nils-herrmann/clean_scirepeval huggingface dataset:
FlowQA: Grasping Flow in History for Conversational Machine Comprehension

Conversational machine comprehension requires a deep understanding of the conversation history. To enable traditional, single-turn models to encode the history comprehensively, we introduce FLOW, a mechanism that can incorporate intermediate representations generated during the process of answering previous questions, through an alternating parallel processing structure. Compared to shallow approaches that concatenate previous questions/answers as input, FLOW integrates the latent semantics of the conversation history more deeply. Our model, FLOWQA, shows superior performance on two recently proposed conversational challenges (+7.2% F1 on CoQA and +4.0% on QuAC). The effectiveness of FLOW also shows in other tasks. By reducing sequential instruction understanding to conversational machine comprehension, FLOWQA outperforms the best models on all three domains in SCONE, with +1.8% to +4.4% improvement in accuracy.
Semantic Deep Learning

Artificial intelligence and machine learning research is dedicated to building intelligent artifacts that can imitate or even transcend the cognitive abilities of human beings. To emulate human cognitive abilities with intelligent artifacts, one must first render machines capable of capturing critical aspects of sensory data, with adequate data representations and performing reasoning and inference with formal knowledge representations. In recent years, the research in deep learning and knowledge engineering has made wide impact on the two problems of data and knowledge representations. Deep learning is a set of machine learning algorithms that attempt to model data representations through many layers of non-linear transformations. Hierarchical, distributed, and efficient data representations can be learned through deep learning models with proper training algorithms. The learned data representation can disentangle the hidden explanation factors and variations in the input data that are critical for further artificial intelligence and machine learning tasks. Additionally, the research in knowledge engineering has frequently focused on modeling the high level human cognitive abilities, such as reasoning, making inferences, and validation. The formal knowledge representation facilitates knowledge reusing and sharing in a machine processable way. It also promotes many advances in the field of semantic data mining which refers to the data mining tasks that systematically incorporate domain knowledge, especially formal semantics, into the data mining process. Empirical studies have attested that formal knowledge representations can make positive influences in all stages of both the data mining and machine learning processes. Inspired by the success of both deep learning and semantic data mining, we hypothesize that formal knowledge representations have the potential to assist in the deep learning process as well. In this report, we summarize the advances in both deep learning and semantic data mining in recent years. We illustrate how learning models with deeper architectures are capable of constructing better data representations for further artificial intelligence and machine learning tasks. We also demonstrate how formal knowledge representation can assist in data mining process at all data mining stages, from various perspectives. At last, we present our thoughts and intuitions on semantic deep learning, which addresses the topic of learning deep data representation with the assistance of formal knowledge representation.
A cooperative heterogeneous Unmanned Autonomous Systems solution for monitoring and inspecting power distribution system

This paper presents a comprehensive solution to monitoring and inspection of power-line distribution system. This involves two robotic platforms which are heterogeneous and cooperative in executing tasks as planned. One of them is Robonwire which is a three-arms robot which moves on the transmission lines while the other is an Octacopter UAV. The UAV inspects the transmission tower while the Robonwire performs both inspection and monitoring of the lines by travelling on them. The Robonwire is capable of moving across and along the transmission lines by expansion and retraction. This enables the robot to handle and avoid the obstacles with minimum effort. The UAV is a vertical take-off rotorcraft comprising eight propellers designed to carry a payload of camera platform and wireless telemetry. In addition to inspection, the octacopter serves as a wireless relay node establishing data transfer between Robonwire and the ground station. An UAV test-bed is proposed to speed up the controller design and tuning of the quadcopter in the laboratory. Both the UAV-platform and Robonwire together provide complementary inspection roles: horizontally and vertically enable a cooperative behaviour. Preliminary simulation and experimental investigations the individual system is presented.
Experimental Evaluations on Ship Autonomous Navigation and Collision Avoidance by Intelligent Guidance

Experimental evaluations on autonomous navigation and collision avoidance of ship maneuvers by intelligent guidance are presented in this paper. These ship maneuvers are conducted on an experimental setup that consists of a navigation and control platform and a vessel model, in which the mathematical formulation presented is actually implemented. The mathematical formulation of the experimental setup is presented under three main sections: vessel traffic monitoring and information system, collision avoidance system, and vessel control system. The physical system of the experimental setup is presented under two main sections: vessel model and navigation and control platform. The vessel model consists of a scaled ship that has been used in this study. The navigation and control platform has been used to control the vessel model and that has been further divided under two sections: hardware structure and software architecture. Therefore, the physical system has been used to conduct ship maneuvers in autonomous navigation and collision avoidance experiments. Finally, several collision avoidance situations with two vessels are considered in this study. The vessel model is considered as the vessel (i.e., own vessel) that makes collision avoidance decisions/actions and the second vessel (i.e., target vessel) that does not take any collision avoidance actions is simulated. Finally, successful experimental results on several collision avoidance situations with two vessels are also presented in this study.
SimpleScalar: An Infrastructure for Computer System Modeling

T o accelerate hardware development, designers often employ software models of the hardware they build. They implement these models in traditional programming languages or hardware description languages, then exercise them with the appropriate workload. Designers can execute programs on these models to validate the performance and correctness of a proposed hardware design. Programmers can use software models to develop and test software before the real hardware becomes available. Although software models are slower than their hardware counterparts, programmers can build and test them in minutes or hours rather than in the months needed to build real hardware. This fast mechanism for design and test provides shorter time to market and much higher quality first silicon. Three critical requirements drive the implementation of a software model: performance, flexibility, and detail. Performance determines the amount of workload the model can exercise given the machine resources available for simulation. Flexibility indicates how well the model is structured to simplify modification, permitting design variants or even completely different designs to be modeled with ease. Detail defines the level of abstraction used to implement the model’s components. A highly detailed model will faithfully simulate all aspects of machine operation, whether or not a particular aspect is important to any metric being measured. In practice, optimizing all three model characteristics in tandem is difficult. Thus, most model implementations optimize only one or two of them, which explains why so many software models exist, even for a single product design. Research models tend to optimize performance and flexibility at the expense of detail. The SimpleScalar toolset provides an infrastructure for simulation and architectural modeling. The toolset can model a variety of platforms ranging from simple unpipelined processors to detailed dynamically scheduled microarchitectures with multiple-level memory hierarchies. For users with more individual needs, SimpleScalar offers a documented and well-structured design, which simplifies extending the toolset to accomplish most architectural modeling tasks. SimpleScalar simulators reproduce computing device operations by executing all program instructions using an interpreter. The toolset’s instruction interpreters support several popular instruction sets, including Alpha, Power PC, x86, and ARM.
Are there basic emotions?

Ortony and Turner's (1990) arguments against those who adopt the view that there are basic emotions are challenged. The evidence on universals in expression and in physiology strongly suggests that there is a biological basis to the emotions that have been studied. Ortony and Turner's reviews of this literature are faulted, and their alternative theoretical explanations do not fit the evidence. The utility of the basic emotions approach is also shown in terms of the research it has generated.
Personalized, interactive tag recommendation for flickr

We study the problem of personalized, interactive tag recommendation for Flickr: While a user enters/selects new tags for a particular picture, the system suggests related tags to her, based on the tags that she or other people have used in the past along with (some of) the tags already entered. The suggested tags are dynamically updated with every additional tag entered/selected. We describe a new algorithm, called Hybrid, which can be applied to this problem, and show that it outperforms previous algorithms. It has only a single tunable parameter, which we found to be very robust.
 Apart from this new algorithm and its detailed analysis, our main contributions are (i) a clean methodology which leads to conservative performance estimates, (ii) showing how classical classification algorithms can be applied to this problem, (iii) introducing a new cost measure, which captures the effort of the whole tagging process, (iv) clearly identifying, when purely local schemes (using only a user's tagging history) can or cannot be improved by global schemes (using everybody's tagging history).
Just Feels Good : Customers ’ Affective Response to Touch and Its Influence on Persuasion

Prior research has assumed that touch has a persuasive effect only if it provides attribute or structural information about a product. Under this view, the role of touch as a persuasive tool is limited. The main purpose of this research is to investigate the persuasive influence of touch as an affective tool in the absence of useful product-related information. The authors find that for people who are motivated to touch because it is fun or interesting, a communication that incorporates touch leads to increased affective response and increased persuasion, particularly when the touch provides neutral or positive sensory feedback. People who are not motivated to touch for fun will also be persuaded by a communication that incorporates touch when they are able to make sense of how the touch is related to the message. The authors explore the effectiveness of different types of touch in generating an affective response, and they replicate the effects on attitudes and behavior in a real-world setting. This research suggests that the marketing implications of touch are more substantial than previously believed. The authors present research implications for direct marketing, product packaging, point-of-purchase displays, and print advertising.
LivDet 2013 Fingerprint Liveness Detection Competition 2013

A spoof or fake is a counterfeit biometric that is used in an attempt to circumvent a biometric sensor Liveness detection distinguishes between live and fake biometric traits. Liveness detection is based on the principle that additional information can be garnered above and beyond the data procured by a standard verification system, and this additional data can be used to verify if a biometric measure is authentic. The Fingerprint Liveness Detection Competition (LivDet) goal is to compare both software-based (Part 1) and hardware-based (Part 2) fingerprint liveness detection methodologies and is open to all academic and industrial institutions. Submissions for the third edition were much more than in the previous editions of LivDet demonstrating a growing interest in the area. We had nine participants (with eleven algorithms) for Part 1 and two submissions for Part 2.
A Study of multilabel text classification and the effect of label hierarchy

Text classification has traditionally been one of the most popular problems in information retreival, natural language processing and machine learning. In the simplest case, the task of text classification  is as follows: A set of training documents T = {X1, X2, ...Xm} , each labelled with a class value from a set of k distinct labels, from the set {1, 2, ..k}, is used to learn a classification model, that captures the relationship between features in the documents and their labels. Subsequently, for a test document with unknown label, the model is used to predict a label. The problem of text classification finds wide appeal in various domains for tasks such as (i) News selection and grouping, (ii) Document organization in digital libraries, websites, social feeds,etc., (iii) Email classification including spam filtering. A variation of this problem in which each document can belong to any number of classes (labels) is referred to as a multilabel text classification problem. An extension to such a problem in which the labels are interrelated by a categorical hierarchy is referred to as a hierarchical text classification problem. In this project, we • study the task of multilabel text classification on real world datasets
Rumor Propagation in Online Social Networks Like Twitter -- A Simulation Study

Rumor has drawn much of the attention of researchers, considering its importance and influence, as well as its complexity. Under different circumstances, the process and results of rumor spreading are also different. The method of agent-based modeling is considered a good way to look into the process of rumor propagation. In this study, we put the theme under the circumstances of online social networks like twitter, which is different from traditional social network websites. Assuming that the structure of these websites is a kind of scale free network, we build a specific agent-based model using Net Logo based on the propagation model of SIR. Taking consideration of the unique property of twitter-like websites in information flow, max spread time and the mechanism of people believing the rumor, we draw the conclusion that compared to the traditional online social networks, the rumor propagation in twitter-like websites is of more efficiency, unpredictability, and a tendency to start the rumor again.
The biological principles of swarm intelligence

The roots of swarm intelligence are deeply embedded in the biological study of self-organized behaviors in social insects. From the routing of traffic in telecommunication networks to the design of control algorithms for groups of autonomous robots, the collective behaviors of these animals have inspired many of the foundational works in this emerging research field. For the first issue of this journal dedicated to swarm intelligence, we review the main biological principles that underlie the organization of insects’ colonies. We begin with some reminders about the decentralized nature of such systems and we describe the underlying mechanisms of complex collective behaviors of social insects, from the concept of stigmergy to the theory of self-organization in biological systems. We emphasize in particular the role of interactions and the importance of bifurcations that appear in the collective output of the colony when some of the system’s parameters change. We then propose to categorize the collective behaviors displayed by insect colonies according to four functions that emerge at the level of the colony and that organize its global behavior. Finally, we address the role of modulations of individual behaviors by disturbances (either environmental or internal to the colony) in the overall flexibility of insect colonies. We conclude that future studies about self-organized biological behaviors should investigate such modulations to better understand how insect colonies adapt to uncertain worlds.
Arthur : Rich Post-Facto Debugging for Production Analytics Applications

Debugging the massive parallel computations that run in today’s datacenters is hard, as they consist of thousands of tasks processing terabytes of data. It is especially hard in production settings, where performance overheads of more than a few percent are unacceptable. To address this challenge, we present Arthur, a new debugger that provides a rich set of analysis tools at close to zero runtime overhead through selective replay of data flow applications. Unlike previous replay debuggers, which add high overheads due to the need to log low-level nondeterministic events, Arthur takes advantage of the structure of large-scale data flow models (e.g., MapReduce), which split work into deterministic tasks for fault tolerance, to minimize its logging cost. We use selective replay to implement a variety of debugging features, including rerunning any task in a single-process debugger; ad-hoc queries on computation state; and forward and backward tracing of records through the computation, which we achieve using a program transformation at replay time. We implement Arthur for Hadoop and Spark, and show that it can be used to find a variety of real bugs.
Camera Trajectory Estimation Using Inertial Sensor Measurements and Structure from Motion Results

This paper describes an approach to estimating the trajectory of a moving camera based on the measurements acquired with an inertial sensor and estimates obtained by applying a structure from motion algorithm to a small set of keyframes in the video sequence. The problem is formulated as an o ine trajectory tting task rather than an online integration problem. This approach avoids many of the issues usually associated with inertial estimation schemes. One of the main advantages of the proposed technique is that it can be applied in situations where approaches based on feature tracking would have signi cant diÆculties. Results obtained by applying the procedure to extended sequences acquired with both conventional and omnidirectional cameras are presented.
Boosting Neural Machine Translation

Training efficiency is one of the main problems for Neural Machine Translation (NMT). Deep networks need for very large data as well as many training iterations to achieve state-of-the-art performance. This results in very high computation cost, slowing down research and industrialisation. In this paper, we propose to alleviate this problem with several training methods based on data boosting and bootstrap with no modifications to the neural network. It imitates the learning process of humans, which typically spend more time when learning “difficult” concepts than easier ones. We experiment on an English-French translation task showing accuracy improvements of up to 1.63 BLEU while saving 20% of training time.
GISMO: a Graphical Interactive Student Monitoring Tool for Course Management Systems

This paper presents GISMO, a graphical interactive student monitoring and tracking system tool that extracts tracking data from an on-line course maintained with a Course Management System, and generates graphical representations that can be explored by course instructors to examine various aspects of distance students. GISMO uses techniques from Information Visualisation to render into an appropriate graphical manner the complex multidimensional student tracking data provided by the Course Management System. GISMO aims to help instructors to become aware of what is happening in their classes and provide a better support to their learners.
Deep Learning Based Link Prediction with Social Pattern and External Attribute Knowledge in Bibliographic Networks

The problem of predicting links for information entities is an important task in network analysis. In this regard, link prediction between authors in bibliographic networks has attracted much attention. However, most of these works only center around exploiting network topology features to do prediction, and other factors affecting link formation are rarely considered. In this paper, we introduce two kinds of novel features based on social pattern and external attribute knowledge (SPEAK), then integrate the SPEAK features and topological features into a deep learning framework using deep neural networks (DNNs). We present the performance based on a real world academic social network from AMiner. Experimental results demonstrate that the SPEAK features can significantly boost the link prediction performance especially when potential links span large geodesic distance. In addition, these features are helpful in understanding the mechanisms behind the link formation.
Cyber Security Analysis of Substation Automation System

The automation of substation is increasing in the modern world. The implementation of SCADA system is necessary for Substation Automation (SA) systems. Generally Substation Automation Systems uses Intelligent Electronic devices (IED) for monitoring, control and protection of substation. Standard protocols used by the Substation Automation systems are IEC 60870-5-104, DNP, IEC 61850, IEC 60870-5-101. In this paper, Modbus protocol is used as communication protocol. Cyber attack is critical issue in SCADA systems. This paper deals with the monitoring of substation and cyber security analysis of SCADA systems.
Load Signature Study—Part I: Basic Concept, Structure, and Methodology

Load signature is the unique consumption pattern intrinsic to each individual electrical appliance/piece of equipment. This paper focus on building a universal platform to better understand and explore the nature of electricity consumption patterns using load signatures and advanced technology, such as feature extraction and intelligent computing. Through this knowledge, we can explore and develop innovative applications to achieve better utilization of resources and develop more intelligent ways of operation. This paper depicts the basic concept, features of load signatures, structure and methodology of applying mathematical programming techniques, pattern recognition tools, and committee decision mechanism to perform load disaggregation. New indices are also introduced to aid our understanding of the nature of load signatures and different disaggregation algorithms.
Facial Signs of Emotional Experience

Spontaneous facial expressions were found to provide accurate information about more specific aspects of emotional experience than just the pleasant versus unpleasant distinction. Videotape records were gathered while subjects viewed motion picture films and then reported on their subjective experience. A new technique for measuring facial movement isolated a particular type of smile that was related to differences in reported happiness between those who showed this action and those who did not, to the intensity of happiness, and to which of two happy experiences was reported as happiest. Those who showed a set of facial actions hypothesized to be signs of various negative affects reported experiencing more negative emotion than those who did not show these actions. How much these facial actions were shown was related to the reported intensity of negative affect. Specific facial actions associated with the experience of disgust were identified.