Reviewerly/exHarmony huggingface dataset:
Graph Enhanced Neural Interaction Model for recommendation

Since user-item interactions in recommender systems can be naturally modeled as a bipartite graph, recent studies have started to incorporate graph neural networks (GNNs) to learn user and item representations. However, existing GNN-based models for recommendation usually emphasize the graph structure but neglect the rich node (i.e., users and items) features or linearly integrate the node features without considering the interactions among these features, which leads to a suboptimal recommendation model. Considering the features (e.g., categorical features) are often discrete, sparse, and high-dimensional, how to jointly utilize the graph structure and node features while considering the feature interactions is a major challenge. In this paper, we propose Graph Enhanced Neural Interaction Model (GENIM), a novel graph recommendation model consisting of three parts: (1) graph convolution layers that recursively propagate the encoded node features on the user-item bipartite graph; (2) the neural feature interaction layer that learns node feature interactions, which contains rich signals for predictive analytics; (3) an optional hashing-based embedding layer that is used to reduce the model size. Extensive experiments conducted on two real-world datasets show that our model outperforms other state-of-the-art solutions.
An adaptive IMM filter for jump Markov systems with inaccurate noise covariances in the presence of missing measurements

This paper derives a Kullback-Leibler average (KLA) based adaptive interacting multiple model (IMM) filter for jump Markov systems with inaccurate noise covariances in the presence of missing measurements. Firstly, a switching error model (SEM) is constructed to model the probability density function of measurement likelihood. The SEM can automatically choose appropriate error model for the real measurement or pure measurement noise by estimating a binary indicator. Secondly, by assigning conjugate priors on inaccurate noise covariances and binary indicator, the state, noise covariances and binary indicator are jointly estimated based on variational Bayesian (VB) technique. Finally, by using the KLA fusion scheme to fuse the conditioned estimates from every mode in the mixing and output phase, an adaptive IMM filter is derived. Simulation results demonstrate that the proposed filter achieves better performance than existing typical algorithms.
Comparing PSO-based clustering over contextual vector embeddings to modern topic modeling

Efficient topic modeling is needed to support applications that aim at identifying main themes from a collection of documents. In the present paper, a reduced vector embedding representation and particle swarm optimization (PSO) are combined to develop a topic modeling strategy that is able to identify representative themes from a large collection of documents. Documents are encoded using a reduced, contextual vector embedding from a general-purpose pre-trained language model (sBERT). A modified PSO algorithm (pPSO) that tracks particle fitness on a dimension-by-dimension basis is then applied to these embeddings to create clusters of related documents. The proposed methodology is demonstrated on two datasets. The first dataset consists of posts from the online health forum r/Cancer and the second dataset is a standard benchmark for topic modeling which consists of a collection of messages posted to 20 different news groups. When compared to the state-of-the-art generative document models (i.e., ETM and NVDM), pPSO is able to produce interpretable clusters. The results indicate that pPSO is able to capture both common topics as well as emergent topics. Moreover, the topic coherence of pPSO is comparable to that of ETM and its topic diversity is comparable to NVDM. The assignment parity of pPSO on a document completion task exceeded 90% for the 20NewsGroups dataset. This rate drops to approximately 30% when pPSO is applied to the same Skip-Gram embedding derived from a limited, corpus-specific vocabulary which is used by ETM and NVDM.
Intensity diffusion: a concealed cause of fringe distortion in fringe projection profilometry

Fringe projection profilometry (FPP) is widely used in optical three-dimensional (3D) measurements because of its high stability. In FPP, fringe distortion is an inevitable and highly complex systematic error that significantly reduces the 3D measurement accuracy. At this point, the existing causes of fringe distortion represented by gamma distortion, high-order harmonics, and image saturation have been effectively analyzed and compensated to restore high-quality fringe images. In this paper, we innovatively reveal a concealed cause of fringe distortion, i.e., intensity diffusion across pixels, which is induced by photocarrier diffusion between photodiodes. To the best of our knowledge, intensity diffusion has not been studied in the field of fringe restoration. Based on the motion of photocarrier diffusion, we theoretically analyze the mechanism of how the intensity diffusion affects FPP. Subsequently, an intensity diffusion model is established for quantifying the diffused intensity in each pixel, and an intensity diffusion correction algorithm is presented to remove the diffused intensity from the fringe images and correct the fringe distortion. Experiments demonstrate the impact of intensity diffusion on FPP, and the 3D measurement results prove the effectiveness of the proposed methods on improving the 3D measurement accuracy by correcting the fringe distortion.
Constructions of (vectorial) bent functions outside the completed Maiorana–McFarland class

Two new classes of bent functions derived from the Maiorana–McFarland (M) class, so-called C and D, were introduced by Carlet (1993) almost three decades ago. In Zhang (2020) sufficient conditions for specifying bent functions in C and D which are outside the completed M class, denoted by M#, were given. Furthermore in Pasalic et al. (2021) the notion of vectorial bent functions which are weakly or strongly outside M#, referring respectively to the case whether some or all nonzero linear combinations (called components) of its coordinate functions are in class C (or D) but provably outside M#, was introduced. In this article we continue the work of finding new instances of vectorial bent functions weakly/strongly outside M# using a different approach. Namely, a generic method for the construction of vectorial bent (n,t)-functions of the form F(x,y)=G(x,y)+H(x,y), n=2m,t|m, was recently proposed in Bapić (2021), where G is a given bent (n,t)-function satisfying certain properties and H is an arbitrary (t,t)-function having certain form. We introduce a new superclass of bent functions SC which contains the classes D0 and C and whose members are provably outside M#. Most notably, using indicators of the form 1L⊥(x,y)+δ0(x) to define members of this class leads for the first time to modifications of the M class performed on sets rather than on affine subspaces. We also show that for suitable choices of H, the function F is a vectorial bent function weakly/strongly outside the class M#. In this context, a new concept of being almost strongly outside M# is introduced and some families of vectorial bent functions with this property are given. Furthermore, we provide two new families of vectorial bent functions strongly outside M# (considered to be an intrinsically hard problem) whose output dimension is greater than 2, thus giving first examples of such functions in the literature.
Bipartite network embedding with Symmetric Neighborhood Convolution

Mining bipartite networks, a ubiquitous type of model particularly developed for networks with two different classes of nodes, is of great importance in various applications. Recently, the advance of network representation learning has provided more tractable, effective and robust methods for several graph mining tasks. However, the strength of representation learning has not been sufficiently explored for bipartite structure, since that most existing learning methods either separately treat the nodes in different classes by defective projections, or independently sample instances for learning by adopting relation-aware random walks. Both methods can lose information and they hardly leverage the symmetry property of bipartite structure directly. In this paper, a novel method called Symmetric Neighborhood Convolution is proposed to fulfill the representation learning task in bipartite networks. The method takes into consideration the topological symmetry property and utilizes one-dimensional convolution kernels to extract features from neighborhood for the nodes in each class. Three different objectives are designated to compare the performance of the proposed convolution-based mechanism. The method is evaluated on five real-world datasets by the tasks of link prediction and recommendation. Comparing to eight competitive baselines, experimental results demonstrate the effectiveness of our method.
Systematic Review of Computer Vision Semantic Analysis in Socially Assistive Robotics

The simultaneous surges in the research on socially assistive robotics and that on computer vision can be seen as a result of the shifting and increasing necessities of our global population, especially towards social care with the expanding population in need of socially assistive robotics. The merging of these fields creates demand for more complex and autonomous solutions, often struggling with the lack of contextual understanding of tasks that semantic analysis can provide and hardware limitations. Solving those issues can provide more comfortable and safer environments for the individuals in most need. This work aimed to understand the current scope of science in the merging fields of computer vision and semantic analysis in lightweight models for robotic assistance. Therefore, we present a systematic review of visual semantics works concerned with assistive robotics. Furthermore, we discuss the trends and possible research gaps in those fields. We detail our research protocol, present the state of the art and future trends, and answer five pertinent research questions. Out of 459 articles, 22 works matching the defined scope were selected, rated in 8 quality criteria relevant to our search, and discussed in depth. Our results point to an emerging field of research with challenging gaps to be explored by the academic community. Data on database study collection, year of publishing, and the discussion of methods and datasets are displayed. We observe that the current methods regarding visual semantic analysis show two main trends. At first, there is an abstraction of contextual data to enable an automated understanding of tasks. We also observed a clearer formalization of model compaction metrics.
Moving Towards Centers: Re-Ranking With Attention and Memory for Re-Identification

Re-ranking utilizes contextual information to optimize the initial ranking list of person or vehicle re-identification (re-ID), which boosts the retrieval performance at post-processing steps. This paper proposes a re-ranking network to predict the correlations between the probe and top-ranked neighbor samples. Specifically, all the feature embeddings of query and gallery images are expanded and enhanced by a linear combination of their neighbors, with the correlation prediction serving as discriminative combination weights. The combination process is equivalent to moving independent embeddings toward the identity centers, improving cluster compactness. For correlation prediction, we first aggregate the contextual information for probe's <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$k$</tex-math></inline-formula> -nearest neighbors via the Transformer encoder. Then, we distill and refine the probe-related features into the Contextual Memory cell via attention mechanism. Like humans that retrieve images by not only considering probe images but also memorizing the retrieved ones, the Contextual Memory produces multi-view descriptions for each instance. Finally, the neighbors are reconstructed with features fetched from the Contextual Memory, and a binary classifier predicts their correlations with the probe. Experiments on six widely-used person and vehicle re-ID benchmarks demonstrate the effectiveness of the proposed method. Especially, our method surpasses the state-of-the-art re-ranking approaches on large-scale datasets by a significant margin, i.e., with an average 4.83% CMC@1 and 14.83% mAP improvements on VERI-Wild, MSMT17, and VehicleID datasets.
Secure EMR Classification and Deduplication Using MapReduce

Healthcare providers generate huge amount of data every day through registration, lab results, prescriptions, and others. This is stored in the form of Electronic Medical Records (EMR) in a central repository. A medical record data is very huge, difficult to read and understand. To give an insight to the professionals in analyzing the different domains a patient belongs to, it is necessary to get pointers to a file before classifying it to a particular department for further analysis. This study provides a EMR processing system to automatically classify EMRs based on the important medical terms using TF-IDF and topic modeling. Automatic Classification of EMRs help the healthcare professionals in taking accurate decisions, providing efficient service, and improves the time taken for processing huge amount of data and in better organizing of patient. The data stored on the cloud may contain duplicate copies of EMR on several storage systems at file level thus increasing the network bandwidth, cost, and consuming storage space. Hence, a deduplication mechanism is required to avoid or reduce the data redundancy. Adapting cloud computing for healthcare systems necessitates sharing patient data with cloud service providers, which creates security concerns as the data may contain diagnosis, medication, laboratory results and medical claims. The main aim of this work is to classify the EMRs as per the specialization using KNN algorithm, optimize storage using deduplication and protect the data using DNA encryption algorithm before uploading to Hadoop. Data redundancy is taken care by implementing deduplication techniques using MD5 hashing. Proposed methodology shows an accuracy of 90% for EMR record classification and handles duplication and security aspects. This in-turn proves the state of the art approach for health care data management.
Distributed Latency Profiling through Critical Path Tracing

Low latency is an important feature for many Google applications such as Search, and latency-analysis tools play a critical role in sustaining low latency at scale. For complex distributed systems that include services that constantly evolve in functionality and data, keeping overall latency to a minimum is a challenging task. In large, real-world distributed systems, existing tools such as RPC telemetry, CPU profiling, and distributed tracing are valuable to understand the subcomponents of the overall system, but are insufficient to perform end-to-end latency analyses in practice. Scalable and accurate fine-grain tracing has made Critical Path Tracing the standard approach for distributed latency analysis for many Google applications, including Google Search.
Spatiotemporal change analysis of environmental quality in mining areas based on long-term landsat images

The mining of ion-adsorption rare-earth elements (REE) in South China has caused severe ecological disturbances in mining areas. Therefore, environmental monitoring and evaluation of REE mining areas have become a prerequisite for the sustainable development of the ion-adsorption REE industry. In this study, a remote sensing-based ecological index (RSEI) model was used to evaluate changes in the environmental quality of REE mining areas. Thirty Landsat images from 1986 to 2019 were used in a case study of the Lingbei mining area in Dingnan County, China. Google Earth images were used for visual interpretation and comparison with the classification results of different types of land damage and restoration for different disturbance levels. The random forest (RF) method was used for classification, and the underlying mechanism of the environmental changes was discussed. The results show that the RSEI accurately reflects the environmental quality of the REE mining areas. The analysis of the spatiotemporal distribution of the environmental quality in the REE mining areas shows that since 1986, mining techniques, such as pool leaching, heap leaching, and in-situ leaching, resulted in surface vegetation removal and topsoil stripping, causing extensive environmental damage. After 2011, the environmental quality was slowly restored due to the optimization of mining technology and the government’s focus on environmental management, resulting in the recovery of mining sites. The changes in the environmental quality of the REE mining areas in different disturbance states indicate that the area affected by human disturbance is 15.44 km2, accounting for 44.43% of the total mining area and 16.31% of the area exhibits serious impacts on the ecological environment. The proposed method provides a quantitative approach to describe the evolution of environmental quality in the mining area over the past 34 years, providing a scientific basis for the sound management of the mining areas to promote the regional sustainable development.
A conservative chaotic system with coexisting chaotic-like attractors and its application in image encryption

Unlike dissipative systems, conservative systems do not have attractors and no attractor reconstruction occurs. Therefore, these systems are more suitable for application in image encryption. On the basis of above appoints, here we develop and propose a conservative system with infinite chaotic-like attractors. The conservative and chaotic characteristics and coexistence chaotic-like attractors are studied using Lyapunov exponents, Poincare maps, and numerical simulation. The results show that the coexistence of chaotic-like attractors has a more complex structure and dynamic behaviour than traditional ones. Additionally, the developed system is further used to design an encryption system for a digital image. Using the coexistence chaotic-like attractor sequence to scramble and diffuse the image can destroy the correlation of adjacent pixels and hide the information of all pixels. The feasibility and security of the encryption scheme are demonstrated through the analysis of key space, histogram, information entropy, key sensitivity and pixel correlation.
Combining user behavioural information at the feature level to enhance continuous authentication systems

The scientific and business communities are proposing new authentication methods more robust than traditional solutions relying on a single security point such as passwords (i.e. “something you know”). User and Entity Behavior Analysis (UEBA) has postulated as an excellent solution to improve authentication systems by performing continuous authentication to extend the authentication process over time. UEBA is based on detecting anomalies in the intrinsic behaviour of each user or entity (i.e. it is based on “something you are/do”). This paper presents a method for performing continuous authentication using UEBA techniques that allows combining information from multiple sources at the feature level. This combination is achieved through a novel Symbolic Aggregate approximation (SAX) using Random Trees Embeddings for each information source, producing a sequence of symbols. Then, these sequences of symbols are combined into a single sequence using temporal information. The resulting sequence of symbols feeds a density-based clustering model that uses a distance based on DNA sequence alignment techniques to extract behavioural cores. Finally, new samples are compared against these cores to detect anomalies using a risk model that evaluates if a behaviour is anomalous (suspected user impersonation). The model has been extensively tested and evaluated against well-known state-of-the-art datasets.
A two-stage maximum entropy approach for time series regression

The maximum entropy bootstrap for time series is a technique that creates a large number of replicates, as elements of an ensemble, for inference purposes, which satisfies the ergodic and the central limit theorems. As an alternative to the use of traditional techniques, this work proposes generalized maximum entropy for the estimation of parameters in all the replicated models. An empirical application and a simulated example illustrate the advantages of this two-stage maximum entropy approach for time series regression modeling, where maximum entropy is used both in data replication and in parameter estimation.
SignDS-FL: Local Differentially Private Federated Learning with Sign-based Dimension Selection

Federated Learning (FL) [ 31 ] is a decentralized learning mechanism that has attracted increasing attention due to its achievements in computational efficiency and privacy preservation. However, recent research highlights that the original FL framework may still reveal sensitive information of clients’ local data from the exchanged local updates and the global model parameters. Local Differential Privacy (LDP), as a rigorous definition of privacy, has been applied to Federated Learning to provide formal privacy guarantees and prevent potential privacy leakage. However, previous LDP-FL solutions suffer from considerable utility loss with an increase of model dimensionality. Recent work [ 29 ] proposed a two-stage framework that mitigates the dimension-dependency problem by first selecting one “important” dimension for each local update and then perturbing the dimension value to construct the sparse privatized update. However, the framework may still suffer from utility loss because of the insufficient per-stage privacy budget and slow model convergence. In this article, we propose an improved framework, SignDS-FL , which shares the concept of dimension selection with Reference [ 29 ], but saves the privacy cost for the value perturbation stage by assigning random sign values to the selected dimensions. Besides using the single-dimension selection algorithms in Reference [ 29 ], we propose an Exponential Mechanism-based Multi-Dimension Selection algorithm that further improves model convergence and accuracy. We evaluate the framework on a number of real-world datasets with both simple logistic regression models and deep neural networks. For training logistic regression models on structured datasets, our framework yields only a \( \sim \) 1%–2% accuracy loss in comparison to a \( \sim \) 5%–15% decrease of accuracy for the baseline methods. For training deep neural networks on image datasets, the accuracy loss of our framework is less than \( 8\% \) and at best only \( 2\% \) . Extensive experimental results show that our framework significantly outperforms the previous LDP-FL solutions and enjoys an advanced utility-privacy balance.
Doc2Vec, SBERT, InferSent, and USE Which embedding technique for noun phrases?

Phrase embedding is a technique of representing phrases in vector space. A very high effort has been made to develop this technique to improve tasking in various natural language processing (NLP) applications. The evaluation of phrase embedding has been presented in many studies, but most of them focused on the intrinsic or extrinsic evaluation process regardless of the type of the phrase (noun phrases, Verb phrases …). In the literature, there is no study evaluating the embedding of noun phrases, knowing that this type is used by many NLP applications, such as automatic key-phrase extraction (AKE), information retrieval, and question answering. In this article, we will present an empirical study to compare the most common phrase embedding techniques, to determine the most suitable for representing noun phrases. Dataset used in the comparison process consists of the noun phrases from the Inspec and SemEval2010 datasets, to which we have added their manually defined synonyms.