Reviewerly/exHarmony huggingface dataset:
A study on graph representations for genetic programming

Graph representations promise several desirable properties for Genetic Programming (GP); multiple-output programs, natural representations of code reuse and, in many cases, an innate mechanism for neutral drift. Each graph GP technique provides a program representation, genetic operators and overarching evolutionary algorithm. This makes it difficult to identify the individual causes of empirical differences, both between these methods and in comparison to traditional GP. In this work, we empirically study the behavior of Cartesian Genetic Programming (CGP), Linear Genetic Programming (LGP), Evolving Graphs by Graph Programming (EGGP) and traditional GP. By fixing some aspects of the configurations, we study the performance of each graph GP method and GP in combination with three different EAs: generational, steady-state and (1 + λ). In general, we find that the best choice of representation, genetic operator and evolutionary algorithm depends on the problem domain. Further, we find that graph GP methods, particularly in combination with the (1 + λ) EA are significantly better on digital circuit synthesis tasks.
Putting Phenomenological Theories to Work in the Design of Self-Tracking Technologies

Existing self-tracking devices have been criticized for perpetuating a dualist, rather than phenomenological, understanding of the self as a separated mind and body. In this paper we answer calls for a phenomenological approach to the design of self-tracking devices. Ambient Cycle is a menstrual cycle tracking device that provides a continuous display of data in the home through coloured light. Through its design and long-term deployment, we found that a phenomenological approach facilitated; 1. the documentation of a diversity in subjective experiences of the enigmatic menstrual cycle; 2. the tracking of positive as well as negative aspects of the menstrual cycle, which challenges wider understandings of the body in society, and 3. novel uses of self-tracked data. We also expand on existing uses of phenomenological theories within HCI to include those that address interactions with the insides of our bodies.
Formation of the Flatreef deposit, northern Bushveld, by hydrodynamic and hydromagmatic processes

Abstract New lithological and whole rock compositional data show that the main platinum-group element (PGE) horizons of the Flatreef succession show strong compositional similarities to the Merensky and Bastard reefs of the western Bushveld Complex (WBC), notably in terms of many immobile and incompatible minor and trace elements such as TiO 2 , Zr, Y, and REE. However, Al 2 O 3 , CaO, and Na 2 O contents are markedly lower in the Flatreef, whereas MgO contents are higher. In view of broadly similar silicate mineral compositions in the Flatreef and the WBC reefs, we suggest that the major element compositional differences between the rocks are largely due to higher modal proportions of orthopyroxene and olivine and lower proportions of plagioclase in the Flatreef. The thickness of the mineralised interval is much greater in the Flatreef than in the WBC (several 10 s of m vs ~ 1 m) and the abundance of sulfides in the Flatreef is typically somewhat higher (on average ~ 1.5% vs ~ 1%). These data, complemented by textural observations, are interpreted to reflect enhanced hydrodynamic crystal sorting accompanied by percolation of sulfide melt through incompletely solidified cumulates. Further genetic constraints are provided by metal data: The concentration of Ni (~ 3000 ppm) in the Flatreef is broadly similar to that in the Merensky Reef of the WBC, but Cu contents are markedly higher (average ~ 1500 ppm vs 700 ppm in the WBC). The concentrations of most PGE are slightly lower (Flatreef ~ 1.5–2 ppm Pt, ~ 100–150 ppb Rh; WBC MR 3.7 ppm Pt, 240 ppb Rh), but Pd has broadly similar contents (Flatreef ~ 1.2–2 ppm; WBC MR 1.54 ppm). The relatively high Cu content of the Flatreef is interpreted as a result of assimilation of Cu sulfides from the sedimentary floor rocks. The reason for the enrichment of Pd relative to Pt, especially in the basal rocks, remains unclear. It could reflect mobilisation of Pd via a fluid phase from the country rocks or the interior of the intrusion, relatively enhanced partitioning of Pd into the sulfides, or relative Pt depletion of the earliest magma pulses in response to Pt alloy fractionation triggered by contamination with reducing country rocks.
KGSNet: Key-Point-Guided Super-Resolution Network for Pedestrian Detection in the Wild

In real-world scenarios (i.e., in the wild), pedestrians are often far from the camera (i.e., small scale), and they often gather together and occlude with each other (i.e., heavily occluded). However, detecting these small-scale and heavily occluded pedestrians remains a challenging problem for the existing pedestrian detection methods. We argue that these problems arise because of two factors: 1) insufficient resolution of feature maps for handling small-scale pedestrians and 2) lack of an effective strategy for extracting body part information that can directly deal with occlusion. To solve the above-mentioned problems, in this article, we propose a key-point-guided super-resolution network (coined KGSNet) for detecting these small-scale and heavily occluded pedestrians in the wild. Specifically, to address factor 1), a super-resolution network is first trained to generate a clear super-resolution pedestrian image from a small-scale one. In the super-resolution network, we exploit key points of the human body to guide the super-resolution network to recover fine details of the human body region for easier pedestrian detection. To address factor 2), a part estimation module is proposed to encode the semantic information of different human body parts where four semantic body parts (i.e., head and upper/middle/bottom body) are extracted based on the key points. Finally, based on the generated clear super-resolved pedestrian patches padded with the extracted semantic body part images at the image level, a classification network is trained to further distinguish pedestrians/backgrounds from the inputted proposal regions. Both proposed networks (i.e., super-resolution network and classification network) are optimized in an alternating manner and trained in an end-to-end fashion. Extensive experiments on the challenging CityPersons data set demonstrate the effectiveness of the proposed method, which achieves superior performance over previous state-of-the-art methods, especially for those small-scale and heavily occluded instances. Beyond this, we also achieve state-of-the-art performance (i.e., 3.89% MR <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-2</sup> on the reasonable subset) on the Caltech data set.
Relevant Data Node Extraction:A Web Data Extraction Method for Non Contagious Data

The Internet is expanding rapidly and millions of HTML pages are created daily. These HTML pages are created by content management systems like Wordpress, Joomla or by other software programs. This software programs query data from single or multiple associated databases & then fill the template with data in web pages to get well-structured data and call this well-structured data as data nodes. This paper proposes a novel technique to detect and extract structured data from web pages. These data nodes are very vital since they provide information about all the structured data. A data extraction technique Relevant Data Node Extraction (RDNE) that automatically mine relevant data nodes from HTML pages is presented. The algorithm in this paper is based on some set of rules that are observed & implemented. Our approach showed excellent results for the proposed technique.
Decisional Support System with Artificial Intelligence oriented on Health Prediction using a Wearable Device and Big Data

The proposed work describes a decision support system (DSS) based on artificial intelligence algorithms and health wearable sensors predicting health status. The discussion is mainly focused on the description of the innovative architecture of the prototype platform related to a research industry project. The platform is able to control different wearable sensors storing data into a Cassandra Big Data system. Support Vector machine (SVM) and Long Short Term Memory (LSTM) algorithms have been applied to experimental datasets, proving the basic function of physiological data prediction. The work is suitable for the implementation of multi-dimensional risk map of health status.
The Impact of Chatbots on Customer Service Performance

The advent of chatbots in customer service solutions received increasing attention by research and practice throughout the last years. However, the relevant dimensions and features for service quality and service performance for chatbots remain quite unclear. Therefore, this research develops and tests a conceptual model for customer service quality and customer service performance in the context of chatbots. Additionally, the impact of the developed service dimensions on different customer relationship metrics is measured across different service channels (hotline versus chatbots). Findings of six independent studies indicate a strong main effect of the conceptualized service dimensions on customer satisfaction, service costs, intention to service reusage, word-of-mouth, and customer loyalty. However, different service dimensions are relevant for chatbots compared to a traditional service hotline.
Weakly-Supervised Classification of HER2 Expression in Breast Cancer Haematoxylin and Eosin Stained Slides

Human epidermal growth factor receptor 2 (HER2) evaluation commonly requires immunohistochemistry (IHC) tests on breast cancer tissue, in addition to the standard haematoxylin and eosin (H&amp;E) staining tests. Additional costs and time spent on further testing might be avoided if HER2 overexpression could be effectively inferred from H&amp;E stained slides, as a preliminary indication of the IHC result. In this paper, we propose the first method that aims to achieve this goal. The proposed method is based on multiple instance learning (MIL), using a convolutional neural network (CNN) that separately processes H&amp;E stained slide tiles and outputs an IHC label. This CNN is pretrained on IHC stained slide tiles but does not use these data during inference/testing. H&amp;E tiles are extracted from invasive tumour areas segmented with the HASHI algorithm. The individual tile labels are then combined to obtain a single label for the whole slide. The network was trained on slides from the HER2 Scoring Contest dataset (HER2SC) and tested on two disjoint subsets of slides from the HER2SC database and the TCGA-TCIA-BRCA (BRCA) collection. The proposed method attained 83.3 % classification accuracy on the HER2SC test set and 53.8 % on the BRCA test set. Although further efforts should be devoted to achieving improved performance, the obtained results are promising, suggesting that it is possible to perform HER2 overexpression classification on H&amp;E stained tissue slides.
Decentralized Poisson Multi-Bernoulli Filtering for Vehicle Tracking

A decentralized Poisson multi-Bernoulli filter is proposed to track multiple vehicles using multiple high-resolution sensors. Independent filters estimate the vehicles' presence, state, and shape using a Gaussian process extent model; a decentralized filter is realized through fusion of the filters posterior densities. An efficient implementation is achieved by parametric state representation, utilization of single hypothesis tracks, and fusion of vehicle information based on a fusion mapping. Numerical results demonstrate the performance.
The role of institutional infrastructures in financial <scp>inclusion‐growth</scp> relations: Evidence from <scp>SSA</scp>

Abstract This paper investigates the role of institutional infrastructures in the financial inclusion‐growth nexus for a panel of 20 countries in sub‐Sahara Africa (SSA). Employing the System Generalized Method of Moments (GMM), the following insightful outcomes are established. First, while there is an unrestricted positive impact of physical access to ATMs and ICT measures of financial inclusion on SSA's growth, only the former was found significant. Second, the four institutional components via economic, political, institutional and general governances were also found to be growth‐spurring. Lastly, countries with low levels of real per capita income are catching=up with other countries with high levels of real income per capita. The empirical evidence of some negative net effects and insignificant marginal impacts are indications that imperfections in the financial markets are sometimes employed to the disadvantage of the poor. On the whole, we established positive effects on growth for the most part. The positive effects are evident because the governance indicators complement financial inclusion in reducing pecuniary constraints hindering credit access and allocation to the poor that deteriorate growth.
Multi-Task Learning for Entity Recommendation and Document Ranking in Web Search

Entity recommendation, providing users with an improved search experience by proactively recommending related entities to a given query, has become an indispensable feature of today’s Web search engine. Existing studies typically only consider the query issued at the current timestep while ignoring the in-session user search behavior (short-term search history) or historical user search behavior across all sessions (long-term search history) when generating entity recommendations. As a consequence, they may fail to recommend entities of interest relevant to a user’s actual information need. In this work, we believe that both short-term and long-term search history convey valuable evidence that could help understand the user’s search intent behind a query, and take both of them into consideration for entity recommendation. Furthermore, there has been little work on exploring whether the use of other companion tasks in Web search such as document ranking as auxiliary tasks could improve the performance of entity recommendation. To this end, we propose a multi-task learning framework with deep neural networks (DNNs) to jointly learn and optimize two companion tasks in Web search engines: entity recommendation and document ranking, which can be easily trained in an end-to-end manner. Specifically, we regard document ranking as an auxiliary task to improve the main task of entity recommendation, where the representations of queries, sessions, and users are shared across all tasks and optimized by the multi-task objective during training. We evaluate our approach using large-scale, real-world search logs of a widely-used commercial Web search engine. We also performed extensive ablation experiments over a number of facets of the proposed multi-task DNN model to figure out their relative importance. The experimental results show that both short-term and long-term search history can bring significant improvements in recommendation effectiveness, and the combination of both outperforms using either of them individually. In addition, the experiments show that the performance of both entity recommendation and document ranking can be significantly improved, which demonstrates the effectiveness of using multi-task learning to jointly optimize the two companion tasks in Web search.
Quantum double-double-slit experiment with momentum entangled photons

Abstract Double-double-slit thought experiment provides profound insight on interference of quantum entangled particles. This paper presents a detailed experimental realisation of quantum double-double-slit thought experiment with momentum entangled photons and theoretical analysis of the experiment. Experiment is configured in such a way that photons are path entangled and each photon can reveal the which-slit path information of the other photon. As a consequence, single photon interference is suppressed. However, two-photon interference pattern appears if locations of detection of photons are correlated without revealing the which-slit path information. It is also shown experimentally and theoretically that two-photon quantum interference disappears when the which-slit path of a photon in the double-double-slit is detected.
Encryption of accounting data using DES algorithm in computing environment

The more and more developed network has caused more and more impact on people’s life and work, providing convenient channels for people’s information exchange, and then improving people’s living and working conditions. However, when data is transmitted through the network, there are hidden security risks, especially important accounting data. Once intercepted and used by criminals, it may cause serious harm to the owner of the data. Based on the above background, the purpose of this article is to study the use of the DES algorithm to encrypt accounting data in a computing environment. This paper proposes an improved quantum genetic algorithm and applies it to the S-box design of the DES algorithm, which improves the non-linearity of the S-box, reduces the differential uniformity, and enhances the security of the DES algorithm. This improved DES algorithm reduces the number of iterations by increasing the key length and iterative processing using a two-round function, which further increases the security of the algorithm and improves the operation speed of the encryption process. It is found that the 64 ciphertexts of the DES algorithm and the number of changed bits compared to the original ciphertext fluctuates around 32 bits, which explains the problems that should be paid attention to when using the DES algorithm to encrypt accounting data. The validity of key characters should be guaranteed to prevent key loss or leakage. Shorter data encryption regular solution.
Role of Discourse Information in Urdu Sentiment Classification

In computational linguistics, sentiment analysis refers to the classification of opinions in a positive class or a negative class. There exist a lot of different methods for sentiment analysis of the English language, but the literature lacks the availability of methods and techniques for Urdu, which is the largely spoken language in the South Asian sub-continent and the national language of Pakistan. The currently available techniques, such as adjective count method known as Bag of Words (BoW), is not sufficient for classification of complex sentiment written in the Urdu language. Also, the performance of available machine-learning techniques (with legacy features), for classification of Urdu sentiments, are not comparable with the achieved accuracy of other languages. In the case of the English language, the discourse information (sub-sentence-level information) boosts the performance of both the BoW method and machine-learning techniques, but there are very few works available that have tested the context-level information for the sentiment analysis of the Urdu language. This research aims to extract the discourse information from the Urdu sentiments and utilise the discourse information to improve the performance and reduce the error rate of existing techniques for Urdu Sentiment classification. The proposed solution extracts the discourse information, suggests a new set of features for machine-learning techniques, and introduces a set of rules to extend the capabilities of the BoW model. The results show that the task has been enhanced significantly and the performance metrics such as recall, precision, and accuracy are increased by 31.25%, 8.46%, and 21.6%, respectively. In future, the proposed technique can be extended to sentiments with more than two sub-opinions, such as for blogs, reviews, and TV talk shows.
Studies on deformation measurement with non-fixed camera using digital image correlation method

A digital image correlation technique with non-fixed camera is developed for deformation measurement in the field since it is difficult to fix cameras when the deformations of a large number of areas need to be measured. Multiple reference and target images of the specimen surface and a feature template are captured from different orientations and corrected before the correlation calculation. The relative positions of the specimen surface and the camera are obtained by the camera calibration method. The specimen surface is divided to several meshes to reconstruct the speckle patterns. The technique is more flexible and convenient than traditional digital image correlation method in measuring the deformation of high altitude facilities such as bridges. In order to test the accuracy of this method, the deformations of an aluminum plate under different loading forces are measured and compared to the values recorded by the tensile tester.
Neural Architecture Search with GBDT.

Neural architecture search (NAS) with an accuracy predictor that predicts the accuracy of candidate architectures has drawn increasing interests due to its simplicity and effectiveness. Previous works employ neural network based predictors which unfortunately cannot well exploit the tabular data representations of network architectures. As decision tree-based models can better handle tabular data, in this paper, we propose to leverage gradient boosting decision tree (GBDT) as the predictor for NAS and demonstrate that it can improve the prediction accuracy and help to find better architectures than neural network based predictors. Moreover, considering that a better and compact search space can ease the search process, we propose to prune the search space gradually according to important features derived from GBDT using an interpreting tool named SHAP. In this way, NAS can be performed by first pruning the search space (using GBDT as a pruner) and then searching a neural architecture (using GBDT as a predictor), which is more efficient and effective. Experiments on NASBench-101 and ImageNet demonstrate the effectiveness of GBDT for NAS: (1) NAS with GBDT predictor finds top-10 architecture (among all the architectures in the search space) with $0.18\%$ test regret on NASBench-101, and achieves $24.2\%$ top-1 error rate on ImageNet; and (2) GBDT based search space pruning and neural architecture search further achieves $23.5\%$ top-1 error rate on ImageNet.
Pragmatics lost?

Abstract This article argues for a definition of online hate speech as a contextualised speech act that is part of a social process of alienation. It suggests that hate speech comes in degrees, is contextual, involves already existing power dynamics, and ‘others’ its targets by creating in/out groups. I first review the various stances towards understanding the phenomenon of online hate speech, including approaches that focus on online hate speech as an interaction shaped by its medium, while also emphasizing the need to consider the role of implicatures in speech acts when defining hate speech. Second, I argue that the relationality of online speech implies that any message is embedded in idiosyncratic socio-cultural norms, and that therefore a ‘one size fits all’ definition of hate speech is elusive. I conclude by suggesting that contextualized hate speech is embedded in a social process of alienation and should be understood as a continuum.