nils-herrmann/clean_scirepeval huggingface dataset:
Topic-based sentiment analysis for the social web: The role of mood and issue-related words

Mike Thelwall, Kevan Buckley Statistical Cybermetrics Research Group, School of Technology, University of Wolverhampton, Wulfruna Street, Wolverhampton WV1 1SB, UK. E-mail: m.thelwall@wlv.ac.uk, K.A.Buckley@wlv.ac.uk Tel: +44 1902 321470  General sentiment analysis for the social web has become increasingly useful to shed light on the role of emotion in online communication and offline events in both academic research and data journalism. Nevertheless, existing general purpose social web sentiment analysis algorithms may not be optimal for texts focussed around specific topics. This article introduces two new methods, mood setting and lexicon extension, to improve the accuracy of topic-specific lexical sentiment strength detection for the social web. Mood setting allows the topic mood to determine the default polarity for ostensibly neutral expressive text. Topic-specific lexicon extension involves adding topic-specific words to the default general sentiment lexicon. Experiments with eight data sets show that both methods can improve sentiment analysis performance in corpora and are recommended when the topic focus is tightest.
Neuromorphic accelerators: A comparison between neuroscience and machine-learning approaches

A vast array of devices, ranging from industrial robots to self-driven cars or smartphones, require increasingly sophisticated processing of real-world input data (image, voice, radio, ...). Interestingly, hardware neural network accelerators are emerging again as attractive candidate architectures for such tasks. The neural network algorithms considered come from two, largely separate, domains: machine-learning and neuroscience. These neural networks have very different characteristics, so it is unclear which approach should be favored for hardware implementation. Yet, few studies compare them from a hardware perspective. We implement both types of networks down to the layout, and we compare the relative merit of each approach in terms of energy, speed, area cost, accuracy and functionality.
 Within the limit of our study (current SNN and machine-learning NN algorithms, current best effort at hardware implementation efforts, and workloads used in this study), our analysis helps dispel the notion that hardware neural network accelerators inspired from neuroscience, such as SNN+STDP, are currently a competitive alternative to hardware neural networks accelerators inspired from machine-learning, such as MLP+BP: not only in terms of accuracy, but also in terms of hardware cost for realistic implementations, which is less expected. However, we also outline that SNN+STDP carry potential for reduced hardware cost compared to machine-learning networks at very large scales, if accuracy issues can be controlled (or for applications where they are less important). We also identify the key sources of inaccuracy of SNN+STDP which are less related to the loss of information due to spike coding than to the nature of the STDP learning algorithm. Finally, we outline that for the category of applications which require permanent online learning and moderate accuracy, SNN+STDP hardware accelerators could be a very cost-efficient solution.
Comparative Validation of Polyp Detection Methods in Video Colonoscopy: Results From the MICCAI 2015 Endoscopic Vision Challenge

Colonoscopy is the gold standard for colon cancer screening though some polyps are still missed, thus preventing early disease detection and treatment. Several computational systems have been proposed to assist polyp detection during colonoscopy but so far without consistent evaluation. The lack of publicly available annotated databases has made it difficult to compare methods and to assess if they achieve performance levels acceptable for clinical use. The Automatic Polyp Detection sub-challenge, conducted as part of the Endoscopic Vision Challenge (http://endovis.grand-challenge.org) at the international conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) in 2015, was an effort to address this need. In this paper, we report the results of this comparative evaluation of polyp detection methods, as well as describe additional experiments to further explore differences between methods. We define performance metrics and provide evaluation databases that allow comparison of multiple methodologies. Results show that convolutional neural networks are the state of the art. Nevertheless, it is also demonstrated that combining different methodologies can lead to an improved overall performance.
Probabilistic Programming with Gaussian Process Memoization

Gaussian Processes (GPs) are widely used tools in statistics, machine learning, robotics, computer vision, and scientific computation. However, despite their popularity, they can be difficult to apply; all but the simplest classification or regression applications require specification and inference over complex covariance functions that do not admit simple analytical posteriors. This paper shows how to embed Gaussian processes in any higherorder probabilistic programming language, using an idiom based on memoization, and demonstrates its utility by implementing and extending classic and state-of-the-art GP applications. The interface to Gaussian processes, called gpmem, takes an arbitrary real-valued computational process as input and returns a statistical emulator that automatically improve as the original process is invoked and its input-output behavior is recorded. The flexibility of gpmem is illustrated via three applications: (i) Robust GP regression with hierarchical hyper-parameter learning, (ii) discovering symbolic expressions from time-series data by fully Bayesian structure learning over kernels generated by a stochastic grammar, and (iii) a bandit formulation of Bayesian optimization with automatic inference and action selection. All applications share a single 50-line Python library and require fewer than 20 lines of probabilistic code each.
ERRoR ANAlysIs AND RETENTIoN-AwARE ERRoR MANAgEMENT FoR NAND FlAsh MEMoRy

With continued scaling of NAND flash memory process technology and multiple bits programmed per cell, NAND flash reliability and endurance are degrading. In our research, we experimentally measure, characterize, analyze, and model error patterns in nanoscale flash memories. Based on the understanding developed using real flash memory chips, we design techniques for more efficient and effective error management than traditionally used costly error correction codes.
Factors Affecting Technology Integration in K-12 Classrooms: A Path Model

The purpose of this study was to examine the direct and indirect effects of teachers’ individual characteristics and perceptions of environmental factors that influence their technology integration in the classroom. A research-based path model was developed to explain causal relationships between these factors and was tested based on data gathered from 1,382 Tennessee public school teachers. The results provided significant evidence that the developed model is useful in explaining factors affecting technology integration and the relationships between the factors.
Geospatial Big Data Handling Theory and Methods: A Review and Research Challenges

DTU Orbit (17/12/2018)  Geospatial Big Data Handling Theory and  A Review and Research Challenges Big data has now become a strong focus of global interest that is increasingly attracting the attention of academia, industry, government and other organizations. Big data can be situated in the disciplinary area of traditional geospatial data handling theory and methods. The increasing volume and varying format of collected geospatial big data presents challenges in storing, managing, processing, analyzing, visualizing and verifying the quality of data. This has implications for the quality of decisions made with big data. Consequently, this position paper of the International Society for Photogrammetry and Remote Sensing (ISPRS) Technical Commission II (TC II) revisits the existing geospatial data handling methods and theories to determine if they are still capable of handling emerging geospatial big data. Further, the paper synthesises problems, major issues and challenges with current developments as well as recommending what needs to be developed further in the near future.
Research Challenges for Enterprise Cloud Computing

Cloud computing represents a shift away from computing as a product that is purchased, to computing as a service that is delivered to consumers over the internet from large-scale data centers – or ‘clouds’. This paper discusses some of the research challenges for cloud computing from an enterprise or organizational perspective, and puts them in context by reviewing the existing body of literature in cloud computing. Various research challenges relating to the following topics are discussed: the organizational changes brought about by cloud computing; the economic and organizational implications of its utility billing model; the security, legal and privacy issues that cloud computing raises. It is important to highlight these research challenges because cloud computing is not simply about a technological improvement of data centers but a fundamental change in how IT is provisioned and used. This type of research has the potential to influence wider adoption of cloud computing in enterprise, and in the consumer market too.
Faster and Cheaper: Parallelizing Large-Scale Matrix Factorization on GPUs

Matrix factorization (MF) is used by many popular algorithms such as collaborative filtering. GPU with massive cores and high memory bandwidth sheds light on accelerating MF much further when appropriately exploiting its architectural characteristics.
 This paper presents cuMF, a CUDA-based matrix factorization library that optimizes alternate least square (ALS) method to solve very large-scale MF. CuMF uses a set of techniques to maximize the performance on single and multiple GPUs. These techniques include smart access of sparse data leveraging GPU memory hierarchy, using data parallelism in conjunction with model parallelism, minimizing the communication overhead among GPUs, and a novel topology-aware parallel reduction scheme.
 With only a single machine with four Nvidia GPU cards, cuMF can be 6-10 times as fast, and 33-100 times as cost-efficient, compared with the state-of-art distributed CPU solutions. Moreover, cuMF can solve the largest matrix factorization problem ever reported in current literature, with impressively good performance.
Analysis of Heart Diseases Dataset using Neural Network Approach

One of the important techniques of Data mining is Classification. Many real world problems in various fields such as business, science, industry and medicine can be solved by using classification approach. Neural Networks have emerged as an important tool for classification. The advantages of Neural Networks helps for efficient classification of given data. In this study a Heart diseases dataset is analyzed using Neural Network approach. To increase the efficiency of the classification process parallel approach is also adopted in the training phase.
An endoscopic continuum testbed for finalizing system characteristics of a surgical robot for NOTES procedures

Natural Orifice Translumenal Endoscopic Surgery (NOTES) has recently attracted lots of attention, promising surgical procedures with fewer complications, better cosmesis, lower pains and faster recovery. Several robotic systems were developed aiming to enable abdominal surgeries in a NOTES manner. Although these robotic systems demonstrated the surgical concept, characteristics which could fully enable NOTES procedures remain unclear. This paper presents the development of an endoscopic continuum testbed for finalizing system characteristics of a surgical robot for NOTES procedures, which include i) deployability (the testbed can be deployed in a folded endoscope configuration and then be unfolded into a working configuration), ii) adequate workspace, iii) sufficient distal dexterity (e.g. suturing capability), and iv) desired mechanics properties (e.g. enough load carrying capability). Continuum mechanisms were implemented in the design and a diameter of 12mm of this testbed in its endoscope configuration was achieved. Results of this paper could be used to form design references for future development of NOTES robots.
Active-Clamped Ripple-Free DC/DC Converter Using an Input–Output Coupled Inductor

A new input-output coupled inductor (IOCI) is proposed for reducing current ripples and magnetic components. Moreover, a current-source-type circuit using active-clamp mechanism and a current doubler with synchronous rectifier are presented to achieve high efficiency in low input-output voltage applications. The configuration of the IOCI is realized by three windings on a common core, and has the properties of an input inductor at the input-side and two output inductors at the output- side. An active clamped ripple-free dc-dc converter using the proposed IOCI is analyzed in detail and optimized for high power efficiency. Experimental results for 80 W (5 V/16 A) at a constant switching frequency of 100 kHz are obtained to show the performance of the proposed converter.
An automated classification system based on the strings of trojan and virus families

Classifying malware correctly is an important research issue for anti-malware software producers. This paper presents an effective and efficient malware classification technique based on string information using several well-known classification algorithms. In our testing we extracted the printable strings from 1367 samples, including unpacked trojans and viruses and clean files. Information describing the printable strings contained in each sample was input to various classification algorithms, including tree-based classifiers, a nearest neighbour algorithm, statistical algorithms and AdaBoost. Using k-fold cross validation on the unpacked malware and clean files, we achieved a classification accuracy of 97%. Our results reveal that strings from library code (rather than malicious code itself) can be utilised to distinguish different malware families.
Automatic segmentation of lung lobes in ct images based on fissures, vessels, and bronchi

Lobewise analysis of the pulmonary parenchyma is of clinical relevance for diagnosing and monitoring pathologies. In this work, a fully automatic lobe segmentation approach is presented, which is based on a previously proposed watershed transformation approach. The proposed extension explicitly considers the pulmonary fissures by including them in the cost image for the watershed segmentation. The fissure structures are found through a tailored feature analysis of the Hessian matrix. The method is evaluated using 42 data sets, and a comparison with manual segmentations yields an average volumetric agreement of 96.8%. In comparison to the previously proposed approach, this method increases segmentation accuracy where the fissures are visible.
Smartphone App Use Among Medical Providers in ACGME Training Programs

The past decade has witnessed the advent of the smartphone, a device armed with computing power, mobility and downloadable “apps,” that has become commonplace within the medical field as both a personal and professional tool. The popularity of medically-related apps suggests that physicians use mobile technology to assist with clinical decision making, yet usage patterns have never been quantified. A digital survey examining smartphone and associated app usage was administered via email to all ACGME training programs. Data regarding respondent specialty, level of training, use of smartphones, use of smartphone apps, desired apps, and commonly used apps were collected and analyzed. Greater than 85% of respondents used a smartphone, of which the iPhone was the most popular (56%). Over half of the respondents reported using apps in their clinical practice; the most commonly used app types were drug guides (79%), medical calculators (18%), coding and billing apps (4%) and pregnancy wheels (4%). The most frequently requested app types were textbook/reference materials (average response: 55%), classification/treatment algorithms (46%) and general medical knowledge (43%). The clinical use of smartphones and apps will likely continue to increase, and we have demonstrated an absence of high-quality and popular apps despite a strong desire among physicians and trainees. This information should be used to guide the development of future healthcare delivery systems; expanded app functionality is almost certain but reliability and ease of use will likely remain major factors in determining the successful integration of apps into clinical practice.
Steganography for inserting message on digital image using least significant bit and AES cryptographic algorithm

Along with advances in information technology, cybercrime techniques also increased. There are several forms of attacks on data and information, such as hackers, crackers, Trojans, etc. The Symantec Intelligence report edition on August 2012 indicated that the attacker selected the target of attacks. The type of data is valuable and confidential. The Hackers selected the target to attack or steal interest information the first and they did not just taking random from a large amount of data. This indication worried because hackers stealing the data more planned. Therefore, today many systems reinforced with various efforts to maintain data security and overcome these attacks. Necessary methods to secure electronic messages that do not fall on those who are not authorized. One alternative is steganography. Cryptography and Steganography are the two major techniques for secret communication. Cryptography converts information from its original form (plaintext) into unreadable form (cipher text); where as in steganography is the art of hiding messages within other data without changing the data to it attaches, so data before and after the process of hiding almost look like the same. There are many different techniques are available for cryptography and steganography. The cryptography suspicion against disguised message is easily recognizable, because of the message disguised by changing the original message becomes as if illegible. While further reduce suspicion steganography disguised as a message hidden in the file. The research designed the application of steganography using Least Significant Bit (LSB) in which the previous message is encrypted using the Advanced Encryption Standard algorithm (AES) and it can restore the previously hidden data. The messages in this form application and hidden text on media digital image so as not to arouse suspicion. The result of research shown the steganography is expected to hide the secret message, so the message is not easy to know other people who are not eligible.
Forced-choice decision-making in modified trolley dilemma situations: a virtual reality and eye tracking study

Based on the frameworks of dual-process theories, we examined the interplay between intuitive and controlled cognitive processes related to moral and social judgments. In a virtual reality (VR) setting we performed an experiment investigating the progression from fast, automatic decisions towards more controlled decisions over multiple trials in the context of a sacrificing scenario. We repeatedly exposed participants to a modified ten-to-one version and to three one-to-one versions of the trolley dilemma in VR and varied avatar properties, such as their gender and ethnicity, and their orientation in space. We also investigated the influence of arousing music on decisions. Our experiment replicated the behavioral pattern observed in studies using text versions of the trolley dilemma, thereby validating the use of virtual environments in research on moral judgments. Additionally, we found a general tendency towards sacrificing male individuals which correlated with socially desirable responding. As indicated by differences in response times, the ten-to-one version of the trolley dilemma seems to be faster to decide than decisions requiring comparisons based on specific avatar properties as a result of differing moral content. Building upon research on music-based emotion induction, we used music to induce emotional arousal on a physiological level as measured by pupil diameter. We found a specific temporal signature displaying a peak in arousal around the moment of decision. This signature occurs independently of the overall arousal level. Furthermore, we found context-dependent gaze durations during sacrificing decisions, leading participants to look prolonged at their victim if they had to choose between avatars differing in gender. Our study confirmed that moral decisions can be explained within the framework of dual-process theories and shows that pupillometric measurements are a promising tool for investigating affective responses in dilemma situations.
Making friends on the fly: Cooperating with new teammates

Robots are being deployed in an increasing variety of environments for longer periods of time. As the number of robots grows, they will increasingly need to interact with other robots. Additionally, the number of companies and research laboratories producing these robots is increasing, leading to the situation where these robots may not share a common communication or coordination protocol. While standards for coordination and communication may be created, we expect that robots will need to additionally reason intelligently about their teammates with limited information. This problem motivates the area of ad hoc teamwork in which an agent may potentially cooperate with a variety of teammates in order to achieve a shared goal. This article focuses on a limited version of the ad hoc teamwork problem in which an agent knows the environmental dynamics and has had past experiences with other teammates, though these experiences may not be representative of the current teammates. To tackle this problem, this article introduces a new general-purpose algorithm, PLASTIC, that reuses knowledge learned from previous teammates or provided by experts to quickly adapt to new teammates. This algorithm is instantiated in two forms: 1) PLASTIC–Model – which builds models of previous teammates’ behaviors and plans behaviors online using these models and 2) PLASTIC–Policy – which learns policies for cooperating with previous teammates and selects among these policies online. We evaluate PLASTIC on two benchmark tasks: the pursuit domain and robot soccer in the RoboCup 2D simulation domain. Recognizing that a key requirement of ad hoc teamwork is adaptability to previously unseen agents, the tests use more than 40 previously unknown teams on the first task and 7 previously unknown teams on the second. While PLASTIC assumes that there is some degree of similarity between the current and past teammates’ behaviors, no steps are taken in the experimental setup to make sure this assumption holds. The teammates ✩This article contains material from 4 prior conference papers [11–14]. Email addresses: sam@cogitai.com (Samuel Barrett), rosenfa@jct.ac.il (Avi Rosenfeld), sarit@cs.biu.ac.il (Sarit Kraus), pstone@cs.utexas.edu (Peter Stone) 1This work was performed while Samuel Barrett was a graduate student at the University of Texas at Austin.   2016 To appear in http://dx.doi.org/10.1016/j.artint.2016.10.005 Artificial Intelligence (AIJ)