Reviewerly/exHarmony huggingface dataset:
Deep Learning Based Approaches for Vehicle Make and Model Recognition

Object detection is largely used in the area of computer vision and is critical for variety of applications. During the development of half a century, object detection methods have been continuously developed, and generated numerous approaches which obtained promising achievements. At present, the approach of object detection has been largely evolved into two categories which are traditional machine learning methods utilizing varied computer vision techniques and deep learning methods. In spite of this evolution, accurate implementation of Vehicle Make and Model Recognition (VMMR) is exacting owing to alike (kindred) appearance of different models of vehicles. Therefore this paper presents machine as well as deep learning techniques along with transfer learning models for car detection where the classification is generally at the extent of Make, Model and Year. In this paper, firstly the existing techniques centered on traditional machine learning are introduced and summarized. Then, two main schools of deep learning methods, Convolutional Neural Network (CNN) and ResNeXt50 are selected for analysis. At the end, the methods mentioned are briefly compared and discussed.
Use of YOLOv4 and Yolov4Tiny for Intelligent Vehicle Detection in Smart City Environments

One of the biggest problems in cities today is the significant increase in the number of motor vehicles. Intelligent traffic control is a fundamental part of controlling city travel. To achieve this goal, it is very important to have sensor technologies capable of identifying the number of vehicles traveling on a road. In this paper, we propose the development of a classifier model capable of reliably counting the number of vehicles in urban areas. In this case, it is proposed the construction of a dataset to carry out the training of a model based on YOLOv4 and YOLOv4Tiny systems that can be embedded in intelligent traffic light systems.
Investigation on the Distribution Characteristics of Chinese Continuing Education Based on the Community Detection Algorithm in Complex Networks

In order to closely fit the characteristics of continuing education, the development of continuing education teaching activities under the network background should not only be combined with the characteristics of professional adult education but also make reasonable use of modern teaching models in the actual teaching process. Based on the community detection algorithm in complex networks, this article makes thorough research and analysis on the complexity of Chinese continuing education by using complex network technology. By establishing the characteristics of vertex degree distribution, average path length, and clustering coefficient of complex networks, it is confirmed that Chinese continuing education has scale-free network characteristics and small-world network characteristics. The three aspects of relationship strength comprehensively analyze the information dissemination speed, scope, interpretation, and application; through the combination of the ant colony algorithm and complex network technology, multiple information dissemination paths are abstracted in Chinese continuing education. The research shows that the application of complex network algorithms can effectively improve the speed and quality of continuing education in China. It is found that the government should increase the number of adult education projects and improve the level of project categories, form key adult education research basis to promote the diversification of research subjects, expand the space for adult education projects to balance regional and provincial differences and attach importance to basic research on adult education, and integrate applied research.
A Phonetic-Semantic Pre-Training Model for Robust Speech Recognition

Robustness is a long-standing challenge for automatic speech recognition (ASR) as the applied environment of any ASR system faces much noisier speech samples than clean training corpora. However, it is impractical to annotate every types of noisy environments. In this work, we propose a novel phonetic-semantic pre-training (PSP) framework that allows a model to effectively improve the performance of ASR against practical noisy environments via seamlessly integrating pre-training, self-supervised learning, and fine-tuning. In particular, there are three fundamental stages in PSP. First, pre-train the phone-to-word transducer (PWT) to map the generated phone sequence to the target text using only unpaired text data; second, continue training the PWT on more complex data generated from an empirical phone-perturbation heuristic, in additional to self-supervised signals by recovering the tainted phones; and third, fine-tune the resultant PWT with real world speech data. We perform experiments on two real-life datasets collected from industrial scenarios and synthetic noisy datasets, which show that the PSP effectively improves the traditional ASR pipeline with relative character error rate (CER) reductions of 28.63% and 26.38%, respectively, in two real-life datasets. It also demonstrates its robustness against synthetic highly noisy speech datasets.
Pattern Recognition of Wushu Routine Action Decomposition Process Based on Kinect

Human action recognition is a hotspot in the fields of computer vision and pattern recognition. Human action recognition technology has created huge social value and considerable economic value for the society. Meeting people’s needs and understanding people’s expressions are the current research focus. Aiming at the problem that the movement cannot be continuously identified and due to a lack of detailed features in the action decomposition pattern recognition in the traditional Wushu routine decomposition process, it is proposed to use Kinect technology to identify the Wushu routine movement decomposition process in the Wushu routine movement decomposition process. This paper analyzes the principle of skeleton tracking and skeleton extraction performed by the Kinect human sensor and uses the Kinect sensor with the Visual Studio 2015 development platform to collect and process the skeleton data of limb movements and defines eight static limb motion samples and four dynamic limbs. The study uses a deep learning neural network algorithm to train and identify the established database of static body movements and uses the same template matching algorithm and K-NN. The recognition effects of the algorithms were compared and analyzed, and it was concluded that the static body motion recognition rates of the three algorithms were all above 90%. In this paper, recognition experiments are carried out on the MSR action 3D database. The influence of different integrated decision-making methods on the recognition results is further discussed and analyzed, and the average method integrated decision-making, which is most suitable for the algorithm model in this paper, is proposed. The results show that the recognition accuracy of the algorithm reaches 98.1%, which proves the feasibility of the preprocessing algorithm.
Study of Unsupervised Machine Learning using Clustering Algorithms

With the rising popularity of AI, taking a look at its deficiencies is significant in perceiving how well AI is applicable. Is it conceivable to apply the grouping with a small dataset? This paper comprises of a writing study, a review and an examination. It examines how two distinct AI calculations DBSCAN(Density-Based Spatial Clustering of Applications with Noise) and K-implies run on a dataset. Making a study where we can see genuinely what the vast majority picked and apply grouping with the information from the review to affirm in the event that the bunching has similar examples as what individuals have picked measurably. It was feasible to recognize designs with grouping calculations utilizing a little dataset. The writing concentrates on show models that the two calculations have been utilized effectively. It's feasible to see designs utilizing DBSCAN and K-implies on a little dataset. The size of the dataset isn't really the main perspective to think about, component and boundary choice are both significant as the need might arise to be tuned and tweaked to the information.
Query-Driven Graph Processing

Graphs are data model abstractions that are becoming pervasive in several real-life applications and practical use cases. In these settings, users primarily focus on entities and their relationships, further enhanced with multiple labels and properties to form the so-called property graphs. The processing of property graphs relies on graph queries that are used to extract and manipulate entities and relationships [2]. Whereas property graphs can be defined in a schema-less fashion, schema constraints and schema concepts are important assets when considering complex graph processing [1, 3, 6]. As witnessed by ongoing standardization activities for property graphs and their query language [5], graph processing systems are prone to enable query-driven, schema-driven or constraint-driven tasks. In this talk, I will focus on our work on graph processing in the static and streaming environments [7, 9], as well as schema inference methods [6], graph constraint and query workload designs tailored for property graphs [1, 4]. I will conclude by pinpointing future directions for graph processing and graph analytics inspired by our recent community-wide vision paper [8].
Retaining Diverse Information in Contrastive Learning Through Multiple Projectors

<i>Contrastive Learning (CL)</i> achieves great success in learning visual representations by comparing two augmented views of the same images. However, this very design removes transformation-dependent visual information from the pre-training, which leads to incomplete representations and is harmful for downstream tasks. It&#x0027;s still an open question to retain such information in the CL pre-training process. In this paper, we propose a <i>Multi-Projector Contrastive Learning (MPCL)</i> to address this issue, which produces multi-view contrastive candidates to retain more comprehensive visual characteristics. In addition, we introduce a contrast regularization to construct multiple projectors as different as possible, thereby facilitating the diversity of preserved information. Finally, to promote a consistent learning process for multi-projector, we design a projector training balance strategy to adjust the learning preference of different projectors. MPCL can be applied to various CL frameworks to effectively protect visual characteristics. Experimental results show that the method performs well on subsequent tasks such as linear and semi-supervised image classification, object detection, and semantic segmentation. Importantly, the visual transformer trained by MPCL improves 2&#x0025; absolute points of linear evaluation beyond the MoCo-v3 on the ImageNet-100 dataset.
A Novel Hybrid Clustering Approach Based on Black Hole Algorithm for Document Clustering

In information retrieval and text mining, document clustering is a big challenge because the amount of document collection has been increasing, day by day.The problem of clustering is NP-hard, use of meta-heuristic algorithms to solve these problems could be an effective method.When the solution space is large, traditional methods are unable to find a solution in a reasonable amount of time.K-means is a heuristic clustering algorithm, two main issues with heuristic algorithms are the early convergence and trapping in local optima.Moreover, finding the right number of clusters is one of the main drawbacks of the k-means algorithm.The correct value of k is always confusing, different researchers used different methods to solve this problem.To overcome these mentioned problems, this study presents a novel Hybrid approach for document clustering.One of the challenges in existing BH algorithm is the input data type.Recently, the algorithm was only accepting textual data.Another flaw in the existing model is that it doesn't choose how many clusters k to form automatically, and the centroids are chosen at random in it.In this paper, we have constructed a Hybrid cluster identification approach which consists of the Elbow method and Silhouette score for cluster k identification.This paper mainly offers three novel combination of model to represent text documents, namely i) K-mean++ -BH + TF-IDF with fix k ii) K-mean++ -BH + W2V with fix k iii) Hybrid Black Hole with automated k.The proposed improvements have validated on the document clustering problem.Cluster analysis based on two evaluation measures, external (Purity) and internal measures (Silhouette score) are used to report the findings.Experiments have been carried out on the four al-phanumeric datasets (Doc50, Reuters, WebKB and News20) as well as on two numeric datasets (Iris and Wine) respectively.The complete result analysis is reported in detail with respect to each research contribution to compare the performance of the proposed algorithm with existing clustering methods.Result shows that the proposed Hybrid BH algorithm outperforms better than the existing clustering methods for all datasets.The clustering of data with and without stop words is examined; additionally, the two alternative word embedding used for data exploration in conjunction with proposed model are also evaluated.In the present study, proposed Hybrid BH algorithm handles the optimal value of k efficiently.This is one of the major contributions of the paper, concluded that Hybrid Black Hole is an effective algorithm for cluster analysis.
New Key-Independent Structural Properties of AES-Like Permutations

The Internet of Things (IoT) technology makes our lives very simple and convenient by interacting with sensors/devices around the world and using the smart data collected from them. However, IoT devices typically have a resource-constrained architecture, rendering them vulnerable to cyberattacks. The advent of lightweight cryptography provides an opportunity to meet the challenges of IoT. With the promotion of 5G (5th generation wireless systems) technology, the amount of data in IoT devices is bound to grow rapidly. The design and analysis of lightweight block cipher is still a hot issue that needs to be solved in the coming period of time, as it responds to the strong push of many national governments to adopt IoT systems in the management of public affairs. MANTIS is a new tweakable block cipher suitable for IoT with the goal of low-latency implementations and it has drawn lots of attention in the form of prior cryptanalysis. This work first reveals a novel property of MANTIS. The characteristic is established under the condition that there is a certain equivalence relation between the input pairs in a particular subspace and it is evidenced by the first introduction of a key-independent distinguisher for 6-round MANTIS. We obtain that the number of input plaintext pairs in the same equivalence class is always divisible by 8. Then, we demonstrate a general and comprehensive proof as why it has to exist. Additionally, we have successfully verified the validity of the distinguisher. Only 216 chosen plaintexts and 222 table lookups computational cost are required to guarantee that the success probability exceeds 99%. Moreover, we discover that the same kind of property holds for other AES-like permutations with the example of the lightweight hash function PHOTON. Finally, we put forward some future explorations in this promising field.
Co-operative Choosing: Machines and Humans Thinking Together to Choose the Right Way

Making machine intelligence was an objective of the last few decades. Here, researchers tried to find out human ways of learning to embed intelligence into machines. There has been a competition to develop autonomous machines. Autonomous machines were expected to do complex and demanding tasks themselves without active human interventions. Slowly we have gone beyond this objective with more expectations from machines. We wanted to deal with scenarios where multiple machines were in the focus. Machine intelligence took the world by storm. Even at times researchers and philosophers debated on singularity. This debate was so hot that books were written, read and became popular—claiming that singularity is near. Are machines really overpowering us, i.e. homo sapiensSapiens? The question on these lines was asked to me even during my talks at many prominent venues across the globe. In my opinion, the challenge was not to overcome machines or compete with them but to remain homo sapiens. Becoming homo sapiens means learning from uncertaintyUncertainty, going for choice rather than selectionSelection and working cooperatively to deal with the problems and above all evolving strategies and learning. Definitely learning cooperatively is one of the key attributes.
Teacher Competency and the Needs of the Learner

In any educational system there are broadly speaking, five clearly defined components. They are: (1) students, their needs and expectations; (2) teachers, their competencies and commitment; (3) infrastructure which includes buildings, libraries, laboratories and classrooms; and (4) syllabi of courses, their framing and execution; and (5) most importantly a clearly conceived and unambiguously stated objective. Detailed discussions on the theoretical aspects of these components can go on ad infinitum. In order to make our discussions meaningful and relevant, it will, of course, be desirable to read our analysis of the related issues in the context of our own situation in the country. It will not be an exaggeration to assert that the area which attracted the least serious attention of those responsible for the governance of our educational system during the post-independence period is education. We may gloat over the unbridled proliferation of educational institutions and explosion of student-population flocking to these institutions. But the big question remains and that is whether our educational system as a whole is founded on any definite objective which alone can provide a sustainable direction. Flexibility in objectives is understandable but a radical deviation from the age-old purpose of liberal education meant for shaping humans into a worthwhile creation of God can only prove disastrous. Education should aim at primarily developing students' personality and chiselling those inherent traits in him with which he is born in God's own image. This assertion may sound too holistic to utilitarian ears. But it has stood the test of time since humanity came into existence
Determining a core view of research quality in empirical software engineering

Research quality is intended to appraise the design and reporting of studies. It comprises a set of standards such as methodological rigor, practical relevance, and conformance to ethical standards. Depending on the perspective, different views of importance are given to the standards for research quality. To investigate the suitability of a conceptual model of research quality to Software Engineering (SE), from the perspective of researchers engaged in Empirical Software Engineering (ESE) research, in order to understand the core value of research quality. We conducted a mixed-methods approach with two distinct group perspectives: (i) a research group; and (ii) the empirical SE research community. Our data collection approach comprised a questionnaire survey and a complementary focus group. We carried out a hierarchical voting prioritization to collect relative values for importance of standards for research quality. In the context of this research, ‘internally valid’, ‘relevant research idea’, and ‘applicable results’ are perceived as the core standards for research quality in empirical SE. The alignment at the research group level was higher compared to that at the community level. The conceptual model was seen to express fairly the standards for research quality in the SE context. It presented limitations regarding its structure and components’ description, which resulted in an updated model.
A Secure Bank Transaction Using Blockchain Computing and Forest Oddity

The Indian banking system has progressed from nationalization to liberalization. The banks are the backbone of any country wherever financial support is required. Security is an important factor that must be followed in any organization to protect its user’s confidential data. This paper removes issues using Blockchain Algorithm SHA-256. The aim of the study is to compare the security and privacy features of selected banks. The study further investigates the bank customers’ perception towards security and privacy concern’ regarding the use of e-banking services. The current framework, various following partner issues that break away at projected tasks using Blockchain algorithm SHA-256 which are extremely traditional and effectively accessible for software engineer action within the event that it needs to interrupt. During this paper, we tend to use SHA-256 calculation for message key age and for info encoding utilized upgraded Blowfish algorithm. When the procedure of this is finished, we tend to boot discover the negotiated server in the cloud framework. For replicas, we tend to utilize Cloudsim, a java primarily based check system.
Blockchain and IoT Unanimity in a Smart Metropolitan Development

The key technological developments in the current era include Blockchain and the Internet of Things in varied application fields. There’s a difference between the above two technologies but the unison of the above two has a wider and practical acceptability in today’s technological scenario. The IoT is responsible for generating a large amount of data in varied fields like healthcare, retail, real-time forecasting, and smart city development. The data is quite useful in terms of big data analytics whereas the Blockchain will promote secure transactions of data along with varied application integration. Smart city can be considered as a prime application area for IoT and there are a lot of progressions among IoT devices, sensors, and actuators. IoT devices are capable enough of communicating among themselves via the Internet using lightweight protocols which results in making them more and more user-friendly. Though the Internet connectivity of IoT devices makes them more vulnerable in regards to data scalability, consistency, and safety, blockchain confirms the integrity of data as it is able in trailing and synchronizing connected devices. As it is based on a decentralized approach, which restricts the condition of a single point of failure thereby forming a steady interoperable safe system. Internet of Things (IoT) requires various tools and techniques for the purpose of information security which necessitates the operative expansion of any IoT cantered smart city. Various security threats are always looking for any ambiguities or loopholes, which can be exploited in any network; security threats are a perilous contest, which needs some action to be taken and implemented instantaneously. The book chapter will be discussing the implications of Blockchain and IoT in a metropolitan urban scenario.