nils-herrmann/clean_scirepeval huggingface dataset:
Recent Developments in Sign Language Recognition : A Review

In the world of sign language, and gestures, a lot of research work has been done over the past three decades. This has brought about a gradual transition from isolated to continuous, and static to dynamic gesture recognition for operations on a limited vocabulary. In present scenario, human machine interactive systems facilitate communication between the deaf, and hearing people in real world situations. In order to improve the accuracy of recognition, many researchers have deployed methods such as HMM, Artificial Neural Networks, and Kinect platform. Effective algorithms for segmentation, classification, pattern matching and recognition have evolved. The main purpose of this paper is to analyze these methods and to effectively compare them, which will enable the reader to reach an optimal solution. This creates both, challenges and opportunities for sign language recognition related research.  Language Recognition, Hidden Markov Model, Artificial Neural Network, Kinect Platform, Fuzzy Logic.
LSAfun--An R package for computations based on Latent Semantic Analysis.

In this article, the R package LSAfun is presented. This package enables a variety of functions and computations based on Vector Semantic Models such as Latent Semantic Analysis (LSA) Landauer, Foltz and Laham (Discourse Processes 25:259-284, 1998), which are procedures to obtain a high-dimensional vector representation for words (and documents) from a text corpus. Such representations are thought to capture the semantic meaning of a word (or document) and allow for semantic similarity comparisons between words to be calculated as the cosine of the angle between their associated vectors. LSAfun uses pre-created LSA spaces and provides functions for (a) Similarity Computations between words, word lists, and documents; (b) Neighborhood Computations, such as obtaining a word's or document's most similar words, (c) plotting such a neighborhood, as well as similarity structures for any word lists, in a two- or three-dimensional approximation using Multidimensional Scaling, (d) Applied Functions, such as computing the coherence of a text, answering multiple choice questions and producing generic text summaries; and (e) Composition Methods for obtaining vector representations for two-word phrases. The purpose of this package is to allow convenient access to computations based on LSA.
NFC antenna for portable device with metal back cover

In this paper, a new Near Field Communication (NFC) antenna solution that can be used for portable devices with metal back cover is proposed. In particular, there are two holes on metal back cover, a slit between the two holes, and antenna coil located behind the metal cover. With such an arrangement, the shielding effect of the metal cover can be totally eliminated. Simulated and measured results of the proposed antenna are presented.
Simulation and analysis of stand-alone photovoltaic system with boost converter using MATLAB/Simulink

Use of renewable energy and in particular solar energy has brought significant attention over the past decades. Photovoltaic (PV) power generation projects are implemented in very large number in many countries. Many research works are carried out to analyze and validate the performance of PV modules. Implementation of experimental set up for PV based power system with DC-DC converter to validate the performance of the system is not always possible due to practical constraints. Software based simulation model helps to analyze the performance of PV and a common circuit based model which could be used for validating any commercial PV module will be more helpful. Simulation of mathematical model for Photovoltaic (PV) module and DC-DC boost converter is presented in this paper. The model presented in this paper can be used as a generalized PV module to analyze the performance of any commercially available PV modules. I-V characteristics and P-V characteristics of PV module under different temperature and irradiation level can be obtained using the model. The design of DC-DC boost converter is also discussed in detail. Simulation of DC-DC converter is performed and the results are obtained from constant DC supply fed converter and PV fed converter.
Multimodal interaction in the car: combining speech and gestures on the steering wheel

Implementing controls in the car becomes a major challenge: The use of simple physical buttons does not scale to the increased number of assistive, comfort, and infotainment functions. Current solutions include hierarchical menus and multi-functional control devices, which increase complexity and visual demand. Another option is speech control, which is not widely accepted, as it does not support visibility of actions, fine-grained feedback, and easy undo of actions. Our approach combines speech and gestures. By using speech for identification of functions, we exploit the visibility of objects in the car (e.g., mirror) and simple access to a wide range of functions equaling a very broad menu. Using gestures for manipulation (e.g., left/right), we provide fine-grained control with immediate feedback and easy undo of actions. In a user-centered process, we determined a set of user-defined gestures as well as common voice commands. For a prototype, we linked this to a car interior and driving simulator. In a study with 16 participants, we explored the impact of this form of multimodal interaction on the driving performance against a baseline using physical buttons. The results indicate that the use of speech and gesture is slower than using buttons but results in a similar driving performance. Users comment in a DALI questionnaire that the visual demand is lower when using speech and gestures.
LSTM based Conversation Models

In this paper, we present a conversational model that incorporates both context and participant role for two-party conversations. Different architectures are explored for integrating participant role and context information into a Long Short-term Memory (LSTM) language model. The conversational model can function as a language model or a language generation model. Experiments on the Ubuntu Dialog Corpus show that our model can capture multiple turn interaction between participants. The proposed method outperforms a traditional LSTM model as measured by language model perplexity and response ranking. Generated responses show characteristic differences between the two participant roles.
ELI: A Complete, Flexible Compiler Construction System

William 111. Waite ver the past five years, our group has developed the Eli’ system to reduce the cost of producing compilers. Eli has been used to construct complete compilers for standard programming languages extensions to standard programming languages, and specialpurpose languages. For the remainder of this article, we will use the term compiler when referring to language processors. One of the most important ways to enhance productivity in software engineering is to provide more appropriate descriptions of problems and their
One-shot learning of generative speech concepts

One-shot learning – the human ability to learn a new concept from just one or a few examples – poses a challenge to traditional learning algorithms, although approaches based on Hierarchical Bayesian models and compositional representations have been making headway. This paper investigates how children and adults readily learn the spoken form of new words from one example – recognizing arbitrary instances of a novel phonological sequence, and excluding non-instances, regardless of speaker identity and acoustic variability. This is an essential step on the way to learning a word’s meaning and learning to use it, and we develop a Hierarchical Bayesian acoustic model that can learn spoken words from one example, utilizing compositions of phoneme-like units that are the product of unsupervised learning. We compare people and computational models on one-shot classification and generation tasks with novel Japanese words, finding that the learned units play an important role in achieving good performance.
Novel Approaches for Privacy Preserving Data Mining in k-Anonymity Model

In privacy preserving data mining, anonymization based approaches have been used to preserve the privacy of an individual. Existing literature addresses various anonymization based approaches for preserving the sensitive private information of an individual. The k-anonymity model is one of the widely used anonymization based approach. However, the anonymization based approaches suffer from the issue of information loss. To minimize the information loss various state-of-the-art anonymization based clustering approaches viz. Greedy k-member algorithm and Systematic clustering algorithm have been proposed. Among them, the Systematic clustering algorithm gives lesser information loss. In addition, these approaches make use of all attributes during the creation of an anonymized database. Therefore, the risk of disclosure of sensitive private data is higher via publication of all the attributes. In this paper, we propose two approaches for minimizing the disclosure risk and preserving the privacy by using systematic clustering algorithm. First approach creates an unequal combination of quasi-identifier and sensitive attribute. Second approach creates an equal combination of quasi-identifier and sensitive attribute. We also evaluate our approach empirically focusing on the information loss and execution time as vital metrics. We illustrate the effectiveness of the proposed approaches by comparing them with the existing clustering algorithms.
Location Privacy-Preserving Task Allocation for Mobile Crowdsensing with Differential Geo-Obfuscation

In traditional mobile crowdsensing applications, organizers need participants’ precise locations for optimal task allocation, e.g., minimizing selected workers’ travel distance to task locations. However, the exposure of their locations raises privacy concerns. Especially for those who are not eventually selected for any task, their location privacy is sacrificed in vain. Hence, in this paper, we propose a location privacy-preserving task allocation framework with geoobfuscation to protect users’ locations during task assignments. Specifically, we make participants obfuscate their reported locations under the guarantee of differential privacy, which can provide privacy protection regardless of adversaries’ prior knowledge and without the involvement of any third-part entity. In order to achieve optimal task allocation with such differential geo-obfuscation, we formulate a mixed-integer non-linear programming problem to minimize the expected travel distance of the selected workers under the constraint of differential privacy. Evaluation results on both simulation and real-world user mobility traces show the effectiveness of our proposed framework. Particularly, our framework outperforms Laplace obfuscation, a state-ofthe-art differential geo-obfuscation mechanism, by achieving 45% less average travel distance on the real-world data.
Location-aware associated data placement for geo-distributed data-intensive applications

Data-intensive applications need to address the problem of how to properly place the set of data items to distributed storage nodes. Traditional techniques use the hashing method to achieve the load balance among nodes such as those in Hadoop and Cassandra, but they do not work efficiently for the requests reading multiple data items in one transaction, especially when the source locations of requests are also distributed. Recent works proposed the managed data placement schemes for online social networks, but have a limited scope of applications due to their focuses. We propose an associated data placement (ADP) scheme, which improves the co-location of associated data and the localized data serving while ensuring the balance between nodes. In ADP, we employ the hypergraph partitioning technique to efficiently partition the set of data items and place them to the distributed nodes, and we also take replicas and incremental adjustment into considerations. Through extensive experiments with both synthesized and trace-based datasets, we evaluate the performance of ADP and demonstrate its effectiveness.
Teamwork in multiteam systems.

The authors examined how networks of teams integrate their efforts to succeed collectively. They proposed that integration processes used to align efforts among multiple teams are important predictors of multiteam performance. The authors used a multiteam system (MTS) simulation to assess how both cross-team and within-team processes relate to MTS performance over multiple performance episodes that differed in terms of required interdependence levels. They found that cross-team processes predicted MTS performance beyond that accounted for by within-team processes. Further, cross-team processes were more important for MTS effectiveness when there were high cross-team interdependence demands as compared with situations in which teams could work more independently. Results are discussed in terms of extending theory and applications from teams to multiteam systems.
Controlled Functional Encryption

Motivated by privacy and usability requirements in various scenarios where existing cryptographic tools (like secure multi-party computation and functional encryption) are not adequate, we introduce a new cryptographic tool called Controlled Functional Encryption (C-FE). As in functional encryption, C-FE allows a user (client) to learn only certain functions of encrypted data, using keys obtained from an authority. However, we allow (and require) the client to send a fresh key request to the authority every time it wants to evaluate a function on a ciphertext. We obtain efficient solutions by carefully combining CCA2 secure public-key encryption (or rerandomizable RCCA secure public-key encryption, depending on the nature of security desired) with Yao's garbled circuit. Our main contributions in this work include developing and for- mally defining the notion of C-FE; designing theoretical and practical constructions of C-FE schemes achieving these definitions for specific and general classes of functions; and evaluating the performance of our constructions on various application scenarios.
Normalizing historical orthography for OCR historical documents using LSTM

Historical text presents numerous challenges for contemporary different techniques, e.g. information retrieval, OCR and POS tagging. In particular, the absence of consistent orthographic conventions in historical text presents difficulties for any system which requires reference to a fixed lexicon accessed by orthographic form. For example, language modeling or retrieval engine for historical text which is produced by OCR systems, where the spelling of words often differ in various way, e.g. one word might have different spellings evolved over time. It is very important to aid those techniques with the rules for automatic mapping of historical wordforms. In this paper, we propose a new technique to model the target modern language by means of a recurrent neural network with long-short term memory architecture. Because the network is recurrent, the considered context is not limited to a fixed size especially due to memory cells which are designed to deal with long-term dependencies. In the set of experiments conducted on the Luther bible database and transform wordforms from Early New High German (ENHG) 14th - 16th centuries to the corresponding modern wordforms in New High German (NHG). We compare our proposed supervised model LSTM to various methods for computing word alignments using statistical, heuristic models. Our new proposed LSTM outperforms the other three state-of-the-art methods. The evaluation shows the accuracy of our model on the known wordforms is 93.90% and on the unknown wordforms is 87.95%, while the accuracy of the existing state-of-the-art combined approach of the wordlist-based and rule-based normalization models is 92.93% for known and 76.88% for unknown tokens. Our proposed LSTM model outperforms on normalizing the modern wordform to historical wordform. The performance on seen tokens is 93.4%, while for unknown tokens is 89.17%.
Privacy, Risk Perception, and Expert Online Behavior: An Exploratory Study of Household End Users

Advances in online technologies have raised new concerns about privacy. A sample of expert household end users was surveyed concerning privacy, risk perceptions, and online behavior intentions. A new e-privacy typology consisting of privacyaware, privacy-suspicious, and privacy-active types was developed from a principal component factor analysis. Results suggest the presence of a privacy hierarchy of effects where awareness leads to suspicion, which subsequently leads to active behavior. An important finding was that privacy-active behavior that was hypothesized to increase the likelihood of online subscription and purchasing was not found to be significant. A further finding was that perceived risk had a strong negative influence on the extent to which respondents participated in online subscription and purchasing. Based on these results, a number of implications for managers and directions for future research are discussed.
Discriminative Recurrent Sparse Auto-Encoders

We present the discriminative recurrent sparse auto-encoder model, comprising a recurrent encoder of rectified linear units, unrolled for a fixed number of iterations, and connected to two linear decoders that reconstruct the input and predict its supervised classification. Training via backpropagation-through-time initially minimizes an unsupervised sparse reconstruction error; the loss function is then augmented with a discriminative term on the supervised classification. The depth implicit in the temporally-unrolled form allows the system to exhibit far more representational power, while keeping the number of trainable parameters fixed. From an initially unstructured network the hidden units differentiate into categorical-units, each of which represents an input prototype with a well-defined class; and part-units representing deformations of these prototypes. The learned organization of the recurrent encoder is hierarchical: part-units are driven directly by the input, whereas the activity of categorical-units builds up over time through interactions with the part-units. Even using a small number of hidden units per layer, discriminative recurrent sparse auto-encoders achieve excellent performance on MNIST.
The QR-code reorganization in illegible snapshots taken by mobile phones

In this paper, we give an algorithm for the analysis and correction of the distorted QR barcode (QR-code) image. The introduced algorithm is based on the code area finding by four corners detection for 2D barcode. We combine Canny edge detection with contours finding algorithms to erase noises and reduce computation and utilize two tangents to approximate the right-bottom point. Then, we give a detail description on how to use inverse perspective transformation in rebuilding a QR-code image from a distorted one. We test our algorithm on images taken by mobile phones. The experiment shows that our algorithm is effective.
Sparse Convolved Gaussian Processes for Multi-output Regression

We present a sparse approximation approach for dependent output Gaussian processes (GP). Employing a latent function framework, we apply the convolution process formalism to establish dependencies between output variables, where each latent function is represented as a GP. Based on these latent functions, we establish an approximation scheme using a conditional independence assumption between the output processes, leading to an approximation of the full covariance which is determined by the locations at which the latent functions are evaluated. We show results of the proposed methodology for synthetic data and real world applications on pollution prediction and a sensor network.
Dirichlet Process Mixture Model for Correcting Technical Variation in Single-Cell Gene Expression Data

We introduce an iterative normalization and clustering method for single-cell gene expression data. The emerging technology of single-cell RNA-seq gives access to gene expression measurements for thousands of cells, allowing discovery and characterization of cell types. However, the data is confounded by technical variation emanating from experimental errors and cell type-specific biases. Current approaches perform a global normalization prior to analyzing biological signals, which does not resolve missing data or variation dependent on latent cell types. Our model is formulated as a hierarchical Bayesian mixture model with cell-specific scalings that aid the iterative normalization and clustering of cells, teasing apart technical variation from biological signals. We demonstrate that this approach is superior to global normalization followed by clustering. We show identifiability and weak convergence guarantees of our method and present a scalable Gibbs inference algorithm. This method improves cluster inference in both synthetic and real single-cell data compared with previous methods, and allows easy interpretation and recovery of the underlying structure and cell types.
Growth and morphological development of laboratory-reared larval and juvenile three-spot gourami Trichogaster trichopterus

Morphological development, including the body proportions, fins, pigmentation and labyrinth organ, in laboratory-hatched larval and juvenile three-spot gourami Trichogaster trichopterus was described. In addition, some wild larval and juvenile specimens were observed for comparison. Body lengths of larvae and juveniles were 2.5 ± 0.1 mm just after hatching (day 0) and 9.2 ± 1.4 mm on day 22, reaching 20.4 ± 5.0 mm on day 40. Aggregate fin ray numbers attained their full complements in juveniles >11.9 mm BL. Preflexion larvae started feeding on day 3 following upper and lower jaw formation, the yolk being completely absorbed by day 11. Subsequently, oblong conical teeth appeared in postflexion larvae >6.4 mm BL (day 13). Melanophores on the body increased with growth, and a large spot started forming at the caudal margin of the body in flexion postlarvae >6.7 mm BL, followed by a second large spot positioned posteriorly on the midline in postflexion larvae >8.6 mm BL. The labyrinth organ differentiated in postflexion larvae >7.9 mm BL (day 19). For eye diameter and the first soft fin ray of pelvic fin length, the proportions in laboratory-reared specimens were smaller than those in wild specimens in 18.5–24.5 mm BL. The pigmentation pattern of laboratory-reared fish did not distinctively differ from that in the wild ones. Comparisons with larval and juvenile morphology of a congener T. pectoralis revealed several distinct differences, particularly in the numbers of myomeres, pigmentations and the proportional length of the first soft fin ray of the pelvic fin.
Bored mondays and focused afternoons: the rhythm of attention and online activity in the workplace

While distractions using digital media have received attention in HCI, understanding engagement in workplace activities has been little explored. We logged digital activity and continually probed perspectives of 32 information workers for five days in situ to understand how attentional states change with context. We present a framework of how engagement and challenge in work relate to focus, boredom, and rote work. Overall, we find more focused attention than boredom in the workplace. Focus peaks mid-afternoon while boredom is highest in early afternoon. People are happiest doing rote work and most stressed doing focused work. On Mondays people are most bored but also most focused. Online activities are associated with different attentional states, showing different patterns at beginning and end of day, and before and after a mid-day break. Our study shows how rhythms of attentional states are associated with context and time, even in a dynamic workplace environment.