mn>2</mml:mn></mml:msup></mml:math><inline-graphic xlink:href="gogoglou-ieq2-2884505.gif"/></alternatives></inline-formula>=0.96 for short term and <inline-formula><tex-math notation="LaTeX">$R^2$</tex-math><alternatives><mml:math><mml:msup><mml:mi>R</mml:mi><mml:mn>2</mml:mn></mml:msup></mml:math><inline-graphic xlink:href="gogoglou-ieq3-2884505.gif"/></alternatives></inline-formula>=0.91 for long term predictions.
Feature selection algorithm for no-reference image quality assessment using natural scene statistics

Images play an essential part in our daily lives and the performance of various imaging applications is dependent on the user’s quality of experience. No-reference image quality assessment (NR-IQA) has gained importance to assess the perceived quality, without using any prior information of the nondistorted version of the image. Different NR-IQA techniques that utilize natural scene statistics classify the distortion type based on groups of features and then these features are used for estimating the image quality score. However, every type of distortion has a different impact on certain sets of features. In this paper, a new feature selection algorithm is proposed for distortion identification based image verity and integration evaluation that selects distinct feature groups for each distortion type. The selection procedure is based on the contribution of each feature on the Spearman rank order correlation constant (SROCC) score. Only those feature groups are used in the prediction model that have majority features with SROCC score greater than mean SROCC score of all the features. The proposed feature selection algorithm for NR-IQA shows better performance in comparison to state-of-the-art NR-IQA techniques and other feature selection algorithms when evaluated on three commonly used databases.
A deep learning based tracking framework for passenger monitoring

A substantial amount of traffic accidents are caused by distracted driving and drowsy behaviour each year. Two important areas when tackling these inadequacies is human-car interaction and the car’s awareness of the passengers. Tracking passenger movements over time can result in a more intelligent vehicle that is able to react to passenger actions, and thereby provide a more secure driver experience. In this thesis, we present a Deep Learning based tracking framework for passenger monitoring. The framework is divided into two separate parts: a human body pose estimator and an object detector. Additionally, we demonstrate the use case of the framework by developing a hand gesture classifier and a body action recognizer upon the framework. The framework and additional modules are optimized for efficiency, and we achieve real-time performance on a Nvidia Jetson TX2 embedded system.
Distributed electric propulsion for small business aircraft a concept-plane for key-technologies investigations.

Electric propulsion for aircraft begins to be effective in the field of leisure aviation mainly for initial training. However, some challenges appear when performance-level needs to be increased to address more demanding application such as business travel even for small passenger capacity. Based on its expertise in all disciplines of aviation design, and in the following of projects dedicated to future Air Transport System, ONERA started exploratory studies, a few years ago, to investigate potential new technologies and concepts that could participate in answering societal need for On-Demand Mobility. The resulting concept-planes illustrate some possible assembly of such key-technologies (KT) which will serve as basis for future research aiming at demonstrating both performance potential and feasibility of the most relevant technologies. On this basis, ONERA started recently a CARNOT funded project, called AMPERE, dedicated to increase maturity of one of those KT which concerns Distributed Electric Propulsion (DEP), from two main points of view: aero-propulsion and command & control using Electric Ducted Fans (EDF). This paper intends to describe concept-planes which illustrate the multidisciplinary balance between KTs and then gives some information regarding the DEP dedicated project mentioned above.
Improving continuous space language models auxiliary features

In this paper we introduce a novel method to improve the continuous space language models using auxiliary features. The suggested auxiliary features include text genre, line length, various types of context vector representations. We report perplexity improvements of around 7.5% of the English Penn Treebank data set. We also report an improvement on a translation task up to 1.1 B LEU point on test by re-scoring the n-best list generated by our phrase-based statistical machine translation system.
The Concept of Social Interest

Social interest was Alfred Adler’s most important personality trait, and it reflects one’s genuine concern for the welfare of all individuals. Several measures of social interest are available, and the Social Interest Index (SII) is one of the most popular in current use. This study is the first to report the results of a confirmatory factor analysis of the SII. Using college students, three models were tested in an effort to find support for the factorial validity of this scale. All analyses showed a poor fit between the theoretical model and scale items. The results paint a fairly negative picture of the factor structure of this important scale.
Solving Jungle Checkers

Jungle Checkers is a simplified version of the traditional Chinese board game Dou Shou Qi. First, we determine the space complexity of Jungle Checkers by counting the number of possible game positions. This number is approximately 2× 10. Furthermore, we try to ultra-weakly solve Jungle Checkers by using the concept of a proof tree: both alpha-beta pruning and proof-number search algorithms are applied. Unfortunately, due to limited hardware resources, we did only manage to solve a tiny part of the game. However, using endgame databases constructed by retrograde analysis we are able to make a number of interesting observations about the game.
The Comparison of the Effectiveness of the Using Compass-Straightedge and A Dynamic Software on Achievement

Bu çalışma ile doğrular ve açılarda geometrik çizimler konusunda, somut araçlar (pergel-cetvel) kullanma ile dinamik bir yazılım kullanmanın öğretmen adaylarının başarılarına etkilerini karşılaştırmak ve matematik öğretmeni adaylarının geometri öğretiminde pergel-cetvel veya dinamik bir yazılım kullanma hakkındaki düşüncelerini ortaya çıkarmak amaçlanmıştır. 24’ü deney-1, 31’i deney-2 grubu olmak üzere toplam 55 matematik öğretmen adayı ile gerçekleştirilen bu çalışmada veriler, başarı testi ve odak grup görüşmesinden elde edilmiştir. Çalışmadan elde edilen verilerin analizi sonucunda, dinamik yazılım kullanan deney-1 grubundaki öğretmen adaylarının başarıları ile pergel-cetvel kullanan deney-2 grubundaki öğretmen adaylarının başarıları arasında anlamlı fark bulunmamıştır. Bununla beraber hem pergel-cetvel hem dinamik yazılım kullanmanın öğretmen adaylarının başarılarına olumlu yönde katkı sağladığı tespit edilmiştir. Deney-1 grubundaki öğretmen adaylarının büyük kısmının, dinamik yazılımın karışık çizimlerde avantajlı olduğunu, zaman tasarrufu sağladığını ve kavramları anlamada etkili olduğunu söylediği görülmüştür. Deney-2 grubundaki öğretmen adaylarının çoğunluğu ise, pergel cetvel ile kalıcı öğrenim sağlandığını, çizim yapmanın kolay olduğunu ve bu araçları kullanmanın zevkli olduğunu belirtmiştir.
Use of auxiliary translation for improving decoding in statistical machine translation

Recently, the concept of driven decoding (DD), has been sucessfully applied to the automatic speech recognition (speech-to-text) task: an auxiliary transcription guide the decoding process. There is a strong interest in applying this concept to statistical machine translation (SMT). This paper presents our approach on this topic. Our first attempt in driven decoding consists in adding several feature functions corresponding to the distance between the current hypothesis decoded and the auxiliary translations available. Experimental results done for a french-to-english machine translation task, in the framework of the WMT 2011 evaluation, show the potential of the DD approach proposed.
Beef Up the Edge: Spectrum-Aware Placement of Edge Computing Services for the Internet of Things

In this paper, we introduce a network entity called point of connection (PoC), which is equipped with customized powerful communication, computing, and storage (CCS) capabilities, and design a data transportation network (DART) of interconnected PoCs to facilitate the provision of Internet of Things (IoT) services. By exploiting the powerful CCS capabilities of PoCs, DART brings both communication and computing services much closer to end devices so that resource-constrained IoT devices could have access to the desired communication and computing services. To achieve the design goals of DART, we further study the spectrum-aware placement of edge computing services. We formulate the service placement as a stochastic mixed-integer optimization problem and propose an enhanced coarse-grained fixing procedure to facilitate efficient solution finding. Through extensive simulations, we demonstrate the effectiveness of the resulting spectrum-aware service placement strategies and the proposed solution approach.
A Comparison Study on Processing ILI Data With Different Filtering Methods

In-line inspection (ILI) data processing is a crucial process on assessing the remaining capacity of corroded pipelines. It is used to generate the RBP (river bottom profile) or M AV (moving average) file which could be further used in the capacity calculations. There are large uncertainties regarding the qualities of ILI data itself, which are dependent on the accuracies of the ILI tool, the reporting method and the condition of the inspected pipelines et. al. It is thus required that the post-process of the ILI data should not add more uncertainties to the capacity check. Challenges exist on generating reliable RBP/MAV from the ILI data on general base. This paper originates from work during the in-house software development for corroded pipeline tool. Three different algorithms on filtering the ILI data are examined. They are the method recommended by DNV-RP-F101 (with modification), the median filter algorithm and the gaussian filter algorithm. The latter two algorithms are from standard methods for image denoising. A comparison study has been performed with several actual ILI data sets. Finally, conclusions and suggestions have been made, which may provide useful hints for industry application.
Paradigm uniformity bias in the learning of Korean verbal inflections

This study explores the role of paradigm uniformity bias in the acquisition of Korean verbal inflections. Paradigm uniformity bias has been proposed in a constraint-based phonological framework, but has rarely been supported by experimental data. This paper provides experimental evidence for paradigm uniformity bias from four- to seven-year-old Korean children learning their native language phonology. Experiment 1 demonstrates that children alter morphological structures in order to produce non-alternating verb forms. Experiment 2 shows that the tendency to adjust morphological structures is rooted in children's preference for uniform paradigms, not in their ignorance of alternations. The results suggest that paradigm uniformity bias plays a role in determining children's preferred production patterns, which favour non-alternating forms even after they have acquired adult-like knowledge of the patterns of alternations.
Phase-aware Speech Enhancement with Deep Complex U-Net

Most deep learning-based models for speech enhancement have mainly focused on estimating the magnitude of spectrogram while reusing the phase from noisy speech for reconstruction. This is due to the difficulty of estimating the phase of clean speech. To improve speech enhancement performance, we tackle the phase estimation problem in three ways. First, we propose Deep Complex U-Net, an advanced U-Net structured model incorporating well-defined complex-valued building blocks to deal with complex-valued spectrograms. Second, we propose a polar coordinate-wise complex-valued masking method to reflect the distribution of complex ideal ratio masks. Third, we define a novel loss function, weighted source-to-distortion ratio (wSDR) loss, which is designed to directly correlate with a quantitative evaluation measure. Our model was evaluated on a mixture of the Voice Bank corpus and DEMAND database, which has been widely used by many deep learning models for speech enhancement. Ablation experiments were conducted on the mixed dataset showing that all three proposed approaches are empirically valid. Experimental results show that the proposed method achieves state-of-the-art performance in all metrics, outperforming previous approaches by a large margin.
Building the leadership capacity to achieve instructional focus and increase student achievement

St. Georges Technical High School (SGTHS), part of the New Castle County Vocational-Technical School District (NCCVTSD), analyzed district internal assessment data and SAT scores from the 2015-2016 school year to determine the needed areas of focus around our implementation of the Common Core State Standards (CCSS). SGTHS did not meet the district’s goal to have 80% of SGTHS students proficient on the SAT. An analysis of the SAT’s Evidence Based Reading and Writing (EBRW) scores indicated that only 56% of St. Georges’ students met proficiency. Based on this data, it is evident that St. Georges needs to focus on literacy development to increase the number of students who are proficient on the EBRW of the SAT and meet or exceed the NCCVT and SGTHS goal. Furthermore, by working towards this goal, St. Georges will work to reduce the college remediation rate for its students. Based on the most recent data, 47% of the students from St. Georges needed collegiate remediation. The St. Georges team analyzed the data and attempted to identify possibilities for improving CCSS implementation. The team determined that literacy; with an emphasis on analysis and synthesis, was a logical starting point for professional learning based on college and career needs. Based on the needs assessment data above, SGTHS will implement a two-phase professional development program personalized to the individual needs of each content and career area. The plan will be a continuation of this past year’s “Lessons in Literacy” initiative that provided teachers with personalized learning
Social connectedness and graduate employability: exploring the professional networks of graduates from business and creative industries

Despite widespread recognition of the importance of social networks to career development and professional learning among experienced professionals and graduates alike, there is evidence to suggest that many students complete undergraduate programs with nascent networks at best, and little idea of how to develop and make use of the affordances of face-to-face and online connections for their careers. Drawing upon data from more than 600 surveys of graduates of Bachelor level programs in Creative Industries and Business fields from three Australian universities, this chapter: describes the professional networks of recent graduates; characterises their levels of social capital and benchmarks current graduate connectedness capability levels to identify opportunities for development in degree programs.
Adaptive quality-of-service provisioning in wireless and mobile networks.

Adaptive Quality-of-Service Provisioning in Wireless and Mobile Networks by Chun-Ting Chou Chair: Kang G. Shin The problem of adaptive QoS provisioning in wireless and mobile networks is studied in this thesis. A mathematical model is established to analyze the impact of adaptive bandwidth allocation on both system performance and user-perceived QoS. With this model, network service providers can dynamically adjust — based on the network load or available network capacity — user bandwidth with controllable degradation on user-perceived QoS. To facilitate adaptive QoS support in time-division multiplexed wireless networks (such as the IEEE 802.11 wireless LANs), a distributed airtime usage control is also developed. By using the proposed airtime control, wireless stations using the contention-based medium access method are shown to be able to provide users the parameterized QoS, which can only be achieved by using the polling-based medium access method in the current IEEE 802.11e standard. The proposed distributed airtime usage control is also shown to be able to provide QoS support in ad hoc IEEE 802.11 wireless LANs. In order to further improve the user’s QoS, the concept of “spectral agility” is introduced to wireless networks (especially, the IEEE 802.11 wireless LANs). An analytical model is established in order to derive the achievable improvement gained by using spectral agility. To fully exploit spectral agility, a comprehensive framework for spectral-agile networks is also developed. This framework and the associated functionalities are integrated with the IEEE 802.11 wireless LAN in the ns-2 simulator to demonstrate the effectiveness of the resulting spectral-agile wireless networks. Finally, the mobility support for QoS provisioning in the IEEE 802.11 wireless LAN is investigated, and a unified smooth-and-fast handoff is developed for both intraand inter-subnet handoffs based on the Inter-Access Point Protocol.
TRECVID 2017: Evaluating Ad-hoc and Instance Video Search, Events Detection, Video Captioning and Hyperlinking

HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L’archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d’enseignement et de recherche français ou étrangers, des laboratoires publics ou privés. TRECVID 2017: Evaluating Ad-hoc and Instance Video Search, Events Detection, Video Captioning, and Hyperlinking George Awad, Asad Butt, Jonathan Fiscus, David Joy, Andrew Delgado, Willie Mcclinton, Martial Michel, Alan Smeaton, Yvette Graham, Wessel Kraaij, et al.