nils-herrmann/clean_scirepeval huggingface dataset:
Adaptable, model-driven security engineering for SaaS cloud-based applications

Software-as-a-service (SaaS) multi-tenancy in cloud-based applications helps service providers to save cost, improve resource utilization, and reduce service customization and maintenance time. This is achieved by sharing of resources and service instances among multiple “tenants” of the cloud-hosted application. However, supporting multi-tenancy adds more complexity to SaaS applications required capabilities. Security is one of these key requirements that must be addressed when engineering multi-tenant SaaS applications. The sharing of resources among tenants—i.e. multi-tenancy—increases tenants’ concerns about the security of their cloud-hosted assets. Compounding this, existing traditional security engineering approaches do not fit well with the multi-tenancy application model where tenants and their security requirements often emerge after the applications and services were first developed. The resultant applications do not usually support diverse security capabilities based on different tenants’ needs, some of which may change at run-time i.e. after cloud application deployment. We introduce a novel model-driven security engineering approach for multi-tenant, cloud-hosted SaaS applications. Our approach is based on externalizing security from the underlying SaaS application, allowing both application/service and security to evolve at runtime. Multiple security sets can be enforced on the same application instance based on different tenants’ security requirements. We use abstract models to capture service provider and multiple tenants’ security requirements and then generate security integration and configurations at runtime. We use dependency injection and dynamic weaving via Aspect-Oriented Programming (AOP) to integrate security within critical application/service entities at runtime. We explain our approach, architecture and implementation details, discuss a usage example, and present an evaluation of our approach on a set of open source web applications.
Hardware design of a neural processing unit for bio-inspired computing

Unsupervised artificial neural networks are now considered as a likely alternative to classical computing models in many application domains. For example, recent neural models defined by neuro-scientists exhibit interesting properties for an execution in embedded and autonomous systems: distributed computing, unsupervised learning, self-adaptation, self-organisation, tolerance. But these properties only emerge from large scale and fully connected neural maps that result in intensive computation coupled with high synaptic communications. We are interested in deploying these powerful models in the embedded context of an autonomous bio-inspired robot learning its environment in realtime. So we study in this paper in what extent these complex models can be simplified and deployed in hardware accelerators compatible with an embedded integration. Thus we propose a Neural Processing Unit designed as a programmable accelerator implementing recent equations close to self-organizing maps and neural fields. The proposed architecture is validated on FPGA devices and compared to state of the art solutions. The trade-off proposed by this dedicated but programmable neural processing unit allows to achieve significant improvements and makes our architecture adapted to many embedded systems.
A weighting k-modes algorithm for subspace clustering of categorical data

Traditional clustering algorithms consider all of the dimensions of an input data set equally. However, in the high dimensional data, a common property is that data points are highly clustered in subspaces, which means classes of objects are categorized in subspaces rather than the entire space. Subspace clustering is an extension of traditional clustering that seeks to find clusters in different subspaces categorical data and its corresponding time complexity is analyzed as well. In the proposed algorithm, an additional step is added to the k-modes clustering process to automatically compute the weight of all dimensions in each cluster by using complement entropy. Furthermore, the attribute weight can be used to identify the subsets of important dimensions that categorize different clusters. The effectiveness of the proposed algorithm is demonstrated with real data sets and synthetic data sets. & 2012 Elsevier B.V. All rights reserved.
Continuously Learning Neural Dialogue Management

We describe a two-step approach for dialogue management in task-oriented spoken dialogue systems. A unified neural network framework is proposed to enable the system to first learn by supervision from a set of dialogue data and then continuously improve its behaviour via reinforcement learning, all using gradientbased algorithms on one single model. The experiments demonstrate the supervised model’s effectiveness in the corpus-based evaluation, with user simulation, and with paid human subjects. The use of reinforcement learning further improves the model’s performance in both interactive settings, especially under higher-noise conditions.
A Language-independent and Compositional Model for Personality Trait Recognition from Short Texts

Many methods have been used to recognise author personality traits from text, typically combining linguistic feature engineering with shallow learning models, e.g. linear regression or Support Vector Machines. This work uses deep-learningbased models and atomic features of text – the characters – to build hierarchical, vectorial word and sentence representations for trait inference. This method, applied to a corpus of tweets, shows state-of-theart performance across five traits and three languages (English, Spanish and Italian) compared with prior work in author profiling. The results, supported by preliminary visualisation work, are encouraging for the ability to detect complex human traits.
Peer Group Analysis – Local Anomaly Detection in Longitudinal Data

Peer group analysis is a new tool for monitoring behavior over time in data mining situations. In particular, the tool detects individual objects that begin to behave in a way distinct from objects to which they had previously been similar. Each object is selected as a target object and is compared with all other objects in the database, using either external comparison criteria or internal criteria summarizing earlier behavior patterns of each object. Based on this comparison, a peer group of objects most similar to the target object is chosen. The behavior of the peer group is then summarized at each subsequent time point, and the behavior of the target object compared with the summary of its peer group. Those target objects exhibiting behavior most different from their peer group summary behavior are flagged as meriting closer investigation. The tool is intended to be part of the data mining process, involving cycling between the detection of objects that behave in anomalous ways and the detailed examination of those objects. Several aspects of peer group analysis can be tuned to the particular application, including the size of the peer group, the width of the moving behavior window being used, the way the peer group is summarized, and the measures of difference between the target object and its peer group summary. We apply the tool in various situations and illustrate its use on a set of credit card transaction data.
Mixing Dirichlet Topic Models and Word Embeddings to Make lda2vec

Distributed dense word vectors have been shown to be effective at capturing tokenlevel semantic and syntactic regularities in language, while topic models can form interpretable representations over documents. In this work, we describe lda2vec, a model that learns dense word vectors jointly with Dirichlet-distributed latent document-level mixtures of topic vectors. In contrast to continuous dense document representations, this formulation produces sparse, interpretable document mixtures through a non-negative simplex constraint. Our method is simple to incorporate into existing automatic differentiation frameworks and allows for unsupervised document representations geared for use by scientists while simultaneously learning word vectors and the linear relationships between them.
The Cluster Between Internet of Things and Social Networks: Review and Research Challenges

The cluster between Internet of Things (IoT) and social networks (SNs) enables the connection of people to the ubiquitous computing universe. In this framework, the information coming from the environment is provided by the IoT, and the SN brings the glue to allow human-to-device interactions. This paper explores the novel paradigm for ubiquitous computing beyond IoT, denoted by Social Internet of Things (SIoT). Although there have been early-stage studies in social-driven IoT, they merely use one or some properties of SIoT to improve a number of specific performance variables. Therefore, this paper first addresses a complete view on SIoT and key perspectives to envision the real ubiquitous computing. Thereafter, a literature review is presented along with the evolutionary history of IoT research from Intranet of Things to SIoT. Finally, this paper proposes a generic SIoT architecture and presents a discussion about enabling technologies, research challenges, and open issues.
Big data analysis using Hadoop cluster

Industries keep a check on all statistics of their business and process this data using various data mining techniques to measure profit trends, revenue, growing markets and interesting opportunities to invest. These statistical records keep on increasing and increase very fast. Unfortunately, as the data grows it becomes a tedious task to process such a large data set and extract meaningful information. Also if the data generated is in various formats, its processing possesses new challenges. Owing to its size, big data is stored in Hadoop Distributed File System (HDFS). In this standard architecture, all the Data Nodes function parallel but functioning of a single Data Node is still in sequential fashion. This paper proposes to execute tasks assigned to a single Data Node in parallel instead of executing them sequentially. We propose to use a bunch of streaming multi-processors (SMs) for each single Data Node. An SM can have various processors and memory and all SMs run in parallel and independently. We process big data which may be coming from different sources in different formats to run parallelly on a Hadoop cluster, use the proposed technique and yield desired results efficiently. We have applied proposed methodology to the raw data of an industrial firm, for doing intelligent business, with a final objective of finding profit generated for the firm and its trends throughout a year. We have done analysis over a yearlong data as trends generally repeat after a year.
An Interactive 3D Holographic Pyramid for Museum Exhibition

In this paper, an interactive holographic system, realized with the aim of creating, exchanging, discussing and disseminating cultural heritage information, is presented. By using low-cost and off-the-shelf devices, the system provides the visitors with a 'floating' computer generated representation of a virtual cultural artefact that, unlike the real one, can be examined in detail through a touchless natural interface. The proposed system is realized in such a way that it can be easily placed in a cultural exhibition without requiring any structural intervention. As such, it could represent a useful instrument complementary to a museum visit thanks to its capacity both to convey different types of digital cultural information and especially to allow the visitor to become an active actor, able to enjoy different perspectives and all the details of the artefact sharing her/his experience with other visitors. The paper describes the system modules and the hardware design to physically realize the pyramid, and details the user interface composed of two main actions designed to obtain a simple exploration of a virtual cultural heritage artefact.
Semi-Supervised Consensus Labeling for Crowdsourcing

Because individual crowd workers often exhibit high variance in annotation accuracy, we often ask multiple crowd workers to label each example to infer a single consensus label. While simple majority vote computes consensus by equally weighting each worker’s vote, weighted voting assigns greater weight to more accurate workers, where accuracy is estimated by inner-annotator agreement (unsupervised) and/or agreement with known expert labels (supervised). In this paper, we investigate the annotation cost vs. consensus accuracy benefit from increasing the amount of expert supervision. To maximize benefit from supervision, we propose a semi-supervised approach which infers consensus labels using both labeled and unlabeled examples. We compare our semi-supervised approach with several existing unsupervised and supervised baselines, evaluating on both synthetic data and Amazon Mechanical Turk data. Results show (a) a very modest amount of supervision can provide significant benefit, and (b) consensus accuracy from full supervision with a large amount of labeled data is matched by our semi-supervised approach with much less supervision.
An All-SiC Three-Phase Buck Rectifier for High-Efficiency Data Center Power Supplies

The low power losses of silicon carbide (SiC) devices provide new opportunities to implement an ultra high-efficiency front-end rectifier for data center power supplies based on a 400-Vdc power distribution architecture, which requires high conversion efficiency in each power conversion stage. This paper presents a 7.5-kW high-efficiency three-phase buck rectifier with 480-Vac,rms input line-to-line voltage and 400-Vdc output voltage using SiC MOSFETs and Schottky diodes. To estimate power devices' losses, which are the dominant portion of total loss, the method of device evaluation and loss calculation is proposed based on a current source topology. This method simulates the current commutation process and estimates devices' losses during switching transients considering devices with and without switching actions in buck rectifier operation. Moreover, the power losses of buck rectifiers based on different combinations of 1200-V power devices are compared. The investigation and comparison demonstrate the benefits of each combination, and the lowest total loss in the all-SiC rectifier is clearly shown. A 7.5-kW prototype of the all-SiC three-phase buck rectifier using liquid cooling is fabricated and tested, with filter design and switching frequency chosen based on loss minimization. A full-load efficiency value greater than 98.5% is achieved.
Organizational Culture ’ s Influence on Creativity and Innovation : A Review of the Literature and Implications for Human Resource Development

The problem and the solution.The majority of the literature on creativity has focused on the individual, yet the social environment can influence both the level and frequency of creative behavior. This article reviews the literature for factors related to organizational culture and climate that act as supports and impediments to organizational creativity and innovation. The work of Amabile, Kanter, Van de Ven, Angle, and others is reviewed and synthesized to provide an integrative understanding of the existing literature. Implications for human resource development research and practice are discussed.
Asymmetric Compact OMT for X-Band SAR Applications

A compact asymmetric orthomode transducer (OMT) with high isolation between the vertical and horizontal Ports is developed for the X-band synthetic aperture radar application. The basic idea of the design is to deploy the combined E- and H-plane bends within the common arm. Moreover, an offset between each polarization axis is introduced to enhance the isolation and decrease the size to be around one-third of most of the existing asymmetric OMTs. The OMT achieves better than 22.5-dB matching level and 65-dB isolation level between the two modes. Good agreement is obtained between measurements and full-wave simulations.
SAND: Towards High-Performance Serverless Computing

Serverless computing has emerged as a new cloud computing paradigm, where an application consists of individual functions that can be separately managed and executed. However, existing serverless platforms normally isolate and execute functions in separate containers, and do not exploit the interactions among functions for performance. These practices lead to high startup delays for function executions and inefficient resource usage. This paper presents SAND, a new serverless computing system that provides lower latency, better resource efficiency and more elasticity than existing serverless platforms. To achieve these properties, SAND introduces two key techniques: 1) application-level sandboxing, and 2) a hierarchical message bus. We have implemented and deployed a complete SAND system. Our results show that SAND outperforms the state-of-the-art serverless platforms significantly. For example, in a commonly-used image processing application, SAND achieves a 43% speedup compared to Apache OpenWhisk.
Noncespaces: Using Randomization to Enforce Information Flow Tracking and Thwart Cross-Site Scripting Attacks

Cross-site scripting (XSS) vulnerabilities are among the most common and serious web application vulnerabilities. XSS vulnerabilities are difficult to prevent because it is difficult for web applications to anticipate client-side semantics. We present Noncespaces, a technique that enables web clients to distinguish between trusted and untrusted content to prevent exploitation of XSS vulnerabilities. Using Noncespaces, a web application randomizes the XML namespace tags in each document before delivering it to the client. As long as the attacker is unable to predict the randomized prefixes, the client can distinguish between trusted content created by the web application and untrusted content provided by the attacker. Noncespaces uses client-side policy enforcement to avoid semantic ambiguities between the client and server. To implement Noncespaces with minimal changes to web applications, we leverage a popular web application architecture to automatically apply Noncespaces to static content processed through a popular PHP template engine. We show that with simple policies Noncespaces thwarts popular XSS attack vectors. As an additional benefit, the client-side policy not only allows a web application to restrict security-relevant capabilities to untrusted content but also narrows the application’s remaining attack vectors, which deserve more scrutiny by security auditors.
A High Energy Efficient Reconfigurable Hybrid Neural Network Processor for Deep Learning Applications

Hybrid neural networks (hybrid-NNs) have been widely used and brought new challenges to NN processors. Thinker is an energy efficient reconfigurable hybrid-NN processor fabricated in 65-nm technology. To achieve high energy efficiency, three optimization techniques are proposed. First, each processing element (PE) supports bit-width adaptive computing to meet various bit-widths of neural layers, which raises computing throughput by 91% and improves energy efficiency by <inline-formula> <tex-math notation="LaTeX">$1.93 \times $ </tex-math></inline-formula> on average. Second, PE array supports on-demand array partitioning and reconfiguration for processing different NNs in parallel, which results in 13.7% improvement of PE utilization and improves energy efficiency by <inline-formula> <tex-math notation="LaTeX">$1.11 \times $ </tex-math></inline-formula>. Third, a fused data pattern-based multi-bank memory system is designed to exploit data reuse and guarantee parallel data access, which improves computing throughput and energy efficiency by <inline-formula> <tex-math notation="LaTeX">$1.11 \times $ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$1.17 \times $ </tex-math></inline-formula>, respectively. Measurement results show that this processor achieves 5.09-TOPS/W energy efficiency at most.
QR Code and Augmented Reality-Supported Mobile English Learning System

Mobile learning highly prioritizes the successful acquisition of context-aware contents from a learning server. A variant of 2D barcodes, the quick response (QR) code, which can be rapidly read using a PDA equipped with a camera and QR code reading software, is considered promising for context-aware applications. This work presents a novel QR code and handheld augmented reality (AR) supported mobile learning (m-learning) system: the handheld English language learning organization (HELLO). In the proposed English learning system, the linked information between context-aware materials and learning zones is defined in the QR codes. Each student follows the guide map displayed on the phone screen to visit learning zones and decrypt QR codes. The detected information is then sent to the learning server to request and receive context-aware learning material wirelessly. Additionally, a 3D animated virtual learning partner is embedded in the learning device based on AR technology, enabling students to complete their context-aware immersive learning. A case study and a survey conducted in a university demonstrate the effectiveness of the proposed m-learning system.
On the concrete hardness of Learning with Errors

The Learning with Errors (LWE) problem has become a central building block of modern cryptographic constructions. This work collects and presents hardness results for concrete instances of LWE. In particular, we discuss algorithms proposed in the literature and give the expected resources required to run them. We consider both generic instances of LWE as well as small secret variants. Since for several methods of solving LWE we require a lattice reduction step, we also review lattice reduction algorithms and use a refined model for estimating their running times. We also give concrete estimates for various families of LWE instances, provide a Sage module for computing these estimates and highlight gaps in the knowledge about algorithms for solving the Learning with Errors problem.
Learning to Detect Salient Objects with Image-Level Supervision

Deep Neural Networks (DNNs) have substantially improved the state-of-the-art in salient object detection. However, training DNNs requires costly pixel-level annotations. In this paper, we leverage the observation that image-level tags provide important cues of foreground salient objects, and develop a weakly supervised learning method for saliency detection using image-level tags only. The Foreground Inference Network (FIN) is introduced for this challenging task. In the first stage of our training method, FIN is jointly trained with a fully convolutional network (FCN) for image-level tag prediction. A global smooth pooling layer is proposed, enabling FCN to assign object category tags to corresponding object regions, while FIN is capable of capturing all potential foreground regions with the predicted saliency maps. In the second stage, FIN is fine-tuned with its predicted saliency maps as ground truth. For refinement of ground truth, an iterative Conditional Random Field is developed to enforce spatial label consistency and further boost performance. Our method alleviates annotation efforts and allows the usage of existing large scale training sets with image-level tags. Our model runs at 60 FPS, outperforms unsupervised ones with a large margin, and achieves comparable or even superior performance than fully supervised counterparts.