nils-herrmann/clean_scirepeval huggingface dataset:
A Supervised Learning Framework for Modeling Director Agent Strategies in Educational Interactive Narrative

Computational models of interactive narrative offer significant potential for creating educational game experiences that are procedurally tailored to individual players and support learning. A key challenge posed by interactive narrative is devising effective director agent models that dynamically sequence story events according to players' actions and needs. In this paper, we describe a supervised machine-learning framework to model director agent strategies in an educational interactive narrative Crystal Island. Findings from two studies with human participants are reported. The first study utilized a Wizard-of-Oz paradigm where human “wizards” directed participants through Crystal Island's mystery storyline by dynamically controlling narrative events in the game environment. Interaction logs yielded training data for machine learning the conditional probabilities of a dynamic Bayesian network (DBN) model of the human wizards' directorial actions. Results indicate that the DBN model achieved significantly higher precision and recall than naive Bayes and bigram model techniques. In the second study, the DBN director agent model was incorporated into the runtime version of Crystal Island, and its impact on students' narrative-centered learning experiences was investigated. Results indicate that machine-learning director agent strategies from human demonstrations yield models that positively shape players' narrative-centered learning and problem-solving experiences.
Distributed controller clustering in software defined networks

Software Defined Networking (SDN) is an emerging promising paradigm for network management because of its centralized network intelligence. However, the centralized control architecture of the software-defined networks (SDNs) brings novel challenges of reliability, scalability, fault tolerance and interoperability. In this paper, we proposed a novel clustered distributed controller architecture in the real setting of SDNs. The distributed cluster implementation comprises of multiple popular SDN controllers. The proposed mechanism is evaluated using a real world network topology running on top of an emulated SDN environment. The result shows that the proposed distributed controller clustering mechanism is able to significantly reduce the average latency from 8.1% to 1.6%, the packet loss from 5.22% to 4.15%, compared to distributed controller without clustering running on HP Virtual Application Network (VAN) SDN and Open Network Operating System (ONOS) controllers respectively. Moreover, proposed method also shows reasonable CPU utilization results. Furthermore, the proposed mechanism makes possible to handle unexpected load fluctuations while maintaining a continuous network operation, even when there is a controller failure. The paper is a potential contribution stepping towards addressing the issues of reliability, scalability, fault tolerance, and inter-operability.
Niacin, an Old Drug, has New Effects on Central Nervous System Disease

Increased serum cholesterol and decreased high-density lipoprotein (HDL) cholesterol level in serum and cerebro-spinal fluid is a risk factor for the development of Alzheimer disease, and also a predictor of cardiovascular events and stroke in epidemiologic studies. Niacin (vitamin B 3 or nicotinic acid) is the most effective medication in current clinical use for increasing HDL cholesterol and it substantially lowers triglycerides and LDL cholesterol. This review provides an update on the role of the increasing HDL cholesterol agent, niacin, as a neuroprotective and neurorestorative agent which promotes angiogenesis and arteriogenesis after stroke and improves neurobehavioral recovery following central nervous system diseases such as stroke, Alzheimer’s disease and multiple sclerosis. The mechanisms underlying the niacin induced neuroprotective and neurorestorative effects after stroke are discussed. The primary focus of this review is on stroke, with shorter discussion on Alzheimer disease and multiple sclerosis.
A single port surgical robot system with novel elbow joint mechanism for high force transmission.

Despite its evident clinical benefits, single-incision laparoscopic surgery (SILS) imposes inherent limitations of collision between external arms and inadequate triangulation because multiple instruments are inserted through a single port at the same time.



A robot platform appropriate for SILS was developed wherein an elbowed instrument can be equipped to easily create surgical triangulation without the interference of robot arms. A novel joint mechanism for a surgical instrument actuated by a rigid link was designed for high torque transmission capability.



The feasibility and effectiveness of the robot was checked through three kinds of preliminary tests: payload, block transfer, and ex vivo test. Measurements showed that the proposed robot has a payload capability >15 N with 7 mm diameter.



The proposed robot is effective and appropriate for SILS, overcoming inadequate triangulation and improving workspace and traction force capability.
COBRA: Toward Provably Efficient Semi-Clairvoyant Scheduling in Data Analytics Systems

Typical data analytics systems abstract jobs as directed acyclic graphs (DAGs). It is crucial to maximize throughput and speedup completions for DAG jobs in practice. Existing works propose clairvoyant schedulers optimizing these goals, however, they assume complete job information as a prior knowledge which limits their applicability. Instead, we remove the complete prior knowledge assumption and rely solely on a partial prior information, which is more practical. And we design a semi-clairvoyant task scheduler Cobra working within each job. Cobra adaptively adjusts its resource desires in a multiplicative-increase multiplicative-decrease (MIMD) manner according to nearly past resource utilizations and the current waiting tasks. On the other hand, Cobra seeks to satisfy task locality preferences by allowing each task to wait for some time that is bounded by a parameterized threshold. Surprisingly, even with the partial prior job information, we theoretically prove, Cobra, when working with the widely used fair job scheduler, is $\mathbf{O}(1)$-competitive with respect to both makespan and average job response time. We experimentally validate that the performance promotion of Cobra in both real system deployment and trace-driven simulations.
A parallel workload model and its implications for processor allocation

We develop a workload model based on the observed behavior of parallel computers at the San Diego Supercomputer Center and the Cornell Theory Center. This model gives us insight into the performance of strategies for scheduling moldable jobs on space-sharing parallel computers. We find that Adaptive Static Partitioning (ASP), which has been reported to work well for other workloads, does not perform as well as strategies that adapt better to system load. The best of the strategies we consider is one that explicitly reduces allocations when load is high (a variation of Sevcik's (1989) A+ strategy).
CrystalBall: Statically analyzing runtime behavior via deep sequence learning

Understanding dynamic program behavior is critical in many stages of the software development lifecycle, for purposes as diverse as optimization, debugging, testing, and security. This paper focuses on the problem of predicting dynamic program behavior statically. We introduce a novel technique to statically identify hot paths that leverages emerging deep learning techniques to take advantage of their ability to learn subtle, complex relationships between sequences of inputs. This approach maps well to the problem of identifying the behavior of sequences of basic blocks in program execution. Our technique is also designed to operate on the compiler's intermediate representation (IR), as opposed to the approaches taken by prior techniques that have focused primarily on source code, giving our approach language-independence. We describe the pitfalls of conventional metrics used for hot path prediction such as accuracy, and motivate the use of Area Under the Receiver Operating Characteristic curve (AUROC). Through a thorough evaluation of our technique on complex applications that include the SPEC CPU2006 benchmarks, we show that our approach achieves an AUROC of 0.85.
XiaoIce Band: A Melody and Arrangement Generation Framework for Pop Music

With the development of knowledge of music composition and the recent increase in demand, an increasing number of companies and research institutes have begun to study the automatic generation of music. However, previous models have limitations when applying to song generation, which requires both the melody and arrangement. Besides, many critical factors related to the quality of a song such as chord progression and rhythm patterns are not well addressed. In particular, the problem of how to ensure the harmony of multi-track music is still underexplored. To this end, we present a focused study on pop music generation, in which we take both chord and rhythm influence of melody generation and the harmony of music arrangement into consideration. We propose an end-to-end melody and arrangement generation framework, called XiaoIce Band, which generates a melody track with several accompany tracks played by several types of instruments. Specifically, we devise a Chord based Rhythm and Melody Cross-Generation Model (CRMCG) to generate melody with chord progressions. Then, we propose a Multi-Instrument Co-Arrangement Model (MICA) using multi-task learning for multi-track music arrangement. Finally, we conduct extensive experiments on a real-world dataset, where the results demonstrate the effectiveness of XiaoIce Band.
Analysis of Policy Agendas: Lessons Learned from Automatic Topic Classification of Croatian Political Texts

Policy agenda research is concerned with measuring the policymaker activities. Topic classification has proven a valuable tool for policy agenda research. However, manual topic coding is extremely costly and time-consuming. Supervised topic classification offers a cost-effective and reliable alternative, yet it introduces new challenges, the most significant of which are the training set coding, classifier design, and accuracy-efficiency trade-off. In this work, we address these challenges in the context of the recently launched Croatian Policy Agendas project. We describe a new policy agenda dataset, explore the many system design choices, and report on the insights gained. Our best-performing model reaches 77% and 68% of F1-score for major topics and subtopics, respectively.
Significant Permission Identification for Machine-Learning-Based Android Malware Detection

The alarming growth rate of malicious apps has become a serious issue that sets back the prosperous mobile ecosystem. A recent report indicates that a new malicious app for Android is introduced every 10 s. To combat this serious malware campaign, we need a scalable malware detection approach that can effectively and efficiently identify malware apps. Numerous malware detection tools have been developed, including system-level and network-level approaches. However, scaling the detection for a large bundle of apps remains a challenging task. In this paper, we introduce Significant Permission IDentification (SigPID), a malware detection system based on permission usage analysis to cope with the rapid increase in the number of Android malware. Instead of extracting and analyzing all Android permissions, we develop three levels of pruning by mining the permission data to identify the most significant permissions that can be effective in distinguishing between benign and malicious apps. SigPID then utilizes machine-learning-based classification methods to classify different families of malware and benign apps. Our evaluation finds that only 22 permissions are significant. We then compare the performance of our approach, using only 22 permissions, against a baseline approach that analyzes all permissions. The results indicate that when a support vector machine is used as the classifier, we can achieve over 90% of precision, recall, accuracy, and F-measure, which are about the same as those produced by the baseline approach while incurring the analysis times that are 4–32 times less than those of using all permissions. Compared against other state-of-the-art approaches,  SigPID is more effective by detecting 93.62% of malware in the dataset and 91.4% unknown/new malware samples.
Cloudlet-based Efficient Data Collection in Wireless Body Area Networks

Wireless Body Area Networks (WBANs) have developed as an effective solution for a wide range of healthcare, military and sports applications. Most of the proposed works studied efficient data collection from individual and traditional WBANs. Cloud computing is a new computing model that is continuously evolving and spreading. This paper presents a novel cloudlet-based efficient data collection system in WBANs. The goal is to have a large scale of monitored data of WBANs to be available at the end user or to the service provider in reliable manner. A prototype of WBANs, including Virtual Machine (VM) and Virtualized Cloudlet (VC) has been proposed for simulation characterizing efficient data collection in WBANs. Using the prototype system, we provide a scalable storage and processing infrastructure for large scale WBANs system. This infrastructure will be efficiently able to handle the large size of data generated by the WBANs system, by storing these data and performing analysis operations on it. The proposed model is fully supporting for WBANs system mobility using cost effective communication technologies of WiFi and cellular which are supported by WBANs and VC systems. This is in contrast of many of available mHealth solutions that is limited for high cost communication technology, such as 3G and LTE. Performance of the proposed prototype is evaluated via an extended version of CloudSim simulator. It is shown that the average power consumption and delay of the collected data is tremendously decreased by increasing the number of VMs and VCs.
A Novel Low Leakage Current VPT(Vertical Pillar Transistor) Integration for 4F2 DRAM Cell Array with sub 40 nm Technology

for 4F2 DRAM Cell Array with sub 40 nm Technology Jae-Man Yoon, Kangyoon Lee, Seung-Bae Park, Seong-Goo Kim, Hyoung-Won Seo, Young-Woong Son, Bong-Soo Kim, Hyun-Woo Chung, Choong-Ho Lee*, Won-Sok Lee* *, Dong-Chan Kim* * *, Donggun Park*, Wonshik Lee and Byung-Il Ryu ATD Team, Device Research Team*, CAEP*, PD Team***, Semiconductor R&D Division, Samsung Electronics Co., San #24, Nongseo-Dong, Kiheung-Gu, Yongin-City, Kyunggi-Do, 449-711, Korea Tel) 82-31-209-4741, Fax) 82-31-209-3274, E-mail)
Signaling Pathways in Melanogenesis

Melanocytes are melanin-producing cells found in skin, hair follicles, eyes, inner ear, bones, heart and brain of humans. They arise from pluripotent neural crest cells and differentiate in response to a complex network of interacting regulatory pathways. Melanins are pigment molecules that are endogenously synthesized by melanocytes. The light absorption of melanin in skin and hair leads to photoreceptor shielding, thermoregulation, photoprotection, camouflage and display coloring. Melanins are also powerful cation chelators and may act as free radical sinks. Melanin formation is a product of complex biochemical events that starts from amino acid tyrosine and its metabolite, dopa. The types and amounts of melanin produced by melanocytes are determined genetically and are influenced by a variety of extrinsic and intrinsic factors such as hormonal changes, inflammation, age and exposure to UV light. These stimuli affect the different pathways in melanogenesis. In this review we will discuss the regulatory mechanisms involved in melanogenesis and explain how intrinsic and extrinsic factors regulate melanin production. We will also explain the regulatory roles of different proteins involved in melanogenesis.
The Effect of Planning Shape on Dyna-style Planning in High-dimensional State Spaces

Dyna is an architecture for reinforcement learning agents that interleaves planning, acting, and learning in an online setting. This architecture aims to make fuller use of limited experience to achieve better performance with fewer environmental interactions. Dyna has been well studied in problems with a tabular representation of states, and has also been extended to some settings with larger state spaces that require function approximation. However, little work has studied Dyna in environments with high-dimensional state spaces like images. In Dyna, the environment model is typically used to generate one-step transitions from selected start states. We applied one-step Dyna to several games from the Arcade Learning Environment and found that the model-based updates offered surprisingly little benefit, even with a perfect model. However, when the model was used to generate longer trajectories of simulated experience, performance improved dramatically. This observation also holds when using a model that is learned from experience; even though the learned model is flawed, it can still be used to accelerate learning.
Framework for Traffic Congestion Management

Traffic Congestion is one of many serious global problems in all great cities resulted from rapid urbanization which always exert negative externalities upon society. The solution of traffic congestion is highly geocentric and due to its heterogeneous nature, curbing congestion is one of the hard tasks for transport planners. It is not possible to suggest unique traffic congestion management framework which could be absolutely applied for every great cities. Conversely, it is quite feasible to develop a framework which could be used with or without minor adjustment to deal with congestion problem. So, the main aim of this paper is to prepare a traffic congestion mitigation framework which will be useful for urban planners, transport planners, civil engineers, transport policy makers, congestion management researchers who are directly or indirectly involved or willing to involve in the task of traffic congestion management. Literature review is the main source of information of this study. In this paper, firstly, traffic congestion is defined on the theoretical point of view and then the causes of traffic congestion are briefly described. After describing the causes, common management measures, using worldwide, are described and framework for supply side and demand side congestion management measures are prepared.
SoMaJo: State-of-the-art tokenization for German web and social media texts

In this paper we describe SoMaJo, a rulebased tokenizer for German web and social media texts that was the best-performing system in the EmpiriST 2015 shared task with an average F1-score of 99.57. We give an overview of the system and the phenomena its rules cover, as well as a detailed error analysis. The tokenizer is available as
How Hard Can It Be? Designing and Implementing a Deployable Multipath TCP

Networks have become multipath: mobile devices have multiple radio interfaces, datacenters have redundant paths and multihoming is the norm for big server farms. Meanwhile, TCP is still only single-path. Is it possible to extend TCP to enable it to support multiple paths for current applications on today’s Internet? The answer is positive. We carefully review the constraints—partly due to various types of middleboxes— that influenced the design of Multipath TCP and show how we handled them to achieve its deployability goals. We report our experience in implementing Multipath TCP in the Linux kernel and we evaluate its performance. Our measurements focus on the algorithms needed to efficiently use paths with different characteristics, notably send and receive buffer tuning and segment reordering. We also compare the performance of our implementation with regular TCP on web servers. Finally, we discuss the lessons learned from designing MPTCP.
From road distraction to safe driving: Evaluating the effects of boredom and gamification on driving behaviour, physiological arousal, and subjective experience

Boredom and low levels of task engagement while driving can pose road safety risks, e.g., inattention during low traffic, routine trips, or semi-automated driving. Digital technology interventions that increase task engagement, e.g., through performance feedback, increased challenge, and incentives (often referred to as ‘gamification’), could therefore offer safety benefits. To explore the impact of such interventions, we conducted experiments in a highfidelity driving simulator with thirty-two participants. In two counterbalanced conditions (control and intervention), we compared driving behaviour, physiological arousal, and subjective experience. Results indicate that the gamified boredom intervention reduced unsafe coping mechanisms such as speeding while promoting anticipatory driving. We can further infer that the intervention not only increased one’s attention and arousal during the intermittent gamification challenges, but that these intermittent stimuli may also help sustain one’s attention and arousal in between challenges and throughout a drive. At the same time, the gamified condition led to slower hazard reactions and short off-road glances. Our contributions deepen our understanding of driver boredom and pave the way for engaging interventions for safety critical tasks.
A Big Data Architecture Design for Smart Grids Based on Random Matrix Theory

Model-based analysis tools, built on assumptions and simplifications, are difficult to handle smart grids with data characterized by volume, velocity, variety, and veracity (i.e., 4Vs data). This paper, using random matrix theory (RMT), motivates data-driven tools to perceive the complex grids in high-dimension; meanwhile, an architecture with detailed procedures is proposed. In algorithm perspective, the architecture performs a high-dimensional analysis and compares the findings with RMT predictions to conduct anomaly detections. Mean spectral radius (MSR), as a statistical indicator, is defined to reflect the correlations of system data in different dimensions. In management mode perspective, a group-work mode is discussed for smart grids operation. This mode breaks through regional limitations for energy flows and data flows, and makes advanced big data analyses possible. For a specific large-scale zone-dividing system with multiple connected utilities, each site, operating under the group-work mode, is able to work out the regional MSR only with its own measured/simulated data. The large-scale interconnected system, in this way, is naturally decoupled from statistical parameters perspective, rather than from engineering models perspective. Furthermore, a comparative analysis of these distributed MSRs, even with imperceptible different raw data, will produce a contour line to detect the event and locate the source. It demonstrates that the architecture is compatible with the block calculation only using the regional small database; beyond that, this architecture, as a data-driven solution, is sensitive to system situation awareness, and practical for real large-scale interconnected systems. Five case studies and their visualizations validate the designed architecture in various fields of power systems. To our best knowledge, this paper is the first attempt to apply big data technology into smart grids.