Reviewerly/exHarmony huggingface dataset:
Human Motion Recognition Using Artificial Intelligence Techniques

The goal of this paper's research is to develop learning methods that promote the automatic analysis and interpretation of human and mime-gestural movement from various perspectives and using various data sources images, video, depth, mocap data, audio, and inertial sensors, for example. Deep neural models are used as well as supervised classification and semi-supervised feature learning modeling temporal dependencies, and their effectiveness in a set of tasks that are fundamental, such as detection, classification, and parameter estimation, is demonstrated as well as user verification. A method for identifying and classifying human actions and gestures based on utilizing multi-dimensional and multi-modal deep learning from visual signals (for example, live stream, depth, and motion - based data). A training strategy that uses, first, individual modalities must be carefully initialized, followed by gradual fusion (called ModDrop) to learn correlations between modalities while preserving the uniqueness of each modality specific representation. In addition, the suggested ModDrop training approach assures that the classifier detect has weak inputs for one or maybe more channels, enabling these to make valid predictions from any amount of data points accessible modalities. In this paper, inertial sensors (such as accelerometers and gyroscopes) embedded in mobile devices collect data are also used.
FLAN: feature-wise latent additive neural models for biological applications

Interpretability has become a necessary feature for machine learning models deployed in critical scenarios, e.g. legal system, healthcare. In these situations, algorithmic decisions may have (potentially negative) long-lasting effects on the end-user affected by the decision. While deep learning models achieve impressive results, they often function as a black-box. Inspired by linear models, we propose a novel class of structurally constrained deep neural networks, which we call FLAN (Feature-wise Latent Additive Networks). Crucially, FLANs process each input feature separately, computing for each of them a representation in a common latent space. These feature-wise latent representations are then simply summed, and the aggregated representation is used for the prediction. These feature-wise representations allow a user to estimate the effect of each individual feature independently from the others, similarly to the way linear models are interpreted.We demonstrate FLAN on a series of benchmark datasets in different biological domains. Our experiments show that FLAN achieves good performances even in complex datasets (e.g. TCR-epitope binding prediction), despite the structural constraint we imposed. On the other hand, this constraint enables us to interpret FLAN by deciphering its decision process, as well as obtaining biological insights (e.g. by identifying the marker genes of different cell populations). In supplementary experiments, we show similar performances also on non-biological datasets.Code and example data are available at https://github.com/phineasng/flan_bio.
Emerging Technology in Business and Finance

In the globalized scenario where technologies are developing continuously with time, these novel methods are affecting the business and finance in the significant way. In this chapter we are going to discuss about the major emerging technologies in the field of entrepreneurship, application development, finance, and business.&#x0D; The authors are going to start with the introduction about the business, finance, entrepreneurship and application development, and the effect of the emerging technologies on these fields and the way in which technologies are developing from time to time, about adoption of these technologies by industries. The changes in the technologies with special reference to developed and developing country will also be the part of this chapter.&#x0D; Moving ahead we are discussing about these technologies in prevailing businesses as well as upcoming business. Some of the technologies we are going to discuss are Embedded Business Intelligence, Amplified Visual Presentation, Augmented Analytics, Cloud Management.&#x0D; Beside these technologies, we are going to cover about the growing automation in the finance sector such as Cloud banking, Robotic process automation, Blockchain, Internet of things, etc.&#x0D; This chapter will cover all the technologies while getting the complete knowledge about what, why, where, when and how it is changing in the present finance and business scenario. Just like the two opposite faces of the coin, one side these emerging technologies are boon for the business and finances then on the other side there are certain risks involved in these technologies, which can be a great threat to our business as well as in our routine life. So, we also discuss about the potential risks associated with these technologies.&#x0D; We will end our chapter by giving our conclusion, precautions, and suggestions on these technologies.
A Visual Analysis Method for Predicting Material Properties Based on Uncertainty

The traditional way of studying fluorinated materials by adjusting parameters throughout multiple trials can no longer meet the needs of the processing and analysis of multi-source, heterogeneous, and numerous complex data. Due to the high confidentiality of fluorinated materials’ data, it is not convenient for the plant to trust the data to third party professionals for processing and analysis. Therefore, this paper introduces a visual analysis method for material performance prediction supporting model selection, MP2-method, which helps with researchers’ independent selection and comparison of different levels of prediction models for different datasets and uses visual analysis to achieve performance prediction of fluorinated materials by adjusting control parameters. In addition, according to the Latin hypercube Markov chain (LHS-MC) model of uncertainty for visual analysis proposed in this paper, the uncertainty of the control-parameter data is reduced, and their prediction accuracy is improved. Finally, the usefulness and reliability of MP2-method are demonstrated through case studies and interviews with domain experts.
The Use of Attentive Knowledge Graph Perceptual Propagation for Improving Recommendations

Collaborative filtering (CF) usually suffers from data sparsity and cold starts. Knowledge graphs (KGs) are widely used to improve recommendation performance. To verify that knowledge graphs can further alleviate the above problems, this paper proposes an end-to-end framework that uses attentive knowledge graph perceptual propagation for recommendations (AKGP). This framework uses a knowledge graph as a source of auxiliary information to extract user–item interaction information and build a sub-knowledge base. The fusion of structural and contextual information is used to construct fine-grained knowledge graphs via knowledge graph embedding methods and to generate initial embedding representations. Through multi-layer propagation, the structured information and historical preference information are embedded into a unified vector space, and the potential user–item vector representation is expanded. This article used a knowledge perception attention module to achieve feature representation, and finally, the model was optimized using the stratified sampling joint learning method. Compared with the baseline model using MovieLens-1M, Last-FM, Book-Crossing and other data sets, the experimental results demonstrate that the model outperforms state-of-the-art KG-based recommendation methods, and the shortcomings of the existing model are improved. The model was applied to product design data and historical maintenance records provided by an automotive parts manufacturing company. The predictions of the recommended system are matched to the product requirements and possible failure records. This helped reduce costs and increase productivity, helping the company to quickly determine the cause of failures and reduce unplanned downtime.
Strike: Stream Cipher Based on Stochastic Lightning Strike Behaviour

There is an increasing need for secure and fast encryption algorithms to support applications and communication protocols, and business models. In this paper, we present an alternative stream cipher (Strike) inspired by the stochastic behaviour of lightning strike phenomena. The novelty and originality of Strike stem from the utilisation of lightning strike behaviour as a source for generating random keystreams for encryption and decryption. Strike consists of three main functions: a function for setting up the security attributes, a function for generating lightning strikes and converting them to a keystream, and a function for plaintext encryption. The proposed stream cipher was tested against several cryptanalysis and statistical attacks in addition to other performance tests. The results show that Strike achieves high throughput on both high- and low-speed devices. Additionally, security analysis shows that our cipher is resistant to cryptanalysis and statistical attacks.
Urban Expressway Renewal Strategy Based on Knowledge Graphs

As an important element of urban infrastructure renewal, urban expressway renewal is of great significance to improve the commuting efficiency of cities (especially metropolitan cities), strengthen the service capacity of urban road facilities, and enhance the quality of cities. Considering the advantages of a knowledge graph in the integration of multi-source data and assisted decision-making based on knowledge reasoning, this paper provides assisted decision support for urban expressway renewal with the help of a knowledge graph. In this paper, we sorted out the concepts related to road renewal from road maintenance standards, designed an ontology concept matching algorithm to extract relevant concepts in existing ontologies, constructed concept models, and built a knowledge graph of expressway renewal with ontology as the carrier. Then, based on road maintenance standards and road properties, this work proposes a knowledge reasoning rule combined with case similarity for an expressway renewal strategy. The final experimental results verified the feasibility of the expressway renewal strategy based on the knowledge graph.
Metric Ensembles Aid in Explainability: A Case Study with Wikipedia Data

In recent years, as machine learning models have become larger and more complex, it has become both more difficult and more important to be able to explain and interpret the results of those models, both to prevent model errors and to inspire confidence for end users of the model. As such, there has been a significant and growing interest in explainability in recent years as a highly desirable trait for a model to have. Similarly, there has been much recent attention on ensemble methods, which aim to aggregate results from multiple (often simple) models or metrics in order to outperform models that optimize for only a single metric. We argue that this latter issue can actually assist with the former: a model that optimizes for several metrics has some base level of explainability baked into the model, and this explainability can be leveraged not only for user confidence but to fine-tune the weights between the metrics themselves in an intuitive way. We demonstrate a case study of such a benefit, in which we obtain clear, explainable results based on an aggregate of five simple metrics of relevance, using Wikipedia data as a proxy for some large text-based recommendation problem. We demonstrate that not only can these metrics’ simplicity and multiplicity be leveraged for explainability, but in fact, that very explainability can lead to an intuitive fine-tuning process that improves the model itself.
Experience of experimental recording of seismic emission of the side array during open mining of coal deposits

The article deals with issues related to the development of seismological equipment and software, in relation to the study of seismic emission as a harbinger of destructive deformations of the side of a coal quarry. Examples of recordings of typical seismic impacts are given. The filtering and event selection algorithms used in the processing of records are shown.
Method for Training and White Boxing DL, BDT, Random Forest and Mind Maps Based on GNN

A method for training and white boxing of deep learning (DL) binary decision trees (BDT), random forest (RF) as well as mind maps (MM) based on graph neural networks (GNN) is proposed. By representing DL, BDT, RF, and MM as graphs, these can be trained by GNN. These learning architectures can be optimized through the proposed method. The proposed method allows representation of the architectures with matrices because the learning architecture can be expressed with graphs. These matrices and graphs are visible, which makes the learning processes visible, and therefore, more accountable. Some examples are shown here to highlight the usefulness of the proposed method, in particular, for learning processes and for ensuring the accountability of DL together with improvement in network architecture.
Non-Markovian quantum Hadamard gate preparation in a hybrid bath: A Lyapunov approach

This paper performs preparation and preservation of the Hadamard gate using the Lyapunov method in a non-Markovian two-level open quantum system embedded in a hybrid bath composed of bosons and fermions, for the first time. To this end, the dynamics of the gate operator corresponds to the unitary and dissipation parts are extracted and the explicit form of equations of decay rate factor in the hybrid environment is attained. Afterwards, the Lyapunov control method based on the matrix logarithm function is used to manipulate the propagator to the desired Hadamard gate and a limitation is defined on control laws in order to have control fields with upper and lower bound that is suitable from the practical point of view. Finally, the numerical simulations are illustrated for various conditions of correlation strength and memory time of baths. The simulation results show that the proposed control fields perfectly prepare the Hadamard gate and can achieve gate error less than 2×10−7 and 4×10−10 considering infinite and finite bounds on the control fields, respectively, with strong coupling between the environment and the system. Moreover, when control is bounded, the fidelity of values very close to 1 can be reached under specific conditions, and maximum fidelity in all cases is more than 0.99, and besides, the issue of excessive control amplitude is tackled. Furthermore, the gate operator can be preserved during the gate processing time under the designed control fields. The proposed approach can be applied to other single-qubit gates.
<scp>AI</scp>‐enabled blockchain and <scp>SDN</scp>‐integrated <scp>IoT</scp> security architecture for cyber‐physical systems

Abstract To address the IoT security problem, in this paper we propose and evaluate the DDoS attack mitigation method based on blockchain, and construct a DDoS abnormal information detection and sharing model. The obtained experimental results show that when the number of decision trees increases, the training time of the DDoS attack detection model based on the RF model grows with a minimum trend of 14 s. The testing time is finally maintained at 1 s, and the recognition accuracy of DDoS attacks keeps improving, ultimately reaching over 99.8%. If the amount of DDoS abnormal traffic information exceeds 100 pieces and 2000 pieces, it only takes 0.1 and 5 s to sign the DDoS abnormal traffic information using ECDSA algorithm digitally. The signature verification only takes 0.1 and 9 s, respectively. And compared to conventional network physical system IoT security architecture, a network physical system IoT security architecture that integrates AI empowerment, blockchain, and SDN integration has a higher joint defense success rate. It can be explained that this scheme will be conducive to promoting joint defense against DDoS attacks and ensuring the security of the Internet of Things.
Super pixels transmission map-based object detection using deep neural network in UAV video

Object detection has become a very prominent subject for research in recent times. This study's main goal is to suggest a technique for video saliency object detection. It seems to sense that using the depth information in photos to detect salient things. Since depth offers abundant information about scene structure, object forms, and other 3D cues. This information is very compatible to distinguish between objects in the foreground and background. As a result of the high object density, small object size, and cluttered background, aerial photos and movies provide results with low precision. In this paper, the proposed SPTM (Super Pixel Transmission Map)-YOLO model, the input RGB image has applied Dark Channel Prior (DCP) method for estimating the transmission map. From the transmission map only, the background probability is estimated with the help of SLIC (simple linear iterative clustering algorithm) superpixel segmentation. That foreground extracted image is further learned with YOLO architecture to detect the objects effectively. For object detection in aerial images, this proposed SPTM-YOLO approach outperforms classic YOLO by up to 6% accuracy. Accurate detection of things that are small in size, partially occluded, and out of view is possible.
A Physical Layer Key Generation Scheme Based on Deep Learning Compensation and Balanced Vector Quantization

Channel reciprocity is the foundation for physical layer key generation, which is influenced by noise, hardware impairments, and synchronization offsets. Weak channel reciprocity will result in a high key disagreement rate (KDR). The existing solutions for improving channel reciprocity cannot achieve satisfactory performance improvements. Furthermore, the existing quantization algorithms generally use one-dimensional channel features to quantize and generate secret keys, which cannot fully utilize channel information. The multidimensional vector quantization technique also needs to improve in terms of randomness and time complexity. This paper proposes a physical layer key generation scheme based on deep learning and balanced vector quantization. Specifically, we build a channel reciprocity compensation network (CRCNet) to learn the mapping relationship between Alice and Bob’s channel measurements. Alice compensates for channel measurements via a trained CRCNet to reduce channel measurement errors between legitimate users and enhance channel reciprocity. We also propose a balanced vector quantization algorithm based on integer linear programming (ILP-BVQ). ILP-BVQ reduces the time complexity of quantization on the basis of ensuring key randomness and a low KDR. Simulation results showed that the proposed CRCNet performs better in terms of channel reciprocity and KDR, while the proposed ILP-BVQ algorithm improves time consumption and key randomness.
Spatiotemporal key region transformer for visual tracking

Abstract Visual tracking is an important field of computer vision research. Although transformer-based trackers have achieved remarkable performance, the transformer structure is globally computationally inefficient, it does not screen important patches, and it cannot focus on key target regions. At the same time, temporal motion features are easily overlooked. To solve these problems, this paper proposes a new method, SKRT, that removes the CNN structure and directly uses a transformer as the backbone network to extract multiframe video features. Then, these feature maps are mixed and superimposed to obtain spatiotemporal information. To focus on important parts efficiently, we use key region extraction to obtain a small set of template and search feature map patches and reinput them into the transformer as a cross-correlation computation. Finally, we predict the position of a tracking object through center-corner prediction. To demonstrate the effectiveness of our method, we conduct experiments on challenging benchmark datasets (GOT-10K, TrackingNet, VOT2018, OTB100, LaSOT), and the results show that SKRT is competitive with other state-of-the-art methods.
Emotion-enriched word embeddings for Turkish

Text, as one of the main communication methods, is frequently used as a data source for natural language processing (NLP) studies. Naturally, our thoughts, expressions, and actions are based on our feelings. Therefore, representing the language better to machines involves the problems of reflecting the actual meaning and also, the detecting emotion of the data source. While representing the textual data, word embeddings (e.g. Word2Vec and GloVe (Global Vectors for Word Representation)) which can extract semantic information are frequently used. However, these models may give unexpected results in sentiment and emotion detection studies because of the limitations of capturing emotive data. Because of occurring frequently in similar contexts, some words carrying opposite emotions may have similar vector representations. Nowadays, enriching the vectors by adding emotion or sentiment data is studied which aim to increase the success in emotion detection or classification tasks. The main purpose is to reorganize the vector space in a way that words having semantically and sentimentally similar in closer locations. In this study, three emotion enrichment models over two semantic embeddings (Word2Vec and GloVe) and a contextual embedding (BERT) (Bidirectional Encoder Representations from Transformers)) are applied to a Turkish dataset. Turkish is an agglutinative language. Thus, it is expected to produce different results in this problem, as it has a different structure from the languages that are frequently studied in this field. Besides, experiments on in-category/opposite-category cosine similarity based on eight emotion categories and classification with sequential minimal optimization, logistic regression and multi-layer perceptron are conducted. According to experimental results emotionally enriched vector representations outperform the original models and give promising results.
Perfect state transfer on weighted bi-Cayley graphs over abelian groups

The study of perfect state transfer on graphs has attracted extensive attention because of its application in quantum information and computation. This paper establishes necessary and sufficient conditions for a weighted bi-Cayley graph having perfect state transfer over any given finite abelian group. As special cases of weighted bi-Cayley graphs, the results in this paper can apply to examine weighted Cayley graphs having perfect state transfer over dihedral groups, generalized dihedral groups, semi-dihedral groups and dicyclic groups.