nils-herrmann/clean_scirepeval huggingface dataset:
Vision based MAV navigation in unknown and unstructured environments

Within the research on Micro Aerial Vehicles (MAVs), the field on flight control and autonomous mission execution is one of the most active. A crucial point is the localization of the vehicle, which is especially difficult in unknown, GPS-denied environments. This paper presents a novel vision based approach, where the vehicle is localized using a downward looking monocular camera. A state-of-the-art visual SLAM algorithm tracks the pose of the camera, while, simultaneously, building an incremental map of the surrounding region. Based on this pose estimation a LQG/LTR based controller stabilizes the vehicle at a desired setpoint, making simple maneuvers possible like take-off, hovering, setpoint following or landing. Experimental data show that this approach efficiently controls a helicopter while navigating through an unknown and unstructured environment. To the best of our knowledge, this is the first work describing a micro aerial vehicle able to navigate through an unexplored environment (independently of any external aid like GPS or artificial beacons), which uses a single camera as only exteroceptive sensor.
Classification and Novel Class Detection in Concept-Drifting Data Streams under Time Constraints

Most existing data stream classification techniques ignore one important aspect of stream data: arrival of a novel class. We address this issue and propose a data stream classification technique that integrates a novel class detection mechanism into traditional classifiers, enabling automatic detection of novel classes before the true labels of the novel class instances arrive. Novel class detection problem becomes more challenging in the presence of concept-drift, when the underlying data distributions evolve in streams. In order to determine whether an instance belongs to a novel class, the classification model sometimes needs to wait for more test instances to discover similarities among those instances. A maximum allowable wait time Tc is imposed as a time constraint to classify a test instance. Furthermore, most existing stream classification approaches assume that the true label of a data point can be accessed immediately after the data point is classified. In reality, a time delay Tl is involved in obtaining the true label of a data point since manual labeling is time consuming. We show how to make fast and correct classification decisions under these constraints and apply them to real benchmark data. Comparison with state-of-the-art stream classification techniques prove the superiority of our approach.
BEGAN: Boundary Equilibrium Generative Adversarial Networks

We propose a new equilibrium enforcing method paired with a loss derived from the Wasserstein distance for training auto-encoder based Generative Adversarial Networks. This method balances the generator and discriminator during training. Additionally, it provides a new approximate convergence measure, fast and stable training and high visual quality. We also derive a way of controlling the trade-off between image diversity and visual quality. We focus on the image generation task, setting a new milestone in visual quality, even at higher resolutions. This is achieved while using a relatively simple model architecture and a standard training procedure.
Habits as knowledge structures: automaticity in goal-directed behavior.

This study tested the idea of habits as a form of goal-directed automatic behavior. Expanding on the idea that habits are mentally represented as associations between goals and actions, it was proposed that goals are capable of activating the habitual action. More specific, when habits are established (e.g., frequent cycling to the university), the very activation of the goal to act (e.g., having to attend lectures at the university) automatically evokes the habitual response (e.g., bicycle). Indeed, it was tested and confirmed that, when behavior is habitual, behavioral responses are activated automatically. In addition, the results of 3 experiments indicated that (a) the automaticity in habits is conditional on the presence of an active goal (cf. goal-dependent automaticity; J. A. Bargh, 1989), supporting the idea that habits are mentally represented as goal-action links, and (b) the formation of implementation intentions (i.e., the creation of a strong mental link between a goal and action) may simulate goal-directed automaticity in habits.
Smart Home Definition and Security Threats

The home of the future should be a smart one, to support us in our daily life. Up to now only a few security incidents in that area are known. Depending on different security analyses, this fact is rather a result of the low spread of Smart Home products than the success of such systems security. Given that Smart Homes become more and more popular, we will consider current incidents and analyses to estimate potential security threats in the future. The definitions of a Smart Home drift widely apart. Thus we first need to define Smart Home for ourselves and additionally provide a way to categorize the big mass of products into smaller groups.
Using Machine Learning Algorithms for Breast Cancer Risk Prediction and Diagnosis

Breast cancer represents one of the diseases that make a high number of deaths every year. It is the most common type of all cancers and the main cause of women’s deaths worldwide. Classification and data mining methods are an effective way to classify data. Especially in medical field, where those methods are widely used in diagnosis and analysis to make decisions. In this paper, a performance comparison between different machine learning algorithms: Support Vector Machine (SVM), Decision Tree (C4.5), Naive Bayes (NB) and k Nearest Neighbors (k-NN) on the Wisconsin Breast Cancer (original) datasets is conducted. The main objective is to assess the correctness in classifying data with respect to efficiency and effectiveness of each algorithm in terms of accuracy, precision, sensitivity and specificity. Experimental results show that SVM gives the highest accuracy (97.13%) with lowest error rate. All experiments are executed within a simulation environment and conducted in WEKA data mining tool.  Peer-review under responsibility of the Conference Program Chairs.
Cell vs. WiFi: on the performance of metro area mobile connections

Cellular and 802.11 WiFi are compelling options for mobile Internet connectivity. The goal of our work is to understand the performance afforded by each of these technologies in diverse environments and use conditions. In this paper, we compare and contrast cellular and WiFi performance using crowd-sourced data from Speedtest.net. Our study considers spatio-temporal performance (upload/download throughput and latency) using over 3 million user-initiated tests from iOS and Android apps in 15 different metro areas collected over a 15 week period. Our basic performance comparisons show that (i) WiFi provides better absolute download/upload throughput, and a higher degree of consistency in performance; (ii) WiFi networks generally deliver lower absolute latency, but the consistency in latency is often better with cellular access; (iii) throughput and latency vary widely depending on the particular access type e.g., HSPA, EVDO, LTE, WiFi, etc.) and service provider. More broadly, our results show that performance consistency for cellular and WiFi is much lower than has been reported for wired broadband. Temporal analysis shows that average performance for cell and WiFi varies with time of day, with the best performance for large metro areas coming at non-peak hours. Spatial analysis shows that performance is highly variable across metro areas, but that there are subregions that offer consistently better performance for cell or WiFi. Comparisons between metro areas show that larger areas provide higher throughput and lower latency than smaller metro areas, suggesting where ISPs have focused their deployment efforts. Finally, our analysis reveals diverse performance characteristics resulting from the rollout of new cell access technologies and service differences among local providers.
A meta-analysis of the five-factor model of personality and academic performance.

This article reports a meta-analysis of personality-academic performance relationships, based on the 5-factor model, in which cumulative sample sizes ranged to over 70,000. Most analyzed studies came from the tertiary level of education, but there were similar aggregate samples from secondary and tertiary education. There was a comparatively smaller sample derived from studies at the primary level. Academic performance was found to correlate significantly with Agreeableness, Conscientiousness, and Openness. Where tested, correlations between Conscientiousness and academic performance were largely independent of intelligence. When secondary academic performance was controlled for, Conscientiousness added as much to the prediction of tertiary academic performance as did intelligence. Strong evidence was found for moderators of correlations. Academic level (primary, secondary, or tertiary), average age of participant, and the interaction between academic level and age significantly moderated correlations with academic performance. Possible explanations for these moderator effects are discussed, and recommendations for future research are provided.
Deep convolutional feature transfer across mobile activity recognition domains, sensor modalities and locations

Kernels in the convolutional layers of deep convolutional networks are believed to act as feature extractors, progressively highlighting more domain-specific features in the upper network layers. Thus lower-level features might be suitable for transfer. We analyse this in wearable activity recognition by reusing kernels learned on a source domain on another target domain. We consider transfer between users, application domains, sensor modalities and sensor locations. We characterize the trade-offs of transferring various convolutional layers along model size, learning speed, recognition performance and training data. Through novel kernel visualisations and comparative evaluations we identify what kernels are predominantly sensitive to, amongst sensor characteristics, motion dynamics and on-body placement. Kernel transfer reduces training time by ~17% without additional complexity. We derive recommendations on when transfer is most suitable.
OutlinAR: an assisted interactive model building system with reduced computational effort

This paper presents a system that allows online building of 3D wireframe models through a combination of user interaction and automated methods from a handheld camera-mouse. Crucially, the model being built is used to concurrently compute camera pose, permitting extendable tracking while enabling the user to edit the model interactively. In contrast to other model building methods that are either off-line and/or automated but computationally intensive, the aim here is to have a system that has low computational requirements and that enables the user to define what is relevant (and what is not) at the time the model is being built. OutlinAR hardware is also developed which simply consists of the combination of a camera with a wide field of view lens and a wheeled computer mouse.
Scaling regression inputs by dividing by two standard deviations.

Interpretation of regression coefficients is sensitive to the scale of the inputs. One method often used to place input variables on a common scale is to divide each numeric variable by its standard deviation. Here we propose dividing each numeric variable by two times its standard deviation, so that the generic comparison is with inputs equal to the mean +/-1 standard deviation. The resulting coefficients are then directly comparable for untransformed binary predictors. We have implemented the procedure as a function in R. We illustrate the method with two simple analyses that are typical of applied modeling: a linear regression of data from the National Election Study and a multilevel logistic regression of data on the prevalence of rodents in New York City apartments. We recommend our rescaling as a default option--an improvement upon the usual approach of including variables in whatever way they are coded in the data file--so that the magnitudes of coefficients can be directly compared as a matter of routine statistical practice.
On the vulnerability of face verification systems to hill-climbing attacks

In this paper, we use a general hill-climbing attack algorithm based on Bayesian adaption to test the vulnerability of two face recognition systems to indirect attacks. The attacking technique uses the scores provided by the matcher to adapt a global distribution computed from an independent set of users, to the local specificities of the client being attacked. The proposed attack is evaluated on an Eigenface-based and a Parts-base face verification system using the XM2VTS database. Experimental results demonstrate that the hill-climbing algorithm is very efficient and is able to bypass over 85% of the attacked accounts (for both face recognition systems). The security flaws flaws of the analyzed system are pointed out and possible countermeasures to avoid them are also proposed.
Building an Arabic Stemmer for Information Retrieval

In TREC 2002 the Berkeley group participated only in the English-Arabic cross-language retrieval (CLIR) track. One Arabic monolingual run and three English-Arabic cross-language runs were submitted. Our approach to the crosslanguage retrieval was to translate the English topics into Arabic using online English-Arabic machine translation systems. The four official runs are named as BKYMON, BKYCL1, BKYCL2, and BKYCL3. The BKYMON is the Arabic monolingual run, and the other three runs are English-to-Arabic cross-language runs. This paper reports on the construction of an Arabic stoplist and two Arabic stemmers, and the experiments on Arabic monolingual retrieval, English-to-Arabic cross-language retrieval.
An efficient algorithm for a class of fused lasso problems

The fused Lasso penalty enforces sparsity in both the coefficients and their successive differences, which is desirable for applications with features ordered in some meaningful way. The resulting problem is, however, challenging to solve, as the fused Lasso penalty is both non-smooth and non-separable. Existing algorithms have high computational complexity and do not scale to large-size problems. In this paper, we propose an Efficient Fused Lasso Algorithm (EFLA) for optimizing this class of problems. One key building block in the proposed EFLA is the Fused Lasso Signal Approximator (FLSA). To efficiently solve FLSA, we propose to reformulate it as the problem of finding an "appropriate" subgradient of the fused penalty at the minimizer, and develop a Subgradient Finding Algorithm (SFA). We further design a restart technique to accelerate the convergence of SFA, by exploiting the special "structures" of both the original and the reformulated FLSA problems. Our empirical evaluations show that, both SFA and EFLA significantly outperform existing solvers. We also demonstrate several applications of the fused Lasso.
Reducing shoulder-surfing by using gaze-based password entry

Shoulder-surfing -- using direct observation techniques, such as looking over someone's shoulder, to get passwords, PINs and other sensitive personal information -- is a problem that has been difficult to overcome. When a user enters information using a keyboard, mouse, touch screen or any traditional input device, a malicious observer may be able to acquire the user's password credentials. We present EyePassword, a system that mitigates the issues of shoulder surfing via a novel approach to user input.
 With EyePassword, a user enters sensitive input (password, PIN, etc.) by selecting from an on-screen keyboard using only the orientation of their pupils (i.e. the position of their gaze on screen), making eavesdropping by a malicious observer largely impractical. We present a number of design choices and discuss their effect on usability and security. We conducted user studies to evaluate the speed, accuracy and user acceptance of our approach. Our results demonstrate that gaze-based password entry requires marginal additional time over using a keyboard, error rates are similar to those of using a keyboard and subjects preferred the gaze-based password entry approach over traditional methods.
A Comprehensive Memory Modeling Tool and Its Application to the Design and Analysis of Future Memory Hierarchies

In this paper we introduce CACTI-D, a significant enhancement of CACTI 5.0. CACTI-D adds support for modeling of commodity DRAM technology and support for main memory DRAM chip organization. CACTI-D enables modeling of the complete memory hierarchy with consistent models all the way from SRAM based L1 caches through main memory DRAMs on DIMMs. We illustrate the potential applicability of CACTI-D in the design and analysis of future memory hierarchies by carrying out a last level cache study for a multicore multithreaded architecture at the 32nm technology node. In this study we use CACTI-D to model all components of the memory hierarchy including L1, L2, last level SRAM, logic process based DRAM or commodity DRAM L3 caches, and main memory DRAM chips. We carry out architectural simulation using benchmarks with large data sets and present results of their execution time, breakdown of power in the memory hierarchy, and system energy-delay product for the different system configurations. We find that commodity DRAM technology is most attractive for stacked last level caches, with significantly lower energy-delay products.
Acoustical Characterization of Gunshots

This paper addresses several practical and theoretical issues encountered in the analysis of gunshot audio recordings. Gunshot recordings have the potential for both tactical detection and forensic evaluation. Such recordings can provide information about speed and trajectory of the projectile, the estimated location of the shooter, and in some cases the type of firearm and ammunition used. However, audio recordings of gunshots typically contain background noise and reverberation due to the gunshot sound reflecting off and diffracting around nearby surfaces, and these effects may limit the reliability of the acoustic estimates. Recordings obtained under carefully controlled conditions are used to demonstrate several key features and limitations of acoustic gunshot analysis.
Optimal design of a wireless power transfer system with multiple self-resonators for an LED TV

This paper proposes an optimal design method in an asymmetric wireless power transfer (WPT) system for a 150 watt LED TV. The WPT system has three self-resonators: a Tx resonator, an Rx resonator, and an intermediate resonator. The Tx and Rx resonators are perpendicular and offset, respectively, to the intermediate resonator in the geometry. For optimal design, the WPT system is analyzed using an equivalent circuit. In particular, a calculation method for mutual inductance in the system is expressed. The calculation results of mutual inductance are used to determine the optimal position of each self-resonator for maximizing the power transfer efficiency. For verification, a WPT system for a 150 watt, 47 inch LED TV is fabricated at 250 kHz. The WPT system exhibits wireless power transfer efficiency of 80%.
Development of a Small Legged Wall Climbing Robot with Passive Suction Cups

In this paper, we develop a small hexapod robot with passive suction cups and realize vertical wall climbing. The passive adhesion mechanisms make the robotic system small and simple, since it requires neither energy nor an additional actuator to stay on the vertical wall. Above all, the passive suction cup is suited to vertical wall climbing because it is strongly attached to a wall with a smooth surface and is simply detached from it by pulling the edge of the cup. These characteristics are intensified according to the decreasing robotic scale. In this study, we developed a small wall climbing robot 12 cm long and 34 g in weight and driven by single degree-of-freedom linkage legs based on the scale effect. Here, the linkage mechanism optimized by a genetic algorithm provided the effective trajectory of the legs and the attachment-detachment cycle. As a result, the robot traveled on the ground at a velocity of 2.7 cm/s and climbed on the vertical wall at a velocity of 2.2 cm/s.
The Search for an Integrated KM Framework

The learning and knowledge that we have, is, at the most, but little compared with that of which we are ignorant. Plato Knowledge management (KM) is a vital and complex topic of current interest to so many in business, government and the community in general, that there is an urgent need to expand the role of empirical research to inform knowledge management practice. However, one of the most striking aspects of knowledge management is the diversity of the field and the lack of universally accepted definitions of the term itself and its derivatives, knowledge and management. As a consequence of the multidisciplinary nature of KM, the terms inevitably hold a difference in meaning and emphasis for different people. The initial chapter of this book addresses the challenges brought about by these differences. This chapter begins with a critical assessment of some diverse frameworks for knowledge management that have been appearing in the international academic literature of many disciplines for some time. Then follows a description of ways that these have led to some holistic and integrated frameworks currently being developed by KM researchers in Australia.
Let Me Answer That for You: Exploiting Broadcast Information in Cellular Networks

Mobile telecommunication has become an important part of our daily lives. Yet, industry standards such as GSM often exclude scenarios with active attackers. Devices participating in communication are seen as trusted and non-malicious. By implementing our own baseband firmware based on OsmocomBB, we violate this trust and are able to evaluate the impact of a rogue device with regard to the usage of broadcast information. Through our analysis we show two new attacks based on the paging procedure used in cellular networks. We demonstrate that for at least GSM, it is feasible to hijack the transmission of mobile terminated services such as calls, perform targeted denial of service attacks against single subscribers and as well against large geographical regions within a metropolitan area.
Exposure to violent video games increases automatic aggressiveness.

The effects of exposure to violent video games on automatic associations with the self were investigated in a sample of 121 students. Playing the violent video game Doom led participants to associate themselves with aggressive traits and actions on the Implicit Association Test. In addition, self-reported prior exposure to violent video games predicted automatic aggressive self-concept, above and beyond self-reported aggression. Results suggest that playing violent video games can lead to the automatic learning of aggressive self-views.
Initial Performance Evaluation of DFT-Spread OFDM Based SC-FDMA for UTRA LTE Uplink

In this paper we present an initial performance evaluation of the 3GPP UTRA long term evolution (UTRA LTE) uplink with baseline settings. The performance results are obtained from a detailed UTRA LTE uplink link level simulator supporting OFDMA and SC-FDMA schemes. The basic transmission scheme for uplink direction is based on single-carrier transmission in the form of DFT spread OFDM with an MMSE receiver. Two antenna configurations, SISO and 1times2 SIMO are considered in the analysis of spectral efficiency in addition to adaptive modulation and coding (AMC) and L1-HARQ. For assessment purposes, the performance results of SC-FDMA are compared with OFDMA. It is shown that 1times2 SIMO greatly increases the spectral efficiency of SC-FDMA making it comparable to OFDMA, especially for high coding rate. Furthermore, SC-FDMA has a flexibility to increase BLER performance by exploiting frequency diversity.