Miracle-dz/UniFAR-FTUs huggingface dataset:
Two-View Geometry estimation using the Rodrigues Rotation Formula

The paper presents the applicability of the Rodrigues Rotation Formula (RRF) in the context of Two-Views Geometry estimation. The Epipolar Constraint is usually formulated as the belonging of an image point to its corresponding Epipolar Line, instead using the RRF we will arrange the same constraint in terms of equivalence between 3D unit-norm vectors. This alternative formulation generates a geometrically meaningful error measure, which according to the presented results, provides a significant improvement of the estimation accuracy with respect to the widely used Sampson error, still maintaining a comparable computational cost.
Route Optimization on Wireless Mobile Ad-hoc Networks

A mobile ad-hoc network (MANET) is formed by a cluster of mobile hosts, each installed with a wireless transceiver, without the assistance of base stations. Due to the transmission range constraint of transceivers, two mobile hosts may communicate with each other indirectly, by having other mobile hosts relay their packets. Several routing protocols have been proposed for a MANET to support the delivery of packets between mobile hosts in a dynamically changing topology 4, 6, 11, 8, 12]. In a MANET, a route, while being used for transmission , may become worse due to host mobility or the system may newly form some better routes. Existing protocols, such as DSR, SSA, AODV, and ZRP, however, will stick on a xed route between a pair of mobile hosts once it is discovered, until the route is expired or broken. In this paper, we show how to enhance these four protocols with route optimization capability, such that the routing paths can be adjusted on-they while they are still being used for delivering packets.
An Implementation of the Programming Language pm2 for PRAM

pm2 is a programming language for PRAM. It is based on Modula-2 with additional elements for the control of parallelism. This report describes the main implementation concepts of pm2, mainly the usage of the PRAM. The compiler from pm2 to PRAM-assembler has been implemented. Processor and memory management of the implementation are simple but adequate and effective enough for the experimental compiler. Moreover the execution times of the primitives match the costs of the primitives used in PRAM-algorithm theory.
The importance of teacher-student relationships for students with emotional and behavioral disorders

Abstract Students with emotional and behavioral disorders (EBD) are at greater risk for academic failure and negative postschool outcomes when compared to other disability categories. It is important to learn how to promote protective factors for students with EBD to foster resilience. The purpose of the study was to examine the impact teachers have on students with EBD and how teachers can be a protective factor to promote resilience. To gain a deeper understanding of the influence teachers have on students with EBD, it is important to recognize the perception students have of their teachers.
Exploiting the P300 paradigm for cognitive biometrics

Automatic identification of a person’s individuality is an important issue today. Brain Computer Interfaces (BCI) which uses EEG as a modality is a promising area for cognitive biometrics. A BCI system could be used to recognise a sequence (say letters, colours or images) by the user. This sequence could form a ‘BrainWord’, which could be used for authentication in a multimodal environment with other technologies for high security applications. In this work, we studied several variations of the well-known P300 BCI paradigm. The influence of irrelevant stimuli during a task was studied by considering the popular Rapid Serial Visual Paradigm (RSVP) . The variation in spatial locations of the presentation stimuli during a task was studied, by designing a Spatially Varying Paradigm . Comparison of classification accuracies and bit rates for eight participants from a BCI perspective, highlights that RSVP paradigm could be exploited effectively for biometrics.
A NEW AUTOMATIC INTERFEROMETRIC METHOD FOR MEASURING THE DEPENDENCE OF MAGNETOELASTIC PROPERTIES ON MAGNETIC FIELDS

The aim of this work is to characterize the high dependence on applied magnetic field of both Young’s modulus and damping, so-called ΔE-effect and Δδ-effect respectively, in some magnetic materials thanks to a new automatic and interferometric method. The characterization of these properties is based on free longitudinal vibration theory in continuous systems. The studied samples are nickel slender rods, with 10 mm in diameter and 110 mm in length, longitudinally placed inside a coil. The excitation of the specimen is obtained thanks to a brief impact caused by a pellet shot with a compressed air system, and the measurement method consists of an interferometric vibrometer based on Doppler Effect. This innovative method achieves a huge amount of advantages over other existing methods, especially precision, resolution and full automation. Attending to the developed studies in nickel, maximum variations relatives to Young’s modulus and logarithmic decrement are 2.5% and 156% respectively.
Semantic Relations Cause Interference in Spoken Language Comprehension When Using Repeated Definite References, Not Pronouns

The choice and processing of referential expressions depend on the referents' status within the discourse, such that pronouns are generally preferred over full repetitive references when the referent is salient. Here we report two visual-world experiments showing that: (1) in spoken language comprehension, this preference is reflected in delayed fixations to referents mentioned after repeated definite references compared with after pronouns; (2) repeated references are processed differently than new references; (3) long-term semantic memory representations affect the processing of pronouns and repeated names differently. Overall, these results support the role of semantic discourse representation in referential processing and reveal important details about how pronouns and full repeated references are processed in the context of these representations. The results suggest the need for modifications to current theoretical accounts of reference processing such as Discourse Prominence Theory and the Informational Load Hypothesis.
The CADENCE Corpus: A New Resource for Inclusive Voice Interface Design

Papers on voice interfaces for people with cognitive impairment or demenita only provide small snapshots of actual interactions, if at all. This is a major obstacle to the development of better interfaces. Transcripts of interactions between users and systems contain rich evidence of typical language patterns, indicate how users conceptualise their computer interlocutor, and highlight key design issues. In this paper, we introduce the CADENCE corpus and outline how it can be used to stimulate replicable research on inclusive voice interfaces. The CADENCE corpus is first data set of its kind to include rich data from people with cognitive impairment and free for research use. The corpus consists of transcribed spoken interactions between older people with and without cognitive impairment and a simulated Intelligent Cognitive Assistant and includes comprehensive data on users' cognitive abilities.
Applying Novel Resampling Strategies To Software Defect Prediction

Due to the tremendous complexity and sophistication of software, improving software reliability is an enormously difficult task. We study the software defect prediction problem, which focuses on predicting which modules will experience a failure during operation. Numerous studies have applied machine learning to software defect prediction; however, skewness in defect-prediction datasets usually undermines the learning algorithms. The resulting classifiers will often never predict the faulty minority class. This problem is well known in machine learning and is often referred to as learning from unbalanced datasets. We examine stratification, a widely used technique for learning unbalanced data that has received little attention in software defect prediction. Our experiments are focused on the SMOTE technique, which is a method of over-sampling minority-class examples. Our goal is to determine if SMOTE can improve recognition of defect-prone modules, and at what cost. Our experiments demonstrate that after SMOTE resampling, we have a more balanced classification. We found an improvement of at least 23% in the average geometric mean classification accuracy on four benchmark datasets.
Using cross-decoder phone coocurrences in phonotactic language recognition

Phonotactic language recognizers are based on the ability of phone decoders to produce phone sequences containing acoustic, phonetic and phonological information, which is partially dependent on the language. Input utterances are decoded and then scored by means of models for the target languages. Commonly, various decoders are applied in parallel and fused at the score level. A kind of complementarity effect is expected when fusing scores, since each decoder is assumed to extract different (and complementary) information from the input utterance. This assumption is supported by the performance improvements attained when fusing systems. However, decodings are processed in a fully uncoupled way, their time alignment (and the information that may be extracted from it) being completely lost. In this paper, a simple approach is proposed, which takes into account time alignment information, by considering cross-decoder phone coocurrences at the frame level. To evaluate the approach, a choice of open software (BUT front-end and phone decoders, SRI-LM toolkit, libSVM, FoCal) is used, and experiments are carried out on the NIST LRE2007 database. Adding phone coocurrences to the baseline phonotactic systems provides slight performance improvements, revealing the potential benefit of using cross-decoder dependencies for language modeling.
A Novel Face Detection Algorithm Based on PCA and Adaboost

This paper studies the feature based face detection algorithm. Based on the principal component analysis, feature vector space is extracted to construct weak classifier. Combined with Adaboost algorithm to construct the strong classifier, an algorithm for face detection is presented. The performance of the algorithm is tested based on MIT+CMU face database, the results show that the algorithm in the running time and detection accuracy is significantly better than the algorithm based on neural network and support vector machine algorithm.
Application of liposomal technologies for delivery of platinum analogs in oncology

Platinum-based chemotherapy, such as cisplatin, oxaliplatin, and carboplatin, is one of the most widely utilized classes of cancer therapeutics. While highly effective, the clinical applications of platinum-based drugs are limited by their toxicity profiles as well as suboptimal pharmacokinetic properties. Therefore, one of the key research areas in oncology has been to develop novel platinum analog drugs and engineer new platinum drug formulations to improve the therapeutic ratio further. Such efforts have led to the development of platinum analogs including nedaplatin, heptaplatin, and lobaplatin. Moreover, reformulating platinum drugs using liposomes has resulted in the development of L-NDPP (Aroplatin™), SPI-77, Lipoplatin™, Lipoxal™, and LiPlaCis®. Liposomes possess several attractive biological activities, including biocompatibility, high drug loading, and improved pharmacokinetics, that are well suited for platinum drug delivery. In this review, we discuss the various platinum drugs and their delivery using liposome-based drug delivery vehicles. We compare and contrast the different liposome platforms as well as speculate on the future of platinum drug delivery research.
Human detection in a challenging situation

Although there have been a lot of studies on human detection in recent years, most of them have some particular requirements. However, some videos may have complicated scenes with low resolution and include both moving and standing people. The paper aims for individual detection under the challenging situation. An EM (Expectation-Maximum) based method has been developed to detect individuals in a common outdoor scenario. A new cluster model is employed in the method for human detection. The method can work well even for low-resolution images. Both moving and standing people can be detected as long as they show some occasional movements, which is satisfied in most cases. Some promising results have been presented and analyzed in a scene with around 100 people.
User-Friendly Chat ( 6 . 857 final project )

Communicating confidentially on the internet is challenging. While strong cryptographic primitives like public-key encryption have been widely available for years, no simple end-to-end encrypted communication apps are widely used. For our 6.857 final project, we designed and implemented a small end-to-end encrypted chat app that builds on a PKI, Keytree, for security. By using an external PKI, our app and protocol are secure yet remain simple.
iPAT: Intelligent privacy-preserving administration tool for IRB applications

Supported by maturing information and communication technology (ICT) technologies, the medical care industry entered its digitization age. However, medical information is highly confidential and involves privacy; even legitimate access can cause privacy infringements. Under the current medical administration system, no established application systems conform to the IRB (Institutional review board) to protect privacy in human subject research. To prevent the privacy of research subjects from being disclosed unintentionally, and to meet the “need-to-know” demands of information management, this study proposes an intelligent privacy-preserving administration tool (iPAT) designed to facilitate human subject research. This study provides the principle and feasibility of iPAT that are introduced and verified through a use case conducted by the head of the research project.
Evaluating Organizational Effectiveness

If organizations are to survive and achieve long-run viability, they must be effective. Yet organizational effectiveness evaluation has been limited by a variety of factors—especially the ambiguity surrounding the concept of organizational effectiveness itself. The long-established approaches used by organizational theorists to define organizational effectiveness have emphasized different perspectives with respect to the organization type and degrees of importance of the various constituency groups comprising the organization. Moreover, definitions of the organization and organizational effectiveness have focused primarily on the dissimilarities among organizations and their constituencies, conceding efforts to identify commonalities. This has led to increased fragmentation of the concept of organizational effectiveness and weakened its utility. Because of its central importance to both researchers and management practitioners, among many other organizational participants, the development of a practical tool that overcomes the deficiencies and challenges in current approaches to organizational effectiveness evaluation is of theoretical and practical value. This dissertation addresses the lack of satisfactory approaches for assessing organizational effectiveness and offers an alternative framework using an evaluation checklist. The Organizational Evaluation Checklist (OEC) outlines a comprehensive process for evaluating organizational effectiveness that specifically addresses the values issues with its inclusion of universal criteria of merit. Two separate investigations were conducted to empirically validate the OEC. The first investigation sought input from subject matter experts and targeted users regarding the merit of the OEC. The second applied the revised OEC in a real-world evaluation to assess the effectiveness of a for-profit organization. Both investigations were keyed to a common set of criteria to evaluate and improve the checklist. The original OEC performed well with respect to its pertinence to organizational effectiveness evaluation, clarity, fairness, and sound theory. The most significant structural change made to the OEC was intended to facilitate the efficient use of the checklist and increase its parsimony. Implications for organizations and the professional practice of evaluation are discussed, along with opportunities for future exploration.
The model checking problem for intuitionistic propositional logic with one variable is AC1-complete

We show that the model checking problem for intuitionistic propositional logic with one variable is complete for logspace-uniform AC1. As basic tool we use the connection between intuitionistic logic and Heyting algebra, and investigate its complexity theoretical aspects. For superintuitionistic logics with one variable, we obtain NC1-completeness for the model checking problem.
Using Reinforcement Learning to Improve the Stability of a Humanoid Robot: Walking on Sloped Terrain

In order to perform a walk on a real environment, humanoid robots need to adapt themselves to the environment, as humans do. One approach to achieve this goal is to use Machine Learning techniques that allow robots to improve their behavior with time. In this paper, we propose a system that uses Reinforcement Learning to learn the action policy that will make a robot walk in an upright position, in a lightly sloped terrain. To validate this proposal, experiments were made with a humanoid robot - a robot for the RoboCup Humanoid League based on DARwIn-OP. The results showed that the robot was able to walk on sloping floors, going up and down ramps, even in situations where the slope angle changes.
Strong Approximation of the Quantile Processes and Its Applications under Strong Mixing Properties

Given some regularity conditions on the distribution F(·) of a random X1, . , Xn emanating from a strictly stationary sequence of random variables satisfying a strong mixing condition, it is shown that the sequence of quantile processes {n12f(F? 1(s))(F? 1n(s) ? F? 1(s)); 0 < s < 1} behaves like a sequence of Brownian bridges {Bn(s); 0
Aggregate vs Disaggregate Data Analysis - a Paradox in the Estimation of a Money Demand Function of Japan Under the Low Interest Rate Policy

We use Japanese aggregate and disaggregate money demand data to show that conflicting inferences can arise. The aggregate data appears to support the contention that there was no stable money demand function. The disaggregate data shows that there was a stable money demand function. Neither was there any indication of the presence of a liquidity trap. Possible sources of discrepancy are explored and the diametrically opposite results between the aggregate and disaggregate analysis are attributed to the neglected heterogeneity among micro units. We provide necessary and sufficient conditions for the existence of cointegrating relations among aggregate variables when heterogeneous cointegration relations among micro units exist. We also conduct simulation analysis to show that when such conditions are violated, it is possible to observe stable micro relations, but unit root phenomenon among macro variables. Moreover, the prediction of aggregate outcomes, using aggregate data is less accurate than the prediction based on micro equations and policy evaluation based on aggregate data ignoring heterogeneity in micro units can be grossly misleading.
Analysis and Lessons from a Publicly Available Google Cluster Trace

System designers in industry are often overwhelmed by large scale data, while researchers in academic often confront a lack of publicly available production data. In this paper, we analyze a large scale production workload trace recently made publicly available by Google. We offer a statistical profile of the data, with several interesting discoveries regarding job arrival patterns, CPU and memory consumptions, task durations, and others. We further perform k-means clustering to identify common groups of jobs, with several methodological departures and different findings compared with prior work on similar data. We also do correlation analysis between job semantics and job behavior, leading to helpful perspectives on capacity planning and system tuning. Our key finding is that while the limited dataset size prevents us from generalizing the trace behaviors observed, the analytical methods we describe nonetheless allow us to extract many system design insights.
Teaching web application development: A case study in a computer science course

Teaching web development in Computer Science undergraduate courses is a difficult task. Often, there is a gap between the students' experiences and the reality in the industry. As a consequence, the students are not always well-prepared once they get the degree. This gap is due to several reasons, such as the complexity of the assignments, the working environment, the frameworks used and the time-frame constraints. In this paper, we report on a case study on how we taught web application development using extreme tutoring and in an apprenticeship manner. The assumption was to take two real web applications as basis for practical teaching. We present the different issues that we faced: the setup of the development framework, the heterogeneity of human resources and the volatility of the environment. We describe how the process evolved positively. The students became independent, and implemented two applications. We conclude with the lessons learned.
Confirmation of the factorial structure of the Japanese short version of the TEMPS-A in psychiatric patients and general adults

Background The Temperament Evaluation of the Memphis, Pisa, Paris, and San Diego Auto-questionnaire (TEMPS-A) is a 110-item questionnaire that assesses five affective temperaments. However, a valid shortened version is desired for large-scale investigations to enhance the compliance of respondents. Methods A confirmatory factor analysis was conducted among 320 psychiatric patients and 61 general adults. The participants completed the Japanese 39-item short version of the TEMPS-A, and a portion of the participants completed the 110-item version. An exploratory factor analysis with the principal factor method and varimax rotation was conducted to identify a more suitable model of the short version of the TEMPS-A. Results The confirmatory factor analysis revealed that the 39-item version exhibited a poor model fit. However, we found that the 18-item version exhibited a firm five-factor structure based on the exploratory factor analysis, and this model exhibited an acceptable model fit. It had good or acceptable internal consistency (Cronbach’s αs: 0.672–0.819). Limitations The majority of the subjects in the present study were patients, and the temperament data may have been affected by psychiatric symptoms. Conclusion A firm five-factor structure was not found in the 39-item short version of the Japanese TEMPS-A. Therefore, an 18-item version was proposed. This new 18-item version of the TEMPS-A might be useful for clinical applications and large-scale investigations.