ピアアセスメントのための項目反応理論と整数計画法を用いたグループ構成最適化

In recent years, large-scale e-learning environments such as Massive Online Open Courses (MOOCs) have become increasingly popular. In such environments, peer assessment, which is mutual assessment among learners, has been used to evaluate reports and programming assignments. When the number of learners increases as in MOOCs, peer assessment is often conducted by dividing learners into multiple groups to reduce the learners’ assessment workload. In this case, however, the accuracy of peer assessment depends on the way to form groups. To solve the problem, this study proposes a group optimization method based on item response theory (IRT) and integer programming. The proposed group optimization method is formulated as an integer programming problem that maximizes the Fisher information, which is a widely used index of ability assessment accuracy in IRT. Experimental results, however, show that the proposed method cannot sufficiently improve the accuracy compared to the random group formulation. To overcome this limitation, this study introduces the concept of external raters and proposes an external rater selection method that assigns a few appropriate external raters to each learner after the groups were formed using the proposed group optimization method. In this study, an external rater is defined as a peer-rater who belongs to different groups. The proposed external rater selection method is formulated as an integer programming problem that maximizes the lower bound of the Fisher information of the estimated ability of the learners by the external raters. Experimental results using both simulated and real-world peer assessment data show that the introduction of external raters is useful to improve the accuracy sufficiently. The result also demonstrates that the proposed external rater selection method based on IRT models can significantly improve the accuracy of ability assessment than the random selection.近年，MOOCsなどの大規模型eラーニングが普及してきた．大規模な数の学習者が参加している場合には，教師が一人で学習者のレポートやプログラム課題などを評価することは難しい．大規模の学習者の評価手法の一つとして，学習者同士によるピアアセスメントが注目されている．MOOCsのように学習者数が多い場合のピアアセスメントは，評価の負担を軽減するために学習者を複数のグループに分割してグループ内のメンバ同士で行うことが多い．しかし，この場合，グループ構成の仕方によって評価結果が大きく変化してしまう問題がある．この問題を解決するために，本研究では，項目反応理論と整数計画法を用いて，グループで行うピアアセスメントの精度を最適化するグループ構成手法を提案する．具体的には，項目反応理論において学習者の能力測定精度を表すフィッシャー情報量を最大化する整数計画問題としてグループ構成問題を定式化する．実験の結果，ランダムグループ構成と比べて，提案手法はおおむね測定精度を改善したが，それは限定的な結果であることが明らかとなった．そこで，本研究ではさらに，異なるグループから数名の学習者を外部評価者として各学習者に割り当て外部評価者選択手法を提案する．シミュレーションと実データ実験により，提案手法を用いることで能力測定精度を大幅に改善できることを示す．電気通信大学201

Fair Cost Sharing Auction Mechanisms in Last Mile Ridesharing

With rapid growth of transportation demands in urban cities, one major challenge is to provide efficient and effective door-to-door service to passengers using the public transportation system. This is commonly known as the Last Mile problem. In this thesis, we consider a dynamic and demand responsive mechanism for Ridesharing on a non-dedicated commercial fleet (such as taxis). This problem is addressed as two sub problems, the first of which is a special type of vehicle routing problems (VRP). The second sub-problem, which is more challenging, is to allocate the cost (i.e. total fare) fairly among passengers. We propose auction mechanisms where we allow passengers to submit their willing payments. We show that our bidding model is budget-balanced, fairness-preserving, and most importantly, incentive-compatible. We also show how the winner determination problem can be solved efficiently. A series of experimental studies are designed to demonstrate the feasibility and efficiency of our proposed mechanisms

Study on model for cutting force when milling SCM440 steel

This article presents empirical study results when milling SCM440 steel. The cutting insert to be used was a TiN coated cutting insert with tool tip radius of 0.5 mm. Experimental process was carried out with 18 experiments according to Box-Behnken matrix, in which cutting speed, feed rate and cutting depth were selected as the input parameters of each experiment. In addition, cutting force was selected as the output parameter. Analysis of experimental results has determined the influence of the input parameters as well as the interaction between them on the output parameters. From the experimental results, a regression model showing the relationship between cutting force and input parameters was built. Box-Cox and Johnson data transformations were applied to construct two other models of cutting force. These three regression models were used to predict cutting force and compare with experimental results. Using parameters including coefficient of determination (R-Sq), adjusted coefficient of determination (R-Sq(adj)) and percentage mean absolute error (% MAE) between the results predicted by the models and the experimental results are the criteria to compare the accuracy of the cutting force models. The results have determined that the two models using two data transformations have higher accuracy than model not using two data transformations. A comparison of the model using the Box-Cox transformation and the model using the Johnson transformation was made with a t-test. The results confirmed that these two models have equal accuracy. Finally, the development direction for the next study is mentioned in this articl

Fast Temporal Wavelet Graph Neural Networks

Spatio-temporal signals forecasting plays an important role in numerous domains, especially in neuroscience and transportation. The task is challenging due to the highly intricate spatial structure, as well as the non-linear temporal dynamics of the network. To facilitate reliable and timely forecast for the human brain and traffic networks, we propose the Fast Temporal Wavelet Graph Neural Networks (FTWGNN) that is both time- and memory-efficient for learning tasks on timeseries data with the underlying graph structure, thanks to the theories of multiresolution analysis and wavelet theory on discrete spaces. We employ Multiresolution Matrix Factorization (MMF) (Kondor et al., 2014) to factorize the highly dense graph structure and compute the corresponding sparse wavelet basis that allows us to construct fast wavelet convolution as the backbone of our novel architecture. Experimental results on real-world PEMS-BAY, METR-LA traffic datasets and AJILE12 ECoG dataset show that FTWGNN is competitive with the state-of-the-arts while maintaining a low computational footprint. Our PyTorch implementation is publicly available at https://github.com/HySonLab/TWGNNComment: arXiv admin note: text overlap with arXiv:2111.0194

Group optimization to maximize peer assessment accuracy using item response theory and integer programming

With the wide spread of large-scale e-learning environments such as MOOCs, peer assessment has been popularly used to measure learner ability. When the number of learners increases, peer assessment is often conducted by dividing learners into multiple groups to reduce the learner\u27s assessment workload. However, in such cases, the peer assessment accuracy depends on the method of forming groups. To resolve that difficulty, this study proposes a group formation method to maximize peer assessment accuracy using item response theory and integer programming. Experimental results, however, have demonstrated that the proposed method does not present sufficiently higher accuracy than a random group formation method does. Therefore, this study further proposes an external rater assignment method that assigns a few outside-group raters to each learner after groups are formed using the proposed group formation method. Through results of simulation and actual data experiments, this study demonstrates that the proposed external rater assignment can substantially improve peer assessment accuracy

Evaluate the Results at Minimum 2-Years of Treating Rotator Cuff Tear by Arthroscopic Surgery

BACKGROUND: Rotator cuff tear (RCT) is a common injury of the shoulder, especially middle-aged people. Nonoperative treatment, cortisone injections are only effective at an early stage. Open surgery causes postoperative atrophy of the deltoid muscle, so results are limited. Arthroscopic rotator cuff repair surgery has been performed in Vietnam for about ten years, with many advantages such as the ability to accurately assess the lesions and less invasive procedure. In order to have a clearer view, we performed a mid-term assessment of the effectiveness of this surgery. AIM: Evaluate results over 2 years of patients with rotator cuff tears treated with arthroscopic surgery and their quality of life. METHOD: A group of 30 patients were diagnosed with RCT and surgery by arthroscopy to treat at Hanoi Medical University Hospital and Saint Paul Hospital between Jun 2015 and April 2017. The results of the surgeries were assessed by the degree of pain, muscle power, motion of the shoulder joint according to UCLA shoulder score. Evaluate the quality of life through the Rotator Cuff-Quality of Life (RC-QoL) index. RESULTS: The average age was 60.7 years. Female / male ratio was 1.3. Thirty-six months ± 6.41 was the average follow-up time (min 27 – max 50 months). The shoulder function is recorded according to UCLA has an average score of 30.9, therein good and excellent result were 90 %. The mean RC-QoL index was 91.5%. CONCLUSION: Treatment of RCT by arthroscopic surgery that has been evaluated for a minimum of 2 years follow-up showed good results and high quality of patient’s life

Analysis of the effect of spray mode on coating porosity and hardness when spraying press screws by the high velocity oxy fuel method

Porosity and coating hardness are two very important properties of the coating. In order to achieve low coating porosity and high hardness, a suitable spray mode is desired. In the particular application for press screws with the complex surface, a suitable spray mode plays a significant role in the formation of the coating properties. This paper employs the Taguchi experimental design method combined with ANOVA analysis to evaluate the impact of the spray mode on the porosity and hardness of the coating while spraying the screw surface using the High Velocity Oxy Fuel (HVOF) method. The injection material used is WC HMSP1060-00 +60 % 4070, with its main components being Nickel and Carbide Wolfram. And the press screw material is 1045 steel. The impactful parameters of the spray mode investigated and tested are the flow rate of spray (F) with a range varying from 25 g/min to 35 g/min, spray distance (D) with a range of values varying from 0.25 m to 0.35 m, and an oxygen/propane ratio (R) from 4 to 6. The analysis shows that the spray mode significantly affects the coating properties, and a suitable set of spray parameters is found to achieve low coating porosity and high coating hardness. The spray mode with the lowest porosity is achieved at a spray rate (F) of 35 g/min, a spray distance (D) of 0.3 m, and an oxygen/propane ratio (R) of 6. The interactions between D and R, as well as between F and D, are statistically significant, influencing each other's effects on porosity. However, the interaction between F and R is relatively low, indicating that changes in one parameter have less impact on porosity when the other parameter is varied. Similarly, for the highest coating hardness, the optimal spray mode includes an F of 35 g/min, D of 0.25 m, and R of 6. There is a significant interaction between F and D, while the interaction between F and R is relatively low. Notably, there is no interaction between F and

DoubleEcho: Mitigating Context-Manipulation Attacks in Copresence Verification

Copresence verification based on context can improve usability and strengthen security of many authentication and access control systems. By sensing and comparing their surroundings, two or more devices can tell whether they are copresent and use this information to make access control decisions. To the best of our knowledge, all context-based copresence verification mechanisms to date are susceptible to context-manipulation attacks. In such attacks, a distributed adversary replicates the same context at the (different) locations of the victim devices, and induces them to believe that they are copresent. In this paper we propose DoubleEcho, a context-based copresence verification technique that leverages acoustic Room Impulse Response (RIR) to mitigate context-manipulation attacks. In DoubleEcho, one device emits a wide-band audible chirp and all participating devices record reflections of the chirp from the surrounding environment. Since RIR is, by its very nature, dependent on the physical surroundings, it constitutes a unique location signature that is hard for an adversary to replicate. We evaluate DoubleEcho by collecting RIR data with various mobile devices and in a range of different locations. We show that DoubleEcho mitigates context-manipulation attacks whereas all other approaches to date are entirely vulnerable to such attacks. DoubleEcho detects copresence (or lack thereof) in roughly 2 seconds and works on commodity devices