nils-herrmann/clean_scirepeval huggingface dataset:
Reading Beside the Lines: Indentation as a Proxy for Complexity Metric

Maintainers face the daunting task of wading through a collection of both new and old revisions, trying to ferret out revisions which warrant personal inspection. One can rank revisions by size/lines of code (LOC), but often, due to the distribution of the size of changes, revisions will be of similar size. If we can't rank revisions by LOC perhaps we can rank by Halstead's and McCabe's complexity metrics? However, these metrics are problematic when applied to code fragments (revisions) written in multiple languages: special parsers are required which may not support the language or dialect used; analysis tools may not understand code fragments. We propose using the statistical moments of indentation as a lightweight, language independent, revision/diff friendly metric which actually proxies classical complexity metrics. We have extensively evaluated our approach against the entire CVS histories of the 278 of the most popular and most active SourceForge projects. We found that our results are linearly correlated and rank-correlated with traditional measures of complexity, suggesting that measuring indentation is a cheap and accurate proxy for code complexity of revisions. Thus ranking revisions by the standard deviation and summation of indentation will be very similar to ranking revisions by complexity.
Assortative mixing in networks.

A network is said to show assortative mixing if the nodes in the network that have many connections tend to be connected to other nodes with many connections. Here we measure mixing patterns in a variety of networks and find that social networks are mostly assortatively mixed, but that technological and biological networks tend to be disassortative. We propose a model of an assortatively mixed network, which we study both analytically and numerically. Within this model we find that networks percolate more easily if they are assortative and that they are also more robust to vertex removal.
(Cross-)Browser Fingerprinting via OS and Hardware Level Features

In this paper, we propose a browser fingerprinting technique that can track users not only within a single browser but also across different browsers on the same machine. Specifically, our approach utilizes many novel OS and hardware level features, such as those from graphics cards, CPU, and installed writing scripts. We extract these features by asking browsers to perform tasks that rely on corresponding OS and hardware functionalities. Our evaluation shows that our approach can successfully identify 99.24% of users as opposed to 90.84% for state of the art on single-browser fingerprinting against the same dataset. Further, our approach can achieve higher uniqueness rate than the only cross-browser approach in the literature with similar stability.
Passive tag-to-tag communication

In this paper, we describe a novel passive RFID system capable of direct tag-to-tag communication in the presence of external radio frequency field. Tags talk by modulating the external field and thus backscattering the commands to each other. We present the system concept and show its hardware implementation based on TI MSP430 microcontroller. We also provide the theoretical model for modulation depth vs. distance which agrees with experimental results (maximum tag-to-tag communication distance). Finally, we discuss possible applications and outline future work.
Learning fuzzy cognitive maps from data by ant colony optimization

Fuzzy Cognitive Maps (FCMs) are a flexible modeling technique with the goal of modeling causal relationships. Traditionally FCMs are developed by experts. We need to learn FCMs directly from data when expert knowledge is not available. The FCM learning problem can be described as the minimization of the difference between the desired response of the system and the estimated response of the learned FCM model. Learning FCMs from data can be a difficult task because of the large number of candidate FCMs. A FCM learning algorithm based on Ant Colony Optimization (ACO) is presented in order to learn FCM models from multiple observed response sequences. Experiments on simulated data suggest that the proposed ACO based FCM learning algorithm is capable of learning FCM with at least 40 nodes. The performance of the algorithm was tested on both single response sequence and multiple response sequences. The test results are compared to several algorithms, such as genetic algorithms and nonlinear Hebbian learning rule based algorithms. The performance of the ACO algorithm is better than these algorithms in several different experiment scenarios in terms of model errors, sensitivities and specificities. The effect of number of response sequences and number of nodes is discussed.
Tactical Decision Making for Lane Changing with Deep Reinforcement Learning

In this paper we consider the problem of autonomous lane changing for self driving cars in a multi-lane, multi-agent setting. We present a framework that demonstrates a more structured and data efficient alternative to end-to-end complete policy learning on problems where the high-level policy is hard to formulate using traditional optimization or rule based methods but well designed low-level controllers are available. The framework uses deep reinforcement learning solely to obtain a high-level policy for tactical decision making, while still maintaining a tight integration with the low-level controller, thus getting the best of both worlds. This is possible with Q-masking, a technique with which we are able to incorporate prior knowledge, constraints and information from a low-level controller, directly in to the learning process thereby simplifying the reward function and making learning faster and efficient. We provide preliminary results in a simulator and show our approach to be more efficient than a greedy baseline, and more successful and safer than human driving.
Truss Decomposition in Massive Networks

The k-truss is a type of cohesive subgraphs proposed recently for the study of networks. While the problem of computing most cohesive subgraphs is NP-hard, there exists a polynomial time algorithm for computing k-truss. Compared with k-core which is also efficient to compute, k-truss represents the “core” of a k-core that keeps the key information of, while filtering out less important information from, the k-core. However, existing algorithms for computing k-truss are inefficient for handling today’s massive networks. We first improve the existing in-memory algorithm for computing k-truss in networks of moderate size. Then, we propose two I/O-efficient algorithms to handle massive networks that cannot fit in main memory. Our experiments on real datasets verify the efficiency of our algorithms and the value of k-truss.
Object Detection Using Strongly-Supervised Deformable Part Models

Deformable part-based models [1, 2] achieve state-of-the-art performance for object detection, but rely on heuristic initialization during training due to the optimization of non-convex cost function. This paper investigates limitations of such an initialization and extends earlier methods using additional supervision. We explore strong supervision in terms of annotated object parts and use it to (i) improve model initialization, (ii) optimize model structure, and (iii) handle partial occlusions. Our method is able to deal with sub-optimal and incomplete annotations of object parts and is shown to benefit from semi-supervised learning setups where part-level annotation is provided for a fraction of positive examples only. Experimental results are reported for the detection of six animal classes in PASCAL VOC 2007 and 2010 datasets. We demonstrate significant improvements in detection performance compared to the LSVM  and the Poselet  object detectors.
Bi-criteria Workflow Tasks Allocation and Scheduling in Cloud Computing Environments

Although there are few efficient algorithms in the literature for scientific workflow tasks allocation and scheduling for heterogeneous resources such as those proposed in grid computing context, they usually require a bounded number of computer resources that cannot be applied in Cloud computing environment. Indeed, unlike grid, elastic computing, such asAmazon's EC2, allows users to allocate and release compute resources on-demand and pay only for what they use. Therefore, it is reasonable to assume that the number of resources is infinite. This feature of Clouds has been called âillusion of infiniteresourcesâ. However, despite the proven benefits of using Cloud to run scientific workflows, users lack guidance for choosing between multiple offering while taking into account several objectives which are often conflicting. On the other side, the workflow tasks allocation and scheduling have been shown to be NP-complete problems. Thus, it is convenient to use heuristic rather than deterministic algorithm. The objective of this paper is to design an allocation strategy for Cloud computing platform. More precisely, we propose three complementary bi-criteria approaches for scheduling workflows on distributed Cloud resources, taking into account the overall execution time and the cost incurred by using a set of resources.
Meet Me where I’m Gazing: How Shared Attention Gaze Affects Human-Robot Handover Timing

In this paper we provide empirical evidence that using humanlike gaze cues during human-robot handovers can improve the timing and perceived quality of the handover event. Handovers serve as the foundation of many human-robot tasks. Fluent, legible handover interactions require appropriate nonverbal cues to signal handover intent, location and timing. Inspired by observations of human-human handovers, we implemented gaze behaviors on a PR2 humanoid robot. The robot handed over water bottles to a total of 102 naïve subjects while varying its gaze behaviour: no gaze, gaze designed to elicit shared attention at the handover location, and the shared attention gaze complemented with a turn-taking cue. We compared subject perception of and reaction time to the robot-initiated handovers across the three gaze conditions. Results indicate that subjects reach for the offered object significantly earlier when a robot provides a shared attention gaze cue during a handover. We also observed a statistical trend of subjects preferring handovers with turn-taking gaze cues over the other conditions. Our work demonstrates that gaze can play a key role in improving user experience of human-robot handovers, and help make handovers fast and fluent.
Development of cable climbing robot for maintenance of suspension bridges

In this paper, we introduce a wheel-based cable climbing robot system developed for maintenance of the suspension bridges. The robot consists of three parts: a wheel based driving mechanism, adhesion mechanism, and safe landing mechanism. The driving mechanism is a combination of pantograph mechanism, and wheels driven by motors. In addition, we propose a special design of safe landing mechanism which can assure the safety of the robot on the cables when the power is lost. Finally, the proposed robotic system is manufactured and validated in the indoor experimental environments.
A Novel Wide-Temperature-Range, 3.9 ppm/$^{\circ}$C CMOS Bandgap Reference Circuit

This paper presents an innovative CMOS Bandgap Reference Generator topology that leads to an improved curvature compensation method over a very wide temperature range. The proposed design was implemented in a standard 0.35 μm CMOS process. The compensation is performed by using only poly-silicon resistors. This is achieved by using a second Op-amp that generates a CTAT current, which is subsequently used to enhance the curvature compensation method. The performance of the circuit was verified experimentally. Measured results have shown temperature coefficients as low as 3.9 ppm/<sup>°</sup>C over a temperature range of 165<sup>°</sup>C ( -15<sup>°</sup>C to 150<sup>°</sup>C ) and temperature coefficients as low as 13.7 ppm/<sup>°</sup>C over an extended temperature range of 200<sup>°</sup>C (-50<sup>°</sup>C to 150<sup>°</sup>C ). In addition the circuit demonstrated very good line regulation performance for a broad range of supply voltages. The measured line regulation at room temperature is 0.039% V.
A Simple Mesh Generator in MATLAB

Creating a mesh is the first step in a wide range of applications, including scientific computing and computer graphics. An unstructured simplex mesh requires a choice of meshpoints (vertex nodes) and a triangulation. We want to offer a short and simple MATLAB code, described in more detail than usual, so the reader can experiment (and add to the code) knowing the underlying principles. We find the node locations by solving for equilibrium in a truss structure (using piecewise linear force-displacement relations) and we reset the topology by the Delaunay algorithm. The geometry is described implicitly by its distance function. In addition to being much shorter and simpler than other meshing techniques, our algorithm typically produces meshes of very high quality. We discuss ways to improve the robustness and the performance, but our aim here is simplicity. Readers can download (and edit) the codes from http://math.mit.edu/~persson/mesh.
A Low-voltage CMOS Op Amp with a Rail-to-rail Constant-gm Input Stage and a Class AB Rail-to-rail Output Stage

Absfrucf In this paper a low-voltage two-stage O p Amp is presented. The O p Amp features rail-to-rail operation and has an @put stage with a constant transconductance (%) over the entire common-mode input range. The input stage consists of an nand a PMOS differential pair connected in parallel. The constant gm is accomplished by regulating the tail-currents with the aid of an MOS translinear (MTL) circuit. The resulting gn is constant within 5%
D2D communications in LoRaWAN Low Power Wide Area Network: From idea to empirical validation

In this paper we advocate the use of device-to-device (D2D) communications in a LoRaWAN Low Power Wide Area Network (LPWAN). After overviewing the critical features of the LoRaWAN technology, we discuss the pros and cons of enabling the D2D communications for it. Subsequently we propose a network-assisted D2D communications protocol and show its feasibility by implementing it on top of a LoRaWAN-certified commercial transceiver. The conducted experiments show the performance of the proposed D2D communications protocol and enable us to assess its performance. More precisely, we show that the D2D communications can reduce the time and energy for data transfer by 6 to 20 times compared to conventional LoRaWAN data transfer mechanisms. In addition, the use of D2D communications may have a positive effect on the network by enabling spatial re-use of the frequency resources. The proposed LoRaWAN D2D communications can be used for a wide variety of applications requiring high coverage, e.g. use cases in distributed smart grid deployments for management and trading.
Cognitive Load Estimation in the Wild

Cognitive load has been shown, over hundreds of validated studies, to be an important variable for understanding human performance. However, establishing practical, non-contact approaches for automated estimation of cognitive load under real-world conditions is far from a solved problem. Toward the goal of designing such a system, we propose two novel vision-based methods for cognitive load estimation, and evaluate them on a large-scale dataset collected under real-world driving conditions. Cognitive load is defined by which of 3 levels of a validated reference task the observed subject was performing. On this 3-class problem, our best proposed method of using 3D convolutional neural networks achieves 86.1% accuracy at predicting task-induced cognitive load in a sample of 92 subjects from video alone. This work uses the driving context as a training and evaluation dataset, but the trained network is not constrained to the driving environment as it requires no calibration and makes no assumptions about the subject's visual appearance, activity, head pose, scale, and perspective.
Analyzing Museum Visitors' Behavior Patterns

Many studies have investigated personalized information presentation in the context of mobile museum guides. In order to provide such a service, information about museum visitors has to be collected and visitors have to be monitored and modelled in a non-intrusive manner. This can be done by using known museum visiting styles to classify the visiting style of visitors as they start their visit. Past research applied ethnographic observations of the behaviour of visitors and qualitative analysis (mainly site studies and interviews with staff) in several museums to define visiting styles. The current work validates past ethnographic research by applying unsupervised learning approaches to visitors classification. By providing quantitative empirical evidence for a qualitative theory we claim that, from the point of view of assessing the suitability of a qualitative theory in a given scenario, this approach is as valid as a manual annotation of museum visiting styles.
Non-orthogonal transmission technology in LTE evolution

Non-orthogonal transmission, although not entirely new to the wireless industry, is gaining more attention due to its promised throughput gain and unique capability to support a large number of simultaneous transmissions within limited resources. In this article, several key techniques for non-orthogonal transmission are discussed. The downlink technique is featured by MUST, which is being specified in 3GPP for mobile broadband services. In the uplink, grantfree schemes such as multi-user shared access and sparse code multiple access, are promising in supporting massive machine-type communication services. The multi-antenna aspect is also addressed in the context of MUST, showing that MIMO technology and non-orthogonal transmission can be used jointly to provide combined gain.
Hippocampal neurogenesis and forgetting

The hippocampus is thought to automatically encode all experience, yet the vast majority of our experiences are not remembered later. Although psychological theories have postulated the existence of decay processes for declarative memory, the corresponding neurobiological mechanisms are unknown. Here we develop the hypothesis that ongoing hippocampal neurogenesis represents a decay process that continually clears memories from the hippocampus. As newborn granule cells integrate into established hippocampal circuits, they form new input and output connections over the course of several weeks. Because successful memory retrieval relies on reinvoking patterns of activity that occurred at the time of encoding (pattern completion), neurogenesis-induced remodeling of hippocampal circuits incrementally reduces the likelihood that a given retrieval cue will reinvoke a previously stored pattern.
Multi-Objective Particle Swarm Optimization with time variant inertia and acceleration coefficients

In this article we describe a novel Particle Swarm Optimization (PSO) approach to multi-objective optimization (MOO), called Time Variant Multi-Objective Particle Swarm Optimization (TV-MOPSO). TV-MOPSO is made adaptive in nature by allowing its vital parameters (viz., inertia weight and acceleration coefficients) to change with iterations. This adaptiveness helps the algorithm to explore the search space more efficiently. A new diversity parameter has been used to ensure sufficient diversity amongst the solutions of the non-dominated fronts, while retaining at the same time the convergence to the Pareto-optimal front. TV-MOPSO has been compared with some recently developed multi-objective PSO techniques and evolutionary algorithms for 11 function optimization problems, using different performance measures.
Bluetooth 5 and Internet of Things: Potential and architecture

This article paves the knowledge about the next generation Bluetooth Standard-BT 5 that will bring some mesmerizing upgrades including increased range, speed, and broadcast messaging capacity. Further, three relevant queries such as what is better about BT 5, why does that matter, and how will it affect IoT have been explained to gather related information so that developers, practitioners, and naive people could formulate BT 5 into IoT based applications while assimilating the need of short range communication in true sense.
Empty LTCC Integrated Waveguide with Compact Transitions for Ultra-low Loss Millimeter-wave Applications

An Empty LTCC (Low Temperature Cofired Ceramics) Integrated Waveguide (E-LIW) for the 57-64 GHz band is proposed. In addition to being air-filled, this SIW-like structure uses long rectangular side-wall vias to reduce conductor and radiation losses. In simulation, the proposed structure shows an insertion loss of 0.084 dB/cm at 60 GHz compared to 0.325 dB/cm, when the same structure is dielectric filled, and 0.48 dB/cm when it is dielectric filled but with conventional cylindrical vias. A very compact transition between E-LIW and CPW is also proposed. A back-to-back configuration including the proposed transition and E-LIW has been fabricated. Measurement results show a maximum total insertion loss of 0.5± 0.2 dB with better than 20 dB return loss for the entire structure and over the entire frequency band of interest. These results are in good agreement with those obtained by 3D field simulation.
Hybrid Digital and Analog Beamforming Design for Large-Scale Antenna Arrays

The potential of using of millimeter wave (mmWave) frequency for future wireless cellular communication systems has motivated the study of large-scale antenna arrays for achieving highly directional beamforming. However, the conventional fully digital beamforming methods which require one radio frequency (RF) chain per antenna element is not viable for large-scale antenna arrays due to the high cost and high power consumption of RF chain components in high frequencies. To address the challenge of this hardware limitation, this paper considers a hybrid beamforming architecture in which the overall beamformer consists of a low-dimensional digital beamformer followed by an RF beamformer implemented using analog phase shifters. Our aim is to show that such an architecture can approach the performance of a fully digital scheme with much fewer number of RF chains. Specifically, this paper establishes that if the number of RF chains is twice the total number of data streams, the hybrid beamforming structure can realize any fully digital beamformer exactly, regardless of the number of antenna elements. For cases with fewer number of RF chains, this paper further considers the hybrid beamforming design problem for both the transmission scenario of a point-to-point multiple-input multiple-output (MIMO) system and a downlink multi-user multiple-input single-output (MU-MISO) system. For each scenario, we propose a heuristic hybrid beamforming design that achieves a performance close to the performance of the fully digital beamforming baseline. Finally, the proposed algorithms are modified for the more practical setting in which only finite resolution phase shifters are available. Numerical simulations show that the proposed schemes are effective even when phase shifters with very low resolution are used.