nils-herrmann/clean_scirepeval huggingface dataset:
Recursive Monte Carlo search for imperfect information games

Perfect information Monte Carlo (PIMC) search is the method of choice for constructing strong Al systems for trick-taking card games. PIMC search evaluates moves in imperfect information games by repeatedly sampling worlds based on state inference and estimating move values by solving the corresponding perfect information scenarios. PIMC search performs well in trick-taking card games despite the fact that it suffers from the strategy fusion problem, whereby the game's information set structure is ignored because moves are evaluated opportunistically in each world. In this paper we describe imperfect information Monte Carlo (IIMC) search, which aims at mitigating this problem by basing move evaluation on more realistic playout sequences rather than perfect information move values. We show that RecPIMC - a recursive IIMC search variant based on perfect information evaluation - performs considerably better than PIMC search in a large class of synthetic imperfect information games and the popular card game of Skat, for which PIMC search is the state-of-the-art cardplay algorithm.
FeatureSmith: Automatically Engineering Features for Malware Detection by Mining the Security Literature

Malware detection increasingly relies on machine learning techniques, which utilize multiple features to separate the malware from the benign apps. The effectiveness of these techniques primarily depends on the manual feature engineering process, based on human knowledge and intuition. However, given the adversaries' efforts to evade detection and the growing volume of publications on malware behaviors, the feature engineering process likely draws from a fraction of the relevant knowledge. We propose an end-to-end approach for automatic feature engineering. We describe techniques for mining documents written in natural language (e.g. scientific papers) and for representing and querying the knowledge about malware in a way that mirrors the human feature engineering process. Specifically, we first identify abstract behaviors that are associated with malware, and then we map these behaviors to concrete features that can be tested experimentally. We implement these ideas in a system called FeatureSmith, which generates a feature set for detecting Android malware. We train a classifier using these features on a large data set of benign and malicious apps. This classifier achieves a 92.5% true positive rate with only 1% false positives, which is comparable to the performance of a state-of-the-art Android malware detector that relies on manually engineered features. In addition, FeatureSmith is able to suggest informative features that are absent from the manually engineered set and to link the features generated to abstract concepts that describe malware behaviors.
Exploiting web search to generate synonyms for entities

Tasks recognizing named entities such as products, people names, or locations from documents have recently received significant attention in the literature. Many solutions to these tasks assume the existence of reference entity tables. An important challenge that needs to be addressed in the entity extraction task is that of ascertaining whether or not a candidate string approximately matches with a named entity in a given reference table.
 Prior approaches have relied on string-based similarity which only compare a candidate string and an entity it matches with. In this paper, we exploit web search engines in order to define new similarity functions. We then develop efficient techniques to facilitate approximate matching in the context of our proposed similarity functions. In an extensive experimental evaluation, we demonstrate the accuracy and efficiency of our techniques.
Modeling documents with a Deep Boltzmann Machine

We introduce a type of Deep Boltzmann Machine (DBM) that is suitable for extracting distributed semantic representations from a large unstructured collection of documents. We overcome the apparent difficulty of training a DBM with judicious parameter tying. This enables an efficient pretraining algorithm and a state initialization scheme for fast inference. The model can be trained just as efficiently as a standard Restricted Boltzmann Machine. Our experiments show that the model assigns better log probability to unseen data than the Replicated Softmax model. Features extracted from our model outperform LDA, Replicated Softmax, and DocNADE models on document retrieval and document classification tasks.
Discriminative acoustic word embeddings: Tecurrent neural network-based approaches

Acoustic word embeddings — fixed-dimensional vector representations of variable-length spoken word segments — have begun to be considered for tasks such as speech recognition and query-by-example search. Such embeddings can be learned discriminatively so that they are similar for speech segments corresponding to the same word, while being dissimilar for segments corresponding to different words. Recent work has found that acoustic word embeddings can outperform dynamic time warping on query-by-example search and related word discrimination tasks. However, the space of embedding models and training approaches is still relatively unexplored. In this paper we present new discriminative embedding models based on recurrent neural networks (RNNs). We consider training losses that have been successful in prior work, in particular a cross entropy loss for word classification and a contrastive loss that explicitly aims to separate same-word and different-word pairs in a “Siamese network” training setting. We find that both classifier-based and Siamese RNN embeddings improve over previously reported results on a word discrimination task, with Siamese RNNs outperforming classification models. In addition, we present analyses of the learned embeddings and the effects of variables such as dimensionality and network structure.
The Chronotron: A Neuron That Learns to Fire Temporally Precise Spike Patterns

In many cases, neurons process information carried by the precise timings of spikes. Here we show how neurons can learn to generate specific temporally precise output spikes in response to input patterns of spikes having precise timings, thus processing and memorizing information that is entirely temporally coded, both as input and as output. We introduce two new supervised learning rules for spiking neurons with temporal coding of information (chronotrons), one that provides high memory capacity (E-learning), and one that has a higher biological plausibility (I-learning). With I-learning, the neuron learns to fire the target spike trains through synaptic changes that are proportional to the synaptic currents at the timings of real and target output spikes. We study these learning rules in computer simulations where we train integrate-and-fire neurons. Both learning rules allow neurons to fire at the desired timings, with sub-millisecond precision. We show how chronotrons can learn to classify their inputs, by firing identical, temporally precise spike trains for different inputs belonging to the same class. When the input is noisy, the classification also leads to noise reduction. We compute lower bounds for the memory capacity of chronotrons and explore the influence of various parameters on chronotrons' performance. The chronotrons can model neurons that encode information in the time of the first spike relative to the onset of salient stimuli or neurons in oscillatory networks that encode information in the phases of spikes relative to the background oscillation. Our results show that firing one spike per cycle optimizes memory capacity in neurons encoding information in the phase of firing relative to a background rhythm.
Unified Chassis Control for Rollover Prevention and Lateral Stability

This paper describes a unified chassis control (UCC) strategy to prevent vehicle rollover and improve vehicle lateral stability. A rollover index (RI), which indicates impending rollover, and a model-based roll state estimator are introduced to detect potential rollover. An RI/lateral stability-based rollover mitigation (ROM) controller is designed to reduce the danger of rollover without loss of vehicle lateral stability by integrating electronic stability control (ESC) and continuous damping control (CDC). Simulation results indicate that significant improvement in vehicle rollover prevention and lateral stability can be expected from the proposed UCC system.
3D Reconstruction of a Moving Point from a Series of 2D Projections

This paper presents a linear solution for reconstructing the 3D trajectory of a moving point from its correspondence in a collection of 2D perspective images, given the 3D spatial pose and time of capture of the cameras that produced each image. Triangulation-based solutions do not apply, as multiple views of the point may not exist at each instant in time. A geometric analysis of the problem is presented and a criterion, called reconstructibility, is defined to precisely characterize the cases when reconstruction is possible, and how accurate it can be. We apply the linear reconstruction algorithm to reconstruct the time evolving 3D structure of several real-world scenes, given a collection of non-coincidental 2D images.
Constructing a personalized e-learning system based on genetic algorithm and case-based reasoning approach

 Fig.1にマスタリーラーニングのアウトラインを示す。 初めに教師はカリキュラムや教材をコンセプトやアイディアが重要であるためレビューする必要がある。 次に教師による診断手段や診断プロセスという形式的評価の計画である。また学習エラーを改善するための Corrective Activitiesの計画の主要な援助でもある。  Corrective Activites 矯正活動にはさまざまな形がとられる。Peer Cross-age Tutoring、コンピュータ支援レッスンなど  Enrichment Activities 問題解決練習の特別なtutoringであり、刺激的で早熟な学習者に実りのある学習となっている。  Formative Assesment B もしCorrective Activitiesが学習者を改善しているのならばこの2回目の評価では体得を行っている。 この2回目の評価は学習者に改善されていることや良い学習者になっていることを示し、強力なモチベーショ ンのデバイスとなる。最後は累積的試験または評価の開発がある。
Analyzing Ancient Maya Glyph Collections with Contextual Shape Descriptors

This paper presents an original approach for shape-based analysis of ancient Maya hieroglyphs based on an interdisciplinary collaboration between computer vision and archeology. Our work is guided by realistic needs of archaeologists and scholars who critically need support for search and retrieval tasks in large Maya imagery collections. Our paper has three main contributions. First, we introduce an overview of our interdisciplinary approach towards the improvement of the documentation, analysis, and preservation of Maya pictographic data. Second, we present an objective evaluation of the performance of two state-of-the-art shape-based contextual descriptors (Shape Context and Generalized Shape Context) in retrieval tasks, using two datasets of syllabic Maya glyphs. Based on the identification of their limitations, we propose a new shape descriptor named Histogram of Orientation Shape Context (HOOSC), which is more robust and suitable for description of Maya hieroglyphs. Third, we present what to our knowledge constitutes the first automatic analysis of visual variability of syllabic glyphs along historical periods and across geographic regions of the ancient Maya world via the HOOSC descriptor. Overall, our approach is promising, as it improves performance on the retrieval task, has been successfully validated under an epigraphic viewpoint, and has the potential of offering both novel insights in archeology and practical solutions for real daily scholar needs.
Combining latent factor model with location features for event-based group recommendation

Groups play an essential role in many social websites which promote users' interactions and accelerate the diffusion of information. Recommending groups that users are really interested to join is significant for both users and social media. While traditional group recommendation problem has been extensively studied, we focus on a new type of the problem, i.e., event-based group recommendation. Unlike the other forms of groups, users join this type of groups mainly for participating offline events organized by group members or inviting other users to attend events sponsored by them. These characteristics determine that previously proposed approaches for group recommendation cannot be adapted to the new problem easily as they ignore the geographical influence and other explicit features of groups and users.
 In this paper, we propose a method called Pairwise Tag enhAnced and featuRe-based Matrix factorIzation for Group recommendAtioN (PTARMIGAN), which considers location features, social features, and implicit patterns simultaneously in a unified model. More specifically, we exploit matrix factorization to model interactions between users and groups. Meanwhile, we incorporate their profile information into pairwise enhanced latent factors respectively. We also utilize the linear model to capture explicit features. Due to the reinforcement between explicit features and implicit patterns, our approach can provide better group recommendations. We conducted a comprehensive performance evaluation on real word data sets and the experimental results demonstrate the effectiveness of our method.
Deep Lambertian Networks

Visual perception is a challenging problem in part due to illumination variations. A possible solution is to first estimate an illumination invariant representation before using it for recognition. The object albedo and surface normals are examples of such representations. In this paper, we introduce a multilayer generative model where the latent variables include the albedo, surface normals, and the light source. Combining Deep Belief Nets with the Lambertian reflectance assumption, our model can learn good priors over the albedo from 2D images. Illumination variations can be explained by changing only the lighting latent variable in our model. By transferring learned knowledge from similar objects, albedo and surface normals estimation from a single image is possible in our model. Experiments demonstrate that our model is able to generalize as well as improve over standard baselines in one-shot face recognition.
Automated Unit Testing of a SCADA Control Software: An Industrial Case Study Based on Action Research

We report in this case-study paper our experience and success story with a practical approach and tool for unit regression testing of a SCADA (Supervisory Control and Data Acquisition) software. The tool uses a black-box specification of the units under test to automatically generate NUnit test code. We then improved the test suite by white-box and mutation testing. The approach and tool were developed in an action-research project to test a commercial large-scale SCADA system called Rocket.
Object-Based Multiple Foreground Video Co-segmentation

We present a video co-segmentation method that uses category-independent object proposals as its basic element and can extract multiple foreground objects in a video set. The use of object elements overcomes limitations of low-level feature representations in separating complex foregrounds and backgrounds. We formulate object-based co-segmentation as a co-selection graph in which regions with foreground-like characteristics are favored while also accounting for intra-video and inter-video foreground coherence. To handle multiple foreground objects, we expand the co-selection graph model into a proposed multi-state selection graph model (MSG) that optimizes the segmentations of different objects jointly. This extension into the MSG can be applied not only to our co-selection graph, but also can be used to turn any standard graph model into a multi-state selection solution that can be optimized directly by the existing energy minimization techniques. Our experiments show that our object-based multiple foreground video co-segmentation method (ObMiC) compares well to related techniques on both single and multiple foreground cases.
Personalized Mathematical Word Problem Generation

Word problems are an established technique for teaching mathematical modeling skills in K-12 education. However, many students find word problems unconnected to their lives, artificial, and uninteresting. Most students find them much more difficult than the corresponding symbolic representations. To account for this phenomenon, an ideal pedagogy might involve an individually crafted progression of unique word problems that form a personalized plot. We propose a novel technique for automatic generation of personalized word problems. In our system, word problems are generated from general specifications using answer-set programming (ASP). The specifications include tutor requirements (properties of a mathematical model), and student requirements (personalization, characters, setting). Our system takes a logical encoding of the specification, synthesizes a word problem narrative and its mathematical model as a labeled logical plot graph, and realizes the problem in natural language. Human judges found our problems as solvable as the textbook problems, with a slightly more artificial language.
When the brain plays music: auditory–motor interactions in music perception and production

Music performance is both a natural human activity, present in all societies, and one of the most complex and demanding cognitive challenges that the human mind can undertake. Unlike most other sensory–motor activities, music performance requires precise timing of several hierarchically organized actions, as well as precise control over pitch interval production, implemented through diverse effectors according to the instrument involved. We review the cognitive neuroscience literature of both motor and auditory domains, highlighting the value of studying interactions between these systems in a musical context, and propose some ideas concerning the role of the premotor cortex in integration of higher order features of music with appropriately timed and organized actions.
The Research Audit Trial – Enhancing Trustworthiness in Qualitative Inquiry

Positivist and interpretivist researchers have different views on how their research outcomes may be evaluated. The issues of validity, reliability and generalisability, used in evaluating positivist studies, are regarded of relatively little significance by many qualitative researchers for judging the merits of their interpretive investigations. In confirming the research, those three canons need at least to be re-conceptualised in order to reflect the keys issues of concern for interpretivists. Some interpretivists address alternative issues such as credibility, dependability and transferability when determining the trustworthiness of their qualitative investigations. A strategy proposed by several authors for establishing the trustworthiness of the qualitative inquiry is the development of a research audit trail. The audit trail enables readers to trace through a researcher’s logic and determine whether the study’s findings may be relied upon as a platform for further enquiry. While recommended in theory, this strategy is rarely implemented in practice. This paper examines the role of the research audit trail in improving the trustworthiness of qualitative research. Further, it documents the development of an audit trail for an empirical qualitative research study that centred on an interpretive evaluation of a new Information and Communication Technology (ICT) student administrative system in the tertiary education sector in the Republic of Ireland. This research study examined the impact of system introduction across five Institutes of Technology (IoTs) through case study research that incorporated multiple evidence sources. The evidence collected was analysed using a grounded theory method, which was supported by qualitative data analysis software. The key concepts and categories that emerged from this process were synthesized into a cross case primary narrative; through reflection the primary narrative was reduced to a higher order narrative that presented the principle findings or key research themes. From this higher order narrative a theoretical conjecture was distilled. Both a physical and intellectual audit trail for this study are presented in this paper. The physical audit trail documents all keys stages of a research study and reflects the key research methodology decisions. The intellectual audit trail, on the other hand, outlines how a researcher’s thinking evolved throughout all phases of the study. Hence, these audit trails make transparent the key decisions taken throughout the research process. The paper concludes by discussing the value of this audit trail process in confirming a qualitative study’s findings.
Encoding Natural Movement as an Agent-Based System: An Investigation into Human Pedestrian Behaviour in the Built Environment

Gibson's ecological theory of perception has received considerable attention within psychology literature, as well as in computer vision and robotics. However, few have applied Gibson's approach to agent-based models of human movement, because the ecological theory requires that individuals have a vision-based mental model of the world, and for large numbers of agents this becomes extremely expensive computationally. Thus, within current pedestrian models, path evaluation is based on calibration from observed data or on sophisticated but deterministic route-choice mechanisms; there is little open-ended behavioural modelling of human-movement patterns. One solution which allows individuals rapid concurrent access to the visual information within an environment is an `exosomatic visual architecture', where the connections between mutually visible locations within a configuration are prestored in a lookup table. Here we demonstrate that, with the aid of an exosomatic visual architecture, it is possible to develop behavioural models in which movement rules originating from Gibson's principle of affordance are utilised. We apply large numbers of agents programmed with these rules to a built-environment example and show that, by varying parameters such as destination selection, field of view, and steps taken between decision points, it is possible to generate aggregate movement levels very similar to those found in an actual building context.
Phone Synchronous Speech Recognition With CTC Lattices

Connectionist temporal classification CTC has recently shown improved performance and efficiency in automatic speech recognition. One popular decoding implementation is to use a CTC model to predict the phone posteriors at each frame and then perform Viterbi beam search on a modified WFST network. This is still within the traditional frame synchronous decoding framework. In this paper, the peaky posterior property of CTC is carefully investigated and it is found that ignoring blank frames will not introduce additional search errors. Based on this phenomenon, a novel phone synchronous decoding framework is proposed by removing tremendous search redundancy due to blank frames, which results in significant search speed up. The framework naturally leads to an extremely compact phone-level acoustic space representation: CTC lattice. With CTC lattice, efficient and effective modular speech recognition approaches, second pass rescoring for large vocabulary continuous speech recognition LVCSR, and phone-based keyword spotting KWS, are also proposed in this paper. Experiments showed that phone synchronous decoding can achieve 3-4 times search speed up without performance degradation compared to frame synchronous decoding. Modular LVCSR with CTC lattice can achieve further WER improvement. KWS with CTC lattice not only achieved significant equal error rate improvement, but also greatly reduced the KWS model size and increased the search speed.