nils-herrmann/clean_scirepeval huggingface dataset:
The clinical anatomy of the cephalic vein in the deltopectoral triangle.

Identification and recognition of the cephalic vein in the deltopectoral triangle is of critical importance when considering emergency catheterization procedures. The aim of our study was to conduct a cadaveric study to access data regarding the topography and the distribution patterns of the cephalic vein as it relates to the deltopectoral triangle. One hundred formalin fixed cadavers were examined. The cephalic vein was found in 95% (190 right and left) specimens, while in the remaining 5% (10) the cephalic vein was absent. In 80% (152) of cases the cephalic vein was found emerging superficially in the lateral portion of the deltopectoral triangle. In 30% (52) of these 152 cases the cephalic vein received one tributary within the deltopectoral triangle, while in 70% (100) of the specimens it received two. In the remaining 20% (38) of cases the cephalic vein was located deep to the deltopectoral fascia and fat and did not emerge through the deltopectoral triangle but was identified medially to the coracobrachialis and inferior to the medial border of the deltoid. In addition, in 4 (0.2%) of the specimens the cephalic vein, after crossing the deltopectoral triangle, ascended anterior and superior to the clavicle to drain into the subclavian vein. In these specimens a collateral branch was observed to communicate between the cephalic and external jugular veins. In 65.2% (124) of the cases the cephalic vein traveled with the deltoid branch of the thoracoacromial trunk. The length of the cephalic vein within the deltopectoral triangle ranged from 3.5 cm to 8.2 cm with a mean of 4.8+/-0.7 cm. The morphometric analysis revealed a mean cephalic vein diameter of 0.8+/-0.1 cm with a range of 0.1 cm to 1.2 cm. The cephalic vein is relatively large and constant, usually allowing for easy cannulation.
Are We There Yet ? Challenging SeqSLAM on a 3000 km Journey Across All Four Seasons

When operating over extended periods of time, an autonomous system will inevitably be faced with severe changes in the appearance of its environment. Coping with such changes is more and more in the focus of current robotics research. In this paper, we foster the development of robust place recognition algorithms in changing environments by describing a new dataset that was recorded during a 728 km long journey in spring, summer, fall, and winter. Approximately 40 hours of full-HD video cover extreme seasonal changes over almost 3000 km in both natural and man-made environments. Furthermore, accurate ground truth information are provided. To our knowledge, this is by far the largest SLAM dataset available at the moment. In addition, we introduce an open source Matlab implementation of the recently published SeqSLAM algorithm and make it available to the community. We benchmark SeqSLAM using the novel dataset and analyse the influence of important parameters and algorithmic steps.
Internet of things for sleep quality monitoring system: A survey

Sleep quality is an important factor for human physical and mental health, day-time performance, and safety. Sufficient sleep quality can reduce risk of chronic disease and mental depression. Sleep helps brain to work properly that can improve productivity and prevent accident because of falling asleep. In order to analyze the sleep quality, reliable continuous monitoring system is required. The emergence of internet-of-things technology has provided a promising opportunity to build a reliable sleep quality monitoring system by leveraging the rapid improvement of sensor and mobile technology. This paper presents the literature study about internet of things for sleep quality monitoring systems. The study is started from the review of sleep quality problem, the importance of sleep quality monitoring, the enabling internet of things technology, and the open issues in this field. Finally, our future research plan for sleep apnea monitoring is presented.
Speaker Recognition Using e–Vectors

Systems based on i–vectors represent the current state–of–the–art in text-independent speaker recognition. Unlike joint factor analysis JFA, which models both speaker and intersession subspaces separately, in the i–vector approach all the important variability is modeled in a single low-dimensional subspace. This paper is based on the observation that JFA estimates a more informative speaker subspace than the “total variability” i–vector subspace, because the latter is obtained by considering each training segment as belonging to a different speaker. We propose a speaker modeling approach that extracts a compact representation of a speech segment, similar to the speaker factors of JFA and to i–vectors, referred to as “e–vector.” Estimating the e–vector subspace follows a procedure similar to i–vector training, but produces a more accurate speaker subspace, as confirmed by the results of a set of tests performed on the NIST 2012 and 2010 Speaker Recognition Evaluations. Simply replacing the i–vectors with e–vectors we get approximately 10% average improvement of the C $_{\text{primary}}$ cost function, using different systems and classifiers. It is worth noting that these performance gains come without any additional memory or computational costs with respect to the standard i–vector systems.
Exploring EEG-based biometrics for user identification and authentication

As human brain activities, represented by EEG brainwave signals, are more confidential, sensitive, and hard to steal and replicate, they hold great promise to provide a far more secure biometric approach for user identification and authentication. In this study, we present an EEG-based biometric security framework. Specifically, we propose to reduce the noise level through ensemble averaging and low-pass filter, extract frequency features using wavelet packet decomposition, and perform classification based on an artificial neural network. We explicitly discuss four different scenarios to emulate different application cases in authentication. Experimental results show that: the classification rates of distinguishing one subject or a small group of individuals (e.g., authorized personnel) from others (e.g., unauthorized personnel) can reach around 90%. However, it is also shown that recognizing each individual subject from a large pool has the worst performance with a classification rate of less than 11%. The side-by-side method shows an improvement on identifying all the subjects with classification rates of around 40%. Our study lays a solid foundation for future investigation of innovative, brainwave-based biometric approaches.
Channel-aware scheduling algorithms for SC-FDMA in LTE uplink

Single-carrier frequency division multiple access (SC-FDMA) has been selected as the uplink access scheme in the UTRA Long Term Evolution (LTE) due to its low peak-to-average power ratio properties compared to orthogonal frequency division multiple access. Nevertheless, in order to achieve such a benefit, it requires a localized allocation of the resource blocks, which naturally imposes a severe constraint on the scheduler design. In this paper, three new channel-aware scheduling algorithms for SC-FDMA are proposed and evaluated in both local and wide area scenarios. Whereas the first maximum expansion (FME) and the recursive maximum expansion (RME) are relative simple solutions to the above-mentioned problem, the minimum area-difference to the envelope (MADE) is a more computational expensive approach, which, on the other hand, performs closer to the optimal combinatorial solution. Simulation results show that adopting a proportional fair metric all the proposed algorithms quickly reach a high level of data-rate fairness. At the same time, they definitely outperform the round-robin scheduling in terms of cell spectral efficiency with gains up to 68.8% in wide area environments.
MOTAG: Moving Target Defense against Internet Denial of Service Attacks

Distributed Denial of Service (DDoS) attacks still pose a significant threat to critical infrastructure and Internet services alike. In this paper, we propose MOTAG, a moving target defense mechanism that secures service access for authenticated clients against flooding DDoS attacks. MOTAG employs a group of dynamic packet indirection proxies to relay data traffic between legitimate clients and the protected servers. Our design can effectively inhibit external attackers' attempts to directly bombard the network infrastructure. As a result, attackers will have to collude with malicious insiders in locating secret proxies and then initiating attacks. However, MOTAG can isolate insider attacks from innocent clients by continuously "moving" secret proxies to new network locations while shuffling client-to-proxy assignments. We develop a greedy shuffling algorithm to minimize the number of proxy re- allocations (shuffles) while maximizing attack isolation. Simulations are used to investigate MOTAG's effectiveness on protecting services of different scales against intensified DDoS attacks.
Internet of Music Things: an edge computing paradigm for opportunistic crowdsensing

Device centric music computation in the era of the Internet is participant-centric data recognition and computation that includes devices such as smartphones, real sound sensors, and computing systems. These participatory devices enhance the progression of Internet of Things, the devices which are responsible for gathering sensor data to the devices as per the requirements of the end users. This contribution analyzes a class of qualitative music composition applications in the context of the Internet of Things that we entitle as the Internet of Music Things. In this work, participated individuals having sensing devices capable of music sensing and computation share data within a group and retrieve information for analyzing and mapping any interconnected processes of common interest. We present the crowdsensing architecture for music composition in this contribution. Musical components like vocal and instrumental performances are provided by a committed edge layer in music crowdsensing architecture for improving computational efficiencies and lessening data traffic in cloud services for information processing and storage. Proposed opportunistic music crowdsensing orchestration organizes a categorical step toward aggregated music composition and sharing within the network. We also discuss an analytical case study of music crowdsensing challenges, clarify the unique features, and demonstrate edge-cloud computing paradigm along with deliberate outcomes. The requirement for four-layer unified crowdsensing archetype is discussed. The data transmission time, power, and relevant energy consumption of the proposed system are analyzed.
View synthesis by trinocular edge matching and transfer

This paper presents a novel automatic method for view synthesis (or image transfer) from a triplet of uncalibrated images based on trinocular edge matching followed by transfer by interpolation, occlusion detection and correction and finally rendering. The edge-based technique proposed here is of general practical relevance because it overcomes most of the problems encountered in other approaches that either rely upon dense correspondence, work in projective space or need explicit camera calibration. Applications range from immersive media and teleconferencing, image interpolation for fast rendering and compression.
Camber measurement system in a hot rolling mill

Camber is one of the most significant defects in the first stages of hot rolling of steel plates. This kind of defect may cause the clogging of finishing mills, but it is also the visible effect of alterations in the process. In this paper we describe the design and implementation of a computer vision system for real-time measurement of camber in a hot rolling mill. Our goal is to provide valuable feedback information to improve AGC operation. As ground truth values are almost impossible to obtain, we have analyzed the relationship among measured camber and other process variables in order to validate our results. The system has proved to be robust, and at the same time there is a strong relationship between known problems in the mill and system readings.
AQBox: An air quality measuring box from COTS gas sensors

In this demo, we demonstrate a functional prototype of an air quality monitoring box (AQBox) built from cheap/commodity off- the-shelf (COTS) sensors. We use a set of MQ gas sensors, a temperature and humidity sensor, a dust sensor and a GPS. We instrument the box, powered by an on-board battery, with a 3G cellular connection to upload sensed data to the cloud. The box is suitable for deploying in developing countries where other means to monitor air quality, such as large expensive environmental sensors affixed to certain locations (such as at weather stations) and use of satellite, is not available or not viable. We shall demonstrate the construction and function of the box as well as the collection and analysis of captured data (both in real-time and offline). Built and deployed in large numbers, we believe, these boxes can be a cheap solution to perpetual air quality monitoring for modern cities.
Severe attacks by dogs: characteristics of the dogs, the victims, and the attack settings.

Sixteen incidents involving dog bites fitting the description "severe" were identified among 5,711 dog bite incidents reported to health departments in five South Carolina counties (population 750,912 in 1980) between July 1, 1979, and June 30, 1982. A "severe" attack was defined as one in which the dog "repeatedly bit or vigorously shook its victim, and the victim or the person intervening had extreme difficulty terminating the attack." Information from health department records was clarified by interviews with animal control officers, health and police officials, and persons with firsthand knowledge of the events. Investigation disclosed that the dogs involved in the 16 severe attacks were reproductively intact males. The median age of the dogs was 3 years. A majority of the attacks were by American Staffordshire terriers, St. Bernards, and cocker spaniels. Ten of the dogs had been aggressive toward people or other dogs before the incident that was investigated. Ten of the 16 victims of severe attacks were 10 years of age or younger; the median age of all 16 victims was 8 years. Twelve of the victims either were members of the family that owned the attacking dog or had had contact with the dog before the attack. Eleven of the victims were bitten on the head, neck, or shoulders. In 88 percent of the cases, the attacks took place in the owner's yard or home, or in the adjoining yard. In 10 of the 16 incidents, members of the victims' families witnessed the attacks. The characteristics of these attacks, only one of which proved fatal, were similar in many respects to those that have been reported for other dog bite incidents that resulted in fatalities. On the basis of this study, the author estimates that a risk of 2 fatalities per 1,000 reported dog bites may exist nationwide. Suggestions made for the prevention of severe attacks focus on changing the behavior of both potential canine attackers and potential victims.
Speed control of stepper motor for collimator jaws positioning based on FPGA implementation

Stepper motor is widely used for precise position and speed control. In this paper speed control of stepper motor is used for collimator jaw positioning based on Field Programmable Gate Array (FPGA) implementation. Collimator consists of Symmetric and Asymmetric movement and can be used in dynamic mode also. In dynamic mode there is variation in the speed of jaw motion during the beam is ON. To provide speed variation stepper motor attached to jaws is controlled using FPGA. The proposed control mechanism uses speed variation technique to rotate motor through desired angle, speed and direction.
Is object localization for free? - Weakly-supervised learning with convolutional neural networks

Successful methods for visual object recognition typically rely on training datasets containing lots of richly annotated images. Detailed image annotation, e.g. by object bounding boxes, however, is both expensive and often subjective. We describe a weakly supervised convolutional neural network (CNN) for object classification that relies only on image-level labels, yet can learn from cluttered scenes containing multiple objects. We quantify its object classification and object location prediction performance on the Pascal VOC 2012 (20 object classes) and the much larger Microsoft COCO (80 object classes) datasets. We find that the network (i) outputs accurate image-level labels, (ii) predicts approximate locations (but not extents) of objects, and (iii) performs comparably to its fully-supervised counterparts using object bounding box annotation for training.
Are specific language impairment and dyslexia distinct disorders?

The purpose of this study was to determine whether specific language impairment (SLI) and dyslexia are distinct developmental disorders.



Study 1 investigated the overlap between SLI identified in kindergarten and dyslexia identified in 2nd, 4th, or 8th grades in a representative sample of 527 children. Study 2 examined phonological processing in a subsample of participants, including 21 children with dyslexia only, 43 children with SLI only, 18 children with SLI and dyslexia, and 165 children with typical language/reading development. Measures of phonological awareness and nonword repetition were considered.



Study 1 showed limited but statistically significant overlap between SLI and dyslexia. Study 2 found that children with dyslexia or a combination of dyslexia and SLI performed significantly less well on measures of phonological processing than did children with SLI only and those with typical development. Children with SLI only showed only mild deficits in phonological processing compared with typical children.



These results support the view that SLI and dyslexia are distinct but potentially comorbid developmental language disorders. A deficit in phonological processing is closely associated with dyslexia but not with SLI when it occurs in the absence of dyslexia.
Behavior and expectations of mobile job seekers: an industry study focusing on job boards

The market share of mobile devices like smartphones and tablets is growing rapidly. These devices are increasingly used to access the services offered on the Internet. Time spent online has started to shift considerably from desktop and laptop computers to mobile connected post-pc devices. In certain areas, mobile usage has already exceeded the access and traffic generated by desktop computers. This development also affects the usage behavior and ex-pectations of job seekers when accessing job ads and other job-related information online. In this context the paper at hand presents a study analyzing the behavior and user expectations of job seekers using mobile devices in Germany. The study shows that the majority of smartphone and tablet users have already accessed job ads and used job search applications ("apps") through these devices. Moreover, many of those respondents who have already accessed job ads with a post-pc device also expect to be able to apply for a job via smartphone and tablet.
A review on trends in production of secondary metabolites from higher plants by in vitro tissue, organ and cell cultures.

Plant cell and tissue cultures can be established routinely under sterile conditions from explants, such as plant leaves, stems, roots, meristems etc for both the ways for multiplication and extraction of secondary metabolites. Strain improvement, methods for the selection of high-producing cell lines, and medium optimizations can lead to an enhancement in secondary metabolite production. However, most often trials with plant cell cultures fail to produce the desired products. In such cases, strategies to improve the production of secondary metabolites must be considered. One of the main problems encountered is the lack of basic knowledge of the biosynthetic routes, and mechanisms responsible for the production of plant metabolites. Where the productivity of the desired metabolites is limited by the lack of particular precursors, biotransformation using an exogenous supply of biosynthetic precursors, genetic manipulation and metabolic engineering may improve the accumulation of compounds. Feedback inhibition of metabolic enzymes as well as inhibition of membrane transport can be eliminated by the accumulation of synthesized products in a second phase introduced into the aqueous medium. Organ cultures and in vitro biomass production often have sites of synthesis and storage of secondary metabolites in separate compartments. Elicitors, compounds triggering the formation of secondary metabolites, can be abiotic or biotic. Natural elicitors include polysaccharides such as pectin and chitosan, which are also used in the immobilization and permeabilization of plant cells. Immobilization with suitable bioreactor system provides several advantages, such as continuous process operation, but for the development of an immobilized plant cell culture process, natural or artifically induced secretion of the accumulated product into the surrounding medium is necessary. The present review highlights the nature, applications, perspective and scale up methods for the production of valuable secondary metabolites in vitro.
How evolution learns to generalise: Using the principles of learning theory to understand the evolution of developmental organisation

One of the most intriguing questions in evolution is how organisms exhibit suitable phenotypic variation to rapidly adapt in novel selective environments. Such variability is crucial for evolvability, but poorly understood. In particular, how can natural selection favour developmental organisations that facilitate adaptive evolution in previously unseen environments? Such a capacity suggests foresight that is incompatible with the short-sighted concept of natural selection. A potential resolution is provided by the idea that evolution may discover and exploit information not only about the particular phenotypes selected in the past, but their underlying structural regularities: new phenotypes, with the same underlying regularities, but novel particulars, may then be useful in new environments. If true, we still need to understand the conditions in which natural selection will discover such deep regularities rather than exploiting 'quick fixes' (i.e., fixes that provide adaptive phenotypes in the short term, but limit future evolvability). Here we argue that the ability of evolution to discover such regularities is formally analogous to learning principles, familiar in humans and machines, that enable generalisation from past experience. Conversely, natural selection that fails to enhance evolvability is directly analogous to the learning problem of over-fitting and the subsequent failure to generalise. We support the conclusion that evolving systems and learning systems are different instantiations of the same algorithmic principles by showing that existing results from the learning domain can be transferred to the evolution domain. Specifically, we show that conditions that alleviate over-fitting in learning systems successfully predict which biological conditions (e.g., environmental variation, regularity, noise or a pressure for developmental simplicity) enhance evolvability. This equivalence provides access to a well-developed theoretical framework from learning theory that enables a characterisation of the general conditions for the evolution of evolvability.