nils-herrmann/clean_scirepeval huggingface dataset:
Artificial Intelligence in Education

In a life-long learning society, learning scenarios can be categorized into five types, which are “classroom learning”, “self-learning”, “inquiry learning”, “learning in doing” and “learning in working”. From a life-wide learning perspective, all these scenarios play vital roles for personal development. How to recognize these learning scenarios (including learning time, learning place, learning peers, learning activities, etc.) and provide the matched learning ways (including learning path, resources, peers, teachers, etc.) are the basis for smart learning environments, however few research could be found to address this problem. In order to solve this problem, we propose a conceptual framework of smart learning engine that is the core of integrated, interactive and intelligent (i) learning environments. The smart learning engine consists of three main functions. The first function is to identify data from student, teacher, subject area, and the environment using wireless sensors, the established learning resources and scenarios, and a learner modeling technology. The acquired data includes prior knowledge, theme-based context, leaner/teacher profile, physical environments, etc. The second function is to compute the best ways of learning based on the learning scenario and learning preference. In detail, this function includes modeling learner’s affective data, building knowledge structure, optimizing knowledge module, and connecting learners. The third function is to deploy personalized and adaptive strategy, resources and tools for students and teachers based on the computed results in the second function. Deploy interactive strategies, learning paces, learning resources, and delivery approaches are the core elements for this function.
Amplified Boomerang Attacks Against Reduced-Round MARS and Serpent

We introduce a new cryptanalytic technique based on Wagner’s boomerang and inside-out attacks. We first describe this new attack in terms of the original boomerang attack, and then demonstrate its use on reduced-round variants of the MARS core and Serpent. Our attack breaks eleven rounds of the MARS core with 2 chosen plaintexts, 2 memory, and 2 partial decryptions. Our attack breaks eight rounds of Serpent with 2 chosen plaintexts, 2 memory, and 2 partial decryptions.
Gentle Adaboost algorithm for weld defect classification

In this paper, we present a new strategy for automatic classification of weld defects in radiographs based on Gentle Adaboost algorithm. Radiographic images were segmented and moment-based features were extracted and given as input to Gentle Adaboost classifier. The performance of our classification system is evaluated using hundreds of radiographic images. The classifier is trained to classify each defect pattern into one of four classes: Crack, Lack of penetration, Porosity, and Solid inclusion. The experimental results show that the Gentle Adaboost classifier is an efficient automatic weld defect classification algorithm and can achieve high accuracy and is faster than support vector machine (SVM) algorithm, for the tested data.
Interactive Narrative: A Novel Application of Artificial Intelligence for Computer Games

Game Artificial Intelligence (Game AI) is a subdiscipline of Artificial Intelligence (AI) and Machine Learning (ML) that explores the ways in which AI and ML can augment player experiences in computer games. Storytelling is an integral part of many modern computer games; within games stories create context, motivate the player, and move the action forward. Interactive Narrative is the use of AI to create and manage stories within games, creating the perception that the player is a character in a dynamically unfolding and responsive story. This paper introduces Game AI and focuses on the open research problems of Interactive
Score and winning prediction in cricket through data mining

Currently, in One Day International (ODI) cricket matches first innings score is predicted on the basis of Current Run Rate which can be calculated as the amount of runs scored per the number of overs bowled. It does not include factors like number of wickets fallen and venue of the match. Furthermore, in second innings there is no method to predict the outcome of the match. In this paper a model has been proposed that has two methods, first predicts the score of first innings not only on the basis of current run rate but also considers number of wickets fallen, venue of the match and batting team. The second method predicts the outcome of the match in the second innings considering the same attributes as of the former method along with the target given to the batting team. These two methods have been implemented using Linear Regression Classifier and Naïve Bayes Classifier for first innings and second innings respectively. In both methods, 5 over intervals have been made from 50 overs of the match and at each interval above mentioned attributes have been recorded of all non-curtailed matches played between 2002 and 2014 of every team independently. It has been found in the results that error in Linear Regression classifier is less than Current Run Rate method in estimating the final score and also accuracy of Naïve Bayes in predicting match outcome has been 68% initially from 0-5 overs to 91% till the end of 45th over.
Dating the colonization of Sahul ( Pleistocene Australia – New Guinea ) : a review of recent research

The date for the initial colonization of Sahul is a key benchmark in human history and the topic of a long-running debate. Most analysts favor either a 40,000 BP or 60,000 BP arrival time, though some have proposed a much earlier date. Here we review data from more than 30 archaeological sites with basal ages >20,000 years reported since 1993, giving special attention to five sites with purported ages >45,000 years. We conclude that while the continent was probably occupied by 42–45,000 BP, earlier arrival dates are not well-supported. This observation undercuts claims for modern human migrations out of Africa and beyond the Levant before 50,000 BP. It also has critical but not yet conclusive implications for arguments about a human role in the extinction of Sahul megafauna.
Automated Demand-driven Resource Scaling in Relational Database-as-a-Service

Relational Database-as-a-Service (DaaS) platforms today support the abstraction of a resource container that guarantees a fixed amount of resources. Tenants are responsible for selecting a container size suitable for their workloads, which they can change to leverage the cloud's elasticity. However, automating this task is daunting for most tenants since estimating resource demands for arbitrary SQL workloads in an RDBMS is complex and challenging. In addition, workloads and resource requirements can vary significantly within minutes to hours, and container sizes vary by orders of magnitude both in the amount of resources as well as monetary cost. We present a solution to enable a DaaS to auto-scale container sizes on behalf of its tenants. Approaches to auto-scale stateless services, such as web servers, that rely on historical resource utilization as the primary signal, often perform poorly for stateful database servers which are significantly more complex. Our solution derives a set of robust signals from database engine telemetry and combines them to significantly improve accuracy of demand estimation for database workloads resulting in more accurate scaling decisions. Our solution raises the abstraction by allowing tenants to reason about monetary budget and query latency rather than resources. We prototyped our approach in Microsoft Azure SQL Database and ran extensive experiments using workloads with realistic time-varying resource demand patterns obtained from production traces. Compared to an approach that uses only resource utilization to estimate demand, our approach results in 1.5x to 3x lower monetary costs while achieving comparable query latencies.
The wave kernel signature: A quantum mechanical approach to shape analysis

We introduce the Wave Kernel Signature (WKS) for characterizing points on non-rigid three-dimensional shapes. The WKS represents the average probability of measuring a quantum mechanical particle at a specific location. By letting vary the energy of the particle, the WKS encodes and separates information from various different Laplace eigenfrequencies. This clear scale separation makes the WKS well suited for a large variety of applications. Both theoretically and in quantitative experiments we demonstrate that the WKS is substantially more discriminative and therefore allows for better feature matching than the commonly used Heat Kernel Signature (HKS). As an application of the WKS in shape analysis we show results on shape matching.
Internet of things (IoT) based smart health care system

A BSN (Body Sensor Network) is a network designed to operate autonomously to connect the various medical sensors and implants located inside and/or outside of the human body; which offers flexible operation and cost saving options to both healthcare professionals and patients. This work illustrates the design and implementation of a smart health monitoring system. Here, a patient can be monitored using a collection of lightweight wearable sensor nodes for real time sensing and analysis of various vital parameters of patients. The devices seamlessly gather and share the information with each other and also store the information, making it possible to collect record and analyze data. Therefore, patients will have high quality services because the system supports medical staff by providing the real-time data gathering, by eliminating the manual data collection and by enabling the monitoring of huge numbers of patients.
Model Checking Interlocking Control Tables

A challenging problem for model checking is represented by railway interlocking systems. It is a well known fact that interlocking systems, due to their inherent complexity related to the high number of variables involved, are not amenable to automatic verification, typically incurring in state space explosion problems. The literature is however quite scarce on data concerning the size of interlocking systems that have been successfully proved with model checking techniques. In this paper we attempt a systematic study of the applicability bounds for general purpose model checkers on this class of systems, by studying the typical characteristics of control tables and their size parameters. The results confirm that, although small scale interlocking systems can be addressed by model checking, interlockings that control medium or large railway yards can not, asking for specialized verification techniques.
Regularization of Neural Networks using DropConnect

We introduce DropConnect, a generalization of Dropout (Hinton et al., 2012), for regularizing large fully-connected layers within neural networks. When training with Dropout, a randomly selected subset of activations are set to zero within each layer. DropConnect instead sets a randomly selected subset of weights within the network to zero. Each unit thus receives input from a random subset of units in the previous layer. We derive a bound on the generalization performance of both Dropout and DropConnect. We then evaluate DropConnect on a range of datasets, comparing to Dropout, and show state-of-the-art results on several image recognition benchmarks by aggregating multiple DropConnect-trained models.
The ODD protocol : a review and first update

The ‘ODD’ (Overview, Design concepts, and Details) protocol was published in 2006 to standardize the published descriptions of individual-based and agent-based models (ABMs). The primary objectives of ODD are to make model descriptions more understandable and complete, thereby making ABMs less subject to criticism for being irreproducible. We have systematically evaluated existing uses of the ODD protocol and identified, as expected, parts of ODD needing improvement and clarification. Accordingly, we revise the definition of ODD to clarify aspects of the original version and thereby facilitate future standardization of ABM descriptions. We discuss frequently raised critiques in ODD but also two emerging, and unanticipated, benefits: ODD improves the rigorous formulation of models and helps make the theoretical foundations of large models more visible. Although the protocol was designed for ABMs, it can help with documenting any large, complex model, alleviating some general objections against such models.
HAWK : Halting Anomaly with Weighted ChoKing to Rescue Well-Behaved TCP Sessions from Shrew DoS Attacks

1 Manuscript submitted to ICDCS 2005 on October 8, 2004. All rights reserved by the authors. This research was supported by an NSF ITR Research Grant under contract number ACI-0325409.  Kai Hwang,  213-740-4470, Fax: 213-740-4418. High availability in network services is crucial for effective large-scale distributed computing. While denial-ofservice (DoS) attacks through massive packet flooding have baffled researchers for years, a new type of even more detrimental attack—shrew attacks (periodic intensive packet bursts with low average rate)—has recently been identified. Shrew attacks can significantly degrade well-behaved TCP sessions and are very difficult to detect, not to mention defend against, due to its low average rate. We propose a new stateful adaptive queue management technique called HAWK (Halting Anomaly with Weighted choKing) which works by judiciously identifying malicious shrew packet flows using a small flow table and dropping such packets decisively to halt the attack such that well-behaved TCP sessions can re-gain their bandwidth shares. Our NS-2 based extensive performance results indicate that HAWK is highly agile in the sense that it can swiftly (e.g., within 5 seconds) identify and then halt shrew attacks with a flow table of tiny size (e.g., 600 bytes). Thus, HAWK is readily amenable to practical implementation in a real environment.
SharesSkew: An algorithm to handle skew for joins in MapReduce

In this paper, we investigate the problem of computing a multiway join in one round of MapReduce when the data may be skewed. We optimize on communication cost, i.e., the amount of data that is transferred from the mappers to the reducers. We identify join attributes values that appear very frequently, Heavy Hitters (HH). We distribute HH valued records to reducers avoiding skew by using an adaptation of the Shares  algorithm to achieve minimum communication cost. Our algorithm is implemented for experimentation and is offered as open source software. Furthermore, we investigate a class of multiway joins for which a simpler variant of the algorithm can handle skew. We offer closed forms for computing the parameters of the algorithm for chain and symmetric joins.
Event detection in baseball video using superimposed caption recognition

We have developed a novel system for baseball video event detection and summarization using superimposed caption text detection and recognition. The system detects different types of semantic level events in baseball video including scoring and last pitch of each batter. The system has two components: event detection and event boundary detection. Event detection is realized by change detection and recognition of game stat texts (such as text information showing in score box). Event boundary detection is achieved using our previously developed algorithm, which detects the pitch view as the event beginning and nonactive view as potential endings of the event. One unique contribution of the system is its capability to accurately detect the semantic level events by combining video text recognition with camera view recognition. Another unique feature is the real-time processing speed by taking advantage of compressed-domain approaches in part of the algorithms such as caption detection. To the best of our knowledge, this is the first system achieving accurate detection of multiple types of high-level semantic events in baseball videos.
Automatic detection and rapid determination of earthquake magnitude by wavelet multiscale analysis of the primary arrival

Earthquake early warning systems must save lives. It is of great importance that networked systems of seismometers be equipped with reliable tools to make rapid determinations of earthquake magnitude in the few to tens of seconds before the damaging ground motion occurs. A new fully automated algorithm based on the discrete wavelet transform detects as well as analyzes the incoming first arrival with great accuracy and precision, estimating the final magnitude to within a single unit from the first few seconds of the P wave.
Recent and Emerging Topics in Wireless Industrial Communications: A Selection

In this paper we discuss a selection of promising and interesting research areas in the design of protocols and systems for wireless industrial communications. We have selected topics that have either emerged as hot topics in the industrial communications community in the last few years (like wireless sensor networks), or which could be worthwhile research topics in the next few years (for example cooperative diversity techniques for error control, cognitive radio/opportunistic spectrum access for mitigation of external interferences).
Packaging a $W$ -Band Integrated Module With an Optimized Flip-Chip Interconnect on an Organic Substrate

This paper, for the first time, presents successful integration of a W-band antenna with an organically flip-chip packaged silicon-germanium (SiGe) low-noise amplifier (LNA). The successful integration requires an optimized flip-chip interconnect. The interconnect performance was optimized by modeling and characterizing the flip-chip transition on a low-loss liquid crystal polymer organic substrate. When the loss of coplanar waveguide (CPW) lines is included, an insertion loss of 0.6 dB per flip-chip-interconnect is measured. If the loss of CPW lines is de-embedded, 0.25 dB of insertion loss is observed. This kind of low-loss flip-chip interconnect is essential for good performance of W-band modules. The module, which we present in this paper, consists of an end-fire Yagi-Uda antenna integrated with an SiGe BiCMOS LNA. The module is 3 mm × 1.9 mm and consumes only 19.2 mW of dc power. We present passive and active E- and H-plane radiation pattern measurements at 87, 90, and 94 GHz. Passive and active antennas both showed a 10-dB bandwidth of 10 GHz. The peak gain of passive and active antennas was 5.2 dBi at 90 GHz and 21.2 dBi at 93 GHz, respectively. The measurements match well with the simulated results.
Accurate electrical battery model capable of predicting runtime and I-V performance

Low power dissipation and maximum battery runtime are crucial in portable electronics. With accurate and efficient circuit and battery models in hand, circuit designers can predict and optimize battery runtime and circuit performance. In this paper, an accurate, intuitive, and comprehensive electrical battery model is proposed and implemented in a Cadence environment. This model accounts for all dynamic characteristics of the battery, from nonlinear open-circuit voltage, current-, temperature-, cycle number-, and storage time-dependent capacity to transient response. A simplified model neglecting the effects of self-discharge, cycle number, and temperature, which are nonconsequential in low-power Li-ion-supplied applications, is validated with experimental data on NiMH and polymer Li-ion batteries. Less than 0.4% runtime error and 30-mV maximum error voltage show that the proposed model predicts both the battery runtime and I-V performance accurately. The model can also be easily extended to other battery and power sourcing technologies.
The Option-Critic Architecture

We first studied the behavior of the intra-option policy gradient algorithm when the initiation sets and subgoals are fixed by hand. In this case, options terminate with probability 0.9 in a hallway state and four of its incoming neighboring states. We chose to parametrize the intra-option policies using the softmax distribution with a one-hot encoding of state-action pairs as basis functions. Learning both intra-option policies and terminations
Improving TCP Throughput over Two-Way Asymmetric Links: Analysis and Solutions

The sharing of a common buffer by TCP data segments and acknowledgments in a network or internet has been known to produce the effect of ack compression, often causing dramatic reductions in throughput. We study several schemes for improving the performance of two-way TCP traffic over asymmetric links where the bandwidths in the two directions may differ substantially, possibly by many orders of magnitude. These approaches reduce the effect of ack compression by carefully controlling the flow of data packets and acknowledgments. We first examine a scheme where acknowledgments are transmitted at a higher priority than data. By analysis and simulation, we show that prioritizing acks can lead to starvation of the low-bandwidth connection. Next, we introduce and analyze a connection-level backpressure mechanism designed to limit the maximum amount of data buffered in the outgoing IP queue of the source of the low-bandwidth connection. We show that this approach, while minimizing the queueing delay for acks, results in unfair bandwidth allocation on the slow link. Finally, our preferred solution separates the acks from data packets in the outgoing queue, and makes use of a connection-level bandwidth allocation mechanism to control their bandwidth shares. We show that this scheme overcomes the limitations of the previous approaches, provides isolation, and enables precise control of the connection throughputs. We present analytical models of the dynamic behavior of each of these approaches, derive closed-form expressions for the expected connection efficiencies in each case, and validate them with simulation results.
The ‘ dark side ’ of leadership personality and transformational leadership : An exploratory study

There is growing interest in dysfunctional dispositions in the workplace, in particular relating to leaders and managers within organizations. This paper reports an exploratory study investigating the relationship between the ‘dark side’ of leadership personality and transformational leadership. Data were collected from a sample of New Zealand business leaders and senior managers (n = 80) from scores on the Hogan Development Survey (HDS; Hogan & Hogan, 1997) and the Multifactor Leadership Questionnaire (MLQ; Bass & Avolio, 1995). Regression analyses revealed the ‘Colorful’ (histrionic) dimension of the HDS to be a positive predictor of transformational leadership, while the ‘Cautious’ (avoidant) and ‘Bold’ (narcissistic) dimensions of the HDS were revealed as negative predictors of transformational leadership. This is one of the first reports to suggest a relationship between histrionic personality and transformational leadership, in a literature preoccupied with the relationship between leadership and narcissism.