nils-herrmann/clean_scirepeval huggingface dataset:
Design Decisions: The Bridge between Rationale and Architecture

Software architecture can be seen as a decision making process; it involves making the right decisions at the right time. Typically, these design decisions are not explicitly represented in the artifacts describing the design. They reside in the minds of the designers and are therefore easily lost. Rationale management is often proposed as a solution, but lacks a close relationship with software architecture artifacts. Explicit modeling of design decisions in the software architecture bridges this gap, as it allows for a close integration of rationale management with software architecture. This improves the understandability of the software architecture. Consequently, the software architecture becomes easier to communicate, maintain and evolve. Furthermore, it allows for analysis, improvement, and reuse of design decisions in the design process.
Data loss and reconstruction in sensor networks

Reconstructing the environment in cyber space by sensory data is a fundamental operation for understanding the physical world in depth. A lot of basic scientific work (e.g., nature discovery, organic evolution) heavily relies on the accuracy of environment reconstruction. However, data loss in wireless sensor networks is common and has its special patterns due to noise, collision, unreliable link, and unexpected damage, which greatly reduces the accuracy of reconstruction. Existing interpolation methods do not consider these patterns and thus fail to provide a satisfactory accuracy when missing data become large. To address this problem, this paper proposes a novel approach based on compressive sensing to reconstruct the massive missing data. Firstly, we analyze the real sensory data from Intel Indoor, GreenOrbs, and Ocean Sense projects. They all exhibit the features of spatial correlation, temporal stability and low-rank structure. Motivated by these observations, we then develop an environmental space time improved compressive sensing (ESTICS) algorithm to optimize the missing data estimation. Finally, the extensive experiments with real-world sensory data shows that the proposed approach significantly outperforms existing solutions in terms of reconstruction accuracy. Typically, ESTICS can successfully reconstruct the environment with less than 20% error in face of 90% missing data.
A first look at cellular network performance during crowded events

During crowded events, cellular networks face voice and data traffic volumes that are often orders of magnitude higher than what they face during routine days. Despite the use of portable base stations for temporarily increasing communication capacity and free Wi-Fi access points for offloading Internet traffic from cellular base stations, crowded events still present significant challenges for cellular network operators looking to reduce dropped call events and improve Internet speeds. For effective cellular network design, management, and optimization, it is crucial to understand how cellular network performance degrades during crowded events, what causes this degradation, and how practical mitigation schemes would perform in real-life crowded events. This paper makes a first step towards this end by characterizing the operational performance of a tier-1 cellular network in the United States during two high-profile crowded events in 2012. We illustrate how the changes in population distribution, user behavior, and application workload during crowded events result in significant voice and data performance degradation, including more than two orders of magnitude increase in connection failures. Our findings suggest two mechanisms that can improve performance without resorting to costly infrastructure changes: radio resource allocation tuning and opportunistic connection sharing. Using trace-driven simulations, we show that more aggressive release of radio resources via 1-2 seconds shorter RRC timeouts as compared to routine days helps to achieve better tradeoff between wasted radio resources, energy consumption, and delay during crowded events; and opportunistic connection sharing can reduce connection failures by 95% when employed by a small number of devices in each cell sector.
Flexible unit A-star trajectory planning for autonomous vehicles on structured road maps

In this paper we propose a trajectory planning approach for autonomous vehicles on structured road maps. Therefore we are using the well-known A∗ optimal path planning algorithm. We generate a safe optimal trajectory through a three-dimensional graph, considering the two-dimensional position and time. (1) The graph is generated dynamically with fixed time differences and flexible distances between nodes, based on the vehicle's velocity, using a structured road map. (2) Furthermore the position of dynamic obstacles is predicted over time along the road lanes. The proposed Flexible Unit A∗  algorithm was tested for real-time applications with execution times of less than 50 ms on the car's main computer. The feasibility and reliability of FU-A∗ is validated by implementing on simulated autonomous car of Freie university “MadeInGermany” using the roadmap of Tempelhof, Berlin.
The Dundee Treebank

We introduce the Dundee Treebank, a Universal Dependencies-style syntactic annotation layer on top of the English side of the Dundee Corpus. As the Dundee Corpus is an important resource for conducting large-scale psycholinguistic research, we aim at facilitating further research in the field by replacing automatic parses with manually assigned syntax. We report on constructing the treebank, performing parsing experiments, as well as replicating a broad-scale psycholinguistic study—now for the first time using manually assigned syntactic dependencies.
Efficient ConvNet for real-time semantic segmentation

Semantic segmentation is a task that covers most of the perception needs of intelligent vehicles in an unified way. ConvNets excel at this task, as they can be trained end-to-end to accurately classify multiple object categories in an image at the pixel level. However, current approaches normally involve complex architectures that are expensive in terms of computational resources and are not feasible for ITS applications. In this paper, we propose a deep architecture that is able to run in real-time while providing accurate semantic segmentation. The core of our ConvNet is a novel layer that uses residual connections and factorized convolutions in order to remain highly efficient while still retaining remarkable performance. Our network is able to run at 83 FPS in a single Titan X, and at more than 7 FPS in a Jetson TX1 (embedded GPU). A comprehensive set of experiments demonstrates that our system, trained from scratch on the challenging Cityscapes dataset, achieves a classification performance that is among the state of the art, while being orders of magnitude faster to compute than other architectures that achieve top precision. This makes our model an ideal approach for scene understanding in intelligent vehicles applications.
SVM-Based Classification of Segmented Airborne LiDAR Point Clouds in Urban Areas

Object-based point cloud analysis (OBPA) is useful for information extraction from airborne LiDAR point clouds. An object-based classification method is proposed for classifying the airborne LiDAR point clouds in urban areas herein. In the process of classification, the surface growing algorithm is employed to make clustering of the point clouds without outliers, thirteen features of the geometry, radiometry, topology and echo characteristics are calculated, a support vector machine (SVM) is utilized to classify the segments, and connected component analysis for 3D point clouds is proposed to optimize the original classification results. Three datasets with different point densities and complexities are employed to test our method. Experiments suggest that the proposed method is capable of making a classification of the urban point clouds with the overall classification accuracy larger than 92.34% and the Kappa coefficient larger than 0.8638, and the classification accuracy is promoted with the increasing of the point density, which is meaningful for various types of applications. Keyword: airborne LiDAR; object-based classification; point clouds; segmentation; SVM OPEN ACCESS Remote Sens. 2013, 5 3750
99.3% Efficiency of three-phase inverter for motor drive using GaN-based Gate Injection Transistors

In this paper, we present a successful operation of Gallium Nitride(GaN)-based three-phase inverter with high efficiency of 99.3% for driving motor at 900W under the carrier frequency of 6kHz. This efficiency well exceeds the value by IGBT (Insulated Gate Bipolar Transistor). This demonstrates that GaN has a great potential for power switching application competing with SiC. Fully reduced on-state resistance in a new normally-off GaN transistor called Gate Injection Transistor (GIT) greatly helps to increase the efficiency. In addition, use of the bidirectional operation of the lateral and compact GITs with synchronous gate driving, the inverter is operated free from fly-wheel diodes which have been connected in parallel with IGBTs in a conventional inverter system.
MillWheel: Fault-Tolerant Stream Processing at Internet Scale

MillWheel is a framework for building low-latency data-processing applications that is widely used at Google. Users specify a directed computation graph and application code for individual nodes, and the system manages persistent state and the continuous flow of records, all within the envelope of the framework’s fault-tolerance guarantees. This paper describes MillWheel’s programming model as well as its implementation. The case study of a continuous anomaly detector in use at Google serves to motivate how many of MillWheel’s features are used. MillWheel’s programming model provides a notion of logical time, making it simple to write time-based aggregations. MillWheel was designed from the outset with fault tolerance and scalability in mind. In practice, we find that MillWheel’s unique combination of scalability, fault tolerance, and a versatile programming model lends itself to a wide variety of problems at Google.
DISTRIBUTED COOPERATIVE CONTROL OFMULTIPLE VEHICLE FORMATIONS USINGSTRUCTURAL POTENTIAL FUNCTIONS

In this paper, we propose a framework for formation stabilization of multiple autonomous vehicles in a distributed fashion. Each vehicle is assumed to have simple dynamics, i.e. a double-integrator, with a directed (or an undirected) information flow over the formation graph of the vehicles. Our goal is to find a distributed control law (with an efficient computational cost) for each vehicle that makes use of limited information regarding the state of other vehicles. Here, the key idea in formation stabilization is the use of natural potential functions obtained from structural constraints of a desired formation in a way that leads to a collision-free, distributed, and bounded state feedback law for each vehicle.
Advertisement Detection and Replacement using Acoustic and Visual Repetition

In this paper, we propose a method for detecting and precisely segmenting repeated sections of broadcast streams. This method allows advertisements to be removed and replaced with new ads in redistributed television material. The detection stage starts from acoustic matches and validates the hypothesized matches using the visual channel. Finally, the precise segmentation uses fine-grain acoustic match profiles to determine start and end-points. The approach is both efficient and robust to broadcast noise and differences in broadcaster signals. Our final result is nearly perfect, with better than 99% precision, at a recall rate of 95% for repeated advertisements
A 3.8mW 8b 1GS/s 2b/cycle interleaving SAR ADC with compact DAC structure

An 8b 1GS/s ADC is presented that interleaves two 2b/cycle SARs. To enhance speed and save power, the prototype utilizes segmentation switching and custom-designed DAC array with high density in a low parasitic layout structure. It operates at 1GS/s from 1V supply without interleaving calibration and consumes 3.8mW of power, exhibiting a FoM of 24fJ/conversion step. The ADC occupies an active area of 0.013mm2 in 65nm CMOS including on-chip offset calibration.
Playing games for security: an efficient exact algorithm for solving Bayesian Stackelberg games

In a class of games known as Stackelberg games, one agent (the leader) must commit to a strategy that can be observed by the other agent (the follower or adversary) before the adversary chooses its own strategy. We consider Bayesian Stackelberg games, in which the leader is uncertain about the types of adversary it may face. Such games are important in security domains, where, for example, a security agent (leader) must commit to a strategy of patrolling certain areas, and a robber (follower) has a chance to observe this strategy over time before choosing its own strategy of where to attack. This paper presents an efficient exact algorithm for finding the optimal strategy for the leader to commit to in these games. This algorithm, DOBSS, is based on a novel and compact mixed-integer linear programming formulation. Compared to the most efficient algorithm known previously for this problem, DOBSS is not only faster, but also leads to higher quality solutions, and does not suffer from problems of infeasibility that were faced by this previous algorithm. Note that DOBSS is at the heart of the ARMOR system that is currently being tested for security scheduling at the Los Angeles International Airport.
Prediction of Human Activity by Discovering Temporal Sequence Patterns

Early prediction of ongoing human activity has become more valuable in a large variety of time-critical applications. To build an effective representation for prediction, human activities can be characterized by a complex temporal composition of constituent simple actions and interacting objects. Different from early detection on short-duration simple actions, we propose a novel framework for long -duration complex activity prediction by discovering three key aspects of activity: Causality, Context-cue, and Predictability. The major contributions of our work include: (1) a general framework is proposed to systematically address the problem of complex activity prediction by mining temporal sequence patterns; (2) probabilistic suffix tree (PST) is introduced to model causal relationships between constituent actions, where both large and small order Markov dependencies between action units are captured; (3) the context-cue, especially interactive objects information, is modeled through sequential pattern mining (SPM), where a series of action and object co-occurrence are encoded as a complex symbolic sequence; (4) we also present a predictive accumulative function (PAF) to depict the predictability of each kind of activity. The effectiveness of our approach is evaluated on two experimental scenarios with two data sets for each: action-only prediction and context-aware prediction. Our method achieves superior performance for predicting global activity classes and local action units.
Predictive control of a single-phase cascaded h-bridge photovoltaic energy conversion system

Grid-connected photovoltaic energy conversion systems are among the fastest growing energy systems of the last five years. Multilevel converters, and particularly the Cascaded H-Bridge (CHB), have attracted much attention for this application due to medium-voltage operation, improved power quality and higher efficiency. The CHB enables the connection of individual PV strings to the dc side of each power cell with independent maximum power point tracking (MPPT). This advantage is in turn a great challenge from a control point of view, since each cell will provide different instantaneous active power, for which modified modulation and control schemes are necessary to avoid voltage drift of the dc-link capacitors. This paper explores a model predictive control method, in which predictions for each possible switching state are computed and evaluated in a cost function, in order to select the appropriate control action. The proposed control scheme is capable of controlling the dc-link voltages to the desired MPPT reference voltage, while injecting sinusoidal current to the grid. Preliminary validation through simulation results are included for a five level CHB interfaced PV system.
Perioral aging--an anthropometric appraisal.

To adequately perform perioral rejuvenation procedures, it is necessary to understand the morphologic changes caused by facial aging. Anthropometric analyses of standardized frontal view and profile photographs could help to investigate such changes. Photographs of 346 male individuals were evaluated using 12 anthropometric indices. Data from two groups of health subjects, the first exhibiting a mean age of nearly 20 and the second of nearly 60 years, were compared. To evaluate the influence of combined nicotine and alcohol abuse, the data of the second group were compared to a third group exhibiting a similar mean age who were known alcohol and nicotine abusers. Comparison of the first to the second group showed significant decrease of the vertical height of upper and lower vermilion and relative enlargement of the cutaneous part of upper and lower lips. This effect was stronger in the upper vermilion and medial upper lips. The sagging of the upper lips led to the appearance of an increased mouth width. In the third group the effect of sagging of the upper lips, and especially its medial portion was significantly higher compared to the second group. The photo-assisted anthropometric measurements investigated gave reproducible results related to perioral aging.
Epileptic seizure prediction and control

Epileptic seizures are manifestations of epilepsy, a serious brain dynamical disorder second only to strokes. Of the world's /spl sim/50 million people with epilepsy, fully 1/3 have seizures that are not controlled by anti-convulsant medication. The field of seizure prediction, in which engineering technologies are used to decode brain signals and search for precursors of impending epileptic seizures, holds great promise to elucidate the dynamical mechanisms underlying the disorder, as well as to enable implantable devices to intervene in time to treat epilepsy. There is currently an explosion of interest in this field in academic centers and medical industry with clinical trials underway to test potential prediction and intervention methodology and devices for Food and Drug Administration (FDA) approval. This invited paper presents an overview of the application of signal processing methodologies based upon the theory of nonlinear dynamics to the problem of seizure prediction. Broader application of these developments to a variety of systems requiring monitoring, forecasting and control is a natural outgrowth of this field.
Exploiting spatial sparsity for estimating channels of hybrid MIMO systems in millimeter wave communications

Hybrid multiple input multiple output (MIMO) systems consist of an analog beamformer with large antenna arrays followed by a digital MIMO processor. Channel estimation for hybrid MIMO systems in millimeter wave (mm-wave) communications is challenging because of the large antenna array and the low signal-to-noise ratio (SNR) before beamforming. In this paper, we propose an open-loop channel estimator for mm-wave hybrid MIMO systems exploiting the sparse nature of mm-wave channels. A sparse signal recovery problem is formulated for channel estimation and solved by the orthogonal matching pursuit (OMP) based methods. A modification of the OMP algorithm, called the multi-grid (MG) OMP, is proposed. It is shown that the MG-OMP can significantly reduce the computational load of the OMP method. A process for designing the training beams is also developed. Specifically, given the analog training beams the baseband processor for beam training is designed. Simulation results demonstrate the advantage of the OMP based methods over the conventional least squares (LS) method and the efficiency of the MG-OMP over the original OMP.
The JAK-STAT pathway: impact on human disease and therapeutic intervention.

The Janus kinase (JAK)-signal transducer of activators of transcription (STAT) pathway is now recognized as an evolutionarily conserved signaling pathway employed by diverse cytokines, interferons, growth factors, and related molecules. This pathway provides an elegant and remarkably straightforward mechanism whereby extracellular factors control gene expression. It thus serves as a fundamental paradigm for how cells sense environmental cues and interpret these signals to regulate cell growth and differentiation. Genetic mutations and polymorphisms are functionally relevant to a variety of human diseases, especially cancer and immune-related conditions. The clinical relevance of the pathway has been confirmed by the emergence of a new class of therapeutics that targets JAKs.
Robotic calligraphy — Learning how to write single strokes of Chinese and Japanese characters

A robot testbed for writing Chinese and Japanese calligraphy characters is presented. Single strokes of the calligraphy characters are represented in a database and initialized with a scanned reference image and a manually chosen initial drawing spline. A learning procedure uses visual feedback to analyze each new iteration of the drawn stroke and updates the drawing spline such that every subsequent drawn stroke becomes more similar to the reference image. The learning procedure can be performed either in simulation, using a simple brush model to create simulated images of the strokes, or with a real robot arm equipped with a calligraphy brush and a camera that captures images of the drawn strokes. Results from both simulations and experiments with the robot arm are presented.
Sentence Embedding for Neural Machine Translation Domain Adaptation

Although new corpora are becoming increasingly available for machine translation, only those that belong to the same or similar domains are typically able to improve translation performance. Recently Neural Machine Translation (NMT) has become prominent in the field. However, most of the existing domain adaptation methods only focus on phrase-based machine translation. In this paper, we exploit the NMT’s internal embedding of the source sentence and use the sentence embedding similarity to select the sentences which are close to in-domain data. The empirical adaptation results on the IWSLT English-French and NIST Chinese-English tasks show that the proposed methods can substantially improve NMT performance by 2.4-9.0 BLEU points, outperforming the existing state-of-the-art baseline by 2.3-4.5 BLEU points.
DV Based Positioning in Ad Hoc Networks

Many ad hoc network protocols and applications assume the knowledge of geographic location of nodes. The absolute position of each networked node is an assumed fact by most sensor networks which can then present the sensed information on a geographical map. Finding position without the aid of GPS in each node of an ad hoc network is important in cases where GPS is either not accessible, or not practical to use due to power, form factor or line of sight conditions. Position would also enable routing in sufficiently isotropic large networks, without the use of large routing tables. We are proposing APS – a localized, distributed, hop by hop positioning algorithm, that works as an extension of both distance vector routing and GPS positioning in order to provide approximate position for all nodes in a network where only a limited fraction of nodes have self positioning capability.