Reviewerly/exHarmony huggingface dataset:
Assessing the robustness of feature-based selection in visual working memory.

Visual working memory (VWM) is a capacity-limited system to temporarily maintain visual information.Attending to information in VWM conveys a benefit, as revealed by the retro-cue effect.For example, when the location of one memory item is retro-cued during VWM maintenance, memory accuracy for that item improves.Attentional selection in VWM can also be feature-based: One feature (e.g., shape) may serve as a retrieval cue for another feature (e.g., color) of the same item.Here, we assessed the scope of feature retro-cue benefits with continuous report of colors and orientations.Across six experiments, we observed robust feature retro-cue benefits with manipulations of the cued and recalled feature dimensions, as well as against different baselines controlling for temporal and interference effects.Furthermore, we replicated with continuous report the hallmark of external feature-based attentionconcurrent selection of multiple items.Mixture modelling indicated that feature retro-cue benefits increased recall probability and sometimes precision, paralleling findings on spatial attention.Importantly, cueing multiple items did not produce costs, indicating that concurrently attended items did not interfere with each other.Lastly, manipulation constraining spatial location to a single position suggested that feature retro-cue benefits persist even when spatial context is not singular, but take longer to emerge.
An enhanced artificial bee colony algorithm based on fitness weighted search strategy

Artificial Bee Colony (ABC) algorithm is a meta-heuristic algorithm, is inspired by the bee’s food search behaviour based on swarm intelligence. Successful applications were performed on many optimization problems using this algorithm rising in popularity over the past few years. The update mechanism of the ABC algorithm, despite the fact that its exploration is good, faces the problem of convergence performance. For solving convergence problem of ABC Algorithm An Artificial Bee Colony Algorithm Based Fitness Weighted Search (ABCFWS) algorithm proposed in this paper. In this approach, an intelligent search space is proposed instead of the random search space of the ABC algorithm. In this method, the fitness values of the food source and the selected neighbour food source are taken as weights and a more balanced search space was found in the direction of the food source with better fitness value. The proposed method has been applied to 28 unconstrained numerical optimization test problems with different characteristics and the results were compared with the ABC algorithm variations. The results show that the proposed method is successful and competitive.
Moving People Tracking and False Track Removing with Infrared Thermal Imaging by a Multirotor

Infrared (IR) thermal imaging can detect the warm temperature of the human body regardless of the light conditions, thus small drones equipped with the IR thermal camera can be utilized to recognize human activity for smart surveillance, road safety, and search and rescue missions. However, the unpredictable motion of the drone poses more challenges than a fixed camera. This paper addresses the detection and tracking of people through IR thermal video captured by a multirotor. For object detection, each frame is first registered with a reference frame to compensate for its coordinates. Then, the objects in each frame are segmented through k-means clustering and morphological operations. Falsely detected objects are removed considering the actual size and the shape of the object. The centroid of the segmented area is considered the measured position for target tracking. The track is initialized with two-point differencing initialization, and the target states are continuously estimated by the interacting multiple model (IMM) filter. The nearest neighbor association rule assigns the measurement to the track. Tracks that move slower than the minimum speed are terminated at the proposed criteria. In the experiments, three videos were captured with a long-wave IR band thermal imaging camera mounted on a multirotor. In the first and second videos, eight pedestrians on a pavement and three hikers on a mountain on winter nights were captured, respectively. In the third video, two walking people with complex backgrounds were captured on a windy summer day. The image characteristics vary between videos depending on the climate and surrounding objects, but the proposed scheme shows the robust performance in all cases; the average root mean squared errors in position and velocity are obtained as 0.08 m and 0.53 m/s, respectively for the first video, 0.06 m and 0.58 m/s, respectively for the second video, and 0.18 m and 1.84 m/s, respectively for the third video. The proposed method reduces false tracks from 10 to 1 in the third video.
An Empirical Investigation of Factors Causing Scope Creep in Agile Global Software Development Context: A Conceptual Model for Project Managers

Scope creep is considered as one of the crucial reasons for the failure of traditional software development projects.The ability to manage and control the change elements on a project, particularly the project scope, is a key to project success.The notion of agile process was introduced to tackle the scope change-related challenges such as scope creep.By adopting an agile-footed process, the development organizations can react to the consistent market changes and client requests.However, continuous change accommodation may negatively impact on the success of the targeted project since the project manager mainly focuses on controlling scope change rather than analyzing its impact on the cost and quality.The agile-process advocates have accepted that this situation could happen even following agile methodology, which prompts on compromising the quality, postponed the plans, increases the cost, plan to modify, and diminished the consumer loyalty.Additionally, the scope-related challenge significantly increases, especially when managing scope creep in Global Software Development (GSD) context.Thus, there is a need to focus on scope creep factors in the context of AGSD.Motivated by this, current work aims at identifying the factors causing scope creep in the context of AGSD.To achieve the targeted objectives, we reported the current stateof-the-art related to existing scope creep models in AGSD context.We performed a systematic literature review and an empirical study to address the formulated research questions.The current study also identifies the additional challenges of scope creep from the industrial perspective.Based on the obtained results, the current work proposes a conceptual model for scope creep to assist the agile practitioners to effectively handle the scope creep, which ultimately increases the project success and forecasts change control effect on a software project.Moreover, the proposed conceptual model's effectiveness is validated through expert judgment and a case study.The obtained promising results ensure the additional aspects of AGSD; hence, the project manager could overcome the project's overall risk by implementing the proposed model.
Adaptive utterance rewriting for conversational search

In a conversational context, a user converses with a system through a sequence of natural-language questions, i.e., utterances. Starting from a given subject, the conversation evolves through sequences of user utterances and system replies. The retrieval of documents relevant to an utterance is difficult due to informal use of natural language in speech and the complexity of understanding the semantic context coming from previous utterances. We adopt the 2019 TREC Conversational Assistant Track (CAsT) framework to experiment with a modular architecture performing in order: (i) automatic utterance understanding and rewriting, (ii) first-stage retrieval of candidate passages for the rewritten utterances, and (iii) neural re-ranking of candidate passages. By understanding the conversational context, we propose adaptive utterance rewriting strategies based on the current utterance and the dialogue evolution of the user with the system. A classifier identifies those utterances lacking context information as well as the dependencies on the previous utterances. Experimentally, we evaluate the proposed architecture in terms of traditional information retrieval metrics at small cutoffs. Results demonstrate the effectiveness of our techniques, achieving an improvement up to 0.6512 (+201%) for [email protected] and 0.4484 (+214%) for [email protected] w.r.t. the CAsT baseline.
Fault Diagnosis of Signal Equipment on the Lanzhou-Xinjiang High-Speed Railway Using Machine Learning for Natural Language Processing

The Lanzhou-Xinjiang (Lan-Xin) high-speed railway is one of the principal sections of the railway network in western China, and signal equipment is of great importance in ensuring the safe and efficient operation of the high-speed railway. Over a long period, in the railway operation and maintenance process, the railway signaling and communications department has recorded a large amount of unstructured text information about equipment faults in the form of natural language. However, due to irregularities in the recording methods of these data, it is difficult to use directly. In this paper, a method based on natural language processing (NLP) was adopted to analyze and classify this information. First, the Latent Dirichlet Allocation (LDA) topic model was used to extract the semantic features of the text, which were then expressed in the corresponding topic feature space. Next, the Support Vector Machine (SVM) algorithm was used to construct a signal equipment fault diagnostic model that reduced the impact of sample data imbalance on the classification accuracy. This was compared and analyzed with the traditional Naive Bayes (NB), Logistic Regression (LR), Random Forest (RF), and K-Nearest Neighbor (KNN) algorithms. This study used signal equipment failure text data from the Lan-Xin high-speed railway to conduct experimental analysis and verify the effectiveness of the proposed method. Experiments showed that the accuracy of the SVM classification algorithm could reach 0.84 after being combined with the LDA topic model, which verifies that the natural language processing method can effectively realize the fault diagnosis of signal equipment and has certain guiding significance for the maintenance of field signal equipment.
An Argumentative Dialogue System for COVID-19 Vaccine Information

Dialogue systems are widely used in AI to support timely and interactive communication with users. We propose a general-purpose dialogue system architecture that leverages computational argumentation to perform reasoning and provide consistent and explainable answers. We illustrate the system using a COVID-19 vaccine information case study.
Hell Hath No Fury? Correcting Bias in the NRC Emotion Lexicon

There have been several attempts to create an accurate and thorough emotion lexicon in English, which identifies the emotional content of words.Of the several commonly used resources, the NRC emotion lexicon (Mohammad and Turney, 2013b) has received the most attention due to its availability, size, and its choice of Plutchik's expressive 8-class emotion model.In this paper we identify a large number of troubling entries in the NRC lexicon, where words that should in most contexts be emotionally neutral, with no affect (e.g., lesbian, stone, mountain), are associated with emotional labels that are inaccurate, nonsensical, pejorative, or, at best, highly contingent and context-dependent (e.g., lesbian labeled as DISGUST and SADNESS, stone as ANGER, or mountain as ANTICIPATION).We describe a procedure for semi-automatically correcting these problems in the NRC, which includes disambiguating POS categories and aligning NRC entries with other emotion lexicons to infer the accuracy of labels.We demonstrate via an experimental benchmark that the quality of the resources is thus improved.We release the revised resource and our code to enable other researchers to reproduce and build upon results 1 .
Adaptive Chaotic Image Encryption Algorithm Based on RNA and Pixel Depth

To prevent the leakage of image content, image encryption technology has received increasing attention. Most current algorithms are only suitable for the images of certain types and cannot update keys in a timely manner. To tackle such problems, we propose an adaptive chaotic image encryption algorithm based on RNA and pixel depth. Firstly, a novel chaotic system, two-dimensional improved Logistic-adjusted-Sine map is designed. Then, we propose a three-dimensional adaptive Arnold transform for scrambling. Secondly, keys are generated by the hash values of the plain image and current time to achieve one-image, one-key, and one-time pad simultaneously. Thirdly, we build a pre-permuted RNA cube for 3D adaptive scrambling by pixel depth, chaotic sequences, and adaptive RNA coding. Finally, selective diffusion combined with pixel depth and RNA operations is performed, in which the RNA operators are determined by the chemical structure and properties of amino acids. Pixel depth is integrated into the whole procedure of parameter generation, scrambling, and diffusion. Experiments and algorithm analyses show that our algorithm has strong security, desirable performance, and a broader scope of application.
Deep Visual Analytics (DVA): Applications, Challenges and Future Directions

A B S T R A C TVisual interactive system (VIS) has been received significant attention for solving various complex problems.However, designing and implementing a novel VIS with the large scale of data is a challenging task.While existing studies have applied various visual analytics (VA) to analyze and visualize insightful information, deep visual analytics (DVA) have considered as a promising technique to provide input evidences and explain system results.In this study, we present several deep learning (DL) techniques for analyzing data with visualization, which summarizes the state-of-the-art review on (i) big data analysis, (ii) cognitive and perception science, (iii) customer behavior analysis, (iv) natural language processing, (v) recommended system, (vi) healthcare analysis, (vii) fintech ecosystem, and (viii) tourism management.We present open research challenges for emerging DVA in the visualization community.We also highlight some key themes from the existing literature that may help to explore for future study.Thus, our goal is to help readers and researchers in DL and VA to understand key aspects in designing VIS for analysing data.
Estimation of Electricity Generation by an Electro-Technical Complex with Photoelectric Panels Using Statistical Methods

This paper presents a computational tool for estimating energy generated by low-power photovoltaic systems based on the specific conditions of the study region since the characteristic energy equation can be obtained considering the main climatological factors affecting these systems in terms of the symmetry or skewness of the random distribution of the generated energy. Furthermore, this paper is aimed at determining any correlation that exists between meteorological variables with respect to the energy generated by 5-kW solar systems in the specific climatic conditions of the Republic of Cuba. The paper also presents the results of the influence of each climate factor on the distribution symmetry of the generated energy of the solar system. Studying symmetry in statistical models is important because they allow us to establish the degree of symmetry (or skewness), which is the probability distribution of a random variable, without having to make a graphical representation of it. Statistical skewness reports the degree to which observations are distributed evenly and proportionally above and below the center (highest) point of the distribution. In the case when the mentioned distribution is balanced, it is called symmetric.
Proposal-free One-stage Referring Expression via Grid-Word Cross-Attention

Referring Expression Comprehension (REC) has become one of the most important tasks in visual reasoning, since it is an essential step for many vision-and-language tasks such as visual question answering. However, it has not been widely used in many downstream tasks because it suffers 1) two-stage methods exist heavy computation cost and inevitable error accumulation, and 2) one-stage methods have to depend on lots of hyper-parameters (such as anchors) to generate bounding box. In this paper, we present a proposal-free one-stage (PFOS) model that is able to regress the region-of-interest from the image, based on a textual query, in an end-to-end manner. Instead of using the dominant anchor proposal fashion, we directly take the dense-grid of image as input for a cross-attention transformer that learns grid-word correspondences. The final bounding box is predicted directly from the image without the time-consuming anchor selection process that previous methods suffer. Our model achieves the state-of-the-art performance on four referring expression datasets with higher efficiency, comparing to previous best one-stage and two-stage methods.
Multi-Hop Fact Checking of Political Claims

Recent work has proposed multi-hop models and datasets for studying complex natural language reasoning. One notable task requiring multi-hop reasoning is fact checking, where a set of connected evidence pieces leads to the final verdict of a claim. However, existing datasets either do not provide annotations for gold evidence pages, or the only dataset which does (FEVER) mostly consists of claims which can be fact-checked with simple reasoning and is constructed artificially. Here, we study more complex claim verification of naturally occurring claims with multiple hops over interconnected evidence chunks. We: 1) construct a small annotated dataset, PolitiHop, of evidence sentences for claim verification; 2) compare it to existing multi-hop datasets; and 3) study how to transfer knowledge from more extensive in- and out-of-domain resources to PolitiHop. We find that the task is complex and achieve the best performance with an architecture that specifically models reasoning over evidence pieces in combination with in-domain transfer learning.
Virtualization Costs: Benchmarking Containers and Virtual Machines Against Bare-Metal

DevOps advocates the usage of Virtualization Technologies (VT), such as Virtual Machines and Containers. However, it is complex to predict how the usage of a given VT will impact on the performance of an application. In this paper, we present a collection of reference benchmarks that developers can use to orient when looking for the best-performing VT w.r.t their application profile. To gather our benchmarks in a resource-wise comprehensive and comparable way, we introduce VTmark: a semi-automatic open-source suite that assembles off-the-shelf tools for benchmarking the different resources used by applications (CPU, RAM, etc.). After performing a survey of VTs in the market, we use VTmark to report the benchmarks of 6 of the most widely adopted and popular ones, namely Docker, KVM, Podman, VMWare Workstation, VirtualBox, and Xen. To validate the accuracy of our reference benchmarks, we show how they correlate with the profile performance of a production-grade application ported and deployed on the considered VTs. Beyond our immediate results, VTmark let us shed light on some contradicting findings in the related literature and, by releasing VTmark , we provide DevOps with an open-source, extendable tool to assess the (resource-wise) costs of VTs.
A Novel Hybrid Methodology of Measuring Sentence Similarity

The problem of measuring sentence similarity is an essential issue in the natural language processing area. It is necessary to measure the similarity between sentences accurately. Sentence similarity measuring is the task of finding semantic symmetry between two sentences, regardless of word order and context of the words. There are many approaches to measuring sentence similarity. Deep learning methodology shows a state-of-the-art performance in many natural language processing fields and is used a lot in sentence similarity measurement methods. However, in the natural language processing field, considering the structure of the sentence or the word structure that makes up the sentence is also important. In this study, we propose a methodology combined with both deep learning methodology and a method considering lexical relationships. Our evaluation metric is the Pearson correlation coefficient and Spearman correlation coefficient. As a result, the proposed method outperforms the current approaches on a KorSTS standard benchmark Korean dataset. Moreover, it performs a maximum of a 65% increase than only using deep learning methodology. Experiments show that our proposed method generally results in better performance than those with only a deep learning model.
Person Search Challenges and Solutions: A Survey

Person search has drawn increasing attention due to its real-world applications and research significance. Person search aims to find a probe person in a gallery of scene images with a wide range of applications, such as criminals search, multicamera tracking, missing person search, etc. Early person search works focused on image-based person search, which uses person image as the search query. Text-based person search is another major person search category that uses free-form natural language as the search query. Person search is challenging, and corresponding solutions are diverse and complex. Therefore, systematic surveys on this topic are essential. This paper surveyed the recent works on image-based and text-based person search from the perspective of challenges and solutions. Specifically, we provide a brief analysis of highly influential person search methods considering the three significant challenges: the discriminative person features, the query-person gap, and the detection-identification inconsistency. We summarise and compare evaluation results. Finally, we discuss open issues and some promising future research directions.
Information-Theoretic Methods in Deep Neural Networks: Recent Advances and Emerging Opportunities

We present a review on the recent advances and emerging opportunities around the theme of analyzing deep neural networks (DNNs) with information-theoretic methods. We first discuss popular information-theoretic quantities and their estimators. We then introduce recent developments on information-theoretic learning principles (e.g., loss functions, regularizers and objectives) and their parameterization with DNNs. We finally briefly review current usages of information-theoretic concepts in a few modern machine learning problems and list a few emerging opportunities.