Reviewerly/exHarmony huggingface dataset:
OMBA: User-Guided Product Representations for Online Market Basket Analysis

Market Basket Analysis (MBA) is a popular technique to identify associations between products, which is crucial for business decision making. Previous studies typically adopt conventional frequent itemset mining algorithms to perform MBA. However, they generally fail to uncover rarely occurring associations among the products at their most granular level. Also, they have limited ability to capture temporal dynamics in associations between products. Hence, we propose OMBA, a novel representation learning technique for Online Market Basket Analysis. OMBA jointly learns representations for products and users such that they preserve the temporal dynamics of product-to-product and user-to-product associations. Subsequently, OMBA proposes a scalable yet effective online method to generate products' associations using their representations. Our extensive experiments on three real-world datasets show that OMBA outperforms state-of-the-art methods by as much as 21%, while emphasizing rarely occurring strong associations and effectively capturing temporal changes in associations.
Analysis of Hybrid Data Security Algorithms for Cloud

Security of data before adopting into the cloud is important because of the security challenges in clouds. The data security of the cloud server must be analyzed before implementing new security features. The data of the cloud owner have problems of attacks externally as well as internally. These attacks can be addressed, and tampering of server data can be handled to ensure confidentiality and integrity in cloud data. The proper use of cryptographic algorithms makes the cloud owner and the data on the cloud premises secured. This paper analyzes how data can be securely stored in the cloud using hybrid cryptographic technique. It also describes how to handle the cloud data security issues. The performances of the proposed algorithm are analyzed with other similar algorithms.
Sequential Inference Methods for Non-Homogeneous Poisson Processes With State-Space Prior

The non-homogeneous Poisson process provides a generalised framework for the modelling of random point data by allowing the intensity of point generation to vary across its domain of interest (time or space). The use of non-homogeneous Poisson processes have arisen in many areas of signal processing and machine learning, but application is still largely limited by its intractable likelihood function and the lack of computationally efficient inference schemes, although some methods do exist for the batch data case. In this paper, we propose for the first time a sequential framework for intensity inference which combines the non-homogeneous model of Poisson data with continuous-time state-space models for their time-varying intensity. This approach enables us to design efficient online inference schemes, for which we propose a novel sequential Markov chain Monte Carlo (SMCMC) algorithm, as is demanded by many applications where point data arrive sequentially and decisions need to be made with low latency. The proposed approach is compared with competing methods on synthetic datasets and tested with high-frequency financial order book data, showing in general improved performance and better computational efficiency than the main batch-based competitor algorithm, and better performance than a simple baseline kernel estimation scheme.
Hand Gesture Recognition System for Parking Applications based on Machine Vision

In this paper, we aim to combine the best features of image recognition along with embedded control system. The system completes the image preprocessing and recognizes the gesture information, and then feeds the recognition results back to the Raspberry Pi through the Bluetooth device. Raspberry Pi sends the corresponding gesture number to STC89C52 according to the recognition result, and STC89C52 converts the gesture number into a motion instruction and sends it to the machine to control the movement of car. For the choice of gestures, we use five different gestures to control the car's forward, backward, turn left.turn right, and stop. The geometric moment feature and histogram feature of the preprocessed gesture image are extracted, and the first two components of the seven feature components and the compactness of the preprocessed gesture image are selected as feature values. Anaconda distribution of Python is used to build a BP neural network, and takes feature value as input. After the BP neural network outputs the recognition results, the embedded system converts the recognition results into control instructions to control the the car movement.
Enhanced Recognition Based Image Authentication Scheme to Save System Time &amp; Memory

This paper presents an authentication scheme that contains images, alphabets, numbers and symbols. The primary aim of this research is to develop the authentication scheme using images as the front end data which makes it easy to remember for the user. But to store that selected images by the user in the backend (database), it takes a large amount of memory as well as processing time for password verification. To prevent this, the proposed method converts all the selected images in the string containing alphabets, numbers & symbols. This method focuses on recognition based image authentication scheme and also provides its two different ways of authentication. Sequential recognition based and Non-Sequential recognition-based image authentication schemes which will provide a facility to the user to verify its password sequentially or non-sequentially. At the user side, this authentication scheme is flexible & easy to remember. And at the system side, it provides low memory consumption and verifies the password in less amount of time. After the scheme is developed, the system time & memory is also calculated and analyzed. It has been observed that this scheme is saving the time and memory making the system advantageous.
Joint Multimodal Embedding and Backtracking Search in Vision-and-Language Navigation

Due to the development of computer vision and natural language processing technologies in recent years, there has been a growing interest in multimodal intelligent tasks that require the ability to concurrently understand various forms of input data such as images and text. Vision-and-language navigation (VLN) require the alignment and grounding of multimodal input data to enable real-time perception of the task status on panoramic images and natural language instruction. This study proposes a novel deep neural network model (JMEBS), with joint multimodal embedding and backtracking search for VLN tasks. The proposed JMEBS model uses a transformer-based joint multimodal embedding module. JMEBS uses both multimodal context and temporal context. It also employs backtracking-enabled greedy local search (BGLS), a novel algorithm with a backtracking feature designed to improve the task success rate and optimize the navigation path, based on the local and global scores related to candidate actions. A novel global scoring method is also used for performance improvement by comparing the partial trajectories searched thus far with a plurality of natural language instructions. The performance of the proposed model on various operations was then experimentally demonstrated and compared with other models using the Matterport3D Simulator and room-to-room (R2R) benchmark datasets.
A formally verified NAT stack

Prior work proved a stateful NAT network function to be, crash-free, memory safe and semantically correct [29]. Their toolchain verifies the network function code while assuming the underlying kernel-bypass framework, drivers, operating system, and hardware to be correct. We extend the toolchain to verify the kernel-bypass framework and a NIC driver in the context of the NAT. We uncover bugs in both the framework and the driver. Our code is publicly available [28].
A Multi-model Organ Segmentation Method Based on Abdominal Ultrasound Image

A multi-model organ segmentation method was proposed in this paper. The modified U-Net deep neural network model and simplified DenseNet deep neural network model was used in our method to segment and classify abdominal organs. Preliminary semantic segmentation results were corrected combined with medical prior knowldege and the characteristics of video frame correlation, so that the fine segmentation of abdominal organs based on ultrasound images could be realized. TPR and SIR of our method reached 0.8172 and 0.7971, superior to similar algorithms in terms of both visual effect and evaluation indexes.
Formation of IT Project Implementation Team

The criteria for forming a project team and its impact on the project success are analyzed in this paper. It is noted that the team is selected for each project at the first stage of its implementation. The project team means a team of like-minded people motivated to fulfill the project.The article suggests an original procedure for forming a project team, which involves the selection of performers according to defined criteria based on weight coefficients assigned by experts. The assessment is based on the competencies acquired by graduates of IT specialties and reflected in educational and professional programs.
VQASTO: Visual Question Answering System for Action Surveillance based on Task Ontology

Question answering (QA) is a popular research topic for its applications in reality. In advance, there are Visual Question Answering (VQA) researches that aim to combine visual and textual information for question answering. Their drawback is the dependence of learning models, which impedes human intervention and interpretation. To the best of our knowledge, most of them concentrate on the general problem or some specific contexts but no one puts the QA system under action surveillance context. In this paper, we propose a QA system based on Task Ontology which is mainly responsible for mapping from a question sentence to corresponding tasks carried out to reach the appropriate answer. The advantages of task ontology are the adoption of human knowledge to solve a specific problem and the reusability. The performance of the system thus heavily depends on subtasks/models. In our scope, we focus on two main subtasks: Pose estimation/tracking and Skeleton-based action recognition. Besides, we give some enhancements to improve the time efficiency of Pose estimation/tracking and propose a new spatial-temporal feature based on drawing skeleton sequence to image for skeleton-based action recognition of videos in the wild. This method, to some extent, can overcome the challenge of bad-shape pose/skeleton produced by Pose estimation on real-world videos. The hard part of Action recognition in VQASTO is that it has to get input from Pose estimation and Pose tracking which is markedly different from having available good skeletons and merely do recognition.
Functional Systems Integrated with a Universal Agent of Artificial Intelligence and Higher Neurocategories

The paper proposes a hybrid functional system equipped with a universal AI agent that implements a strong AI variant. The system is a versatile prototype for medical prosthetics. The task is to find formalisms for modeling strong AI within neural networks based on higher-dimensional categories and convolutional polycategories. n-categories are reduced according to the theorem below to convolutional polycategories with convolutions of a special form. The necessity of using functional systems hierarchies based on the highest G-neurocategories of hierarchies is shown, examples of such hierarchies are given. The listed results are new and original.
Real-time gesture recognition in complex background based on deep learning

The non-contact gesture recognition system is essential to enhance the immersive experience of human-computer interaction. In recent years, the gesture recognition network is difficult to deal with complex backgrounds and the diversity of shooting angles, which limits its scope of application. This paper proposes a two Staged gesture recognition network: firstly locate the gesture through the detection network, and then use the classifier to identify the gesture category in the localized area. Through BiFPN for efficient multi-scale semantic fusion, the network can reach 96.73% gesture recognition accuracy in complex background. At the same time, the network is further compressed through pruning and weighting, so that the trained network can be deployed on embedded terminals to realize real-time gesture recognition.
An Integrated Model to Email Spam Classification Using an Enhanced Grasshopper Optimization Algorithm to Train a Multilayer Perceptron Neural Network

Email is an important communication that the Internet has made available. One of the significance is seen in the great ease in which immediate transmission of internet data is done during email transmission. This great ease emerges with a major issue which is the continuous increase in spam emails. Thus, the need for a spam email detector. The versatility and adaptability of the nature of spam influenced past innovations. However, previous techniques have been weakened. This study introduces an email detection model that is designed based on use of an improved version of the grasshopper optimization algorithm to train a Multilayer Perceptron in classifying emails as ham and spam. To validate the performance of EGOA, executed on the spam email dataset are utilized, then the performance was relatively compared with popular search algorithms. The implementation demonstrates that EGOA introduces the best results with high accuracy of up to 96.09%.
Formal Explanation and Mechanisms of Conceptual Representation

This paper reviews evidence that formal explanation is intrinsic to an important class of generic knowledge and to the mechanisms proposed for representing this type of knowledge. Formal explanations are preferentially generated and rated as better when the characterizing property is thought to be an aspect of being the kind of thing in question compared to cases where it is not, but is merely prevalent among things of this kind, or causally connected to it. These and other data suggest that formal explanations unify two deep characteristics of human cognition: (i) the pervasive tendency to think and talk about things as instances of kinds, and (ii) the drive to understand and explain. In identifying something as an instance of a kind, we understand some of its properties as being due to its being the kind of thing it is. The cognitive mechanism that provides the basis for thinking of things as instances of kinds also provides the means for thinking about a kind and the properties that the kind has by virtue of its being the kind of thing it is. This mechanism represents generic knowledge that involves formal explanation in a non-propositional, non-predicational manner via co-indexation. Furthermore, the connection between the kind and the property is cognitively represented as immediate (i.e. there is nothing that mediates between being a dog and barking), and explanatory. The final section speculates that the mechanisms that represent generic knowledge that involves formal explanations may shed light on the representation and acquisition of Aristotelian first principles.
Crosstalk: Emotion and Misunderstanding in Online Debate

Starting with an overview of the role of emotion in political practice, this chapter considers how people negotiate emotion and reason when discussing politics, and the evidence that online political discussion can be a very visceral and affective experience which can have marked ripple effects in people’s offline lives. It shows how designed-in features exploit a tendency to fast-thinking emotional responses, but not necessarily in a way that supports constructive engagement. People can have profound emotional responses owing to the difficulties of using social media and the challenge of reading others and understanding difference, which often came as a surprise. Very personal factors could influence reactions and behaviour, but these might not be visible to other users. Moral judgements about others could involve objectification and were prone to double standards. From participants’ own vivid and personal accounts, we see how this directly supports politically dysfunctional behaviours such as othering, polarisation and echo chamber formation. There are varying levels of self-awareness of how individual behaviour contributes to these phenomena, but this can develop over time.
Software Developer Recommendation in Terms of Reducing Bug Tossing Length

In close software development, it is easy for the project manager to recommend the right developer to resolve a bug that is reported by an end-user. However, in the case of open-source software developments, where most developers are engaged on different project either on the same or different repositories. Due to their agile involvement on repositories, bug triaging might be slow and increases the Bug Tossing Length (BTL) which is encounter as the time between reporting and resolving bugs. In open-source software repositories like GitHub, numerous developers are involved with well-known projects to resolve the issue reported by end-users. The assignment of the reported bug to an appropriate developer may lead to a reduced BTL time. Though, several metrics based and Machine Learning (ML) based approaches have been introduced to recommend the appropriate developer on the bases of several parameters. However, few studies are related to the recommendation of developers on the bases of their historical information regarding their attempts to reduce the BTL. To address this issue, we have proposed a new approach to recommend a developer for bug triaging on the bases of their involvement in reducing the BTL. In the proposed study, the model is trained once and new bug reports are automatically assigned to relevant developers. In this regard, we exploit the proposed methodology through using the XGBoost, Support Vector Machine, Random Forest, Decision Tree, KNearest Neighbor, and Naïve Bayes for the recommendation of the developer for a reported bug. We used widely-known two datasets namely Eclipse, and Mozilla. The experimental result indicate the effectiveness of proposed methodology in terms of developer recommendation for a new reported bug.
Impact of global dimming/brightening on estimating Angström-Prescott parameters based on geographically weighted regression in China

Abstract Variations of solar radiation is related to energy planning and architectural designing. It is important to estimate solar radiation from other climate factors because of the limited measurement solar radiation data. In this study, temporospatial changes of solar radiation and sunshine hour are analysed for global dimming/brightening periods based on meteorological data in China. Results show that the trends of solar radiation and sunshine hour are both decreasing dramatically for 1961–1990 (the global dimming period) but levelling off for 1991–2010 (the global brightening period), which indicates that sunshine hour can be used the reliable indicator of solar radiation. Spatially, the change trends of solar radiation are consistent with sunshine hour in 36 stations during the period of 1961–1990, but they are consistent in just 27 stations during the period of 1991–2010. To assess the impact of changing solar radiation on Angstrom-Prescott parameters (a and b) in the equation of solar radiation estimation from sunshine hour, geographically weighted regression is developed to calculate a and b. There is not dramatic difference in the spatial patterns of a and b for the period of 1961–2010 and 1961–1990. However, there is significantly inconsistent changes in a and b during the global brightening period compared with the global dimming period, which suggests that global dimming/brightening has important impact on estimating solar radiation from sunshine hour, with dramatic variability among different regions.
COMPASS-AR

A description and psychometric validation is offered of the evaluation instrument COMPASS-Ar, conceived to check the level of competence achieved by future teachers—after participating in the ITINER-Ar Project (2018-19)—in the collaborative design of augmented didactic itineraries (ADIs) using augmented reality. This instrument—assessed by experts—consists of 30 items to measure the acquired didactic, digital, socio-collaborative, and creative competences involved in ADI development on the basis of an analytical rubric—created ad hoc and validated by means of the Delphi method—which specifies the requirements of each score through a (1-5) Likert-type scale. After evaluating a sample of 322 subjects, the assessment of reliability provided a Cronbach's alpha = .979. Construct validity was tested using factor analysis. Both the Kaiser-Meyer-Olkin test and Bartlett's sphericity test revealed that the instrument under study has a unidimensional nature. It can be concluded that this is a valid and reliable instrument.
Bayesian anomaly detection and classification for noisy data

Statistical uncertainties are rarely incorporated into machine learning algorithms, especially for anomaly detection. Here we present the Bayesian Anomaly Detection And Classification (BADAC) formalism, which provides a unified statistical approach to classification and anomaly detection within a hierarchical Bayesian framework. BADAC deals with uncertainties by marginalising over the unknown, true, value of the data. Using simulated data with Gaussian noise as an example, BADAC is shown to be superior to standard algorithms in both classification and anomaly detection performance in the presence of uncertainties. Additionally, BADAC provides well-calibrated classification probabilities, valuable for use in scientific pipelines. We show that BADAC can work in online mode and is fairly robust to model errors, which can be diagnosed through model-selection methods. In addition it can perform unsupervised new class detection and can naturally be extended to search for anomalous subsets of data. BADAC is therefore ideal where computational cost is not a limiting factor and statistical rigour is important. We discuss approximations to speed up BADAC, such as the use of Gaussian processes, and finally introduce a new metric, the Rank-Weighted Score (RWS), that is particularly suited to evaluating an algorithm’s ability to detect anomalies.
Detection of Near Duplicates over Graph Datasets Using Pruning

Graphs are widely used formalism to model data in various domains such as natural language processing, chemoinformatics, computer vision, information retrieval and software engineering. Finding similar graphs is essential for many applications in these domains. Graph isomorphism finds exact duplicate graphs. However, it fails to quantify similarity and it's computationally expensive. To overcome both these bottlenecks, a number of graph similarity measures have been proposed. Graph Similarity Self-Join (GSSJ) is the problem of finding all pairs of graphs that have similarity score above a predefined threshold. For a graph dataset with n graphs, Naive solution involves similarity score computation for all (n/2) pairs of graphs. This problem is both compute and data intensive. Existing algorithms for this problem support only graph edit distance as the similarity measure. Overarching goal of this research is to develop algorithms for graph similarity self-join that support multiple graph similarity measures. Major contribution of this research will be better indexing mechanisms for graphs and tight bounds on graph similarity.