Reviewerly/exHarmony huggingface dataset:
Survey on Novelty Detection using Machine Learning Techniques

Novelty detection affords to identify data patterns that stray strikingly from the normal behavior.it allows a good identification and classification of objects which were not known during the learning phase of the model.In this article, we will introduce an organized and comprehensive review of the study on novelty detection.We have grouped existing methods into three classes.Statistical Based techniques, Machine Learning Based techniques and Deep Learning Based techniques.In addition, we provide a discussion on application domains of novelty detection, and for each category, we have defined the novelty, cited the most used dataset, as well as a description and perspectives of the latest work carried out in this domain.Our article is developed with the aim of facilitating to researchers a better understanding of the interest of using novelty detection in the various fields mentioned in the article, as well as to clarify the different existing novelty detection methods.
Lissy: Experimenting with On-Chain Order Books

Financial regulators have long-standing concerns about fully decentralized exchanges that run ‘on-chain’ without any obvious regulatory hooks. The popularity of Uniswap, an automated market makers (AMM), made these concerns a reality. AMMs implement a lightweight dealer-based trading system, but they are unlike anything on Wall Street, require fees intrinsically, and are susceptible to front-running attacks. This leaves the following research questions we address in this paper: (1) are conventional (i.e., order books), secure (i.e., resistant to front-running and price manipulation) and fully decentralized exchanges feasible on a public blockchain like Ethereum, (2) what is the performance profile, and (3) how much do Layer 2 techniques (e.g., Arbitrum) increase performance? To answer these questions, we implement, benchmark, and experiment with an Ethereum-based call market exchange called Lissy. We confirm the functionality is too heavy for Ethereum today (you cannot expect to exceed a few hundred trade executions per block) but show it scales dramatically (99.88% gas cost reduction) on Arbitrum.
An Attentional Model for Earthquake Prediction Using Seismic Data

Natural disaster prediction is one of the main concerns of authorities globally. Disasters cause large-scale psychological, social, and economic damage; therefore, techniques to predict such events are essential to minimize their impacts. However, despite all efforts to estimate the occurrence of a disaster, making an accurate and robust forecast is a challenging task. In recent years, Deep Learning techniques have innovated several fields by learning the factors that contribute to the phenomena generation; also, biologically inspired concepts such as attention have provided cheap ways to allocate computational resources by consuming only the necessary information to solve the task. This work aims to develop and evaluate a feature-based temporal attentional model to predict the time remaining for an earthquake event by consuming seismic waves from the LANL Earthquake dataset. The proposed models comprehend four distinct architectures based on 1D-CNN, LSTM, and LSTM with self-attention mechanisms. Experimental results indicated that attention plays a vital role in learning temporal relations, with models achieving state-of-the-art results in the task.
Propagation-Aware Social Recommendation by Transfer Learning

Social-aware recommendation approaches have been recognized as an effective way to solve the data sparsity issue of traditional recommender systems. The assumption behind is that the knowledge in social user-user connections can be shared and transferred to the domain of user-item interactions, whereby to help learn user preferences. However, most existing approaches merely adopt the first-order connections among users during transfer learning, ignoring those connections in higher orders. We argue that better recommendation performance can also benefit from high-order social relations. In this paper, we propose a novel Propagation-aware Transfer Learning Network (PTLN) based on the propagation of social relations. We aim to better mine the sharing knowledge hidden in social networks and thus further improve recommendation performance. Specifically, we explore social influence in two aspects: (a) higher-order friends have been taken into consideration by order bias; (b) different friends in the same order will have distinct importance for recommendation by an attention mechanism. Besides, we design a novel regularization to bridge the gap between social relations and user-item interactions. We conduct extensive experiments on two real-world datasets and beat other counterparts in terms of ranking accuracy, especially for the cold-start users with few historical interactions.
A Novel Non-binary Associative Memory based on Continuous Recurrent Neural Networks

Neural networks have important applications in associative memory design. In the design of associative memory, it is often necessary to obtain the weight values among the neurons through learning, and the calculation complexity is often high. This will lead to some limitations in the implementation of associative memory by neural networks. Based on continuous recurrent neural network, a new kind of gain type activation function is constructed to realize non-binary associative memory in this paper. The weights are designed under the networks parameters inequality constraints instead of learning from training data. Meanwhile, the influence of time delay is considered. Under the external input way of neural network, the global stability criterion is proposed, and the impact of self-regulating parameters of neural networks to the recall accuracy of patterns is analyzed. Finally, the numerical simulation examples are carried out to verify the proposed model and the paper is summarized in the end.
Exploring Temporal Functional Dependencies Between Latent Skills in Cognitive Diagnostic Models

Cognitive Diagnostic Models (CDMs) provide diagnostic information about a subject’s skill profile by specifying relationships between the student’s latent skill profile, item characteristics, and student responses to those items. In this study, we analyze the ASSISTment Math 2004–2005 dataset to explore whether latent skill profiles estimated at one assessment point are predictive of latent skill profiles at a subsequent assessment point. In addition, we wanted to investigate the hypothesis that latent skills evolve independently. We found that latent skills estimated at subsequent assessment points were predicted by latent skills at previous assessment points when these were spaced at least six weeks apart. In addition, we found evidence that latent skills may not independently evolve despite the fact that this is a common assumption in longitudinal modeling. Some comments are then provided regarding how such results might inform the design of new longitudinal cognitive diagnostic models of student learning in semester-long courses.
Design and implementation of face recognition system based on convolutional neural network

Abstract The main features of face recognition are easy to use, quick to update, high flexibility, high accuracy, high environmental adaptability and so on. This passage of facial recognition technology based on convolutional neural network, which combines the present popular Python and Opencv Keras to identify development system, will make face recognition more efficient and more intelligent. And convolutional neural network based on face recognition data can improve accuracy and reduce the error rate through training. This experiment makes efforts to perfect the training model and enhance the influence of face recognition, so it could be more influential and smarter in higher precision applicable areas.
Knowledge Graph Completion Based on the Joint Embedding of Entities

Abstract As the last step in the process of knowledge graph construction, knowledge graph completion predicts and inferences new factual triples according to the existing knowledge, which is of great significance to the downstream application of the knowledge graph. The knowledge graph completion technology learns representations of entities and relations in the knowledge graph and reconstructs triples factual relations in the representation space to measure their effectiveness, so as to predict and supplement entities and relations. At present, Graph Convolutional Network (GCN) is widely used in entity modeling, which combines the graph structure information to learn the vector representation of entities. However, most algorithms only focus on static knowledge graph completion, ignoring the important role of time in the knowledge graph, and the modeling of entities is not comprehensive enough. In this paper, a knowledge graph completion based on the joint embedding of entities is proposed, which combines structural feature embedding of the graph and dynamic embedding of entities to carry out multi-level learning of entities to improve the effect of entity prediction. Structural feature embedding of the graph takes advantage of the powerful feature aggregation capability of GCN to integrate the knowledge graph as the topological structure and node features of graph data into the representation learning of entities. Dynamic embedding of entities takes advantage of the characteristics that entities will behave in different states at different times and divides the entity features into two parts for learning. One part of the features changes dynamically over time and the other part retains its static properties. In this paper, the probability model ConvTransE is used to construct the model of relations and triples, ConvTransE uses the convolutional neural network to extract the semantic information of the triples and predict the missing elements. We have conducted experiments on public datasets and achieved better results on Hit@K and MRR.
Effect of Dialogue Structure and Memory on Language Emergence in a Multi-task Game

In language emergence, neural agents engage in finite-length conversations using a finite set of symbols to reach a given goal. In such systems, two key factors can determine the dialogue structure; the size of the symbol set and the conversation length. During training, agents invent and assign meanings to the symbols without any external supervision. Existing studies do not investigate how these models behave when they train under multiple tasks requiring different levels of coordination and information exchange. Moreover, only a handful of work discusses the relationship between the dialogue structure and the performance. In this paper, we formulate a game environment where neural agents simultaneously learn on heterogeneous tasks. Using our setup, we investigate how the dialogue structure and the agent’s capability of processing memory affect the agent performance across multiple tasks. We observed that memory capacity non-linearly affects the task performances, where the nature of the task influences this non-linearity. In contrast, the performance gain obtained by varying the dialogue structure is mostly task-independent. We further observed that agents prefer smaller symbol sets with longer conversation lengths than the converse.
Tensor products of coherent configurations

A Cartesian decomposition of a coherent configuration $${\cal X}$$ is defined as a special set of its parabolics that form a Cartesian decomposition of the underlying set. It turns out that every tensor decomposition of $${\cal X}$$ comes from a certain Cartesian decomposition. It is proved that if the coherent configuration $${\cal X}$$ is thick, then there is a unique maximal Cartesian decomposition of $${\cal X}$$ , i.e., there is exactly one internal tensor decomposition of $${\cal X}$$ into indecomposable components. In particular, this implies an analog of the Krull-Schmidt theorem for the thick coherent configurations. A polynomial-time algorithm for finding the maximal Cartesian decomposition of a thick coherent configuration is constructed.
Types and Solutions of Signal Crosstalk of Vibration Acquisition System on Spacecraft

Analog switches which have great influence on the resolution of a whole acquisition system, are widely used in analog signal acquisition system of spacecraft and civil. Signal crosstalk is one of most difficult problems in Multi-input analog switches. A data acquisition system on spacecraft has many channels, large amount of data and high precision requirements. In order to improve the sampling-rate and reduce the noise of a data acquisition system, it is especially important to effectively reduce the crosstalk of multi-input analog switch. This paper introduces the types and solutions of signal crosstalk of analog switch in a vibration acquisition system. They effectively reduced signal crosstalk of the vibration acquisition system and improved the sampling-rate of the vibration acquisition system.
Assessment of the causality of the measured values based on external features

Unlike correlation of values, causation of values is a philosophical concept. In mathematics, a phenomenon that is certain, and a phenomenon that is random, are clearly defined. These phenomena may be deemed dependent (they can be expressed by a formula) or independent in the case of a completely random phenomenon. In all other correlation cases, dependence as causality may only be derived from experience and from the external features of the measured object. These potential causality manifestations may in some cases differ from complete randomness based on the external parameters of a series of the observed measured values. By providing a mathematical description of the external parameters, an acceptable assessment of the causality may be achieved. This type of assessment does not need to determine the causality exactly. Its value may lie in the area of troubleshooting where it may reveal an eventually incorrect measurement procedure. It can also point to specific properties of the object measured, that could be missed if a different manner of assessment were used.
Towards Conditional Quantum Phase Gates Based on Strongly-Coupled Charged Quantum Dot-Micropillar Cavities

The realization of quantum-photonic networks is a key challenge for optical quantum technologies. To scale such networks to many nodes and long distances, an efficient interface between stationary and flying qubits is necessary. One of the front-runners towards this goal is a spin-photon interface realised with a single charged QD with a localized electron spin that is strongly coupled to a micropillar cavity [1] . Through the strong spin-photon coupling, the polarization state of the optical field is controlled by the QD spin, thus modulating the cavity’s transmission properties. Controlling the polarization state over a wide angle is particularly useful for realizing high-fidelity polarization states and conditional phase quantum gates (e.g. CNOT) for photon-based quantum computing.
Learning to Plan via a Multi-step Policy Regression Method

We propose a new approach to increase inference performance in environments that require a specific sequence of actions in order to be solved. This is for example the case for maze environments where ideally an optimal path is determined. Instead of learning a policy for a single step, we want to learn a policy that can predict n actions in advance. Our proposed method called policy horizon regression (PHR) uses knowledge of the environment sampled by A2C to learn an n dimensional policy vector in a policy distillation setup which yields n sequential actions per observation. We test our method on the MiniGrid and Pong environments and show drastic speedup during inference time by successfully predicting sequences of actions on a single observation.
Research on Image Recognition for License Plate from Classifier Algorithm

Classification algorithm based on Adaboost and Harr-like features is quite mature to be used in the field of face detection. In this paper, the traditional Adaboost algorithm is improved and the vehicle positioning module is added. At the same time, this algorithm is applied to vehicle license plate location and it greatly improves the accuracy of license plate location. License plate location is to distinguish the plate under the complex background environment. In license plate recognition, it is the most critical step. Its accuracy directly affects the accuracy of license plate recognition. In this aritical, a new algorithm is proprosed, and two simulation softwares are combined to make results of license plate location and recognition accuracy improved significantly.
Gain-Loss Evaluation-Based Generic Selection for Steganalysis Feature

Fewer contribution feature components in the image high-dimensional steganalysis feature are able to increase the spatio-temporal complexity of detecting the stego images, and even reduce the detection accuracy. In order to maintain or even improve the detection accuracy while effectively reducing the dimension of the DCTR steganalysis feature, this paper proposes a new selection approach for DCTR feature. First, the asymmetric distortion factor and information gain ratio of each feature component are improved to measure the difference between the symmetric cover and stego features, which provides the theoretical basis for selecting the feature components that contribute to a great degree to detecting the stego images. Additionally, the feature components are arranged in descending order rely on the two measurement criteria, which provides the basis for deleting the components. Based on the above, removing feature components that are ranked larger differently according to two criteria. Ultimately, the preserved feature components are used as the final selected feature for training and detection. Comparison experiments with existing classical approaches indicate that this approach can effectively reduce the feature dimension while maintaining or even improving the detection accuracy. At the same time, it can reduce the detection spatio-temporal complexity of the stego images.
The Effect of Boldness Program on Increasing the Level of Non-verbal Communication Skills of Autism Children

The aim of this study was to investigate the effectiveness of group assertiveness program on increasing the level of social skills of autism spectrum disorders children.Using the social skills questionnaire (Ahmadi et al. 1997), children whose communication skills score was less than 100 were identified.These individuals were selected as a sample and then randomly divided into experimental and control groups.The statistical sample of this study included 30 autism children who were randomly selected.The experimental and control groups were divided into two groups of 15 people.Then, the group training program was taught to the experimental group in 10 sessions.After this training, social skills tests were performed on both groups.Finally, Data were Analise.According to the results, The experimental group is children who were trained in a daring program; They performed better than the control group in the non-verbal communication skills test.That is, the group assertiveness program was effective in enhancing the nonverbal communication skills of children on the autism spectrum.Generally.General education, formal adult education, tutoring and various educational materials that should be provided to them by various centers.Some forms of exercise-behavior are often used in the training of courage and ascension.
Code Generation from Images Using Neural Networks

In this paper, we have proposed and demonstrated a method that generates graphical user interface front end code from user drawn screen images. We apply classical computer vision techniques viz. canny edge detection, contour box detection, dilation and erosion for image, and two deep learning techniques of text detector and convolutional neural networks. The process of creating an application starts with a graphical user interface designer, creating a layout that becomes the blueprint for the web developer. The web developer sends the layout back to the designer with desired changes. This process is iterative, and extra efforts are spent to recreate the user interface code. We provide a unique way to automate this iterative process, to give a template code from the layout image.
Near-null interferometric test of aspheric cylinders utilizing a partial null lens

Aspheric cylinders are more advantageous than cylinder with spherical cross section in optical design and aberration correction of high-energy laser systems because additional shape parameters can be introduced. To overcome the limited test accuracy and the insufficient research on the testability of the flexible null test method for aspheric cylinders, a near-null interferometric test method for aspheric cylinders was developed utilizing a partial null lens. A coaxial configuration design was employed. Accordingly, only one translation motion was required for testing various aspheric cylinders in one shot. Using the proposed test configuration and the developed near-null data processing method, test accuracy about λ/20 (λ = 632.8 nm) root-mean-square are easier to guarantee compared with those of the existing stitching-required method with off-axis configuration. Further, the testability of the proposed method was analyzed following the development of the partial null theory. The analysis shows the testable surfaces are equidistant surfaces with nearly constant k·R products (k·R = 211.625 mm). The testability results can serve as a good reference for engineers who intend to use aspheric cylinders in high-energy laser systems and can further promote the development of high-energy laser systems. A near-null test system was established. Its simple configuration, moderate test accuracy, and flexible test capacity were successfully demonstrated based on aspheric cylinders measurements.
U-Net-Based Segmentation for Electrical Lines and Its Application to Real-Time Maintenance Algorithm for Electricity Facilities

Inspections for maintenance of electricity facility are performed by patrols which are always in a potentially dangerous traffic accident. In this study, we address the inspection procedure with leveraging advanced deep learning algorithms on images for patrollers to focus on driving to reduce the danger, assuming that facility images are obtained by photographing automatically with a monocular camera during patrols. Toward the goal with restriction of our concerns on an electrical line and related devices, it is initially proposed a new image segmentation algorithm for electrical lines, U-Net-based CNN model, which can be used as a basic step of real-time maintenance algorithms for electricity facilities. It is then introduced a novel inference algorithm for the connectivity between an insulator and an electrical line, as one realization of real-time maintenance algorithms. Experiments demonstrate that the both proposed algorithms are effective and efficient enough to be feasible for real-time algorithms. The proposed methods expect not only to help dangerous labor done by patrollers but also to save lots of money and time by products.