Miracle-dz/UniFAR-FTUs huggingface dataset:
3D Ground Truth Generation Using Pre-Trained Deep Neural Networks

Training three-dimensional object detectors on publicly available data has been limited to small datasets due to the significant effort required to generate annotations. The difficulty of labeling in 3D using 2.5D sensors, such as LIDAR, is attributed to the high spatial reasoning skills required to deal with occlusion and partial viewpoints. Additionally, the current methods to label 3D objects are cognitively demanding because they require frequent task switching. Reducing task complexity and the amount of task switching done by annotators is key to reducing the effort and time required to generate 3D bounding box annotations. The following work seeks to reduce the burden on the annotators by leverage existing 3D object detectors. This work introduces a novel ground truth generation method that combines human supervision with pre-trained neural networks to generate per-instance 3D point cloud segmentation, 3D bounding boxes, and class annotations. The annotators provide object anchor clicks which behave as seeds to generate instance segmentation results in 3D. The points that belong to each instance are then used to regress object centroids, bounding box dimensions, and object orientation. The deep neural network model that is used to generate the segmentation masks and bounding box parameters is based on the PointNet architecture. The following method relies on the existing KITTI dataset to analyze the quality of the generated ground truth. The neural network model is trained on the KITTI training split and the 3D bounding box outputs are generated with annotation clicks collected from the validation split. The validation split of KITTI detection dataset contains 3,712 frames of pointcloud and image scenes, and it took 16.35 hours to label with the method used in this study. Based on these results, the proposed approach is 19 times faster than the latest published 3D object annotation scheme. Additionally, the annotators spent less time per object as the number of objects in the scenes increases, making our apporach a very efficient for multi-object labeling. The quality of the generated 3D bounding boxes, using the proposed method is compared against the KITTI ground truth. The model performs on par with the current state-of-the-art 3D detectors, and the labeling procedure does not negatively impact the output quality of the bounding boxes. The proposed scheme is also applied to previously unseen data from the Autonomoose self-driving vehicle to demonstrate the generalization capabilities of the network.
Compact feature learning for multimedia retrieval

Efficient multimedia search has attracted much attention in recent years due to the exponential growth of data available. Extracting representative compact features for nearest neighbor (NN) search and approximate nearest neighbor (ANN) search has played an important but challenging role in multimedia retrieval systems. This is because efficient compact features require two essential properties: (1) It should be able to capture high quality information from raw data; (2) It needs to be of small dimensionality to support fast search with low memory costs. While several compact feature learning algorithms have been proposed in the literature and some of them have achieved reasonably good performance in retrieval benchmark datasets, there is still some room for further improvement. Hence, this thesis is dedicated to developing several compact feature learning algorithms for different multimedia search systems using machine learning concepts. Particularly, we present four compact feature learning methods. Experimental results in benchmark retrieval datasets and comparisons with popular feature learning and hashing methods have demonstrated the effectiveness of our proposed methods.
Novel approaches for air temperature prediction: A comparison of four hybrid evolutionary fuzzy models

The application of a novel method of adaptive neuro‐fuzzy inference system (ANFIS) for the prediction of air temperature is investigated. The paper discusses the improvement of the ANFIS when used with genetic algorithm (GA), particle swarm optimization (PSO), ant colony optimization for continuous domains (ACOR) and differential evolution (DE). For this purpose, three input of multiple variables are selected in order to predict monthly minimum, average and maximum air temperatures for 34 meteorological stations in Iran. The co‐efficient of determination (R2), root mean square error (RMSE), mean absolute error (MAE) and Nash–Sutcliffe efficiency (NSE) are used as evaluation criteria. A comparison of suggested fuzzy models indicates that the ANFIS with the GA has the best performance in the prediction of maximum temperatures. It decreases the RMSE of the classic ANFIS model in the validation stage from 1.22 to 1.12°C for Mashhad, from 1.26 to 1.01°C for Zahedan, from 1.20 to 0.98°C for Ahvaz, from 1.76 to 1.24°C for Rasht and from 1.21 to 0.95°C for Tabriz.
Confident alternate test implementation

Being able to check whether an IC is functional or not after the manufacturing process is very difficult. Particularly for analog and Radio Frequency (RF) circuits, test equipment and procedures required have a major impact on the circuits cost. An interesting approach to reduce the impact of the test cost is to measure parameters requiring low cost test resources and correlate these measurements, called indirect measurements, with the targeted specifications. This is known as indirect test technique because there is no direct measurement for these specifications, which requires so expensive test equipment and an important testing time, but these specifications are estimated w.r. t "low-cost measurements". While this approach seems attractive, it is only viable if we are able to establish a sufficient accuracy for the performance estimation and if this estimation remains stable and independent from the circuits sets under test.The main goal of this thesis is to implement a robust and effective indirect test strategy for a given application and to improve test decisions based on data analysis.To be able to build this strategy, we have brought various contributions. Initially, we have defined new metric developed in this thesis to assess the reliability of the estimated performances. Secondly, we have analyzed and defined a strategy for the construction of an optimal model. This latter includes a data preprocessing followed by a comparative analysis of different methods of indirect measurement selection. Then, we have proposed a strategy for a confidant exploration of the indirect measurement space in order to build several best models that can be used later to solve trust and optimization issues. Comparative studies were performed on 2 experimental data sets by using both of the conventional and the developed metrics to evaluate the robustness of each solution in an objective way.Finally, we have developed a comprehensive strategy based on an efficient implementation of the redundancy techniques w.r. t to the build models. This strategy has greatly improved the robustness and the effectiveness of the decision plan based on the obtained measurements. This strategy is adaptable to any context in terms of compromise between the test cost, the confidence level and the expected precision.
Algorithms for Bohemian Matrices

This thesis develops several algorithms for working with matrices whose entries are multivariate polynomials in a set of parameters. Such parametric linear systems often appear in biology and engineering applications where the parameters represent physical properties of the system. Some computations on parametric matrices, such as the rank and Jordan canonical form, are discontinuous in the parameter values. Understanding where these discontinuities occur provides a greater understanding of the underlying system. Algorithms for computing a complete case discussion of the rank, Zigzag form, and the Jordan canonical form of parametric matrices are presented. These algorithms use the theory of regular chains to provide a unified framework allowing for algebraic or semi-algebraic constraints on the parameters. Corresponding implementations for each algorithm in the Maple computer algebra system are provided. In some applications, all entries may be parameters whose values are limited to finite sets of integers. Such matrices appear in applications such as graph theory where matrix entries are limited to the sets {0,+1}, or {−1, 0,+1}. These types of parametric matrices can be explored using different techniques and exhibit many interesting properties. A family of Bohemian matrices is a set of low to moderate dimension matrices where the entries are independently sampled from a finite set of integers of bounded height. Properties of Bohemian matrices are studied including the distributions of their eigenvalues, symmetries, and integer sequences arising from properties of the families. These sequences provide connections to other areas of mathematics and have been archived in the Characteristic Polynomial Database. A study of two families of structured matrices: upper Hessenberg and upper Hessenberg Toeplitz, and properties of their characteristic polynomials are presented.
Culture, Environment and E-learning as Factor in Student Performance (Case Studies in Management Accounting Study Programs)

The aim of the study is to solve the problem of decreasing student academic performance which is influenced by satisfaction in using e-learning, the quality of e-learning systems, environment, and organizational culture. The research method used is verification by using structural equational modeling test with partial least square approach, primary data obtained from questionnaires designed by researchers. Respondents are students who are in management accounting courses with the number of samples using the power analysis. The results of the study answer the problems that occur that there is no evidence of environmental uncertainty, organizational culture towards e-learning. But, e-learning affects user satisfaction, and user satisfaction influences student academic performance. The results of the study are expected to provide input for managers to consider the application of e-learning system learning in management accounting courses. The uniqueness of this research is the development of a success model of information systems from DeLone & McLane, shown by the addition of exogenous variables, namely the environment and organizational culture that were not previously present in the model.
An Effective DRAM Address Remapping for Mitigating Rowhammer Errors

A rowhammer error represents a loss of data stored in a DRAM cell caused by electromagnetic interference due to repetitive access to the same and/or adjacent rows. Due to the concentrated occurrence of rowhammer errors in specific rows and columns, these errors cannot be corrected by the conventional error correcting code (ECC) commonly used in DRAM devices. Previous techniques avoid these errors by having additional refresh operations that require additional hardware resources and/or power consumption. This paper proposes a different approach to handle rowhammer errors by distributing them across different DRAM rows and columns so that the attack cells are not concentrated on specific rows and columns. To this end, the distribution of rowhammer errors is observed with experiments using several commercial DRAM devices by employing state-of-the-art rowhammer attack techniques. The observation of the rowhammer errors concentrated in specific rows and columns underlies the proposal of an effective DRAM address remapping scheme for re-distribution of rowhammer errors. By using different address mappings to different chips and arrays in a DIMM, the proposed remapping effectively distributes errors over different rows and columns. As a result, the proposed remapping scheme decreases the possibility of multiple errors in a single word, and consequently, reduces uncorrectable errors under single error or single symbol correcting ECC. Experimental results with commercial DIMMs show that the proposed scheme reduces uncorrectable errors by about 95 percent while incurring a small additional hardware cost.
On the Word Alignment from Neural Machine Translation

Prior researches suggest that neural machine translation (NMT) captures word alignment through its attention mechanism, however, this paper finds attention may almost fail to capture word alignment for some NMT models. This paper thereby proposes two methods to induce word alignment which are general and agnostic to specific NMT models. Experiments show that both methods induce much better word alignment than attention. This paper further visualizes the translation through the word alignment induced by NMT. In particular, it analyzes the effect of alignment errors on translation errors at word level and its quantitative analysis over many testing examples consistently demonstrate that alignment errors are likely to lead to translation errors measured by different metrics.
The Performance Information Processing Framework: Four Cognitive Models of Performance Information Use

The question of how public managers use public sector performance information received significant scholarly attention in recent years. The promise of performance management systems was to rationalize the decision making process by creating objective performance metrics that citizens, political officials, and public managers could use to assess the performance of public organizations. Some theoretical work suggests, however, that there is a certain subjectivity to these data, which arises from an individual’s role in their organization or broader political environment. Furthermore, a recent spate of experimental work in this area suggests subjectivity might also arise, at the individual level, through cognitive bias. I bridge these two bodies of scholarship with a framework of performance information processing, which incorporates four models of political information use into the story of how public managers use performance information. I suggest that cognitive bias can contribute to the subjectivity of performance information when public managers process performance information. In other words, a model of meaning avoidance suggests that managers accurately receive performance information from management systems, but that cognitive biases influence the ways in which they interpret or act upon that information. In this essay, I provide empirical evidence for this model. I show that despite different presentations, public managers can accurately recount the objective information they saw when asked to recall it. I also provide evidence that despite being equally aware of objective raw performance metrics, public managers exhibit evidence of cognitive bias when asked to interpret the meaning of that information. This study contributes to the broader discussion of how individuals use performance information.
ViVoVAD: a Voice Activity Detection Tool based on Recurrent Neural Networks

Voice Activity Detection (VAD) aims to distinguishcorrectly those audio segments containing humanspeech. In this paper we present our latest approachto the VAD task that relies on the modellingcapabilities of Bidirectional Long Short TermMemory (BLSTM) layers to classify every frame inan audio signal as speech or non-speech
What Makes a Good Counselor? Learning to Distinguish between High-quality and Low-quality Counseling Conversations

The quality of a counseling intervention relies highly on the active collaboration between clients and counselors. In this paper, we explore several linguistic aspects of the collaboration process occurring during counseling conversations. Specifically, we address the differences between high-quality and low-quality counseling. Our approach examines participants’ turn-by-turn interaction, their linguistic alignment, the sentiment expressed by speakers during the conversation, as well as the different topics being discussed. Our results suggest important language differences in low- and high-quality counseling, which we further use to derive linguistic features able to capture the differences between the two groups. These features are then used to build automatic classifiers that can predict counseling quality with accuracies of up to 88%.
NodeSketch: Highly-Efficient Graph Embeddings via Recursive Sketching

Embeddings have become a key paradigm to learn graph representations and facilitate downstream graph analysis tasks. Existing graph embedding techniques either sample a large number of node pairs from a graph to learn node embeddings via stochastic optimization, or factorize a high-order proximity/adjacency matrix of the graph via expensive matrix factorization. However, these techniques usually require significant computational resources for the learning process, which hinders their applications on large-scale graphs. Moreover, the cosine similarity preserved by these techniques shows suboptimal efficiency in downstream graph analysis tasks, compared to Hamming similarity, for example. To address these issues, we propose NodeSketch, a highly-efficient graph embedding technique preserving high-order node proximity via recursive sketching. Specifically, built on top of an efficient data-independent hashing/sketching technique, NodeSketch generates node embeddings in Hamming space. For an input graph, it starts by sketching the self-loop-augmented adjacency matrix of the graph to output low-order node embeddings, and then recursively generates k-order node embeddings based on the self-loop-augmented adjacency matrix and (k-1)-order node embeddings. Our extensive evaluation compares NodeSketch against a sizable collection of state-of-the-art techniques using five real-world graphs on two graph analysis tasks. The results show that NodeSketch achieves state-of-the-art performance compared to these techniques, while showing significant speedup of 9x-372x in the embedding learning process and 1.19x-1.68x speedup when performing downstream graph analysis tasks.
Reinforcement Learning to Reach Equilibrium Flow on Roads in Transportation System

Traffic congestion threats the vitality of cities and the welfare of citizens. Transportation systems are using various technologies to allow users to adapt and have a different decision on transportation modes. Modification and improvement of these systems affect commuters 19 perspective and social welfare. In this study, the effect of equilibrium road flow on commuters 19 utilities with a different type of transportation mode will be discussed. A simple network with two modes of transportation will be illustrated to test the efficiency of minority game and reinforcement learning in commuters 19 daily trip decision making based on time and mode. The artificial society of agents is simulated to analyze the results. Reinforcement Learning to Reach Equilibrium Flow on Roads in Transportation System Hajar Baghcheband CISTER Research Centre, ISEP, Polytechnic Institute of Porto Porto, Portugal hajar@isep.ipp. pt Abstract—Traffic congestion threats the vitality of cities and the welfare of citizens. Transportation systems are using various technologies to allow users to adapt and have a different decision on transportation modes. Modification and improvement of these systems affect commuters’ perspective and social welfare. In this study, the effect of equilibrium road flow on commuters’ utilities with a different type of transportation mode will be discussed. A simple network with two modes of transportation will be illustrated to test the efficiency of minority game and reinforcement learning in commuters’ daily trip decision making based on time and mode. The artificial society of agents is simulated to analyze the results.Traffic congestion threats the vitality of cities and the welfare of citizens. Transportation systems are using various technologies to allow users to adapt and have a different decision on transportation modes. Modification and improvement of these systems affect commuters’ perspective and social welfare. In this study, the effect of equilibrium road flow on commuters’ utilities with a different type of transportation mode will be discussed. A simple network with two modes of transportation will be illustrated to test the efficiency of minority game and reinforcement learning in commuters’ daily trip decision making based on time and mode. The artificial society of agents is simulated to analyze the results. Keywords—transportation system, minority game, reinforcement learning, multi-agent system, simulation
Urban Parcel Grouping Method Based on Urban Form and Functional Connectivity Characterisation

The grouping of parcel data based on proximity is a pre-processing step of GIS and a key link of urban structure recognition for regional function discovery and urban planning. Currently, most literature abstracts parcels into points and clusters parcels based on their attribute similarity, which produces a large number of coarse granularity functional regions or discrete distribution of parcels that is inconsistent with human cognition. In this paper, we propose a novel parcel grouping method to optimise this issue, which considers both the urban morphology and the urban functional connectivity. Infiltration behaviours of urban components provide a basis for exploring the correlation between morphology mechanism and functional connectivity of urban areas. We measured the infiltration behaviours among adjacent parcels and concluded that the occurrence of infiltration behaviours often appears in the form of groups, which indicated the practical significance of parcel grouping. Our method employed two parcel morphology indicators: the similarity of the line segments and the compactness of the distribution. The line segment similarity was used to establish the adjacent relationship among parcels and the compactness was used to optimise the grouping result in obtain a satisfactory visual expression. In our study, constrained Delaunay triangulation, Hausdorff distance, and graph theory were employed to construct the proximity, delineate the parcel adjacency matrix, and implement the grouping of parcels. We applied this method for grouping urban parcel data of Beijing and verified the rationality of grouping results based on the quantified results of infiltration behaviours. Our method proved to take a good account of infiltration behaviours and satisfied human cognition, compared with a k-means++ method. We also presented a case using Xicheng District in Beijing to demonstrate the practicability of the method. The result showed that our method obtained fine-grained groups while ensuring functional regions-integrity.
Abstractive Text Summarization Based on Deep Learning and Semantic Content Generalization

This work proposes a novel framework for enhancing abstractive text summarization based on the combination of deep learning techniques along with semantic data transformations. Initially, a theoretical model for semantic-based text generalization is introduced and used in conjunction with a deep encoder-decoder architecture in order to produce a summary in generalized form. Subsequently, a methodology is proposed which transforms the aforementioned generalized summary into human-readable form, retaining at the same time important informational aspects of the original text and addressing the problem of out-of-vocabulary or rare words. The overall approach is evaluated on two popular datasets with encouraging results.