nils-herrmann/clean_scirepeval huggingface dataset:
Improving generalization ability in a puzzle game using reinforcement learning

Nowadays machine learning has attracted much attention. In order to apply it to various problems without relearning, its generalization ability is needed. Geometry Friends is a puzzle game where a player has to collect all targets in a two-dimensional world, and it is used in some artificial intel­ligence competitions. Although sufficient generalization ability is needed to apply the machine learning to this game, such machine learning methods are not proposed yet. In this paper, we propose a method based on reinforcement learning in which the generalization ability is improved for Geometry Friends.
A Hybrid model of Neural Network Approach for Speaker independent Word Recognition

—Speech Recognition by computer is a process where speech signals are automatically converted into the corresponding sequence of words in text. When the training and testing conditions are not similar, statistical speech recognition algorithms suffer from severe degradation in recognition accuracy. So we depend on intelligent and recognizable sounds for common communications. In this research, word inputs are recognized by the system and executed in the form of text corresponding to the input word. In this paper, we propose a hybrid model by using a fully connected hidden layer between the input state nodes and the output. We have proposed a new objective function for the neural network using a combined framework of statistical and neural network based classifiers. We have used the hybrid model of Radial Basis Function and the Pattern Matching method. The system was trained by Indian English word consisting of 50 words uttered by 20 male speakers and 20 female speakers. The test samples comprised 30 words spoken by a different set of 20 male speakers and 20 female speakers. The recognition accuracy is found to be 91% which is well above the previous results.
Haematoma in a hydrocele of the canal of Nuck mimicking a Richter’s hernia

We report a haematoma in a hydrocele of the canal of Nuck in a 69-year-old female. She presented with a right-sided groin swelling, the differential for which included an irreducible inguinal hernia or haematoma given her aspirin and clopidegrel use. Successful treatment involved evacuation of the haematoma with excision of the sac. Despite a high index of suspicion for a haematoma, these swellings should ideally be explored given the potential for co-existence of a hernia.
How to Use Bitcoin to Design Fair Protocols

We study a model of fairness in secure computation in which an adversarial party that aborts on receiving output is forced to pay a mutually predefined monetary penalty. We then show how the Bitcoin network can be used to achieve the above notion of fairness in the two-party as well as the multiparty setting (with a dishonest majority). In particular, we propose new ideal functionalities and protocols for fair secure computation and fair lottery in this model. One of our main contributions is the definition of an ideal primitive, which we call F CR (CR stands for “claim-or-refund”), that formalizes and abstracts the exact properties we require from the Bitcoin network to achieve our goals. Naturally, this abstraction allows us to design fair protocols in a hybrid model in which parties have access to the F CR functionality, and is otherwise independent of the Bitcoin ecosystem. We also show an efficient realization of F CR that requires only two Bitcoin transactions to be made on the network. Our constructions also enjoy high efficiency. In a multiparty setting, our protocols only require a constant number of calls to F CR per party on top of a standard multiparty secure computation protocol. Our fair multiparty lottery protocol improves over previous solutions which required a quadratic number of Bitcoin transactions.
LCD based Micro – Stereolithography : A Novel Technique for Rapid Prototyping

The LCD based Micro stereolithography (μSLA) can be developed to produce highly precise, three-dimensional (3D) microstructures from broad selection of functional materials, especially bio-compatible materials. In principle, μSLA utilizes focused light to scan over the surface of a photo-curable resin, which undergoes photo-polymerization and forms solid microstructures. It provides an engineering platform for various applications, such as Microelectromechanical Systems (MEMS), integrated photonics, tissue engineering, and Tetra hertz (THz) metamaterial synthesis. The μSLA fabricated devices, containing complex engineered microstructures which are covered with selfassembled functional groups, can work as a unique interface between the nanometer scale functional group and macro-scale bio-medical samples, therefore can find applications in Bio-MEMS. The basic concepts of LCD based Micro stereolithography and a case study of manufacturing a bevel gear using photosensitive resins reinforced with ceramic nanoparticles is presented in this paper.
Someone to Read with: Design of and Experiences with an In-Home Learning Companion Robot for Reading

The development of literacy and reading proficiency is a building block of lifelong learning that must be supported both in the classroom and at home. While the promise of interactive learning technologies has widely been demonstrated, little is known about how an interactive robot might play a role in this development. We used eight design features based on recommendations from interest-development and human-robot-interaction literatures to design an in-home learning companion robot for children aged 11--12. The robot was used as a technology probe to explore families' (N=8) habits and views about reading, how a reading technology might be used, and how children perceived reading with the robot. Our results indicate reading with the learning companion to be a way to socially engage with reading, which may promote the development of reading interest and ability. We discuss design and research implications based on our findings.
A UML-based pattern specification technique

Informally described design patterns are useful for communicating proven solutions for recurring design problems to developers, but they cannot be used as compliance points against which solutions that claim to conform to the patterns are checked. Pattern specification languages that utilize mathematical notation provide the needed formality, but often at the expense of usability. We present a rigorous and practical technique for specifying pattern solutions expressed in the unified modeling language (UML). The specification technique paves the way for the development of tools that support rigorous application of design patterns to UML design models. The technique has been used to create specifications of solutions for several popular design patterns. We illustrate the use of the technique by specifying observer and visitor pattern solutions.
Liposomal-encapsulated Ascorbic Acid: Influence on Vitamin C Bioavailability and Capacity to Protect Against Ischemia–Reperfusion Injury

Intravenous administration of vitamin C has been shown to decrease oxidative stress and, in some instances, improve physiological function in adult humans. Oral vitamin C administration is typically less effective than intravenous, due in part to inferior vitamin C bioavailability. The purpose of this study was to determine the efficacy of oral delivery of vitamin C encapsulated in liposomes. On 4 separate randomly ordered occasions, 11 men and women were administered an oral placebo, or 4 g of vitamin C via oral, oral liposomal, or intravenous delivery. The data indicate that oral delivery of 4 g of vitamin C encapsulated in liposomes (1) produces circulating concentrations of vitamin C that are greater than unencapsulated oral but less than intravenous administration and (2) provides protection from ischemia-reperfusion-mediated oxidative stress that is similar to the protection provided by unencapsulated oral and intravenous administrations.
Quo vadis, PUF?: Trends and challenges of emerging physical-disorder based security

The physical unclonable function (PUF) has emerged as a popular and widely studied security primitive based on the randomness of the underlying physical medium. To date, most of the research emphasis has been placed on finding new ways to measure randomness, hardware realization and analysis of a few initially proposed structures, and conventional secret-key based protocols. In this work, we present our subjective analysis of the emerging and future trends in this area that aim to change the scope, widen the application domain, and make a lasting impact. We emphasize on the development of new PUF-based primitives and paradigms, robust protocols, public-key protocols, digital PUFs, new technologies, implementations, metrics and tests for evaluation/validation, as well as relevant attacks and countermeasures.
Application-awareness in SDN

We present a framework, Atlas, which incorporates application-awareness into Software-Defined Networking (SDN), which is currently capable of L2/3/4-based policy enforcement but agnostic to higher layers. Atlas enables fine-grained, accurate and scalable application classification in SDN. It employs a machine learning (ML) based traffic classification technique, a crowd-sourcing approach to obtain ground truth data and leverages SDN's data reporting mechanism and centralized control. We prototype Atlas on HP Labs wireless networks and observe 94% accuracy on average, for top 40 Android applications.
Open-Domain Neural Conversational Agents : The Step Towards Artificial General Intelligence

Development of conversational agents started half century ago and since then it has transformed into a technology that is accessible in various aspects in everyday life. This paper presents a survey current state-of-the-art in the open domain neural conversational agent research and future research directions towards Artificial General Intelligence (AGI) creation. In order to create a conversational agent which is able to pass the Turing Test, numerous research efforts are focused on open-domain dialogue system. This paper will present latest research in domain of Neural Network reasoning and logical association, sentiment analysis and real-time learning approaches applied to open domain neural conversational agents. As an effort to provide future research directions, current cuttingedge approaches applied to open domain neural conversational agents, current cutting-edge approaches in rationale generation and the state-of-the-art research directions in alternative training methods will be discussed in this paper. Artificial intelligence; deep learning; neural networks; open domain chatbots; conversational agents
RPL in a nutshell: A survey

1389-1286/$ see front matter 2012 Elsevier B.V http://dx.doi.org/10.1016/j.comnet.2012.06.016 ⇑ Corresponding author.  E-mail addresses: olfa.gaddour@enis.rnu.tn (O isep.ipp.pt (A. Koubâa). IPv6 Routing Protocol for Low Power and Lossy Networks (RPL) is a routing protocol specifically designed for Low power and Lossy Networks (LLN) compliant with the 6LoWPAN protocol. It currently shows up as an RFC proposed by the IETF ROLL working group. However, RPL has gained a lot of maturity and is attracting increasing interest in the research community. The absence of surveys about RPL motivates us to write this paper, with the objective to provide a quick introduction to RPL. In addition, we present the most relevant research efforts made around RPL routing protocol that pertain to its performance evaluation, implementation, experimentation, deployment and improvement. We also present an experimental performance evaluation of RPL for different network settings to understand the impact of the protocol attributes on the network behavior, namely in terms of convergence time, energy, packet loss and packet delay. Finally, we point out open research challenges on the RPL design. We believe that this survey will pave the way for interested researchers to understand its behavior and contributes for further relevant research works.
Answering Table Queries on the Web using Column Keywords

We present the design of a structured search engine which returns a multi-column table in response to a query consisting of keywords describing each of its columns. We answer such queries by exploiting the millions of tables on the Web because these are much richer sources of structured knowledge than free-format text. However, a corpus of tables harvested from arbitrary HTML web pages presents huge challenges of diversity and redundancy not seen in centrally edited knowledge bases. We concentrate on one concrete task in this paper. Given a set of Web tables T1, . . . , Tn, and a query Q with q sets of keywords Q1, . . . , Qq, decide for each Ti if it is relevant to Q and if so, identify the mapping between the columns of Ti and query columns. We represent this task as a graphical model that jointly maps all tables by incorporating diverse sources of clues spanning matches in different parts of the table, corpus-wide co-occurrence statistics, and content overlap across table columns. We define a novel query segmentation model for matching keywords to table columns, and a robust mechanism of exploiting content overlap across table columns. We design efficient inference algorithms based on bipartite matching and constrained graph cuts to solve the joint labeling task. Experiments on a workload of 59 queries over a 25 million web table corpus shows significant boost in accuracy over baseline IR methods.
Hand-held RCM/OCT probe for assessing skin burns

A hand-held probe combining high-resolution reflectance confocal microscopy (RCM) and optical coherence tomography (OCT) within the same optical path was developed and preliminarily tested for assessing skin burns. The preliminary results show that these two optical technologies have complementary capabilities that can help the clinician to more rapidly identify the dermal-epidermal junction, determine the integrity of the epidermal layer, and determine tissue perfusion status.
( Crowd ) funding Innovation , Financing Constraints and Real Effects ∗

Financing through crowdfunding is growing rapidly, especially for start-ups. Investment financing via crowdfunding is integrated with the real side of the firm as future consumers may potentially provide all or part of the required resources. We derive the optimal pre-sale crowdfunding contract of a financially constrained monopolist and analyze its implications for production, investment and welfare. Crowdfunding contracts may serve as a price-discrimination mechanism, forcing pivotal consumers to pay a premium above the future spot price, thus increasing profits. When raising funds is costly, entrepreneurs balance the benefits from price discrimination against the costs of external financing, and respond to tighter financing constraints by decreasing the degree of price discrimination and increasing production. When crowdfunding is available, reducing the cost of capital, a common policy instrument used for spurring innovation, may unintentionally reduce production and welfare.
Modeling biological motor control for human locomotion with functional electrical stimulation

This paper develops a novel control system for functional electrical stimulation (FES) locomotion, which aims to generate normal locomotion for paraplegics via FES. It explores the possibility of applying ideas from biology to engineering. The neural control mechanism of the biological motor system, the central pattern generator, has been adopted in the control system design. Some artificial control techniques such as neural network control, fuzzy logic, control and impedance control are incorporated to refine the control performance. Several types of sensory feedback are integrated to endow this control system with an adaptive ability. A musculoskeletal model with 7 segments and 18 muscles is constructed for the simulation study. Satisfactory simulation results are achieved under this FES control system, which indicates a promising technique for the potential application of FES locomotion in future.
A Combined CNN and LSTM Model for Arabic Sentiment Analysis

Deep neural networks have shown good data modelling capabilities when dealing with challenging and large datasets from a wide range of application areas. Convolutional Neural Networks (CNNs) offer advantages in selecting good features and Long Short-Term Memory (LSTM) networks have proven good abilities of learning sequential data. Both approaches have been reported to provide improved results in areas such image processing, voice recognition, language translation and other Natural Language Processing (NLP) tasks. Sentiment classification for short text messages from Twitter is a challenging task, and the complexity increases for Arabic language sentiment classification tasks because Arabic is a rich language in morphology. In addition, the availability of accurate pre-processing tools for Arabic is another current limitation, along with limited research available in this area. In this paper, we investigate the benefits of integrating CNNs and LSTMs and report obtained improved accuracy for Arabic sentiment analysis on different datasets. Additionally, we seek to consider the morphological diversity of particular Arabic words by using different sentiment classification levels.
Subset feature learning for fine-grained category classification

Fine-grained categorisation has been a challenging problem due to small inter-class variation, large intra-class variation and low number of training images. We propose a learning system which first clusters visually similar classes and then learns deep convolutional neural network features specific to each subset. Experiments on the popular fine-grained Caltech-UCSD bird dataset show that the proposed method outperforms recent fine-grained categorisation methods under the most difficult setting: no bounding boxes are presented at test time. It achieves a mean accuracy of 77.5%, compared to the previous best performance of 73.2%. We also show that progressive transfer learning allows us to first learn domain-generic features (for bird classification) which can then be adapted to specific set of bird classes, yielding improvements in accuracy.
Methods for biological data integration: perspectives and challenges.

Rapid technological advances have led to the production of different types of biological data and enabled construction of complex networks with various types of interactions between diverse biological entities. Standard network data analysis methods were shown to be limited in dealing with such heterogeneous networked data and consequently, new methods for integrative data analyses have been proposed. The integrative methods can collectively mine multiple types of biological data and produce more holistic, systems-level biological insights. We survey recent methods for collective mining (integration) of various types of networked biological data. We compare different state-of-the-art methods for data integration and highlight their advantages and disadvantages in addressing important biological problems. We identify the important computational challenges of these methods and provide a general guideline for which methods are suited for specific biological problems, or specific data types. Moreover, we propose that recent non-negative matrix factorization-based approaches may become the integration methodology of choice, as they are well suited and accurate in dealing with heterogeneous data and have many opportunities for further development.
An α Helix to β Barrel Domain Switch Transforms the Transcription Factor RfaH into a Translation Factor

NusG homologs regulate transcription and coupled processes in all living organisms. The Escherichia coli (E. coli) two-domain paralogs NusG and RfaH have conformationally identical N-terminal domains (NTDs) but dramatically different carboxy-terminal domains (CTDs), a β barrel in NusG and an α hairpin in RfaH. Both NTDs interact with elongating RNA polymerase (RNAP) to reduce pausing. In NusG, NTD and CTD are completely independent, and NusG-CTD interacts with termination factor Rho or ribosomal protein S10. In contrast, RfaH-CTD makes extensive contacts with RfaH-NTD to mask an RNAP-binding site therein. Upon RfaH interaction with its DNA target, the operon polarity suppressor (ops) DNA, RfaH-CTD is released, allowing RfaH-NTD to bind to RNAP. Here, we show that the released RfaH-CTD completely refolds from an all-α to an all-β conformation identical to that of NusG-CTD. As a consequence, RfaH-CTD binding to S10 is enabled and translation of RfaH-controlled operons is strongly potentiated.
Applications of texture mapping to volume and flow visualization

We describe six visualization methods which take advantage of hardware polygon scan conversion, texture mapping, and compositing, to give interactive viewing of 3D scalar fields, and motion for 3D flows. For volume rendering, these are splatting of an optimized 3D reconstruction filter, and tetrahedral cell projection using a texture map to provide the exponential per pixel necessary for accurate opacity calculation. For flows, these are the above tetrahedral projection method for rendering the “flow volume” dyed after passing through a dye releasing polygon, “splatting” of cycled anisotropic textures to provide flow direction and motion visualization, splatting motion blurred particles to indicate flow velocity, and advecting a texture directly to show the flow motion. All these techniques are tailored to take advantage of existing graphics pipelines to produce interactive visualization tools.
Crimeware-as-a-service - A survey of commoditized crimeware in the underground market

Crimeware-as-a-service (CaaS) has become a prominent component of the underground economy. CaaS provides a new dimension to cyber crime by making it more organized, automated, and accessible to criminals with limited technical skills. This paper dissects CaaS and explains the essence of the underground economy that has grown around it. The paper also describes the various crimeware services that are provided in the underground
Exploring use cases for telepresence robots

Telepresence robots can be thought of as embodied video conferencing on wheels. Companies producing these robots imagine them being used in a wide variety of situations (e.g., ad-hoc conversations at the office, inspections and troubleshooting at factories, and patient rounds at medical facilities). In July and August 2010, we examined office-related use cases in a series of studies using two prototype robots (Anybots' QB and VGo Communications' VGo). In this paper, we present two studies: conference room meetings (n=6) and moving hallway conversations (n=24). We discuss who might benefit from using telepresence robots, in what scenarios, and the features that telepresence robots must incorporate for use in ad-hoc interactions.