Reviewerly/exHarmony huggingface dataset:
Hadoop MapReduce for Parallel Genetic Algorithm to Solve Traveling Salesman Problem

Achieving an optimal solution for NP-complete problems is a big challenge nowadays.The paper deals with the Traveling Salesman Problem (TSP) one of the most important combinatorial optimization problems in this class.We investigated the Parallel Genetic Algorithm to solve TSP.We proposed a general platform based on Hadoop MapReduce approach for implementing parallel genetic algorithms.Two versions of parallel genetic algorithms (PGA) are implemented, a Parallel Genetic Algorithm with Islands Model (IPGA) and a new model named an Elite Parallel Genetic Algorithm using MapReduce (EPGA) which improve the population diversity of the IPGA.The two PGAs and the sequential version of the algorithm (SGA) were compared in terms of quality of solutions, execution time, speedup and Hadoop overhead.The experimental study revealed that both PGA models outperform the SGA in terms of execution time, solution quality when the problem size is increased.The computational results show that the EPGA model outperforms the IPGA in term of solution quality with almost similar running time for all the considered datasets and clusters.Genetic Algorithms with MapReduce platform provide better performance for solving large-scale problems.
A Supervised Learning Model for the Automatic Assessment of Language Levels Based on Learner Errors

This paper focuses on the use of technology in language learning. Language training requires the need to group learners homogeneously and to provide them with instant feedback on their productions such as errors [8, 15, 17] or proficiency levels. A possible approach is to assess writings from students and assign them with a level. This paper analyses the possibility of automatically predicting Common European Framework of Reference (CEFR) language levels on the basis of manually annotated errors in a written learner corpus [9, 11]. The research question is to evaluate the predictive power of errors in terms of levels and to identify which error types appear to be criterial features in determining interlanguage stages. Results show that specific errors such as punctuation, spelling and verb tense are significant at specific CEFR levels.
Silicon Photonics for Quantum Communication

Silicon quantum photonics, capable to integrate large numbers of optical components with CMOS-compatible fabrication technology and reliable control of quantum states, is expected to play a critical role in future quantum communication. In this talk, we will introduce our recent results of silicon photonics for quantum communication, including high-dimensional silicon chip-to-chip quantum key distribution, chip-to-chip quantum teleportation, on-chip generation of high-dimensional quantum entanglement, and Hong-Ou-Mandel interference between two different III-V/silicon hybrid lasers towards future measurement device independent quantum communication.
Multi-Level Fine-Grained Interactions for Collaborative Filtering

In recent years, review-based collaborative filtering (CF) has been extensively studied, which is an combination between natural language processing (NLP) and recommender systems. The core pattern behind CF is to first model user and item, and then adopts a relatively primitive interaction between them for personalized recommendation. This pattern is very similar to the issue of sequence matching in NLP, where sequence 1 and sequence 2 are matched with a fine-grained interaction leading to a better result. Therefore, there is a tremendous room for further improvement in current review-based CF to release the power of fine-grained interaction. To this end, we treat the user review set and item review set as two sequences, and design a multi-level matching attention layer for fine-grained interaction. In addition, we devise the aspect-level and review-level attention to measure the contribution of each review. Extensive experiments on 24 public datasets show that the proposed model consistently outperforms the state-of-the-art approaches. More importantly, by selecting the relevant reviews according to the aspect attention score and review attention score, we can observe which specific item aspects that user mainly concerned and which item characteristic highly matched with the user preference, in which the recommendation interpretability can be enhanced.
Model analytics for defect prediction based on design-level metrics and sampling techniques

Predicting software defects in the early stages of the software development life cycle, such as the design and requirement analysis phase, provides significant economic advantages for software companies. Model analytics for defect prediction lets quality assurance groups build prediction models earlier and predict the defect-prone components before the testing phase for in-depth testing. In this study, we demonstrate that Machine Learning-based defect prediction models using design-level metrics in conjunction with data sampling techniques are effective in finding software defects. We show that design-level attributes have a strong correlation with the probability of defects and the SMOTE data sampling approach improves the performance of prediction models. When design-level metrics are applied, the Adaboost ensemble method provides the best performance to detect the minority class samples.
Fake Review Detection using Principal Component Analysis and Active Learning

E-commerce proved its importance based on the fact where time is the essence.People are relying on e-commerce more than before.With e-commerce comes a huge amount of user feedback based on the products they buy.As the internet has become cheaper and easy to get, more people are getting connected through different social media and platform where they are expressing product-related feedbacks.With the rise of e-commerce, people are relying more on product reviews to get a clear view and user experience.But there is no convincing way to authenticate the reviews posted on products on ecommerce websites.To generate more revenue and fulfill some immoral benefits, some sellers are making investments and hiring people to post fake reviews.These fake reviews are generated to convince people to buy the product.To detect these fake reviews, several methodologies were introduced.Most of the models are supervised models which rely on pseudo fake reviews or large scale labeled dataset.In this paper, a model has been proposed with a new technique which combines two different types of learning methods (active and supervised) by creating a manually labeled dataset.This model has 4 different filtering phases that are based on TF-IDF, Countvectorizer and n-gram features of the review content and then Principal Component Analysis to reduce the feature set.It achieves a very encouraging result while working on 2000 reviews from Amazon.In the best case precision, recall, and fscore are slightly above 91% and the accuracy achieved is up to 90%.After comparing the results with similar successful methods where PCA is used as a feature selection technique, it is quite clear that the proposed model is efficient and encouraging.
Communicating Arsenic’s Risks

Arsenic is a naturally occurring toxic metalloid that has many human health implications. Its strong prevalence in the bedrock and thus much of the well water in New England puts many private well owners at risk. It is also found in food products, particularly those that contain rice. Despite the documented health risks, arsenic is not high on the list of concerns for residents of the region. This study will describe two types of environmental communication efforts that have been undertaken by the Dartmouth Toxic Metals Superfund Research Program (DTMSRP)-the development and evaluation of a comprehensive website, Arsenic and You, and a mental models research approach to better understand the disconnect between expert and community perceptions of arsenic risk. We find that there are knowledge gaps between the two, particularly regarding the origin of arsenic in drinking water and food, the necessity of testing well water, and the process for treating water that is above recommended limits. Moreover, the mental models approach provides a structured framework for better understanding these gaps. A website can address some of these disconnects, and it is important to have a "one-stop shop" for vetted information on the risks and steps to reduce exposure.
Human Behavior Recognition Based on Motion Data Analysis

The development of sensor technologies and smart devices has made it possible to realize real-time data acquisition of human beings. Human behavior monitoring is the process of obtaining activity information with wearables and computer technology. In this paper, we design a data preprocessing method based on the data collected by a single three-axis accelerometer. We first use Butterworth filter as low-pass filtering to remove the noise. Then, we propose a KGA algorithm to remove abnormal data and smooth them at the same time. This method uses genetic algorithm to optimize the parameters of Kalman filter. After that, we use a threshold-based method to identify falls that are harmful to the elderly. The key point of this method is to distinguish falls from people’s daily activities. According to the characteristics of human falls, we extract eigenvalues that can effectively distinguish daily activities from falls. In addition, we use cross-validation to determine the threshold of the method. The results show that in the analysis of 11 kinds of human daily activities and 15 types of falls, our method can distinguish 15 types of falls. The recognition recall rate in our method reaches 99.1%.
Purity-Based Continuity Bounds for von Neumann Entropy

Abstract We propose continuity bounds for the von Neumann entropy of qubits whose difference in purity is bounded. Considering the purity difference of two qubits to capture the notion of distance between them results into bounds which are demonstrably tighter than the trace distance-based existing continuity bounds of quantum states. Continuity bounds can be utilized in bounding the information-theoretic quantities which are generally difficult to compute.
Explicit Motion Risk Representation

This paper presents a formal definition and explicit representation of robot motion risk. Currently, robot motion risk has not been formally defined, but has already been used in motion and path planning. Risk is either implicitly represented as model uncertainty using probabilistic approaches, where the definition of risk is somewhat avoided, or explicitly modeled as a simple function of states, without a formal definition. In this work, we provide formal reasoning behind what risk is for robot motion and propose a formal definition of risk in terms of a sequence of motion, namely path. Mathematical approaches to represent motion risk are also presented, which is in accordance with our risk definition and properties. The definition and representation of risk provide a meaningful way to evaluate or construct robot motion or path plans. The understanding of risk is even of greater interest for the search and rescue community: the deconstructed environments cast extra risk onto the robot, since they are working under extreme conditions. A proper risk representation has the potential to reduce robot failure in the field.
A Look at Multiplicity Distributions via Modified Combinants

The experimentally measured multiplicity distributions exhibit, after a closer inspection, the peculiarly enhanced void probability and the oscillatory behavior of modified combinants. We show that both these features can be used as additional sources of information, not yet fully explored, on the mechanism of multiparticle production. We provide their theoretical understanding within the class of compound distributions.
Optimization of the classical interference visibility of an asymmetric Mach–Zehnder interferometer based on planar lightwave circuit technology

Silicon-based devices offer great potential for quantum key distribution (QKD) with the benefits of miniaturization, scalability, complexity, and improved performance. Based on the planar lightwave circuit technology, a 200&#x00A0;ps delay-time asymmetric Mach&#x2013;Zehnder interferometer (AMZI) decoding chip was designed and fabricated that can adjust the power ratio of the two arms by introducing a variable beam splitter. The measured delay time is approximately 200&#x00A0;ps, and the two output pulses&#x2019; peaks can be balanced by loading an appropriate voltage on the variable beam splitter. The classical interference visibility of the AMZI versus temperature was studied, and it is highly temperature dependent. The interference visibility can reach as high as 99.72% under appropriate temperature control. This AMZI can act as a passive decoder in fiber-based QKD systems.
Correction to: Could stock hedge Bitcoin risk(s) and vice versa?

There were some inconsistencies in Table 4, 5, and 7 in the online PDF. The corrected tables are given below. The original article has been corrected.
Ensampling Data Prediction using Sparse Data in Mobile Intelligent System

&lt;p&gt;Rain is an important source of water. The Indian economy is heavily dependent on agriculture and the livelihood of Indian farmer largely depends on rains. The farms are more dependent on rainfall than any other water resource. As we observe there is a lot of climate change in recent years due to industrialization, because of these climate changes there are a lot of floods and droughts. So, it might be helpful to predict the rainfall beforehand and take necessary precautions to protect crops and other sorts of damages that might occur due to the irregular rainfall. So, we present a model that could reasonably predict the future rainfall using very fewer variables. The reason why we developed a model which uses sparse data is that sometimes it could be hard to obtain a large amount of data due to lack or improper working of recording equipment and so on. So, it will be good to have a working model in those situations and applications are implemented.This paper affords a overview of system learning and offers a brief take a look at on distinctive machine gaining knowledge of strategies together with their programs on mobile devices. It also affords an outline of overall performance-associated parameters of gadget studying techniques useful for mobile devices.&lt;/p&gt;
A Fast and Furious Bayesian Network and Its Application of Identifying Colon Cancer to Liver Metastasis Gene Regulatory Networks

Bayesian networks is a powerful method for identifying causal relationships among variables. However, as the network size increases, the time complexity of searching the optimal structure grows exponentially. We proposed a novel search algorithm - Fast and Furious Bayesian Network (FFBN). Compared to the existing greedy search algorithm, FFBN uses significantly fewer model configuration rules to determine the causal direction of edges when constructing the Bayesian network, which leads to greatly improved computational speed. We benchmarked the performance of FFBN by reconstructing gene regulatory networks (GRNs) from two DREAM5 challenge datasets: a synthetic dataset and a larger yeast transcriptome dataset. In both datasets, FFBN shows a much faster speed than the existing greedy search algorithm, while maintaining equally good or better performance in recall and precision. We then constructed three whole transcriptome GRNs for primary liver cancer (PL), primary colon cancer (PC) and colon to liver metastasis (CLM) expression data, which the existing greedy search algorithms failed. Three GRNs contain 12,099 common genes. Unprecedentedly, our newly developed FFBN algorithm is able to build up GRNs at a scale larger than 10,000 genes. Using FFBN, we discovered that CLM has its unique cancer molecular mechanisms and shares a certain degree of similarity with both PL and PC.
Parameterized Intractability of Even Set and Shortest Vector Problem

The <?TeX $k$?> -Even Set problem is a parameterized variant of the Minimum Distance Problem of linear codes over <?TeX $\mathbb {F}_2$?> , which can be stated as follows: given a generator matrix <?TeX $\mathbf {A}$?> and an integer <?TeX $k$?> , determine whether the code generated by <?TeX $\mathbf {A}$?> has distance at most <?TeX $k$?> , or, in other words, whether there is a nonzero vector <?TeX ${\mathbf {x}}$?> such that <?TeX $\mathbf {A} {\mathbf {x}}$?> has at most <?TeX $k$?> nonzero coordinates. The question of whether <?TeX $k$?> -Even Set is fixed parameter tractable (FPT) parameterized by the distance <?TeX $k$?> has been repeatedly raised in the literature; in fact, it is one of the few remaining open questions from the seminal book of Downey and Fellows [1999]. In this work, we show that <?TeX $k$?> -Even Set is W [1]-hard under randomized reductions. We also consider the parameterized <?TeX $k$?> -Shortest Vector Problem (SVP) , in which we are given a lattice whose basis vectors are integral and an integer <?TeX $k$?> , and the goal is to determine whether the norm of the shortest vector (in the <?TeX $\ell _p$?> norm for some fixed <?TeX $p$?> ) is at most <?TeX $k$?> . Similar to <?TeX $k$?> -Even Set, understanding the complexity of this problem is also a long-standing open question in the field of Parameterized Complexity. We show that, for any <?TeX $p \gt 1$?> , <?TeX $k$?> -SVP is W [1]-hard to approximate (under randomized reductions) to some constant factor.
Unsupervised Evaluation of Human Translation Quality

Even though machine translation (MT) systems have reached impressive performances in cross-lingual translation tasks, the quality of MT is still far behind professional human translations (HTs) due to the complexity in natural languages, especially for terminologies in different domains. Therefore, HTs are still widely demanded in practice. However, the quality of HT is also imperfect and vary significantly depending on the experience and knowledge of the translators. Evaluating the quality of HT in an automatic manner has faced many challenges. Although bilingual speakers are able to assess the translation quality, manually checking the accuracy of translations is expensive and time-consuming. In this paper, we propose an unsupervised method to evaluate the quality of HT without requiring any labelled data. We compare a range of methods for automatically grading HTs and observe the Bidirectional Minimum Word Mover’s distance (BiMWMD) to produce gradings that correlate well with humans.
Analysis of Error Dependencies on Newhope

Among many submissions to NIST post-quantum cryptography (PQC) project, NewHope is a promising key encapsulation mechanism (KEM) based on the Ring-Learning with errors (Ring-LWE) problem. Since NewHope is an indistinguishability (IND)-chosen ciphertext attack secure KEM by applying the Fujisaki-Okamoto transform to an IND-chosen plaintext attack secure public-key encryption, accurate calculation of decryption failure rate (DFR) is required to guarantee resilience against attacks that exploit decryption failures. However, the current upper bound (UB) on DFR of NewHope is rather loose because the compression noise, the effect of encoding/decoding of NewHope, and the approximation effect of centered binomial distribution are not fully considered. Furthermore, since NewHope is a Ring-LWE based cryptography, there is a problem of error dependency among error coefficients, which makes accurate DFR calculation difficult. In this paper, we derive much tighter UB on DFR than the current UB by using constraint relaxation and union bound. Especially, the above-mentioned factors are all considered in the derivation of new UB and the centered binomial distribution is not approximated. Since the error dependency is also considered, the new UB is much closer to the real DFR than the current UB. Furthermore, the new UB is parameterized by using Chernoff-Cramer bound to facilitate the calculation of new UB for the parameters of NewHope. Since the new UB is much lower than the DFR requirement of PQC, this DFR margin can be used to improve NewHope. As a result, the security level and bandwidth efficiency of NewHope are improved by 7.2 % and 5.9 %, respectively.
Metadata Documentation Practices at ARL Institutional Repositories

This paper describes an investigation into whether institutional repositories at member libraries of the Association of Research Libraries (ARL) provide publicly available metadata documentation via the repositories' websites. The study compares the results of a September 2017 survey with findings from a review of ARL institutional repository websites. Slightly more than half of ARL institutional repositories do provide metadata documentation. Implications of the results are discussed, including specific research questions for moving forward. A call to reimagine documentation as a tool of transparency is delivered.
The Significant Effect of Parameter Tuning on Software Vulnerability Prediction Models

Vulnerability prediction is one of the critical issues for researchers in the software industry. Technically, a vulnerability predictor is a machine learning model trained to identify vulnerable and non-vulnerable modules. Recent studies have shown that the performance of these models can be affected when the default parameter settings are used. Unfortunately, most studies in literature present their results using the default parameter settings. This study investigates the extent to which parameter optimization affect the performance of vulnerability prediction models. To evaluate our procedure, we conducted an empirical study on three open-source vulnerability datasets, namely Drupal, Moodle and PHPMyAdmin using five machine learning algorithms. Surprisingly, we found that in all cases of the 3 datasets studied, our models provided a significant increase in precision and accuracy against the benchmark study. In conclusion, software engineers can use the results obtained from this study when building data miners for identifying vulnerable modules.
One-Hot Residue Logarithmic Number Systems

Switching behavior and dynamic power consumption of arithmetic circuits are influenced by the distribution of operands as well as the number system used to encode them. Binary integer encoding may cause severe switching fluctuation; the integer Residue Number System (RNS) reduces this by breaking the integer into smaller moduli, which in turn may use either binary or one-hot encoding. One-hot switching is nearly consistent regardless of operand distribution, but this comes at the cost of increased bit width. Reals are represented by mapping integers, such as well-known examples of fixed point and Floating Point (FP). A more unusual system is the Logarithmic Number System (LNS) that takes the logarithm of the absolute value of the real (its sign is encoded separately) into an integer. Combining one-hot, RNS and LNS offers real arithmetic circuits with nearly uniform switching at the cost of some complexity in word size, addition, conversion and sign-detection.