nils-herrmann/clean_scirepeval huggingface dataset:
A Blended Deep Learning Approach for Predicting User Intended Actions

User intended actions are widely seen in many areas. Forecasting these actions and taking proactive measures to optimize business outcome is a crucial step towards sustaining the steady business growth. In this work, we focus on predicting attrition, which is one of typical user intended actions. Conventional attrition predictive modeling strategies suffer a few inherent drawbacks. To overcome these limitations, we propose a novel end-to-end learning scheme to keep track of the evolution of attrition patterns for the predictive modeling. It integrates user activity logs, dynamic and static user profiles based on multi-path learning. It exploits historical user records by establishing a decaying multi-snapshot technique. And finally it employs the precedent user intentions via guiding them to the subsequent learning procedure. As a result, it addresses all disadvantages of conventional methods. We evaluate our methodology on two public data repositories and one private user usage dataset provided by Adobe Creative Cloud. The extensive experiments demonstrate that it can offer the appealing performance in comparison with several existing approaches as rated by different popular metrics. Furthermore, we introduce an advanced interpretation and visualization strategy to effectively characterize the periodicity of user activity logs. It can help to pinpoint important factors that are critical to user attrition and retention and thus suggests actionable improvement targets for business practice. Our work will provide useful insights into the prediction and elucidation of other user intended actions as well.
Type synthesis of 3T1R 4-DOF parallel manipulators based on screw theory

3T1R four-degrees-of-freedom (DOF) parallel manipulators (3T1R-PMs) are the parallel counterparts of the 4-DOF SCARA serial robots. In a 3T1R-PM, the moving platform can generate 3T1R motion (also called Schonflies motion), which refers to a rotation about any axis with a given direction in conjunction with 3-DOF translations. A method is proposed for the type synthesis of 3T1R-PMs based on screw theory. The wrench systems of a 3T1R parallel kinematic chain (3T1R-PKC) and its legs are first analyzed. A general procedure is then proposed for the type synthesis of 3T1R-PMs. The type synthesis of legs for 3T1R-PKCs, the type synthesis of 3T1R-PKCs, as well as the selection of actuated joints of 3T1R-PMs, are dealt with in sequence. 3T1R-PKCs with and without inactive joints are synthesized. The phenomenon of dependent joint groups in a 3T1R-PKC is revealed for the first time. Several 3T1R-PMs with identical type of legs are obtained.
The goodness of pronunciation algorithm: a detailed performance study

An inventory was compiled of pronunciation errors frequently made by foreigners speaking Dutch. On the basis of this inventory artificial errors were created in a native development corpus, which in turn were used to optimize thresholds for the Goodness of Pronunciation (GOP) algorithm. In the current study the GOP algorithm is evaluated in three different ways: (1) using a native test corpus with artificial errors which reflect errors frequently made by non-natives, (2) within an actual application used by non-natives for practicing pronunciation, and (3) post-hoc, using the recorded interactions of the pronunciation training application, to determine what the performance of the algorithm would have been if optimal speaker and phone specific thresholds had been used. The results show that the performance of the GOP algorithm was satisfactory and that the procedure by which thresholds were determined by simulating realistic pronunciation errors was appropriate, because performance on the artificially introduced errors closely approximated performance on real data. This finding is particularly welcome if we consider that, in general, paucity of data is a common problem in this kind of research. Furthermore, it appeared that post-hoc threshold optimization only led to a slight increase in performance.
Learning shape correspondence with anisotropic convolutional neural networks

Establishing correspondence between shapes is a fundamental problem in geometry processing, arising in a wide variety of applications. The problem is especially difficult in the setting of non-isometric deformations, as well as in the presence of topological noise and missing parts, mainly due to the limited capability to model such deformations axiomatically. Several recent works showed that invariance to complex shape transformations can be learned from examples. In this paper, we introduce an intrinsic convolutional neural network architecture based on anisotropic diffusion kernels, which we term Anisotropic Convolutional Neural Network (ACNN). In our construction, we generalize convolutions to non-Euclidean domains by constructing a set of oriented anisotropic diffusion kernels, creating in this way a local intrinsic polar representation of the data (‘patch’), which is then correlated with a filter. Several cascades of such filters, linear, and non-linear operators are stacked to form a deep neural network whose parameters are learned by minimizing a task-specific cost. We use ACNNs to effectively learn intrinsic dense correspondences between deformable shapes in very challenging settings, achieving state-of-the-art results on some of the most difficult recent correspondence benchmarks.
CLASSIFICATION USING NAÏVE BAYES , VSM AND POS TAGGER

This research focuses on Text Classification. Text classification is the task of automatically sorting a set of documents into categories from a predefined set. The domain of this research is the combination of information ret rieval (IR) technology, Data mining and machine learning (ML) technology. This research will outline the fundamental traits of the technologies involved. This research uses three text classification algorithms (Naive Bayes, VSM for text classificatio n and the new technique -Use of Stanford Tagger for text classific ation) to classify documents into different categories, which is trained on two different datasets (20 Newsgroups and New news dataset for five categories).In regards to the above classifica tion strategies, Naïve Bayes is potentially good at serving as a tex classification model due to its simplicity.
A Novel Single-Stage High-Power-Factor AC-to-DC LED Driving Circuit With Leakage Inductance Energy Recycling

This paper proposes a novel single-stage ac-to-dc light-emitting-diode (LED) driver circuit. A buck-boost power factor (PF) corrector is integrated with a flyback converter. A recycling path is built to recover the inductive leakage energy. In this way, the presented circuit can provide not only high PF and low total harmonic distortion but also high conversion efficiency and low switching voltage spikes. Above 0.95 PF and 90% efficiency are obtained from an 8-W LED lamp driver prototype.
Creating natural dialogs in the carnegie mellon communicator system

The Carnegie Mellon Communicator system helps users create complex travel itineraries through a conversational interface. Itineraries consist of (multi-leg) flights, hotel and car reservations and are built from actual travel information for North America, obtained from the Web. The system manages dialog using a schema-based approach. Schemas correspond to major units of task information (such as a flight leg) and define conversational topics, or foci of interaction, meaningful to the user.
Designing Guiding Systems for Brain-Computer Interfaces

Brain-Computer Interface (BCI) community has focused the majority of its research efforts on signal processing and machine learning, mostly neglecting the human in the loop. Guiding users on how to use a BCI is crucial in order to teach them to produce stable brain patterns. In this work, we explore the instructions and feedback for BCIs in order to provide a systematic taxonomy to describe the BCI guiding systems. The purpose of our work is to give necessary clues to the researchers and designers in Human-Computer Interaction (HCI) in making the fusion between BCIs and HCI more fruitful but also to better understand the possibilities BCIs can provide to them.
Accurate Optical Flow via Direct Cost Volume Processing

We present an optical flow estimation approach that operates on the full four-dimensional cost volume. This direct approach shares the structural benefits of leading stereo matching pipelines, which are known to yield high accuracy. To this day, such approaches have been considered impractical due to the size of the cost volume. We show that the full four-dimensional cost volume can be constructed in a fraction of a second due to its regularity. We then exploit this regularity further by adapting semi-global matching to the four-dimensional setting. This yields a pipeline that achieves significantly higher accuracy than state-of-the-art optical flow methods while being faster than most. Our approach outperforms all published general-purpose optical flow methods on both Sintel and KITTI 2015 benchmarks.
A Privacy-Aware Authentication Scheme for Distributed Mobile Cloud Computing Services

In modern societies, the number of mobile users has dramatically risen in recent years. In this paper, an efficient authentication scheme for distributed mobile cloud computing services is proposed. The proposed scheme provides security and convenience for mobile users to access multiple mobile cloud computing services from multiple service providers using only a single private key. The security strength of the proposed scheme is based on bilinear pairing cryptosystem and dynamic nonce generation. In addition, the scheme supports mutual authentication, key exchange, user anonymity, and user untraceability. From system implementation point of view, verification tables are not required for the trusted smart card generator (SCG) service and cloud computing service providers when adopting the proposed scheme. In consequence, this scheme reduces the usage of memory spaces on these corresponding service providers. In one mobile user authentication session, only the targeted cloud service provider needs to interact with the service requestor (user). The trusted SCG serves as the secure key distributor for distributed cloud service providers and mobile clients. In the proposed scheme, the trusted SCG service is not involved in individual user authentication process. With this design, our scheme reduces authentication processing time required by communication and computation between cloud service providers and traditional trusted third party service. Formal security proof and performance analyses are conducted to show that the scheme is both secure and efficient.
CCDN: Content-Centric Data Center Networks

Data center networks continually seek higher network performance to meet the ever increasing application demand. Recently, researchers are exploring the method to enhance the data center network performance by intelligent caching and increasing the access points for hot data chunks. Motivated by this, we come up with a simple yet useful caching mechanism for generic data centers, i.e., a server caches a data chunk after an application on it reads the chunk from the file system, and then uses the cached chunk to serve subsequent chunk requests from nearby servers. To turn the basic idea above into a practical system and address the challenges behind it, we design content-centric data center networks CCDNs, which exploits an innovative combination of content-based forwarding and location [Internet Protocol IP]-based forwarding in switches, to correctly locate the target server for a data chunk on a fully distributed basis. Furthermore, CCDN enhances traditional content-based forwarding to determine the nearest target server, and enhances traditional location IP-based forwarding to make high utilization of the precious memory space in switches. Extensive simulations based on real-world workloads and experiments on a test bed built with NetFPGA prototypes show that, even with a small portion of the server’s storage as cache e.g., 3% and with a modest content forwarding information base size e.g., 1000 entries in switches, CCDN can improve the average throughput to get data chunks by 43% compared with a pure Hadoop File System HDFS system in a real data center.
Chat-App Decryption Key Extraction

Recent years have seen a pervasive usage of mobile-based instant messaging apps, which are popularly known as chat apps. On users’ mobile devices, chat logs are usually stored encrypted. This paper is concerned with discovering the decryption key of chat-log database files as they are used by popular chat apps like WhatsApp and WeChat. We propose a systematic and generalized information-flow based approach to recovering the decryption key by taking advantage of both static and dynamic analyses. We show that, despite the employed code obfuscation techniques, we can perform the key discovery process on relevant code portions. Furthermore, to the best of our knowledge, we are the first to detail the employed string de-obfuscation, encrypted database file structure, and decryption-key formulation of the latest WhatsApp with crypt12 database. We also demonstrate how our keyextraction techniques can decrypt encrypted WhatsApp and WeChat database files that originate from a target device. Additionally, we show how we can construct a version of WhatsApp or WeChat that simulates the key generation processes of a remote target device, and recover the keys. Lastly, we analyze why our technique can work on widely-popular chat apps, and mention measures that can be adopted by chat-app developers to better protect the privacy of billions of their users.
Speed vs . Accuracy : Designing an Optimal ASR System for Spontaneous Non-Native Speech in a Real-Time Application

Automatic dialog interaction with remote interlocutors is a difficult application area for speech recognition technology because of the limited acoustic context, poor signal representation, high variability of spontaneous speech and limited time available to do the recognition of noncanonical spoken production. We present the speech recognition system for the non-native dialog applications that we are currently developing. We find that our system broadly matches human performance; that minimum Bayes risk decoding improves accuracy, and that the posterior probabilities have good power towards predicting errors. We also explore the temporal distribution of errors made by the recognizer with online speaker adaptation, the frequency of errors among auto-semantic and function words, as well as the distribution of error rates among the heterogeneous speaker population. Our findings motivate further development directions for dialog speech recognition systems.
Introduction to smart learning analytics: foundations and developments in video-based learning

Smart learning has become a new term to describe technological and social developments (e.g., Big and Open Data, Internet of Things, RFID, and NFC) enable effective, efficient, engaging and personalized learning. Collecting and combining learning analytics coming from different channels can clearly provide valuable information in designing and developing smart learning. Although, the potential of learning analytics to enable smart learning is very promising area, it remains non-investigated and even ill-defined concept. The paper defines the subset of learning analytics that focuses on supporting the features and the processes of smart learning, under the term Smart Learning Analytics. This is followed by a brief discussion on the prospects and drawbacks of Smart Learning Analytics and their recent foundations and developments in the area of Video-Based Learning. Drawing from our experience with the recent international workshops in Smart Environments and Analytics in Video-Based Learning, we present the state-of-the-art developments as well as the four selected contributions. The paper further draws attention to the great potential and need for research in the area of Smart Learning Analytics.
Evaluating Sketchiness as a Visual Variable for the Depiction of Qualitative Uncertainty

We report on results of a series of user studies on the perception of four visual variables that are commonly used in the literature to depict uncertainty. To the best of our knowledge, we provide the first formal evaluation of the use of these variables to facilitate an easier reading of uncertainty in visualizations that rely on line graphical primitives. In addition to blur, dashing and grayscale, we investigate the use of `sketchiness' as a visual variable because it conveys visual impreciseness that may be associated with data quality. Inspired by work in non-photorealistic rendering and by the features of hand-drawn lines, we generate line trajectories that resemble hand-drawn strokes of various levels of proficiency-ranging from child to adult strokes-where the amount of perturbations in the line corresponds to the level of uncertainty in the data. Our results show that sketchiness is a viable alternative for the visualization of uncertainty in lines and is as intuitive as blur; although people subjectively prefer dashing style over blur, grayscale and sketchiness. We discuss advantages and limitations of each technique and conclude with design considerations on how to deploy these visual variables to effectively depict various levels of uncertainty for line marks.
Predicting the co-evolution of event and Knowledge Graphs

Knowledge graphs have evolved as flexible and powerful means for representing general world knowledge. Typical examples are DBpedia, Yago, or the Google Knowledge Graph, which all started off by representing information derived from Wikipedia and were then greatly expanded. In this paper we use the concept of a knowledge graph to present information about specific classes of entities, such as patients or users. The knowledge graph represents all that is known about the entities and their relationships and the goal is to integrate and exploit that information for prediction and decision support. In previous papers it was shown that embedding learning, a.k.a. representation learning, is capable of modelling large-scale semantic knowledge graphs, by exploiting information that describes the context of an entity in the knowledge graph. In Machine Learning we often map the knowledge graph to a tensor representation. Then we learn the latent representations of the entities that compose the tensor and use them to predict unobserved facts. However knowledge graphs represent the current status of the world and therefore they lack of a temporal dimension, which means we can only use them to predict facts about the present moment. In this paper we introduce an additional set of tensors that contain temporal information. Each of this event tensors contains all the events that occurred on a particular time step. Our goal will be to predict the events that will happen in future time steps, using for that task both dynamic information from the previous event tensors and static information that is stored in the knowledge graph. Therefore, this architecture allows us to fuse static and dynamic information to predict future events. We present experiments showing how this model performs well in multiple scenarios: medical data, a recommendation engine and sensor data.
Attributing physical and biological impacts to anthropogenic climate change

Significant changes in physical and biological systems are occurring on all continents and in most oceans, with a concentration of available data in Europe and North America. Most of these changes are in the direction expected with warming temperature. Here we show that these changes in natural systems since at least 1970 are occurring in regions of observed temperature increases, and that these temperature increases at continental scales cannot be explained by natural climate variations alone. Given the conclusions from the Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report that most of the observed increase in global average temperatures since the mid-twentieth century is very likely to be due to the observed increase in anthropogenic greenhouse gas concentrations, and furthermore that it is likely that there has been significant anthropogenic warming over the past 50 years averaged over each continent except Antarctica, we conclude that anthropogenic climate change is having a significant impact on physical and biological systems globally and in some continents.
Design Opportunities for Wearable Devices in Learning to Climb

In this paper, we present a field study on the learning of climbing aimed at defining the design space of wearable devices to support beginners. Three main findings have emerged from our study. First, climbing has a strong emotional impact on beginners; therefore, learning to climb requires mastering new motor patterns as well as negative emotions, such as stress and fear. Second, the feeling of danger that climbers often experience can be mitigated by trust in the climbing partner and the perception of her active presence. Finally, a big problem in climbing is the communication difficulty between the climbing partners and between climber and instructor. We conclude the paper presenting four design considerations for the design of wearable devices meant to support the learning of climbing by providing the actors involved with augmented communication. Such augmented communication should address both the physical and the emotional difficulties of this sport.
Optical Packet and Burst Switching Technologies for the Future Photonic Internet

This paper reviews advanced optical burst switching (OBS) and optical packet switching (OPS) technologies and discusses their roles in the future photonic Internet. Discussions include optoelectronic and optical systems technologies as well as systems integration into viable network elements (OBS and OPS routers). Optical label switching (OLS) offers a unified multiple-service platform with effective and agile utilization of the available optical bandwidth in support of voice, data, and multimedia services on the Internet Protocol. In particular, OLS routers with wavelength routing switching fabrics and parallel optical labeling allow forwarding of asynchronously arriving variable-length packets, bursts, and circuits. By exploiting contention resolution in wavelength, time, and space domains, the OLS routers can achieve high throughput without resorting to a store-and-forward method associated with large buffer requirements. Testbed demonstrations employing OLS edge routers show high-performance networking in support of multimedia and data communications applications over the photonic Internet with optical packets and bursts switched directly at the optical layer
Reducing the effect of email interruptions on employees

It is generally assumed that because it is not necessary to react to email messages when they arrive, employees will read their messages in their own time with minimum interruption to their work. This research has shown that email messages do have some disruptive effect by interrupting the user. Employees at the Danwood Group in the UK were monitored to see how they used email. It was found that most employees had their email software check for incoming messages every 5min and responded to the arrival of a message within 6 s. A recovery time between finishing reading the email and returning to normal work also existed though it was shorter than published recovery times for a telephone interrupt. This analysis has suggested that a number of methods can be employed to reduce this interrupt effect. Employee training, changing the settings and modes of using the email software and the introduction of a one line email facility are all shown to have beneficial effects. This has led to a series of recommendations that will enable the Danwood Group to make better use of email communication and increase employee effectiveness. r 2003