nils-herrmann/clean_scirepeval huggingface dataset:
SentiBench - a benchmark comparison of state-of-the-practice sentiment analysis methods

In the last few years thousands of scientific papers have investigated sentiment analysis, several startups that measure opinions on real data have emerged and a number of innovative products related to this theme have been developed. There are multiple methods for measuring sentiments, including lexical-based and supervised machine learning methods. Despite the vast interest on the theme and wide popularity of some methods, it is unclear which one is better for identifying the polarity (i.e., positive or negative) of a message. Accordingly, there is a strong need to conduct a thorough apple-to-apple comparison of sentiment analysis methods, as they are used in practice, across multiple datasets originated from different data sources. Such a comparison is key for understanding the potential limitations, advantages, and disadvantages of popular methods. This article aims at filling this gap by presenting a benchmark comparison of twenty-four popular sentiment analysis methods (which we call the state-of-the-practice methods). Our evaluation is based on a benchmark of eighteen labeled datasets, covering messages posted on social networks, movie and product reviews, as well as opinions and comments in news articles. Our results highlight the extent to which the prediction performance of these methods varies considerably across datasets. Aiming at boosting the development of this research area, we open the methods’ codes and datasets used in this article, deploying them in a benchmark system, which provides an open API for accessing and comparing sentence-level sentiment analysis methods.
Analysis of CMOS low-dropout regulator — Power-supply rejection ratio

This paper provides a detailed analysis of the power-supply rejection ratio (PSRR) of low-dropout (LDO) regulator. The paper includes circuit modeling of a generic LDO with signal injection at its supply. Based on the modeling, the transfer function of PSRR is derived. Thorough analysis of the locations of poles and zeros obtained from the transfer function is carried out, and then recommendations to improve PSRR are given. The proposed model and the achieved results are verified by circuit simulations using BSEVI models of a commercial CMOS 0.35-μm technology. The results reveal good agreement between the modeling and the PSRR property of a LDO.
An Empirical Study on the Integrated Framework of e-CRM in Online Shopping: Evaluating the Relationships Among Perceived Value, Satisfaction, and Trust Based on Customers' Perspectives

Based on customer cognitive, affective and conative experiences in Internet online shopping, this study, from customers’ perspectives, develops a conceptual framework for e-CRM to explain the psychological process that customers maintain a long-term exchange relationship with specific online retailer. The conceptual framework proposes a series of causal linkages among the key variables affecting customer commitment to specific online retailer, such as perceived value (as cognitive belief), satisfaction (as affective experience) and trust (as conative relationship intention). Three key exogenous variables affecting Internet online shopping experiences, such as perceived service quality, perceived product quality, and perceived price fairness, are integrated into the framework. This study empirically tested and supported a large part of the proposed framework and the causal linkages within it. The empirical results highlight some managerial implications for successfully developing and implementing a strategy for e-CRM.
On blockchain-based anonymized dataset distribution platform

In this paper, we design a distributed platform for anonymized dataset trading without any centralized trusted third party. The platform consists of peers and consensus-based blockchain mechanism, and each peer acts as a data broker, data receiver, or verifier for blockchain in a data transfer transaction. A data broker collects data from data owners under their consent for data trading. The Privacy Policy Manager (PPM) manages the consent information and confirms them on behalf of data owners, when data distribution is requested from data broker. We implement a prototype system of the platform using an open-source blockchain mechanism, Hyperledger Fabric, and provide evaluation results of the prototype system.
On the Impact of Touch ID on iPhone Passcodes

Smartphones today store large amounts of data that can be confidential, private or sensitive. To protect such data, all mobile OSs have a phone lock mechanism, a mechanism that requires user authentication before granting access to applications and data on the phone. iPhone’s unlocking secret (a.k.a., passcode in Apple’s terminology) is also used to derive a key for encrypting data on the device. Recently, Apple has introduced Touch ID, that allows a fingerprint-based authentication to be used for unlocking an iPhone. The intuition behind the technology was that its usability would allow users to use stronger passcodes for locking their iOS devices, without substantially sacrificing usability. To this date, it is unclear, however, if users take advantage of Touch ID technology and if they, indeed, employ stronger passcodes. It is the main objective and the contribution of this paper to fill this knowledge gap. In order to answer this question, we conducted three user studies (a) an in-person survey with 90 participants, (b) interviews with 21 participants, and (c) an online survey with 374 Amazon Mechanical Turks. Overall, we found that users do not take an advantage of Touch ID and use weak unlocking secrets, mainly 4-digit PINs, similarly to those users who do not use Touch ID. To our surprise, we found that more than 30% of the participants in each group did not know that they could use passwords instead of 4-digit PINs. Some other participants indicated that they adopted PINs due to better usability, in comparison to passwords. Most of the participants agreed that Touch ID, indeed, offers usability benefits, such as convenience, speed and ease of use. Finally, we found that there is a disconnect between users’ desires for security that their passcodes have to offer and the reality. In particular, only 12% of participants correctly estimated the security their passcodes provide.
Software watermarking via opaque predicates: Implementation, analysis, and attacks

Within the software industry software piracy is a great concern. In this article we address this issue through a prevention technique called software watermarking. Depending on how a software watermark is applied it can be used to discourage piracy; as proof of authorship or purchase; or to track the source of the illegal redistribution. In particular we analyze an algorithm originally proposed by Geneviève Arboit in A Method for Watermarking Java Programs via Opaque Predicates. This watermarking technique embeds the watermark by adding opaque predicates to the application. We have found that the Arboit technique does withstand some forms of attack and has a respectable data-rate. However, it is susceptible to a variety of distortive attacks. One unanswered question in the area of software watermarking is whether dynamic algorithms are inherently more resilient to attacks than static algorithms. We have implemented and empirically evaluated both static and dynamic versions within the SANDMARK framework.
Predicting query performance

We develop a method for predicting query performance by computing the relative entropy between a query language model and the corresponding collection language model. The resulting clarity score measures the coherence of the language usage in documents whose models are likely to generate the query. We suggest that clarity scores measure the ambiguity of a query with respect to a collection of documents and show that they correlate positively with average precision in a variety of TREC test sets. Thus, the clarity score may be used to identify ineffective queries, on average, without relevance information. We develop an algorithm for automatically setting the clarity score threshold between predicted poorly-performing queries and acceptable queries and validate it using TREC data. In particular, we compare the automatic thresholds to optimum thresholds and also check how frequently results as good are achieved in sampling experiments that randomly assign queries to the two classes.
MPQA 3.0: An Entity/Event-Level Sentiment Corpus

This paper presents an annotation scheme for adding entity and event target annotations to the MPQA corpus, a rich span-annotated opinion corpus. The new corpus promises to be a valuable new resource for developing systems for entity/event-level sentiment analysis. Such systems, in turn, would be valuable in NLP applications such as Automatic Question Answering. We introduce the idea of entity and event targets (eTargets), describe the annotation scheme, and present the results of an agreement study.
Learning hierarchical sparse features for RGB-(D) object recognition

Recently introduced RGB-D cameras are capable of providing high quality synchronized videos of both color and depth. With its advanced sensing capabilities, this technology represents an opportunity to significantly increase the capabilities of object recognition. It also raises the problem of developing expressive features for the color and depth channels of these sensors. In this paper we introduce hierarchical matching pursuit (HMP) for RGB-D data. As a multi-layer sparse coding network, HMP builds feature hierarchies layer by layer with an increasing receptive field size to capture abstract representations from raw RGB-D data. HMP uses sparse coding to learn codebooks at each layer in an unsupervised way and builds hierarchical feature representations from the learned codebooks in conjunction with orthogonal matching pursuit, spatial pooling and contrast normalization. Extensive experiments on various datasets indicate that the features learned with our approach enable superior object recognition results using linear support vector machines.
Development of a Low-Profile Circularly Polarized Cavity-Backed Antenna Using HMSIW Technique

In this paper, two single fed low-profile cavity-backed planar slot antennas for circular polarization (CP) applications are first introduced by half mode substrate integrated waveguide (HMSIW) technique. One of the structures presents right handed CP (RHCP), while the other one offers left handed CP (LHCP). A single layer of low cost printed circuit board (PCB) is employed for both antennas providing low-cost, lightweight, and also easy integration with planar circuits. An inset microstrip line is used to excite two orthogonal quarter-wave length patch modes with required phase difference for generating CP wave. The new proposed antennas are successfully designed and fabricated. Measured results are in good agreement with those obtained by numerical investigation using HFSS. Results exhibit that both antennas present the advantages of conventional cavity backed antennas including high gain and high front to back ratio (FTBR).
Semi-Amortized Variational Autoencoders

Amortized variational inference (AVI) replaces instance-specific local inference with a global inference network. While AVI has enabled efficient training of deep generative models such as variational autoencoders (VAE), recent empirical work suggests that inference networks can produce suboptimal variational parameters. We propose a hybrid approach, to use AVI to initialize the variational parameters and run stochastic variational inference (SVI) to refine them. Crucially, the local SVI procedure is itself differentiable, so the inference network and generative model can be trained end-to-end with gradient-based optimization. This semi-amortized approach enables the use of rich generative models without experiencing the posterior-collapse phenomenon common in training VAEs for problems like text generation. Experiments show this approach outperforms strong autoregressive and variational baselines on standard text and image datasets.
Mobile music touch: mobile tactile stimulation for passive learning

Mobile Music Touch (MMT) helps teach users to play piano melodies while they perform other tasks. MMT is a lightweight, wireless haptic music instruction system consisting of fingerless gloves and a mobile Bluetooth enabled computing device, such as a mobile phone. Passages to be learned are loaded into the mobile phone and are played repeatedly while the user performs other tasks. As each note of the music plays, vibrators on each finger in the gloves activate, indicating which finger is used to play each note. We present two studies on the efficacy of MMT. The first measures 16 subjects' ability to play a passage after using MMT for 30 minutes while performing a reading comprehension test. The MMT system was significantly more effective than a control condition where the passage was played repeatedly but the subjects' fingers were not vibrated. The second study compares the amount of time required for 10 subjects to replay short, randomly generated passages using passive training versus active training. Participants with no piano experience could repeat the passages after passive training while subjects with piano experience often could not.
Clustering Paraphrases by Word Sense

Automatically generated databases of English paraphrases have the drawback that they return a single list of paraphrases for an input word or phrase. This means that all senses of polysemous words are grouped together, unlike WordNet which partitions different senses into separate synsets. We present a new method for clustering paraphrases by word sense, and apply it to the Paraphrase Database (PPDB). We investigate the performance of hierarchical and spectral clustering algorithms, and systematically explore different ways of defining the similarity matrix that they use as input. Our method produces sense clusters that are qualitatively and quantitatively good, and that represent a substantial improvement to the PPDB resource.
Nonverbal social withdrawal in depression: Evidence from manual and automatic analyses

The relationship between nonverbal behavior and severity of depression was investigated by following depressed participants over the course of treatment and video recording a series of clinical interviews. Facial expressions and head pose were analyzed from video using manual and automatic systems. Both systems were highly consistent for FACS action units (AUs) and showed similar effects for change over time in depression severity. When symptom severity was high, participants made fewer affiliative facial expressions (AUs 12 and 15) and more non-affiliative facial expressions (AU 14). Participants also exhibited diminished head motion (i.e., amplitude and velocity) when symptom severity was high. These results are consistent with the Social Withdrawal hypothesis: that depressed individuals use nonverbal behavior to maintain or increase interpersonal distance. As individuals recover, they send more signals indicating a willingness to affiliate. The finding that automatic facial expression analysis was both consistent with manual coding and revealed the same pattern of findings suggests that automatic facial expression analysis may be ready to relieve the burden of manual coding in behavioral and clinical science.
ASL Recognition Based on a Coupling Between HMMs and 3D Motion Analysis

We present a framework for recognizing isolated and continuous American Sign Language (ASL) sentences from three-dimensional data. The data are obtained by using physics-based three-dimensional tracking methods and then presented as input to Hidden Markov Models (HMMs) for recognition. To improve recognition performance, we model context-dependent HMMs and present a novel method of coupling three-dimensional computer vision methods and HMMs by temporally segmenting the data stream with vision methods. We then use the geometric properties of the segments to constrain the HMM framework for recognition. We show in experiments with a 53 sign vocabulary that three-dimensional features outperform two-dimensional features in recognition performance. Furthermore, we demonstrate that contextdependent modeling and the coupling of vision methods and HMMs improve the accuracy of continuous ASL recognition.
Data formats for high efficiency coding of Lytro-Illum light fields

Light fields captured by the Lytro-Illum camera are the first to appear in the consumer market, capable of providing refocused pictures at acceptable spatial resolution and quality. Since this is partially due to sampling of a huge number of light rays, efficient compression methods are required to store and exchange light field data. This paper presents a performance study of HEVC-compatible coding of Lytro-Illum light fields using different data formats for standard coding. The efficiency of 5 different light field data formats are evaluated using a data set of 12 light field images and the standard HEVC coding configurations of Still-Image Profile, All-Intra, Low Delay B and P and Random Access. Unexpectedly, the results show that relative performance is not consistent across all coding configurations, raising new research questions regarding standard coding of Lytro-Illum light fields using HEVC. Most importantly, the proposed data formats greatly increase HEVC performance.
Activity analysis based on low sample rate smart meters

Activity analysis disaggregates utility consumption from smart meters into specific usage that associates with human activities. It can not only help residents better manage their consumption for sustainable lifestyle, but also allow utility managers to devise conservation programs. Existing research efforts on disaggregating consumption focus on analyzing consumption features with high sample rates (mainly between 1 Hz ~ 1MHz). However, many smart meter deployments support sample rates at most 1/900 Hz, which challenges activity analysis with occurrences of parallel activities, difficulty of aligning events, and lack of consumption features. We propose a novel statistical framework for disaggregation on coarse granular smart meter readings by modeling fixture characteristics, household behavior, and activity correlations. This framework has been implemented into two approaches for different application scenarios, and has been deployed to serve over 300 pilot households in Dubuque, IA. Interesting activity-level consumption patterns have been identified, and the evaluation on both real and synthetic datasets has shown high accuracy on discovering washer and shower.
Model-Based Testing of SDN Firewalls: A Case Study

In Software defined networking (SDN), security mechanisms such as firewalls need to deal with dynamic network environments. This raises challenges for quality assurance of these security mechanisms. This paper presents a case study on model-based testing of SDN firewall programs. The subject program is the firewall module of Floodlight, one of the most popular SDN platforms in Java. We model the expected firewall behavior with function nets, a modeling formalism in the MISTA tool. The test code is generated automatically by MISTA. The result of our case study shows that the generated model-based tests have achieved much higher mutation coverage than the existing Junit tests in the Floodlight firewall program. This indicates that model-based testing can be a viable option for quality assurance of SDN-based firewall programs.
Fast and accurate map merging for multi-robot systems

We present a new algorithm for merging occupancy grid maps produced by multiple robots exploring the same environment. The algorithm produces a set of possible transformations needed to merge two maps, i.e translations and rotations. Each transformation is weighted, thus allowing to distinguish uncertain situations, and enabling to track multiple cases when ambiguities arise. Transformations are produced extracting some spectral information from the maps. The approach is deterministic, non-iterative, and fast. The algorithm has been tested on public available datasets, as well as on maps produced by two robots concurrently exploring both indoor and outdoor environments. Throughout the experimental validation stage the technique we propose consistently merged maps exhibiting very different characteristics.
A Usability Study of Awareness Widgets in a Shared Workspace Groupware System

Workspace awareness is knowledge about others’ interaction with a shared workspace. Groupware systems provide only limited information about other participants, often compromising workspace awareness. This paper describes a usability study of several widgets designed to help maintain awareness in a groupware workspace. These widgets include a miniature view, a radar view, a multiuser scrollbar, a glance function, and a “what you see is what I do” view. The study examined the widgets’ information content, how easily people could interpret them, and whether they were useful or distracting. Observations, questionnaires, and interviews indicate that the miniature and radar displays are useful and valuable for tasks involving spatial manipulation of artifacts.
Predicting Process Behaviour using Deep Learning

Predicting business process behaviour is an important aspect of business process management. Motivated by research in natural language processing, this paper describes an application of deep learning with recurrent neural networks to the problem of predicting the next event in a business process. This is both a novel method in process prediction, which has largely relied on explicit process models, and also a novel application of deep learning methods. The approach is evaluated on two real datasets and our results surpass the state-of-the-art in prediction precision.
EEG artifact elimination by extraction of ICA-component features using image processing algorithms

Artifact rejection is a central issue when dealing with electroencephalogram recordings. Although independent component analysis (ICA) separates data in linearly independent components (IC), the classification of these components as artifact or EEG signal still requires visual inspection by experts. In this paper, we achieve automated artifact elimination using linear discriminant analysis (LDA) for classification of feature vectors extracted from ICA components via image processing algorithms. We compare the performance of this automated classifier to visual classification by experts and identify range filtering as a feature extraction method with great potential for automated IC artifact recognition (accuracy rate 88%). We obtain almost the same level of recognition performance for geometric features and local binary pattern (LBP) features. Compared to the existing automated solutions the proposed method has two main advantages: First, it does not depend on direct recording of artifact signals, which then, e.g. have to be subtracted from the contaminated EEG. Second, it is not limited to a specific number or type of artifact. In summary, the present method is an automatic, reliable, real-time capable and practical tool that reduces the time intensive manual selection of ICs for artifact removal. The results are very promising despite the relatively small channel resolution of 25 electrodes.