nils-herrmann/clean_scirepeval huggingface dataset:
Tactical Language and Culture Training Systems: Using AI to Teach Foreign Languages and Cultures

72 AI MAGAZINE The Tactical Language and Culture Training System (TLCTS) helps people quickly acquire functional skills in foreign languages and cultures. It includes interactive lessons that focus on particular communicative skills and interactive games that apply those skills. Heavy emphasis is placed on spoken communication: learners must learn to speak the foreign language to complete the lessons and play the games. It focuses on the language and cultural skills needed to accomplish particular types of tasks and gives learners rich, realistic opportunities to practice achieving those tasks. Several TLCTS courses have been developed so far. Tactical Iraqi, Tactical Pashto, and Tactical French are in widespread use by U.S. marines and soldiers, and increasingly by military service members in other countries. Additional courses are being developed for use by business executives, workers for nongovernmental organizations, and high school and college students. While precise numbers are impossible to obtain (we do not control copies made by the U.S. government), over 40,000 and as many as 60,000 people have trained so far with TLCTS courses. More than 1000 people download copies of TLCTS courses each month, either for their own use or to set up computer language labs and redistribute copies to students. Just one training site, the military advisor training center at Fort Riley, Kansas, trains approximately 10,000 people annually. Artificial intelligence technologies play multiple essential functions in TLCTS. Speech is the primary input modality, so automated speech recognition tailored to foreign language learners is essential. TLCTS courses are populated with “virtual humans” that engage in dialogue with learners. AI techniques are used to model the decision processes of the virtual humans and to support the generation of their behavior. This makes it possible to give learners extensive conversational practice. Learner modeling software continually monitors each learner’s application of communication skills to estimate the learner’s
The Security Development Lifecycle in the Context of Accreditation Policies and Standards

The proposed security development lifecycle (SecDLC) model delivers a perpetual cycle of information security management and refinement. Using real-world examples, the authors show how SecDLC ensures the goals of preserving, monitoring, and improving security practices, policies, and standards in private and public sectors. The authors describe the four phases of SecDLC, comparing and contrasting them to existing security development models.
Leadership for safety: industrial experience.

The importance of leadership for effective safety management has been the focus of research attention in industry for a number of years, especially in energy and manufacturing sectors. In contrast, very little research into leadership and safety has been carried out in medical settings. A selective review of the industrial safety literature for leadership research with possible application in health care was undertaken. Emerging findings show the importance of participative, transformational styles for safety performance at all levels of management. Transactional styles with attention to monitoring and reinforcement of workers' safety behaviours have been shown to be effective at the supervisory level. Middle managers need to be involved in safety and foster open communication, while ensuring compliance with safety systems. They should allow supervisors a degree of autonomy for safety initiatives. Senior managers have a prime influence on the organisation's safety culture. They need to continuously demonstrate a visible commitment to safety, best indicated by the time they devote to safety matters.
Exploratory spatio-temporal visualization: an analytical review

Current software tools for visualization of spatio-temporal data, on the one hand, utilize the opportunities provided by modern computer technologies, on the other hand, incorporate the legacy from the conventional cartography. We have considered existing visualization-based techniques for exploratory analysis of spatio-temporal data from two perspectives: (1) what types of spatio-temporal data they are applicable to; (2) what exploratory tasks they can potentially support. The technique investigation has been based on an operational typology of spatio-temporal data and analytical tasks we specially devised for this purpose. The result of the study is a structured inventory of existing exploratory techniques related to the types of data and tasks they are appropriate for. This result is potentially helpful for data analysts—users of geovisualization tools: it provides guidelines for selection of proper exploratory techniques depending on the characteristics of data to analyze and the goals of analysis. At the same time the inventory as well as the suggested typology of tasks could be useful for tool designers and developers of various domain-specific geovisualization applications. The designers can, on the one hand, see what task types are insufficiently supported by the existing tools and direct their creative activities towards filling the gaps, on the other hand, use the techniques described as basic elements for building new, more sophisticated ones. The application developers can, on the one hand, use the task and data typology in the analysis of potential user needs, on the other hand, appropriately select and combine existing tools in order to satisfy these needs. r 2003 Corresponding author.  E-mail addresses: natalia.andrienko@ais.fraunhofer.de (N. Andrienko), gatalsky@ais.fhg.de (P. Gatalsky). 926X/$ see front matter r 2003 -926X(03)00046-6
Depth Transfer: Depth Extraction from Video Using Non-Parametric Sampling

We describe a technique that automatically generates plausible depth maps from videos using non-parametric depth sampling. We demonstrate our technique in cases where past methods fail (non-translating cameras and dynamic scenes). Our technique is applicable to single images as well as videos. For videos, we use local motion cues to improve the inferred depth maps, while optical flow is used to ensure temporal depth consistency. For training and evaluation, we use a Kinect-based system to collect a large data set containing stereoscopic videos with known depths. We show that our depth estimation technique outperforms the state-of-the-art on benchmark databases. Our technique can be used to automatically convert a monoscopic video into stereo for 3D visualization, and we demonstrate this through a variety of visually pleasing results for indoor and outdoor scenes, including results from the feature film Charade.
Multi-task learning for Joint Language Understanding and Dialogue State Tracking

This paper presents a novel approach for multi-task learning of language understanding (LU) and dialogue state tracking (DST) in task-oriented dialogue systems. Multi-task training enables the sharing of the neural network layers responsible for encoding the user utterance for both LU and DST and improves performance while reducing the number of network parameters. In our proposed framework, DST operates on a set of candidate values for each slot that has been mentioned so far. These candidate sets are generated using LU slot annotations for the current user utterance, dialogue acts corresponding to the preceding system utterance and the dialogue state estimated for the previous turn, enabling DST to handle slots with a large or unbounded set of possible values and deal with slot values not seen during training. Furthermore, to bridge the gap between training and inference, we investigate the use of scheduled sampling on LU output for the current user utterance as well as the DST output for the preceding turn.
Fundamentals of Digital Forensic Evidence

Digital forensic evidence consists of exhibits, each consisting of a sequence of bits, presented by witnesses in a legal matter, to help jurors establish the facts of the case and support or refute legal theories of the case. The exhibits should be introduced and presented and/or challenged by properly qualified people using a properly applied methodology that addresses the legal theories at issue. The tie between technical issues associated with the digital forensic evidence and the legal theories is the job of expert witnesses.
The Contemporary Theory of Metaphor

inferences as metaphorical spatial inferences Spatial inferences are characterized by the topological structure of image-schemas. We have seen cases such as CATEGORIES ARE CONTAINERS and LINEAR SCALES ARE PATHS where image-schema structure is preserved by metaphor and where abstract inferences about categories and linear scales are metaphorical versions of spatial inferences about containers and paths. The Invariance Principle hypothesizes that imageschema structure is always preserved by metaphor. The Invariance Principle raises the possibility that a great many, if not all, abstract inferences are actually metaphorical versions of spatial inferences that are inherent in the topological structure of imageschemas. What I will do now is turn to other cases of basic, but abstract, concepts to see what evidence there is for the claim that such concepts are fundamentally characterized by metaphor.
The microbiome-gut-brain axis during early life regulates the hippocampal serotonergic system in a sex-dependent manner

Bacterial colonisation of the intestine has a major role in the post-natal development and maturation of the immune and endocrine systems. These processes are key factors underpinning central nervous system (CNS) signalling. Regulation of the microbiome–gut–brain axis is essential for maintaining homeostasis, including that of the CNS. However, there is a paucity of data pertaining to the influence of microbiome on the serotonergic system. Germ-free (GF) animals represent an effective preclinical tool to investigate such phenomena. Here we show that male GF animals have a significant elevation in the hippocampal concentration of 5-hydroxytryptamine and 5-hydroxyindoleacetic acid, its main metabolite, compared with conventionally colonised control animals. Moreover, this alteration is sex specific in contrast with the immunological and neuroendocrine effects which are evident in both sexes. Concentrations of tryptophan, the precursor of serotonin, are increased in the plasma of male GF animals, suggesting a humoral route through which the microbiota can influence CNS serotonergic neurotransmission. Interestingly, colonisation of the GF animals post weaning is insufficient to reverse the CNS neurochemical consequences in adulthood of an absent microbiota in early life despite the peripheral availability of tryptophan being restored to baseline values. In addition, reduced anxiety in GF animals is also normalised following restoration of the intestinal microbiota. These results demonstrate that CNS neurotransmission can be profoundly disturbed by the absence of a normal gut microbiota and that this aberrant neurochemical, but not behavioural, profile is resistant to restoration of a normal gut flora in later life.
Melody Generation for Pop Music via Word Representation of Musical Properties

Automatic melody generation for pop music has been a long-time aspiration for both AI researchers and musicians. However, learning to generate euphonious melody has turned out to be highly challenging due to a number of factors. Representation of multivariate property of notes has been one of the primary challenges. It is also difficult to remain in the permissible spectrum of musical variety, outside of which would be perceived as a plain random play without auditory pleasantness. Observing the conventional structure of pop music poses further challenges. In this paper, we propose to represent each note and its properties as a unique ‘word,’ thus lessening the prospect of misalignments between the properties, as well as reducing the complexity of learning. We also enforce regularization policies on the range of notes, thus encouraging the generated melody to stay close to what humans would find easy to follow. Furthermore, we generate melody conditioned on song part information, thus replicating the overall structure of a full song. Experimental results demonstrate that our model can generate auditorily pleasant songs that are more indistinguishable from human-written ones than previous models.
CETS : Compiler-Enforced Temporal Safety for C Santosh Nagarakatte Jianzhou

Temporal memory safety errors, such as dangling pointer dereferences and double frees, are a prevalent source of software bugs in unmanaged languages such as C. Existing schemes that attempt to retrofit temporal safety for such languages have high runtime overheads and/or are incomplete, thereby limiting their effectiveness as debugging aids. This paper presents CETS, a compile-time transformation for detecting all violations of temporal safety in C programs. Inspired by existing approaches, CETS maintains a unique identifier with each object, associates this metadata with the pointers in a disjoint metadata space to retain memory layout compatibility, and checks that the object is still allocated on pointer dereferences. A formal proof shows that this is sufficient to provide temporal safety even in the presence of arbitrary casts if the program contains no spatial safety violations. Our CETS prototype employs both temporal check removal optimizations and traditional compiler optimizations to achieve a runtime overhead of just 48% on average. When combined with a spatial-checking system, the average overall overhead is 116% for complete memory safety. Disciplines Computer Sciences Comments Santosh Nagarakatte, Jianzhou Zhao, Milo M. K. Martin, and Steve Zdancewic.  Compiler-Enforced Temporal Safety for C. In  2010. doi>10.1145/1806651.1806657  This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in   http://doi.acm.org/ 10.1145/1806651.1806657 This conference paper is available at ScholarlyCommons: http://repository.upenn.edu/cis_papers/576 CETS: Compiler-Enforced Temporal Safety for C Santosh Nagarakatte Jianzhou Zhao Milo M. K. Martin Steve Zdancewic Computer and Information Sciences Department, University of Pennsylvania santoshn@cis.upenn.edu jianzhou@cis.upenn.edu milom@cis.upenn.edu stevez@cis.upenn.edu
Systematic review of effects of current transtibial prosthetic socket designs--Part 2: Quantitative outcomes.

This review is an attempt to untangle the complexity of transtibial prosthetic socket fit and perhaps find some indication of whether a particular prosthetic socket type might be best for a given situation. In addition, we identified knowledge gaps, thus providing direction for possible future research. We followed the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, using medical subject headings and standard key words to search for articles in relevant databases. No restrictions were made on study design and type of outcome measure used. From the obtained search results (n = 1,863), 35 articles were included. The relevant data were entered into a predefined data form that included the Downs and Black risk of bias assessment checklist. This article presents the results from the systematic review of the quantitative outcomes (n = 27 articles). Trends indicate that vacuum-assisted suction sockets improve gait symmetry, volume control, and residual limb health more than other socket designs. Hydrostatic sockets seem to create less inconsistent socket fittings, reducing a problem that greatly influences outcome measures. Knowledge gaps exist in the understanding of clinically meaningful changes in socket fit and its effect on biomechanical outcomes. Further, safe and comfortable pressure thresholds under various conditions should be determined through a systematic approach.
SoftRank: optimizing non-smooth rank metrics

We address the problem of learning large complex ranking functions. Most IR applications use evaluation metrics that depend only upon the ranks of documents. However, most ranking functions generate document scores, which are sorted to produce a ranking. Hence IR metrics are innately non-smooth with respect to the scores, due to the sort. Unfortunately, many machine learning algorithms require the gradient of a training objective in order to perform the optimization of the model parameters,and because IR metrics are non-smooth,we need to find a smooth proxy objective that can be used for training. We present a new family of training objectives that are derived from the rank distributions of documents, induced by smoothed scores. We call this approach SoftRank. We focus on a smoothed approximation to Normalized Discounted Cumulative Gain (NDCG), called SoftNDCG and we compare it with three other training objectives in the recent literature. We present two main results. First, SoftRank yields a very good way of optimizing NDCG. Second, we show that it is possible to achieve state of the art test set NDCG results by optimizing a soft NDCG objective on the training set with a different discount function
Improving feature selection techniques for machine learning

As a commonly used technique in data preprocessing for machine learning, feature selection identifies important features and removes irrelevant, redundant or noise features to reduce the dimensionality of feature space. It improves efficiency, accuracy and comprehensibility of the models built by learning algorithms. Feature selection techniques have been widely employed in a variety of applications, such as genomic analysis, information retrieval, and text categorization. Researchers have introduced many feature selection algorithms with different selection criteria. However, it has been discovered that no single criterion is best for all applications. We proposed a hybrid feature selection framework called based on genetic algorithms (GAs) that employs a target learning algorithm to evaluate features, a wrapper method. We call it hybrid genetic feature selection (HGFS) framework. The advantages of this approach include the ability to accommodate multiple feature selection criteria and find small subsets of features that perform well for the target algorithm. The experiments on genomic data demonstrate that ours is a robust and effective approach that can find subsets of features with higher classification accuracy and/or smaller size compared to each individual feature selection algorithm. A common characteristic of text categorization tasks is multi-label classification with a great number of features, which makes wrapper methods time-consuming and impractical. We proposed a simple filter (non-wrapper) approach called Relation Strength and Frequency Variance (RSFV) measure. The basic idea is that informative features are those that are highly correlated with the class and distribute most differently among all classes. The approach is compared with two well-known feature selection methods in the experiments on two standard text corpora. The experiments show that RSFV generate equal or better performance than the others in many cases.  Feature selection, Gene selection, Term selection, Dimension Reduction, Genetic algorithm, Text categorization, Text classification IMPROVING FEATURE SELECTION TECHNIQUES FOR
Inductive Learning of Answer Set Programs from Noisy Examples

In recent years, non-monotonic Inductive Logic Programming has received growing interest. Specifically, several new learning frameworks and algorithms have been introduced for learning under the answer set semantics, allowing the learning of common-sense knowledge involving defaults and exceptions, which are essential aspects of human reasoning. In this paper, we present a noise-tolerant generalisation of the learning from answer sets framework. We evaluate our ILASP3 system, both on synthetic and on real datasets, represented in the new framework. In particular, we show that on many of the datasets ILASP3 achieves a higher accuracy than other ILP systems that have previously been applied to the datasets, including a recently proposed differentiable learning framework.
Cross-domain security of cyber-physical systems

The interaction between the cyber domain and the physical domain components and processes can be leveraged to enhance the security of the cyber-physical system. In order to do so, we must first analyze various cyber domain and physical domain information flows, and characterize the relation between them using model functions. In this paper, we present a notion of cross-domain security of cyber-physical systems, whereby we present a security analysis framework that can be used for generating novel cross-domain attack models, attack detection methods, etc. We demonstrate how information flows such as discrete domain signal flows and continuous domain energy flows in the cyber and physical domain can be used to generate model functions using data-driven estimation, and use this model functions for performing various cross-domain security analysis. We also demonstrate the practical applicability of the cross-domain security analysis framework using the cyber-physical manufacturing system as a case study.
Distributed Autonomous Online Learning: Regrets and Intrinsic Privacy-Preserving Properties

Online learning has become increasingly popular on handling massive data. The sequential nature of online learning, however, requires a centralized learner to store data and update parameters. In this paper, we consider online learning with distributed data sources. The autonomous learners update local parameters based on local data sources and periodically exchange information with a small subset of neighbors in a communication network. We derive the regret bound for strongly convex functions that generalizes the work by Ram et al. for convex functions. More importantly, we show that our algorithm has intrinsic privacy-preserving properties, and we prove the sufficient and necessary conditions for privacy preservation in the network. These conditions imply that for networks with greater-than-one connectivity, a malicious learner cannot reconstruct the subgradients (and sensitive raw data) of other learners, which makes our algorithm appealing in privacy-sensitive applications.
CiteSeerx: an architecture and web service design for an academic document search engine

CiteSeer is a scientific literature digital library and search engine which automatically crawls and indexes scientific documents in the field of computer and information science. After serving as a public search engine for nearly ten years, CiteSeer is starting to have scaling problems for handling of more documents, adding new feature and more users. Its monolithic architecture design prevents it from effectively making use of new web technologies and providing new services. After analyzing the current system problems, we propose a new architecture and data model, CiteSeerx. CiteSeerx that will overcome the existing problems as well as provide scalability and better performance plus new services and system features.
Embodiment of Wearable Augmented Reality Technology in Tourism Experiences

The increasing use of wearable devices for tourism purposes sets the stage for a critical discussion on technological mediation in tourism experiences. This paper provides a theoretical reflection on the phenomenon of embodiment relation in technological mediation and then assesses the embodiment of wearable augmented reality technology in a tourism attraction. The findings suggest that technology embodiment is a multidimensional construct consisting of ownership, location, and agency. These support the concept of technology withdrawal, where technology disappears as it becomes part of human actions, and contest the interplay of subjectivity and intentionality between humans and technology in situated experiences such as tourism. It was also found that technology embodiment affects enjoyment and enhances experience with tourism attractions.