Reviewerly/exHarmony huggingface dataset:
Weighted Neural Collaborative Filtering: Deep Implicit Recommendation with Weighted Positive and Negative Feedback

Being able to generate personalized recommendations is a widespread objective in (online) retail. The focus of this research is to estimate the relevance of user-item combinations based on previous interactions using implicit feedback. We do this in a situation where interactions are often repeated, focusing on new ones. We bring two weighting schemes of positive and negative implicit feedback together into a single Weighted Matrix Factorization (WMF) model to handle the uncertainty associated with implicit preference information. Next, we bring the concept of these weighting schemes to a Deep Learning framework by introducing a Neural Weighted Matrix Factorization model (NeuWMF). We experiment with different weights, loss functions, and regularization terms, and evaluate both models using purchase data from an online supermarket. Our WMF model with both weighted positive and negative feedbacks gives superior performance in terms of NDCG and HR over regular WMF models. Even better results are obtained by our NeuWMF model, which is better capable of capturing the complex patterns behind item preferences. Especially the weighting of positive terms gives an extra boost compared to the state-of-the-art NeuMF model. We confirm the practical use of our model results in an experiment on real customer interactions.
MUTUAL: Multi-Domain Sentiment Classification via Uncertainty Sampling

Multi-domain sentiment classification trains a classifier using multiple domains and then tests the classifier on one of the domains. Importantly, no domain is assumed to have sufficient labeled data; instead, the goal is leveraging information between domains, making multi-domain sentiment classification a very realistic scenario. Typically, labeled data is costly because humans must classify it manually. In this context, we propose the MUTUAL approach that learns general and domain-specific sentence embeddings that are also context-aware due to the attention mechanism. In this work, we propose using a stacked BiLSTM-based Autoencoder with an attention mechanism to generate the two above-mentioned types of sentence embeddings. Then, using the Jensen-Shannon (JS) distance, the general sentence embeddings of the four most similar domains to the target domain are selected. The selected general sentence embeddings and the domain-specific embeddings are concatenated and fed into a dense layer for training. Evaluation results on public datasets with 16 different domains demonstrate the efficiency of our model. In addition, we propose an active learning algorithm that first applies the elliptic envelope for outlier removal to a pool of unlabeled data that the MUTUAL model then classifies. Next, the most uncertain data points are selected to be labeled based on the least confidence metric. The experiments show higher accuracy for querying 38% of the original data than random sampling.
Aging and rejuvenating strategies for fading windows in multi-label classification on data streams

Combining the challenges of streaming data and multi-label learning, the task of mining a drifting, multi-label data stream requires methods that can accurately predict labelsets, adapt to various types of concept drift and run fast enough to process each data point before the next arrives. To achieve greater accuracy, many multi-label algorithms use computationally expensive techniques, such as multiple adaptive windows, with little concern for runtime and memory complexity. We present Aging and Rejuvenating kNN (ARkNN) which uses simple resources and efficient strategies to weight instances based on age, predictive performance, and similarity to the incoming data. We break down ARkNN into its component strategies to show the impact of each and experimentally compare ARkNN to seven state-of-the-art methods for learning from multi-label data streams. We demonstrate that it is possible to achieve competitive performance in multi-label classification on streams without sacrificing runtime and memory use, and without using complex and computationally expensive dual memory strategies.
An active learning budget-based oversampling approach for partially labeled multi-class imbalanced data streams

Learning classification models from multi-class imbalanced data streams is a challenging task in machine learning. Moreover, there is a common assumption that all instances are labeled and available for the training phase. However, this is not realistic in real-world scenarios when learning from partially labeled data. In this work, we propose an active learning method based on labeling budget that can tackle multi-class imbalance data, concept drift, and limited access to labels. The proposed method combines information from budget constraints and dynamic class ratios to generate new relevant instances. We performed experiments on 18 real-world data streams and 11 semi-synthetic data streams, under different labeling budgets, in order to evaluate the performance of the proposed method under a varied set of scenarios. The experimental study showed that our oversampling method was able to improve the performance of state-of-the-art classifiers for multi-class imbalanced data streams under strict budgets and outperforms previously proposed oversampling methods in the domain.
Scalable Coercion-Resistant E-Voting under Weaker Trust Assumptions

Electronic voting (e-voting) is regularly used in many countries and organizations for legally binding elections. In order to conduct such elections securely, numerous e-voting systems have been proposed over the last few decades. Notably, some of these systems were designed to provide coercion-resistance. This property protects against potential adversaries trying to swing an election by coercing voters.
PoRt: Non-Interactive Continuous Availability Proof of Replicated Storage

Secure cryptographic storage is one of the most important issues that both businesses and end-users take into account before moving their data to either centralized clouds or blockchain-based decentralized storage marketplace. Recent work [4] formalizes the notion of Proof of Storage-Time (PoSt) which enables storage servers to demonstrate non-interactive continuous availability of outsourced data in a publicly verifiable way. The work also proposes a stateful compact PoSt construction, while leaving the stateless and transparent PoSt with support for proof of replication as an open problem. In this paper, we consider this problem by constructing a proof system that enables servers to simultaneously demonstrate continuous availability and dedication of unique storage resources for encoded replicas of a data file in a stateless and publicly verifiable way. We first formalize Proof of Replication-Time (PoRt) by extending PoSt formal definition and security model to provide support for replications. Then, we provide a concrete instantiation of PoRt by designing a lightweight replica encoding algorithm where replicas' failures are efficiently located through an efficient comparison-based verification process, after the data deposit period ends. PoRt's proofs are aggregatable: the prover can take several sequentially generated proofs and efficiently aggregate them into a single, succinct proof. The protocol is also stateless in the sense that the client can efficiently extend the deposit period by incrementally updating the tags and without requiring to download the outsourced file replicas. We also demonstrate feasible extensions of PoRt to support dynamic data updates, and be transparent to enable its direct use in decentralized storage networks, a property not supported in previous proposals. Finally, PoRt's verification cost is independent of both outsourced file size and deposit length.
A Hierarchical Game-Theoretic Decision-Making for Cooperative Multiagent Systems Under the Presence of Adversarial Agents

Underlying relationships among Multi-Agent Systems (MAS) in hazardous scenarios can be represented as Game-theoretic models. This paper proposes a new hierarchical network-based model called Game-theoretic Utility Tree (GUT), which decomposes high-level strategies into executable low-level actions for cooperative MAS decisions. It combines with a new payoff measure based on agent needs for real-time strategy games. We present an Explore game domain, where we measure the performance of MAS achieving tasks from the perspective of balancing the success probability and system costs. We evaluate the GUT approach against state-of-the-art methods that greedily rely on rewards of the composite actions. Conclusive results on extensive numerical simulations indicate that GUT can organize more complex relationships among MAS cooperation, helping the group achieve challenging tasks with lower costs and higher winning rates. Furthermore, we demonstrated the applicability of the GUT using the simulator-hardware testbed - Robotarium. The performances verified the effectiveness of the GUT in the real robot application and validated that the GUT could effectively organize MAS cooperation strategies, helping the group with fewer advantages achieve higher performance.
Finite Multivariate McDonald's Beta Mixture Model Learning Approach in Medical Applications

In this paper, we present a finite mixture model with a generalization of Beta distribution called the McDonald's Beta Mixture Model (McBMM). The parameters of the McBMM are estimated via the maximum likelihood estimation technique and EM algorithm using the Newton-Raphson technique as an iterative approach that assists in computing the updated parameters. We apply our proposed model to medical applications namely, targeting treatment for heart disease patients based on clinical data, breast tissue analysis considering histopathological images, and malaria detection using histological images. Compared to the Gaussian mixture model (GMM), the Beta mixture model performs better on data with strictly bounded values and asymmetric distribution. Three real-world datasets are modelled using the new McBMM, showing that this model fits better than the GMM and has better accuracy.
Call Reason Prediction using Hierarchical Models

We propose a supervised method to predict a reason for making call to customer contact centers by exploiting domain knowledge wherein domain experts propose a few reason codes to label the calls. They also mark an utterance in the call that would indicate the reason for making the call, this is called a marked utterance. Thus we solve a two-level prediction problem for a call, first to classify whether an utterance is marked, and then predict a reason code around this marked utterance as a label for the call. We use hierarchical models (LSTM or BERT layered on top of a transformer) for modeling purpose. We carry out comprehensive experiments using our internal (labeled) call data set, marked by domain experts, and achieve some interesting results like the accuracy of prediction of reason codes leaps to 85% using hierarchical model.
Robust Kalman Filter for Systems With Colored Heavy-Tailed Process and Measurement Noises

In this brief, we consider the robust state estimation for a linear system with colored heavy-tailed process and measurement noises. We employ the state augmentation and measurement differencing methods to whiten the colored noise and use the Student’s t distribution to model the heavy-tailed property, which makes a new state space model with the augmented state vector. The posterior estimation of the system state, inaccurate scale matrices, and auxiliary parameters are jointly inferred with the variational Bayes method by constructing the hierarchical Gaussian forms of the prediction and likelihood probability density functions and selecting the proper prior distributions of the scale matrices and auxiliary parameters. A typical target tracking simulation is given to confirm the performance of the proposed robust Kalman filter.
A Multi-Swarm PSO Approach to Large-Scale Task Scheduling in a Sustainable Supply Chain Datacenter

Supply chain management is a vital part of ensuring service quality and production efficiency in industrial applications. With the development of cloud computing and data intelligence in modern industries, datacenters have become an important basic support for intelligent applications. However, the increase in the number and complexity of tasks makes datacenters face increasingly heavy task processing demands. Therefore, there are problems of long task completion time and long response time in the task scheduling process of the data center. A multi-swarm particle swarm optimization task scheduling approach based on load balancing is proposed in this paper, aiming to reduce the maximum completion time and response time in task scheduling. The proposed approach improves the fitness evaluation function of particle swarms to facilitate load balancing. The new adaptive inertia weight and initialization method design can improve the search efficiency and convergence speed of particles. Meanwhile, the multi-swarm design can avoid the problem of particles falling into local optimum as much as possible. Finally, the proposed algorithm is verified experimentally using the task dataset released by Alibaba datacenter, and compared with other benchmark algorithms. The results show that the proposed algorithm can improve the task scheduling performance of the datacenter in supply chain management when dealing with different workloads and changes in the number of elastic machines.
A Novel Color Image Watermarking Method with Adaptive Scaling Factor Using Similarity-Based Edge Region

This study aimed to deal with three challenges: robustness, imperceptibility, and capacity in the image watermarking field. To reach a high capacity, a novel similarity-based edge detection algorithm was developed that finds more edge points than traditional techniques. The colored watermark image was created by inserting a randomly generated message on the edge points detected by this algorithm. To ensure robustness and imperceptibility, watermark and cover images were combined in the high-frequency subbands using Discrete Wavelet Transform and Singular Value Decomposition. In the watermarking stage, the watermark image was weighted by the adaptive scaling factor calculated by the standard deviation of the similarity image. According to the results, the proposed edge-based color image watermarking technique has achieved high payload capacity, imperceptibility, and robustness to all attacks. In addition, the highest performance values were obtained against rotation attack, to which sufficient robustness has not been reached in the related studies.
Unsupervised Forecasting and Anomaly Detection of ADLs in single-resident elderly smart homes

As the aging population increases, predictive health applications for the elderly can provide opportunities for more independent living, increase cost efficiency and improve the quality of health services for senior citizens. Human activity recognition within IoT-based smart homes can enable detection of early health risks related to mild cognitive impairment by providing proactive measurements and interventions to both the elderly and supporting healthcare givers. In this paper, we develop and evaluate a method to forecast activities of daily living (ADL) and detect anomalous behaviour using motion sensor data from smart homes. We build a predictive Multivariate long short term memory (LSTM) model for forecasting activities and evaluate it using data from six real-world smart homes. Further, we use Mahalanobis distance to identify anomalies in user behaviors based on predictions and actual values. In all of the datasets used for forecasting both duration of stay and level of activities using duration of activeness/stillness features, the max NMAE error was about 6%, the values show that the performance of LSTM for predicting the direct next activity versus the seven coming activities are close.
A Lightweight Authentication and Privacy Preservation Scheme for MQTT

Message Queuing Telemetry Transport (MQTT) is a protocol commonly used in smart IoT applications. The protocol reduces the resource saturation but does not implement appropriate security mechanisms. There have been attempts to add security features to MQTT; however, they do not take into account the resource-constrained nature of IoT devices. The Cipher-text Policy Attribute-Based Encryption (CP-ABE) scheme provides fine-grained access to topic-related data and adequate data storage on MQTT server. In this work, we propose an Improved CP-ABE (ICP-ABE) scheme integrated with a lightweight symmetric encryption algorithm - PRESENT. The new scheme separates the roles of attribute auditing and key extraction. By using a blind key, MQTT servers verify the identity of sender nodes without knowing the sender's attributes. The PRESENT algorithm is employed in the proposed scheme in order to securely share such blind keys between clients. The efficiency of the scheme is evaluated in terms of throughput, packet delivery ratio, network delay, and execution time.
Solving an industry-inspired generalization of lifelong MAPF problem including multiple delivery locations

In this study, we worked on a new industry-inspired generalization of the multi-agent path finding (MAPF) problem. In this new problem, incoming jobs should be assigned to agents in a way that minimizes the total path cost. In this problem, definition of a job is visiting a certain coordinate by the agent. In addition, the agents must keep the following tasks on their own lists and decide the order of visiting the tasks assigned to them by minimizing the total distance traveled. This new structure can be applied to many real-world problems like cleaning an area with autonomous robots, automated warehouses and area protection with autonomous robots. In solving this problem, three different problems must be solved; to which agent a new job will be assigned, in what order the agents will handle that job, and finally, how agents will act without colliding with each other when the agents have multiple destinations. This problem is different from the job-distribution problems in the MAPF literature since the optimization criterion is total path cost and agents have to decide the order of the jobs to be visited. For this reason, new job-distribution methods that meet these goals are presented in this study. Five new heuristic algorithms that produce fast solutions for job-distribution are presented. Furthermore, we presented one brute force algorithm to find the optimal path-cost solution and compare with the heuristics created. Experiments conducted on benchmark maps have demonstrated that the job-distribution algorithm yielding the best performance resulted in path costs that were at most three to three and a half percent higher compared to the brute force approach. Additionally, the heuristics developed were found to operate at least ten times faster in terms of processing time when compared to the brute force approach. The CBS-D*-lite algorithm was previously presented in the literature and shown to produce good results in incremental environments. With these modifications, agents can have multiple destinations and collisions between the agent paths are avoided.
Adaptive interacting multiple model for underwater maneuvering target tracking with one-step randomly delayed measurements

The existing interacting multiple model (IMM) suffers from two problems when tracking an underwater maneuvering target: 1) the conventional nonlinear filters in IMM cannot work on the condition of delayed measurement, 2) the inefficiency of model switching in IMM may degrade the tracking performance. In order to handle the delayed measurement, a novel Bayesian filter framework under two Gaussian assumptions is proposed. The new extended Kalman filter (EKF) and unscented Kalman filter (UKF) are developed as two variations of the proposed filter. In addition, to solve the problem of low switching efficiency between IMM models, a transfer probability corrected function is designed to adaptively update the transfer probability matrix (TPM) in IMM. The simulation results show that the proposed algorithm outperforms the existing IMM-EKF and IMM-UKF.
POEM: A prototype cross and emphasis network for few-shot semantic segmentation

To tackle the demand for large labeled datasets and poor unseen class generalization for segmentation tasks, few-shot segmentation (FSS) has received widespread attention, which utilizes prototypical learning to achieve significant progress. However, the prototypes generated by averaging the global object information still suffer from the appearance diversity within the target class. Motivated by the fact that humans only need part attributes rather than all attributions to recognize objects from a particular class, we propose an effective prototype cross and emphasis segmentation network (POEM) to provide more precise guidance in the feature matching process. Firstly, we present a regional crossed referencing module to improve the prototypical learning strategy, which mines the relevant attributes of the target objects in both support and query images and customizes the prototypes for the query images. Secondly, we develop a foreground-emphasized reasoning module to reduce the negative influence caused by complex background clutter, in which the foreground and background attributions are regarded as fundamental information and auxiliary information, respectively. Extensive experiments on two standard datasets have demonstrated the superiority of POEM over the state-of-the-art methods.
Completable sets of orthogonal product states with minimal nonlocality

Since the theory of quantum nonlocality without entanglement was proposed, great achievements have been made in the local distinguishability of bipartite orthogonal quantum states, but there are still many unresolved problems. In this paper, we first propose the concept of minimal nonlocality to characterize the lower bound of the amount of the elements contained in a nonlocal set of orthogonal product states. A nonlocal set of orthogonal product states has minimal nonlocality if and only if the set can be distinguished by local operations and classical communication (LOCC) after only a certain state of the set is removed. Then we give a novel method to construct completable sets with minimal nonlocality in bipartite quantum system. Compared with the existing results, the completable sets we construct have the least number of elements in the same quantum systems. Finally, we extend our construction method of completable nonlocal set with fewer elements in bipartite system to tripartite or even more multipartite quantum systems.
BMISP: Bidirectional mapping of image signal processing pipeline

After being processed by the image signal processing (ISP) pipeline in digital cameras, the sRGB images are nonlinear, and thus are not suitable for the computer vision tasks which work best in a linear color space. Therefore, mapping nonlinear sRGB images back to a linear color space is a highly valuable task. To achieve an accurate mapping, this paper proposes a framework based on convolutional neural networks, which models the ISP pipeline in both reverse and forward directions. In particular, for the reverse mapping, a U-net structure is applied to extract features from a given sRGB image, and the extracted features are utilized to estimate the linear and nonlinear transformations in the ISP pipeline. For the forward mapping, the original sRGB image is used as a guidance to embed the camera-style information. To incorporate the encoded prior knowledge, affine transformations are employed to modulate the features. Experiments demonstrate that the proposed framework is able to achieve the state-of-the-art performance.