nils-herrmann/clean_scirepeval huggingface dataset:
The Way They Move: Tracking Multiple Targets with Similar Appearance

We introduce a computationally efficient algorithm for multi-object tracking by detection that addresses four main challenges: appearance similarity among targets, missing data due to targets being out of the field of view or occluded behind other objects, crossing trajectories, and camera motion. The proposed method uses motion dynamics as a cue to distinguish targets with similar appearance, minimize target mis-identification and recover missing data. Computational efficiency is achieved by using a Generalized Linear Assignment (GLA) coupled with efficient procedures to recover missing data and estimate the complexity of the underlying dynamics. The proposed approach works with track lets of arbitrary length and does not assume a dynamical model a priori, yet it captures the overall motion dynamics of the targets. Experiments using challenging videos show that this framework can handle complex target motions, non-stationary cameras and long occlusions, on scenarios where appearance cues are not available or poor.
Adopting Continuous Delivery and Deployment: Impacts on Team Structures, Collaboration and Responsibilities

Continuous Delivery and Deployment (CD) practices aim to deliver software features more frequently and reliably. While some efforts have been made to study different aspects of CD practices, a little empirical work has been reported on the impact of CD on team structures, collaboration and team members' responsibilities.  Our goal is to empirically investigate how Development (Dev) and Operations (Ops) teams are organized in software industry for adopting CD practices. Furthermore, we explore the potential impact of practicing CD on collaboration and team members' responsibilities. We conducted a mixed-method empirical study, which collected data from 21 in-depth, semi-structured interviews in 19 organizations and a survey with 93 software practitioners.  There are four common types of team structures (i.e., (1) separate Dev and Ops teams with higher collaboration; (2) separate Dev and Ops teams with facilitator(s) in the middle; (3) small Ops team with more responsibilities for Dev team; (4) no visible Ops team) for organizing Dev and Ops teams to effectively initiate and adopt CD practices. Our study also provides insights into how software organizations actually improve collaboration among teams and team members for practicing CD. Furthermore, we highlight new responsibilities and skills (e.g., monitoring and logging skills), which are needed in this regard.
Modeling Radiometric Uncertainty for Vision with Tone-Mapped Color Images

To produce images that are suitable for display, tone-mapping is widely used in digital cameras to map linear color measurements into narrow gamuts with limited dynamic range. This introduces non-linear distortion that must be undone, through a radiometric calibration process, before computer vision systems can analyze such photographs radiometrically. This paper considers the inherent uncertainty of undoing the effects of tone-mapping. We observe that this uncertainty varies substantially across color space, making some pixels more reliable than others. We introduce a model for this uncertainty and a method for fitting it to a given camera or imaging pipeline. Once fit, the model provides for each pixel in a tone-mapped digital photograph a probability distribution over linear scene colors that could have induced it. We demonstrate how these distributions can be useful for visual inference by incorporating them into estimation algorithms for a representative set of vision tasks.
PDF-TREX: An Approach for Recognizing and Extracting Tables from PDF Documents

This paper presents PDF-TREX, an heuristic approach for table recognition and extraction from PDF documents.The heuristics starts from an initial set of basic content elements and aligns and groups them, in bottom-up way by considering only their spatial features, in order to identify tabular arrangements of information. The scope of the approach is to recognize tables contained in PDF documents as a 2-dimensional grid on a Cartesian plane and extract them as a set of cells equipped by 2-dimensional coordinates. Experiments, carried out on a dataset composed of tables contained in documents coming from different domains, shows that the approach is well performing in recognizing table cells.The approach aims at improving PDF document annotation and information extraction by providing an output that can be further processed for understanding table and document contents.
A 1.2–12 GHz Sliced Notch Antenna Array

Historically, Vivaldi arrays are known to suffer from high cross-polarization when scanning in the nonprincipal planes—a fault without a universal solution. In this paper, a solution to this issue is proposed in the form of a new Vivaldi-type array with low cross-polarization termed the Sliced Notch Antenna (SNA) array. For the first proof-of-concept demonstration, simulations and measurements are comparatively presented for two single-polarized <inline-formula> <tex-math notation="LaTeX">$19 \times 19$ </tex-math></inline-formula> arrays—the proposed SNA and its Vivaldi counterpart—each operating over a 1.2–12 GHz (10:1) band. Both arrays are built using typical vertically integrated printed-circuit board cards, and are designed to exhibit VSWR < 2.5 within a 60° scan cone over most of the 10:1 band as infinite arrays. Measurement results compare very favorably with full-wave finite array simulations that include array truncation effects. The SNA array element demonstrates well-behaved polarization performance versus frequency, with more than 20 dB of D-plane <inline-formula> <tex-math notation="LaTeX">$\theta \!=\!45 {^{\circ }}$ </tex-math></inline-formula> polarization purity improvement at the high frequency. Moreover, the SNA element also: 1) offers better suppression of classical Vivaldi E-plane scan blindnesses; 2) requires fewer plated through vias for stripline-based designs; and 3) allows relaxed adjacent element electrical contact requirements for dual-polarized arrangements.
Compressing LSTMs into CNNs

We show that a deep convolutional network with an architecture inspired by the models used in image recognition can yield accuracy similar to a long-short term memory (LSTM) network, which achieves the state-of-the-art performance on the standard Switchboard automatic speech recognition task. Moreover, we demonstrate that merging the knowledge in the CNN and LSTM models via model compression further improves the accuracy of the convolutional model.
Diagnosis and treatment of lichen planus.

Lichen planus is a chronic, inflammatory, autoimmune disease that affects the skin, oral mucosa, genital mucosa, scalp, and nails. Lichen planus lesions are described using the six P's (planar [flat-topped], purple, polygonal, pruritic, papules, plaques). Onset is usually acute, affecting the flexor surfaces of the wrists, forearms, and legs. The lesions are often covered by lacy, reticular, white lines known as Wickham striae. Classic cases of lichen planus may be diagnosed clinically, but a 4-mm punch biopsy is often helpful and is required for more atypical cases. High-potency topical corticosteroids are first-line therapy for all forms of lichen planus, including cutaneous, genital, and mucosal erosive lesions. In addition to clobetasol, topical tacrolimus appears to be an effective treatment for vulvovaginal lichen planus. Topical corticosteroids are also first-line therapy for mucosal erosive lichen planus. Systemic corticosteroids should be considered for severe, widespread lichen planus involving oral, cutaneous, or genital sites. Referral to a dermatologist for systemic therapy with acitretin (an expensive and toxic oral retinoid) or an oral immunosuppressant should be considered for patients with severe lichen planus that does not respond to topical treatment. Lichen planus may resolve spontaneously within one to two years, although recurrences are common. However, lichen planus on mucous membranes may be more persistent and resistant to treatment.
NGS QC Toolkit: A Toolkit for Quality Control of Next Generation Sequencing Data

Next generation sequencing (NGS) technologies provide a high-throughput means to generate large amount of sequence data. However, quality control (QC) of sequence data generated from these technologies is extremely important for meaningful downstream analysis. Further, highly efficient and fast processing tools are required to handle the large volume of datasets. Here, we have developed an application, NGS QC Toolkit, for quality check and filtering of high-quality data. This toolkit is a standalone and open source application freely available at http://www.nipgr.res.in/ngsqctoolkit.html. All the tools in the application have been implemented in Perl programming language. The toolkit is comprised of user-friendly tools for QC of sequencing data generated using Roche 454 and Illumina platforms, and additional tools to aid QC (sequence format converter and trimming tools) and analysis (statistics tools). A variety of options have been provided to facilitate the QC at user-defined parameters. The toolkit is expected to be very useful for the QC of NGS data to facilitate better downstream analysis.
A Practical Guide to Sentiment Annotation: Challenges and Solutions

Sentences and tweets are often annotated for sentiment simply by asking respondents to label them as positive, negative, or neutral. This works well for simple expressions of sentiment; however, for many other types of sentences, respondents are unsure of how to annotate, and produce inconsistent labels. In this paper, we outline several types of sentences that are particularly challenging for manual sentiment annotation. Next we propose two annotation schemes that address these challenges, and list benefits and limitations for both.
Hybrid Evolutionary Algorithms for Graph Coloring

A recent and very promising approach for combinatorial optimization is to embed local search into the framework of evolutionary algorithms. In this paper, we present such hybrid algorithms for the graph coloring problem. These algorithms combine a new class of highly specialized crossover operators and a well-known tabu search algorithm. Experiments of such a hybrid algorithm are carried out on large DIMACS Challenge benchmark graphs. Results prove very competitive with and even better than those of state-of-the-art algorithms. Analysis of the behavior of the algorithm sheds light on ways to further improvement.
Twitter Improves Seasonal Influenza Prediction

Seasonal influenza epidemics causes severe illnesses and 250,000 to 500,000 deaths worldwide each year. Other pandemics like the 1918 “Spanish Flu” may change into a devastating one. Reducing the impact of these threats is of paramount importance for health authorities, and studies have shown that effective interventions can be taken to contain the epidemics, if early detection can be made. In this paper, we introduce the Social Network Enabled Flu Trends (SNEFT), a continuous data collection framework which monitors flu related tweets and track the emergence and spread of an influenza. We show that text mining significantly enhances the correlation between the Twitter and the Influenza like Illness (ILI) rates provided by Centers for Disease Control and Prevention (CDC). For accurate prediction, we implemented an auto-regression with exogenous input (ARX) model which uses current Twitter data, and CDC ILI rates from previous weeks to predict current influenza statistics. Our results show that, while previous ILI data from CDC offer a true (but delayed) assessment of a flu epidemic, Twitter data provides a real-time assessment of the current epidemic condition and can be used to compensate for the lack of current ILI data. We observe that the Twitter data is highly correlated with the ILI rates across different regions within USA and can be used to effectively improve the accuracy of our prediction. Our age-based flu prediction analysis indicates that for most of the regions, Twitter data best fit the age groups of 5-24 and 25-49 years, correlating well with the fact that these are likely, the most active user age groups on Twitter. Therefore, Twitter data can act as supplementary indicator to gauge influenza within a population and helps discovering flu trends ahead of CDC.
Microcontroller Based Heart Rate Monitor using Fingertip Sensors

This paper presents the design and development of a microcontroller based heart rate monitor using fingertip sensor. The device uses the optical technology to detect the flow of blood through the finger and offers the advantage of portability over tape-based recording systems. The important feature of this research is the use of Discrete Fourier Transforms to analyse the ECG signal in order to measure the heart rate. Evaluation of the device on real signals shows accuracy in heart rate estimation, even under intense physical activity. The performance of HRM device was compared with ECG signal represented on an oscilloscope and manual pulse measurement of heartbeat, giving excellent results. Our proposed Heart Rate Measuring (HRM) device is economical and user friendly.
Working memory and language: an overview.

Working memory involves the temporary storage and manipulation of information that is assumed to be necessary for a wide range of complex cognitive activities. In 1974, Baddeley and Hitch proposed that it could be divided into three subsystems, one concerned with verbal and acoustic information, the phonological loop, a second, the visuospatial sketchpad providing its visual equivalent, while both are dependent upon a third attentionally-limited control system, the central executive. A fourth subsystem, the episodic buffer, has recently been proposed. These are described in turn, with particular reference to implications for both the normal processing of language, and its potential disorders. The reader will be introduced to the concept of a multi-component working memory. Particular emphasis will be placed on the phonological loop component, and (a) its fractionation into a storage and processing component, (b) the neuropsychological evidence for this distinction, and (c) its implication for both native and second language learning. This will be followed by (d) a brief overview of the visuospatial sketchpad and its possible role in language, culminating in (e) discussion of the higher-level control functions of working memory which include (f) the central executive and its multi-dimensional storage system, the episodic buffer. An attempt throughout is made to link the model to its role in both normal and disordered language functions.
Fingerprint Image Enhancement Algorithm and Performance Evaluation

Fingerprint identification is one of the most important biometric technologies which has drawn a substantial amount of attention recently. The uniqueness of a fingerprint is exclusively determined by the local ridge characteristics and their relationships. Very important. Fingerprint images get degraded and corrupted due to variations in skin and impression conditions. Thus, image enhancement techniques are employed prior to minutiae extraction. However, the performance of a minutiae extraction algorithm relies heavily on the quality of the input fingerprint images. Here introducing a fast fingerprint enhancement algorithm, which can adaptively improve the clarity of ridge and valley structures of fingerprint images based on the estimated local ridge orientation and frequency and evaluated the performance of the image enhancement algorithm using the goodness index of the extracted minutiae and the accuracy of an online fingerprint verification system.
Searching optimal product bundles by means of GA-based Engine and Market Basket Analysis

The product bundling problem is a challenging task in the e-Commerce domain. We propose a generative engine in order to find the bundle of products that best satisfies user requirements and, at the same time, seller needs such as the minimization of the dead stocks and the maximization of net income. The proposed system named Intelligent Bundle Suggestion and Generation (IBSAG) is designed in order to satisfy these requirements. Market Basket Analysis supports the system in user requirement elicitation task. Experimental results prove the ability of system in finding the optimal tradeoff between different and conflicting constraints.
Optimized Bidding of a EV Aggregation Agent in the Electricity Market

An electric vehicle (EV) aggregation agent, as a commercial middleman between electricity market and EV owners, participates with bids for purchasing electrical energy and selling secondary reserve. This paper presents an optimization approach to support the aggregation agent participating in the day-ahead and secondary reserve sessions, and identifies the input variables that need to be forecasted or estimated. Results are presented for two years (2009 and 2010) of the Iberian market, and considering perfect and naïve forecast for all variables of the problem.
Assessing the Impact of Service Workers on the Energy Efficiency of Progressive Web Apps

Mobile web apps represent a large share of the Internet today. However, they still lag behind native apps in terms of user experience. Progressive Web Apps (PWAs) are a new technology introduced by Google that aims at bridging this gap, with a set of APIs known as service workers at its core. Goal. In this paper, we present an empirical study that evaluates the impact of service workers on the energy efficiency of PWAs, when operating in different network conditions on two different generations of mobile devices.  We designed an empirical experiment with two main factors: the use of service workers and the type of network available (2G or WiFi). We performed the experiment by running a total of 7 PWAs on two devices (an LG G2 and a Nexus 6P) that we evaluated as blocking factor. Our response variable is the energy consumption of the devices.  Our results show that service workers do not have a significant impact over the energy consumption of the two devices, regardless of the network conditions. Also, no interaction was detected between the two factors. However, some patterns in the data show different behaviors among PWAs.  This paper represents a first empirical investigation on PWAs. Our results show that the PWA and service workers technology is promising in terms of energy efficiency.
Security and privacy framework for ubiquitous healthcare IoT devices

With the support of the wearable devices, healthcare services started a new phase in serving patients need. The new technology adds more facilities and luxury to the healthcare services, Also changes patients' lifestyles from the traditional way of monitoring to the remote home monitoring. Such new approach faces many challenges related to security as sensitive data get transferred through different type of channels. They are four main dimensions in terms of security scope such as trusted sensing, computation, communication, privacy and digital forensics. In this paper we will try to focus on the security challenges of the wearable devices and IoT and their advantages in healthcare sectors.
Learning to Identify Metaphors from a Corpus of Proverbs

In this paper, we experiment with a resource consisting of metaphorically annotated proverbs on the task of word-level metaphor recognition. We observe that existing feature sets do not perform well on this data. We design a novel set of features to better capture the peculiar nature of proverbs and we demonstrate that these new features are significantly more effective on the metaphorically dense proverb data.
Per-packet load-balanced, low-latency routing for clos-based data center networks

Clos-based networks including Fat-tree and VL2 are being built in data centers, but existing per-flow based routing causes low network utilization and long latency tail. In this paper, by studying the structural properties of Fat-tree and VL2, we propose a per-packet round-robin based routing algorithm called Digit-Reversal Bouncing (DRB). DRB achieves perfect packet interleaving. Our analysis and simulations show that, compared with random-based load-balancing algorithms, DRB results in smaller and bounded queues even when traffic load approaches 100%, and it uses smaller re-sequencing buffer for absorbing out-of-order packet arrivals. Our implementation demonstrates that our design can be readily implemented with commodity switches. Experiments on our testbed, a Fat-tree with 54 servers, confirm our analysis and simulations, and further show that our design handles network failures in 1-2 seconds and has the desirable graceful performance degradation property.
Textile Pressure Sensor for Muscle Activity and Motion Detection

This paper presents a pure textile, capacitive pressure sensor designed for integration into clothing to measure pressure on human body. The applications fields cover all domains where a soft and bendable sensor with a high local resolution is needed, e.g. in rehabilitation, pressure-sore prevention or motion detection due to muscle activities. We developed several textile sensors with spatial resolution of 2 times 2 cm and an average error below 4 percent within the measurement range 0 to 10 N/cm2. Applied on the upper arm the textile pressure sensor determines the deflection of the forearm between 0 and 135 degrees due to the muscle bending.
Depth kernel descriptors for object recognition

Consumer depth cameras, such as the Microsoft Kinect, are capable of providing frames of dense depth values at real time. One fundamental question in utilizing depth cameras is how to best extract features from depth frames. Motivated by local descriptors on images, in particular kernel descriptors, we develop a set of kernel features on depth images that model size, 3D shape, and depth edges in a single framework. Through extensive experiments on object recognition, we show that (1) our local features capture different aspects of cues from a depth frame/view that complement one another; (2) our kernel features significantly outperform traditional 3D features (e.g. Spin images); and (3) we significantly improve the capabilities of depth and RGB-D (color+depth) recognition, achieving 10–15% improvement in accuracy over the state of the art.
Design and Development of the Cable Actuated Finger Exoskeleton for Hand Rehabilitation Following Stroke

Finger impairment following stroke results in significant deficits in hand manipulation and the performance of everyday tasks. While recent advances in rehabilitation robotics have shown promise for facilitating functional improvement, it remains unclear how best to employ these devices to maximize benefits. Current devices for the hand, however, lack the capacity to fully explore the space of possible training paradigms. Particularly, they cannot provide the independent joint control and levels of velocity and torque required. To fill this need, we have developed a prototype for one digit, the cable actuated finger exoskeleton (CAFE), a three-degree-of-freedom robotic exoskeleton for the index finger. This paper presents the design and development of the CAFE, with performance testing results.