Reviewerly/exHarmony huggingface dataset:
Mining Largest Maximal Quasi-Cliques

Quasi-cliques are dense incomplete subgraphs of a graph that generalize the notion of cliques. Enumerating quasi-cliques from a graph is a robust way to detect densely connected structures with applications in bioinformatics and social network analysis. However, enumerating quasi-cliques in a graph is a challenging problem, even harder than the problem of enumerating cliques. We consider the enumeration of top- k degree-based quasi-cliques and make the following contributions: (1) we show that even the problem of detecting whether a given quasi-clique is maximal (i.e., not contained within another quasi-clique) is NP-hard. (2) We present a novel heuristic algorithm K ernel QC to enumerate the k largest quasi-cliques in a graph. Our method is based on identifying kernels of extremely dense subgraphs within a graph, followed by growing subgraphs around these kernels, to arrive at quasi-cliques with the required densities. (3) Experimental results show that our algorithm accurately enumerates quasi-cliques from a graph, is much faster than current state-of-the-art methods for quasi-clique enumeration (often more than three orders of magnitude faster), and can scale to larger graphs than current methods.
A variable scale case-based reasoning method for evidence location in digital forensics

With the increasing usage of information technology on the criminal side, the digital forensic analysis, especially multimedia forensics, becomes an emerging technique for cybercrime investigators to improve examination efficiency. The study focuses on the digital triage problem for evidence location during the automatic forensic process. After defining the multi-scale knowledge base for storing digital forensic investigators’ prior knowledge, a variable scale case-based reasoning method (VSCBR) is proposed to support investigators predicting evidential areas. The variable-scale clustering algorithm based on the scale transformation strategy (VSC-STS) is also put forward, which could identify highly similar past cases containing candidate evidence in the case reuse and revise phase. A case study is established using a real 15.9 GB bidding case dataset, which contains both text bidding documents and image technical drawings. Numerical experimental results show that the validation of the proposed VSC-STS is significantly improved compared with the traditional single-scale clustering algorithm, and it is insensitive to the initial parameter threshold. Moreover, the proposed method VSCBR is able to help investigators locate suspicious rule-violating evidences in practice. • The multi-scale knowledge base is established to solve the time varying problem • The scale transformation strategies are proposed to optimize attribute selection in the scale transformation process. • A variable scale clustering algorithm based on the scale transformation strategy is proposed to solve the random attribute selection. • A variable scale case-based reasoning method is proposed to support investigators predicting evidential areas.
Procedural generation of dungeons’ maps and locked-door missions through an evolutionary algorithm validated with players

The present research introduces an evolutionary algorithm able to procedurally generate dungeon maps containing locked door missions. The evolutionary algorithm evolves a tree structure, which encodes dungeons, aiming to generate levels close to the input configuration provided by a game designer. The tree structure holds information about the number of rooms, connections between them, and their position within a 2D map. The proposed encoding also allows evolving semantic information about the narrative of the game. This is done by feasibly setting keys and locks throughout the dungeons for locked door missions. The generated dungeons are then evaluated computationally and as a proof of concept using an adventure game prototype. A total of 70 players evaluated the contents and the results show that the procedurally generated levels are perceived as more human-made, fun, and difficult than their human-made counterparts for most cases.
Towards Content Provider Aware Recommender Systems

Most existing recommender systems focus primarily on matching users (content consumers) to content which maximizes user satisfaction on the platform. It is increasingly obvious, however, that content providers have a critical influence on user satisfaction through content creation, largely determining the content pool available for recommendation. A natural question thus arises: can we design recommenders taking into account the long-term utility of both users and content providers? By doing so, we hope to sustain more content providers and a more diverse content pool for long-term user satisfaction. Understanding the full impact of recommendations on both user and content provider groups is challenging. This paper aims to serve as a research investigation of one approach toward building a content provider aware recommender, and evaluating its impact in a simulated setup.
Deep Deterministic Policy Gradient Based on Double Network Prioritized Experience Replay

The traditional deep deterministic policy gradient (DDPG) algorithm has the disadvantages of slow convergence velocity and ease of falling into the local optimum. From these two perspectives, a DDPG algorithm based on the double network prioritized experience replay mechanism (DNPER-DDPG) is proposed in this paper. Firstly, the value function is approximated by introducing the idea of two neural networks, and the minimum of the action value functions generated by the two networks is selected as the updated value of the actor policy network, which reduces the incidence of local optimal policy. Then, the Q values obtained by the two networks and the immediate reward obtained by the environment are used as the criteria for prioritization, and the importance of the samples in the experience replay mechanism is divided to improve the convergence speed of the algorithm. Finally, the improved method is demonstrated in the classic control environment of OpenAI Gym, and the results show that the proposed method has increased convergence speed and cumulative reward compared with the comparison algorithm.
Learning Recommender Systems with Implicit Feedback via Soft Target Enhancement

One-hot encoder accompanied by a softmax loss has become the default configuration to deal with the multiclass problem, and is also prevalent in deep learning (DL) based recommender systems (RS). The standard learning process of such methods is to fit the model outputs to a one-hot encoding of the ground truth, referred to as the hard target. However, it is known that these hard targets largely ignore the ambiguity of unobserved feedback in RS, and thus may lead to sub-optimal generalization performance. In this work, we propose SoftRec, a new RS optimization framework to enhance item recommendation. The core idea is that we add additional supervisory signals - well-designed soft targets - for each instance so as to better guide the recommender learning. Meanwhile, we carefully investigate the impacts of specific soft target distributions by instantiating the SoftRec with a series of strategies, including item-based, user-based, and model-based. To verify the effectiveness of SoftRec, we conduct extensive experiments on two public recommendation datasets by using various deep recommendation architectures. The experimental results show that our methods achieve superior performance compared with the standard optimization approaches. Moreover, SoftRec could also exhibit strong performance in cold-start scenarios where user-item interaction has higher sparsity.
Survey on usage and concerns of a COVID-19 contact tracing application in Japan

We aimed to identify the concerns, current implementation status and correct usage, and factors inhibiting implementation and correct use of a COVID-19 contact tracing application among the ordinary citizens in Japan.This was a cross-sectional study based on an internet survey completed by 2013 participants who were selected among registrants of an Internet research company between September 8 and 13, 2020.Participants completed an online survey that included thoughts and concerns about the application, status of use, and questions about whether the application was being used correctly. We performed multiple logistic regression analysis to clarify the association between the use of the app and sociodemographic factors and user concerns.Of the 2013 respondents, 429 (21.3%) participants reported using this application, but only 60.8% of them used it correctly. The percentage of those having some concerns about the application ranged from 45.9% to 75.5%, with the highest percentage being 'doubts about effectiveness of apps for preventing spread of infection'. Multiple logistic regression analysis revealed, the main concerns inhibiting application use were insufficient knowledge of how to use it, privacy concerns, doubts about the effectiveness of the app, and concerns about battery consumption and communication costs. Additionally, the prevalence of the application was lower for lower-income individuals.The findings suggest that income may create inequalities in the efficacy and effectiveness of COVID-19 contact tracing applications. Awareness activity strategies to dispel such concerns and support low-income individuals may be needed.
Reversible Data Hiding Scheme Based on VQ Prediction and Adaptive Parametric Binary Tree Labeling for Encrypted Images

In this paper, we propose a reversible data hiding scheme for the encrypted images (RDHEI) based on vector quantization (VQ) prediction and parametric binary tree labeling (PBTL). VQ compression is a lossy image compression method, the difference between the original image and the decompressed image is small when the length of codebook is sufficient. Thus, VQ can be applied as a tool for pixel value prediction. Based on VQ prediction, PBTL method is applied to label the embeddable and non-embeddable pixels. Through adaptive setting of parameters, the modified PBTL can provide optimal pixel labeling strategies and thus maximize the overall embedding capacity. Furthermore, the VQ index and the secret data are stream ciphered to avoid leakage of the image content and secret information. Different metrics are used to show that the marked encrypted images are highly secure. In comparison with several state-of-the-art schemes, our scheme outperforms the related works in embedding rate for two commonly applied image databases. In addition, extraction of the secret data and recovery of the original image can be operated separately according to authorization.
Performance and Cost Comparison of Cloud Services for Deep Learning Workload

Many organizations are migrating their on-premise artificial intelligence workloads to the cloud due to the availability of cost-effective and highly scalable infrastructure, software and platform services. To ease the process of migration, many cloud vendors provide services, frameworks and tools that can be used for deployment of applications on cloud infrastructure. Finding the most appropriate service and infrastructure for a given application that results in a desired performance at minimal cost, is a challenge.
Requirements Engineering Tools: An Evaluation

“If you don't know where you are going, any road will get you there." Alice from Alice in Wonderland was told this obvious piece of wisdom when she asked for directions. We all know this wisdom from navigating through the fog of insufficient requirements when working on projects. Clear goals can be achieved; unclear goals are sure to be missed. Requirements engineering (RE) is the disciplined and systematic approach (i.e., "engineering") for elicitation, documentation, analysis, agreement, verification, and management of requirements while considering market, technical, and economic goals. "Disciplined" is about culture, and "systematic" demands process and tools, which is our focus here.
SU(1,1) interferometry with parity measurement

We present a new operator method in the Heisenberg representation to obtain the signal of parity measurement within a lossless SU(1,1) interferometer. Based on this method, it is convenient to derive the parity signal directly in terms of input states, including general Gaussian or non-Gaussian state. As applications, we revisit the signal of parity measurement within an SU(1,1) interferometer when a coherent or thermal state and a squeezed vacuum state are considered as input states. In addition, we also obtain the parity signal of a Fock state when it passes through an SU(1,1) interferometer, which is also a new result. Therefore, the operator method proposed in this work may bring convenience to the study of quantum metrology, particularly the phase estimation based on an SU(1,1) interferometer.
Deep-Learning-Empowered 3D Reconstruction for Dehazed Images in IoT-Enhanced Smart Cities

With increasingly more smart cameras deployed in infrastructure and commercial buildings, 3D reconstruction can quickly obtain cities' information and improve the efficiency of government services. Images collected in outdo... | Find, read and cite all the research you need on Tech Science Press
Research on chaotic flying sparrow search algorithm

Abstract The sparrow search algorithm has attracted much attention due to its excellent characteristics, but it still has shortcomings such as falling into the local optimum and relying on the initial population stage. In order to improve these shortcomings, the chaotic flying sparrow search algorithm is proposed. In the initialization, the chaotic mapping based on random variables is introduced to make the population distribution more uniform and speed up the optimization efficiency of the population. In the discoverer stage, the dynamic adaptive search strategy and levy flight mechanism are used to increase the search range and flexibility, and the random walk strategy is introduced to make the follower’s search more detailed and avoid premature phenomenon. The effectiveness of the improved algorithm is verified by six standard test functions, and the introduction of a variety of strategies greatly enhances the optimization ability of the algorithm.
A Recommender System for Crowdsourcing Food Rescue Platforms

The challenges of food waste and insecurity arise in wealthy and developing nations alike, impacting millions of livelihoods. The ongoing pandemic only exacerbates the problem. A major force to combat food waste and insecurity, food rescue (FR) organizations match food donations to the non-profits that serve low-resource communities. Since they rely on external volunteers to pick up and deliver the food, some FRs use web-based mobile applications to reach the right set of volunteers. In this paper, we propose the first machine learning based model to improve volunteer engagement in the food waste and security domain. We (1) develop a recommender system to send push notifications to the most likely volunteers for each given rescue, (2) leverage a mathematical programming based approach to diversify our recommendations, and (3) propose an online algorithm to dynamically select the volunteers to notify without the knowledge of future rescues. Our recommendation system improves the hit ratio from 44% achieved by the previous method to 73%. A pilot study of our method is scheduled to take place in the near future.
Root Causes of Interaction Issues in Agile Software Development Teams: Status and Perspectives

It has been more than two decades since the formulation of the values and principles for the enactment of Agile methods. Since then, the software development industry increasingly began using such methods, that are various. At the core of all Agile methods, however, lies an assumption: it is the interactions between people, not just processes and plans, that allows for successful product delivery and projects productivity. Hence, Agile methods typically focus on how groups of individuals with different but often complementary skill-sets can mutually interact or self-organise to build high-performing teams capable of developing, testing, and delivering software valuable for the users and stakeholders. The validation of such methodologies in the software development industry nevertheless requires a careful analysis of the potential challenges and impediments that agile teams may encounter during their collaboration; so, a practical investigation over the root causes of these potential issues. Our aim in this paper is twofold. Firstly, we want to gather all currently defined root causes of interaction issues and impediments in agile teams. Secondly, we intend to review and systematically analyse the most common of them in order to build appropriate mitigation strategies, which -we hope- will contribute to decreasing their potential occurrences in future works.
HAL: Hybrid active learning for efficient labeling in medical domain

The success of the deep convolutional neural networks in computer vision tasks mainly relies on massive labeled training data. However, in the field of medical images, it is difficult to construct large labeled datasets since the labeling of medical images is time-consuming, labor-intensive, and medical expertise demanded. To meet the challenge, we propose a hybrid active learning framework HAL for efficient labeling in the medical domain, which integrates active learning into deep learning to reduce the cost of manual labeling and take the advantages of deep neural networks. The proposed HAL utilizes a hybrid sampling strategy considering both sample diversity and prediction loss simultaneously. The effectiveness and efficiency of proposed HAL are validated on three medical image datasets. The experimental results show that the proposed HAL outperforms several state-of-the-art active learning methods. On the Hyper-Kvasir Dataset, with only 10% of the labels, the HAL achieves 95% performance of the deep learning method trained on the entire dataset. The quantitative and qualitative analysis proves that HAL can greatly reduce the number of labels needed for training a deep neural network, which is robust to address efficient labeling problems even with imbalanced data distribution.
Differentially private graph publishing with degree distribution preservation

The goal of privacy-preserving graph publishing is to protect individual privacy in released graph data while preserving data utility. Degree distribution, serving as fundamental operations for many graph analysis tasks, is a crucial data utility. Yet, existing methods using differential privacy (DP) cannot well preserve degree distribution, since they distill a graph into a set of structural statistics (e.g. dK-series, etc.) that only captures local degree correlations, and require massive noise added to mask the change of a single edge. Recently Generative Adversarial Network for graphs (NetGAN) plays a key role in machine learning, due to its ability to capture the local and global degree distribution of the graph via biased random walks. Further, it allows us to move the burden of privacy-preserving to the learning procedure of its discriminator, rather than the extracted structure features. Inspired by this, we propose Priv-GAN, a private publishing model based on NetGAN. Instead of distilling and then publishing graphs, we publish the Priv-GAN model that is trained using the original data in a DP manner. With Priv-GAN, data holders are able to produce synthetic graph data with degree distribution preservation. Compared to alternative solutions, ours highlights that (i) a private Langevin with gradient estimate is designed as an optimizer for discriminator, which provides a theoretical gradient upper bound and achieves DP by adding noise to the gradients; and (ii) importantly, the error bound of the noisy Langevin method is theoretically analyzed, which demonstrates that with appropriate parameter settings, Priv-GAN is able to maintain high utility guarantees. Experimental results confirm our theoretical findings and the efficacy of Priv-GAN.
Labelled calculi for quantified modal logics with definite descriptions

Abstract We introduce labelled sequent calculi for quantified modal logics with definite descriptions. We prove that these calculi have the good structural properties of G3-style calculi. In particular, all rules are height-preserving invertible, weakening and contraction are height-preserving admissible and cut is syntactically admissible. Finally, we show that each calculus gives a proof-theoretic characterization of validity in the corresponding class of models.
Analysis on the Channel Prediction Accuracy of Deep Learning-based Approach

In recent days, the vehicular communication system (VCS) plays an important role in driving safety and traffic information. In VCS, one of the most important factors that affects the system performance is the channel prediction. The accurate channel prediction is a necessary part for secure vehicle-to-vehicle communication. The channel prediction in VCS has many challenges and these challenges reduce VCS performance. In this paper, we analyze the impact of the deep learning-based channel prediction algorithm for vehicle-to-vehicle communication to improve the channel prediction accuracy of VCS. We consider the algorithm called channel adaptive transmission (CAT) which uses the long short-term memory (LSTM) networks for channel prediction. The proposed approach achieves 2.6 dBm of root mean square error and over 97% of prediction accuracy. The result shows that this algorithm can be utilized efficiently in channel prediction.
Bursting through the blocks in the human mind

research-article Open AccessBursting through the blocks in the human mind: enhancing creativity with extended reality technologies Share on Authors: Xinhui Hu University of Oulu University of OuluView Profile , Vijayakumar Nanjappan University of Oulu University of OuluView Profile , Georgi V. Georgiev University of Oulu University of OuluView Profile Authors Info & Claims InteractionsVolume 28Issue 3May - June 2021 pp 57–61https://doi.org/10.1145/3460114Online:27 April 2021Publication History 0citation693DownloadsMetricsTotal Citations0Total Downloads693Last 12 Months693Last 6 weeks33 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteView all FormatsPDF
Learning multi-level weight-centric features for few-shot learning

Few-shot learning is currently enjoying a considerable resurgence of interest, aided by the recent advance of deep learning. Contemporary approaches based on weight-generation scheme delivers a straightforward and flexible solution to the problem. However, they did not fully consider both the representation power for unseen categories and weight generation capacity in feature learning, making it a significant performance bottleneck. This paper proposes a multi-level weight-centric feature learning to give full play to feature extractor's dual roles in few-shot learning. Our proposed method consists of two essential techniques: a weight-centric training strategy to improve the features' prototype-ability and a multi-level feature incorporating a mid- and relation-level information. The former increases the feasibility of constructing a discriminative decision boundary based on a few samples. Simultaneously, the latter helps improve the transferability for characterizing novel classes and preserve classification capability for base classes. We extensively evaluate our approach to low-shot classification benchmarks. Experiments demonstrate our proposed method significantly outperforms its counterparts in both standard and generalized settings and using different network backbones.