nils-herrmann/clean_scirepeval huggingface dataset:
Plyometric Training Favors Optimizing Muscle–Tendon Behavior during Depth Jumping

The purpose of the present study was to elucidate how plyometric training improves stretch-shortening cycle (SSC) exercise performance in terms of muscle strength, tendon stiffness, and muscle-tendon behavior during SSC exercise. Eleven men were assigned to a training group and ten to a control group. Subjects in the training group performed depth jumps (DJ) using only the ankle joint for 12 weeks. Before and after the period, we observed reaction forces at foot, muscle-tendon behavior of the gastrocnemius, and electromyographic activities of the triceps surae and tibialis anterior during DJ. Maximal static plantar flexion strength and Achilles tendon stiffness were also determined. In the training group, maximal strength remained unchanged while tendon stiffness increased. The force impulse of DJ increased, with a shorter contact time and larger reaction force over the latter half of braking and initial half of propulsion phases. In the latter half of braking phase, the average electromyographic activity (mEMG) increased in the triceps surae and decreased in tibialis anterior, while fascicle behavior of the gastrocnemius remained unchanged. In the initial half of propulsion, mEMG of triceps surae and shortening velocity of gastrocnemius fascicle decreased, while shortening velocity of the tendon increased. These results suggest that the following mechanisms play an important role in improving SSC exercise performance through plyometric training: (1) optimization of muscle-tendon behavior of the agonists, associated with alteration in the neuromuscular activity during SSC exercise and increase in tendon stiffness and (2) decrease in the neuromuscular activity of antagonists during a counter movement.
Paraphrase Generation with Deep Reinforcement Learning

Automatic generation of paraphrases from a given sentence is an important yet challenging task in natural language processing (NLP). In this paper, we present a deep reinforcement learning approach to paraphrase generation. Specifically, we propose a new framework for the task, which consists of a generator and an evaluator, both of which are learned from data. The generator, built as a sequenceto-sequence learning model, can produce paraphrases given a sentence. The evaluator, constructed as a deep matching model, can judge whether two sentences are paraphrases of each other. The generator is first trained by deep learning and then further fine-tuned by reinforcement learning in which the reward is given by the evaluator. For the learning of the evaluator, we propose two methods based on supervised learning and inverse reinforcement learning respectively, depending on the type of available training data. Experimental results on two datasets demonstrate the proposed models (the generators) can produce more accurate paraphrases and outperform the stateof-the-art methods in paraphrase generation in both automatic evaluation and human evaluation.
A Theoretical Model of Augmented Reality Acceptance

Recent tourism research increasingly explored the opportunities of using Augmented Reality (AR) in order to boost tourism and increase the value for tourists while travelling within a destination. The Technology Acceptance Model (TAM) has been applied to a number of research disciplines, lately also AR however, studies focusing on the tourism context are still scarce. As this field is expected to increase in importance rapidly due to technological advancements and research into functionality, acceptance and usefulness, it is important to identify what the basic requirements are for AR to be accepted by users. Furthermore, the provision of a conceptual model provides researchers with a starting point on which they can base their future research. Therefore, this paper proposes an AR acceptance model including five external variables that might be included in future AR acceptance research.
Personality within Information Systems Research: a literature Analysis

After integrating five higher-order personality traits in an extended model of technology acceptance, Devaraj et al. (2008) called for further research including personality in information systems research to understand the formation of perceptual beliefs and behaviors in more detail. To assist such future research endeavors, this article gives an overview on prior research discussing personality within the six plus two journals of the AIS Senior Basket (MISQ, ISR, JMIS, JAIS, EJIS, ISJ, JSIS, JIT) 1 . Therefore, the Theory of a Person approach (ToP) derived from psychology research serves as the underlying conceptual matrix. Within the literature analysis, we identify 30 articles discussing personality traits on distinct hierarchical levels in three fields of information systems research. Results of the literature analysis reveal a shift of examined traits over the last years. In addition, research gaps are identified so that propositions are derived. Further research results and implications are discussed within the article.
Pattern Recognition and Computer Vision

Currently, most methods of object detection are monocularbased. However, due to the sensitivity to color, these methods can not handle many hard samples. With the depth information, disparity maps are helpful to get over this problem. In this paper, we propose the asymmetric two-stream networks for RGB-Disparity based object detection. Our method consists of two networks, Disparity Representations Mining Network (DRMN) and Muti-Modal Detection Network (MMDN), to combine RGB and disparity data for more accurate detection. Unlike normal two-stream networks, our model is asymmetric because of the different capacity of RGB and disparity data. We are the first to propose a deep learning based framework utilizing only binocular information for object detection. The experiment results on KITTI and our proposed BPD dataset demonstrate that our method can achieve a significant increase in performance efficiently and get the state-of-the-art.
Models of object recognition

Understanding how biological visual systems recognize objects is one of the ultimate goals in computational neuroscience. From the computational viewpoint of learning, different recognition tasks, such as categorization and identification, are similar, representing different trade-offs between specificity and invariance. Thus, the different tasks do not require different classes of models. We briefly review some recent trends in computational vision and then focus on feedforward, view-based models that are supported by psychophysical and physiological data.
Metabolism and cancer: the circadian clock connection

Circadian rhythms govern a remarkable variety of metabolic and physiological functions. Accumulating epidemiological and genetic evidence indicates that the disruption of circadian rhythms might be directly linked to cancer. Intriguingly, several molecular gears constituting the clock machinery have been found to establish functional interplays with regulators of the cell cycle, and alterations in clock function could lead to aberrant cellular proliferation. In addition, connections between the circadian clock and cellular metabolism have been identified that are regulated by chromatin remodelling. This suggests that abnormal metabolism in cancer could also be a consequence of a disrupted circadian clock. Therefore, a comprehensive understanding of the molecular links that connect the circadian clock to the cell cycle and metabolism could provide therapeutic benefit against certain human neoplasias.
A normal ano-genital exam: sexual abuse or not?

Sexual abuse is a problem of epidemic proportions in the United States. Pediatric nurse practitioners (PNPs) are at the forefront of providing care to children and families. The PNP is in a unique position to educate patients and families regarding sexual abuse and dispel common myths associated with sexual abuse. One such myth is that a normal ano-genital examination is synonymous with the absence of sexual abuse. This article will provide primary care providers, including PNPs, with a framework for understanding why a normal ano-genital examination does not negate the possibility of sexual abuse/assault. Normal ano-genital anatomy, changes that occur with puberty, and physical properties related to the genitalia and anus will be discussed. Photos will provide visualization of both normal variants of the pre-pubertal hymen and genitalia as well as changes that occur with puberty. Implications for practice for PNPs will be discussed.
State of the Art Report: Music Emotion Recognition: A State of the Art Review

This paper surveys the state of the art in automatic emotion recognition in music. Music is oftentimes referred to as a “language of emotion” , and it is natural for us to categorize music in terms of its emotional associations. Myriad features, such as harmony, timbre, interpretation, and lyrics affect emotion, and the mood of a piece may also change over its duration. But in developing automated systems to organize music in terms of emotional content, we are faced with a problem that oftentimes lacks a welldefined answer; there may be considerable disagreement regarding the perception and interpretation of the emotions of a song or ambiguity within the piece itself. When compared to other music information retrieval tasks (e.g., genre identification), the identification of musical mood is still in its early stages, though it has received increasing attention in recent years. In this paper we explore a wide range of research in music emotion recognition, particularly focusing on methods that use contextual text information (e.g., websites, tags, and lyrics) and content-based approaches, as well as systems combining multiple feature domains.
A high gain patch antenna using negative permeability metamaterial structures

In this paper, a high gain patch antenna realized by using negative permeability electromagnetic metamaterials is presented to operate at 5–5.5 GHz. The proposed metamaterial cell is investigated and analyzed by numerical methods. We found that the proposed metamaterial structure provides a negative permeability from 5 GHz to 5.5 GHz, which overlaps with the operating frequency band of the reference microstrip antenna. By periodically loading a number of the proposed metamaterial cells on the substrate around the patch antenna, the gain of the antenna at operating frequency is enhanced by about 2.5 dBi, while there is no effect on the operating bandwidth of the reference antenna.
A Known Plaintext Attack on the PKZIP Stream Cipher

Abs t r ac t . The PKZIP program is one of the more widely used archive/ compression programs on personM, computers. It also has many compatible variants on other computers~ and is used by most BBS's and ftp sites to compress their archives. PKZIP provides a stream cipher which allows users to scramble files with variable length keys (passwords). In this paper we describe a known pla.intext attack on this cipher, which can find the internal representation of the key within a few hours on a personal computer using a few hundred bytes of known plaintext. In many cases, the actual user keys can also be found from the internal representation. We conclude that the PKZIP cipher is weak, and should not be used to protect valuable data.
Cloud Computing Technologies and Applications

In a nutshell, the existing Internet provides to us content in the forms of videos, emails and information served up in web pages. With Cloud Computing, the next generation of Internet will allow us to “buy” IT services from a web portal, drastic expanding the types of merchandise available beyond those on e-commerce sites such as eBay and Taobao. We would be able to rent from a virtual storefront the basic necessities to build a virtual data center: such as CPU, memory, storage, and add on top of that the middleware necessary: web application servers, databases, enterprise server bus, etc. as the platform(s) to support the applications we would like to either rent from an Independent Software Vendor (ISV) or develop ourselves. Together this is what we call as “IT as a Service,” or ITaaS, bundled to us the end users as a virtual data center. Within ITaaS, there are three layers starting with Infrastructure as a Service, or IaaS, comprised of the physical assets we can see and touch: servers, storage, and networking switches. At the IaaS level, what cloud computing service provider can offer is basic computing and storage capability, such as the cloud computing center founded by IBM in Wuxi Software Park and Amazon EC2. Taking computing power provision as an example, the basic unit provided is the server, including CPU, memory, storage, operating system and system monitoring software. In order to allow users to customize their own server environment, server template technology is resorted to, which means binding certain server configuration and the operating system and software together, and providing customized functions as required at the same time. Using virtualization technology, we could provide as little as 0.1 CPU in a virtual machine to the end user, therefore drastically increasing the utilization potential of a physical server to multiple users.
Cloud Computing Benefits, risks and recommendations for information security

Cloud computing is a new way of delivering computing resources, not a new technology. Computing services ranging from data storage and processing to software, such as email handling, are now available instantly, commitment-free and on-demand. Since we are in a time of belt-tightening, this new economic model for computing has found fertile ground and is seeing massive global investment. According to IDC’s analysis, the worldwide forecast for cloud services in 2009 will be in the order of $17.4bn. The estimation for 2013 amounts to $44.2bn, with the European market ranging from €971m in 2008 to €6,005m in 2013 .
A nonlinear observer approach for concurrent estimation of pose, IMU bias and camera-to-IMU rotation

This paper concerns the problem of pose estimation for an inertial-visual sensor. It is well known that IMU bias, and calibration errors between camera and IMU frames can impair the achievement of high-quality estimates through the fusion of visual and inertial data. The main contribution of this work is the design of new observers to estimate pose, IMU bias and camera-to-IMU rotation. The observers design relies on an extension of the so-called passive complementary filter on SO(3). Stability of the observers is established using Lyapunov functions under adequate observability conditions. Experimental results are presented to assess this approach.
Modified Playfair Cipher using Rectangular Matrix

One of the well known polyalphabetic ciphers is the Playfair cipher. In this cipher digrams or groups of 2 letters in the plain text is converted to cipher text digrams during encryption using a key. Similarly during decryption cipher text digrams are converted to plain text digrams using the same key. However the original 5 x 5 Playfair cipher can support only 25 uppercase alphabets. To overcome this drawback we propose a rectangular matrix having 10 columns and 9 rows which can support almost all the printable characters including white space. This paper analyses the original Playfair cipher, the different variations that have been proposed and the modified Playfair cipher that we propose. Cryptanalysis is done to show that the proposed cipher is a strong one.
Composable DevOps: Automated Ontology Based DevOps Maturity Analysis

In this era of the emerging digitized, mobilized, and cloudified enterprises, the concept of the "compos able business" is the most critical piece which ties everything together. The digital enterprise is here, and its prime characteristic is that is essentially detaches and segregates existing businesses and reassembles them according to market demands. Every industry, from transportation to eyewear is up for disruption, and developers are in the forefront of this movement. In turn, these developers are under intense pressure to accelerate time to market. The compos able enterprise approach requires a reconsideration of traditional models of the entire IT organization. These organization and their processes need to be broken up into components that follow certain key design principles such as The Minimal Functions with least Dependencies, portability, Shared Knowledge, Predictable Contracts and Maximized Human Value. The last three bullet points encapsulate the very definition of DevOps . The concept of better integration between Development and Operations is a valuable objective. The goal is to foster measurable incremental cultural change to derive most overall value out of the union of people, process and technology. But the cultural issues, reward models, and risk allocation create obvious barriers in attaining those goals. The common industry belief is to use the compos able enterprise framework to build a platform using the right tools and you will have attained DevOps nirvana. In this paper we will explore valuable lessons learned from our mistakes in tool centric adoption of IT Infrastructure Library (ITIL) . We will also show how we applied those lessons to develop a lightweight compos able/contextual DevOps framework that learns and measure itself to avoid those cultural pitfalls.
Transfer Learning and Sentence Level Features for Named Entity Recognition on Tweets

We present our system for the WNUT 2017 Named Entity Recognition challenge on Twitter data. We describe two modifications of a basic neural network architecture for sequence tagging. First, we show how we exploit additional labeled data, where the Named Entity tags differ from the target task. Then, we propose a way to incorporate sentence level features. Our system uses both methods and ranked second for entity level annotations, achieving an F1-score of 40.78, and second for surface form annotations, achieving an F1score of 39.33.
Sales Promotion and Purchasing Intention : Applying the Technology Acceptance Model in Consumer-to-Consumer

This paper studies the impact of sales promotion on consumers’ intention to purchase online in the setting of a consumer-to-consumer (C2C) marketplace. The research is grounded in the theory of Technology Acceptance Model (TAM). We test a number of hypotheses empirically about the relationship between online sales promotion, TAM and C2C online purchasing intention. The finding of this study suggests that, by increasing consumers’ perception of ease of use and by decreasing consumer’s perceived risk, online sales promotion has a significant impact on their purchasing intention.
Explainability by Parsing: Neural Module Tree Networks for Natural Language Visual Grounding

Grounding natural language in images essentially requires composite visual reasoning. However, existing methods over-simplify the composite nature of language into a monolithic sentence embedding or a coarse composition of subject-predicate-object triplet. They might perform well on short phrases, but generally fail in longer sentences, mainly due to the over-fitting to certain vision-language bias. In this paper, we propose to ground natural language in an intuitive, explainable, and composite fashion as it should be. In particular, we develop a novel modular network called Neural Module Tree network (NMTREE) that regularizes the visual grounding along the dependency parsing tree of the sentence, where each node is a module network that calculates or accumulates the grounding score in a bottom-up direction where as needed. NMTREE disentangles the visual grounding from the composite reasoning, allowing the former to only focus on primitive and easyto-generalize patterns. To reduce the impact of parsing errors, we train the modules and their assembly end-to-end by using the Gumbel-Softmax approximation and its straightthrough gradient estimator, accounting for the discrete process of module selection. Overall, the proposed NMTREE not only consistently outperforms the state-of-the-arts on several benchmarks and tasks, but also shows explainable reasoning in grounding score calculation. Therefore, NMTREE shows a good direction in closing the gap between explainability and performance.
Comparison of anatomic and non-anatomic hepatic resection for hepatocellular carcinoma.

The aim of the present study was to compare the prognostic impact of anatomic resection (AR) versus non-anatomic resection (NAR) on patient survival after resection of a single hepatocellular carcinoma (HCC).



To control for confounding variable distributions, a 1-to-1 propensity score match was applied to compare the outcomes of AR and NAR. Among 710 patients with a primary, solitary HCC of <5.0 cm in diameter that was resectable by either AR or NAR from 2003 to 2007 in Japan and Korea, 355 patients underwent NAR and 355 underwent AR of at least one section with complete removal of the portal territory containing the tumor.



Overall survival (OS) was better in the AR than NAR group (hazard ratio 1.67, 95% confidence interval 1.28-2.19, P < 0.001) while disease-free survival showed no significant difference. Significantly fewer patients in the AR than NAR group developed intrahepatic HCC recurrence and multiple intrahepatic recurrences. Patients with poorly differentiated HCC who underwent AR had improved disease-free survival and OS.



Anatomic resection decreases the risk of tumor recurrence and improves OS in patients with a primary, solitary HCC of <5.0 cm in diameter.
Recommendation with Social Relationships via Deep Learning

While users trust the selections of their social friends in recommendation systems, the preferences of friends do not necessarily match. In this study, we introduce a deep learning approach to learn both about user preferences and the social influence of friends when generating recommendations. In our model we design a deep learning architecture by stacking multiple marginalized Denoising Autoencoders. We define a joint objective function to enforce the latent representation of social relationships in the Autoencoder's hidden layer to be as close as possible to the users' latent representation when factorizing the user-item matrix. We formulate a joint objective function as a minimization problem to learn both user preferences and friends' social influence and we present an optimization algorithm to solve the joint minimization problem. Our experiments on four benchmark datasets show that the proposed approach achieves high recommendation accuracy, compared to other state-of-the-art methods.
Removable partial dentures: clinical concepts.

This article provides a review of the traditional clinical concepts for the design and fabrication of removable partial dentures (RPDs). Although classic theories and rules for RPD designs have been presented and should be followed, excellent clinical care for partially edentulous patients may also be achieved with computer-aided design/computer-aided manufacturing technology and unique blended designs. These nontraditional RPD designs and fabrication methods provide for improved fit, function, and esthetics by using computer-aided design software, composite resin for contours and morphology of abutment teeth, metal support structures for long edentulous spans and collapsed occlusal vertical dimensions, and flexible, nylon thermoplastic material for metal-supported clasp assemblies.