Reviewerly/exHarmony huggingface dataset:
A Cluster-Weighted Kernel K-Means Method for Multi-View Clustering

Clustering by jointly exploiting information from multiple views can yield better performance than clustering on one single view. Some existing multi-view clustering methods aim at learning a weight for each view to determine its contribution to the final solution. However, the view-weighted scheme can only indicate the overall importance of a view, which fails to recognize the importance of each inner cluster of a view. A view with higher weight cannot guarantee all clusters in this view have higher importance than them in other views. In this paper, we propose a cluster-weighted kernel k-means method for multi-view clustering. Each inner cluster of each view is assigned a weight, which is learned based on the intra-cluster similarity of the cluster compared with all its corresponding clusters in different views, to make the cluster with higher intra-cluster similarity have a higher weight among the corresponding clusters. The cluster labels are learned simultaneously with the cluster weights in an alternative updating way, by minimizing the weighted sum-of-squared errors of the kernel k-means. Compared with the view-weighted scheme, the cluster-weighted scheme enhances the interpretability for the clustering results. Experimental results on both synthetic and real data sets demonstrate the effectiveness of the proposed method.
SetRank: A Setwise Bayesian Approach for Collaborative Ranking from Implicit Feedback

The recent development of online recommender systems has a focus on collaborative ranking from implicit feedback, such as user clicks and purchases. Different from explicit ratings, which reflect graded user preferences, the implicit feedback only generates positive and unobserved labels. While considerable efforts have been made in this direction, the well-known pairwise and listwise approaches have still been limited by various challenges. Specifically, for the pairwise approaches, the assumption of independent pairwise preference is not always held in practice. Also, the listwise approaches cannot efficiently accommodate “ties” due to the precondition of the entire list permutation. To this end, in this paper, we propose a novel setwise Bayesian approach for collaborative ranking, namely SetRank, to inherently accommodate the characteristics of implicit feedback in recommender system. Specifically, SetRank aims at maximizing the posterior probability of novel setwise preference comparisons and can be implemented with matrix factorization and neural networks. Meanwhile, we also present the theoretical analysis of SetRank to show that the bound of excess risk can be proportional to √M/N, where M and N are the numbers of items and users, respectively. Finally, extensive experiments on four real-world datasets clearly validate the superiority of SetRank compared with various state-of-the-art baselines.
Nonadiabatic holonomic three-qubit controlled gates realized by one-shot implementation

Nonadiabatic holonomic gates provide a promising way to realize high-fidelity quantum gates because of their robustness and high-speed implementation. Compared with quantum computation that consists of many nonadiabatic holonomic one-qubit and two-qubit gates, the nonadiabatic holonomic multiqubit gate, which can reduce the number of quantum gates, is more efficient to construct simpler and faster complicated quantum computation. We propose the simplest scheme to realize nonadiabatic holonomic three-qubit controlled gates. On the one hand, the parameters we set to realize the three-qubit controlled gates are all time independent, which makes it easy to control and operate the quantum systems. On the other hand, our scheme avoids the combination of the elementary gates and realizes the three-qubit controlled gates only by one-shot implementation.
A word or two about nonwords: Frequency, semantic neighborhood density, and orthography-to-semantics consistency effects for nonwords in the lexical decision task.

For the most part, the effects of lexical-distributional properties of words on visual word recognition are well-established. More uncertainty remains, however, about the influence of these properties on lexical processing for nonwords. The work presented here investigates the mechanisms that guide nonword processing through an analysis of lexical decision latencies for 18,547 words and 27,079 nonwords in the British Lexicon Project (Keuleers, Lacey, Rastle, & Brysbaert, 2012) using piecewise generalized mixed models (pamms; Bender, Groll, & Scheipl, 2018; Bender & Scheipl, 2018; Bender, Scheipl, Hartl, Day, & Kuchenhoff, 2018). The pamm analysis of the data revealed 2 novel effects for nonwords in the lexical-decision task. First, whereas previous studies reported effects of base word frequency, the current study is the first to document a true nonword frequency effect. Second, we report effects of semantic neighborhood density and orthography-to-semantics consistency; not only for words, but also for nonwords. The effects of frequency, semantic neighborhood density and orthography-to-semantics consistency are facilitatory for words, but inhibitory for nonwords. The pamm analysis offers insights into the temporal development of the effects of lexical-distributional variables that are not available through more traditional analysis techniques and that shed new light on lexical processing in visual word recognition tasks. The implications of the reported results for models of visual word recognition are discussed. (PsycInfo Database Record (c) 2021 APA, all rights reserved).
Learning with Comments: An Analysis of Comments and Community on Stack Overflow

Stack Overflow (SO) has become a primary source for learning how to code, with community features supporting asking and answering questions, upvoting to signify approval of content, and comments to extend questions and answers. While past research has considered the value of posts, often based on upvoting, little has examined the role of comments. Beyond value in explaining code, comments may offer new ways of looking at problems, clarifications of questions or answers, and socially supportive community interactions. To understand the role of comments, a content analysis was conducted to evaluate the key purposes of comments. A coding schema of nine comment categories was developed from open coding on a set of 40 posts and used to classify comments in a larger dataset of 2323 comments from 50 threads over a 6-month period. Results provide insight into the way the comments support learning, knowledge development, and the SO community, and the use and usefulness of the comment feature.
Agriculture Blockchain Service Platform for Farm-to-Fork Traceability with IoT Sensors

The traceability of agricultural products has always been one of the most important things that customers care about. For the past decades, the trend of applying Internet of Things (IoT) technology to agriculture has been on an exponential rise and has also shown great success in different aspects. Not only crops can be irrigated and taken care of more precisely, but more reliable production history can be created based on the environmental data collected by IoT sensors. To achieve a trustable traceability for agricultural production process, data security becomes a major factor after the collection of massive data via IoT. By using blockchain technology for data storing, integrity and reliability can be ensured due to its immutability. Furthermore, when dealing with several components (producer, processor, distributor…etc.) on a food production chain, blockchain also has the ability to connect untrusted nodes and restrict them in following certain protocols. This study designs and implements an agriculture blockchain service platform for farm-to-fork traceability with IoT sensors. The Ethereum blockchain technology is employed and smart contracts are also designed to support tampering-proof data storing and provide reliable financial transactions which may happen throughout the entire food production chain.
Hyperspectral microscopic imaging for automatic detection of head and neck squamous cell carcinoma using histologic image and machine learning

The purpose of this study is to develop hyperspectral imaging (HSI) for automatic detection of head and neck cancer cells on histologic slides. A compact hyperspectral microscopic system is developed in this study. Histologic slides from 15 patients with squamous cell carcinoma (SCC) of the larynx and hypopharynx are imaged with the system. The proposed nuclei segmentation method based on Principle Component Analysis (PCA) can extract most nuclei in the hyperspectral image without extracting other sub-cellular components. Both spectra-based support vector machine (SVM) and patchbased convolutional neural network (CNN) are used for nuclei classification. CNNs were trained with both hyperspectral images and pseudo RGB images of extracted nuclei, in order to evaluate the usefulness of extra information provided by hyperspectral imaging. The average accuracy of spectra-based SVM classification is 68%. The average AUC and average accuracy of the HSI patch-based CNN classification is 93.7 and 82.4%, respectively. The hyperspectral microscopic imaging and classification methods provide an automatic tool to aid pathologists in detecting SCC on histologic slides.
Toward Active and Passive Confidentiality Attacks on Cryptocurrency Off-chain Networks

Cryptocurrency off-chain networks such as Lightning (e.g., Bitcoin) or Raiden (e.g., Ethereum) aim to increase the scalability of traditional on-chain transactions. To support nodes in learning about possible paths to route their transactions, these networks need to provide gossip and probing mechanisms. This paper explores whether these mechanisms may be exploited to infer sensitive information about the flow of transactions, and eventually harm privacy. In particular, we identify two threats, related to an active and a passive adversary. The first is a probing attack: here the adversary aims to detect the maximum amount which is transferable in a given direction over a target channel by actively probing it and differentiating the response messages it receives. The second is a timing attack: the adversary discovers how close the destination of a routed payment actually is, by acting as a passive man-in-the middle and analyzing the time deltas between sent messages and their corresponding responses. We then analyze the limitations of these attacks and propose remediations for scenarios in which they are able to produce accurate results.
Enhancing Network Forensics with Particle Swarm and Deep Learning: The Particle Deep Framework

The popularity of IoT smart things is rising, due to the automation they provide and its effects on productivity. However, it has been proven that IoT devices are vulnerable to both well established and new IoT-specific attack vectors. In this paper, we propose the Particle Deep Framework, a new network forensic framework for IoT networks that utilised Particle Swarm Optimisation to tune the hyperparameters of a deep MLP model and improve its performance. The PDF is trained and validated using Bot-IoT dataset, a contemporary network-traffic dataset that combines normal IoT and non-IoT traffic, with well known botnet-related attacks. Through experimentation, we show that the performance of a deep MLP model is vastly improved, achieving an accuracy of 99.9% and false alarm rate of close to 0%.
Multi-objective Optimization using NSGA II for service composition in IoT

Internet of Things (IoT) provides an innovative worldwide view to the things which are fit for communicating with one another. Each thing like temperature sensor, pressure sensor provides its functionality in the form of web service on the Internet. As the number of things connecting to the IoT network are increasing, thus one of the important challenges is to address the optimal selection of the web service to fulfil the user request. As the user requirements cannot be fulfilled using a single service so various services are composed to serve the IoT based system applications. This paper presents the usage of multi-objective metaheuristic search algorithm to provide optimal solution to Service Composition Problem (SCP) in IoT. Furthermore, this paper illustrates the use of one of the popular algorithms namely Non-dominated Sorting Genetic Algorithm (NSGA II). This paper looks at the way and methodology utilized to solve SCP in IoT. QoS parameters have become an important criterion for optimization. This algorithm is utilized to solve Quality of Service (QoS) based SCP. The results obtained are evaluated by analysing the no. of points plotted by pareto optimal curve. NSGA II is used to obtain the pareto fronts.
Feature relevance quantification in explainable AI: A causal problem

We discuss promising recent contributions on quantifying feature relevance using Shapley values, where we observed some confusion on which probability distribution is the right one for dropped features. We argue that the confusion is based on not carefully distinguishing between observational and interventional conditional probabilities and try a clarification based on Pearl's seminal work on causality. We conclude that unconditional rather than conditional expectations provide the right notion of dropping features in contradiction to the theoretical justification of the software package SHAP. Parts of SHAP are unaffected because unconditional expectations (which we argue to be conceptually right) are used as approximation for the conditional ones, which encouraged others to `improve' SHAP in a way that we believe to be flawed.
Tiki-taka algorithm: a novel metaheuristic inspired by football playing style

Purpose Metaheuristic algorithms have been commonly used as an optimisation tool in various fields. However, optimisation of real-world problems has become increasingly challenging with to increase in system complexity. This situation has become a pull factor to introduce an efficient metaheuristic. This study aims to propose a novel sport-inspired algorithm based on a football playing style called tiki-taka. Design/methodology/approach The tiki-taka football style is characterised by short passing, player positioning and maintaining possession. This style aims to dominate the ball possession and defeat opponents using its tactical superiority. The proposed tiki-taka algorithm (TTA) simulates the short passing and player positioning behaviour for optimisation. The algorithm was tested using 19 benchmark functions and five engineering design problems. The performance of the proposed algorithm was compared with 11 other metaheuristics from sport-based, highly cited and recent algorithms. Findings The results showed that the TTA is extremely competitive, ranking first and second on 84% of benchmark problems. The proposed algorithm performs best in two engineering design problems and ranks second in the three remaining problems. Originality/value The originality of the proposed algorithm is the short passing strategy that exploits a nearby player to move to a better position.
Silhouette Index as Clustering Evaluation Tool

Silhouette index is commonly used in cluster analysis for finding the optimal number of clusters, as well as for final clustering validation and evaluation as a synthetic indicator allowing to measure the general quality of clustering (relative compactness and separability of clusters—see Walesiak and Gatnar in Statystyczna analiza danych z wykorzystaniem programu R. PWN, Warszawa, p. 420, 2009). Its advantage is low computational complexity and simple interpretation rules. Recently, some proposals have appeared to use this index directly as basis of clustering algorithms. The paper is a tryout of the evaluation of such approach. In the paper examples, when the “mechanical” use of the silhouette index leads to the results that do not correspond to the actual structure of the classes are shown, the recommendations on the principles of the correct application of the index are presented.
Exploiting Multi-Modal Features from Pre-Trained Networks for Alzheimer’s Dementia Recognition

Collecting and accessing a large amount of medical data is very time-consuming and laborious, not only because it is difficult to find specific patients but also because it is required to resolve the confidentiality of a patient's medical records. On the other hand, there are deep learning models, trained on easily collectible, large scale datasets such as Youtube or Wikipedia, offering useful representations. It could therefore be very advantageous to utilize the features from these pre-trained networks for handling a small amount of data at hand. In this work, we exploit various multi-modal features extracted from pre-trained networks to recognize Alzheimer's Dementia using a neural network, with a small dataset provided by the ADReSS Challenge at INTERSPEECH 2020. The challenge regards to discern patients suspicious of Alzheimer's Dementia by providing acoustic and textual data. With the multi-modal features, we modify a Convolutional Recurrent Neural Network based structure to perform classification and regression tasks simultaneously and is capable of computing conversations with variable lengths. Our test results surpass baseline's accuracy by 18.75%, and our validation result for the regression task shows the possibility of classifying 4 classes of cognitive impairment with an accuracy of 78.70%.
Will I Sound Like Me? Improving Persona Consistency in Dialogues through Pragmatic Self-Consciousness

We explore the task of improving persona consistency of dialogue agents. Recent models tackling consistency often train with additional Natural Language Inference (NLI) labels or attach trained extra modules to the generative agent for maintaining consistency. However, such additional labels and training can be demanding. Also, we find even the best-performing persona-based agents are insensitive to contradictory words. Inspired by social cognition and pragmatics, we endow existing dialogue agents with public self-consciousness on the fly through an imaginary listener. Our approach, based on the Rational Speech Acts framework (Frank and Goodman, 2012), can enforce dialogue agents to refrain from uttering contradiction. We further extend the framework by learning the distractor selection, which has been usually done manually or randomly. Results on Dialogue NLI (Welleck et al., 2019) and PersonaChat (Zhang et al., 2018) dataset show that our approach reduces contradiction and improves consistency of existing dialogue models. Moreover, we show that it can be generalized to improve context-consistency beyond persona in dialogues.
Semantic Equivalent Adversarial Data Augmentation for Visual Question Answering

Visual Question Answering (VQA) has achieved great success thanks to the fast development of deep neural networks (DNN). On the other hand, the data augmentation, as one of the major tricks for DNN, has been widely used in many computer vision tasks. However, there are few works studying the data augmentation problem for VQA and none of the existing image based augmentation schemes (such as rotation and flipping) can be directly applied to VQA due to its semantic structure – an $$\langle image, question, answer\rangle $$ triplet needs to be maintained correctly. For example, a direction related Question-Answer (QA) pair may not be true if the associated image is rotated or flipped. In this paper, instead of directly manipulating images and questions, we use generated adversarial examples for both images and questions as the augmented data. The augmented examples do not change the visual properties presented in the image as well as the semantic meaning of the question, the correctness of the $$\langle image, question, answer\rangle $$ is thus still maintained. We then use adversarial learning to train a classic VQA model (BUTD) with our augmented data. We find that we not only improve the overall performance on VQAv2, but also can withstand adversarial attack effectively, compared to the baseline model. The source code is available at https://github.com/zaynmi/seada-vqa .
Communicative Message Passing for Inductive Relation Reasoning

Relation prediction for knowledge graphs aims at predicting missing relationships between entities. Despite the importance of inductive relation prediction, most previous works are limited to a transductive setting and cannot process previously unseen entities. The recent proposed subgraph-based relation reasoning models provided alternatives to predict links from the subgraph structure surrounding a candidate triplet inductively. However, we observe that these methods often neglect the directed nature of the extracted subgraph and weaken the role of relation information in the subgraph modeling. As a result, they fail to effectively handle the asymmetric/anti-symmetric triplets and produce insufficient embeddings for the target triplets. To this end, we introduce a Communicative Message Passing neural network for Inductive reLation rEasoning, CoMPILE, that reasons over local directed subgraph structures and has a vigorous inductive bias to process entity-independent semantic relations. In contrast to existing models, CoMPILE strengthens the message interactions between edges and entitles through a communicative kernel and enables a sufficient flow of relation information. Moreover, we demonstrate that CoMPILE can naturally handle asymmetric/anti-symmetric relations without the need for explosively increasing the number of model parameters by extracting the directed enclosing subgraphs. Extensive experiments show substantial performance gains in comparison to state-of-the-art methods on commonly used benchmark datasets with variant inductive settings.
DARTS-: Robustly Stepping out of Performance Collapse Without Indicators

Despite the fast development of differentiable architecture search (DARTS), it suffers from a standing instability issue regarding searching performance, which extremely limits its application. Existing robustifying methods draw clues from the outcome instead of finding out the causing factor. Various indicators such as Hessian eigenvalues are proposed as a signal of performance collapse, and the searching should be stopped once an indicator reaches a preset threshold. However, these methods tend to easily reject good architectures if thresholds are inappropriately set, let alone the searching is intrinsically noisy. In this paper, we undertake a more subtle and direct approach to resolve the collapse. We first demonstrate that skip connections with a learnable architectural coefficient can easily recover from a disadvantageous state and become dominant. We conjecture that skip connections profit too much from this privilege, hence causing the collapse for the derived model. Therefore, we propose to factor out this benefit with an auxiliary skip connection, ensuring a fairer competition for all operations. Extensive experiments on various datasets verify that our approach can substantially improve the robustness of DARTS. Our code is available at https://github.com/Meituan-AutoML/DARTS-