nils-herrmann/clean_scirepeval huggingface dataset:
Teaching Smart People How to Learn

A ny company that aspires to succeed in the tougher business environment of the 1990s must rst resolve a basic dilemma: success in the marketplace increasingly depends on learning, yet most people don't know how to learn. What's more, those members of the organization that many assume to be the best at learning are, in fact, not very good at it. I am talking about the well-educated, high-powered, high-commitment professionals who occupy key leadership positions in the modern corporation. Most companies not only have tremendous difculty addressing this learning dilemma ; they aren't even aware that it exists. The reason: they misunderstand what learning is and how to bring it about. As a result, they tend to make two mistakes in their efforts to become a learning organization. First, most people dene learning too narrowly as mere ''problem solving,'' so they focus on identifying and correcting errors in the external environment. Solving problems is important. But if learning is to persist, managers and employees must also look inward. They need to reect critically on their own behavior, identify the ways they often inadvertently contribute to the organization's problems, and then change how they act. In particular, they must learn how the very way they go about dening and solving problems can be a source of problems in its own right. I have coined the terms ''single loop'' and ''double loop'' learning to capture this crucial distinction. To give a simple analogy: a thermostat that automatically turns on the heat whenever the temperature in a room drops below 68 degrees is a good example of single-loop learning. A thermostat that could ask, ''Why am I set at 68 degrees?'' and then explore whether or not some other temperature might more economically achieve the goal of heating the room would be engaging in double-loop learning. Highly skilled professionals are frequently very good at single-loop learning. After all, they have spent much of their lives acquiring academic credentials, mastering one or a number of intellectual disciplines, and applying those disciplines to solve real-world problems. But ironically, this very fact helps explain why professionals are often so bad at double-loop learning. Put simply, because many professionals are almost always successful at what they do, they rarely experience failure. And because they have rarely failed, they have never learned how to learn from failure. So whenever their single-loop learning strategies go wrong, they become defensive, …
The New DLR Flight Dynamics Library Gertjan Looye

An overview of the new Modelica Flight Dynamics Library of the German Aerospace Center DLR is given. This library is intended for construction of multi-disciplinary flight dynamics models of rigid and flexible flight vehicles. The environment models provide the functionality to cover on-ground operations up to flight at high speeds and high altitudes. The resulting models may be used in various fields and stages of the aircraft development process, like flight control law design, as well as for real-time flight simulation.
BA-NET: DENSE BUNDLE ADJUSTMENT NETWORKS

This paper introduces a network architecture to solve the structure-from-motion (SfM) problem via feature-metric bundle adjustment (BA), which explicitly enforces multi-view geometry constraints in the form of feature-metric error. The whole pipeline is differentiable, so that the network can learn suitable features that make the BA problem more tractable. Furthermore, this work introduces a novel depth parameterization to recover dense per-pixel depth. The network first generates several basis depth maps according to the input image, and optimizes the final depth as a linear combination of these basis depth maps via feature-metric BA. The basis depth maps generator is also learned via end-to-end training. The whole system nicely combines domain knowledge (i.e. hard-coded multi-view geometry constraints) and deep learning (i.e. feature learning and basis depth maps learning) to address the challenging dense SfM problem. Experiments on large scale real data prove the success of the proposed method.
A Hardware Design Language for Efficient Control of Timing Channels

Information security can be compromised by leakage via low-level hardware features. One recently prominent example is cache probing attacks, which rely on timing channels created by caches. We introduce a hardware design language, SecVerilog, which makes it possible to statically analyze information flow at the hardware level. With SecVerilog, systems can be built with verifiable control of timing channels and other information channels. SecVerilog is Verilog, extended with expressive type annotations that enable precise reasoning about information flow. It also comes with rigorous formal assurance: we prove that SecVerilog enforces timing-sensitive noninterference and thus ensures secure information flow. By building a secure MIPS processor and its caches, we demonstrate that SecVerilog makes it possible to build complex hardware designs with verified security, yet with low overhead in time, space, and HW designer effort.
The use of optical flow for road navigation

This paper describes procedures for obtaining a reliable and dense optical flow from image sequences taken by a television (TV) camera mounted on a car moving in usual outdoor scenarios. The optical flow can be computed from these image sequences by using several techniques. Differential techniques to compute the optical flow do not provide adequate results, because of a poor texture in images and the presence of shocks and vibrations experienced by the TV camera during image acquisition. By using correlation based techniques and by correcting the optical flows for shocks and vibrations, useful sequences of optical flows can be obtained. When the car is moving along a flat road and the optical axis of the TV camera is parallel to the ground, the motion field is expected to be almost quadratic and have a specific structure. As a consequence the egomotion can be estimated from this optical flow and information on the speed and the angular velocity of the moving vehicle are obtained. By analyzing the optical flow it is possible to recover also a coarse segmentation of the flow, in which objects moving with a different speed are identified. By combining information from intensity edges a better localization of motion boundaries are obtained. These results suggest that the optical flow can be successfully used by a vision system for assisting a driver in a vehicle moving in usual streets and motorways.
Opinion Fraud Detection via Neural Autoencoder Decision Forest

Online reviews play an important role in influencing buyers’ daily purchase decisions. However, fake and meaningless reviews, which cannot reflect users’ genuine purchase experience and opinions, widely exist on the Web and pose great challenges for users to make right choices. Therefore, it is desirable to build a fair model that evaluates the quality of products by distinguishing spamming reviews. We present an end-to-end trainable unified model to leverage the appealing properties from Autoencoder and random forest. A stochastic decision tree model is implemented to guide the global parameter learning process. Extensive experiments were conducted on a large Amazon review dataset. The proposed model consistently outperforms a series of compared methods.
Agile Software Development Methods: Review and Analysis

Agile – denoting “the quality of being agile; readiness for motion; nimbleness, activity, dexterity in motion” – software development methods are attempting to offer an answer to the eager business community asking for lighter weight along with faster and nimbler software development processes. This is especially the case with the rapidly growing and volatile Internet software industry as well as for the emerging mobile application environment. The new agile methods have evoked a substantial amount of literature and debates. However, academic research on the subject is still scarce, as most of existing publications are written by practitioners or consultants. The aim of this publication is to begin filling this gap by systematically reviewing the existing literature on agile software development methodologies. This publication has three purposes. First, it proposes a definition and a classification of agile software development approaches. Second, it analyses ten software development methods that can be characterized as being ”agile” against the defined criteria. Third, it compares these methods and highlights their similarities and differences. Based on this analysis, future research needs are identified and discussed.
Real Time Identification and Prevention of Doziness using Face Recognition System

A large number of road accidents in the world occur due to driver’s not taking proper breaks while driving long distances. This System is used to detect fatigue behaviour of the driver well and before any accident occurs. In this system driver’s facial appearance is extracted via high speed camera which is placed in car in front of driver. This camera will capture the frames at every instant of time. Each of this frame will be processed using image processing algorithm. The mouth and eye region is separated from each of the frames and analyzed for concluding whether the driver is dozy or not. The system draws conclusion if the eyes are found closed for five consecutive frame. If doziness is detected, a signal alert is triggered. In case accidents occurs, message is sent to the desired number.
Pondering the Concept of Abstraction in (Illustrative) Visualization

We explore the concept of abstraction as it is used in visualization, with the ultimate goal of understanding and formally defining it. Researchers so far have used the concept of abstraction largely by intuition without a precise meaning. This lack of specificity left questions on the characteristics of abstraction, its variants, its control, or its ultimate potential for visualization and, in particular, illustrative visualization mostly unanswered. In this paper we thus provide a first formalization of the abstraction concept and discuss how this formalization affects the application of abstraction in a variety of visualization scenarios. Based on this discussion, we derive a number of open questions still waiting to be answered, thus formulating a research agenda for the use of abstraction for the visual representation and exploration of data. This paper, therefore, is intended to provide a contribution to the discussion of the theoretical foundations of our field, rather than attempting to provide a completed and final theory.
2000-01-0288 Validation and Use of SIMULINK Integrated , High Fidelity , Engine-In-Vehicle Simulation of the International Class VI Truck

This work presents the development, validation and use of a SIMULINK integrated vehicle system simulation composed of engine, driveline and vehicle dynamics modules. The engine model links the appropriate number of single-cylinder modules, featuring thermodynamic models of the in-cylinder processes with transient capabilities to ensure high fidelity predictions. A detailed fuel injection control module is also included. The engine is coupled to the driveline, which consists of the torque converter, transmission, differential and prop shaft and drive shafts. An enhanced version of the point mass model is used to account for vehicle dynamics in the longitudinal and heave directions. A vehicle speed controller replaces the operator and allows the feedforward simulation to follow a prescribed vehicle speed schedule. For the particular case reported here, the simulation is configured for the International 4700 series, Class VI, 4x2 delivery truck powered by a V8 turbocharged, intercooled diesel engine. The integrated vehicle simulation is validated against transient data measured on the proving ground. Comparisons of predicted and measured responses of engine and vehicle variables during vehicle acceleration from 0 to 60 mph and from 30 to 50 mph show very good agreement. The simulation is also used to study trade-offs involved in redesigning control strategies for improved performance of the vehicle system.
Integrated information theory of consciousness: an updated account.

This article presents an updated account of integrated information theory of consciousness (IIT) and some of its implications. IIT stems from thought experiments that lead to phenomenological axioms and ontological postulates. The information axiom asserts that every experience is one out of many, i.e. specific - it is what it is by differing in its particular way from a large repertoire of alternatives. The integration axiom asserts that each experience is one, i.e. unified - it cannot be reduced to independent components. The exclusion axiom asserts that every experience is definite - it is limited to particular things and not others and flows at a particular speed and resolution. IIT formalizes these intuitions with three postulates. The information postulate states that only "differences that make a difference" from the intrinsic perspective of a system matter: a mechanism generates cause-effect information if its present state has specific past causes and specific future effects within a system. The integration postulate states that only information that is irreducible matters: mechanisms generate integrated information only to the extent that the information they generate cannot be partitioned into that generated within independent components. The exclusion postulate states that only maxima of integrated information matter: a mechanism specifies only one maximally irreducible set of past causes and future effects - a concept. A complex is a set of elements specifying a maximally irreducible constellation of concepts, where the maximum is evaluated at the optimal spatio-temporal scale. Its concepts specify a maximally integrated conceptual information structure or quale, which is identical with an experience. Finally, changes in information integration upon exposure to the environment reflect a system's ability to match the causal structure of the world. After introducing an updated definition of information integration and related quantities, the article presents some theoretical considerations about the relationship between information and causation and about the relational structure of concepts within a quale. It also explores the relationship between the temporal grain size of information integration and the dynamic of metastable states in the corticothalamic complex. Finally, it summarizes how IIT accounts for empirical findings about the neural substrate of consciousness, and how various aspects of phenomenology may in principle be addressed in terms of the geometry of information integration.
Digital image integrity - a survey of protection and verification techniques

We are currently on a verge of a revolution in digital photography. Developments in computational imaging and adoption of artificial intelligence have spawned new editing techniques that give impressive results in astonishingly short timeframes. The advent of multi-sensor and multi-lens cameras will further challenge many existing integrity verification techniques. As a result, it will be necessary to re-evaluate our notion of image authenticity and look for new techniques that could work efficiently in this new reality. The goal of this paper is to thoroughly review existing techniques for protection and verification of digital image integrity. In contrast to other recent surveys, the discussion covers the most important developments both in active protection and in passive forensic analysis techniques. Existing approaches are analyzed with respect to their capabilities, fundamental limitations, and prospective attack vectors. Whenever possible, the discussion is supplemented with real operation examples and a list of available implementations. Finally, the paper reviews resources available in the research community, including public data-sets and commercial or open-source software. The paper concludes by discussing relevant developments in computational imaging and highlighting future challenges and open research problems.
A Mobile-Based Diet Monitoring System for Obesity Management.

Personal diet management is key to fighting the obesity epidemic. Recent advances in smartphones and wearable sensor technologies have empowered automated food monitoring through food image processing and eating episode detection, with the goal to conquer drawbacks of traditional food journaling that is labour intensive, inaccurate, and low adherent. In this paper, we present a new interactive mobile system that enables automated food recognition and assessment based on user food images and provides dietary intervention while tracking users' dietary and physical activities. In addition to using techniques in computer vision and machine learning, one unique feature of this system is the realization of real-time energy balance monitoring through metabolic network simulation. As a proof of concept, we have demonstrated the use of this system through an Android application.
Combination of feature selection and optimized fuzzy apriori rules: the case of credit scoring

Credit scoring is an important topic and banks collect different data from their loan applicants to make appropriate and correct decisions. Rule bases are favourite in credit decision making because of their ability to explicitly distinguish between good and bad applicants. This paper, uses four feature selection approaches as features pre-processing combined with fuzzy apriori. These methods are stepwise regression, Classification And Regression Tree (CART), correlation matrix and Principle Component Analysis (PCA). Particle Swarm is applied to find the best fuzzy apriori rules by searching different support and confidence. Considering Australian and German University of California at Irvine (UCI) and an Iranian bank datasets, different feature selections methods are compared in terms of accuracy, number of rules and number of features. The results are compared using T test; it reveals that fuzzy apriori combined with PCA creates a compact rule base and shows better results than the single fuzzy apriori model and other combined feature selection methods.
An Infrastructure Approach to Context-Aware Computing

The Context Toolkit (Dey, Salber, and Abowd 2001 [this special issue]) is only one of many possible architectures for supporting context-aware applications. In this essay, we look at the trade-offs involved with a service infrastructure approach to context-aware computing. We describe the advantages that a service infrastructure for contextawareness has over other approaches, outline some of the core technical challenges that must be addressed before such an infrastructure can be built, and point out promising research directions for overcoming these challenges.
Experimental kinematics for wheeled skid-steer mobile robots

This work aims at improving real-time motion control and dead-reckoning of wheeled skid-steer vehicles by considering the effects of slippage, but without introducing the complexity of dynamics computations in the loop. This traction scheme is found both in many off-the-shelf mobile robots due to its mechanical simplicity and in outdoor applications due to its maneuverability. In previous works, we reported a method to experimentally obtain an optimized kinematic model for skid-steer tracked vehicles based on the boundedness of the instantaneous centers of rotation (ICRs) of treads on the motion plane. This paper provides further insight on this method, which is now proposed for wheeled skid-steer vehicles. It has been successfully applied to a popular research robotic platform, pioneer P3-AT, with different kinds of tires and terrain types.
n ensemble learning framework for anomaly detection in building nergy consumption

During building operation, a significant amount of energy is wasted due to equipment and humanrelated faults. To reduce waste, today’s smart buildings monitor energy usage with the aim of identifying abnormal consumption behaviour and notifying the building manager to implement appropriate energysaving procedures. To this end, this research proposes a new pattern-based anomaly classifier, the collective contextual anomaly detection using sliding window (CCAD-SW) framework. The CCAD-SW framework identifies anomalous consumption patterns using overlapping sliding windows. To enhance the anomaly detection capacity of the CCAD-SW, this research also proposes the ensemble anomaly detection (EAD) framework. The EAD is a generic framework that combines several anomaly detection classifiers
Short-term load forecasting using lifting scheme and ARIMA models

Short-term load forecasting is achieved using a lifting scheme and autoregressive integrated moving average (ARIMA) models. The lifting scheme is a general and flexible approach for constructing bi-orthogonal wavelets that are usually in the spatial domain. The lifting scheme is embedded into the ARIMA models to enhance forecasting accuracy. Based on wavelet multi-revolution analysis (MRA) results, the lifting scheme decomposes the original load series into different sub-series at different revolution levels, which display the different frequency characteristic of a load. The sub-series are then forecast using properly fitted ARIMA models. Finally, forecasting results at different levels are reconstructed to generate an original load prediction by the inverse lifting scheme. In this study, the Coeflet 12 wavelet is factored into lifting scheme steps. The proposed algorithm was tested by applying it to different practical load data types from the Taipower Company in 2007 for one-day-ahead load forecasting. Simulation results indicate that the forecasting performance of the proposed approach is superior to that of the back-propagation network (BPN) algorithm and traditional ARIMA models.
Robust Audio Hashing for Content Identification

Nowadays most audio content identification systems are based on watermarking technology. In this paper we present a different technology, referred to as robust audio hashing. By extracting robust features and translating them into a bit string, we get an object called a robust hash. Content can then be identified by comparing hash values of a received audio clip with the hash values of previously stored original audio clips. A distinguishing feature of the proposed hash scheme is its ability to extract a bit string for every so many milliseconds. More precisely, for every windowed time interval a hash value of 32 bits is computed by thresholding energy differences of several frequency bands. A sequence of 256 hash values, corresponding to approximately 3 seconds of audio, can uniquely identify a song. Experimental results show that the proposed scheme is robust against severe compression, but bit errors do occur. This implies that searching and matching is a non-trivial task for large databases. For instance, a brute force search approach is already prohibitive for databases containing hash values of more than 100 songs. Therefore we propose a scheme that exploits a structured search algorithm that allows searching databases containing over 100,000 songs.
Frequency Tracking Control for a Capacitor-Charging Parallel Resonant Converter with Phase-Locked Loop

This study investigates a phase-locked loop (PLL) controlled parallel resonant converter (PRC) for a pulse power capacitor charging application. The dynamic nature of the capacitor charging is such that it causes a shift in the resonant frequency of the PRC. Using the proposed control method, the PRC can be optimized to operate with its maximum power capability and guarantee ZVS operation, even when the input voltage and resonant tank parameters vary. The detailed implementation of the PLL controller, as well as the determination of dead-time and leading time, is presented in this paper. Simulation and experimental results verify the performance of the proposed control method.