nils-herrmann/clean_scirepeval huggingface dataset:
FALCON: Feature Driven Selective Classification for Energy-Efficient Image Recognition

Machine-learning algorithms have shown outstanding image recognition/classification performance for computer vision applications. However, the compute and energy requirement for implementing such classifier models for large-scale problems is quite high. In this paper, we propose feature driven selective classification (FALCON) inspired by the biological visual attention mechanism in the brain to optimize the energy-efficiency of machine-learning classifiers. We use the consensus in the characteristic features (color/texture) across images in a dataset to decompose the original classification problem and construct a tree of classifiers (nodes) with a generic-to-specific transition in the classification hierarchy. The initial nodes of the tree separate the instances based on feature information and selectively enable the latter nodes to perform object specific classification. The proposed methodology allows selective activation of only those branches and nodes of the classification tree that are relevant to the input while keeping the remaining nodes idle. Additionally, we propose a programmable and scalable neuromorphic engine (NeuE) that utilizes arrays of specialized neural computational elements to execute the FALCON-based classifier models for diverse datasets. The structure of FALCON facilitates the reuse of nodes while scaling up from small classification problems to larger ones thus allowing us to construct classifier implementations that are significantly more efficient. We evaluate our approach for a 12-object classification task on the Caltech101 dataset and ten-object task on CIFAR-10 dataset by constructing FALCON models on the NeuE platform in 45-nm technology. Our results demonstrate up to  $3.66\boldsymbol \times $  improvement in energy-efficiency for no loss in output quality, and even higher improvements of up to  $5.91\boldsymbol \times $  with 3.9% accuracy loss compared to an optimized baseline network. In addition, FALCON shows an improvement in training time of up to  $1.96\boldsymbol \times $  as compared to the traditional classification approach.
Highly Automated Driving on Freeways in Real Traffic Using a Probabilistic Framework

A system, particularly a decision-making concept, that facilitates highly automated driving on freeways in real traffic is presented. The system is capable of conducting fully automated lane change (LC) maneuvers with no need for driver approval. Due to the application in real traffic, a robust functionality and the general safety of all traffic participants are among the main requirements. Regarding these requirements, the consideration of measurement uncertainties demonstrates a major challenge. For this reason, a fully integrated probabilistic concept is developed. By means of this approach, uncertainties are regarded in the entire process of determining driving maneuvers. While this also includes perception tasks, this contribution puts a focus on the driving strategy and the decision-making process for the execution of driving maneuvers. With this approach, the BMW Group Research and Technology managed to drive 100% automated in real traffic on the freeway A9 from Munich to Ingolstadt, showing a robust, comfortable, and safe driving behavior, even during multiple automated LC maneuvers.
A Study of Index Structures for Main Memory Database Management Systems

One approach to achieving high performance in a database management system is to store the database in main memorv rather than on disk. -One can then design new data structures aid algorithms oriented towards making eflicient use of CPU cycles and memory space rather than minimizing disk accesses and &ing disk space efliciently. In this paper we present some results on index structures from an ongoing study of main memory database management systems. We propose a new index structure, the T Tree, and we compare it to existing index structures in a main memory database environment. Our results indicate that the T Tree provides good overall performance in main memory.
The strength of random search on automated program repair

Automated program repair recently received considerable attentions, and many techniques on this research area have been proposed. Among them, two genetic-programming-based techniques, GenProg and Par, have shown the promising results. In particular, GenProg has been used as the baseline technique to check the repair effectiveness of new techniques in much literature. Although GenProg and Par have shown their strong ability of fixing real-life bugs in nontrivial programs, to what extent GenProg and Par can benefit from genetic programming, used by them to guide the patch search process, is still unknown. 
 To address the question, we present a new automated repair technique using random search, which is commonly considered much simpler than genetic programming, and implement a prototype tool called RSRepair. Experiment on 7 programs with 24 versions shipping with real-life bugs suggests that RSRepair, in most cases (23/24), outperforms GenProg in terms of both repair effectiveness (requiring fewer patch trials) and efficiency (requiring fewer test case executions), justifying the stronger strength of random search over genetic programming. According to experimental results, we suggest that every proposed technique using optimization algorithm should check its effectiveness by comparing it with random search.
Vortex: An Optimizing Compiler for Object-Oriented Languages

Previously, techniques such as class hierarchy analysis and profile-guided receiver class prediction have been demonstrated to greatly improve the performance of applications written in pure object-oriented languages, but the degree to which these results are transferable to applications written in hybrid languages has been unclear. In part to answer this question, we have developed the Vortex compiler infrastructure, a language-independent optimizing compiler for object-oriented languages, with front-ends for Cecil, C++, Java, and Modula-3. In this paper, we describe the Vortex compiler's intermediate language, internal structure, and optimization suite, and then we report the results of experiments assessing the effectiveness of different combinations of optimizations on sizable applications across these four languages. We characterize the benchmark programs in terms of a collection of static and dynamic metrics, intended to quantify aspects of the "object-orientedness" of a program.
The Precision-Recall Plot Is More Informative than the ROC Plot When Evaluating Binary Classifiers on Imbalanced Datasets

Binary classifiers are routinely evaluated with performance measures such as sensitivity and specificity, and performance is frequently illustrated with Receiver Operating Characteristics (ROC) plots. Alternative measures such as positive predictive value (PPV) and the associated Precision/Recall (PRC) plots are used less frequently. Many bioinformatics studies develop and evaluate classifiers that are to be applied to strongly imbalanced datasets in which the number of negatives outweighs the number of positives significantly. While ROC plots are visually appealing and provide an overview of a classifier's performance across a wide range of specificities, one can ask whether ROC plots could be misleading when applied in imbalanced classification scenarios. We show here that the visual interpretability of ROC plots in the context of imbalanced datasets can be deceptive with respect to conclusions about the reliability of classification performance, owing to an intuitive but wrong interpretation of specificity. PRC plots, on the other hand, can provide the viewer with an accurate prediction of future classification performance due to the fact that they evaluate the fraction of true positives among positive predictions. Our findings have potential implications for the interpretation of a large number of studies that use ROC plots on imbalanced datasets.
Overview of information flow tracking techniques based on taint analysis for Android

Smartphones today are ubiquitous source of sensitive information. Information leakage instances on the smartphones are on the rise because of exponential growth in smartphone market. Android is the most widely used operating system on smartphones. Many information flow tracking and information leakage detection techniques are developed on Android operating system. Taint analysis is commonly used data flow analysis technique which tracks the flow of sensitive information and its leakage. This paper provides an overview of existing Information flow tracking techniques based on the Taint analysis for android applications. It is observed that static analysis techniques look at the complete program code and all possible paths of execution before its run, whereas dynamic analysis looks at the instructions executed in the program-run in the real time. We provide in depth analysis of both static and dynamic taint analysis approaches.
Computational Light Routing: 3D Printed Optical Fibers for Sensing and Display

Despite recent interest in digital fabrication, there are still few algorithms that provide control over how light propagates inside a solid object. Existing methods either work only on the surface or restrict themselves to light diffusion in volumes. We use multi-material 3D printing to fabricate objects with embedded optical fibers, exploiting total internal reflection to guide light inside an object. We introduce automatic fiber design algorithms together with new manufacturing techniques to route light between two arbitrary surfaces. Our implicit algorithm optimizes light transmission by minimizing fiber curvature and maximizing fiber separation while respecting constraints such as fiber arrival angle. We also discuss the influence of different printable materials and fiber geometry on light propagation in the volume and the light angular distribution when exiting the fiber. Our methods enable new applications such as surface displays of arbitrary shape, touch-based painting of surfaces, and sensing a hemispherical light distribution in a single shot.
A Design Methodology for Miniaturized Power Dividers Using Periodically Loaded Slow Wave Structure With Dual-Band Applications

This paper proposes an analytically-based approach for the design of a miniaturized single-band and dual-band two-way Wilkinson power divider. This miniaturization is achieved by realizing the power divider's impedance transformers using slow wave structures. These slow wave structures are designed by periodically loading transmission lines with capacitances, which reduces the phase velocity of the propagating waves and hence engender higher electric lengths using smaller physical lengths. The dispersive analysis of the slow wave structure used is included in the design approach to ensure a smooth nondispersive transmission line operation in the case of dual-band applications. The design methodology is validated with the design of a single-band, reduced size, two-way Wilkinson power divider at 850 and 620 MHz. An approximate length reduction of 25%-35% is achieved with this technique. For dual-band applications, this paper describes the design of a reduced size, two-way Wilkinson power divider for dual-band global system for mobile communications and code division multiple access applications at 850 and 1960 MHz, respectively. An overall reduction factor of 28%, in terms of chip area occupied by the circuit, is achieved. The electromagnetic simulation and experimental results validate the design approach. The circuit is realized with microstrip technology, which can be easily fabricated using conventional printed circuit processes.
Time series prediction based on NARX neural networks: An advanced approach

The NARX network is a dynamical neural architecture commonly used for input-output modeling of nonlinear dynamical systems. When applied to time series prediction, the NARX network is designed as a feedforward time delay neural network (TDNN), i.e., without the feedback loop of delayed outputs, reducing substantially its predictive performance. In this paper, it is shown that the original architecture of the NARX network can be easily and efficiently applied to prediction of time series using embedding theory to reconstruct the input of NARX network. We evaluate the proposed approach using a real-world data set, which is the vibration data measured from a Co2 compressor. The results show that the proposed approach consistently outperforms standard neural network based predictors, such as the TDNN architecture.
Injectable fillers: review of material and properties.

With an increasing understanding of the aging process and the rapidly growing interest in minimally invasive treatments, injectable facial fillers have changed the perspective for the treatment and rejuvenation of the aging face. Other than autologous fat and certain preformed implants, the collagen family products were the only Food and Drug Administration approved soft tissue fillers. But the overwhelming interest in soft tissue fillers had led to the increase in research and development of other products including bioengineered nonpermanent implants and permanent alloplastic implants. As multiple injectable soft tissue fillers and biostimulators are continuously becoming available, it is important to understand the biophysical properties inherent in each, as these constitute the clinical characteristics of the product. This article will review the materials and properties of the currently available soft tissue fillers: hyaluronic acid, calcium hydroxylapatite, poly-l-lactic acid, polymethylmethacrylate, and autologous fat (and aspirated tissue including stem cells).
Study on the MFCC similarity-based voice activity detection algorithm

The accuracy of voice activity detection (VAD) is one of the most important factors which influence the capability of the speech recognition system, how to detect the endpoint precisely in noise environment is still a difficult task. In this paper, we proposed a new VAD method based on Mel-frequency cepstral coefficients (MFCC) similarity. We first extracts the MFCC of a voice signal for each frame, followed by calculating the MFCC Euclidean distance and MFCC correlation coefficient of the test frame and the background noise, Finally, give the experimental results. The results show that at low SNR circumstance, MFCC similarity detection method is better than traditional short-term energy method. Compared with Euclidean distance measure method, correlation coefficient is better.
Face liveness detection using variable focusing

As Face Recognition(FR) technology becomes more mature and commercially available in the market, many different anti-spoofing techniques have been recently developed to enhance the security, reliability, and effectiveness of FR systems. As a part of anti-spoofing techniques, face liveness detection plays an important role to make FR systems be more secured from various attacks. In this paper, we propose a novel method for face liveness detection by using focus, which is one of camera functions. In order to identify fake faces (e.g. 2D pictures), our approach utilizes the variation of pixel values by focusing between two images sequentially taken in different focuses. The experimental result shows that our focus-based approach is a new method that can significantly increase the level of difficulty of spoof attacks, which is a way to improve the security of FR systems. The performance is evaluated and the proposed method achieves 100% fake detection in a given DoF(Depth of Field).
Learning Subsequential Transducers for Pattern Recognition Interpretation Tasks

The “interpretation” framework in Pattern Recognition (PR) arises in the many cases in which the more classical paradigm of “classification” is not properly applicable, generally because the number of classes is rather large, or simply because the concept of “class” does not hold. A very general way of representing the results of Interpretations of given objects or data is in terms of sentences of a “Semantic Language” in which the actions to be performed for each different object or datum are described. Interpretation can therefore be conveniently formalized through the concept of Formal Transduction, giving rise to the central PR problem of how to automatically learn a transducer from a training set of examples of the desired input-output behavior. This paper presents a formalization of the stated transducer learning problem, as well as an effective and efficient method for the inductive learning of an important class of transducers, namely, the class of Subsequential Tranducers. The capabilities of subsequential transductions are illustrated through a series of experiments which also show the high effectiveness of the proposed learning method to obtain very accurate and compact transducers for the corresponding tasks.  Work partially supported by the Spanish CICYT under grant TIC-0448/89 ©  1993.
Environment-Independent Task Specifications via GLTL

We propose a new task-specification language for Markov decision processes that is designed to be an improvement over reward functions by being environment independent. The language is a variant of Linear Temporal Logic (LTL) that is extended to probabilistic specifications in a way that permits approximations to be learned in finite time. We provide several small environments that demonstrate the advantages of our geometric LTL (GLTL) language and illustrate how it can be used to specify standard reinforcementlearning tasks straightforwardly.
HEART score to further risk stratify patients with low TIMI scores.

The ability to risk stratify patients presenting to the emergency department (ED) with potential acute coronary syndrome (ACS) is critical. The thrombolysis in myocardial infarction (TIMI) risk score can risk stratify ED patients with potential ACS but cannot identify patients safe for ED discharge. The symptom-based HEART score identifies very low-risk patients. Our hypothesis was that patients with a TIMI score of 0 or 1 may be stratified further with the HEART score to identify a group of patients at less than 1% risk of 30-day cardiovascular events.



We conducted a secondary analysis of a prospective cohort study in a tertiary care hospital ED. Patients with potential ACS who were >30 years of age were included. Data collected included demographics, history, electrocardiogram, laboratories, and components of the TIMI and HEART scores. Follow-up was conducted by structured record review and phone. The main outcome was a composite of death, acute myocardial infarction, or revascularization at 30 days.



There were 8815 patients enrolled (mean age, 52.8 ± 15.1 years; 57% women, and 69% black). At 30 days, the composite event rate was 8.0% (660 patients): 108 deaths, 410 acute myocardial infarction, and 301 revascularizations. Of the 485 patients with both a TIMI score of 0 and a HEART score of 0, there were no cardiovascular events (95% confidence interval, 0-0.8%); but no other score combination had an upper limit confidence interval less than 1%.



At all levels of TIMI score, the HEART score was able to further substratify patients with respect to 30-day risk. A HEART score of 0 in a patient with a TIMI of 0 identified a group of patients at less than 1% risk for 30-day adverse events.
Cloud computing services: taxonomy and comparison

Cloud computing is a highly discussed topic in the technical and economic world, and many of the big players of the software industry have entered the development of cloud services. Several companies what to explore the possibilities and benefits of incorporating such cloud computing services in their business, as well as the possibilities to offer own cloud services. However, with the amount of cloud computing services increasing quickly, the need for a taxonomy framework rises. This paper examines the available cloud computing services and identifies and explains their main characteristics. Next, this paper organizes these characteristics and proposes a tree-structured taxonomy. This taxonomy allows quick classifications of the different cloud computing services and makes it easier to compare them. Based on existing taxonomies, this taxonomy provides more detailed characteristics and hierarchies. Additionally, the taxonomy offers a common terminology and baseline information for easy communication. Finally, the taxonomy is explained and verified using existing cloud services as examples.
Small-Size Octaband Monopole Antenna in an LTE/WWAN Mobile Phone

This letter proposes a small-size printed antenna with multiband WWAN/LTE operation in a mobile phone by introducing a novel loop parasitic shorted strip and a C-shaped ground plane. The obtained impedance bandwidths across LTE and WWAN operating bands approach 277 and 1176 MHz, respectively. The proposed printed monopole antenna reduces the antenna size by at least 22% since the overall antenna size is only 35 × 10 × 0.8 mm3. The measured peak gains and antenna efficiencies are approximately 2.2/3.1 dBi and 76%/82% for the LTE/WWAN bands, respectively.
Gaming in the clouds: QoE and the users' perspective

Cloud Gaming is a new kind of service, which combines the successful concepts of Cloud Computing and Online Gaming. It provides the entire game experience to the users remotely from a data center. The player is no longer dependent on a specific type or quality of gaming hardware, but is able to use common devices. The end device only needs a broadband internet connection and the ability to display High Definition (HD) video. While this may reduce hardware costs for users and increase the revenue for developers by leaving out the retail chain, it also raises new challenges for service quality in terms of bandwidth and latency for the underlying network. In this paper we present the results of a subjective user study we conducted into the user-perceived quality of experience (QoE) in Cloud Gaming. We design a measurement environment, that emulates this new type of service, define tests for users to assess the QoE, derive Key Influence Factors (KIF) and influences of content and perception from our results.
Control architecture for a tail-sitter unmanned air vehicle

This paper details the control and guidance architecture for the T-wing tail-sitter unmanned air vehicle, (UAV). The T-wing is a vertical take off and landing (VTOL) UAV that is capable of both wing-born horizontal flight and propeller born vertical mode flight including hover and descent. During low-speed vertical flight the T-wing uses propeller wash over its aerodynamic surfaces to effect control. At the lowest level, the vehicle uses a mixture of classical and LQR controllers for angular rate and translational velocity control. These low-level controllers are directed by a series of proportional guidance controllers for the vertical, horizontal and transition flight modes that allow the vehicle to achieve autonomous waypoint navigation. The control design for the T-wing is complicated by the large differences in vehicle dynamics between vertical and horizontal flight; the difficulty of accurately predicting the low-speed vehicle aerodynamics; and the basic instability of the vertical flight mode. This paper considers the control design problem for the T-wing in light of these factors. In particular it focuses on the integration of all the different types and levels of controllers into a full flight-vehicle control system.