Reviewerly/exHarmony huggingface dataset:
The Promise of Blockchain for the Construction Industry: A Governance Lens

This chapter outlines the promise of blockchain for the construction industry. Blockchain is an opportunity to create novel forms of economic coordination toward better collaboration within and across the built asset life cycle phases. Ongoing research tends to focus on blockchain to increase trust in existing processes. Instead, we argue blockchain's disruptive potential is the creation of novel economic coordination. Therefore, we intend to advance the thinking around the promise of blockchain as an institutional innovation in the construction industry. First, we explain how the underlying cryptoeconomic governance mechanisms of blockchain can facilitate new decentralized coordination mechanisms between both humans and machines. Next, we provide an alternative vision for the governance of construction 4.0 to explain how cryptoeconomic coordination can address long-standing problems in the construction industry. Finally, we propose an adoption framework that can guide researchers and practitioners to explore the promise of blockchain and cryptoeconomics for the construction industry.
A Hybrid Sperm Swarm Optimization and Genetic Algorithm for Unimodal and Multimodal Optimization Problems

A good exploration ability can ensure that the method jumps out of local optimum in multimodal problems and a good exploitation can ensure an algorithm converge faster to global optimum values. So, this paper proposes a new hybrid sperm swarm optimization and genetic algorithm to obtain global optimal solutions termed HSSOGA which is developed based on the concept of balancing the exploration and exploitation capability by merging Sperm Swarm Optimization (SSO), which has a fast convergence rate, and a Genetic Algorithm (GA) that can explore a search domain efficiently. To ensure that the proposed method delivers good performance, it is evaluated with 11 standard test function problems consisting of 5 unimodal and 6 multimodal functions. The proposed HSSOGA set is compared with conventional GA and SSO methods, as well as with several hybrid methods such as Hybrid Firefly and Particle Swarm Optimization (HFPSO), hybrid Simulated Annealing and Genetic Algorithm (SAGA), Hybrid Particle Swarm Optimization and Genetic Algorithm (HFPSO), hybrid Particle Swarm Optimization and Grey Wolf Optimization (PSOGWO), and closely related Hybrid Sperm Swarm Optimization and Gravitational Search Algorithm (HSSOGSA). The results are evaluated in terms of each method’s best fitness, mean, standard deviation, and convergence rates. The numerical experiment results show that HSSOGA has better convergence towards the true global optimum values as compared to the conventional and existing hybrid methods in most unimodal and multimodal test function problems.
Arabic Handwritten Character Recognition Based on Convolution Neural Networks

Automatic handwriting recognition is a useful task for many applications. The main Research has focused on the Latin languages. However, few approaches have been proposed for the Arabic language due to the specific and complex features of handwritten Arabic text. In this paper, we propose a Deep Learning (DL) approach for Arabic character recognition using proposed model of convolutional neural networks (CNN). In our work, we dealt with the specific features of Arabic text, in particular the variation of the shape of characters according to its position in the word based a new model of CNN network. In the experimental evaluation, we use hijja dataset in train and test steps. Obtained results prove the efficiency of our model, achieving accuracy of 95% on the Hijja dataset.
Enabling Large-Capacity Reversible Data Hiding Over Encrypted JPEG Bitstreams

Cloud computing offers advantages in handling the exponential growth of images but also entails privacy concerns on outsourced private images. Reversible data hiding (RDH) over encrypted images has emerged as an effective technique for securely storing and managing confidential images in the cloud. Most existing schemes only work on uncompressed images. However, almost all images are transmitted and stored in compressed formats such as JPEG. Recently, some RDH schemes over encrypted JPEG bitstreams have been developed, but these works have some disadvantages such as a small embedding capacity (particularly for low quality factors), damage to the JPEG format, and file size expansion. In this study, we propose a permutation-based embedding technique that allows the embedding of significantly more data than existing techniques. Using the proposed embedding technique, we further design a large-capacity RDH scheme over encrypted JPEG bitstreams, in which a grouping method is designed to boost the number of embeddable blocks. The designed RDH scheme allows a content owner to encrypt a JPEG bitstream before uploading it to a cloud server. The cloud server can embed additional data (e.g., copyright and identification information) into the encrypted JPEG bitstream for storage, management, or other processing purpose. A receiver can losslessly recover the original JPEG bitstream using a decryption key. Comprehensive evaluation results demonstrate that our proposed design can achieve approximately twice the average embedding capacity compared to the best prior scheme while preserving the file format without file size expansion.
Self-supervised learning and semi-supervised learning for multi-sequence medical image classification

Multi-sequence medical images are playing an increasingly important role in disease diagnosis because different sequences can provide complementary information. At the same time, deep learning-based methods have been widely used in computer aided diagnosis, but most of the recent success profoundly relies on large amounts of carefully labeled data, which is time-consuming and costly, especially when multiple sequences need to be labeled. To reduce the human effort of labeling multi-sequence medical images, we present a new self-supervised learning method MI-SelfL, a new semi-supervised learning method MI-SemiL, and a combined method MI-SESEL, and all these methods can exploit unlabeled data by exploring the intrinsic relation and the complementarity between multi-sequence images. We conducted extensive experiments on two tasks, hepatocellular carcinoma grading using dynamic contrast enhanced Magnetic Resonance Imaging (MRI) and prostate cancer classification using multiparametric MRI. The results show that compared with the fully-supervised learning baseline, MI-SelfL and MI-SemiL can both improve the model performance, whereas the combined method MI-SESEL can further improve it.
Structural Persistence in Language Models: Priming as a Window into Abstract Language Representations

Abstract We investigate the extent to which modern neural language models are susceptible to structural priming, the phenomenon whereby the structure of a sentence makes the same structure more probable in a follow-up sentence. We explore how priming can be used to study the potential of these models to learn abstract structural information, which is a prerequisite for good performance on tasks that require natural language understanding skills. We introduce a novel metric and release Prime-LM, a large corpus where we control for various linguistic factors that interact with priming strength. We find that Transformer models indeed show evidence of structural priming, but also that the generalizations they learned are to some extent modulated by semantic information. Our experiments also show that the representations acquired by the models may not only encode abstract sequential structure but involve certain level of hierarchical syntactic information. More generally, our study shows that the priming paradigm is a useful, additional tool for gaining insights into the capacities of language models and opens the door to future priming-based investigations that probe the model’s internal states.1
Reasoning and interaction for social artificial intelligence

Current work on multi-agent systems at King’s College London is extensive, though largely based in two research groups within the Department of Informatics: the Distributed Artificial Intelligence (DAI) thematic group and the Reasoning &amp; Planning (RAP) thematic group. DAI combines AI expertise with political and economic theories and data, to explore social and technological contexts of interacting intelligent entities. It develops computational models for analysing social, political and economic phenomena to improve the effectiveness and fairness of policies and regulations, and combines intelligent agent systems, software engineering, norms, trust and reputation, agent-based simulation, communication and provenance of data, knowledge engineering, crowd computing and semantic technologies, and algorithmic game theory and computational social choice, to address problems arising in autonomous systems, financial markets, privacy and security, urban living and health. RAP conducts research in symbolic models for reasoning involving argumentation, knowledge representation, planning, and other related areas, including development of logical models of argumentation-based reasoning and decision-making, and their usage for explainable AI and integration of machine and human reasoning, as well as combining planning and argumentation methodologies for strategic argumentation.
Towards the Evaluation of Recommender Systems with Impressions

In Recommender Systems, impressions are a relatively new type of information that records all products previously shown to the users. They are also a complex source of information, combining the effects of the recommender system that generated them, search results, or business rules that may select specific products for recommendations. The fact that the user interacted with a specific item given a list of recommended ones may benefit from a richer interaction signal, in which some items the user did not interact with may be considered negative interactions. This work presents a preliminary evaluation of recommendation models with impressions. First, impressions are characterized by describing their assumptions, signals, and challenges. Then, an evaluation study with impressions is described. The study's goal is two-fold: to measure the effects of impressions data on properly-tuned recommendation models using current open-source datasets and disentangle the signals within impressions data. Preliminary results suggest that impressions data and signals are nuanced, complex, and effective at improving the recommendation quality of recommenders. This work publishes the source code, datasets, and scripts used in the evaluation to promote reproducibility in the domain.
RecSys Challenge 2022: Fashion Purchase Prediction

The RecSys 2022 Challenge was a session-based recommendation task in the fashion domain. The dataset was supplied by Dressipi. Given session data consisting of views and purchases, as well as content data representing the fashion characteristics of the items, the task was to predict which item was purchased at the end of the session. The challenge ran for 3 months with a public leaderboard and final result on a separate hidden test set. There were over 300 teams that submitted a solution to the leaderboard and about 50 that submitted a solution for the final test set. The winning team achieved a MRR score of 0.216 which means that the correct target item was on average ranked 5th in the list of predictions. We identify some interesting common themes among the solutions in this paper and the winning approaches are presented in the workshop.
A Lightweight Transformer for Next-Item Product Recommendation

We apply a transformer using sequential browse history to generate next-item product recommendations. Interpreting the learned item embeddings, we show that the model is able to implicitly learn price, popularity, style and functionality attributes without being explicitly passed these features during training. Our real-life test of this model on Wayfair’s different international stores show mixed results (but overall win). Diagnosing the cause, we identify a useful metric (average number of customers browsing each product) to ensure good model convergence. We also find limitations of using standard metrics like recall and nDCG, which do not correctly account for the positional effects of showing items on the Wayfair website, and empirically determine a more accurate discount factor.
Don’t recommend the obvious: estimate probability ratios

Sequential recommender systems are becoming widespread in the online retail and streaming industry. These systems are often trained to predict the next item given a sequence of a user's recent actions, and standard evaluation metrics reward systems that can identify the most probable items that might appear next. However, some recent papers instead evaluate recommendation systems with popularity-sampled metrics, which measure how well the model can find a user's next item when hidden amongst generally-popular items. We argue that these popularity-sampled metrics are more appropriate for recommender systems, because the most probable items for a user often include generally-popular items. If the probability that a customer will watch Toy Story is not much more probable than for the average customer, then the movie isn't especially relevant for them and we should not recommend it. This paper shows that optimizing popularity-sampled metrics is closely related to estimating point-wise mutual information (PMI). We propose and compare two techniques to fit PMI directly, which both improve popularity-sampled metrics for state-of-the-art recommender systems. The improvements are large compared to differences between recently-proposed model architectures.
ProtoMF: Prototype-based Matrix Factorization for Effective and Explainable Recommendations

Recent studies show the benefits of reformulating common machine learning models through the concept of prototypes – representatives of the underlying data, used to calculate the prediction score as a linear combination of similarities of a data point to prototypes. Such prototype-based formulation of a model, in addition to preserving (sometimes enhancing) the performance, enables explainability of the model's decisions, as the prediction can be linearly broken down into the contributions of distinct definable prototypes. Following this direction, we extend the idea of prototypes to the recommender system domain by introducing ProtoMF, a novel collaborative filtering algorithm. ProtoMF learns sets of user/item prototypes that represent the general consumption characteristics of users/items in the underlying dataset. Using these prototypes, ProtoMF then represents users and items as vectors of similarities to the corresponding prototypes. These user/item representations are ultimately leveraged to make recommendations that are both effective in terms of accuracy metrics, and explainable through the interpretation of prototypes' contributions to the affinity scores. We conduct experiments on three datasets to assess both the effectiveness and the explainability of ProtoMF. Addressing the former, we show that ProtoMF exhibits higher Hit Ratio and NDCG compared to other relevant collaborative filtering approaches. As for the latter, we qualitatively show how ProtoMF can provide explainable recommendations and how its explanation capabilities can expose the existence of statistical biases in the learned representations, which we exemplify for the case of gender bias.
Improved sine algorithm for global optimization

An improved sine cosine algorithm (abbreviated MSA) is presented to overcome the disadvantages of the sine cosine algorithm (SCA), such as its low accuracy, premature convergence, and slow local convergence. We changed the method for setting the conversion parameters of the SCA algorithm from a linear decline into a nonlinear decline in order to optimize the timing of global exploration and local exploration. In order to improve the convergence accuracy and to increase the convergence speed of the SCA, an inertia weight is introduced in the position update equation. Ten high-dimensional complex benchmark functions are simulated with five improved SCA algorithms, under the same particle numbers and maximum iteration times. According to our experiments, the MSA algorithm is not only much better than two of the improved SCA algorithms in optimization accuracy but also better than the other three improved SCA algorithms in terms of stability.
Multi-scale graph learning for ovarian tumor segmentation from CT images

Ovarian cancer is the gynecological malignant tumor with low early diagnosis rate and high mortality. Automated and reliable segmentation of ovarian tumor plays an essential role in ovarian cancer radiotherapy, which assists radiologists to assess cancer progresses and make fast therapeutic schedules. However, ovarian cancer is characterized by polycentric tumors and the tumors are irregular in size, which makes the tumor segmentation task challenging. In this paper, a multi-scale graph learning U-Net (MGLU-Net) is proposed for ovarian tumor segmentation. The network adopts an encoder-decoder architecture as the basic backbone. The feature maps of different scales generated by the encoder are projected into graphs with different sizes. On this basis, the graph learning module is designed to jointly exploit local information and long-range dependencies among ovarian tumors at different locations. Meanwhile, multiple graphs with different sizes are constructed to handle the size variation problem. A channel attention module is further designed to highlight the informative features from the channel domain. Extensive experiments are conducted on a real ovarian CT image dataset and Sliver07 challenge dataset. Experiments demonstrate that the proposed method shows superior performance than the state-of-the-art methods.
The XP Stabiliser Formalism: a Generalisation of the Pauli Stabiliser Formalism with Arbitrary Phases

We propose an extension to the Pauli stabiliser formalism that includes fractional $2\pi/N$ rotations around the $Z$ axis for some integer $N$. The resulting generalised stabiliser formalism - denoted the XP stabiliser formalism - allows for a wider range of states and codespaces to be represented. We describe the states which arise in the formalism, and demonstrate an equivalence between XP stabiliser states and 'weighted hypergraph states' - a generalisation of both hypergraph and weighted graph states. Given an arbitrary set of XP operators, we present algorithms for determining the codespace and logical operators for an XP code. Finally, we consider whether measurements of XP operators on XP codes can be classically simulated.
A hybrid data‐level sampling approach in learning from skewed user‐click data for click fraud detection in online advertising

Abstract One of the challenging issues in user‐click data of online advertising is the uneven class distribution which biases classification models. Resampling the data is a popular choice for obtaining class balance. However, oversampling results in overfitting, whilst under‐sampling results in information loss. Moreover, enhancing separability between samples, where the classes overlap closer to the decision boundary, is another challenge, which requires a careful pruning of instances towards increasing the separability in data space. Therefore, in this work, a new hybrid data sampling algorithm SMOTEOSS is designed and evaluated, concatenating the synthetic minority oversampling technique (SMOTE) followed by one‐sided selection (OSS) to balance the class distribution. The working of SMOTEOSS is twofold‐ first, it oversamples the under‐represented class distribution using the SMOTE by generating synthetic instances. However, the generation of synthetic instances closer to the decision boundary directly influences the learning model's decision‐making. Utilising OSS, the proposed method then identifies TOMEKLINKS and eliminates the noisy majority instances whilst eliminating the redundant instances. The proposed method's effectiveness is validated on the FDMA 2012 dataset against 10 state‐of‐the‐art sampling methods utilising the gradient tree boosting learning model. To authenticate SMOTEOSS, a fair comparison is made by conducting experiments on other 10 benchmark imbalanced datasets using 10‐fold cross‐validation. Performance is measured using average precision, recall, F1‐score, G‐mean, the area under curve (AUC) and reduction rate. Results showed that the designed hybrid methodology is an efficient alternative to existing sampling methods. The Wilcoxon signed‐rank test is employed to demonstrate significant differences amidst the proposed and conventional sampling algorithms.
On Decoding Strategies for Neural Text Generators

Abstract When generating text from probabilistic models, the chosen decoding strategy has a profound effect on the resulting text. Yet the properties elicited by various decoding strategies do not always transfer across natural language generation tasks. For example, while mode-seeking methods like beam search perform remarkably well for machine translation, they have been observed to lead to incoherent and repetitive text in story generation. Despite such observations, the effectiveness of decoding strategies is often assessed on only a single task. This work—in contrast—provides a comprehensive analysis of the interaction between language generation tasks and decoding strategies. Specifically, we measure changes in attributes of generated text as a function of both decoding strategy and task using human and automatic evaluation. Our results reveal both previously observed and novel findings. For example, the nature of the diversity–quality trade-off in language generation is very task-specific; the length bias often attributed to beam search is not constant across tasks. https://github.com/gianwiher/decoding-NLG
POI Recommendation Method of Neural Matrix Factorization Integrating Auxiliary Attribute Information

Point-of-interest (POI) recommendation is the prevalent personalized service in location-based social networks (LBSNs). A single use of matrix factorization (MF) or deep neural networks cannot effectively capture the complex structure of user–POI interactions. In addition, to alleviate the data-sparsity problem, current methods primarily introduce the auxiliary information of users and POIs. Auxiliary information is often judged to be equally valued, which will dissipate some of the valuable information. Hence, we propose a novel POI recommendation method fusing auxiliary attribute information based on the neural matrix factorization, integrating the convolutional neural network and attention mechanism (NueMF-CAA). First, the k-means and term frequency–inverse document frequency (TF-IDF) algorithms are used to mine the auxiliary attribute information of users and POIs from user check-in data to alleviate the data-sparsity problem. A convolutional neural network and an attention mechanism are applied to learn the expression of auxiliary attribute information and distinguish the importance of auxiliary attribute information, respectively. Then, the auxiliary attribute information feature vectors of users and POIs are concatenated with their respective latent feature vectors to obtain the complete latent feature vectors of users and POIs. Meanwhile, generalized matrix factorization (GMF) and multilayer perceptron (MLP) are used to learn the linear and nonlinear interactions between users and POIs, respectively, and the last hidden layer is connected to integrate the two parts to alleviate the implicit feedback problem and make the recommendation results more interpretable. Experiments on two real-world datasets, the Foursquare dataset and the Weibo dataset, demonstrate that the proposed method significantly improves the evaluation metrics—hit ratio (HR) and normalized discounted cumulative gain (NDCG).