nils-herrmann/clean_scirepeval huggingface dataset:
Text mining and its potential applications in systems biology.

With biomedical literature increasing at a rate of several thousand papers per week, it is impossible to keep abreast of all developments; therefore, automated means to manage the information overload are required. Text mining techniques, which involve the processes of information retrieval, information extraction and data mining, provide a means of solving this. By adding meaning to text, these techniques produce a more structured analysis of textual knowledge than simple word searches, and can provide powerful tools for the production and analysis of systems biology models.
Engaging Engineering Students with Gamification

Well-designed games are good motivators by nature, as they imbue players with clear goals and a sense of reward and fulfillment, thus encouraging them to persist and endure in their quests. Recently, this motivational power has started to be applied to non- game contexts, a practice known as Gamification. This adds gaming elements to non-game processes, motivating users to adopt new behaviors, such as improving their physical condition, working more, or learning something new. This paper describes an experiment in which game-like elements were used to improve the delivery of a Master's level College course, including scoring, levels, leaderboards, challenges and badges. To assess how gamification impacted the learning experience, we compare the gamified course to its non-gamified version from the previous year, using different performance measures. We also assessed student satisfaction as compared to other regular courses in the same academic context. Results were very encouraging, showing significant increases ranging from lecture attendance to online participation, proactive behaviors and perusing the course reference materials. Moreover, students considered the gamified instance to be more motivating, interesting and easier to learn as compared to other courses. We finalize by discussing the implications of these results on the design of future gamified learning experiences.
Spontaneous vs. posed facial behavior: automatic analysis of brow actions

Past research on automatic facial expression analysis has focused mostly on the recognition of prototypic expressions of discrete emotions rather than on the analysis of dynamic changes over time, although the importance of temporal dynamics of facial expressions for interpretation of the observed facial behavior has been acknowledged for over 20 years. For instance, it has been shown that the temporal dynamics of spontaneous and volitional smiles are fundamentally different from each other. In this work, we argue that the same holds for the temporal dynamics of brow actions and show that velocity, duration, and order of occurrence of brow actions are highly relevant parameters for distinguishing posed from spontaneous brow actions. The proposed system for discrimination between volitional and spontaneous brow actions is based on automatic detection of Action Units (AUs) and their temporal segments (onset, apex, offset) produced by movements of the eyebrows. For each temporal segment of an activated AU, we compute a number of mid-level feature parameters including the maximal intensity, duration, and order of occurrence. We use Gentle Boost to select the most important of these parameters. The selected parameters are used further to train Relevance Vector Machines to determine per temporal segment of an activated AU whether the action was displayed spontaneously or volitionally. Finally, a probabilistic decision function determines the class (spontaneous or posed) for the entire brow action. When tested on 189 samples taken from three different sets of spontaneous and volitional facial data, we attain a 90.7% correct recognition rate.
Are People Really Social in Porn 2.0?

Social Web 2.0 features have become a vital component in a variety of multimedia systems, e.g., YouTube and Last.fm. Interestingly, adult video websites are also starting to adopt these Web 2.0 principles, giving rise to the term “Porn 2.0”. This paper examines a large Porn 2.0 social network, through data covering 563k users. We explore a number of unusual behavioural aspects that set this apart from more traditional multimedia social networks. We particularly focus on the role of gender and sexuality, to understand how these different groups behave. A number of key differences are discovered relating to social demographics, modalities of interaction and content consumption habits, shedding light on this understudied area of online activity.
Efficient Semantic Communication via Compatible Beliefs

In previous works, Juba and Sudan  and Goldreich, Juba and Sudan  considered the idea of “semantic communication”, wherein two players, a user and a server, attempt to communicate with each other without any prior common language (or communication) protocol. They showed that if communication was goal-oriented and the user could sense progress towards the goal (or verify when it has been achieved), then meaningful communication is possible, in that the user’s goal can be achieved whenever the server is helpful. A principal criticism of their result has been that it is inefficient: in order to determine the “right” protocol to communicate with the server, the user enumerates protocols and tries them out with the server until it finds one that allows it to achieve its goal. They also show settings in which such enumeration is essentially the best possible solution. In this work we introduce definitions which allow for efficient behavior in practice. Roughly, we measure the performance of users and servers against their own “beliefs” about natural protocols. We show that if user and server are efficient with respect to their own beliefs and their beliefs are (even just slightly) compatible with each other, then they can achieve their goals very efficiently. We show that this model allows sufficiently “broad-minded” servers to talk with “exponentially” many different users in polynomial time, while dismissing the “counterexamples” in the previous work as being “narrow-minded,” or based on “incompatible beliefs.” ∗Portions of this work are presented in modified form in the first author’s Ph.D. thesis. [5, Chapter 4]. Research supported in part by NSF Awards CCF-0915155 and CCF-0939370.
Control of a Biomimetic "Soft-actuated" 10DoF Lower Body Exoskeleton

The successful motor rehabilitation of stroke, traumatic brain/spinal cord/sport injured patients requires a highly intensive and task-specific therapy based approach. Significant budget, time and logistic constraints limits a direct hand-to-hand therapy approach, so that intelligent assistive machines may offer a solution to promote motor recovery and obtain a better understanding of human motor control. This paper will address the development of a lower limb exoskeleton legs for force augmentation and active assistive walking training. The twin wearable legs are powered by pneumatic muscle actuators (pMAs), an experimental low mass high power to weight and volume actuation system. In addition, the pMA being pneumatic produces a more natural muscle like contact and as such can be considered a soft and biomimetic actuation system. This capacity to "replicate" the function of natural muscle and inherent safety is extremely important when working in close proximity to humans. The integration of the components sections and testing of the performance will also be considered to show how the structure and actuators can be combined to produce the various systems needed for a highly flexible/low weight clinically viable rehabilitation exoskeleton
Tension propagation analysis of novel robotized surgical platform for transumbilical single-port access surgery

In this paper, tension propagation analysis of a newly designed multi-DOF robotic platform for single-port access surgery (SPS) is presented. The analysis is based on instantaneous kinematics of the proposed 6-DOF surgical instrument, and provides the decision criteria for estimating the payload of a surgical instrument according to its pose changes and specifications of a driving-wire. Also, the wire-tension and the number of reduction ratio to manage such a payload can be estimated, quantitatively. The analysis begins with derivation of the power transmission efficiency through wire-interfaces from each instrument joint to an actuator. Based on the energy conservation law and the capstan equation, we modeled the degradation of power transmission efficiency due to 1) the reducer called wire-reduction mechanism, 2) bending of proximal instrument joints, and 3) bending of hyper-redundant guide tube. Based on the analysis, the tension of driving-wires was computed according to various manipulation poses and loading conditions. In our experiment, a newly designed surgical instrument successfully managed the external load of 1kgf, which was applied to the end effector of a surgical manipulator.
Multi-class cost-sensitive boosting with p-norm loss functions

We propose a family of novel cost-sensitive boosting methods for multi-class classification by applying the theory of gradient boosting to p-norm based cost functionals. We establish theoretical guarantees including proof of convergence and convergence rates for the proposed methods. Our theoretical treatment provides interpretations for some of the existing algorithms in terms of the proposed family, including a generalization of the costing algorithm, DSE and GBSE-t, and the Average Cost method. We also experimentally evaluate the performance of our new algorithms against existing methods of cost sensitive boosting, including AdaCost, CSB2, and AdaBoost.M2 with cost-sensitive weight initialization. We show that our proposed scheme generally achieves superior results in terms of cost minimization and, with the use of higher order p-norm loss in certain cases, consistently outperforms the comparison methods, thus establishing its empirical advantage.
Efficient Lane and Vehicle Detection with Integrated Synergies (ELVIS)

On-road vehicle detection and lane detection are integral parts of most advanced driver assistance systems (ADAS). In this paper, we introduce an integrated approach called Efficient Lane and Vehicle detection with Integrated Synergies (ELVIS), that exploits the inherent synergies between lane and on-road vehicle detection to improve the overall computational efficiency without compromising on the robustness of both the tasks. Detailed evaluations show that the vehicle detection component of ELVIS shows at least 50% lesser false alarms with equal or better detection rates, and reducing the computational costs by over 90% as compared to state-of-the-art vehicle detection methods. Similarly, the lane detection component shows more reliable lane feature extraction with average computation costs that are at least 35% lesser than existing techniques.
Doubly fed induction generator uising back-toback PWM converters and its application to variable-speed wind-energy generation

The paper describes the engineering and design of a doubly fed induction generator (DFIG), using back-to-back PWM voltage-source converters in the rotor circuit. A vector-control scheme for the supply-side PWM converter results in independent control of active and reactive power drawn from the supply, while ensuring sinusoidal supply currents. Vector control of the rotor-connected converter provides for wide speed-range operation; the vector scheme is embedded in control loops which enable optimal speed tracking for maximum energy capture from the wind. An experimental rig, which represents a 1.5 kW variable speed wind-energy generation system is described, and experimental results are given that illustrate the excellent performance characteristics of the system. The paper considers a grid-connected system; a further paper will describe a stand-alone system.
Improved Laplacian Smoothing of Noisy Surface Meshes

This paper presents a technique for smoothing polygonal surface meshes that avoids the well-known problem of deformation and shrinkage caused by many smoothing methods, like e.g. the Laplacian algorithm. The basic idea is to push the vertices of the smoothed mesh back towards their previous locations. This technique can be also used in order to smooth unstructured point sets, by reconstructing a surface mesh to which the smoothing technique is applied. The key observation is that a surface mesh which is not necessarily topologically correct, but which can efficiently be reconstructed, is sufficient for that purpose.
Mapping Words to the World in Infancy : Infants ’ Expectations for Count Nouns and Adjectives

Threeexperimentsdocument that14-month-old infants’construalofobjects (e.g.,purple animals) is influenced by naming, that they can distinguish between the grammatical form noun and adjective, and that they treat this distinction as relevant to meaning. In each experiment, infants extended novel nouns (e.g., “This one is a blicket”) specifically to object categories (e.g., animal), and not to object properties (e.g., purple things). This robust noun–category link is related to grammatical form and not to surface differences in the presentation of novel words (Experiment 3). Infants’extensions of novel adjectives (e.g., “This one is blickish”) were more fragile: They extended adjectives specifically to object properties when the property was color (Experiment 1), but revealed a less precise mapping when the property was texture (Experiment 2). These results reveal that by 14 months, infants distinguish between grammatical forms and utilize these distinctions in determining the meaning of novel words.
PSHAPE: Automatically Combining Gadgets for Arbitrary Method Execution

Return-Oriented Programming (ROP) is the cornerstone of today’s exploits. Yet, building ROP chains is predominantly a manual task, enjoying limited tool support. Many of the available tools contain bugs, are not tailored to the needs of exploit development in the real world and do not offer practical support to analysts, which is why they are seldom used for any tasks beyond gadget discovery. We present PSHAPE (P ractical Support for Half-Automated P rogram Exploitation), a tool which assists analysts in exploit development. It discovers gadgets, chains gadgets together, and ensures that side effects such as register dereferences do not crash the program. Furthermore, we introduce the notion of gadget summaries, a compact representation of the effects a gadget or a chain of gadgets has on memory and registers. These semantic summaries enable analysts to quickly determine the usefulness of long, complex gadgets that use a lot of aliasing or involve memory accesses. Case studies on nine real binaries representing 147 MiB of code show PSHAPE’s usefulness: it automatically builds usable ROP chains for nine out of eleven scenarios.
Supersizing self-supervision: Learning to grasp from 50K tries and 700 robot hours

Current model free learning-based robot grasping approaches exploit human-labeled datasets for training the models. However, there are two problems with such a methodology: (a) since each object can be grasped in multiple ways, manually labeling grasp locations is not a trivial task; (b) human labeling is biased by semantics. While there have been attempts to train robots using trial-and-error experiments, the amount of data used in such experiments remains substantially low and hence makes the learner prone to over-fitting. In this paper, we take the leap of increasing the available training data to 40 times more than prior work, leading to a dataset size of 50K data points collected over 700 hours of robot grasping attempts. This allows us to train a Convolutional Neural Network (CNN) for the task of predicting grasp locations without severe overfitting. In our formulation, we recast the regression problem to an 18-way binary classification over image patches. We also present a multi-stage learning approach where a CNN trained in one stage is used to collect hard negatives in subsequent stages. Our experiments clearly show the benefit of using large-scale datasets (and multi-stage training) for the task of grasping. We also compare to several baselines and show state-of-the-art performance on generalization to unseen objects for grasping.
Deep Learning for Quality Assessment in Live Video Streaming

Video content providers put stringent requirements on the quality assessment methods realized on their services. They need to be accurate, real-time, adaptable to new content, and scalable as the video set grows. In this letter, we introduce a novel automated and computationally efficient video assessment method. It enables accurate real-time (online) analysis of delivered quality in an adaptable and scalable manner. Offline deep unsupervised learning processes are employed at the server side and inexpensive no-reference measurements at the client side. This provides both real-time assessment and performance comparable to the full reference counterpart, while maintaining its no-reference characteristics. We tested our approach on the LIMP Video Quality Database (an extensive packet loss impaired video set) obtaining a correlation between <inline-formula><tex-math notation="LaTeX">$78\%$</tex-math> </inline-formula> and <inline-formula><tex-math notation="LaTeX">$91\%$</tex-math></inline-formula> to the FR benchmark (the video quality metric). Due to its unsupervised learning essence, our method is flexible and dynamically adaptable to new content and scalable with the number of videos.
SeDaSC: Secure Data Sharing in Clouds

Cloud storage is an application of clouds that liberates organizations from establishing in-house data storage systems. However, cloud storage gives rise to security concerns. In case of group-shared data, the data face both cloud-specific and conventional insider threats. Secure data sharing among a group that counters insider threats of legitimate yet malicious users is an important research issue. In this paper, we propose the Secure Data Sharing in Clouds (SeDaSC) methodology that provides: 1) data confidentiality and integrity; 2) access control; 3) data sharing (forwarding) without using compute-intensive reencryption; 4) insider threat security; and 5) forward and backward access control. The SeDaSC methodology encrypts a file with a single encryption key. Two different key shares for each of the users are generated, with the user only getting one share. The possession of a single share of a key allows the SeDaSC methodology to counter the insider threats. The other key share is stored by a trusted third party, which is called the cryptographic server. The SeDaSC methodology is applicable to conventional and mobile cloud computing environments. We implement a working prototype of the SeDaSC methodology and evaluate its performance based on the time consumed during various operations. We formally verify the working of SeDaSC by using high-level Petri nets, the Satisfiability Modulo Theories Library, and a Z3 solver. The results proved to be encouraging and show that SeDaSC has the potential to be effectively used for secure data sharing in the cloud.
Some key differences between a happy life and a meaningful life

Being happy and finding life meaningful overlap, but there are important differences. A large survey revealed multiple differing predictors of happiness (controlling for meaning) and meaningfulness (controlling for happiness). Satisfying one’s needs and wants increased happiness but was largely irrelevant to meaningfulness. Happiness was largely present oriented, whereas meaningfulness involves integrating past, present, and future. For example, thinking about future and past was associated with high meaningfulness but low happiness. Happiness was linked to being a taker rather than a giver, whereas meaningfulness went with being a giver rather than a taker. Higher levels of worry, stress, and anxiety were linked to higher meaningfulness but lower happiness. Concerns with personal identity and expressing the self contributed to meaning but not happiness. We offer brief composite sketches of the unhappy but meaningful life and of the happy but meaningless life.
Integrating Emotional Cues into a Framework for Dialogue Management

Emotions are very important in human-human communication but are usually ignored in human-computer interaction. Recent work focuses on recognition and generation of emotions as well as emotion driven behavior. Our work focuses on the use of emotions in dialogue systems that can be used with speech input or as well in multi-modal environments.This paper describes a framework for using emotional cues in a dialogue system and their informational characterization. We describe emotion models that can be integrated into the dialogue system and can be used in different domains and tasks. Our application of the dialogue system is planned to model multi-modal human-computer-interaction with a humanoid robotic system.
RankMBPR: Rank-Aware Mutual Bayesian Personalized Ranking for Item Recommendation

Previous works indicated that pairwise methods are stateofthe-art approaches to fit users’ taste from implicit feedback. In this paper, we argue that constructing item pairwise samples for a fixed user is insufficient, because taste differences between two users with respect to a same item can not be explicitly distinguished. Moreover, the rank position of positive items are not used as a metric to measure the learning magnitude in the next step. Therefore, we firstly define a confidence function to dynamically control the learning step-size for updating model parameters. Sequently, we introduce a generic way to construct mutual pairwise loss from both users’ and items’ perspective. Instead of useroriented pairwise sampling strategy alone, we incorporate item pairwise samples into a popular pairwise learning framework, bayesian personalized ranking (BPR), and propose mutual bayesian personalized ranking (MBPR) method. In addition, a rank-aware adaptively sampling strategy is proposed to come up with the final approach, called RankMBPR. Empirical studies are carried out on four real-world datasets, and experimental results in several metrics demonstrate the efficiency and effectiveness of our proposed method, comparing with other baseline algorithms.
ns-3 project goals

This paper reports on the project plan to develop a new major version of the popular ns-2 networking simulator. The authors have organized an NSF-funded, four-year community infrastructure project to develop the next version of ns. The project will also be oriented towards community development and open source software practices to encourage participation from the broader research and educational community. The purpose of this paper is to expand on the goals and initial design concepts for this new software development effort.
Recognizing the Absence of Opposing Arguments in Persuasive Essays

In this paper, we introduce an approach for recognizing the absence of opposing arguments in persuasive essays. We model this task as a binary document classification and show that adversative transitions in combination with unigrams and syntactic production rules significantly outperform a challenging heuristic baseline. Our approach yields an accuracy of 75.6% and 84% of human performance in a persuasive essay corpus with various topics.