Reviewerly/exHarmony huggingface dataset:
Performing Multi-Target Regression via a Parameter Sharing-Based Deep Network

Multi-target regression (MTR) comprises the prediction of multiple continuous target variables from a common set of input variables. There are two major challenges when addressing the MTR problem: the exploration of the inter-target dependencies and the modeling of complex input-output relationships. This paper proposes a neural network model that is able to simultaneously address these two challenges in a flexible way. A deep architecture well suited for learning multiple continuous outputs is designed, providing some flexibility to model the inter-target relationships by sharing network parameters as well as the possibility to exploit target-specific patterns by learning a set of nonshared parameters for each target. The effectiveness of the proposal is analyzed through an extensive experimental study on 18 datasets, demonstrating the benefits of using a shared representation that exploits the commonalities between target variables. According to the experimental results, the proposed model is competitive with respect to the state-of-the-art in MTR.
I'm a Giant

Advances in tracking technology and wireless headsets enable walking as a means of locomotion in Virtual Reality. When exploring virtual environments larger than room-scale, it is often desirable to increase users' perceived walking speed, for which we investigate three methods. (1) Ground-Level Scaling increases users' avatar size, allowing them to walk farther. (2) Eye-Level Scaling enables users to walk through a World in Miniature, while maintaining a street-level view. (3) Seven-League Boots amplifies users' movements along their walking path. We conduct a study comparing these methods and find that users feel most embodied using Ground-Level Scaling and consequently increase their stride length. Using Seven-League Boots, unlike the other two methods, diminishes positional accuracy at high gains, and users modify their walking behavior to compensate for the lack of control. We conclude with a discussion on each technique's strength and weaknesses and the types of situation they might be appropriate for.
Adversarial Training in Affective Computing and Sentiment Analysis: Recent Advances and Perspectives [Review Article]

Over the past few years, adversarial training has become an extremely active research topic and has been successfully applied to various Artificial Intelligence (AI) domains. As a potentially crucial technique for the development of the next generation of emotional AI systems, we herein provide a comprehensive overview of the application of adversarial training to affective computing and sentiment analysis. Various representative adversarial training algorithms are explained and discussed accordingly, aimed at tackling diverse challenges associated with emotional AI systems. Further, we highlight a range of potential future research directions. We expect that this overview will help facilitate the development of adversarial training for affective computing and sentiment analysis in both the academic and industrial communities.
May AI?

Design ideation is a prime creative activity in design. However, it is challenging to support computationally due to its quickly evolving and exploratory nature. The paper presents cooperative contextual bandits (CCB) as a machine-learning method for interactive ideation support. A CCB can learn to propose domain-relevant contributions and adapt their exploration/exploitation strategy. We developed a CCB for an interactive design ideation tool that 1) suggests inspirational and situationally relevant materials ("may AI?"); 2) explores and exploits inspirational materials with the designer; and 3) explains its suggestions to aid reflection. The application case of digital mood board design is presented, wherein visual inspirational materials are collected and curated in collages. In a controlled study, 14 of 16 professional designers preferred the CCB-augmented tool. The CCB approach holds promise for ideation activities wherein adaptive and steerable support is welcome but designers must retain full outcome control.
Empirical Comparison of Area under ROC curve (AUC) and Mathew Correlation Coefficient (MCC) for Evaluating Machine Learning Algorithms on Imbalanced Datasets for Binary Classification

A common challenge encountered when trying to perform classifications and comparing classifiers is selecting a suitable performance metric. This is particularly important when the data has class-imbalance problems. Area under the Receiver Operating Characteristic Curve (AUC) has been commonly used by the machine learning community in such situations, and recently researchers are starting to use Matthew Correlation Coefficient (MCC), especially in biomedical research. However, there is no empirical study that has been conducted to compare the suitability of the two metrics. In this paper, the aim of this study is to provide insights about how AUC and MCC are compared to each other when used with classical machine learning algorithms over a range of imbalanced datasets. In our study, we utilize an earlier-proposed criteria for comparing metrics based on the degree of consistency and degree of Discriminancy to compare AUC against MCC. We carry out experiments using four machine learning algorithms on 54 imbalanced datasets, with imbalance ratios ranging from 1% to 10%. The results demonstrate that both AUC and MCC are statistically consistent with each other; however AUC is more discriminating than MCC. The same observation is noticed when evaluated on 23 balanced datasets. This suggests AUC to be a better measure than MCC in evaluating and comparing classification algorithms.
Integrating Local CNN and Global CNN for Script Identification in Natural Scene Images

Script identification in natural scene images is a key pre-step for text recognition and is also an indispensable condition for automatic text understanding systems that are designed for multi-language environments. In this paper, we present a novel framework integrating Local CNN and Global CNN both of which are based on ResNet-20 for script identification. We first obtain a lot of patches and segmented images based on the aspect ratios of the images. Subsequently, these patches and segmented images are used as inputs to Local CNN and Global CNN for training, respectively. Finally, to get the final results, the Adaboost algorithm is used to combine the results of Local CNN and Global CNN for decision-level fusion. Benefiting from such a strategy, Local CNN fully exploits the local features of the image, effectively revealing subtle differences among the scripts that are difficult to distinguish such as English, Greek, and Russian. Moreover, Global CNN mines the global features of the image to improve the accuracy of script identification. The experimental results demonstrate that our approach has a good performance on four public datasets.
FS-PEKS: Lattice-based Forward Secure Public-key Encryption with Keyword Search for Cloud-assisted Industrial Internet of Things

Cloud-assisted Industrial Internet of Things (IIoT) relies on cloud computing to provide massive data storage services. To ensure the confidentiality, sensitive industrial data need to be encrypted before being outsourced to cloud storage server. Public-key encryption with keyword search (PEKS) enables users to search target encrypted data by keywords. However, most existing PEKS schemes are based on conventional hardness assumptions, which are vulnerable to adversaries equipped with quantum computers in the near future. Moreover, they suffer from key exposure, and thus the security would be broken once the keys are compromised. In this paper, we propose a forward secure PEKS scheme (FS-PEKS) based on lattice assumptions for cloud-assisted IIoT, which is post-quantum secure. We integrate a lattice-based delegation mechanism into FS-PEKS to achieve forward security, such that the security of the system is still guaranteed even the keys are compromised by the adversaries. We define the first formal security model on forward security of PEKS, and prove the security of FS-PEKS under the model. As the keywords of industrial data are with inherently low entropy, we further extend FS-PEKS to resist insider keyword guessing attacks (IKGA). The comprehensive performance evaluation demonstrates that FS-PEKS is practical for cloud-assisted IIoT.
Swarm intelligence for clustering — A systematic review with new perspectives on data mining

The increase in available data has attracted the interest in clustering approaches as a way of coherently aggregating them and identify patterns in big data. Hence, Swarm Intelligence techniques can outperform standard clustering techniques in some real problems. Indeed, they can replace standard techniques in some cases. The knowledge regarding the problem is not enough to select the best algorithm. It is also necessary to unveil which techniques are relevant in the literature. This paper presents a systematic mapping review on recent investigations of swarm-inspired algorithms to tackle clustering problems. We selected 161 articles from the most important scientific databases, which were published over the last six years. We discuss many aspects, such as the most used fitness functions, validation indexes, encoding schemes, hybrid proposals, frequent applications, among others. We provide an overview of how to apply the swarm methods together with a critical analysis of the current and future perspectives in the field.
An efficient method for mining high utility closed itemsets

Mining closed high utility itemsets (CHUIs) involves finding a representative set of HUIs that is usually smaller than that of HUIs but can generate the full HUIs without loss of information. Researchers have therefore shown interest in this problem, and many methods have been proposed for mining CHUIs effectively, of which CHUI-Miner and EFIM-Closed are the two most efficient algorithms. However, these face performance issues when mining CHUIs from sparse datasets. In this paper, we propose a method for the effective mining of CHUIs in both dense and sparse datasets. We first modify the compact utility list structure in the HMiner algorithm to reduce the mining time, and then develop backward and forward checking methods using the most recently explored CHUIs and combine this with candidate building for the next levels. Finally, we apply pruning strategies to reduce the search space for the generation of CHUIs. Our experimental results show that the proposed algorithm, called HMiner-Closed, is more efficient than the state-of-the-art algorithms for both dense and sparse datasets.
QoS Prediction for Mobile Edge Service Recommendation With Auto-Encoder

In the mobile edge computing environment, there are a large number of mobile edge services which are the carriers of various mobile intelligent applications. So how to recommend the most suitable candidate from such a huge number of available services is an urgent task, especially the recommendation task based on quality-of-service (QoS). In traditional service recommendation, collaborative filtering (CF) has been studied in academia and industry. However, due to the mobility of users and services, there exist several defects that limit the application of the CF-based methods, especially in an edge computing environment. The most important problem is the cold-start. In this paper, we propose an ensemble model which combines the model-based CF and neighborhood-based CF. Our approach has two phases, i.e., global features learning and local features learning. In the first phase, to alleviate the cold-start problem, we propose an improved auto-encoder which deals with sparse inputs by pre-computing an estimate of the missing QoS values and can obtain the effective hidden features by capturing the complex structure of the QoS records. In the second phase, to further improve prediction accuracy, a novel computation method is proposed based on Euclidean distance that aims to address the overestimation problem. We introduce two new concepts, common invocation factor and invocation frequency factor, in similarity computation. Then we propose three prediction models, containing two individual models and one hybrid model. The two individual models are proposed to utilize user similar neighbors and service similar neighbors, and the hybrid model is to utilize all neighbors. The experiments conducted in a real-world dataset show that our models can produce superior prediction results and are not sensitive to parameter settings.
The Curse of Knowledge in Visual Data Communication

A viewer can extract many potential patterns from any set of visualized data values.But that means that two people can see different patterns in the same visualization, potentially leading to miscommunication.Here, we show that when people are primed to see one pattern in the data as visually salient, they believe that naïve viewers will experience the same visual salience.Participants were told one of multiple backstories about political events that affected public polling data, before viewing a graph that depicted those data.One pattern in the data was particularly visually salient to them given the backstory that they heard.They then predicted what naïve viewers would most visually salient on the visualization.They were strongly influenced by their own knowledge, despite explicit instructions to ignore it, predicting that others would find the same patterns to be most visually salient.This result reflects a psychological phenomenon known as the curse of knowledge, where an expert struggles to re-create the state of mind of a novice.The present findings show that the curse of knowledge also plagues the visual perception of data, explaining why people can fail to connect with audiences when they communicate patterns in data.
Human Detection in Thermal Imaging Using YOLO

In this paper, we consider the problem of automatic detection of humans in thermal videos and images. The thermal videos are recorded on a meadow with a small forest with up to three persons present on the scene at different positions and ranges from the camera. To simulate realistic conditions that can happen during surveillance and monitoring of protected areas, all videos are recorded at night but different weather conditions--clear weather, rain, and fog. We present the results of human detection on a custom dataset of thermal videos using the out-of-the-box YOLO convolutional neural network and the YOLO network trained on a subset of our dataset. YOLO is an object detector pretrained on the COCO image dataset of RGB images of various object classes. Test experimental results have shown significantly improved performance of human detection in thermal imaging in terms of average precision for trained YOLO model over the original model.
Fuzzy logic based multi document summarization with improved sentence scoring and redundancy removal technique

Nowadays abundant amount of information is available on Internet which makes it difficult for the users to locate desired information. Automatic methods are needed to efficiently sieve and scavenge useful information from the Internet. Text summarization is identified and accepted as one of the solutions to find desired contents from one or more documents. The objective of proposed multi-document summarization is to gain good content coverage with information diversity. The proposed statistical feature based model utilizes the fuzzy model to deal with the imprecise and uncertainty of feature weight. Redundancy removal using cosine similarity is presented as enrichment to proposed work. The proposed approach is compared with DUC (Document Understanding Conference) participant systems and other summarization systems such as TexLexAn, ItemSum, Yago Summarizer, MSSF and PatSum using ROUGE measure on dataset DUC 2004. The experimental results show that our proposed work achieves a significant performance improvement over the other summarizers.
Lattice-Based Zero-Knowledge Proofs: New Techniques for Shorter and Faster Constructions and Applications

We devise new techniques for design and analysis of efficient lattice-based zero-knowledge proofs (ZKP). First, we introduce one-shot proof techniques for non-linear polynomial relations of degree $$k\ge 2$$ , where the protocol achieves a negligible soundness error in a single execution, and thus performs significantly better in both computation and communication compared to prior protocols requiring multiple repetitions. Such proofs with degree $$k\ge 2$$ have been crucial ingredients for important privacy-preserving protocols in the discrete logarithm setting, such as Bulletproofs (IEEE S&P ’18) and arithmetic circuit arguments (EUROCRYPT ’16). In contrast, one-shot proofs in lattice-based cryptography have previously only been shown for the linear case ( $$k=1$$ ) and a very specific quadratic case ( $$k=2$$ ), which are obtained as a special case of our technique. Moreover, we introduce two speedup techniques for lattice-based ZKPs: a CRT-packing technique supporting “inter-slot” operations, and “NTT-friendly” tools that permit the use of fully-splitting rings. The former technique comes at almost no cost to the proof length, and the latter one barely increases it, which can be compensated for by tweaking the rejection sampling parameters while still having faster computation overall. To illustrate the utility of our techniques, we show how to use them to build efficient relaxed proofs for important relations, namely proof of commitment to bits, one-out-of-many proof, range proof and set membership proof. Despite their relaxed nature, we further show how our proof systems can be used as building blocks for advanced cryptographic tools such as ring signatures. Our ring signature achieves a dramatic improvement in length over all the existing proposals from lattices at the same security level. The computational evaluation also shows that our construction is highly likely to outperform all the relevant works in running times. Being efficient in both aspects, our ring signature is particularly suitable for both small-scale and large-scale applications such as cryptocurrencies and e-voting systems. No trusted setup is required for any of our proposals.
A placement architecture for a container as a service (CaaS) in a cloud environment

Unlike a traditional virtual machine (VM), a container is an emerging lightweight virtualization technology that operates at the operating system level to encapsulate a task and its library dependencies for execution. The Container as a Service (CaaS) strategy is gaining in popularity and is likely to become a prominent type of cloud service model. Placing container instances on virtual machine instances is a classical scheduling problem. Previous research has focused separately on either virtual machine placement on physical machines (PMs) or container, or only tasks without containerization, placement on virtual machines. However, this approach leads to underutilized or overutilized PMs as well as underutilized or overutilized VMs. Thus, there is a growing research interest in developing a container placement algorithm that considers the utilization of both instantiated VMs and used PMs simultaneously. The goal of this study is to improve resource utilization, in terms of number of CPU cores and memory size for both VMs and PMs, and to minimize the number of instantiated VMs and active PMs in a cloud environment. The proposed placement architecture employs scheduling heuristics, namely, Best Fit (BF) and Max Fit (MF), based on a fitness function that simultaneously evaluates the remaining resource waste of both PMs and VMs. In addition, another meta-heuristic placement algorithm is proposed that uses Ant Colony Optimization based on Best Fit (ACO-BF) with the proposed fitness function. Experimental results show that the proposed ACO-BF placement algorithm outperforms the BF and MF heuristics and maintains significant improvement of the resource utilization of both VMs and PMs.
Enabling Efficient and Privacy-Preserving Aggregation Communication and Function Query for Fog Computing-Based Smart Grid

Compared with the traditional grid, smart grid involves a lot of advanced technologies and applications. However, due to rapid development, it faces a challenge to balance privacy, security, efficiency, and functionality. In this paper, we build a fog computing-based smart grid model and then present an efficient and privacy-preserving scheme which supports aggregation communication and function query based on the proposed model. To the best of our knowledge, this is the first concrete solution focusing on aspects of aggregation communication and data availability (e.g., function query) simultaneously in smart grid. In doing so, we utilize geographically distributed massive fog nodes and a centralized cloud server to achieve low-latency communication and electricity data storage. Encrypted under a double trapdoor cryptosystem, the usage data can be efficiently aggregated by fog nodes. Which supports the service provider to dynamically control and distribute the electricity. With outsourcing the encrypted usage data to the cloud, our scheme allows the service provider to launch various function queries on encrypted usage data, which is necessary for its services (e.g., billing), while letting users to have control of their own data. Therefore, our scheme can be applied to more complex smart grid applications compared with other solutions. Security proofs and analyses show that our scheme guarantees data privacy, confidentiality, authentication, and integrity. Finally, we compare our scheme with related works in terms of functionalities and most importantly, implement the scheme in a simulation environment. Experimental results indicate that our scheme is efficient with low computation and communication overheads.
TWiCe

Computer systems using DRAM are exposed to row-hammer (RH) attacks, which can flip data in a DRAM row without directly accessing a row but by frequently activating its adjacent ones. There have been a number of proposals to prevent RH, but they either incur large area overhead, suffer from noticeable performance drop on adversarial memory access patterns, or provide probabilistic protection with no capability to detect attacks.
Towards reliable named entity recognition in the biomedical domain

Automatic biomedical named entity recognition (BioNER) is a key task in biomedical information extraction. For some time, state-of-the-art BioNER has been dominated by machine learning methods, particularly conditional random fields (CRFs), with a recent focus on deep learning. However, recent work has suggested that the high performance of CRFs for BioNER may not generalize to corpora other than the one it was trained on. In our analysis, we find that a popular deep learning-based approach to BioNER, known as bidirectional long short-term memory network-conditional random field (BiLSTM-CRF), is correspondingly poor at generalizing. To address this, we evaluate three modifications of BiLSTM-CRF for BioNER to improve generalization: improved regularization via variational dropout, transfer learning and multi-task learning.We measure the effect that each strategy has when training/testing on the same corpus ('in-corpus' performance) and when training on one corpus and evaluating on another ('out-of-corpus' performance), our measure of the model's ability to generalize. We found that variational dropout improves out-of-corpus performance by an average of 4.62%, transfer learning by 6.48% and multi-task learning by 8.42%. The maximal increase we identified combines multi-task learning and variational dropout, which boosts out-of-corpus performance by 10.75%. Furthermore, we make available a new open-source tool, called Saber that implements our best BioNER models.Source code for our biomedical IE tool is available at https://github.com/BaderLab/saber. Corpora and other resources used in this study are available at https://github.com/BaderLab/Towards-reliable-BioNER.Supplementary data are available at Bioinformatics online.