nils-herrmann/clean_scirepeval huggingface dataset:
Cash demand forecasting in ATMs by clustering and neural networks

To improve ATMs’ cash demand forecasts, this paper advocates the prediction of cash demand for groups of ATMs with similar day-of-the week cash demand patterns. We first clustered ATM centers into ATM clusters having similar day-of-the week withdrawal patterns. To retrieve “day-of-the-week” withdrawal seasonality parameters (effect of a Monday, etc) we built a time series model for each ATMs. For clustering, the succession of 7 continuous daily withdrawal seasonality parameters of ATMs is discretized. Next, the similarity between the different ATMs’ discretized daily withdrawal seasonality sequence is measured by the Sequence Alignment Method (SAM). For each cluster of ATMs, four neural networks viz., general regression neural network (GRNN), multi layer feed forward neural network (MLFF), group method of data handling (GMDH) and wavelet neural network (WNN) are built to predict an ATM center’s cash demand. The proposed methodology is applied on the NN5 competition dataset. We observed that GRNN yielded the best result of 18.44% symmetric mean absolute percentage error (SMAPE), which is better than the result of Andrawis et al. (2011). This is due to clustering followed by a forecasting phase. Further, the proposed approach yielded much smaller SMAPE values than the approach of direct prediction on the entire sample without clustering. From a managerial perspective, the clusterwise cash demand forecast helps the bank’s top management to design similar cash replenishment plans for all the ATMs in the same cluster. This cluster-level replenishment plans could result in saving huge operational costs for ATMs operating in a similar geographical region.
Automated Fault Tree Analysis from AADL Models

Cyber-physical systems, used in domains such as avionics or medical devices, perform critical functions where a fault might have catastrophic consequences (mission failure, severe injuries, etc.). Their development is guided by rigorous practice standards that prescribe safety analysis methods in order to verify that failure have been correctly evaluated and/or mitigated. This laborintensive practice typically focuses system safety analysis on system engineering activities.
 As reliance on software for system operation grows, embedded software systems have become a major source of hazard contributors. Studies show that late discovery of errors in embedded software system have resulted in costly rework, making up as much as 50% of the total software system cost. Automation of the safety analysis process is key to extending safety analysis to the software system and to accommodate system evolution.
 In this paper we discuss three elements that are key to safety analysis automation in the context of fault tree analysis (FTA). First, generation of fault trees from annotated architecture models consistently reflects architecture changes in safety analysis results. Second, use of a taxonomy of failure effects ensures coverage of potential hazard contributors is achieved. Third, common cause failures are identified based on architecture information and reflected appropriately in probabilistic fault tree analysis. The approach utilizes the SAE Architecture Analysis & Design Language (AADL) standard and the recently published revised Error Model Annex V2 (EMV2) standard to represent annotated architecture models of systems and embedded software systems.
 The approach takes into account error sources specified with an EMV2 error propagation type taxonomy and occurrence probabilities as well as direct and indirect propagation paths between system components identified in the architecture model to generate a fault graph and apply transformations into a fault tree representation to support common mode analysis, cut set determination and probabilistic analysis.
Arc-swift: A Novel Transition System for Dependency Parsing

Transition-based dependency parsers often need sequences of local shift and reduce operations to produce certain attachments. Correct individual decisions hence require global information about the sentence context and mistakes cause error propagation. This paper proposes a novel transition system, arc-swift, that enables direct attachments between tokens farther apart with a single transition. This allows the parser to leverage lexical information more directly in transition decisions. Hence, arc-swift can achieve significantly better performance with a very small beam size. Our parsers reduce error by 3.7–7.6% relative to those using existing transition systems on the Penn Treebank dependency parsing task and English Universal Dependencies.
Hardware Implementation of the Time-Triggered Ethernet Controller

Time-triggered (TT) Ethernet is a novel communication system that integrates real-time and non-real-time traffic into a single communication architecture. A TT Ethernet system consists od a set of nodes interconnected by a specific switch called TT Ethernet switch. A node consist of a TT Ethernet communication controller that executes the TT Ethernet protocol and a host computer that executes the user application. The protocol distinguishes between event-triggered (ET) and time-triggered (TT) Ethernet traffic. Time-triggered traffic is scheduled and transmitted with a predictable transmission delay, whereas event-triggered traffic is transmitted on a best-effort basis. The event-triggered traffic in TT Ethernet is handled in conformance with the existing Ethernet standards of the IEEE. This paper presents the design of the TT Ethernet communication controller optimized to be implemented in hardware. The paper describes a prototypical implementation using a custom built hardware platform and presents the results of evaluation experiments.
Some New Parallel Mechanisms Containing the Planar Four-Bar Parallelogram

A parallelogram allows the output link to remain at a fixed orientation with respect to an input link, for which it acts as a unique role in the design of parallel mechanisms. In this paper, the unique role of a parallelogram is used completely to design some new parallel mechanisms with two to six degrees of freedom (DoFs). In these mechanisms, some with three DoFs possess the advantage of very high rotational capability and some with two DoFs have the translational output of a rigid body. More than that, the design concept is also applied first to some parallel mechanisms to improve the systems’ rotational capability. The parallel mechanisms proposed in this paper have wide applications in industrial robots, simulators, micromanipulators, parallel kinematics machines, and any other manipulation devices in which high rotational capability and stiffness are needed. Especially, the paper provides new concepts of the design of novel parallel mechanisms and the improvement of rotational capability for such systems. parallel mechanism, degrees of freedom, rotational capability, mechanical design, parallelogram
Occlusion and Abandoned Object Detection for Surveillance Applications

Object detection is an important step in any video analysis. Difficulties of the object detection are finding hidden objects and finding unrecognized objects. Although many algorithms have been developed to avoid them as outliers, occlusion boundaries could potentially provide useful information about the scene’s structure and composition. A novel framework for blob based occluded object detection is proposed. A technique that can be used to detect occlusion is presented. It detects and tracks the occluded objects in video sequences captured by a fixed camera in crowded environment with occlusions. Initially the background subtraction is modeled by a Mixture of Gaussians technique (MOG). Pedestrians are detected using the pedestrian detector by computing the Histogram of Oriented Gradients descriptors (HOG), using a linear Support Vector Machine (SVM) as the classifier. In this work, a recognition and tracking system is built to detect the abandoned objects in the public transportation area such as train stations, airports etc. Several experiments were conducted to demonstrate the effectiveness of the proposed approach. The results show the robustness and effectiveness of the proposed method.  Occlusion, Histograms of Oriented Gradients descriptors, Support Vector Machine, mixture of Gaussians techniques, Blob, abandoned object.
OpenFace 2.0: Facial Behavior Analysis Toolkit

Over the past few years, there has been an increased interest in automatic facial behavior analysis and understanding. We present OpenFace 2.0 - a tool intended for computer vision and machine learning researchers, affective computing community and people interested in building interactive applications based on facial behavior analysis. OpenFace 2.0 is an extension of OpenFace toolkit and is capable of more accurate facial landmark detection, head pose estimation, facial action unit recognition, and eye-gaze estimation. The computer vision algorithms which represent the core of OpenFace 2.0 demonstrate state-of-the-art results in all of the above mentioned tasks. Furthermore, our tool is capable of real-time performance and is able to run from a simple webcam without any specialist hardware. Finally, unlike a lot of modern approaches or toolkits, OpenFace 2.0 source code for training models and running them is freely available for research purposes.
Teaching culture in the 21 st century language classroom

The paper presents an overview of the research on teaching culture and describes effective pedagogical practices that can be integrated into the second language curriculum. Particularly, this overview tries to advance an approach for teaching culture and language through the theoretical construct of the 3Ps (Products, Practices, Perspectives), combined with an inquiry-based teaching approach utilizing instructional technology. This approach promotes student motivation and engagement that can help overcome past issues of stereotyping and lack of intercultural awareness. The authors summarize the research articles illustrating how teachers successfully integrate digital media together with inquiry learning into instruction to create a rich and meaningful environment in which students interact with authentic data and build their own understanding of a foreign culture’s products, practices, and perspectives. In addition, the authors review the articles that describe more traditional methods of teaching culture and demonstrate how they can be enhanced with technology. “The digital revolution is far more significant than the invention of writing or even of printing. It offers the potential for humans to learn new ways of thinking and organizing social structures.” Douglas Engelbard (1997) The advent of the Standards for Foreign Language Learning in the 21st Century (National Standards in Foreign Language Education Project, 1999) drew attention to the vital role of culture in language classrooms and defined culture as a fundamental part of the second language (L2) learning 5
An interactive ambient visualization for code smells

Code smells are characteristics of software that indicate that code may have a design problem. Code smells have been proposed as a way for programmers to recognize the need for restructuring their software. Because code smells can go unnoticed while programmers are working, tools called smell detectors have been developed to alert programmers to the presence of smells in their code, and to help them understand the cause of those smells. In this paper, we propose a novel smell detector called Stench Blossom that provides an interactive ambient visualization designed to first give programmers a quick, high-level overview of the smells in their code, and then, if they wish, to help in understanding the sources of those code smells. We also describe a laboratory experiment with 12 programmers that tests several hypotheses about our tool. Our findings suggest that programmers can use our tool effectively to identify smells and to make refactoring judgements. This is partly because the tool serves as a memory aid, and partly because it is more reliable and easier to use than heuristics for analyzing smells.
Medicinal properties of terpenes found in Cannabis sativa and Humulus lupulus.

Cannabaceae plants Cannabis sativa L. and Humulus lupulus L. are rich in terpenes - both are typically comprised of terpenes as up to 3-5% of the dry-mass of the female inflorescence. Terpenes of cannabis and hops are typically simple mono- and sesquiterpenes derived from two and three isoprene units, respectively. Some terpenes are relatively well known for their potential in biomedicine and have been used in traditional medicine for centuries, while others are yet to be studied in detail. The current, comprehensive review presents terpenes found in cannabis and hops. Terpenes' medicinal properties are supported by numerous in vitro, animal and clinical trials and show anti-inflammatory, antioxidant, analgesic, anticonvulsive, antidepressant, anxiolytic, anticancer, antitumor, neuroprotective, anti-mutagenic, anti-allergic, antibiotic and anti-diabetic attributes, among others. Because of the very low toxicity, these terpenes are already widely used as food additives and in cosmetic products. Thus, they have been proven safe and well-tolerated.
Authenticating query results in edge computing

Edge computing pushes application logic and the underlying data to the edge of the network, with the aim of improving availability and scalability. As the edge servers are not necessarily secure, there must be provisions for validating their outputs. This paper proposes a mechanism that creates a verification object (VO) for checking the integrity of each query result produced by an edge server - that values in the result tuples are not tampered with, and that no spurious tuples are introduced. The primary advantages of our proposed mechanism are that the VO is independent of the database size, and that relational operations can still be fulfilled by the edge servers. These advantages reduce transmission load and processing at the clients. We also show how insert and delete transactions can be supported.
FastHybrid: A Hybrid Model for Efficient Answer Selection

Answer selection is a core component in any question-answering systems. It aims to select correct answer sentences for a given question from a pool of candidate sentences. In recent years, many deep learning methods have been proposed and shown excellent results for this task. However, these methods typically require extensive parameter (and hyper-parameter) tuning, which gives rise to efficiency issues for large-scale datasets, and potentially makes them less portable across new datasets and domains (as re-tuning is usually required). In this paper, we propose an extremely efficient hybrid model (FastHybrid) that tackles the problem from both an accuracy and scalability point of view. FastHybrid is a light-weight model that requires little tuning and adaptation across different domains. It combines a fast deep model (which will be introduced in the method section) with an initial information retrieval model to effectively and efficiently handle answer selection. We introduce a new efficient attention mechanism in the hybrid model and demonstrate its effectiveness on several QA datasets. Experimental results show that although the hybrid uses no training data, its accuracy is often on-par with supervised deep learning techniques, while significantly reducing training and tuning costs across different domains.
Knowledge Distillation for Bilingual Dictionary Induction

Leveraging zero-shot learning to learn mapping functions between vector spaces of different languages is a promising approach to bilingual dictionary induction. However, methods using this approach have not yet achieved high accuracy on the task. In this paper, we propose a bridging approach, where our main contribution is a knowledge distillation training objective. As teachers, rich resource translation paths are exploited in this role. And as learners, translation paths involving low resource languages learn from the teachers. Our training objective allows seamless addition of teacher translation paths for any given low resource pair. Since our approach relies on the quality of monolingual word embeddings, we also propose to enhance vector representations of both the source and target language with linguistic information. Our experiments on various languages show large performance gains from our distillation training objective, obtaining as high as 17% accuracy improvements.
Minimal Change and Bounded Incremental Parsing

Ideally, the time that an incremental algorithm uses to process a change should be a fimction of the size of the change rather than, say, the size of the entire current input. Based o n a formalization of "the set of things changed" by an increInental modification, this paper investigates how and to what extent it is possibh~' to give such a guarantee for a chart-ba.se(l parsing frmnework and discusses the general utility of a tninlmality notion in incremental processing) 1 I n t r o d u c t i o n
A Review of Particle Image Velocimetry for Fish Migration

Understanding the flow characteristic in fishways is crucial for efficient fish migration. Flow characteristic measurements can generally provide quantitative information of velocity distributions in such passages; Particle Image Velocimetry (PIV) has become one of the most versatile techniques to disclose flow fields in general and in fishways, in particular. This paper firstly gives an overview of fish migration along with fish ladders and then the application of PIV measurements on the fish migration process. The overview shows that the quantitative and detailed turbulent flow information in fish ladders obtained by PIV is critical for analyzing turbulent properties and validating numerical results.
Using Pivots to Speed-Up k-Medoids Clustering

Clustering is a key technique within the KDD process, with k-means, and the more general k-medoids, being well-known incremental partition-based clustering algorithms. A fundamental issue within this class of algorithms is to find an initial set of medians (or medoids) that improves the efficiency of the algorithms (e.g., accelerating its convergence to a solution), at the same time that it improves its effectiveness (e.g., finding more meaningful clusters). Thus, in this article we aim at providing a technique that, given a set of elements, quickly finds a very small number of elements as medoid candidates for this set, allowing to improve both the efficiency and effectiveness of existing clustering algorithms. We target the class of k-medoids algorithms in general, and propose a technique that selects a well-positioned subset of central elements to serve as the initial set of medoids for the clustering process. Our technique leads to a substantially smaller amount of distance calculations, thus improving the algorithm’s efficiency when compared to existing methods, without sacrificing effectiveness. A salient feature of our proposed technique is that it is not a new k-medoid clustering algorithm per se, rather, it can be used in conjunction with any existing clustering algorithm that is based on the k-medoid paradigm. Experimental results, using both synthetic and real datasets, confirm the efficiency, effectiveness and scalability of the proposed technique.
Mediterranean diet and health status: an updated meta-analysis and a proposal for a literature-based adherence score.

To update previous meta-analyses of cohort studies that investigated the association between the Mediterranean diet and health status and to utilize data coming from all of the cohort studies for proposing a literature-based adherence score to the Mediterranean diet.



We conducted a comprehensive literature search through all electronic databases up to June 2013.



Cohort prospective studies investigating adherence to the Mediterranean diet and health outcomes. Cut-off values of food groups used to compute the adherence score were obtained.



The updated search was performed in an overall population of 4 172 412 subjects, with eighteen recent studies that were not present in the previous meta-analyses.



A 2-point increase in adherence score to the Mediterranean diet was reported to determine an 8 % reduction of overall mortality (relative risk = 0·92; 95 % CI 0·91, 0·93), a 10 % reduced risk of CVD (relative risk = 0·90; 95 % CI 0·87, 0·92) and a 4 % reduction of neoplastic disease (relative risk = 0·96; 95 % CI 0·95, 0·97). We utilized data coming from all cohort studies available in the literature for proposing a literature-based adherence score. Such a score ranges from 0 (minimal adherence) to 18 (maximal adherence) points and includes three different categories of consumption for each food group composing the Mediterranean diet.



The Mediterranean diet was found to be a healthy dietary pattern in terms of morbidity and mortality. By using data from the cohort studies we proposed a literature-based adherence score that can represent an easy tool for the estimation of adherence to the Mediterranean diet also at the individual level.
Adding Concurrency to Smart Contracts

Modern cryptocurrency systems, such as Ethereum, permit complex financial transactions through scripts called smart contracts. These smart contracts are executed many, many times, always without real concurrency. First, all smart contracts are serially executed by miners before appending them to the blockchain. Later, those contracts are serially re-executed by validators to verify that the smart contracts were executed correctly by miners. Serial execution limits system throughput and fails to exploit today's concurrent multicore and cluster architectures. Nevertheless, serial execution appears to be required: contracts share state, and contract programming languages have a serial semantics.
 This paper presents a novel way to permit miners and validators to execute smart contracts in parallel, based on techniques adapted from software transactional memory. Miners execute smart contracts speculatively in parallel, allowing non-conflicting contracts to proceed concurrently, and "discovering" a serializable concurrent schedule for a block's transactions, This schedule is captured and encoded as a deterministic fork-join program used by validators to re-execute the miner's parallel schedule deterministically but concurrently.
 Smart contract benchmarks run on a JVM with ScalaSTM show that a speedup of 1.33x can be obtained for miners and 1.69x for validators with just three concurrent threads.
Reinforcement Learning with Soft State Aggregation

It is widely accepted that the use of more compact representations than lookup tables is crucial to scaling reinforcement learning (RL) algorithms to real-world problems. Unfortunately almost all of the theory of reinforcement learning assumes lookup table representations. In this paper we address the pressing issue of combining function approximation and RL, and present 1) a function approximator based on a simple extension to state aggregation (a commonly used form of compact representation), namely soft state aggregation, 2) a theory of convergence for RL with arbitrary, but fixed, soft state aggregation, 3) a novel intuitive understanding of the effect of state aggregation on online RL, and 4) a new heuristic adaptive state aggregation algorithm that finds improved compact representations by exploiting the non-discrete nature of soft state aggregation. Preliminary empirical results are also presented.
Security Architecture for the Internet Protocol

Internet Drafts are draft documents valid for a maximum of 6 months. Internet Drafts may be updated, replaced, or obsoleted by other documents at any time. It is not appropriate to use Internet Drafts as reference material or to cite them other than as "work in progress". This particular Internet Draft is a product of the IETF's IP Security (IPsec) working group. It is intended that a future version of this draft be submitted to the IESG for publication as a Draft Standard RFC. Comments about this draft may be sent to the author or to the IPsec WG mailing list <ipsec@tis.com>.