nils-herrmann/clean_scirepeval huggingface dataset:
Insider threat detection model for the cloud

Cloud computing is a revolutionary technology that is changing the way people and organizations conduct business. It promises to help organizations save money on IT expenditure while increasing reliability, efficiency and productivity. However, despite the potential benefits that the cloud promises its users, it is facing some security challenges. Insider threats are some of the growing security concerns that are hindering the adoption of the cloud. Cloud providers are faced with a challenge of monitoring usage patterns of users so as to ensure that malicious insiders do not compromise the security of customer data and applications. Solutions are still needed to ensure that the data stored in the cloud is secure from malicious insiders of the cloud service provider. This paper presents an Insider Threat Detection Model that can be used to detect suspicious insider activities. An experimental system was designed to implement this model. This system uses sequential rule mining to detect malicious users by comparing incoming events against user profiles.
Social cognitive neuroscience: where are we heading?

Humans crave the company of others and suffer profoundly if temporarily isolated from society. Much of the brain must have evolved to deal with social communication and we are increasingly learning more about the neurophysiological basis of social cognition. Here, we explore some of the reasons why social cognitive neuroscience is captivating the interest of many researchers. We focus on its future, and what we believe are priority areas for further research.
MT/IE: Cross-lingual Open Information Extraction with Neural Sequence-to-Sequence Models

Cross-lingual information extraction is the task of distilling facts from foreign language (e.g. Chinese text) into representations in another language that is preferred by the user (e.g. English tuples). Conventional pipeline solutions decompose the task as machine translation followed by information extraction (or vice versa). We propose a joint solution with a neural sequence model, and show that it outperforms the pipeline in a cross-lingual open information extraction setting by 1-4 BLEU and 0.5-0.8 F1.
Topic-based Evaluation for Conversational Bots

Dialog evaluation is a challenging problem, especially for non task-oriented dialogs where conversational success is not well-defined. We propose to evaluate dialog quality using topic-based metrics that describe the ability of a conversational bot to sustain coherent and engaging conversations on a topic, and the diversity of topics that a bot can handle. To detect conversation topics per utterance, we adopt Deep Average Networks (DAN) and train a topic classifier on a variety of question and query data categorized into multiple topics. We propose a novel extension to DAN by adding a topic-word attention table that allows the system to jointly capture topic keywords in an utterance and perform topic classification. We compare our proposed topic based metrics with the ratings provided by users and show that our metrics both correlate with and complement human judgment. Our analysis is performed on tens of thousands of real human-bot dialogs from the Alexa Prize competition and highlights user expectations for conversational bots.
CondenseNet: An Efficient DenseNet Using Learned Group Convolutions

Deep neural networks are increasingly used on mobile devices, where computational resources are limited. In this paper we develop CondenseNet, a novel network architecture with unprecedented efficiency. It combines dense connectivity with a novel module called learned group convolution. The dense connectivity facilitates feature re-use in the network, whereas learned group convolutions remove connections between layers for which this feature re-use is superfluous. At test time, our model can be implemented using standard group convolutions, allowing for efficient computation in practice. Our experiments show that CondenseNets are far more efficient than state-of-the-art compact convolutional networks such as ShuffleNets.
Deep-Cascade: Cascading 3D Deep Neural Networks for Fast Anomaly Detection and Localization in Crowded Scenes

This paper proposes a fast and reliable method for anomaly detection and localization in video data showing crowded scenes. Time-efficient anomaly localization is an ongoing challenge and subject of this paper. We propose a cubic-patch-based method, characterised by a cascade of classifiers, which makes use of an advanced feature-learning approach. Our cascade of classifiers has two main stages. First, a light but deep 3D auto-encoder is used for early identification of “many” normal cubic patches. This deep network operates on small cubic patches as being the first stage, before carefully resizing the remaining candidates of interest, and evaluating those at the second stage using a more complex and deeper 3D convolutional neural network (CNN). We divide the deep auto-encoder and the CNN into multiple sub-stages, which operate as cascaded classifiers. Shallow layers of the cascaded deep networks (designed as Gaussian classifiers, acting as weak single-class classifiers) detect “simple” normal patches, such as background patches and more complex normal patches, are detected at deeper layers. It is shown that the proposed novel technique (a cascade of two cascaded classifiers) performs comparable to current top-performing detection and localization methods on standard benchmarks, but outperforms those in general with respect to required computation time.
Google Earth for the Study of Ancient Civilizations

Satellite imaging is well known as a useful tool in many scientific disciplines and various applications. Google Earth, with its free access, is now - thanks to increasing resolution and precision - such a tool. It improves the visualization and dissemination of scientific data, and opens doors to new discoveries. For example, many Nasca geoglyphs are now visible to Google Earth and so are the orientations of Chinese pyramids, which appear to be laid out with the aid of a magnetic compass. Google Earth can also "see" a previously unknown "Monte Alban II" close to the well known "Monte Alban" in the Valley of Oaxaca (Mexico), as well as prehistoric causeways in Mesoamerica (namely, in north Yucatan) and in the Chaco valley, New Mexico. We find that Google Earth can save time and resources significantly: before, during and after field measurements.
Learning Fine-Grained Expressions to Solve Math Word Problems

This paper presents a novel templatebased method to solve math word problems. This method learns the mappings between math concept phrases in math word problems and their math expressions from training data. For each equation template, we automatically construct a rich template sketch by aggregating information from various problems with the same template. Our approach is implemented in a two-stage system. It first retrieves a few relevant equation system templates and aligns numbers in math word problems to those templates for candidate equation generation. It then does a fine-grained inference to obtain the final answer. Experiment results show that our method achieves an accuracy of 28.4% on the linear Dolphin18K benchmark, which is 10% (54% relative) higher than previous stateof-the-art systems while achieving an accuracy increase of 12% (59% relative) on the TS6 benchmark subset.
Theory and Experiments on Vector Quantized Autoencoders

Deep neural networks with discrete latent variables offer the promise of better symbolic reasoning, and learning abstractions that are more useful to new tasks. There has been a surge in interest in discrete latent variable models, however, despite several recent improvements, the training of discrete latent variable models has remained challenging and their performance has mostly failed to match their continuous counterparts. Recent work on vector quantized autoencoders (VQVAE) has made substantial progress in this direction, with its perplexity almost matching that of a VAE on datasets such as CIFAR-10. In this work, we investigate an alternate training technique for VQ-VAE, inspired by its connection to the Expectation Maximization (EM) algorithm. Training the discrete bottleneck with EM helps us achieve better image generation results on CIFAR-10, and together with knowledge distillation, allows us to develop a non-autoregressive machine translation model whose accuracy almost matches a strong greedy autoregressive baseline Transformer, while being 3.3 times faster at inference.
Visual-Inertial Sensor Fusion: Localization, Mapping and Sensor-to-Sensor Self-calibration

Visual and inertial sensors, in combination, are able to provide accurate motion estimates and are well-suited for use in many robot navigation tasks. However, correct data fusion, and hence overall performance, depends on careful calibration of the rigid body transform between the sensors. Obtaining this calibration information is typically difficult and time-consuming, and normally requires additional equipment. In this paper we describe an algorithm, based on the unscented Kalman filter, for self-calibration of the transform between a camera and an inertial measurement unit (IMU). Our formulation rests on a differential geometric analysis of the observability of the camera-IMU system; this analysis shows that the sensor-to-sensor transform, the IMU gyroscope and accelerometer biases, the local gravity vector, and the metric scene structure can be recovered from camera and IMU measurements alone. While calibrating the transform we simultaneously localize the IMU and build a map of the surroundings – all without additional hardware or prior knowledge about the environment in which a robot is operating. We present results from simulation studies and from experiments with a monocular camera and a low-cost IMU, which demonstrate accurate estimation of both the calibration parameters and the local scene structure.
PERFORMANCE AND DECISION MAKING

■  Theory and research on small group performance and decision making is reviewed. Recent trends in group performance research have found that process gains as well as losses are possible, and both are frequently explained by situational and procedural contexts that differentially affect motivation and resource coordination. Research has continued on classic topics (e.g., brainstorming, group goal setting, stress, and group performance) and relatively new areas (e.g., collective induction). Group decision making research has focused on preference combination for continuous response distributions and group information processing. New approaches (e.g., group-level signal detection) and traditional topics (e.g., groupthink) are discussed. New directions, such as nonlinear dynamic systems, evolutionary adaptation, and technological advances, should keep small group research vigorous well into the future.
Trichoderma-plant -pathogen interactions

Biological control involves the use of beneficial organisms, their genes, and/or products, such as metabolites, that reduce the negative effects of plant pathogens and promote positive responses by the plant. Disease suppression, as mediated by biocontrol agents, is the consequence of the interactions between the plant, pathogens, and the microbial community. Antagonists belonging to the genus Trichoderma are among the most commonly isolated soil fungi. Due to their ability to protect plants and contain pathogen populations under different soil conditions, these fungi have been widely studied and commercially marketed as biopesticides, biofertilizers and soil amendments. Trichoderma spp. also produce numerous biologically active compounds, including cell wall degrading enzymes, and secondary metabolites. Studies of the three-way relationship established with Trichoderma, the plant and the pathogen are aimed at unravelling the mechanisms involved in partner recognition and the cross-talk used to maintain the beneficial association between the fungal antagonist and the plant. Several strategies have been used to identify the molecular factors involved in this complex tripartite interaction including genomics, proteomics and, more recently, metabolomics, in order to enhance our understanding. This review presents recent advances and findings regarding the biocontrol-resulting events that take place during the Trichoderma–plant–pathogen interaction. We focus our attention on the biological aspects of this topic, highlighting the novel findings concerning the role of Trichoderma in disease suppression. A better understanding of these factors is expected to enhance not only the rapid identification of effective strains and their applications but also indicate the potentials for improvement of natural strains of Trichoderma. r 2007 Elsevier Ltd. All rights reserved.
Q-learning policies for a single agent foraging tasks

Policies play an important role in balancing the trade-off between exploration and exploitation problem in q-learning. Pure exploration degrades the performance of the q-learning but increases the flexibility to adapt in a dynamic environment. On the other hand pure exploitation drives the learning process to locally optimal solutions. In this paper, a single agent foraging task has been modeled incorporating the available policies reported in the open literature to address the exploration and exploitation issues. Policies namely greedy, e-greedy, Boltzmann distribution, Simulated An-nealing(SA)algorithm and random search are used to study their performances in the foraging task and the results are presented.
A Character-Aware Encoder for Neural Machine Translation

This article proposes a novel character-aware neural machine translation (NMT) model that views the input sequences as sequences of characters rather than words. On the use of row convolution (Amodei et al., 2015), the encoder of the proposed model composes word-level information from the input sequences of characters automatically. Since our model doesn’t rely on the boundaries between each word (as the whitespace boundaries in English), it is also applied to languages without explicit word segmentations (like Chinese). Experimental results on Chinese-English translation tasks show that the proposed character-aware NMT model can achieve comparable translation performance with the traditional word based NMT models. Despite the target side is still word based, the proposed model is able to generate much less unknown words.
Dual Motion GAN for Future-Flow Embedded Video Prediction

Future frame prediction in videos is a promising avenue for unsupervised video representation learning. Video frames are naturally generated by the inherent pixel flows from preceding frames based on the appearance and motion dynamics in the video. However, existing methods focus on directly hallucinating pixel values, resulting in blurry predictions. In this paper, we develop a dual motion Generative Adversarial Net (GAN) architecture, which learns to explicitly enforce future-frame predictions to be consistent with the pixel-wise flows in the video through a duallearning mechanism. The primal future-frame prediction and dual future-flow prediction form a closed loop, generating informative feedback signals to each other for better video prediction. To make both synthesized future frames and flows indistinguishable from reality, a dual adversarial training method is proposed to ensure that the futureflow prediction is able to help infer realistic future-frames, while the future-frame prediction in turn leads to realistic optical flows. Our dual motion GAN also handles natural motion uncertainty in different pixel locations with a new probabilistic motion encoder, which is based on variational autoencoders. Extensive experiments demonstrate that the proposed dual motion GAN significantly outperforms stateof-the-art approaches on synthesizing new video frames and predicting future flows. Our model generalizes well across diverse visual scenes and shows superiority in unsupervised video representation learning.
Churn prediction in subscription services: An application of support vector machines while comparing two parameter-selection techniques

CRM gains increasing importance due to intensive competition and saturated markets. With the purpose of retaining customers, academics as well as practitioners find it crucial to build a churn prediction model that is as accurate as possible. This study applies support vector machines in a newspaper subscription context in order to construct a churn model with a higher predictive performance. Moreover, a comparison is made between two parameter-selection techniques, needed to implement support vector machines. Both techniques are based on grid search and cross-validation. Afterwards, the predictive performance of both kinds of support vector machine models is benchmarked to logistic regression and random forests. Our study shows that support vector machines show good generalization performance when applied to noisy marketing data. Nevertheless, the parameter optimization procedure plays an important role in the predictive performance. We show that only when the optimal parameter selection procedure is applied, support vector machines outperform traditional logistic regression, whereas random forests outperform both kinds of support vector machines. As a substantive contribution, an overview of the most important churn drivers is given. Unlike ample research, monetary value and frequency do not play an important role in explaining churn in this subscription-services application. Even though most important churn predictors belong to the category of variables describing the subscription, the influence of several client/company-interaction variables can not be neglected.
Hotel revenue management – a critical literature review

Th e paper presents a literature review of the main concepts of hotel revenue management (RM) and current state-of-the-art of its theoretical research. Th e article emphasises on the diff erent directions of hotel RM research and is structured around the elements of the hotel RM system and the stages of RM process. Th e elements of the hotel RM system discussed in the paper include hotel RM centres (room division, F&B, function rooms, spa & fi tness facilities, golf courses, casino and gambling facilities, and other additional services), data and information, the pricing (price discrimination, dynamic pricing, lowest price guarantee) and non-pricing (overbookings, length of stay control, room availability guarantee) RM tools, the RM software, and the RM team. Th e stages of RM process have been identifi ed as goal setting, collection of data and information, data analysis, forecasting, decision making, implementation and monitoring. Additionally, special attention is paid to ethical considerations in RM practice, the connections between RM and customer relationship management, and the legal aspect of RM. Finally, the article outlines future research perspectives and discloses potential evolution of RM in future.
Data reorganization in memory using 3D-stacked DRAM

In this paper we focus on common data reorganization operations such as shuffle, pack/unpack, swap, transpose, and layout transformations. Although these operations simply relocate the data in the memory, they are costly on conventional systems mainly due to inefficient access patterns, limited data reuse and roundtrip data traversal throughout the memory hierarchy. This paper presents a two pronged approach for efficient data reorganization, which combines (i) a proposed DRAM-aware reshape accelerator integrated within 3D-stacked DRAM, and (ii) a mathematical framework that is used to represent and optimize the reorganization operations.
 We evaluate our proposed system through two major use cases. First, we demonstrate the reshape accelerator in performing a physical address remapping via data layout transform to utilize the internal parallelism/locality of the 3D-stacked DRAM structure more efficiently for general purpose workloads. Then, we focus on offloading and accelerating commonly used data reorganization routines selected from the Intel Math Kernel Library package. We evaluate the energy and performance benefits of our approach by comparing it against existing optimized implementations on state-of-the-art GPUs and CPUs. For the various test cases, in-memory data reorganization provides orders of magnitude performance and energy efficiency improvements via low overhead hardware.
Targeted Storyfying: Creating Stories About Particular Events

The present paper proposes a computational model of the task of building a story from a set of events that have been observed in the world. For the purposes of the paper, a story is considered to be a particular type of sequential discourse, that includes a beginning, a complication and a resolution, concerns a character that can be clearly identified as a protagonist, and ends with a certain sense of closure. Starting from prior approaches to this task, the paper addresses the problem of how to target particular events to act as the core of the desired story. Two different heuristics – imaginative interpretation and imaginative enrichment – are proposed, one favouring faithful rendering of the observed events and the other favouring strong cohesive plots. The heuristics are tested over a simple case study based on finding interesting plots to tell inspired by the movements of pieces in a chess game.
Multi-Domain and Multi-Task Learning for Human Action Recognition

Domain-invariant (view-invariant and modality-invariant) feature representation is essential for human action recognition. Moreover, given a discriminative visual representation, it is critical to discover the latent correlations among multiple actions in order to facilitate action modeling. To address these problems, we propose a multi-domain and multi-task learning (MDMTL) method to: 1) extract domain-invariant information for multi-view and multi-modal action representation and 2) explore the relatedness among multiple action categories. Specifically, we present a sparse transfer learning-based method to co-embed multi-domain (multi-view and multi-modality) data into a single common space for discriminative feature learning. Additionally, visual feature learning is incorporated into the multi-task learning framework, with the Frobenius-norm regularization term and the sparse constraint term, for joint task modeling and task relatedness-induced feature learning. To the best of our knowledge, MDMTL is the first supervised framework to jointly realize domain-invariant feature learning and task modeling for multi-domain action recognition. Experiments conducted on the INRIA Xmas Motion Acquisition Sequences data set, the MSR Daily Activity 3D (DailyActivity3D) data set, and the Multi-modal & Multi-view & Interactive data set, which is the most recent and largest multi-view and multi-model action recognition data set, demonstrate the superiority of MDMTL over the state-of-the-art approaches.
Education data mining: Mining MOOCs videos using metadata based approach

Due to the growing interest in data mining and the educational system, educational data mining is the emerging topic for research community. The various techniques of data mining like classification and clustering can be applied to bring out hidden knowledge from the educational data. Web video mining is retrieving the content using data mining techniques from World Wide Web. There are two approaches for web video mining using traditional image processing (signal processing) and metadata based approach. In this paper, we focus on the education data mining and precisely MOOCs which constitute a new modality of e-learning and clustering techniques. We present a methodology that can be used for mining Moocs videos using metadata as leading contribution for knowledge discovery.