Reviewerly/exHarmony huggingface dataset:
Phrase Based Information Retrieval Analysis in Various Search Engines Using Machine Learning Algorithms

Query-based information retrieval is an essential part of the web search engine. Many researchers have applied different types of web mining technologies to find more relevant information based on the keyword but are not able to know the correct meaning of the term (keyword) single, multiword or phrases. In this paper we address this problem of searching phrases. In this work the phrase searching process is three-fold as whole Phrase, Sequence of term in phrase and Mingle of the term in the phrase. Here the user enters a query as phrases that is passed to various search engines and retrieves the top ‘n’ list of web pages. Initially preprocessing is performed on the Sequence of Keyword in phrase and Mingle of the keyword in the phrase. Then feature extraction is done based on the web pages in the various search engines using term Frequency-Inverse Document Frequency method. Following the feature extraction, grouping of the top ‘n’ list of web pages from various search engines based on the parameter as title-based, snippet-based, content-based, address-based, link-based, uniform resource locator-based, and co-occurrence-based calculation is done using LBG clustering algorithm. Then identified the unique link from the above grouping of web pages from the various search engines using SVM classifier and assigned the rank value to the unique link web pages are done using proposed ranking algorithm. Finally it is observed from this experiment that precision, recall, f-measure, accuracy, speed, and error rate show significant improvement than the traditional search engines.
Incorporating Hierarchical Domain Information to Disambiguate Very Short Queries

Users often express their information needs using incomplete or ambiguous queries of only one or two terms in length, particularly in the Web environments. The ambiguity of short queries is a recognized problem for information retrieval (IR) systems. In this study, we investigate various approaches for incorporating hierarchical domain information into IR models such that the domain specification resolves the ambiguity. To this end, we develop practical models for constructing evaluation datasets from existing corpora. In terms of effectiveness, we further study the trade-off between a short query and its domain specification information. In doing so, we find that domains with the highest number of relevant documents are not always the best ones to select. We also evaluate the utility of a domain hierarchy and find that incorporating the hierarchical structure of a collection into the retrieval model could have a high impact on short query disambiguation.
Describing Datasets in Wikidata

We propose to use Wikidata to provide metadata for datasets when the traditional approach via Schema.org is not feasible. We describe and discuss the proposal, and believe that the process described in this paper can help with increasing findability and accessibility of certain datasets.
The Construction and Annotation of a Semantically Enriched Database: The Mandarin VerbNet and Its NLP Applications

Linguistic analysis has been questioned of its applicational value. However, with the proper framework, linguistic wisdom may be needed in the last mile of big data era. This paper introduces a lexical semantic database, Mandarin VerbNet, which is built from a frame-based constructional approach, as a linguistically motivated, computationally feasible, and Chinese-appropriate resource. The study also tests the validity of adopting the semantically-enriched and constructionally-cued features for the task of Word Sense Disambiguation. Four Chinese polysemous verbs of emotion are chosen for a pilot trial. With the annotation of frame-specific features, the different senses can be detected with a high performance rate. The results consistently show significant improvement for all the disambiguation tasks over the baselines, with the weight average F∆max. up to 0.337. The impressive results indicate the discriminative effect of frame-based constructional information, and ultimately points to the potential advantage of employing deep linguistic annotation in NLP applications.
A Multi-Source Collection of Event-Labeled News Documents

In this paper, we present a collection of news documents labeled at the level of crisp events. Compared to other publicly-available collections, our dataset is made of heterogeneous documents published by popular news channels on different platforms in the same temporal window and, therefore, dealing with roughly the same events and topics.
Beating uncertainty in racing bot evolution through enhanced exploration and pole position selection

One of the main problems in the design through optimization of car racing bots is the inherent noise in the optimization process: besides the fact that the fitness is a heuristic based on what we think are the keys to success and as such just a surrogate for the ultimate objective, winning races, fitness itself is uncertain due to the stochastic behavior of racing conditions and the rest of the (simulated) racers. The fuzzy-based genetic controller for the car racing simulator TORCS we have defined in previous works is based on two fuzzy sub-controllers, one for deciding on the wheel steering angle and another to set the car target speed at the next simulation tick. They are both optimized by means of an Evolutionary Algorithm, which considers an already tested fitness function focused on the maximization of the average speed during the race and the minimization of the car damage. The noisy environment asks for keeping diversity high during evolution, that is why we have added a Blend Crossover (BLX-α) operator, which is, besides, able to exploit current results at the same time it explores. Additionally, we try to address uncertainty in selection by introducing a novel selection policy of parents based in races, where the individuals are grouped and compete against others in several races, so just the firsts ranked will remain in the population as parents. Several experiments have been conducted, testing the value of the different controllers. The results show that the combination of a dynamic BLX-α crossover operator plus the pole position selection policy clearly beats the rest of approaches. Moreover, in the comparison of this controller with one of the participants of the prestigious international Simulated Car Racing Championship, our autonomous driver obtains much better results than the opponent.
On the Prospects of Multiport Devices for Photon-Number-Resolving Detection

Ideal photon-number-resolving detectors form a class of important optical components in quantum optics and quantum information theory. In this article, we theoretically investigate the potential of multiport devices having reconstruction performances approaching that of the Fock-state measurement. By recognizing that all multiport devices are minimally complete, we first provide a general analytical framework to describe the tomographic accuracy (or quality) of these devices. Next, we show that a perfect multiport device with an infinite number of output ports functions as either the Fock-state measurement when photon losses are absent or binomial mixtures of Fock-state measurements when photon losses are present and derive their respective expressions for the tomographic transfer function. This function is the scaled asymptotic mean squared error of the reconstructed photon-number distributions uniformly averaged over all distributions in the probability simplex. We then supply more general analytical formulas for the transfer function for finite numbers of output ports in both the absence and presence of photon losses. The effects of photon losses on the photon-number resolving power of both infinite- and finite-size multiport devices are also investigated.
Musical Borrowing in Sonification

Sonification presents some challenges in communicating information, particularly because of the large difference between possible data to sound mappings and cognitively valid mappings. It is an information transmission process which can be described through the Shannon-Weaver Theory of Mathematical Communication. Musical borrowing is proposed as a method in sonification which can aid the information transmission process as the composer’s and listener’s shared musical knowledge is used. This article describes the compositional process of Wasgiischwashäsch (2017) which uses Rossini’s William Tell Overture (1829) to sonify datasets relating to climate change in Switzerland. It concludes that the familiarity of audiences with the original piece, and the humorous effect produced by the distortion of a well-known piece, contribute to a more effective transmission process.
Diagnoses Detection in Short Snippets of Narrative Medical Texts

Data extraction from narrative medical texts is a significant task to enable secondary use of medical data. Supervised learning algorithms show good results in natural language processing (NLP) tasks. We have developed a NLP framework based on supervised machine learning for entity extraction from medical texts. The framework is language independent and entities independent as long as an appropriately labeled dataset is given. The framework is based on vector representation of words and a neural network as a classifier. We have trained and evaluated the framework on two different text corpuses: diagnoses paragraphs written in German and medical records written in Russian. The neural network hyperparameters were adjusted for every dataset to get better classification results. Finally, accuracy, standard deviation, and standard error were calculated for both network models engaging 10-folds cross-validation. The obtained accuracy is 97,64% for Russian texts and 96,81% for German ones.
Deep Learning in the IT Curriculum

Deep learning is a form of neural-network based machine learning that is becoming prevalent in many IT endeavors. It is therefore important to expose IT undergraduate students to it somewhere in the curriculum. However, deep learning has mathematical foundations that may be beyond the reach of the average undergraduate student. A course focusing on creative AI has been developed to introduce deep learning from the practitioner perspective. Specifically, the application of deep learning in human creative activities, such as in art, music, and multimedia, forms the basis of our educational approach. The major topics of this course are presented, along with the software tools being utilized. Sample laboratory assignments and projects are introduced, and the results of measuring various student outcomes across two semesters are presented. Overall, the use of deep learning in applications mimicking human creativity can serve the purposes of getting students excited about machine learning and also help enhance their core knowledge of programming and the utilization of standard library tools.
Distributed Compression of Correlated Classical-Quantum Sources

We resume the investigation of the problem of independent local compression of correlated quantum sources, the classical case of which is covered by the celebrated Slepian-Wolf theorem. We focus specifically on classical-quantum (cq) sources, for which one edge of the rate region, corresponding to the compression of the classical part, using the quantum part as side information at the decoder, was previously determined by Devetak and Winter [Phys. Rev. A 68, 042301 (2003)]. Whereas the Devetak-Winter protocol attains a rate-sum equal to the von Neumann entropy of the joint source, here we show that the full rate region is much more complex, due to the partially quantum nature of the source. In particular, in the opposite case of compressing the quantum part of the source, using the classical part as side information at the decoder, typically the rate sum is strictly larger than the von Neumann entropy of the total source.We determine the full rate region in the generic case, showing that, apart from the Devetak-Winter point, all other points in the achievable region have a rate sum strictly larger than the joint entropy. We can interpret the difference as the price paid for the quantum encoder being ignorant of the classical side information. In the general case, we give an achievable rate region, via protocols that are built on the decoupling principle, and the principles of quantum state merging and quantum state redistribution. Our achievable region is matched almost by a single-letter converse, which however still involves asymptotic errors and an unbounded auxiliary system.
Towards Designing and Performance Analysis of Evolving Higher Order Neural Networks for Modeling and Forecasting Exchange Rate Time Series Data

Achieving improved prediction accuracy with minimal input data and computationally less complex model is a challenge in financial time series forecasting research. Constructing the model from training data and evaluate it on test data is a common methodology which requires lots of human interventions. This paper developed three evolving higher order neural networks (EHONN) and evaluated their performances on modeling and forecasting five exchange rate time series. A Pi-Sigma neural network (PSNN) is used as base model. The optimal architectures of PSNN are evolved on fly with three evolutionary learning methods, i.e. genetic algorithm (GA), differential evolution (DE), and particle swarm optimization (PSO), therefore forming three hybrid models. The single layer tunable weight and biases of PSNN contributed less complexity to the hybrid models. The models are evaluated on five real exchange rate datasets, compared with other state-of-art models trained similarly and found better. Further, statistical test are conducted to justify the significance of the proposed models.
Classification of Online Medical Discourse by Modified Co-Training

Online health communities have become an important resource that both patients and family members rely on to gain medical information and interact with contributors and experts outside the clinical setting. However, few studies have focused on analysing sentiment in medical forum discourse. Analysing sentiment expressed by members of a health community in medical forum discourse can be of significant value, such as by identifying characteristics of an information space, determining themes that predominate among a large dataset, and allowing people to summarize topics within a big data set. In this paper, we identify sentiment expressed in online medical forums discussing Lyme Disease and lupus. In many sentiment analysis applications, the labelled training datasets are fairly expensive to obtain, whereas unlabelled datasets are readily available. Therefore, we propose an adaptation of a well-known semi-supervised learning technique, in which co-training is implemented by combining labelled and unlabelled data. We use domain-independent and domain-dependent sentiment features as two distinct sets of views of a post and utilize them in order to identify the multi-class sentiment expressed using an adaptive co-training model. The results of our experiments demonstrate the effectiveness of the proposed approach.
A Day in the Life: Personas of Professional Communicators at Work

This article uses personas to illustrate the range of technical communication knowledge work developed through its practitioners—to articulate the functions, characteristics, traits, skills, and workplace styles of positions someone in the field might pursue. Recent research has provided valuable data about the expanding and evolving skill sets of the technical/professional communicator. We build on that by triangulating the data from an analysis of job postings, a survey of technical communicators, and interviews and embedded observations of practitioners to develop personas of technical/professional communicators on the job. The personas can help students, programs, and practitioners understand and navigate the many types of roles that are available in the field.
Multiple Perspective Answer Reranking for Multi-passage Reading Comprehension

This study focuses on multi-passage Machine Reading Comprehension (MRC) task. Prior work has shown that retriever, reader pipeline model could improve overall performance. However, the pipeline model relies heavily on retriever component since inferior retrieved documents would significantly degrade the performance. In this study, we proposed a new multi-perspective answer reranking technique that considers all documents to verify the confidence of candidate answers; such nuanced technique can carefully distinguish candidate answers to improve performance. Specifically, we rearrange the order of traditional pipeline model and make a posterior answer reranking instead of prior passage reranking. In addition, new proposed pre-trained language model BERT is also introduced here. Experiments with Chinese multi-passage dataset DuReader show that our model achieves competitive performance.
Proposing a Pareto-VIKOR Ranking Method for Enhancing Parallel Coordinates Visualization

Data visualization is an essential step in data science to get better interpretation to analyse data. The parallel coordinates plot (PCP) is a well-known method to visualize high-dimensional (D > 3) data without dimension reduction. In large-scale datasest, PCP may fail because of many clutters and crossing lines in the plot. The order of coordinates is one of the parameters in PCP which can affect on the performance of this method. Finding the best order can be considered as a multi-criteria comparison task based on different metrics such as minimizing the number of crossing lines between adjacent coordinates and the maximizing the pairwise correlation coefcient values. In order to improve the visualization of data using PCP, this paper presents a multi-metric Pareto-VIKOR ranking (PVRPCP), a new method which determines the best order of coordinates based on optimizing two or more metrics. The method consists of evaluating all possible coordinates permutations based on evaluation metrics and applying non-dominated sorting algorithm (NDS) to obtain the Pareto-front ranks (PF). The solutions on each Pareto front are then ranked by VIKOR, a multi-criteria decision making measure. In order to evaluate the effectiveness of the the proposed method in data visualization, we also designed several multi-dimensional benchmarks to represent the effect of ordering in PCP. In addition to author-created benchmarks, several multi-objective function benchmarks and real-world datasets are utilized to evaluate the proposed method. The experimental results show that the PVRPCP offers improved PCP visualization compared to the original order in terms of both utilized metrics.
Financial Services: The Largest Blockchain Market

Blockchain technology could be used in the insurance, payments, and financial services industries to store property records, clear and settle accounts, and ensure the validity and execution of contractual arrangements. Blockchain enables contracts to be executed when special conditions are met or when a financial instrument meets a certain benchmark.
Parallel computation of the Burrows Wheeler Transform in compact space

The Burrows-Wheeler Transform (BWT) has become since its introduction a key tool for representing large text collections in compressed space while supporting indexed searching: on a text of length n over an alphabet of size σ, it requires O(nlg⁡σ) bits of space, instead of the O(nlg⁡n) bits required by classical indexes. A challenge for its adoption is to build it within O(nlg⁡σ) bits as well. There are some sequential algorithms building it within this space, but no such a parallel algorithm. In this paper we present a PRAM CREW algorithm to build the BWT using O(nlg⁡n) work, O(lg3⁡n/lg⁡σ) depth, and O(nlg⁡σ) bits.
Linguistic Feature Representation with Statistical Relational Learning for Readability Assessment

Traditional NLP model for readability assessment represents document as vector of words or vector of linguistic features that may be sparse, discrete, and ignoring the latent relations among features. We observe from data and linguistics theory that a document’s linguistic features are not necessarily conditionally independent. To capture the latent relations among linguistic features, we propose to build feature graphs and learn distributed representation with Statistical Relational Learning. We then project the document vectors onto the linguistic feature embedding space to produce linguistic feature knowledge-enriched document representation. We showcase this idea with Chinese L1 readability classification experiments and achieve positive results. Our proposed model performs better than traditional vector space models and other embedding based models for current data set and deserves further exploration.
Operational Lessons from Chameleon

Chameleon is a large-scale, deeply reconfigurable testbed built to support Computer Science experimentation. Unlike traditional systems of this kind, Chameleon has been configured using an adaptation of a mainstream open source infrastructure cloud system called OpenStack. In this paper, we discuss operational challenges for experimental testbeds and explain what impact they have on the profile of the operating team. We then discuss methods we developed to alleviate the operational burden and show how they can be used in practice. We conclude with a discussion of our interaction with the user community and describe how experimental platforms create a high potential for community involvement.
Instruments of Articulation

Building on the first author's hybrid/augmented violin practice and the second author's work with responsive media environments, we build and reflect on collectively-played room-scale instruments that afford the precision and nuance of an individually-played real-time gestural media system. We consider gestural instruments designed for the interplay of action and perception at the sensorimotor level bypassing tokenization of features of activity and sensors. Our gesturally-modulated media instruments are based not on models or a priori schemata but driven by continuous adaptation to contingent activity and state of the event, as well as compositional intent. We think of such performable, expressive systems as instruments of articulation rather than of representation. Our work is motivated by a progression from phenomenological interpretations of individually-played instruments through non-anthropocentric notions of lived experience, to ecosystemic approaches to ensembles of real-time instruments, people and processes concurrently co-articulating an event.
RDMA Load Balancing via Data Partition

With the development of data center networks, traditional TCP/IP cannot support the demand in data centers. Remote Direct Memory Access (RDMA) technology could improve the performance of DCN significantly because of high throughput and low latency. However, load balancing is a key issue in RDMA which has not been solved distribute. This paper will propose an algorithm to solve the load balance problem in RDMA on application layer without hardware changing. The main idea is to divide data to chunks and data chunks on multiple reachable paths for transmission. However, it is no trivial to find the optimal chunk size and the path number, some empirical values are found by varieties of experiments and tests. Moreover, the chunk allocation scheme also needs to consider the traffic condition in DCNs to find more free paths to transmit. We evaluate the algorithm in application layer with ns3 simulator. The experiment results show that with our algorithm the completion time can decrease 81.03% at most.