Reviewerly/exHarmony huggingface dataset:
Word embedding dimensionality reduction using dynamic variance thresholding (DyVaT)

Natural language processing (NLP) provides a framework for large-scale text analysis. One common processing method uses vector space models (VSMs) which embed word attributes, called features, into highly dimensional vectors. Comprehensive VSMs are generated on sources such as the GoogleNews archive. A thesaurus, a collection of semantically-related words, can be created for a particular root word using cosine similarity with a given VSM. Many methods have been developed to reduce the complexity of these models by maintaining useful semantic information while discarding non-informative features. One such method, variance thresholding, retains high-variance features above a manually-determined threshold, providing higher differentiation between words for classification purposes. Our research developed a dimension-reducing methodology called dynamic variance thresholding (DyVaT). DyVaT reduces the specificity of word embeddings by maintaining low-variance features, allowing for a broader thesaurus preserving semantic similarity. A dynamic variance threshold, determining which low-variance features are retained, is selected using the kneedle algorithm, improving the current results. Our test case for examining the efficiency of DyVat in creating a contextual thesaurus is the visual, auditory and kinesthetic learning style context. We conclude that DyVaT is a valid method for generating loosely-connected word collections with potential uses in NLP classification or clustering tasks.
Efficient Multi-Agent Communication via Shapley Message Value

Utilizing messages from teammates is crucial in cooperative multi-agent tasks due to the partially observable nature of the environment. Naively asking messages from all teammates without pruning may confuse individual agents, hindering the learning process and impairing the whole system's performance. Most previous work either utilizes a gate or employs an attention mechanism to extract relatively important messages. However, they do not explicitly evaluate each message's value, failing to learn an efficient communication protocol in more complex scenarios. To tackle this issue, we model the teammates of an agent as a message coalition and calculate the Shapley Message Value (SMV) of each agent within it. SMV reflects the contribution of each message to an agent and redundant messages can be spotted in this way effectively. On top of that, we design a novel framework named Shapley Message Selector (SMS), which learns to predict the SMVs of teammates for an agent solely based on local information so that the agent can only query those teammates with positive SMVs. Empirically, we demonstrate that our method can prune redundant messages and achieve comparable or better performance in various multi-agent cooperative scenarios than full communication settings and existing strong baselines.
EMGC²F: Efficient Multi-view Graph Clustering with Comprehensive Fusion

This paper proposes an Efficient Multi-view Graph Clustering with Comprehensive Fusion (EMGC²F) model and a corresponding efficient optimization algorithm to address multi-view graph clustering tasks effectively and efficiently. Compared to existing works, our proposals have the following highlights: 1) EMGC²F directly finds a consistent cluster indicator matrix with a Super Nodes Similarity Minimization module from multiple views, which avoids time-consuming spectral decomposition in previous works. 2) EMGC²F comprehensively mines information from multiple views. More formally, it captures the consistency of multiple views via a Cross-view Nearest Neighbors Voting (CN²V) mechanism, meanwhile capturing the importance of multiple views via an adaptive weighted-learning mechanism. 3) EMGC²F is a parameter-free model and the time complexity of the proposed algorithm is far less than existing works, demonstrating the practicability. Empirical results on several benchmark datasets demonstrate that our proposals outperform SOTA competitors both in effectiveness and efficiency.
HAPZSL: A hybrid attention prototype network for knowledge graph zero-shot relational learning

Knowledge graph (KG) is becoming increasingly important in current information systems. To expand the coverage of KG, traditional knowledge graph completion research needs to collect enough training instances for newly added relations. However, high-quality training instances are usually difficult to collect. To alleviate this problem, knowledge graph zero-shot relational learning (KGZSL) has attracted more and more attention in recent years. The common method of KGZSL is to leverage Generative Adversarial Networks (GANs) to establish the connection between existing relation descriptions and the knowledge graph domain. However, traditional KGZSL methods often encounter training stability and model collapse problems, which means that these methods cannot stably generate high-quality relation vectors. Inspired by recent advances in prototype networks, we propose an end-to-end hybrid attention prototype framework to generate more plausible relational embeddings and address model collapse and training stability problems in previous studies. The core idea of our method is to obtain the relation prototype and the entity pair representation through the description encoder and the hybrid attention mechanism (HAM), then input them into a latent space, forcing the entity pair to be closer to their relation prototype. Experimental results on the knowledge graph zero-shot relational learning dataset demonstrate that our HAPZSL has a faster convergence speed and achieves state-of-the-art performance on this task.
Spatiotemporal cue fusion-based saliency extraction and its application in video compression

Extracting salient regions plays an important role in computer vision tasks, e.g., object detection, recognition and video compression. Previous saliency detection study is mostly conducted on individual frames and tends to extract saliency with spatial cues. The development of various motion feature further extends the saliency concept to the motion saliency from videos. In contrast to image-based saliency extraction, video-based saliency extraction is more challenging due to the complicated distractors, e.g., the background dynamics and shadows. In this paper, we propose a novel saliency extraction method by fusing temporal and spatial cues. In specific, the long-term and short-term variations are comprehensively fused to extract the temporal cue, which is then utilized to establish the background guidance for generating the spatial cue. Herein, the long-term variations and spatial cues jointly highlight the contrast between objects and the background, which can solve the problem caused by shadows. The short-term variations contribute to the removal of background dynamics. Spatiotemporal cues are fully exploited to constrain the saliency extraction across frames. The saliency extraction performance of our method is demonstrated by comparing it to both unsupervised and supervised methods. Moreover, this novel saliency extraction model is applied in the video compression tasks, helping to accelerate the video compression task and achieve a larger PSNR value for the region of interest (ROI).
Non-rigid multi-modal brain image registration based on two-stage generative adversarial nets

This paper has proposed the two-stage generative adversarial nets (GAN) based multi-modal brain image registration method. The first stage uses the GAN to indirectly recover image deformation and the second stage employs the GAN to directly estimate the registered image. At the first stage, the GAN uses structural representations of reference and float images generated by the improved modality independent neighborhood descriptor (MIND) as inputs, and outputs the predicted structural representation of the registered image. At the second stage, the GAN estimates the intensities of the registered image according to the generated structural representations at the first stage. The two GANs designed based on the U-Net are trained using the different loss functions related to mean square error, mean structural similarity (MSSIM) and the least squares loss. Experiments on the 3D brain images from the Atlas, BrainWeb and RIRE datasets show that compared with state-of-the-art registration methods based on sum of squared differences on entropy images (ESSD), MIND, self-similarity context (SSC), voxelmorph (Morph), volume tweening network (VTN) and adversarial similarity network (ASN), the proposed method has competitive or better registration performance in terms of dice value, MSSIM and computational efficiency.
Jomodevi: A joint motion and depth visibility prediction algorithm for perceived stereoscopic 3D quality

In this paper, we introduce a completely blind and unsupervised no-reference model that performs the quality assessment of stereoscopic 3D videos by using joint motion and depth visibility. In order to achieve such objective metric, first we compute the correlation map between the motion vector map and the depth map, and then we study the natural scene statistics of the estimated correlation map. We empirically model these statistics with Univariate Generalized Gaussian Distribution (UGGD) and compute the UGGD parameters at multi-scale and multi-orient steerable subband decomposition. It is shown that the estimated UGGD features are capable of discriminating distortions. We then compute the mean vector and the covariance matrix from the estimated UGGD features of the correlation maps of motion vector and the depth maps of undistorted and distorted S3D videos. This is followed by the computation of the Wave Hedges distance between the mean vectors of the undistorted and distorted contents, and the Bhattacharyya distance is measured between the corresponding covariance matrices. We pool these distance measures to achieve the overall joint motion and depth quality of S3D videos. The efficiency of the proposed algorithm is evaluated on the well-known IRCCYN, LFOVIAS3DPh1 and LFOVIAS3DPh2 S3D video datasets. Our model demonstrates robust and consistent performance across all distortion types and shows competitive performance against other 2D and 3D image and video quality assessment algorithms. Furthermore, since the proposed algorithm is completely blind, it does not require any training and testing analysis on the content features and the subjective scores.
Robust data hiding scheme through distinct keypoint selection exploiting modified Bilateral-Laplacian SIFT with encoding pipeline

SIFT (Scale Invariant Feature Transform) has recently been used to secure multimedia materials from geometric and non-geometric attacks. SIFT is commonly used in Computer Vision to identify and describe local features in images using the Difference of Gaussian (DoG) method, which has a high processing cost and a strong affinity towards the edge, resulting in a low contrast point. By introducing modified SIFT (mSIFT), which selects the most distinguishable key point with their feature descriptors, an attempt has been made to construct a rotation and scaling resistant data hiding strategy in this study. Unlike SIFT, a Bilateral-Laplacian filter was employed to determine the extrema, which took into account both geometric and photometric distance to reduce noise and retain the edge. This adds to the benefits of the proposed scheme's resistance against geometric attacks, even when the tampering rate is large. Furthermore, during data embedding, the suggested technique achieves excellent security by utilizing Arnold chaotic function, pseudorandom sequence generator, and a newly created unique encoding pipeline. The proposed technique is secure and resistant to various steganographic attacks while preserving good visual quality, according to experimental results and analysis, as well as comparisons with existing state-of-the-art methods.
Building a Visual Semantics Aware Object Hierarchy

The semantic gap is defined as the difference between the linguistic representations of the same concept, which usually leads to misunderstanding between individuals with different knowledge backgrounds. Since linguistically annotated images are extensively used for training machine learning models, semantic gap problem (SGP) also results in inevitable bias on image annotations and further leads to poor performance on current computer vision tasks. To address this problem, we propose a novel unsupervised method to build visual semantics aware object hierarchy, aiming to get a classification model by learning from pure-visual information and to dissipate the bias of linguistic representations caused by SGP. Our intuition in this paper comes from real-world knowledge representation where concepts are hierarchically organized, and each concept can be described by a set of features rather than a linguistic annotation, namely visual semantic. The evaluation consists of two parts, firstly we apply the constructed hierarchy on the object recognition task and then we compare our visual hierarchy and existing lexical hierarchies to show the validity of our method. The preliminary results reveal the efficiency and potential of our proposed method.
Insight into Voting Problem Complexity Using Randomized Classes

The first step in classifying the complexity of an NP problem is typically showing the problem in P or NP-complete. This has been a successful first step for many problems, including voting problems. However, in this paper we show that this may not always be the best first step. We consider the problem of constructive control by replacing voters (CCRV) introduced by Loreggia et al. [2015, https://dl.acm.org/doi/10.5555/2772879.2773411] for the scoring rule First-Last, which is defined by (1, 0, ..., 0, -1). We show that this problem is equivalent to Exact Perfect Bipartite Matching, and so CCRV for First-Last can be determined in random polynomial time. So on the one hand, if CCRV for First-Last is NP-complete then RP = NP, which is extremely unlikely. On the other hand, showing that CCRV for First-Last is in P would also show that Exact Perfect Bipartite Matching is in P, which would solve a well-studied 40-year-old open problem. Considering RP as an option for classifying problems can also help classify problems that until now had escaped classification. For example, the sole open problem in the comprehensive table from Erdélyi et al. [2021, https://doi.org/10.1007/s10458-021-09523-9] is CCRV for 2-Approval. We show that this problem is in RP, and thus easy since it is widely assumed that P = RP.
In Data We Trust: The Logic of Trust-Based Beliefs

The paper proposes a data-centred approach to reasoning about the interplay between trust and beliefs. At its core, is the modality "under the assumption that one dataset is trustworthy, another dataset informs a belief in a statement". The main technical result is a sound and complete logical system capturing the properties of this modality.
Toward reducing cross-shard transaction overhead in sharded blockchains

Sharding is a promising approach to high-performance blockchains and has been extensively explored in academia recently. However, sharding also introduces cross-shard transactions, which require expensive inter-shard coordination to ensure state consistency. Such transactions significantly limit the performance of sharded blockchains.
A multi-level caching architecture for stateful stream computation

Stream processing is used for real-time applications that deal with large volumes, velocities, and varieties of data. Stream processing frameworks discretize continuous data streams to apply computations on smaller batches. For real-time stream-based data analytics algorithms, the intermediate states of computations might need to be retained in memory until the query is complete. Thus, a massive surge in memory demand needs to be satisfied to run these algorithms successfully. However, a worker/server node in a computing cluster may have limited memory capacity. In addition, multiple parallel processes might be running concurrently, sharing the primary memory. As a result, a streaming application might fail to run or complete due to a memory shortage. Although spilling state information to the disk can alleviate the problem by allowing the query to finish, it will cause significant performance overhead. An in-memory-based object store as the state backend will also perform poorly due to the added communications with the external object store and serializing/deserializing the objects. This paper proposes a multi-level caching architecture to mitigate the surge of memory demand from the processes running complex stateful streaming applications. The multiple levels of the cache span across the process heap space, in-memory distributed object store, and secondary storage. The objects/states required in the computation are always served from the fastest level of the cache to boost the application performance. We also provide a multi-level caching library in Java which can be used to implement scalable streaming algorithms. The underlying cache management completely abstracts the multi-level cache implementation from the application and handles seamless migration of states/objects across different levels of the cache. In addition, the multi-layer caching architecture is configurable (i.e., an application can choose to leave out a cache level.) The experimental results demonstrate that our proposed multi-level caching approach for state management can manage large computational windows and improve the performance of an actual streaming application up to three times compared to the in-memory object store-based state backend.
Knowledge graph stream processing at the edge

We present a knowledge graph management system designed to run on Edge computing devices that handles high-frequency data streams. During the design phase, we took into account the inherent limitations of the devices, i.e., limited computing power and storage space, as well as the expectations of applications, e.g., low latency, high throughput, and intelligent data management. This results in a compact, decompression-free, in-memory, streaming-enabled RDF store that supports continuous querying and some forms of reasoning. The system addresses efficient query processing of data continuously arriving at a fast pace and is well-adapted to event-driven applications such as anomaly and risk detection. We empirically emphasize its accuracy, robustness, latency, and throughput properties on a real-world IoT setting originating from the energy management domain.
Machine Learning Techniques For Software Component Reusability

Software reuse has considerably reduced the products time to market with increased reliability and productivity. Widespread use of software reuse, agile software development, User stories and component-based development methods can be observed nowadays. To reap the advantages of software reuse developers can access it from the repositories. The repositories are generated through different techniques used in data mining. In order to predict the reusability of a component machine learning techniques is used. This article provides a method of integration of agile, user stories and component-based developments methods using artificial neural network. The factors that determine the reusability of software are studied and corresponding software metrics are input to the proposed classifier. The metrics used for the study are Cyclomatic Complexity (CC), Design Structure Quality Index (DSQI), Cohesion, weighted method per class (WMP), Bugs, Coupling. Depth Inheritance Tree (DIT), Number of children (NOC). The classifier was tested with some standard classifiers namely Random Forest, Logit Boost, J48 and ESRT used in machine learning and the accuracy achieved is 91.8%.
A Smart Electric Wheelchair with Multipurpose Health Monitoring System

With the growing number of physically disabled person in the present age, the usage of wheelchair has become unprecedented. Electric autonomous smart wheelchairs have emerged as the go to caring module for the disabled people. The proposed electric health monitoring smart wheelchair system is intended to be an all-in-one electric wheelchair that surpasses its previous predecessors. It allows the user to independently move around both indoor and outdoor using 3 different mode of operation – gesture control for controlling via neck movement, Joystick control & voice command module. It is incorporated with ultrasonic sonar edge detection module & obstacles detection both front and back to ensure safety of the patient. A variety of sensors such as heart rate sensor, PIR sensor, load cell, temperature & humidity detection are attached to keep monitoring the patient while he/she is sitting on the wheelchair. In the event of any accident or anomaly in the health monitoring data, the system will notify the relative and affiliated medical institution via SMS combined with GPS location. The proposed wheelchair system also incorporates retractable solar panel to charge the battery while operating outdoors. The proposed prototype model has been tested in a local area and the practical simulation data gained is promising. As a whole, the proposed prototype has a great deal to offer to the disabled population.
Multi-Objective Cuckoo Search Optimization Algorithm for Optimal Resource Allocation in Cloud Environment

Cloud technology refer to a pool of virtualized heterogeneous computational resources which are served to different customers as a service through the internet. These virtualized resources get provisioned to cloud users dynamically based on demand. however, dealing with resources in such a dynamic environment is still a challenging task. Deciding the way, the available cloud computational resources get allocated to the workload requires efficient provisioning and allocation techniques. The clients need to operate their various applications on cloud servers with variant Quality of Service (QoS) requirements based on the workloads and type of application whereas the objective of the cloud providers is effectively reducing the energy consumption by optimizing the CPU and bandwidth utilization and maintaining the Service Level Agreement (SLA). This, however, makes resource provisioning and allocation a very complicated task. This article implements the meta-heuristic Multi-Objective Cuckoo Search Algorithm (MOCSA) for efficient resource allocation to dynamic upcoming user workloads and scheduling user tasks on cloud VMs, which aim at minimizing the energy consumption with efficient utilization of CPU and bandwidth. The implementation and simulation experiments were conducted on the Cloudsim simulation toolkit. The evaluation results proved that MOCSA performed well in terms of performance, energy consumption, CPU utilization, bandwidth utilization, and SLA violation.
Face Recognition and Adversarial Masking Techniques

Face recognition is a system where people can rely on it to do various types of tasks from differentiating people to authentication. There are as many developments to it as the exploitations to it. The most common ones being physical ones which we can’t overcome, and adversarial attacks for which we can develop something to prevent them or remove them as a threat. The rise of deep learning and neural networks brought various opportunities and applications such as object detection and text-to-speech into modern society. Yet, despite the seemingly high accuracy, neural networks (and most machine learning models) could actually suffer from data, namely adversarial examples, that are manipulated very slightly from original training samples. In fact, past researches have indicated that as long as you recognize the "correct" method to vary your data, you'll force your network to perform poorly on data which may not seem to be visually. Our proposed system tries to overcome the problems faced by the existing models. It makes a face mesh for every person and stores it in a dataset remembering the faces, hence recognizing the faces in future using features from the mesh.