Reviewerly/exHarmony huggingface dataset:
Detecting and localizing the anomalies in video surveillance using deep neuralnetwork with advanced feature descriptor

The anomaly detection in the video surveillance is the need of the hour to detect the anomalous events or objects under the emergency conditions. The main aim of the research is to develop an automatic anomaly detection strategy for the detection of anomaly in surveillance videos. Initially, the input surveillance video is subjected to object detection using threshold method and the object tracking is performed using the Minimum output sum of squared error tracking algorithm (MOSSE). Once the objects are tracked, the statistical and advanced texture features are extracted using the statistical models and advanced texture descriptor. Then, the feature vector with statistical and textual features is fed as input to the deep convolutional neural network (DCNN) classifier that classifies the video as normal or abnormal. If any anomalies are detected in the surveillance video, the object localization is done to track the location of the abnormal object in the video. The comparative analysis of the methods reveals the better performance of the deep CNN classifier with a higher accuracy of 92.15%.
Detecting and Identifying Storage issues using Blockchain Technology

Blockchain technology has emerged and developed in recent years, proving the need and availability across several sectors. The data volume of the blockchain keeps on increasing because its features like data can only be added but not allowed to be deleted. Presently the size of the entire bitcoin blockchain ledger has crossed more than 350 GB. Many nodes are not able to join the network due to the huge demand for storage capacity and bandwidth to synchronize data with the network. This prevents the expansion of the decentralized network and also hinders blockchain technology development. Cloud computing is the method of storing, processing, and managing data over the internet using a network of remote computers instead of a local host or a desktop computer. It is having some challenges like trustworthiness, data management, data security, and compliance. This paper provides a comprehensive overview of blockchain technology and cloud technology. We discuss blockchain features, cloud technology, and different types of databases. In addition, technical challenges and recent advancements are also concisely mentioned.
Monitoring of clinical signs of intravenous infusion patients with ZigBee wireless technology

At present, intravenous infusion in clinical medicine is a very important treatment method in medical care, and it is the main work content of medical staff to ensure the safety and effectiveness of intravenous infusion process. The monitoring system of infusion patient’s physical signs is integrated with sensor technology, bio-electronic technology, and computer network technology. By means of sensor detection, program control and data processing, the automatic detection and control of intravenous infusion process and real-time monitoring of the pulse information are realized. The monitoring equipment has been unable to adapt to the rapid development of medical technology. In this article, based on ZigBee module, automatic control of drip rate, abnormal alarm, real-time liquid crystal display of infusion progress, and other functions are realized without manual intervention. At the same time, the hardware part of the lower computer combines the detection of ECG and pulse to design a multi-parameter monitoring system of vital signs, and realizes the parameter synchronization management requirements of the monitoring terminal based on the local area network. The test proves that the research content in this article is higher than the traditional clinical management mode in terms of monitoring data consistency and data synchronization management accuracy, which can not only reduce the labor intensity of nursing staff, improve work efficiency, but also greatly reduce the potential risks in the nursing process, which is the future application trend of clinical medical nursing work.
A Research Paper on Biometric based ATM System

Abstract: Biometrics-based authentication is a potential alternative to password-based authentication. Of all biometric methods, face-based identification is one of the most convenient one. In ATM systems, facial images are captured using a high resolution camera. Bank security measures can play an important role in preventing customer attacks. These measures are of paramount importance in addressing the weaknesses of civil lawsuits. Banks must meet the criteria to provide their customers with a secure banking environment. This paper focuses on the increased vulnerability and criminal activity of automated teller machines (ATMs), not the bank themselves. Both customers and bankers. Keywords: Automated Teller Machine (ATM), Camera, Verification, Crime, E-Banking.
A Hybrid Metaheuristic Approach for Automatic Clustering of Breast Cancer

Breast cancer is one of the most widely prevalent cancer in both men and women, however, it is far more common in women. Early diagnosis can help reduce the risk of metastasis to other organs and reduce death rates. Many research studies managed to cluster Breast cancer datasets and help in the accurate detection of tumors nevertheless a few ones could detect the number of clusters automatically. Clustering aims to partition the data into malignant or benign tumors. Recently researchers focused on automatic metaheuristic techniques reforming them to solve the clustering problem. In this paper, a new hybrid technique is proposed to determine the number of clusters with no need of prior information. The genetic algorithm is hybridized with cuckoo search algorithm to automatically cluster the Wisconsin Breast Cancer dataset. Also, a study among obtained results showed that the hybrid genetic cuckoo search algorithm outperformed the standard genetic algorithm and cuckoo search algorithm achieving an adjusted rand index of 0.84 and an adjusted mutual information of 0.74 and accuracy of 84 percent. Moreover, hybrid genetic cuckoo search algorithm was compared to the competing algorithms mentioned in the literature review showing a superior performance.
On‐line estimation of power model parameters for skid‐steer robots with applications in mission energy use prediction

Abstract This paper presents algorithms to predict the energy used by a skid‐steer robot to complete a given path, including the refinement of that prediction during operation. The vast majority of commercially available ground mobile robots utilize skid‐steer technology due to the robustness and maneuverability of the design. However, the complex track/terrain interactions developed during skid‐steer maneuvers make traction‐related power losses difficult to model. Accurate estimates of skid‐steer power and energy usage enable more intelligent mission planning decisions to be made. In this study, a skid‐steer power model that is linear in three unknown parameters was used and tested extensively on two skid‐steer vehicles, one tracked and the other wheeled. Repeated testing shows good repeatability of estimated parameters, and results indicate that the tracked vehicle exhibits a much larger sensitivity to the surface types tested than the wheeled vehicle. The skid‐steer power model is then combined with a kinematic model of skid‐steer movement and a trajectory control algorithm to predict the energy required to complete a given traversal path. This is useful for estimating energy demands for retro‐traversal, an application demonstrated in this study. The energy predictions are further refined through the mapping of power model parameters onto terrain types using overhead imagery of the area of operation. The maps are used to predict what terrain types will be encountered along the planned path to improve the estimate of the energy required.
QEN: Applicable Taxonomy Completion via Evaluating Full Taxonomic Relations

Taxonomy is a fundamental type of knowledge graph for a wide range of web applications like searching and recommendation systems. To keep a taxonomy automatically updated with the latest concepts, the taxonomy completion task matches a pair of proper hypernym and hyponym in the original taxonomy with the new concept as its parent and child. Previous solutions utilize term embeddings as input and only evaluate the parent-child relations between the new concept and the hypernym-hyponym pair. Such methods ignore the important sibling relations, and are not applicable in reality since term embeddings are not available for the latest concepts. They also suffer from the relational noise of the “pseudo-leaf” node, which is a null node acting as a node’s hyponym to enable the new concept to be a leaf node. To tackle the above drawbacks, we propose the Quadruple Evaluation Network (QEN), a novel taxonomy completion framework that utilizes easily accessible term descriptions as input, and applies pretrained language model and code attention for accurate inference while reducing online computation. QEN evaluates both parent-child and sibling relations to both enhance the accuracy and reduce the noise brought by pseudo-leaf. Extensive experiments on three real-world datasets in different domains with different sizes and term description sources prove the effectiveness and robustness of QEN on overall performance and especially the performance for adding non-leaf nodes, which largely surpasses previous methods and achieves the new state-of-the-art of the task.1
Session-Enhanced Graph Neural Network Recommendation Model (SE-GNNRM)

Session-based recommendation aims to predict anonymous user actions. Many existing session recommendation models do not fully consider the impact of similar sessions on recommendation performance. Graph neural networks can better capture the conversion relationship of items within a session, but some intra-session conversion relationships are not conducive to recommendation, which requires model learning more representative session embeddings. To solve these problems, an improved session-enhanced graph neural network recommendation model, namely SE-GNNRM, is proposed in this paper. In our model, the complex transitions relationship of items and more representative item features are captured through graph neural network and self-attention mechanism in the encoding stage. Then, the attention mechanism is employed to combine short-term and long-term preferences to construct a global session graph and capture similar session information by using a graph attention network fused with similarity. In order to prove the effectiveness of the constructed SE-GNNRM model, three public data sets are selected here. The experiment results show that the SE-GNNRM outperforms the existing baseline models and is an effective model for session-based recommendation.
Innovating Novel Online Social Spaces with Diverse Middle School Girls

Leveraging social media as a domain of high relevance in the lives of most young adolescents, we led a synchronous virtual design workshop with 17 ethnically diverse, and geographically-dispersed middle school girls (aged 11-14) to co-create novel ICT experiences. Our participatory workshop centered on social media innovation, collaboration, and computational design. We present the culminating design ideas of novel online social spaces, focused on positive experiences for adolescent girls, produced in small-groups, and a thematic analysis of the idea generation and collaboration processes. We reflect on the strengths of utilizing social media as a domain for computing exploration with diverse adolescent girls, the role of facilitators in a synchronous virtual design workshop, and the technical infrastructure that can enable age-appropriate scaffolding for active participation and use of participatory design principles embedded within educational workshops with this population.
Collecting and Reporting Race and Ethnicity Data in HCI

Engaging racially and ethnically diverse participants in Human-Computer Interaction (HCI) research is critical for creating safe, inclusive, and equitable technology. However, it remains unclear why and how HCI researchers collect study participants’ race and ethnicity. Through a systematic literature analysis of 2016–2021 CHI proceedings and a survey with 15 authors who published in these proceedings, we found that reporting race and ethnicity of participants is uncommon and that HCI researchers are far from consensus on the collection and analysis of this data. Because a majority (>90%) of the articles that report participants’ race and ethnicity are conducted in the United States, we focused our discussion on race and ethnicity accordingly. In future work, we plan to investigate considerations and best practices for collecting and analyzing race and ethnicity data in a global context.
Memory-Augmented Insider Threat Detection with Temporal-Spatial Fusion

Insider threat detection is important for the smooth operation and security protection of an organizational system. Most existing detection models establish historical baseline by reconstructing single-day and individual user behaviors, and then treat any outlier of the baseline as a threat. However, such methods ignore the temporal and spatial correlations between different activities, which result in an unsatisfying performance. To address such an issue, we propose a novel insider threat detection method, namely, Memory-Augmented Insider Threat Detection (MAITD), in this paper. Such an idea is motivated by the observation that the combination of individual model that focuses on historical baseline and group model that represents peer baseline can effectively identify the low-signal yet long-lasting insider threats, and reduce the possibility of false positives. To illustrate, our MAITD captures the temporal and spatial correlation of user behaviors by constructing compound behavioral matrix and common group model, and combines specific application scenarios to integrate the detection results. Moreover, it introduces the memory-augmented network into autoencoder to enlarge the reconstruction error of abnormal samples, thereby reducing the false negative rate. The experimental results on CERT dataset show that the instance-based and user-based AUCs of MAITD reach up to 87.54% and 94.56%, respectively, which significantly outperform previous works.
Deep Piecewise Hashing for Efficient Hamming Space Retrieval

Hamming space retrieval can achieve constant-time image search, which is more efficient than linear scan. In Hamming space retrieval, the data points inside the Hamming ball imply retrievable while the data points outside are irretrievable. Therefore, it is crucial to explicitly characterize the Hamming ball. However, for the existing Hamming space retrieval methods, many similar points are found close to the outside of the Hamming ball while many dissimilar points are found close to the query point, leading to the decline of both retrieval accuracy and recall. In this paper, we present a novel method named Deep Piecewise Hashing (DPH), for Efficient Hamming Space Retrieval. A piecewise loss is elaborately designed to guide the learning of hash codes. Meanwhile, a piecewise probability distribution is introduced in the proposed loss function. The piecewise probability distribution pays more attention to the learning of those "marginal" similar points. It considers both discrimination and robustness for the dissimilar points inside the Hamming ball. Comprehensive experiments on two datasets, MS-COCO and NUS-WIDE, demonstrate that DPH can yield state-of-the-art Hamming space retrieval performance.
Neural Speech Synthesis on a Shoestring: Improving the Efficiency of Lpcnet

Neural speech synthesis models can synthesize high quality speech but typically require a high computational complexity to do so. In previous work, we introduced LPCNet, which uses linear prediction to significantly reduce the complexity of neural synthesis. In this work, we further improve the efficiency of LPCNet – targeting both algorithmic and computational improvements – to make it usable on a wide variety of devices. We demonstrate an improvement in synthesis quality while operating 2.5x faster. The resulting open-source <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> LPCNet algorithm can perform real-time neural synthesis on most existing phones and is even usable in some embedded devices.
On the Importance of Different Frequency Bins for Speaker Verification

The majority of modern speaker verification systems take spectral analysis-based features as input, which contains multiple frequency bins. Naturally, there would be a question of whether all different frequency bins contribute equally to the speaker verification system performance? In this paper, we propose the frequency reweighting layer (FRL) to automatically learn and balance the importance of different frequency bins. This new layer can be freely inserted into the original speaker embedding learner once or multiple times at different layers, with an ignorable number of new parameters. Based on the proposed novel architecture, a set of experiments are designed and carried out on the VoxCeleb1 dataset, which not only achieves superior performance but also exhibits an interesting weight distribution – the lower frequencies matter more.
Investment decision in cloud gaming-based businesses opportunities: An analysis of the cloud gaming industry

Cloud gaming is a way of playing video games that involve remote servers in a data center. The need to download and install games on an end device is eliminated by cloud gaming. Cloud processing is intensive, and streaming is an essential component of cloud gaming which depends on several factors; thus, it faces several challenges in terms of investment decisions in the cloud-based gaming industry for investors. This study aims to provide a brief overview of the current cloud gaming market, posing challenges and opportunities for cloud gaming-based businesses and intends to compare four major cloud gaming platforms (CGP) based on their delivery services, the availability, pricing, features, streaming quality, and various games that the platform offers. Moreover, this study has carried out a SWOT analysis of the cloud game industry from a literature review. This research outlines six challenges that impact investment decisions and company potential. These include device requirements, streaming issues, cyber-attack risk, operational challenges, latency, and bandwidth. Furthermore, the paper also provides collaborative approach using the latest technologies solutions.
Adaptive Maximum Entropy Graph-Guided Fast Locality Discriminant Analysis

Linear discriminant analysis (LDA) aims to find a low-dimensional space in which data points in the same class are to be close to each other while keeping data points from different classes apart. To improve the robustness of LDA to non-Gaussian distribution data, most existing discriminant analysis methods extend LDA by approximating the underlying manifold of data. However, these methods suffer from the following problems: 1) local affinity or reconstruction coefficients are learned on the basis of the relationships of all data pairs, which would lead to a sharp increase in the amount of computation and 2) they learn the manifold information in the original space, ignoring the interference of the noise and redundant features. Motivated by these challenges, this article represents a novel discriminant analysis model, called fast and adaptive locality discriminant analysis (FALDA), to improve the efficiency and robustness. First, with the anchor-based strategy, a bipartite graph of each class is constructed to characterize the local structure of data. Since the number of anchor points is far less than that of data points, learning of fuzzy membership relationships between data points and anchor points within each class can save training time. Second, a maximum entropy regularization is introduced to control the uniformity of the weights of graphs and avoid the trivial solution. Third, the above relationships are updated adaptively in the process of dimensionality reduction, which can suppress the interference of the noise and redundant features. Fourth, the whitening constraint is imposed on the projection matrix to remove the relevance between features and restrict the total scatter of data in the subspace. Last but not the least, data with complex distribution can be explicitly divided into sub-blocks according to the learned anchor points (or subclass center points). We test our proposed method on synthetic data, benchmark datasets, and imbalanced datasets. Promising experimental results demonstrate the success of this novel model.
Learnable Structural Semantic Readout for Graph Classification

With the great success of deep learning in various domains, graph neural networks (GNNs) also become a dominant approach to graph classification. By the help of a global readout operation that simply aggregates all node (or node-cluster) representations, existing GNN classifiers obtain a graph-level representation of an input graph and predict its class label using the representation. However, such global aggregation does not consider the structural information of each node, which results in information loss on the global structure. In this work, we propose structural semantic readout (SSRead) to summarize the node representations at the position-level, which allows to model the position-specific weight parameters for classification as well as to effectively capture the graph semantic relevant to the global structure. Given an input graph, SSRead aims to identify structurally-meaningful positions by using the semantic alignment between its nodes and structural prototypes, which encode the prototypical features of each position. The structural prototypes are optimized to minimize the alignment cost for all training graphs, while the other GNN parameters are trained to predict the class labels. Our experimental results demonstrate that SSRead significantly improves the classification performance and interpretability of GNN classifiers while being compatible with a variety of aggregation functions, GNN architectures, and learning frameworks.
Multistream Neural Architectures for Cued Speech Recognition Using a Pre-Trained Visual Feature Extractor and Constrained CTC Decoding

This paper proposes a simple and effective approach for automatic recognition of Cued Speech (CS), a visual communication tool that helps people with hearing impairment to understand spoken language with the help of hand gestures that can uniquely identify the uttered phonemes in complement to lip-reading. The proposed approach is based on a pre-trained hand and lips tracker used for visual feature extraction and a phonetic decoder based on a multistream recurrent neural network trained with connectionist temporal classification loss and combined with a pronunciation lexicon. The proposed system is evaluated on an updated version of the French CS dataset CSF18 for which the phonetic transcription has been manually checked and corrected. With a decoding accuracy at the phonetic level of 70.88%, the proposed system outperforms our previous CNN-HMM decoder and competes with more complex baselines.