nils-herrmann/clean_scirepeval huggingface dataset:
5G New Radio: Waveform, Frame Structure, Multiple Access, and Initial Access

Different from conventional mobile networks designed to optimize the transmission efficiency of one particular service (e.g., streaming voice/ video) primarily, the industry and academia are reaching an agreement that 5G mobile networks are projected to sustain manifold wireless requirements, including higher mobility, higher data rates, and lower latency. For this purpose, 3GPP has launched the standardization activity for the first phase 5G system in Release 15 named New Radio (NR). To fully understand this crucial technology, this article offers a comprehensive overview of the state-of-the-art development of NR, including deployment scenarios, numerologies, frame structure, new waveform, multiple access, initial/random access procedure, and enhanced carrier aggregation (CA) for resource requests and data transmissions. The provided insights thus facilitate knowledge of design and practice for further features of NR.
Extraction of the Euclidean skeleton based on a connectivity criterion

The skeleton is essential for general shape representation. The commonly required properties of a skeletonization algorithm are that the extracted skeleton should be accurate; robust to noise, position and rotation; able to reconstruct the original object; and able to produce a connected skeleton in order to preserve its topological and hierarchical properties. However, the use of a discrete image presents a lot of problems that may in9uence the extraction of the skeleton. Moreover, most of the methods are memory-intensive and computationally intensive, and require a complex data structure. In this paper, we propose a fast, e;cient and accurate skeletonization method for the extraction of a well-connected Euclidean skeleton based on a signed sequential Euclidean distance map. A connectivity criterion is proposed, which can be used to determine whether a given pixel is a skeleton point independently. The criterion is based on a set of point pairs along the object boundary, which are the nearest contour points to the pixel under consideration and its 8 neighbors. Our proposed method generates a connected Euclidean skeleton with a single pixel width without requiring a linking algorithm or iteration process. Experiments show that the runtime of our algorithm is faster than the distance transformation and is linearly proportional to the number of pixels of an image. ? 2002 Pattern Recognition Society.
Be Fast, Cheap and in Control with SwitchKV

SwitchKV is a new key-value store system design that combines high-performance cache nodes with resourceconstrained backend nodes to provide load balancing in the face of unpredictable workload skew. The cache nodes absorb the hottest queries so that no individual backend node is over-burdened. Compared with previous designs, SwitchKV exploits SDN techniques and deeply optimized switch hardware to enable efficient contentbased routing. Programmable network switches keep track of cached keys and route requests to the appropriate nodes at line speed, based on keys encoded in packet headers. A new hybrid caching strategy keeps cache and switch forwarding rules updated with low overhead and ensures that system load is always well-balanced under rapidly changing workloads. Our evaluation results demonstrate that SwitchKV can achieve up to 5× throughput and 3× latency improvements over traditional system designs.
I am Robot: (Deep) Learning to Break Semantic Image CAPTCHAs

Since their inception, captchas have been widely used for preventing fraudsters from performing illicit actions. Nevertheless, economic incentives have resulted in an arms race, where fraudsters develop automated solvers and, in turn, captcha services tweak their design to break the solvers. Recent work, however, presented a generic attack that can be applied to any text-based captcha scheme. Fittingly, Google recently unveiled the latest version of reCaptcha. The goal of their new system is twofold, to minimize the effort for legitimate users, while requiring tasks that are more challenging to computers than text recognition. ReCaptcha is driven by an "advanced risk analysis system" that evaluates requests and selects the difficulty of the captcha that will be returned. Users may be required to click in a checkbox, or solve a challenge by identifying images with similar content. In this paper, we conduct a comprehensive study of reCaptcha, and explore how the risk analysis process is influenced by each aspect of the request. Through extensive experimentation, we identify flaws that allow adversaries to effortlessly influence the risk analysis, bypass restrictions, and deploy large-scale attacks. Subsequently, we design a novel low-cost attack that leverages deep learning technologies for the semantic annotation of images. Our system is extremely effective, automatically solving 70.78% of the image reCaptcha challenges, while requiring only 19 seconds per challenge. We also apply our attack to the Facebook image captcha and achieve an accuracy of 83.5%. Based on our experimental findings, we propose a series of safeguards and modifications for impacting the scalability and accuracy of our attacks. Overall, while our study focuses on reCaptcha, our findings have wide implications, as the semantic information conveyed via images is increasingly within the realm of automated reasoning, the future of captchas relies on the exploration of novel directions.
Single-baseline polarimetric SAR interferometry

The objective of this paper is to examine the application of single-baseline polarimetric SAR interferometry to the remote sensing and measurement of structure over forested terrain. For this, a polarimetric coherent scattering model for vegetation cover suitable for the estimation of forest parameters from interferometric observables is introduced, discussed and validated. Based on this model, an inversion algorithm which allows the estimation of forest parameters such as tree height, average extinction, and underlying topography from single-baseline fully polarimetric interferometric data is addressed. The performance of the inversion algorithm is demonstrated using fully polarimetric single baseline experimental data acquired by DLR’s E-SAR system at L-band.
Human-Intent Detection and Physically Interactive Control of a Robot Without Force Sensors

In this paper, a physically interactive control scheme is developed for a manipulator robot arm. The human touches the robot and applies force in order to make it behave as he/she likes. The communication between the robot and the human is maintained by a physical contact with no sensors. The intent of the human is estimated by observing the change in control effort. The robot receives the estimated human intent and updates its position reference accordingly. The developed method uses the principle of conservation of zero momentum for position-controlled systems. A switching scheme is developed that goes between the modes of pure impedance control with a fixed-position reference and interactive control under human intent. The switching mechanism uses neither a physical switch nor a sensor; it observes the human intent and puts the robot into interactive mode, if there is any. When the human intent disappears, the robot goes into the pure-impedance-control mode, thus stabilizing in the left position.
Towards reproducibility in recommender-systems research

Numerous recommendation approaches are in use today. However, comparing their effectiveness is a challenging task because evaluation results are rarely reproducible. In this article, we examine the challenge of reproducibility in recommender-system research. We conduct experiments using Plista’s news recommender system, and Docear’s research-paper recommender system. The experiments show that there are large discrepancies in the effectiveness of identical recommendation approaches in only slightly different scenarios, as well as large discrepancies for slightly different approaches in identical scenarios. For example, in one news-recommendation scenario, the performance of a content-based filtering approach was twice as high as the second-best approach, while in another scenario the same content-based filtering approach was the worst performing approach. We found several determinants that may contribute to the large discrepancies observed in recommendation effectiveness. Determinants we examined include user characteristics (gender and age), datasets, weighting schemes, the time at which recommendations were shown, and user-model size. Some of the determinants have interdependencies. For instance, the optimal size of an algorithms’ user model depended on users’ age. Since minor variations in approaches and scenarios can lead to significant changes in a recommendation approach’s performance, ensuring reproducibility of experimental results is difficult. We discuss these findings and conclude that to ensure reproducibility, the recommender-system community needs to (1) survey other research fields and learn from them, (2) find a common understanding of reproducibility, (3) identify and understand the determinants that affect reproducibility, (4) conduct more comprehensive experiments, (5) modernize publication practices, (6) foster the development and use of recommendation frameworks, and (7) establish best-practice guidelines for recommender-systems research.
GeoLife: A Collaborative Social Networking Service among User, Location and Trajectory

People travel in the real world and leave their location history in a form of trajectories. These trajectories do not only connect locations in the physical world but also bridge the gap between people and locations. This paper introduces a social networking service, called GeoLife, which aims to understand trajectories, locations and users, and mine the correlation between users and locations in terms of user-generated GPS trajectories. GeoLife offers three key applications scenarios: 1) sharing life experiences based on GPS trajectories; 2) generic travel recommendations, e.g., the top interesting locations, travel sequences among locations and travel experts in a given region; and 3) personalized friend and location recommendation.
An agri-food supply chain traceability system for China based on RFID & blockchain technology

For the past few years, food safety has become an outstanding problem in China. Since traditional agri-food logistics pattern can not match the demands of the market anymore, building an agri-food supply chain traceability system is becoming more and more urgent. In this paper, we study the utilization and development situation of RFID (Radio-Frequency IDentification) and blockchain technology first, and then we analyze the advantages and disadvantages of using RFID and blockchain technology in building the agri-food supply chain traceability system; finally, we demonstrate the building process of this system. It can realize the traceability with trusted information in the entire agri-food supply chain, which would effectively guarantee the food safety, by gathering, transferring and sharing the authentic data of agri-food in production, processing, warehousing, distribution and selling links.
X-Diff: An Effective Change Detection Algorithm for XML Documents

XML has become the de facto standard format for web publishing and data transportation. Since online information changes frequently, being able to quickly detect changes in XML documents is important to Internet query systems, search engines, and continuous query systems. Previous work in change detection on XML, or other hierarchically structured documents, used an ordered tree model, in which left-to-right order among siblings is important and it can affect the change result. This paper argues that an unordered model (only ancestor relationships are significant) is more suitable for most database applications. Using an unordered model, change detection is substantially harder than using the ordered model, but the change result that it generates is more accurate. This paper proposes X-Diff, an effective algorithm that integrates key XML structure characteristics with standard tree-to-tree correction techniques. The algorithm is analyzed and compared with XyDiff [CAM02], a published XML diff algorithm. An experimental evaluation on both algorithms is provided.
About the cortical origin of the low-delta and high-gamma rhythms observed in EEG signals during treadmill walking

This paper presents a spectral and time-frequency analysis of EEG signals recorded on seven healthy subjects walking on a treadmill at three different speeds. An accelerometer was placed on the head of the subjects in order to record the shocks undergone by the EEG electrodes during walking. Our results indicate that up to 15 harmonics of the fundamental stepping frequency may pollute EEG signals, depending on the walking speed and also on the electrode location. This finding may call into question some conclusions drawn in previous EEG studies where low-delta band (especially around 1 Hz, the fundamental stepping frequency) had been announced as being the seat of angular and linear kinematics control of the lower limbs during walk. Additionally, our analysis reveals that EEG and accelerometer signals exhibit similar time-frequency properties, especially in frequency bands extending up to 150 Hz, suggesting that previous conclusions claiming the activation of high-gamma rhythms during walking may have been drawn on the basis of insufficiently cleaned EEG signals. Our results are put in perspective with recent EEG studies related to locomotion and extensively discussed in particular by focusing on the low-delta and high-gamma bands.
Design requirements for technologies that encourage physical activity

Overweight and obesity are a global epidemic, with over one billion overweight adults worldwide (300+ million of whom are obese). Obesity is linked to several serious health problems and medical conditions. Medical experts agree that physical activity is critical to maintaining fitness, reducing weight, and improving health, yet many people have difficulty increasing and maintaining physical activity in everyday life. Clinical studies have shown that health benefits can occur from simply increasing the number of steps one takes each day and that social support can motivate people to stay active. In this paper, we describe Houston, a prototype mobile phone application for encouraging activity by sharing step count with friends. We also present four design requirements for technologies that encourage physical activity that we derived from a three-week long in situ pilot study that was conducted with women who wanted to increase their physical activity.
[Diced cartilage wrapped in deep temporal aponeurosis (DC-F): A new technique in augmentation rhinoplasty].

Diced cartilage rolled up in a fascia (DC-F) is a recent technique developed by Rollin K Daniel. It consists to tailor make a composite graft composed by pieces of cartilage cut in small dices wrapped in a layer of deep temporal aponeurosis. This initially malleable graft allows an effective dorsum augmentation (1 to 10 mm), adjustable until the end of the operation and even post operatively. The indications are all the primary and secondary augmentation rhinoplasties. However, the elective indications are the secondary augmentation rhinoplasties with cartilaginous donor site depletion, or when cartilaginous grafts are of poor quality (insufficient length, multifragmented...), or finally when the recipient site is uneven or asymmetrical. We report our experience of 20 patients operated in 2006 and 2007, with one year minimal follow-up. All the cases are relative or absolute saddle noses, idiopathic, post-traumatic or iatrogenic. Moreover, two patients also had a concomitant chin augmentation with DC-F. No case of displacement or resorption was noted. We modified certain technical points in order to make this technique even more powerful and predictable.
A Multiresolution Spline with Application to Image Mosaics

We define a multiresolution spline technique for combining two or more images into a larger image mosaic. In this procedure, the images to be splined are first decomposed into a set of band-pass filtered component images. Next, the component images in each spatial frequency hand are assembled into a corresponding bandpass mosaic. In this step, component images are joined using a weighted average within a transition zone which is proportional in size to the wave lengths represented in the band. Finally, these band-pass mosaic images are summed to obtain the desired image mosaic. In this way, the spline is matched to the scale of features within the images themselves. When coarse features occur near borders, these are blended gradually over a relatively large distance without blurring or otherwise degrading finer image details in the neighborhood of th e border.
A novel low-loss modulation strategy for high-power bi-directional buck+boost converters

A novel low-loss, constant-frequency, zero-voltage-switching (ZVS) modulation strategy for bi-directional, cascaded, buck-boost DC/DC converters, used in a hybrid electrical vehicle (HEV), is presented and its benefits over state-of-the-art converters and soft-switching solutions are discussed in a comparative evaluation. To obtain ZVS with the purposed modulation strategy, the buck+boost inductance is selected and the switches are gated in a way that the inductor current has a negative offset current at the beginning and the end of each pulse period. This allows the MOSFET switches to turn on when the anti-parallel body diode is conducting. As the novel modulation strategy is a software-only solution, there are no additional expenses for active or passive components compared to conventional modulation implementations. Furthermore, an analytical and simulation investigation predicts an excellent efficiency over the complete operating range and a higher power density for a multi-phase converter equipped with the low-loss modulation. Experimental measurements performed with a converter prototype verify the mode of operation and the ZVS principle.
Evolving Players for an Ancient Game: Hnefatafl

Hnefatafl is an ancient Norse game - an ancestor of chess. In this paper, we report on the development of computer players for this game. In the spirit of Blondie24, we evolve neural networks as board evaluation functions for different versions of the game. An unusual aspect of this game is that there is no general agreement on the rules: it is no longer much played, and game historians attempt to infer the rules from scraps of historical texts, with ambiguities often resolved on gut feeling as to what the rules must have been in order to achieve a balanced game. We offer the evolutionary method as a means by which to judge the merits of alternative rule sets
CSI-Based Fingerprinting for Indoor Localization: A Deep Learning Approach

With the fast-growing demand of location-based services in indoor environments, indoor positioning based on fingerprinting has attracted significant interest due to its high accuracy. In this paper, we present a novel deep-learning-based indoor fingerprinting system using channel state information (CSI), which is termed DeepFi. Based on three hypotheses on CSI, the DeepFi system architecture includes an offline training phase and an online localization phase. In the offline training phase, deep learning is utilized to train all the weights of a deep network as fingerprints. Moreover, a greedy learning algorithm is used to train the weights layer by layer to reduce complexity. In the online localization phase, we use a probabilistic method based on the radial basis function to obtain the estimated location. Experimental results are presented to confirm that DeepFi can effectively reduce location error, compared with three existing methods in two representative indoor environments.
Recent Advances in Flexible and Stretchable Bio-Electronic Devices Integrated with Nanomaterials.

Flexible and stretchable electronics and optoelectronics configured in soft, water resistant formats uniquely address seminal challenges in biomedicine. Over the past decade, there has been enormous progress in the materials, designs, and manufacturing processes for flexible/stretchable system subcomponents, including transistors, amplifiers, bio-sensors, actuators, light emitting diodes, photodetector arrays, photovoltaics, energy storage elements, and bare die integrated circuits. Nanomaterials prepared using top-down processing approaches and synthesis-based bottom-up methods have helped resolve the intrinsic mechanical mismatch between rigid/planar devices and soft/curvilinear biological structures, thereby enabling a broad range of non-invasive, minimally invasive, and implantable systems to address challenges in biomedicine. Integration of therapeutic functional nanomaterials with soft bioelectronics demonstrates therapeutics in combination with unconventional diagnostics capabilities. Recent advances in soft materials, devices, and integrated systems are reviewes, with representative examples that highlight the utility of soft bioelectronics for advanced medical diagnostics and therapies.
Multi-space Variational Encoder-Decoders for Semi-supervised Labeled Sequence Transduction

Labeled sequence transduction is a task of transforming one sequence into another sequence that satisfies desiderata specified by a set of labels. In this paper we propose multi-space variational encoderdecoders, a new model for labeled sequence transduction with semi-supervised learning. The generative model can use neural networks to handle both discrete and continuous latent variables to exploit various features of data. Experiments show that our model provides not only a powerful supervised framework but also can effectively take advantage of the unlabeled data. On the SIGMORPHON morphological inflection benchmark, our model outperforms single-model state-ofart results by a large margin for the majority of
Smart collaboration between humans and machines based on mutual understanding

To improve the safety and comfort of a human-machine system, the machine needs to ‘know,’ in a real time manner, the human operator in the system. The machine’s assistance to the human can be fine tuned if the machine is able to sense the human’s state and intent. Related to this point, this paper discusses issues of human trust in automation, automation surprises, responsibility and authority. Examples are given of a driver assistance system for advanced automobile.
Mining Twitter Feeds for Software User Requirements

Twitter enables large populations of end-users of software to publicly share their experiences and concerns about software systems in the form of micro-blogs. Such data can be collected and classified to help software developers infer users' needs, detect bugs in their code, and plan for future releases of their systems. However, automatically capturing, classifying, and presenting useful tweets is not a trivial task. Challenges stem from the scale of the data available, its unique format, diverse nature, and high percentage of irrelevant information and spam. Motivated by these challenges, this paper reports on a three-fold study that is aimed at leveraging Twitter as a main source of software user requirements. The main objective is to enable a responsive, interactive, and adaptive data-driven requirements engineering process. Our analysis is conducted using 4,000 tweets collected from the Twitter feeds of 10 software systems sampled from a broad range of application domains. The results reveal that around 50% of collected tweets contain useful technical information. The results also show that text classifiers such as Support Vector Machines and Naive Bayes can be very effective in capturing and categorizing technically informative tweets. Additionally, the paper describes and evaluates multiple summarization strategies for generating meaningful summaries of informative software-relevant tweets.