Reviewerly/exHarmony huggingface dataset:
Requirement Engineering Challenges in Agile Software Development

Agile software development has large success rate due to its benefits and promising nature but natively where the size of the project is small. Requirement engineering (RE) is crucial as in each software development life cycle, “Requirements” play a vital role. Though agile provides values to customer’s business needs, changing requirement, and interaction, we also have to face impediments in agile, many of which are related to requirement challenges. This article aims to find out the challenges being faced during requirement engineering of agile projects. Many research studies have been conducted on requirement challenges which are somehow biased, no suggestions are given to improve the agile development process, and the research does not highlight large-scale agile development challenges. Hence, this article covers all the challenges discussed above and presents a comprehensive overview of agile models from requirement engineering perspective. The findings and results can be very helpful for software industry to improve development process as well as for researchers who want to work further in this direction.
The Beta distributed PSO, β-PSO, with application to Inverse Kinematics

this paper introduced a new Particle swarm optimization, PSO, variant where Beta profiles are used to manage exploration and exploitation behaviors of the swarm. A key issue in PSO is that it is missing a clear separation between the exploration behavior and the exploitation behavior of the swarm. In Beta-PSO exploration/ exploitations phases are clearly identified and particles evolve differently in each phase. A couple of beta distributions are used to control both phases. In the exploration phase, particles evolve based on a pseudo-normal beta profile, while in the exploitation phase a decreasing exponential beta distribution is used. The proposed method is applied to solve the inverse kinematic problem of a 6 axes generic robot arm. Results showed that the quadratic error of Beta-PSO was about 5.5 e-17, while QPSO solutions were at the level of 5.6e-l0; SSA returned 6.6 e-8 error and classical PSO was at about 1.7 e-3. Those results were confirmed with the Wilcoxon non parametric comparison which clearly showed that Beta-PSO is better than the classical PSO, quantum PSO, and SSA for this application.
AraStance: A Multi-Country and Multi-Domain Dataset of Arabic Stance Detection for Fact Checking

With the continuing spread of misinformation and disinformation online, it is of increasing importance to develop combating mechanisms at scale in the form of automated systems that support multiple languages.One task of interest is claim veracity prediction, which can be addressed using stance detection with respect to relevant documents retrieved online.To this end, we present our new Arabic Stance Detection dataset (AraStance) of 4,063 claimarticle pairs from a diverse set of sources comprising three fact-checking websites and one news website.AraStance covers false and true claims from multiple domains (e.g., politics, sports, health) and several Arab countries, and it is well-balanced between related and unrelated documents with respect to the claims.We benchmark AraStance, along with two other stance detection datasets, using a number of BERT-based models.Our best model achieves an accuracy of 85% and a macro F1 score of 78%, which leaves room for improvement and reflects the challenging nature of AraStance and the task of stance detection in general.
Pruning-then-Expanding Model for Domain Adaptation of Neural Machine Translation

Domain Adaptation is widely used in practical applications of neural machine translation, which aims to achieve good performance on both general domain and in-domain data.However, the existing methods for domain adaptation usually suffer from catastrophic forgetting, large domain divergence, and model explosion.To address these three problems, we propose a method of "divide and conquer" which is based on the importance of neurons or parameters for the translation model.In this method, we first prune the model and only keep the important neurons or parameters, making them responsible for both generaldomain and in-domain translation.Then we further train the pruned model supervised by the original whole model with knowledge distillation.Last we expand the model to the original size and fine-tune the added parameters for the in-domain translation.We conducted experiments on different language pairs and domains and the results show that our method can achieve significant improvements compared with several strong baselines.
Unsupervised Opinion Summarization with Content Planning

The recent success of deep learning techniques for abstractive summarization is predicated on the availability of large-scale datasets. When summarizing reviews (e.g., for products or movies), such training data is neither available nor can be easily sourced, motivating the development of methods which rely on synthetic datasets for supervised training. We show that explicitly incorporating content planning in a summarization model not only yields output of higher quality, but also allows the creation of synthetic datasets which are more natural, resembling real world document-summary pairs. Our content plans take the form of aspect and sentiment distributions which we induce from data without access to expensive annotations. Synthetic datasets are created by sampling pseudo-reviews from a Dirichlet distribution parametrized by our content planner, while our model generates summaries based on input reviews and induced content plans. Experimental results on three domains show that our approach outperforms competitive models in generating informative, coherent, and fluent summaries that capture opinion consensus.
Automatic question generation based on sentence structure analysis using machine learning approach

Automatic question generation is one of the most challenging tasks of Natural Language Processing. It requires "bidirectional" language processing: firstly, the system has to understand the input text (Natural Language Understanding) and it then has to generate questions also in the form of text (Natural Language Generation). In this article, we introduce our framework for generating the factual questions from unstructured text in the English language. It uses a combination of traditional linguistic approaches based on sentence patterns with several machine learning methods. We firstly obtain lexical, syntactic and semantic information from an input text and we then construct a hierarchical set of patterns for each sentence. The set of features is extracted from the patterns and it is then used for automated learning of new transformation rules. Our learning process is totally data-driven because the transformation rules are obtained from a set of initial sentence-question pairs. The advantages of this approach lie in a simple expansion of new transformation rules which allows us to generate various types of questions and also in the continuous improvement of the system by reinforcement learning. The framework also includes a question evaluation module which estimates the quality of generated questions. It serves as a filter for selecting the best questions and eliminating incorrect ones or duplicates. We have performed several experiments to evaluate the correctness of generated questions and we have also compared our system with several state-of-the-art systems. Our results indicate that the quality of generated questions outperforms the state-of-the-art systems and our questions are also comparable to questions created by humans. We have also created and published an interface with all created datasets and evaluated questions, so it is possible to follow up on our work.
NeurST: Neural Speech Translation Toolkit

NeurST is an open-source toolkit for neural speech translation.The toolkit mainly focuses on end-to-end speech translation, which is easy to use, modify, and extend to advanced speech translation research and products.NeurST aims at facilitating the speech translation research for NLP researchers and building reliable benchmarks for this field.It provides step-by-step recipes for feature extraction, data preprocessing, distributed training, and evaluation.In this paper, we will introduce the framework design of NeurST and show experimental results for different benchmark datasets, which can be regarded as reliable baselines for future research.The toolkit is publicly available at
The binomial of interaction and visualization in digital news media: consolidation, standardization and future challenges

Interaction and visualization together yield an interesting, fruitful, and promising combination for producing content in digital news media. In an era in which the press no longer exclusively provides the news, interaction and visualization combined in innovative products for the public are powerful value propositions for the media. Together, they are capable of winning readers’ loyalty and engagement, both of which are crucial for the media’s sustainability. In this work, we present a review of the literature and formulate the theoretical bases for this binomial pairing and its main components, which, we argue, should be available to citizens, the interests of whom journalism must defend if it aspires to be viable.
ERR is not C/W/L: Exploring the Relationship Between Expected Reciprocal Rank and Other Metrics

We explore the relationship between expected reciprocal rank (ERR) and the metrics that are available under the C/W/L framework. On the surface, it appears that the user browsing model associated with ERR can be directly injected into a C/W/L arrangement, to produce system measurements equivalent to those generated from ERR. That assumption is now known to be invalid, and demonstration of the impossibility of ERR being described via C/W/L choices forms the first part of our work. Given that ERR cannot be accommodated within the C/W/L framework, we then explore the extent to which practical use of ERR correlates with metrics that do fit within the C/W/L user browsing model. In this part of the investigation we present a range of shallow-evaluation C/W/L variants that have very high correlation with ERR when compared in experiments involving a large number of TREC runs. That is, while ERR itself is not a C/W/L metric, there are other weighted-precision computations that fit with the user model assumed by C/W/L, and yield system comparisons almost indistinguishable from those generated via the use of ERR.
Target Search Algorithm for AUV Based on Real-Time Perception Maps in Unknown Environment

For the problem of AUV target searches in unknown underwater environments, a target search algorithm for AUVs based on a real-time perception map is proposed. Real-time perception maps, including target existence probability maps, uncertainty maps, and pheromone maps and their updating rules, are established. Attraction source maps and search status maps based on the environmental information detected by the AUV are established. The maps are used for the AUV to search for corner areas that are unknown to a high degree and areas with low coverage around the current location. At the same time, a release mechanism for attraction and revisiting pheromones is established by combining a neural excitation network algorithm to make the gradient spread in the pheromone grid map. By setting up a search revenue function based on real-time perception maps, an AUV search decision-making method is established. When the AUV finds a suspected target, the AUV approaches the suspected target. The path planning of the AUV is carried out through an improved artificial potential field method. The short-distance confirmation of the target and obstacle avoidance in the search process are realized. The simulation results show that the algorithm has high search efficiency. Additionally, when the target exists in a corner area, the probability of the AUV to quickly search for the target is fast and feasible.
New quantum codes from skew constacyclic codes

For an odd prime p and positive integers m and , let F p m be the finite field with p m elements andThus R ,m is a finite commutative non-chain ring of order p 2 m with characteristic p.In this paper, we aim to construct quantum codes from skew constacyclic codes over R ,m .First, we discuss the structures of skew constacyclic codes and determine their Euclidean dual codes.Then a relation between these codes and their Euclidean duals has been obtained.Finally, with the help of a duality-preserving Gray map and the CSS construction, many MDS and better non-binary quantum codes are obtained as compared to the best-known quantum codes available in the literature.
Just Noticeable Difference for Deep Machine Vision

As an important perceptual characteristic of the Human Visual System (HVS), the Just Noticeable Difference (JND) has been studied for decades with image and video processing (e.g., perceptual visual signal compression). However, there is little exploration on the existence of JND for the Deep Machine Vision (DMV), although the DMV has made great strides in many machine vision tasks. In this paper, we take an initial attempt, and demonstrate that the DMV has the JND, termed as the DMV-JND. We then propose a JND model for the image classification task in the DMV. It has been discovered that the DMV can tolerate distorted images with average PSNR of only 9.56dB (the lower the better), by generating JND via unsupervised learning with the proposed DMV-JND-NET. In particular, a semantic-guided redundancy assessment strategy is designed to restrain the magnitude and spatial distribution of the DMV-JND. Experimental results on image classification demonstrate that we successfully find the JND for deep machine vision. Our DMV-JND facilitates a possible direction for DMV-oriented image and video compression, watermarking, quality assessment, deep neural network security, and so on.
Learning Nondeterministic Real-Time Automata

We present an active learning algorithm named NRTALearning for nondeterministic real-time automata (NRTAs). Real-time automata (RTAs) are a subclass of timed automata with only one clock which resets at each transition. First, we prove the corresponding Myhill-Nerode theorem for real-time languages. Then we show that there exists a unique minimal deterministic real-time automaton (DRTA) recognizing a given real-time language, but the same does not hold for NRTAs. We thus define a special kind of NRTAs, named residual real-time automata (RRTAs), and prove that there exists a minimal RRTA to recognize any given real-time language. This transforms the learning problem of NRTAs to the learning problem of RRTAs. After describing the learning algorithm in detail, we prove its correctness and polynomial complexity. In addition, based on the corresponding Myhill-Nerode theorem, we extend the existing active learning algorithm NL* for nondeterministic finite automata to learn RRTAs. We evaluate and compare the two algorithms on two benchmarks consisting of randomly generated NRTAs and rational regular expressions. The results show that NRTALearning generally performs fewer membership queries and more equivalence queries than the extended NL* algorithm, and the learnt NRTAs have much fewer locations than the corresponding minimal DRTAs. We also conduct a case study using a model of scheduling of final testing of integrated circuits.
Short Text Mining for Classifying Educational Objectives and Outcomes

Most of the international accreditation bodies in engineering education (e.g., ABET) and outcome-based educational systems have based their assessments on learning outcomes and program educational objectives. However, mapping program educational objectives (PEOs) to student outcomes (SOs) is a challenging and time-consuming task, especially for a new program which is applying for ABET-EAC (American Board for Engineering and Technology the American Board for Engineering and Technology—Engineering Accreditation Commission) accreditation. In addition, ABET needs to automatically ensure that the mapping (classification) is reasonable and correct. The classification also plays a vital role in the assessment of students’ learning. Since the PEOs are expressed as short text, they do not contain enough semantic meaning and information, and consequently they suffer from high sparseness, multidimensionality and the curse of dimensionality. In this work, a novel associative short text classification technique is proposed to map PEOs to SOs. The datasets are extracted from 152 self-study reports (SSRs) that were produced in operational settings in an engineering program accredited by ABET-EAC. The datasets are processed and transformed into a representational form appropriate for association rule mining. The extracted rules are utilized as delegate classifiers to map PEOs to SOs. The proposed associative classification of the mapping of PEOs to SOs has shown promising results, which can simplify the classification of short text and avoid many problems caused by enriching short text based on external resources that are not related or relevant to the dataset.
On the properties of the asymptotic incompatibility measure in multiparameter quantum estimation

Abstract We address the use of asymptotic incompatibility (AI) to assess the quantumness of a multiparameter quantum statistical model. AI is a recently introduced measure which quantifies the difference between the Holevo and the symmetric logarithmic derivative (SLD) scalar bounds, and can be evaluated using only the SLD operators of the model. At first, we evaluate analytically the AI of the most general quantum statistical models involving two-level (qubit) and single-mode Gaussian continuous-variable quantum systems, and prove that AI is a simple monotonous function of the state purity. Then, we numerically investigate the same problem for qudits ( d -dimensional quantum systems, with 2 &lt; d ⩽ 4), showing that, while in general AI is not in general a function of purity, we have enough numerical evidence to conclude that the maximum amount of AI is attainable only for quantum statistical models characterized by a purity larger than <?CDATA ${\mu }_{\mathsf{min}}=1/(d-1)$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll"> <mml:msub> <mml:mrow> <mml:mi>μ</mml:mi> </mml:mrow> <mml:mrow> <mml:mi mathvariant="sans-serif">min</mml:mi> </mml:mrow> </mml:msub> <mml:mo>=</mml:mo> <mml:mn>1</mml:mn> <mml:mo>/</mml:mo> <mml:mrow> <mml:mo stretchy="false">(</mml:mo> <mml:mrow> <mml:mi>d</mml:mi> <mml:mo>−</mml:mo> <mml:mn>1</mml:mn> </mml:mrow> <mml:mo stretchy="false">)</mml:mo> </mml:mrow> </mml:math> . In addition, by parametrizing qudit states as thermal (Gibbs) states, numerical results suggest that, once the spectrum of the Hamiltonian is fixed, the AI measure is in one-to-one correspondence with the fictitious temperature parameter β characterizing the family of density operators. Finally, by studying in detail the definition and properties of the AI measure we find that: (i) given a quantum statistical model, one can readily identify the maximum number of asymptotically compatible parameters; (ii) the AI of a quantum statistical model bounds from above the AI of any sub-model that can be defined by fixing one or more of the original unknown parameters (or functions thereof), leading to possibly useful bounds on the AI of models involving noisy quantum dynamics.
The Future is not One-dimensional: Complex Event Schema Induction by Graph Modeling for Event Prediction

Event schemas encode knowledge of stereotypical structures of events and their connections.As events unfold, schemas are crucial to act as a scaffolding.Previous work on event schema induction focuses either on atomic events or linear temporal event sequences, ignoring the interplay between events via arguments and argument relations.We introduce a new concept of Temporal Complex Event Schema: a graph-based schema representation that encompasses events, arguments, temporal connections and argument relations.In addition, we propose a Temporal Event Graph Model that predicts event instances following the temporal complex event schema.To build and evaluate such schemas, we release a new schema learning corpus containing 6,399 documents accompanied with event graphs, and we have manually constructed gold-standard schemas.Intrinsic evaluations by schema matching and instance graph perplexity, prove the superior quality of our probabilistic graph schema library compared to linear representations.Extrinsic evaluation on schema-guided future event prediction further demonstrates the predictive power of our event graph model, significantly outperforming human schemas and baselines by more than 23.8% on HITS@1. 1
Molecumentary: Adaptable Narrated Documentaries Using Molecular Visualization

We present a method for producing documentary-style content using real-time scientific visualization. We introduce molecumentaries, i.e., molecular documentaries featuring structural models from molecular biology, created through adaptable methods instead of the rigid traditional production pipeline. Our work is motivated by the rapid evolution of scientific visualization and it potential in science dissemination. Without some form of explanation or guidance, however, novices and lay-persons often find it difficult to gain insights from the visualization itself. We integrate such knowledge using the verbal channel and provide it along an engaging visual presentation. To realize the synthesis of a molecumentary, we provide technical solutions along two major production steps: (1) preparing a story structure and (2) turning the story into a concrete narrative. In the first step, we compile information about the model from heterogeneous sources into a story graph. We combine local knowledge with external sources to complete the story graph and enrich the final result. In the second step, we synthesize a narrative, i.e., story elements presented in sequence, using the story graph. We then traverse the story graph and generate a virtual tour, using automated camera and visualization transitions. We turn texts written by domain experts into verbal representations using text-to-speech functionality and provide them as a commentary. Using the described framework, we synthesize fly-throughs with descriptions: automatic ones that mimic a manually authored documentary or semi-automatic ones which guide the documentary narrative solely through curated textual input.
Resource-Aware Time Series Imaging Classification for Wireless Link Layer Anomalies

The number of end devices that use the last-mile wireless connectivity is dramatically increasing with the rise of smart infrastructures and requires reliable functioning to support smooth and efficient business processes. To efficiently manage such massive wireless networks, more advanced and accurate network monitoring and malfunction detection solutions are required. In this article, we perform a first-time analysis of image-based representation techniques for wireless anomaly detection using recurrence plots (RPs) and Gramian angular fields and propose a new deep learning architecture enabling accurate anomaly detection. We elaborate on the design considerations for developing a resource-aware architecture and propose a new model using time series to image transformation using RPs. We show that the proposed model: 1) outperforms the one based on Gramian angular fields by up to 14% points; 2) outperforms classical ML models using dynamic time warping by up to 24% points; 3) outperforms or performs on par with mainstream architectures, such as AlexNet and VGG11 while having their weights and up to ≈8% of their computational complexity; and d) outperforms the state of the art in the respective application area by up to 55% points. Finally, we also explain on randomly chosen examples how the classifier takes decisions.