nils-herrmann/clean_scirepeval huggingface dataset:
Understanding how deep neural networks learn face expressions
Venue: 2016 International Joint Conference on Neural Networks (IJCNN)

Deep neural networks have been used successfully for several different computer vision related tasks, including facial expression recognition. In spite of the good results, it is still not clear why these networks achieve such good recognition rates. One way to learn more about deep neural networks is to visualise and understand what they are learning, and to do so techniques such as deconvolution could play a significant role. In this paper, we train a Convolutional Neural Network (CNN) and Lateral Inhibition Pyramidal Neural Network (LIPNet) to learn facial expressions. Then, we use the deconvolution process to visualise the learned features of the CNN and we introduce a novel mechanism for visualising the internal representation of the LIPNet. We perform a series of experiments, training our networks with the Cohn Kanade data set and show what kind of facial structures compose the learned emotion expression representation. Then, we use the trained networks to recognise images from the Jaffe data set and demonstrate that the learned representations are present in different face images, emphasizing the generalization aspects of these networks. We discuss the different representations that each network learns and how they differ from each other. We also discuss how each learned representation contributes to the recognition process and how they can be compared to the emotional notation Facial Action Coding System Facs. Finally, we explain how the principles of invariance, redundancy and filtering, common for deep networks, contribute to the learned features and to the facial expression recognition task in general.
SFEM: Structural feature extraction methodology for the detection of malicious office documents using machine learning methods
Venue: Expert Syst. Appl.

Office documents are used extensively by individuals and organizations. Most users consider these documents safe for use. Unfortunately, Office documents can contain malicious components and perform harmful operations. Attackers increasingly take advantage of naive users and leverage Office documents in order to launch sophisticated advanced persistent threat (APT) and ransomware attacks. Recently, targeted cyber attacks against organizations have been initiated with emails containing malicious attachments. Since most email servers do not allow the attachment of executable files to emails, attackers prefer to use of non executable files (e.g. documents) for malicious purposes. Existing anti virus engines primarily use signature based detection methods, and therefore fail to detect new unknown malicious code which has been embedded in an Office document. Machine learning methods have been shown to be effective at detecting known and unknown malware in various domains, however, to the best of our knowledge, machine learning methods have not been used for the detection of malicious XML based Office documents .docx, .xlsx, .pptx, .odt, .ods, etc. In this paper we present a novel structural feature extraction methodology (SFEM) for XML based Office documents. SFEM extracts discriminative features from documents, based on their structure. We leveraged SFEM's features with machine learning algorithms for effective detection of malicious .docx documents. We extensively evaluated SFEM with machine learning classifiers using a representative collection (16,938 .docx documents collected "from the wild" which contains ~4.9% malicious and ~95.1% benign documents. We examined 1,600 unique configurations based on different combinations of feature extraction, feature selection, feature representation, top feature selection methods, and machine learning classifiers. The results show that machine learning algorithms trained on features provided by SFEM successfully detect new unknown malicious .docx documents. The Random Forest classifier achieves the highest detection rates, with an AUC of 99.12% and true positive rate (TPR) of 97% that is accompanied by a false positive rate (FPR) of 4.9% In comparison, the best anti virus engine achieves a TPR which is ~25% lower.
Speed limit and ramp meter control for traffic flow networks

The control of traffic flow can be related to different applications. In this work, a method to manage variable speed limits combined with coordinated ramp metering within the framework of the Lighthill Whitham Richards (LWR) network model is introduced. Following a 'first discretize then optimize' approach, the first order optimality system is derived and the switch of speeds at certain fixed points in time is explained, together with the boundary control for the ramp metering. Sequential quadratic programming methods are used to solve the control problem numerically. For application purposes, experimental setups are presented wherein variable speed limits are used as a traffic guidance system to avoid traffic jams on highway interchanges and on ramps.
Toward a Blueprint for Trauma Informed Service Delivery in Schools

Recognition of the benefits to trauma informed approaches is expanding, along with commensurate interest in extending delivery within school systems. Although information about trauma informed approaches has quickly burgeoned, systematic attention to integration within multitiered service delivery frameworks has not occurred yet is essential to accurate, durable, and scalable implementation. In addition, there is a critical need to concurrently build a strong evidence base regarding trauma informed service delivery in schools. In this paper, the literatures on trauma informed approaches and multitiered frameworks for school based service delivery are connected with the goal to provide suggestions toward building blueprints for trauma informed service delivery in schools. Drawing from the literature on implementation blueprints for school wide positive behavior supports, sections are organized around current knowledge about trauma informed approaches with regard to blueprints for (a) implementation, (b) professional development, and (c) evaluation. Critical issues, strategy recommendations, and directions for research are discussed.
PRAGMATIC MUNICIPALISM: LOCAL GOVERNMENT SERVICE DELIVERY AFTER THE GREAT RECESSION

Since the Great Recession, some have argued that local governments have become 'austerity machines' that cut and privatize services and undermine unions. We conducted a national survey of US municipalities in 2012 to examine how service provision level and delivery methods are related to local stress and capacity, controlling for community need and place characteristics. We find that local governments are balancing the pressures of stress with community needs. They use alternative revenue sources and service delivery methods (privatization and cooperation) to maintain services. Unionization is not a barrier to innovation. Further, we find that ethnically diverse suburbs are providing more services than other suburbs, thus acting more like metro core cities. We find that the Great Recession has not dramatically shifted local government behaviour to a 'new normal' of fiscal austerity. Instead, we find municipalities practising 'pragmatic municipalism' to maintain their public role.
Alternate Service Delivery Models in Cancer Genetic Counseling: A Mini Review
Venue: Front. Oncol.

Demand for cancer genetic counseling has grown rapidly in recent years as germline genomic information has become increasingly incorporated into cancer care, and the field has entered the public consciousness through high profile celebrity publications. Increased demand and existing variability in the availability of trained cancer genetics clinicians place a priority on developing and evaluating alternate service delivery models for genetic counseling. This mini review summarizes the state of science regarding service delivery models, such as telephone counseling, telegenetics, and group counseling. Research on comparative effectiveness of these models in traditional individual, in person genetic counseling has been promising for improving access to care in a manner acceptable to patients. Yet, it has not fully evaluated the short and long term patient and system level outcomes that will help answer the question of whether these models achieve the same beneficial psychosocial and behavioral outcomes as traditional cancer genetic counseling. We propose a research agenda focused on comparative effectiveness of available service delivery models and how to match models to patients and practice settings. Only through this rigorous research can clinicians and systems find the optimal balance of clinical quality, ready and secure access to care, and financial sustainability. Such research will be integral to achieving the promise of genomic medicine in oncology.
What Does Hotel Location Mean for the Online Consumer? Text Analytics Using Online Reviews
Venue: ENTER

With the continuing growth of online distribution channels how to inspire and support travellers in their online search and purchase process of the hotel product is an ongoing yet challenging task. While its importance to both the management and the consumer has been extensively documented, hotel location has been mostly treated as unidimensional construct within the literature on online travel information search and purchase of hotel product. In this study we mine a large quantity of online reviews associated with a sample of hotel properties in Manhattan, New York City in order to understand how hotel location is perceived by customers. The findings suggest that there are several value related factors associated with hotel location and hotel properties can be distinguished using these factors. This study gains useful insights into the meaning of hotel location within a tourist destination and offers implications for recommender system development as well as for hotel marketing and promotion.
SherLock vs Moriarty: A Smartphone Dataset for Cybersecurity Research
Venue: AISec@CCS

In this paper we describe and share with the research community, a significant smartphone dataset obtained from an ongoing long term data collection experiment. The dataset currently contains 10 billion data records from 30 users collected over a period of 1.6 years and an additional 20 users for 6 months (totaling 50 active users currently participating in the experiment) The experiment involves two smartphone agents: SherLock and Moriarty. SherLock collects a wide variety of software and sensor data at a high sample rate. Moriarty perpetrates various attacks on the user and logs its activities, thus providing labels for the SherLock dataset. The primary purpose of the dataset is to help security professionals and academic researchers in developing innovative methods of implicitly detecting malicious behavior in smartphones. Specifically, from data obtainable without superuser (root) privileges. To demonstrate possible uses of the dataset, we perform a basic malware analysis and evaluate a method of continuous user authentication.
Analysis of Intel's Haswell Microarchitecture Using the ECM Model and Microbenchmarks
Venue: ARCS

This paper presents an in depth analysis of Intel's Haswell microarchitecture for streaming loop kernels. Among the new features examined are the dual ring Uncore design, Cluster on Die mode, Uncore Frequency Scaling, enhancements such as new and improved execution units, as well as improvements throughout the memory hierarchy. The Execution Cache Memory diagnostic performance model is used together with a generic set of microbenchmarks to quantify the efficiency of the microarchitecture. The set of microbenchmarks is chosen in a way that it can serve as a blueprint for other streaming loop kernels.
Decision Trees and Random Forests: Machine Learning Techniques to Classify Rare Events

The article introduces machine learning algorithms for political scientists. These approaches should not be seen as a new method for old problems. Rather, it is important to understand the different logic of the machine learning approach. Here, data is analyzed without theoretical assumptions about possible causalities. Models are optimized according to their accuracy and robustness. While the computer can do this work more or less alone, it is the researcher's duty to make sense of these models afterward. Visualization of machine learning results, therefore, becomes very important and is in the focus of this paper. The methods that are presented and compared are decision trees, bagging, and random forests. The latter are more advanced versions of the former, relying on bootstrapping procedures. To demonstrate these methods, extreme shifts in the US budget and their connection to the attention of political actors are analyzed. The paper presents a comparison of the accuracy of different models based on ROC curves and shows how to interpret random forest models with the help of visualizations. The aim of the paper is to provide an example, how these methods can be used in political science and to highlight possible pitfalls as well as advantages of machine learning.
Designing for digital transformation: lessons for information systems research from the study of ICT and societal challenges

That IS researchers interested in societal or business change should expand their definitions of theory to include theories of the problem and theories of the solution; however, any single paper should only have to work with one of these two alternative theory types. That IS researchers interested in societal or business change should explicitly define the ICT artifact in both broad and specific ways, include affordances and constraints provided by the ICT artifact, and explicitly examine the unintended consequences of the ICT artifact.
IT innovation strategy: managing the implementation communication and its generated knowledge through the use of an ICT tool
Venue: J. Knowl. Manag.

Without effective implementation, no information technology (IT) strategy can succeed. There has been much re search into IT planning, but few studies have developed one of the most important phases of IT strategy: IT Implementation. IT implementation can be improved at information and communication technology (ICT) organizations through the use of organization learning models (OLM) and the implementation of ICT tools. This paper has two purposes define an OLM framework that determines the best practices to increase knowledge at individual, group and/organizational levels, and define and implement an ICT tool to facilitate the integration and institutionalization of the OLM. The ICT tool is based on the technology roadmapping technique that allows an organization to manage at an executive level what, when and how the IT strategy is going to be implemented.  This paper is based on a case study performed at an ICT organization that provides ICT services to financial institutions. The study was carried out in 2014. It analyzed over 24,000 projects, which translated into an equivalent of more than 18 million man hours. The proposal was assessed at a very large ICT organization.  This paper proposes a framework called SPIDER to effectively implement OLMs based on Big Data management principles for monitoring and reporting current status of IT innovation strategies. These kinds of approaches contribute to solve the problems identified in the state of the art regarding the communication and monitoring the implementation status of IT innovation strategies. During this research work, several factors that are essential to implement these kinds of approaches in large banking organizations were identified: effort required to elaborate the monitoring and reporting activities; easiness to understand the reported information; detailed planning of the implementation program; and focus on communication efficiency.  This paper determined the best practices to manage knowledge generated during the implementation of an IT strategy. Additionally, this paper defined and implemented an ICT tool to properly communicate and monitor the implementation of an IT innovation strategy, based on technological roadmap techniques.
Evidence for Intensive Aphasia Therapy: Consideration of Theories From Neuroscience and Cognitive Psychology
Venue: PM R the journal of injury, function, and rehabilitation

Treatment intensity is a critical component to the delivery of speech language pathology and rehabilitation services. Within aphasia rehabilitation, however, insufficient evidence currently exists to guide clinical decision making with respect to the optimal treatment intensity. This review considers perspectives from 2 key bodies of research, the neuroscience and cognitive psychology literature, with respect to the scheduling of aphasia rehabilitation services. Neuroscience research suggests that intensive training is a key element of rehabilitation and is necessary to achieve functional and neurologic changes after a stroke occurs. In contrast, the cognitive psychology literature suggests that optimal long term learning is achieved when training is provided in a distributed or nonintensive schedule. These perspectives are evaluated and discussed with respect to the current evidence for treatment intensity in aphasia rehabilitation. In addition, directions for future research are identified, including study design, methods of defining and measuring treatment intensity, and selection of outcome measures in aphasia rehabilitation.
An Introduction To Applied Cognitive Psychology

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Robust adaptive control of a bio inspired robot manipulator using bat algorithm
Venue: Expert Syst. Appl.

A new combined control law (AFOPIDSMC) proposed for chattering reduction.We apply an adaptive controller for updating FOPID parameters.A bio inspired bat algorithm used for tuning the proposed controller parameters.The stability of the proposed controller is proved by Lyapunov theory.The simulation results show the effectiveness of the proposed control. This paper proposes a novel adaptive fractional order PID sliding mode controller (AFOPIDSMC) using a Bat algorithm to control of a Caterpillar robot manipulator. A fractional order PID (FOPID) control is applied to improve both trajectory tracking and robustness. Sliding mode controller (SMC) is one of the control methods which provides high robustness and low tracking error. Using hybridization, a new combined control law is proposed for chattering reduction by means of FOPID controller and high trajectory tracking through using SMC. Then, an adaptive controller design motivated from the SMC is applied for updating FOPID parameters. A metaheuristic approach, the Bat search algorithm based on the echolocation behavior of bats is applied for optimal design of the Caterpillar robot in order to tune the parameter AFOPIDSMC controllers (BA AFOPIDSMC) To study the effectiveness of Bat algorithm, its performance is compared with five other controllers such as PID, FOPID, SMC, AFOPIDSMC and PSO AFOPIDSMC. The stability of the AFOPIDSMC controller is proved by Lyapunov theory. Numerical simulation results completely indicate the advantage of BA AFOPIDSMC for trajectory tracking and chattering reduction.
Forecasting Stock Market Volatility using GARCH Models: Evidence from the Indian Stock Market

The present article attempts to modelling and forecasting the volatility of the BSE SENSEX Index returns of Indian stock market, using daily data covering a period from 1, July 1997 to 31, December 2015. The forecasting models that are considered in this study range from the symmetric GARCH (1,1) model to asymmetric GARCH models (including Exponential GARCH (1,1) and Threshold GARCH (1,1) models) On the basis of out of the sample forecasts and a majority of evaluation measures our result shows that the asymmetric GARCH models do perform better in forecasting conditional variance of the BSE SENSEX returns rather than the symmetric GARCH model, confirming the presence of leverage effect. Our findings are consistent with the evidence of Banerjee and Sarkar (2006) and Srinivasan (2015) that reveals relatively asymmetric GARCH models are found superior in forecasting the conditional variance of Indian stock market returns rather than the parsimonious symmetric GARCH models.
Development and evaluation of a newborn care education programme in primiparous mothers in Nepal.
Venue: Midwifery

BACKGROUND the health and survival of newborns depend on high levels of attention and care from caregivers. The growth and development of some infants are unhealthy because of their mother's or caregiver's lack of knowledge or the use of inappropriate or traditional child rearing practices that may be harmful. OBJECTIVE to develop a newborn care educational programme and evaluate its impact on infant and maternal health in Nepal. DESIGN a randomised controlled trial. PARTICIPANTS one hundred and forty three mothers were randomly assigned to the intervention (n=69) and control (n=74) groups. Eligible participants were primiparous mothers who had given birth to a single, full term, healthy infant, and were without a history of obstetric, medical, or psychological problems. METHODS prior to being discharged from the postnatal unit, the intervention group received our structured newborn care education programme, which consisted of one on one educational sessions lasting 10 15minutes each and one postpartum follow up telephone support within two weeks after discharge, in addition to the hospital's routine general newborn care education. The control group received only the regular general newborn care education. Outcomes were measured by using Newborn care Knowledge Questionnaires, Karitane Parenting Confidence Scale, State Trait Anxiety Inventory for Adults and infant health and care status. FINDINGS the number of mothers attending the health centre due to the sickness of their babies was significantly decreased in the intervention group compared to the control group. Moreover, the intervention group had significant increases in newborn care knowledge and confidence, and decreases in anxiety, compared with the control group.  the structured newborn care education programme enhanced the infant and mother health. Moreover, it increased maternal knowledge of newborn care and maternal confidence; and reduced anxiety in primiparous mothers. Thus, this educational programme could be integrated into routine educational programs to promote maternal and infant well being in Nepalese society.