nils-herrmann/clean_scirepeval huggingface dataset:
The “Physics” of Notations: Toward a Scientific Basis for Constructing Visual Notations in Software Engineering

Visual notations form an integral part of the language of software engineering (SE). Yet historically, SE researchers and notation designers have ignored or undervalued issues of visual representation. In evaluating and comparing notations, details of visual syntax are rarely discussed. In designing notations, the majority of effort is spent on semantics, with graphical conventions largely an afterthought. Typically, no design rationale, scientific or otherwise, is provided for visual representation choices. While SE has developed mature methods for evaluating and designing semantics, it lacks equivalent methods for visual syntax. This paper defines a set of principles for designing cognitively effective visual notations: ones that are optimized for human communication and problem solving. Together these form a design theory, called the Physics of Notations as it focuses on the physical (perceptual) properties of notations rather than their logical (semantic) properties. The principles were synthesized from theory and empirical evidence from a wide range of fields and rest on an explicit theory of how visual notations communicate. They can be used to evaluate, compare, and improve existing visual notations as well as to construct new ones. The paper identifies serious design flaws in some of the leading SE notations, together with practical suggestions for improving them. It also showcases some examples of visual notation design excellence from SE and other fields.
A 12 b 75 MS/s pipelined ADC using open-loop residue amplification

The multi-bit first stage of a 12 b 75 MS/s pipelined ADC uses an open-loop gain stage to achieve more than 60% residue amplifier power savings over a conventional implementation. Statistical background calibration removes linear and nonlinear residue errors in the digital domain. The prototype IC achieves 68.2 dB SNR, -76 dB THD, occupies 7.9 mm/sup 2/ in 0.35 /spl mu/m CMOS and consumes 290 mW at 3 V.
Electrical signs of selective attention in the human brain.

Auditory evoked potentials were recorded from the vertex of subjects who listened selectively to a series of tone pips in one ear and ignored concurrent tone pips in the other ear. The negative component of the evoked potential peaking at 80 to 110 milliseconds was substantially larger for the attended tones. This negative component indexed a stimulus set mode of selective attention toward the tone pips in one ear. A late positive component peaking at 250 to 400 milliseconds reflected the response set established to recognize infrequent, higher pitched tone pips in the attended series.
Spoofing speech detection using temporal convolutional neural network

Spoofing speech detection aims to differentiate spoofing speech from natural speech. Frame-based features are usually used in most of previous works. Although multiple frames or dynamic features are used to form a super-vector to represent the temporal information, the time span covered by these features are not sufficient. Most of the systems failed to detect the non-vocoder or unit selection based spoofing attacks. In this work, we propose to use a temporal convolutional neural network (CNN) based classifier for spoofing speech detection. The temporal CNN first convolves the feature trajectories with a set of filters, then extract the maximum responses of these filters within a time window using a max-pooling layer. Due to the use of max-pooling, we can extract useful information from a long temporal span without concatenating a large number of neighbouring frames, as in feedforward deep neural network (DNN). Five types of feature are employed to access the performance of proposed classifier. Experimental results on ASVspoof 2015 corpus show that the temporal CNN based classifier is effective for synthetic speech detection. Specifically, the proposed method brings a significant performance boost for the unit selection based spoofing speech detection.
Efficient partial shape matching using Smith-Waterman algorithm

This paper presents an efficient partial shape matching method based on the Smith-Waterman algorithm. For two contours of m and n points respectively, the complexity of our method to find similar parts is only O(mn). In addition to this improvement in efficiency, we also obtain comparable accurate matching with fewer shape descriptors. Also, in contrast to arbitrary distance functions that are used by previous methods, we use a probabilistic similarity measurement, p-value, to evaluate the similarity of two shapes. Our experiments on several public shape databases indicate that our method outperforms state-of-the-art global and partial shape matching algorithms in various scenarios.
A Lyapunov Optimization Approach for Green Cellular Networks With Hybrid Energy Supplies

Powering cellular networks with renewable energy sources via energy harvesting (EH) have recently been proposed as a promising solution for green networking. However, with intermittent and random energy arrivals, it is challenging to provide satisfactory quality of service (QoS) in EH networks. To enjoy the greenness brought by EH while overcoming the instability of the renewable energy sources, hybrid energy supply (HES) networks that are powered by both EH and the electric grid have emerged as a new paradigm for green communications. In this paper, we will propose new design methodologies for HES green cellular networks with the help of Lyapunov optimization techniques. The network service cost, which addresses both the grid energy consumption and achievable QoS, is adopted as the performance metric, and it is optimized via base station assignment and power control (BAPC). Our main contribution is a low-complexity online algorithm to minimize the long-term average network service cost, namely, the Lyapunov optimization-based BAPC (LBAPC) algorithm. One main advantage of this algorithm is that the decisions depend only on the instantaneous side information without requiring distribution information of channels and EH processes. To determine the network operation, we only need to solve a deterministic per-time slot problem, for which an efficient inner-outer optimization algorithm is proposed. Moreover, the proposed algorithm is shown to be asymptotically optimal via rigorous analysis. Finally, sample simulation results are presented to verify the theoretical analysis as well as validate the effectiveness of the proposed algorithm.
ECvisual: a visualization tool for elliptic curve based ciphers

This paper describes a visualization tool ECvisual that helps students understand and instructors teach elliptic curve based ciphers. This tool permits the user to visualize elliptic curves over the real field and over a finite field of prime order, perform arithmetic operations, do encryption and decryption, and convert plaintext to a point on an elliptic curve. The demo mode of ECvisual can be used for classroom presentation and self-study. With the practice mode, the user may go through steps in finite field computations, encryption, decryption and plaintext conversion. The user may compute the output for each operation check each answer for correctness. This helps students understand the primitive operations and how they are used in an elliptic curve cipher. The opportunity for self-study provides an instructor greater flexibility in selecting a lecture pace for this detail-filled material. Classroom evaluation was positive and very encouraging.
Observer-Based Piecewise Affine Output Feedback Controller Synthesis of Continuous-Time T–S Fuzzy Affine Dynamic Systems Using Quantized Measurements

This paper is concerned with the problem of robust H∞ output feedback control for a class of continuous-time Takagi-Sugeno (T-S) fuzzy affine dynamic systems using quantized measurements. The objective is to design a suitable observer-based dynamic output feedback controller that guarantees the global stability of the resulting closed-loop fuzzy system with a prescribed H∞ disturbance attenuation level. Based on common/piecewise quadratic Lyapunov functions combined with S-procedure and some matrix inequality convexification techniques, some new results are developed to the controller synthesis for the underlying continuous-time T-S fuzzy affine systems with unmeasurable premise variables. All the solutions to the problem are formulated in the form of linear matrix inequalities (LMIs). Finally, two simulation examples are provided to illustrate the advantages of the proposed approaches.
Document clustering by concept factorization

In this paper, we propose a new data clustering method called concept factorization that models each concept as a linear combination of the data points, and each data point as a linear combination of the concepts. With this model, the data clustering task is accomplished by computing the two sets of linear coefficients, and this linear coefficients computation is carried out by finding the non-negative solution that minimizes the reconstruction error of the data points. The cluster label of each data point can be easily derived from the obtained linear coefficients. This method differs from the method of clustering based on non-negative matrix factorization (NMF) \citeXu03 in that it can be applied to data containing negative values and the method can be implemented in the kernel space. Our experimental results show that the proposed data clustering method and its variations performs best among 11 algorithms and their variations that we have evaluated on both TDT2 and Reuters-21578 corpus. In addition to its good performance, the new method also has the merit in its easy and reliable derivation of the clustering results.
Can DNS-Based Blacklists Keep Up with Bots?

Many Internet Service Providers (ISPs), anti-virus companies, and enterprise email vendors use Domain Name System-based Blackhole Lists (DNSBLs) to keep track of IP addresses that originate spam, so that future emails sent from these IP addresses can be rejected out-of-hand. DNSBL operators populate blocking lists based on complaints from recipients of spam, who report the IP address of the relay from which the unwanted email was sent. To be effective in blocking spam, information in the blacklist must have the following properties:
Toward Covert Iris Biometric Recognition: Experimental Results From the NICE Contests

This paper announces and discusses the experimental results from the Noisy Iris Challenge Evaluation (NICE), an iris biometric evaluation initiative that received worldwide participation and whose main innovation is the use of heavily degraded data acquired in the visible wavelength and uncontrolled setups, with subjects moving and at widely varying distances. The NICE contest included two separate phases: 1) the NICE.I evaluated iris segmentation and noise detection techniques and 2) the NICE:II evaluated encoding and matching strategies for biometric signatures. Further, we give the performance values observed when fusing recognition methods at the score level, which was observed to outperform any isolated recognition strategy. These results provide an objective estimate of the potential of such recognition systems and should be regarded as reference values for further improvements of this technology, which-if successful-may significantly broaden the applicability of iris biometric systems to domains where the subjects cannot be expected to cooperate.
SHREC ’ 16 : Partial Matching of Deformable Shapes

Matching deformable 3D shapes under partiality transformations is a challenging problem that has received limited focus in the computer vision and graphics communities. With this benchmark, we explore and thoroughly investigate the robustness of existing matching methods in this challenging task. Participants are asked to provide a point-to-point correspondence (either sparse or dense) between deformable shapes undergoing different kinds of partiality transformations, resulting in a total of 400 matching problems to be solved for each method – making this benchmark the biggest and most challenging of its kind. Five matching algorithms were evaluated in the contest; this paper presents the details of the dataset, the adopted evaluation measures, and shows thorough comparisons among all competing methods.
Do you Need a Blockchain?

Blockchain is being praised as a technological innovation which allows to revolutionize how society trades and interacts. This reputation is in particular attributable to its properties of allowing mutually mistrusting entities to exchange financial value and interact without relying on a trusted third party. A blockchain moreover provides an integrity protected data storage and allows to provide process transparency. In this paper we critically analyze whether a blockchain is indeed the appropriate technical solution for a particular application scenario. We differentiate between permissionless (e.g., Bitcoin/Ethereum) and permissioned (e.g. Hyperledger/Corda) blockchains and contrast their properties to those of a centrally managed database. We provide a structured methodology to determine the appropriate technical solution to solve a particular application problem. Given our methodology, we analyze in depth three use cases - Supply Chain Management, Interbank and International Payments, and Decentralized Autonomous Organizations and conclude the article with an outlook for further opportunities.
A robust compliant grasper via shape deposition manufacturing

Joint compliance can enable successful robot grasping despite uncertainties in target object location. Compliance also enhances manipulator robustness by minimizing contact forces in the event of unintended contacts or impacts. In this paper, we describe the design, fabrication, and evaluation of a novel compliant robotic grasper constructed using polymer-based shape deposition manufacturing. Joints are formed by elastomeric flexures, and actuator and sensor components are embedded in tough rigid polymers. The result is a robot gripper with the functionality of conventional metal prototypes for grasping in unstructured environments but with robustness properties that allow for large forces due to inadvertent contact.
Matrix Completion for Weakly-Supervised Multi-Label Image Classification

In the last few years, image classification has become an incredibly active research topic, with widespread applications. Most methods for visual recognition are fully supervised, as they make use of bounding boxes or pixelwise segmentations to locate objects of interest. However, this type of manual labeling is time consuming, error prone and it has been shown that manual segmentations are not necessarily the optimal spatial enclosure for object classifiers. This paper proposes a weakly-supervised system for multi-label image classification. In this setting, training images are annotated with a set of keywords describing their contents, but the visual concepts are not explicitly segmented in the images. We formulate the weakly-supervised image classification as a low-rank matrix completion problem. Compared to previous work, our proposed framework has three advantages: (1) Unlike existing solutions based on multiple-instance learning methods, our model is convex. We propose two alternative algorithms for matrix completion specifically tailored to visual data, and prove their convergence. (2) Unlike existing discriminative methods, our algorithm is robust to labeling errors, background noise and partial occlusions. (3) Our method can potentially be used for semantic segmentation. Experimental validation on several data sets shows that our method outperforms state-of-the-art classification algorithms, while effectively capturing each class appearance.
ARA: a robust audit to prevent free-riding in P2P networks

A number of solutions have been proposed to address the free-riding problem in peer-to-peer file sharing systems. The solutions are either imperfect-they allow some users to cheat the system with malicious behavior, or expensive-they require human intervention, require servers, or incur high mental transaction costs. The authors proposed a method to address these weaknesses. Specifically, a utility function was introduced to capture contributions made by a user and an auditing scheme to ensure the integrity of a utility function's values. The method enabled to reduce cheating by a malicious peer: it is shown that this approach can efficiently detect malicious peers with a probability over 98%.
Robust Detection of Anomalies via Sparse Methods

The problem of anomaly detection is a critical topic across application domains and is the subject of extensive research. Applications include finding frauds and intrusions, warning on robot safety, and many others. Standard approaches in this field exploit simple or complex system models, created by experts using detailed domain knowledge. In this paper, we put forth a statistics-based anomaly detector motivated by the fact that anomalies are sparse by their very nature. Powerful sparsity directed algorithms—namely Robust Principal Component Analysis and the Group Fused LASSO—form the basis of the methodology. Our novel unsupervised single-step solution imposes a convex optimisation task on the vector time series data of the monitored system by employing group-structured, switching and robust regularisation techniques. We evaluated our method on data generated by using a Baxter robot arm that was disturbed randomly by a human operator. Our procedure was able to outperform two baseline schemes in terms of F1 score. Generalisations to more complex dynamical scenarios are desired.
A Low-Power High-Precision Comparator With Time-Domain Bulk-Tuned Offset Cancellation

A novel time-domain bulk-tuned offset cancellation technique is applied to a low-power high-precision dynamic comparator to reduce its input-referred offset with minimal additional power consumption and delay. The design has been fabricated in a commercially available 0.5-μm process. Measurement results of 10 circuits show a reduction of offset standard deviation from 5.415 mV to 50.57 μV, improved by a factor of 107.1. The offset cancellation scheme does not introduce observable offset or noise, and can achieve fast and robust convergence with a wide range of common mode input. Operating at a supply of 5 V and clock frequency of 200 kHz, the comparator together with the OC circuitry consumes 4.65 μW of power, or 23 pJ of energy per comparison.
Generalizing matrix factorization through flexible regression priors

Predicting user "ratings" on items is a crucial task in recommender systems. Matrix factorization methods that computes a low-rank approximation of the incomplete user-item rating matrix provide state-of-the-art performance, especially for users and items with several past ratings (warm starts). However, it is a challenge to generalize such methods to users and items with few or no past ratings (cold starts). Prior work [4][32] have generalized matrix factorization to include both user and item features for performing better regularization of factors as well as provide a model for smooth transition from cold starts to warm starts. However, the features were incorporated via linear regression on factor estimates. In this paper, we generalize this process to allow for arbitrary regression models like decision trees, boosting, LASSO, etc. The key advantage of our approach is the ease of computing --- any new regression procedure can be incorporated by "plugging" in a standard regression routine into a few intermediate steps of our model fitting procedure. With this flexibility, one can leverage a large body of work on regression modeling, variable selection, and model interpretation. We demonstrate the usefulness of this generalization using the MovieLens and Yahoo! Buzz datasets.
An Efficient Parameter Extraction Methodology for the EKV MOST Model

This paper presents a new parameter extraction methodology, based on an accurate and continuous MOS model dedicated to low-voltage and low-current analog circuit design and simulation (EKV MOST Model). The extraction procedure provides the key parameters from the pinch-off versus gate voltage characteristic, measured at constant current from a device biased in moderate inversion. Unique parameter sets, suitable for statistical analysis, describe the device behavior in all operating regions and over all device geometries. This efficient and simple method is shown to be accurate for both submicron bulk CMOS and fully depleted SOI technologies.  The requirements for good MOS analog simulation models such as accuracy and continuity of the largeand small-signal characteristics are well established [1][2]. Continuity of the largeand small-signal characteristics from weak to strong inversion is one of the main features of the Enz-Krummenacher-Vittoz or EKV MOS transistor model [3][4][5]. One of the basic concepts of this model is the pinch-off voltage. A constant current bias is used to measure the pinch-off voltage versus gate voltage characteristic in moderate inversion (MI). This measure allows for an efficient and simple characterization method to be formulated for the most important model parameters as the threshold voltage and the other parameters related to the channel doping, using a single measured characteristic. The same principle is applied for various geometries, including shortand narrow-channel devices, and forms the major part of the complete characterization methodology. The simplicity of the model and the relatively small number of parameters to be extracted eases the parameter extraction. This is of particular importance if large statistical data are to be gathered. This method has been validated on a large number of different CMOS processes. To show its flexibility as well as the abilities of the model, results are presented for submicron bulk and fully depleted SOI technologies. SHORT DESCRIPTION OF THE STATIC MODEL A detailed description of the model formulation can be found in ; important concepts are shortly recalled here since they form the basis of the parameter extraction. A set of 13 intrinsic parameters is used for first and second order effects, listed in Table I. Unlike most other MOS simulation models, in the EKV model the gate, source and drain voltages, VG , VS and VD , are all referred to the substrate in order to preserve the intrinsic symmetry of the device. The Pinch-off Voltage The threshold voltage VTO, which is consequently also referred to the bulk, is defined as the gate voltage for which the inversion charge forming the channel is zero at equilibrium. The pinch-off voltage VP corresponds to the value of the channel potential Vch for which the inversion charge becomes zero in a non-equilibrium situation. VP can be directly related to VG :
SST: Single-Stream Temporal Action Proposals

Our paper presents a new approach for temporal detection of human actions in long, untrimmed video sequences. We introduce Single-Stream Temporal Action Proposals (SST), a new effective and efficient deep architecture for the generation of temporal action proposals. Our network can run continuously in a single stream over very long input video sequences, without the need to divide input into short overlapping clips or temporal windows for batch processing. We demonstrate empirically that our model outperforms the state-of-the-art on the task of temporal action proposal generation, while achieving some of the fastest processing speeds in the literature. Finally, we demonstrate that using SST proposals in conjunction with existing action classifiers results in improved state-of-the-art temporal action detection performance.