Reviewerly/exHarmony huggingface dataset:
HNGraph: Parallel Graph Processing in Hybrid Memory Based NUMA Systems

Hybrid memories exacerbate the asymmetry of memory access latencies in Non-Uniform Memory Access (NUMA) systems due to the vast performance gap between DRAM and Nonvolatile Memory (NVM). Since most graph processing systems have not considered the memory heterogeneity of NUMA nodes, they have sub-optimal performance due to improper data placement and access strategies. This paper proposes HNGraph, a graph processing framework for hybrid memory based NUMA systems. It mainly focuses on performance improvement by reducing random accesses to both local and remote NVM nodes. First, HNGraph assembles most random memory accesses in DRAM by exploiting a degree-aware partitioning strategy, which distributes high-degree and low-degree vertices to DRAM and NVM nodes, respectively. Second, we propose an adaptive graph processing model, which uses a hybrid inter-node communication mechanism to adapt to the asymmetric access latency between NVM and DRAM nodes. In DRAM nodes, we exploit a message passing communication model for remote random NVM updates. In NVM nodes, we use shared memory primitives to access remote DRAM directly. We evaluate the performance of HNGraph using different graph algorithms on typical datasets. Experimental results show that HNGraph can improve the application performance by 43.8% and 30.6% on average compared with the state-of-the-art graph processing systems GBBS and Polymer, respectively.
Eloquent: Improving Text Editing for Mobile

We present Eloquent, an exploration of new interaction techniques for text editing on mobile. Our prototype combines techniques for targeting, selection, and command menus. We demonstrate preliminary results in the form of a text editor, whose design was informed by feedback from users of existing systems. With Eloquent, selecting and acting on text can be done with a single gesture for both novice and expert users.
Flow Plugin Network for Conditional Generation

Generative models have gained many researchers' attention in the last years resulting in models such as StyleGAN for human face generation or PointFlow for 3D point cloud generation. However, by default, we cannot control its sampling process, i.e., we cannot generate a sample with a specific set of attributes. The current approach is model retraining with additional inputs and different architecture, which requires time and computational resources. We propose a novel approach that enables generating objects with a given set of attributes without retraining the base model. For this purpose, we utilize the normalizing flow models - Conditional Masked Autoregressive Flow, and Conditional Real NVP, as a Flow Plugin Network (FPN).
Selective-Opening Security for Public-Key Encryption in the Presence of Parameter Subversion

In public-key encryption (PKE), ciphertexts received by a receiver may be possibly correlated and the security of a PKE relies on honestly generated system parameters. Security against selective opening attacks (SOA) for PKE guarantees that even when an attacker has broken into a subset of honestly generated ciphertexts and opened them (i.e., seeing plaintexts and random bits), the unopened ciphertexts remain secure. While security against parameter subversion attacks (PSA) for PKE requires that even when the public system parameters are maliciously generated, a PKE scheme should be secure. In this paper, we initiate the study of PKE secure against both SOA and PSA. To capture SOA and PSA simultaneously, we formulate a new security notion called indistinguishability under selective opening attacks and parameter subversion attacks (IND-SO-PSA). Further, we define the lossy trapdoor function and all-but-many lossy trapdoor function in the presence of PSA (LTF-PSA and ABM-LTF-PSA correspondingly) and propose an instantiation with the efficiently-embeddable group (EG). Applying these new primitives, we construct a PKE scheme that is proven to be IND-SO-PSA secure.
Multi-Modal Motion Planning Using Composite Pose Graph Optimization

In this paper, we present a motion planning framework for multi-modal vehicle dynamics. Our proposed algorithm employs transcription of the optimization objective function, vehicle dynamics, and state and control constraints into sparse factor graphs, which—combined with mode transition constraints—constitute a composite pose graph. By formulating the multi-modal motion planning problem in composite pose graph form, we enable utilization of efficient techniques for optimization on sparse graphs, such as those widely applied in dual estimation problems, e.g., simultaneous localization and mapping (SLAM). The resulting motion planning algorithm optimizes the multi-modal trajectory, including the location of mode transitions, and is guided by the pose graph optimization process to eliminate unnecessary transitions, enabling efficient discovery of optimized mode sequences from rough initial guesses. We demonstrate multi-modal trajectory optimization in both simulation and real-world experiments for vehicles with various dynamics models, such as an airplane with taxi and flight modes, and a vertical take-off and landing (VTOL) fixed-wing aircraft that transitions between hover and horizontal flight modes.
Few-Shot NLU with Vector Projection Distance and Abstract Triangular CRF

Data sparsity problem is a key challenge of Natural Language Understanding (NLU), especially for a new target domain. By training an NLU model in source domains and applying the model to an arbitrary target domain directly (even without fine-tuning), few-shot NLU becomes crucial to mitigate the data scarcity issue. In this paper, we propose to improve prototypical networks with vector projection distance and abstract triangular Conditional Random Field (CRF) for the few-shot NLU. The vector projection distance exploits projections of contextual word embeddings on label vectors as word-label similarities, which is equivalent to a normalized linear model. The abstract triangular CRF learns domain-agnostic label transitions for joint intent classification and slot filling tasks. Extensive experiments demonstrate that our proposed methods can significantly surpass strong baselines. Specifically, our approach can achieve a new state-of-the-art on two few-shot NLU benchmarks (Few-Joint and SNIPS) in Chinese and English without fine-tuning on target domains.
Design of a Sensor System for a Minimalistic Walking Robot with Two Degrees of Freedom

This research considers the design of a sensor system for walking robot which can be used for coverage path planning and precise surface exploration. The paper presents an updated mechanical design of a minimalistic walking robot with a fixed step size. The robot has only two degrees of freedom. The specific mechanical design prevents the accumulation of any odometry error and does not require any external navigation system. The robot is equipped with a variety of distance and tactile sensors which allow precise exploration of the surface and any high or low spots of the surface to be located. This paper proposes an approach for path planning and surface exploration by using the proposed sensor system. Despite that the robot is based on a minimalistic approach, it can move and operate on complex surfaces. Also, by altering its movement direction it can effectively use its sensor system for distant global object scanning or for local exploration of the walking surface. The conducted experiment showed that the presented in this research mechanical design of a walking robot with fixed step can be used for precise surface exploration.
Systematic Comparison of Data Models used in Mapping Knowledge Organization Systems

Abstract This study explores six projects in mapping Knowledge Organization Systems (schemas, taxonomies, ontologies), and examines the data models used in these projects. We discuss (1) the two most common data models presented in these projects, namely tabular model, and tree model; (2) the implicit and explicit information shown in the data models; and (3) the information gains and losses of each model. We found that though the tree model maintains the hierarchical structure of the KOS while a tabular model flattens out the structure, both models can potentially obscure the information of the original KOS. This makes it problematic if we want to reconstruct a mapping project based on the outcomes manifested in either model. Our findings contribute to the Knowledge Organization literature by showing how choices of data models can impact the results in mapping schemas, taxonomies, and ontologies.
Developing Secure Healthcare Video Consultations for Corona Virus (COVID-19) Pandemic

Many health networks became increasingly interactive in implementing a consulting approach to telemedicine before the COVID-19 pandemic. To mitigate patient trafficking and reduce the virus exposure in health centers, several GPs, physicians and people in the video were consulted during the pandemic at the start. Video and smartphone consultations will allow well-insulated and high-risk medical practitioners to maintain their patient care security. Video appointments include diabetes, obesity, hypertension, stroke, mental health, chemotherapy and chronic pain. Many urgent diseases, including an emergency triage for the eye, may also be used for online consultations and triages. The COVID-19 pandemic shows that healthcare option for healthy healthcare and the potential to increase to a minimum, such as video consultations, have grown quickly. The dissemination of COVID-19 viruses now aims at extending the use of Video-Health Consultations by exchanging insights and simulations of health consultations and saving costs and healthcare practices as a consequence of the COVID-19 pandemic. Our paper focuses on video consulting privacy. This essay further presents the advantages and inconveniences of video consultation and its implementation.‏ This paper suggests the most recent video encryption method known as high efficiency video coding selective encryption (HEVC SE). Our video consultation schema has been improved to secure video streaming on a low calculation overhead, with the same bit rate and to ensure compatibility with the video format. The contribution is made with RC5, a low complexity computer, to encrypt subsets of bin-strings binarized in the HEVC sense using the context-adaptive binary arithmetic coding (CABAC) method through the bypass binary arithmetic coding. This sequence of binstrings consists of a non-zero differential transforming cosine (DCT) coefficient bit, MVD sign bits, remainder absolute DCT suffixes and absolute MVD suffixes. This paper also examines the efficiency assessment of the use of the RC5 with its modes of operations in the HEVC CABAC SE proposed. This study chooses the best operating mode for RC5 to be used for the healthcare video consultation application. Security analysis, such as histogram analysis, correlation coefficient testing and key sensitivity testing, is presented to protect against brute force and statistical attacks for the proposed schema.
Complete/incomplete multi‐view subspace clustering via soft block‐diagonal‐induced regulariser

This study proposes a novel multi-view soft block diagonal representation framework for clustering complete and incomplete multi-view data. First, given that the multi-view self-representation model offers better performance in exploring the intrinsic structure of multi-view data, it can be nicely adopted to individually construct a graph for each view. Second, since an ideal block diagonal graph is beneficial for clustering, a ‘soft’ block diagonal affinity matrix is constructed by fusing multiple previous graphs. The soft diagonal block regulariser encourages a matrix to approximately have (not exactly) K diagonal blocks, where K is the number of clusters. This strategy adds robustness to noise and outliers. Third, to handle incomplete multi-view data, multiple indicator matrices are utilised, which can mark the position of missing elements of each view. Finally, the alternative direction of multipliers algorithm is employed to optimise the proposed model, and the corresponding algorithm complexity and convergence are also analysed. Extensive experimental results on several real-world datasets achieve the best performance among the state-of-the-art complete and incomplete clustering methods, which proves the effectiveness of the proposed methods.
Improved Privacy-Ensuring Data-Fusion and Service Recommendation for Users in Smart Cities

Smart cities are the most notable examples of cyber-physical-social systems, and produce an enormous amount of industrial data related to enterprise businesses, healthcare facilities, transportation, security & surveillance, pollution and carbon emissions etc. Such multi-source data is inherently heterogeneous in nature, and therefore in order to achieve reasonably effective and efficient utilization of such data we almost always require data fusion and mining. However, such data analyses could mean the breach of the privacy of the individuals if the user data is used as it is. Privacy-preserving information consolidation and analysis approaches are therefore being developed for such use cases. In this work, an improved privacy-ensuring data fusion and service recommendation approach targeted towards users in smart city environments is presented. The approach starts with first obfuscating user-data to eliminate user-specific information (thereby ensuring user privacy), and creates equivalent time-aware indices corresponding to the user data, which are forwarded to the cloud for data fusion and service recommendation. Extensive experiments, performed on a real-world dataset, demonstrate the superiority of the proposed approach over its contemporary counterparts.
Object Relocation Visual Tracking based on Siamese network

Siamese network based trackers treat the visual tracking task as a similarity matching process, which significantly increases the performance of trackers. However, there are still many remaining problems as following. 1) The lightweight backbone of Siamese network limits its feature representation ability, and the tracker is prone to failure under the interference distractors (e.g., similar objects or background) or large viewing angle changes. 2) The single template could not well handle the shape changing of object. 3) Lack of arbiter-corrector mechanism, the tracker cannot workwell once losing the object. To solve these problems, we firstly proposed a novel U-shaped arbiter that could automatically judge whether the tracker fails or not according to a distance histogram. This histogram is built by counting the Euclidean distance between multiple candidate object centers. Secondly, Kuhn-Munkres algorithm is introduced to choose the winner set from a dynamic template set constructed through a backtracking procedure, and relocation of the object is realized by comparing the Structural Similarity between the winner set and the template set. The experiments show that the proposed tracker achieves better performance on visual tracking benchmarks, i.e., VOT2018, OTBIOO, GOT-lOk and LaSOT, against the state-of-the-art methods.
Manufacturing Process Anomaly Detection Using Adversarial Autoencoder with Multiple Discriminator

When unexpected problems occur in manufacturing process, it is necessary to configure an anomaly detection system to monitor and control them. Abnormal data are critical because they cause a decrease in yield and poor quality. If abnormal data is not detected, the process continues and the loss becomes greater. Abnormal data have fewer numbers than normal data, resulting in class imbalance problems. Therefore, we solve the data imbalance problem by learning distribution of normal data only. Unlike conventional methods, adversarial autoencoder (AAE) is able to create distributions similar to the original data through competitive learning using discriminator. This paper proposes adversarial autoencoder with multiple discriminators, a method to learn the distribution of normal data more accurately by adding two discriminators to AAE. We use Long Short-Term Memory (LSTM) layer to fit the time series characteristics. Experiments confirm that the method proposed in this paper show great anomaly detection performance.
Variational Autoencoder with Interactive Attention for Affective Text Generation

Human language has the ability to convey the speaker's emotions, such as happiness, sadness, or anger. Existing text generation methods mainly focus on the sequence-to-sequence (Seq2Seq) model that applied an encoder to transform the input text into latent representation and a decoder to generate texts from the latent representation. To control the sentiment of the generated text, these models usually concatenate a disentangled feature into the latent representation. However, such a method is only suitable for short texts, since the sentiment information may gradually dissipate as the text becomes longer. To address this issue, a variational autoencoder with interactive variation attention was proposed in this study. Unlike the previous method of directly connecting sentiment information with the latent variables to control text generation, the proposed model adds the sentiment information into variational attention with a dynamic update mechanism. At each timestep, the model leverage both the variational attention and hidden representation to decode and predict the target word and then uses the generated results to update the emotional information in attention. It can keep track of the attention history, which encourages the attention-based VAE to control better the sentiment and content of generating text. The empirical experiments were conducted using the SST dataset to evaluate the generation performance of the proposed model. The comparative results showed that the proposed method outperformed the other methods for affective text generation. In addition, it can still maintain accurate sentiment information and sentences smoothness even in the longer text.
A Learning-Based Fault Localization Approach Using Subset of Likely and Dynamic Invariants

Fault localization is one of the main tasks of software debugging. Developers spend a lot of time, cost, and effort to locate the faults correctly manually. For reducing this effort, many automatic fault localization techniques have been proposed, which inputs test suites and outputs a sorted list of faulty entities of the program. For further enhancement in this area, we developed a system called SILearning, which is based on invariant analysis. It learns from some existing fixed bugs to locate faulty methods in the program. It combines machine-learned ranking, program invariant differences, and spectrum-based fault localization (SBFL). Using the execution of test cases and code coverage analysis, it obtains each method’s invariant differences and the suspiciousness value based on the program spectral location and uses them as features for ranking the faulty methods. The experimental analysis of SILearning has been performed on the dataset of real fault which is extracted from the database Defects4J. The tools used in this research are Daikon and cobertura for detection of the invariants and code coverage, respectively. The results show that SILearning performs better when combined features are utilized and can successfully locate the faulty methods on average for 76.1, 90.4, 108.2, 123, and 143.5 at the top positions of 1, 2, 3, 4, and 5.
Reliable Distributed Fuzzy Discretizer for Associative Classification of Big Data

Data Mining is an essential task because the digital world creates huge data daily. Associative classification is one of the data mining task which is used to carry out classification of data, based on the demand of knowledge users. Most of the associative classification algorithms are not able to analyze the big data which are mostly continuous in nature. This leads to the interest of analyzing the existing discretization algorithms which converts continuous data into discrete values and the development of novel discretizer Reliable Distributed Fuzzy Discretizer for big data set. Many discretizers suffer the problem of over splitting the partitions. Our proposed method is implemented in distributed fuzzy environment and aims to avoid over splitting of partitions by introducing a novel stopping criteria. Proposed discretization method is compared with existing distributed fuzzy partitioning method and achieved good accuracy in the performance of associative classifiers.
Ubiquitous Music-Making Systems: A Critical Overview of Current Practices

Ubiquitous music-making systems can provide innovative forms of interaction for music creators. It supports everyday creativity with no limitations on the venue or time needed for music creation activities to take place. The paper introduces the concept of ubiquitous music-making and explains the concept through a critical overview of current practices. It then discusses issues and challenges existing in the current ubiquitous music-making systems, which can inform areas that need future improvements.
Stream-Based Lossless Data Compression

Abstract In this chapter, we introduce aspects of applying data-compression techniques. First, we study the background of recent communication data paths. The focus of this chapter is a fast lossless data-compression mechanism that handles data streams completely. A data stream comprises continuous data with no termination of the massive data generated by sources such as movies and sensors. In this chapter, we introduce LCA-SLT and LCA-DLT, which accept the data streams, as well as several implementations of these stream-based compression techniques. We also show optimization techniques for optimal implementation in hardware.
Meemi: A simple method for post-processing and integrating cross-lingual word embeddings

Abstract Word embeddings have become a standard resource in the toolset of any Natural Language Processing practitioner. While monolingual word embeddings encode information about words in the context of a particular language, cross-lingual embeddings define a multilingual space where word embeddings from two or more languages are integrated together. Current state-of-the-art approaches learn these embeddings by aligning two disjoint monolingual vector spaces through an orthogonal transformation which preserves the structure of the monolingual counterparts. In this work, we propose to apply an additional transformation after this initial alignment step, which aims to bring the vector representations of a given word and its translations closer to their average. Since this additional transformation is non-orthogonal, it also affects the structure of the monolingual spaces. We show that our approach both improves the integration of the monolingual spaces and the quality of the monolingual spaces themselves. Furthermore, because our transformation can be applied to an arbitrary number of languages, we are able to effectively obtain a truly multilingual space. The resulting (monolingual and multilingual) spaces show consistent gains over the current state-of-the-art in standard intrinsic tasks, namely dictionary induction and word similarity, as well as in extrinsic tasks such as cross-lingual hypernym discovery and cross-lingual natural language inference.