nils-herrmann/clean_scirepeval huggingface dataset:
Recurrent Highway Networks

Many sequential processing tasks require complex nonlinear transition functions from one step to the next. However, recurrent neural networks with “deep" transition functions remain difficult to train, even when using Long Short-Term Memory (LSTM) networks. We introduce a novel theoretical analysis of recurrent networks based on Geršgorin’s circle theorem that illuminates several modeling and optimization issues and improves our understanding of the LSTM cell. Based on this analysis we propose Recurrent Highway Networks, which are deep not only in time but also in space, extending the LSTM architecture to larger step-to-step transition depths. Experiments demonstrate that the proposed architecture results in powerful and efficient models benefiting from up to 10 layers in the recurrent transition. On the Penn Treebank language modeling corpus, a single network outperforms all previous ensemble results with a perplexity of 66.0 on the test set. On the larger Hutter Prize Wikipedia dataset, a single network again significantly outperforms all previous results with an entropy of 1.32 bits per character on the test set.
Device-to-device communication in 5G cellular networks

Device-to-device (D2D) communication commonly refers to a type of technology that enable devices to communicate directly with each other without communication infrastructures such as access points (APs) or base stations (BSs). Bluetooth and WiFi-Direct are the two most popular D2D techniques, both working in the unlicensed industrial, scientific and medical (ISM) bands. Cellular networks, on the other hand, do not support direct over-the-air communications between users and devices. However, with the emergence of context-aware applications and the accelerating growth of Machine-to-Machine (M2M) applications, D2D communication plays an increasingly important role. It facilitates the discovery of geographically close devices, and enables direct communications between these proximate devices, which improves communication capability and reduces communication delay and power consumption. To embrace the emerging market that requires D2D communications, mobile operators and vendors are accepting D2D as a part of the fourth generation (4G) Long Term Evolution (LTE)-Advanced standard in 3rd Generation Partnership Project (3GPP) Release 12.
Business models for open government data

The commercial re-use of open government data is broadly expected to generate economic value. However, the practice and study of this trend is still in its infancy. In particular, the issue of value creation in the commercial re-use open government data remains largely unknown. This study aims to further understand how open government data is used to develop commercial products and services. Grounded in the comprehensive data obtained from a sample of 500 US firms that use open government data as part of their business model, we propose a taxonomy that encompasses three business model archetypes (enablers, facilitators, and integrators). Furthermore, we discuss the value proposition of each business model archetype, and subsequently present a framework that describes the value created in the context of the open government data ecosystem. Our framework can be used by both scholars and practitioners in the field of open government data to effectively frame the debate of the value created by the commercial re-use of open government data. Simultaneously, our work can be of benefit to entrepreneurs as it provides a systematic overview, as well as practical insights, of the growing use of open government data in the private sector.
Methodological Framework for Data Warehouse Design

Though designing a data warehouse requires techniques completely different from those adopted for operational systems, no significant effort has been made so far to develop a complete and consistent design methodology for data warehouses. In this paper we outline a general methodological framework for data warehouse design, based on our Dimensional Fact Model (DFM). After analyzing the existing information system and collecting the user requirements, conceptual design is carried out semi-automatically starting from the operational database scheme. A workload is then characterized in terms of data volumes and expected queries, to be used as the input of the logical and physical design phases whose output is the final scheme for the data warehouse.
Partial 3-D Correspondence from Shape Extremities

We present a 3-D correspondence method to match the geometric extremities of two shapes which are partially isometric. We consider the most general setting of the isometric partial shape correspondence problem, in which shapes to be matched may have multiple common parts at arbitrary scales as well as parts that are not similar. Our rank-and-vote-and-combine algorithm identifies and ranks potentially correct matches by exploring the space of all possible partial maps between coarsely sampled extremities. The qualified top-ranked matchings are then subjected to a more detailed analysis at a denser resolution and assigned with confidence values that accumulate into a vote matrix. A minimum weight perfect matching algorithm is finally iterated to combine the accumulated votes into an optimal (partial) mapping between shape extremities, which can further be extended to a denser map. We test the performance of our method on several data sets and benchmarks in comparison with state of the art.
Moral Decision Making in Autonomous Systems: Enforcement, Moral Emotions, Dignity, Trust, and Deception

As humans are being progressively pushed further downstream in the decision-making process of autonomous systems, the need arises to ensure that moral standards, however defined, are adhered to by these robotic artifacts. While meaningful inroads have been made in this area regarding the use of ethical lethal military robots, including work by our laboratory, these needs transcend the warfighting domain and are pervasive, extending to eldercare, robot nannies, and other forms of service and entertainment robotic platforms. This paper presents an overview of the spectrum and specter of ethical issues raised by the advent of these systems, and various technical results obtained to date by our research group, geared towards managing ethical behavior in autonomous robots in relation to humanity. This includes: 1) the use of an ethical governor capable of restricting robotic behavior to predefined social norms; 2) an ethical adaptor which draws upon the moral emotions to allow a system to constructively and proactively modify its behavior based on the consequences of its actions; 3) the development of models of robotic trust in humans and its dual, deception, drawing on psychological models of interdependence theory; and 4) concluding with an approach towards the maintenance of dignity in human-robot relationships.
Optimizing deep learning hyper-parameters through an evolutionary algorithm

There has been a recent surge of success in utilizing Deep Learning (DL) in imaging and speech applications for its relatively automatic feature generation and, in particular for convolutional neural networks (CNNs), high accuracy classification abilities. While these models learn their parameters through data-driven methods, model selection (as architecture construction) through hyper-parameter choices remains a tedious and highly intuition driven task. To address this, Multi-node Evolutionary Neural Networks for Deep Learning (MENNDL) is proposed as a method for automating network selection on computational clusters through hyper-parameter optimization performed via genetic algorithms.
Optimal Boolean Matrix Decomposition: Application to Role Engineering

A decomposition of a binary matrix into two matrices gives a set of basis vectors and their appropriate combination to form the original matrix. Such decomposition solutions are useful in a number of application domains including text mining, role engineering as well as knowledge discovery. While a binary matrix can be decomposed in several ways, however, certain decompositions better characterize the semantics associated with the original matrix in a succinct but comprehensive way. Indeed, one can find different decompositions optimizing different criteria matching various semantics. In this paper, we first present a number of variants to the optimal Boolean matrix decomposition problem that have pragmatic implications. We then present a unified framework for modeling the optimal binary matrix decomposition and its variants using binary integer programming. Such modeling allows us to directly adopt the huge body of heuristic solutions and tools developed for binary integer programming. Although the proposed solutions are applicable to any domain of interest, for providing more meaningful discussions and results, in this paper, we present the binary matrix decomposition problem in a role engineering context, whose goal is to discover an optimal and correct set of roles from existing permissions, referred to as the role mining problem (RMP). This problem has gained significant interest in recent years as role based access control has become a popular means of enforcing security in databases. We consider several variants of the above basic RMP, including the min-noise RMP, delta-approximate RMP and edge-RMP. Solutions to each of them aid security administrators in specific scenarios. We then model these variants as Boolean matrix decomposition and present efficient heuristics to solve them.
An Automated Teamwork Infrastructure for Heterogeneous Software Agents and Humans

Agent integration architectures enable a heterogeneous, distributed set of agents to work together to address problems of greater complexity than those addressed by the individual agents themselves. Unfortunately, integrating software agents and humans to perform real-world tasks in a large-scale system remains difficult, especially due to three main challenges: ensuring robust execution in the face of a dynamic environment, providing abstract task specifications without all the low-level coordination details, and finding appropriate agents for inclusion in the overall system. To address these challenges, our Teamcore project provides the integration architecture with general-purpose teamwork coordination capabilities. We make each agent team-ready by providing it with a proxy capable of general teamwork reasoning. Thus, a key novelty and strength of our framework is that powerful teamwork capabilities are built into its foundations by providing the proxies themselves with a teamwork model. Given this teamwork model, the Teamcore proxies addresses the first agent integration challenge, robust execution, by automatically generating the required coordination actions for the agents they represent. We can also exploit the proxies' reusable general teamwork knowledge to address the second agent integration challenge. Through team-oriented programming, a developer specifies a hierarchical organization and its goals and plans, abstracting away from coordination details. Finally, KARMA, our Knowledgeable Agent Resources Manager Assistant, can aid the developer in conquering the third agent integration challenge by locating agents that match the specified organization's requirements. Our integration architecture enables teamwork among agents with no coordination capabilities, and it establishes and automates consistent teamwork among agents with some coordination capabilities. Thus, team-oriented programming provides a level of abstraction that can be used on top of previous approaches to agent-oriented programming. We illustrate how the Teamcore architecture successfully addressed the challenges of agent integration in two application domains: simulated rehearsal of a military evacuation mission and facilitation of human collaboration.
Choice overload : A conceptual review and meta-analysis

Despite the voluminous evidence in support of the paradoxical finding that providing individuals with more options can be detrimental to choice, the question of whether and when large assortments impede choice remains open. Even though extant research has identified a variety of antecedents and consequences of choice overload, the findings of the individual studies fail to come together into a cohesive understanding of when large assortments can benefit choice and when they can be detrimental to choice. In a meta-analysis of 99 observations (N = 7202) reported by prior research, we identify four key factors—choice set complexity, decision task difficulty, preference uncertainty, and decision goal—that moderate the impact of assortment size on choice overload. We further show that each of these four factors has a reliable and significant impact on choice overload, whereby higher levels of decision task difficulty, greater choice set complexity, higher preference uncertainty, and a more prominent, effort-minimizing goal facilitate choice overload. We also find that four of the measures of choice overload used in prior research— satisfaction/confidence, regret, choice deferral, and switching likelihood—are equally powerful measures of choice overload and can be used interchangeably. Finally, we document that when moderating variables are taken into account the overall effect of assortment size on choice overload is significant—a finding counter to the data reported by prior meta-analytic research.
Learning Textual Graph Patterns to Detect Causal Event Relations

This paper presents a novel method for discovering causal relations between events encoded in text. In order to determine if two events from the same sentence are in a causal relation or not, we first build a graph representation of the sentence that encodes lexical, syntactic, and semantic information. In a second step, we automatically extract multiple graph patterns (or subgraphs) from such graph representations and sort them according to their relevance in determining the causality between two events from the same sentence. Finally, in order to decide if these events are causal or not, we train a binary classifier based on what graph patterns can be mapped to the graph representation associated with the two events. Our experimental results show that capturing the feature dependencies of causal event relations using a graph representation significantly outperforms an existing method that uses a flat representation of features.
Joint-sequence models for grapheme-to-phoneme conversion

Grapheme-to-phoneme conversion is the task of finding the pronunciation of a word given its written form. It has important applications in text-to-speech and speech recognition. Joint-sequence models are a simple and theoretically stringent probabilistic framework that is applicable to this problem. This article provides a selfcontained and detailed description of this method. We present a novel estimation algorithm and demonstrate high accuracy on a variety of databases. Moreover we study the impact of the maximum approximation in training and transcription, the interaction of model size parameters, n-best list generation, confidence measures, and phoneme-to-grapheme conversion. Our software implementation of the method proposed in this work is available under an Open Source license.
Principal Manifolds and Nonlinear Dimension Reduction via Local Tangent Space Alignment

Nonlinear manifold learning from unorganized data points is a very challenging unsupervised learning and data visualization problem with a great variety of applications. In this paper we present a new algorithm for manifold learning and nonlinear dimension reduction. Based on a set of unorganized data points sampled with noise from the manifold, we represent the local geometry of the manifold using tangent spaces learned by fitting an affine subspace in a neighborhood of each data point. Those tangent spaces are aligned to give the internal global coordinates of the data points with respect to the underlying manifold by way of a partial eigendecomposition of the neighborhood connection matrix. We present a careful error analysis of our algorithm and show that the reconstruction errors are of second-order accuracy. We illustrate our algorithm using curves and surfaces both in 2D/3D and higher dimensional Euclidean spaces, and 64-by-64 pixel face images with various pose and lighting conditions. We also address several theoretical and algorithmic issues for further research and improvements.
Proposal for a Conceptual Framework for Educators to Describe and Design MOOCs

Massive Open Online Courses (MOOCs) are a disruptive trend in education. Several initiatives have emerged during the last months to give support to MOOCs, and many educators have started offering courses as MOOCs in different areas and disciplines. However, designing a MOOC is not an easy task. Educators need to face not only pedagogical issues, but also other issues of logistical, technological and financial nature, as well as how these issues relate and constrain each other. Currently, little guidance is available for educators to address the design of MOOCs from scratch keeping a balance between all these issues. This paper proposes a conceptual framework for supporting educators in the description and design of MOOCs called the MOOC Canvas. The MOOC Canvas defines eleven interrelated issues that are addressed through a set of questions, offering a visual and understandable guidance for educators during the MOOC design process. As a practical usage example, this paper shows how the MOOC Canvas captures the description and design of a real 9-week MOOC. An analysis of the different elements of the course shed some light on the usage of the MOOC Canvas as a mechanism to address the description and design of MOOCs.
Plug-in privacy for Smart Metering billing

Traditional electricity meters are replaced by Smart Meters in customers’ households. Smart Meters collect fine-grained utility consumption profiles from customers, which in turn enables the introduction of dynamic, time-of-use tariffs. However, the fine-grained usage data that is compiled in this process also allows to infer the inhabitant’s personal schedules and habits. We propose a privacy-preserving protocol that enables billing with time-of-use tariffs without disclosing the actual consumption profile to the supplier. Our approach relies on a zero-knowledge proof based on Pedersen Commitments performed by a plug-in privacy component that is put into the communication link between Smart Meter and supplier’s back-end system. We require no changes to the Smart Meter hardware and only small changes to the software of Smart Meter and back-end system. In this paper we describe the functional and privacy requirements, the specification and security proof of our solution and give a performance evaluation of a prototypical implementation.
A Wizard-of-Oz Study on A Non-Task-Oriented Dialog Systems That Reacts to User Engagement

In this paper, we describe a system that reacts to both possible system breakdowns and low user engagement with a set of conversational strategies. These general strategies reduce the number of inappropriate responses and produce better user engagement. We also found that a system that reacts to both possible system breakdowns and low user engagement is rated by both experts and non-experts as having better overall user engagement compared to a system that only reacts to possible system breakdowns. We argue that for non-task-oriented systems we should optimize on both system response appropriateness and user engagement. We also found that apart from making the system response appropriate, funny and provocative responses can also lead to better user engagement. On the other hand, short appropriate responses, such as “Yes” or “No” can lead to decreased user engagement. We will use these findings to further improve our system.
Leader election algorithms for mobile ad hoc networks

We present two new leader election algorithms for mobile ad hoc networks. The algorithms ensure that eventually each connected component of the topology graph has exactly one leader. The algorithms are based on a routing algorithm called TORA , which in turn is based on an algorithm by Gafni and Bertsekas . The algorithm require nodes to communicate with only their current neighbors, making it well suited to the ad hoc environment. The first algorithm is for a single topology change and is provided with a proof of correctness. The second algorithm tolerates multiple concurrent topology changes.
Detecting Anomalies in Activities of Daily Living of Elderly Residents via Energy Disaggregation and Cox Processes

Monitoring the health of the elderly living independently in their own homes is a key issue in building sustainable healthcare models which support a country's ageing population. Existing approaches have typically proposed remotely monitoring the behaviour of a household's occupants through the use of additional sensors. However the costs and privacy concerns of such sensors have significantly limited their potential for widespread adoption. In contrast, in this paper we propose an approach which detects Activities of Daily Living, which we use as a proxy for the health of the household residents. Our approach detects appliance usage from existing smart meter data, from which the unique daily routines of the household occupants are learned automatically via a log Gaussian Cox process. We evaluate our approach using two real-world data sets, and show it is able to detect over 80% of kettle uses while generating less than 10% false positives. Furthermore, our approach allows earlier interventions in households with a consistent routine and fewer false alarms in the remaining households, relative to a fixed-time intervention benchmark.
Challenges for protecting the privacy of health information: required certification can leave common vulnerabilities undetected

The use of electronic health record (EHR) systems by medical professionals enables the electronic exchange of patient data, yielding cost and quality of care benefits. The United States American Recovery and Reinvestment Act (ARRA) of 2009 provides up to $34 billion for meaningful use of certified EHR systems. But, will these certified EHR systems provide the infrastructure for secure patient data exchange? As a window into the ability of current and emerging certification criteria to expose security vulnerabilities, we performed exploratory security analysis on a proprietary and an open source EHR. We were able to exploit a range of common code-level and design-level vulnerabilities. These common vulnerabilities would have remained undetected by the 2011 security certification test scripts from the Certification Commission for Health Information Technology, the most widely used certification process for EHR systems. The consequences of these exploits included, but were not limited to: exposing all users' login information, the ability of any user to view or edit health records for any patient, and creating a denial of service for all users. Based upon our results, we suggest that an enhanced set of security test scripts be used as entry criteria to the EHR certification process. Before certification bodies spend the time to certify that an EHR application is functionally complete, they should have confidence that the software system meets a basic level of security competence.