nils-herrmann/clean_scirepeval huggingface dataset:
EURIDICE — IoT applied to logistics using the Intelligent Cargo concept

In an interconnected world, the need to exchange information across domain's boundaries is increasingly common, the concern is rapidly moving towards defining the content that needs to be consumed by numerous and different actors using different platforms and/or software solutions, since the internal processes have been consolidated and optimized. The transport logistics sector is no exception, the main issue in this domain is for ICT applications that allow to tag, monitor and transmit information about the freight along the whole transport chain, thus guaranteeing an efficient communication among the supply chain actors for a prompt intervention and resolution in case of problems and in general to increase the transport efficiency. The paper will present the technical aspects of the solutions developed within the EURIDICE project and will link them to the concrete in-field applications that make the platform a great innovation and a step towards the realization of the Future Internet.
Multiple Granularity Descriptors for Fine-Grained Categorization

Fine-grained categorization, which aims to distinguish subordinate-level categories such as bird species or dog breeds, is an extremely challenging task. This is due to two main issues: how to localize discriminative regions for recognition and how to learn sophisticated features for representation. Neither of them is easy to handle if there is insufficient labeled data. We leverage the fact that a subordinate-level object already has other labels in its ontology tree. These "free" labels can be used to train a series of CNN-based classifiers, each specialized at one grain level. The internal representations of these networks have different region of interests, allowing the construction of multi-grained descriptors that encode informative and discriminative features covering all the grain levels. Our multiple granularity framework can be learned with the weakest supervision, requiring only image-level label and avoiding the use of labor-intensive bounding box or part annotations. Experimental results on three challenging fine-grained image datasets demonstrate that our approach outperforms state-of-the-art algorithms, including those requiring strong labels.
Segmentation-free handwritten Chinese text recognition with LSTM-RNN

We present initial results on the use of Multi-Dimensional Long-Short Term Memory Recurrent Neural Networks (MDLSTM-RNN) in recognizing lines of handwritten Chinese text without explicit segmentation of the characters. In fact, most of Chinese text recognizers in the literature perform a pre-segmentation of text image into characters. This can be a drawback, as explicit segmentation is an extra step before recognizing the text, and the errors made at this stage have direct impact on the performance of the whole system. MDLSTM-RNN is now a state-of-the-art technology that provides the best performance on languages with Latin and Arabic characters, hence we propose to apply RNN on Chinese text recognition. Our results on the data from the Task 4 in ICDAR 2013 competition for handwritten Chinese recognition are comparable in performance with the best reported systems.
PX-CGRA: Polymorphic approximate coarse-grained reconfigurable architecture

Coarse-Grained Reconfigurable Architectures (CGRAs) provide tradeoff between the energy-efficiency of Application Specific Integrated Circuits (ASICs) and the flexibility of General Purpose Processors (GPPs). State-of-the-art CGRAs only support exact architectures and precise application executions. However, a majority of the streaming applications such as multimedia and digital signal processing, which are amenable to CGRAs, are inherently error resilient. Therefore, these applications can greatly benefit from the emerging trend of Approximate Computing that leverages this error-resiliency to provide higher energy efficiency proportional to the tolerable accuracy loss (can even be constrained). This paper, for the first time, introduces the novel concept of Polymorphic Approximate CGRA (PX-CGRA) that employs heterogeneous tiles of Polymorphic-Approximated ALU Clusters (PACs) connected in a 2-D mesh style connection. These PACs can implement different approximate modes as well as accurate modes depending upon their selected configuration as per the run-time requirements of executing applications. For designing an efficient PX-CGRA, we propose a bottom-up design flow. In addition, the flow of application mapping on PX-CGRA is discussed including accuracy-level mapping, scheduling, and binding steps. To comprehensively evaluate the efficacy of the proposed CGRA, the complete PX-CGRA architecture in different sizes as well as with different PACs configurations are synthesized using a 15-nm FinFET technology. Our results show up to 15%–45% energy efficiency improvement for 5%–35% output quality degradation, respectively, when compared to the state-of-the-art exact-mode CGRA. Our proposed architecture and design methodology enable a new era of accuracy-configurable CGRAs to provide significant energy gains.
Risk Factors in Adolescence: The Case of Gambling, Videogame Playing, and the Internet

It has been noted that adolescents may be more susceptible to pathological gambling. Not only is it usually illegal, but it appears to be related to high levels of problem gambling and other delinquent activities such as illicit drug taking and alcohol abuse. This paper examines risk factors not only in adolescent gambling but also in videogame playing (which shares many similarities with gambling). There appear to be three main forms of adolescent gambling that have been widely researched. Adolescent gambling activities and general risk factors in adolescent gambling are provided. As well, the influence of technology on adolescents in the form of both videogames and the Internet are examined. It is argued that technologically advanced forms of gambling may be highly appealing to adolescents.
BCD8sP: An advanced 0.16 μm technology platform with state of the art power devices

Advanced 0.16 μm BCD technology platform offering dense logic transistors (1.8 V-5 V CMOS) and high performance analog features has been developed. Thanks to dedicated field plate optimization, body and drain engineering, state of the art power devices (8 V to 42 V rated) have been obtained ensuring large Safe Operating Areas with best RONXAREA-BVDSS tradeoff.
Comparison between android and iOS Operating System in terms of security

This paper compares between android and iPhone Operating System (iOS) mobile operating systems (MOS) that available in the market which is more specific on the security issue. These issues are reportedly the concern of not only the mobile customers but also the software developers. In achieving security requirements, the MOS developers need to know how to achieve the criteria. The security requirements for MOS are Application Sandboxing, Memory Randomization, Encryption, Data Storage Format and Built-in Antivirus. Application sandboxing enforces permissions, privileges, directories, entitlements and kernel access for a mobile app. Memory randomization ensures that the memory regions of mobile application as well as system shared libraries are all randomized at device and application start-up. Encryption is performed on disk or filer/folder level and also at the interprocess communication level. It is difficult to speak in favor or against the android or the iOS operating system in terms of better security. The way of using the device plays a major role in determining the security level. In terms of storage, all data are stored in Data Storage Format. Data can be stored at internal storage or external storage. To protect the MOS from virus attacks, antivirus need to be installed for increasing security areas.
A Lower-Back Robotic Exoskeleton: Industrial Handling Augmentation Used to Provide Spinal Support

A lower-back exoskeleton prototype designed to provide back support for industrial workers who manually handle heavy materials is presented in this article. Reducing spinal loads during these tasks can reduce the risk of work-related back injuries. Biomechanical studies show that compression of the lumbar spine is a key risk factor for musculoskeletal injuries. To address this issue, we present a wearable exoskeleton designed to provide back support and reduce lumbar spine compression. To provide effective assistance and avoid injury to muscles or tendons, we apply a continuous torque of approximately 40 Nm on both hip joints to actively assist both hip abduction/adduction (HAA) and hip flexion/extension (HFE). Each actuation unit includes a modular and a compact series-elastic actuator (SEA) with a clutch. The SEA provides mechanical compliance at the interface between the exoskeleton and the user, and the clutches can automatically disengage the torque between the exoskeleton and the user. These experimental results show that the exoskeleton can lower lumbar compression by reducing the need for muscular activity in the spine. Furthermore, powering both HFE and HAA can effectively reduce the lumbar spinal loading user experience when lifting and lowering objects while in a twisted posture.
Deep Learning + Student Modeling + Clustering: a Recipe for Effective Automatic Short Answer Grading

In this work we tackled the task of Automatic Short Answer Grading (ASAG). While conventional ASAG research makes prediction mainly based on student answers referred as Answer-based, we leveraged the information about questions and student models into consideration. More specifically, we explore the Answer-based, Question, and Student models individually, and subsequently in various combined and composite models through feature engineering. Additionally, we extend the exploration of machine learning methods by utilizing Deep Belief Networks (DBN) together with other five classic classifiers. Our experimental results show that our proposed feature engineering models significantly out-performed the conventional Answer-based model and among the six machine learning classifiers, DBN is the best followed by SVM, and Naive Bayes is the worst.
Network Decoupling: From Regular to Depthwise Separable Convolutions

Depthwise separable convolution has shown great efficiency in network design, but requires time-consuming training procedure with full training-set available. This paper first analyzes the mathematical relationship between regular convolutions and depthwise separable convolutions, and proves that the former one could be approximated with the latter one in closed form. We show depthwise separable convolutions are principal components of regular convolutions. And then we propose network decoupling (ND), a training-free method to accelerate convolutional neural networks (CNNs) by transferring pre-trained CNN models into the MobileNet-like depthwise separable convolution structure, with a promising speedup yet negligible accuracy loss. We further verify through experiments that the proposed method is orthogonal to other training-free methods like channel decomposition, spatial decomposition, etc. Combining the proposed method with them will bring even larger CNN speedup. For instance, ND itself achieves about 2× speedup for the widely used VGG16, and combined with other methods, it reaches 3.7× speedup with graceful accuracy degradation. We demonstrate that ND is widely applicable to classification networks like ResNet, and object detection network like SSD300.
Riderless bicycle with gyroscopic balancer controlled by FSMC and AFSMC

A Riderless bicycle has been developed with a gyroscopic balancer controller by a Fuzzy Sliding Mode Controller (FSMC) and an Adaptive Fuzzy Sliding Mode Controller (AFSMC). The FSMC controller was first implemented because it has better performance on controlling nonlinear systems than the one with PID control. The FSMC can also reduce the chattering phenomenon caused by SMC and the effect of linearizing a nonlinear system. Compared with other balancers, the gyroscopic balancer has a couple of advantages, such as faster system response, lower mass ratio of balancer to bicycle and relatively larger moment. To demonstrate the attributes stated above, we designed and conducted experiments, including the balancing of unmoving bicycle, unmoving bicycle with external impacts, as well as the bicycle moving forward and turning. The experimental results show that the bicycle can overcome jolts, uneven terrain and external disturbances. Furthermore, since the results of experiments are consistent with the ones of the simulation, it validates the derived bicycle dynamics model with the gyroscopic balancer and proves its robustness. However, the system's ability to resist continuous disturbance is not strong enough because of the limitation on the tilt angle of the gyroscopic balancer. Hence, we modified the control strategy by using AFSMC despite the fact that the FSMC performed better than PID control. From the simulations in Section IV, it shows that the AFSMC has better performance at resisting continuous disturbances than FSMC does. Furthermore, the abilities to balance the unmoving bicycle or moving bicycle are no less than FSMC. Thus, the AFSMC is employed to replace the FSMC. The designs of adaptive law and estimation law of AFSMC are based on the Lyapunov function to ensure the stability of the system. Experiments of the bicycle controlled by AFSMC are currently being conducted.
Extra-Specific Multiword Expressions for Language-Endowed Intelligent Agents

Language-endowed intelligent agents benefit from leveraging lexical knowledge falling at different points along a spectrum of compositionality. This means that robust computational lexicons should include not only the compositional expectations of argument-taking words, but also non-compositional collocations (idioms), semi-compositional collocations that might be difficult for an agent to interpret (e.g., standard metaphors), and even collocations that could be compositionally analyzed but are so frequently encountered that recording their meaning increases the efficiency of interpretation. In this paper we argue that yet another type of stringto-meaning mapping can also be useful to intelligent agents: remembered semantic analyses of actual text inputs. These can be viewed as super-specific multi-word expressions whose recorded interpretations mimic a person’s memories of knowledge previously learned from language input. These differ from typical annotated corpora in two ways. First, they provide a full, context-sensitive semantic interpretation rather than select features. Second, they are are formulated in the ontologically-grounded metalanguage used in a particular agent environment, meaning that the interpretations contribute to the dynamically evolving cognitive capabilites of agents configured in that environment.
Psychological Well-Being in Adult Life

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Information Privacy Concerns among novice and Expert Users of SoLoMo

SoLoMo (Social-Local-Mobile) services are becoming dominant to the huge population of semiliterate users living in emerging economies due to low costs and ubiquity. However, usage of SoLoMo services is still susceptible by information privacy concerns. Studies typically addressed the ways to mitigate information privacy concerns for the literate users and not the semi-literate users. To fill the gap of semi-literate users and have a better understanding of the information privacy concerns among different communities, this study draws upon theories of privacy calculus, familiarity, intrinsic and extrinsic motivation and protection motivation to identify the precursors of information privacy concerns related to SoLoMo services and users’ expertise. The proposed research model is empirically tested in a laboratory experiment. The findings show that the two channels (covert and overt) of delivering SoLoMo services affect the degree of information privacy concerns between the literate and semi-literate users. Implications for improving usage intentions and mitigating information privacy concerns for SoLoMo services for different types of mobile applications are discussed.
Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition

Dynamics of human body skeletons convey significant information for human action recognition. Conventional approaches for modeling skeletons usually rely on hand-crafted parts or traversal rules, thus resulting in limited expressive power and difficulties of generalization. In this work, we propose a novel model of dynamic skeletons called SpatialTemporal Graph Convolutional Networks (ST-GCN), which moves beyond the limitations of previous methods by automatically learning both the spatial and temporal patterns from data. This formulation not only leads to greater expressive power but also stronger generalization capability. On two large datasets, Kinetics and NTU-RGBD, it achieves substantial improvements over mainstream methods.
Defeating fake-quality MP3

MP3 is the most popular audio format nowadays in our daily life,e.g., music on the Internet are often of this format. However, low bit rate MP3s are sometimes transcoded to high bit rate, resulting in fake-quality MP3, to seek commercial benefit. This paper presents a method to defeat this kind of fake-quality MP3. Our experimental study and theoretical analysis have demonstrated that there are fewer MDCT coefficients of small values in fake-quality MP3 than in normal MP3. We use the numbers of small-value MDCT coefficients as features to discriminate fake-quality MP3 from normal MP3. Experimental results show the effectiveness of the proposed method. To our best knowledge, this work is the first one to find out fake-quality MP3.
Theorieentwicklung in der Akzeptanzforschung: Entwicklung eines Modells auf Basis einer qualitativen Studie

Die Untersuchung der Akzeptanz von Technologien im Allgemeinen und Software im Besonderen ist ein fruchtbares Feld in der angloamerikanischen Forschung der Disziplinen (Management) Information Systems und der deutschen Wirtschaftsinformatik. Trotz zahlreicher Untersuchungen, die ihren Ursprung in dem Technologie-Akzeptanzmodell und verwandten Theorien haben, mehren sich Beiträge, welche die Defizite bisherigerer Studien und Forschungsansätze herausstellen. Eine wesentliche Ursache ist Fokussierung auf quantitative Forschungsmethoden, wie wir anhand von Metastudien und einer eigenen Literaturrecherche aufzeigen. Während quantitative Verfahren sich in der Regel gut für eine Überprüfung gegebener Theorien eignen, ist ihr Beitrag für die Bildung neuer Theorien begrenzt. Im vorliegenden Beitrag wird aufgezeigt, wie ein qualitatives Verfahren zur besseren Theoriebildung genutzt werden kann. Am Beispiel der Untersuchung der Akzeptanz von Projektmanagement-Software (PMS) kann aufgezeigt werden, dass dieses Vorgehen zu neuen Konstrukten führt, während einige Konstrukte bestehender Akzeptanz-Theorien nicht bestätigt werden konnten.
Evaluating a Convolutional Neural Network on Short-Wave Infra-Red Images

Machine learning algorithms, both traditional and neuralnetwork-based, have been tested against RGB facial images for years, but these algorithms are prone to fail when illumination conditions are insufficient, for example, at night or when images are taken from long distances. Short-Wave Infra-Red (SWIR) illumination provides a much higher intensity and a much more ambient structure than visible light, which makes it better suited for face recognition in different conditions. However, current neural networks require lots of training data, which is not available in the SWIR domain. In this paper, we examine the ability of a convolutional neural network, specifically, the VGG Face network, which was trained on visible spectrum images, to work on SWIR images. Utilizing a dataset containing both RGB and SWIR images, we hypothesize that the VGG Face network will perform well both on facial images taken in RGB and SWIR wavelengths. We expect that the features extracted with VGG Face are independent of the actual wavelengths that the images were taken with. Thus, face recognition with VGG Face is possible between the RGB and SWIR domains. We find that VGG Face performs reasonable on some of the SWIR wavelengths. We can almost reach the same recognition performance when using composite images built from three SWIR wavelengths probing on RGB.
Blind Speech Separation and Enhancement With GCC-NMF

We present a blind source separation algorithm named GCC-NMF that combines unsupervised dictionary learning via non-negative matrix factorization NMF with spatial localization via the generalized cross correlation GCC method. Dictionary learning is performed on the mixture signal, with separation subsequently achieved by grouping dictionary atoms, at each point in time, according to their spatial origins. The resulting source separation algorithm is simple yet flexible, requiring no prior knowledge or information. Separation quality is evaluated for three tasks using stereo recordings from the publicly available SiSEC signal separation evaluation campaign: 3 and 4 concurrent speakers in reverberant environments, speech mixed with real-world background noise, and noisy recordings of a moving speaker. Performance is quantified using perceptually motivated and SNR-based measures with the PEASS and BSS Eval toolkits, respectively. We evaluate the effects of model parameters on separation quality, and compare our approach with other unsupervised and semi-supervised speech separation and enhancement approaches. We show that GCC-NMF is a flexible source separation algorithm, outperforming task-specific approaches in each of the three settings, including both blind as well as several informed approaches that require prior knowledge or information.
A survey of domain adaptation for statistical machine translation

Differences in domains of language use between training data and test data have often been reported to result in performance degradation for phrase-based machine translation models. Throughout the past decade or so, a large body of work aimed at exploring domain-adaptation methods to improve system performance in the face of such domain differences. This paper provides a systematic survey of domain-adaptation methods for phrase-based machine-translation systems. The survey starts out with outlining the sources of errors in various components of phrase-based models due to domain change, including lexical selection, reordering and optimization. Subsequently, it outlines the different research lines to domain adaptation in the literature, and surveys the existing work within these research lines, discussing how these approaches differ and how they relate to each other.
Active Learning for Deep Object Detection

The great success that deep models have achieved in the past is mainly owed to large amounts of labeled training data. However, the acquisition of labeled data for new tasks aside from existing benchmarks is both challenging and costly. Active learning can make the process of labeling new data more efficient by selecting unlabeled samples which, when labeled, are expected to improve the model the most. In this paper, we combine a novel method of active learning for object detection with an incremental learning scheme (Käding et al., 2016b) to enable continuous exploration of new unlabeled datasets. We propose a set of uncertaintybased active learning metrics suitable for most object detectors. Furthermore, we present an approach to leverage class imbalances during sample selection. All methods are evaluated systematically in a continuous exploration context on the PASCAL VOC 2012 dataset (Everingham et al., 2010).