Reviewerly/exHarmony huggingface dataset:
Do Commonly Recommended Preventive Actions Deter Identity Theft Victimization? Findings from NCVS Identity Theft Surveys

It remains unknown if taking commonly used preventive actions is related to identity theft. In the current study, we use a dataset featuring over 220,000 respondents to the National Crime Victimization Survey Identity Theft Supplement (NCVS ITS). The survey was conducted by the Bureau of Justice Statistics (BJS) first in 2012, then again in 2014, and once more in 2016. The findings reported here suggest that demographic variables (e.g., gender, income) and types of online activities (e.g., frequency of online shopping) are significantly related to identity theft victimization. An interesting additional finding is that among seven distinct types of preventive actions listed in the NCVS ITS survey (frequently checking credit reports, frequently changing passwords for financial accounts, employing purchase credit monitoring, shredding documents containing personal information, monitoring bank statements for suspect charges, using security software programs, and purchasing identity theft protection), shredding documents with personal information ALONE is significantly negatively related to identity theft victimization. All six other preventive actions are either positively related or unrelated to identity theft victimization. These findings generate practical implications and, most importantly, raise the question of whether some newly-fashioned preventive actions might provide better protection from identity theft protection.
Mathematical Modeling and Simulation of Online Teaching Effect Evaluation Based on Decision Tree Algorithm

With the support of big data technology, the field of education is also facing new problems and opportunities. Network teaching has become the mainstream means of higher education. In order to explore the changes of students’ learning effect in the process of online teaching, this paper proposes to build an online teaching effect evaluation model with the support of data mining technology and decision tree algorithm. This paper records the factors and objects that reflect the teaching effect in network teaching and traditional teaching, respectively. A decision tree algorithm is used to divide the attributes of influencing factors from relevant rules. Using the Kirschner model to build the evaluation system, add two attribute elements: students’ teaching evaluation and teachers’ self-evaluation. Data mining technology is used to preprocess and clean up the sample set, which improves the accuracy of the calculation results. In the evaluation model, the association rule algorithm is also constructed to classify the data of the same element type and delete the data of different elements after marking. Through this evaluation model, teachers can accurately judge students’ learning interests and improve students’ academic performance. The results show that compared with the traditional data mining algorithm, the decision tree algorithm has obvious advantages in computing speed and accuracy.
Branching Pomsets for Choreographies

Choreographic languages describe possible sequences of interactions among a set of agents. Typical models are based on languages or automata over sending and receiving actions. Pomsets provide a more compact alternative by using a partial order over these actions and by not making explicit the possible interleaving of concurrent actions. However, pomsets offer no compact representation of choices. For example, if an agent Alice can send one of two possible messages to Bob three times, one would need a set of 2 * 2 * 2 distinct pomsets to represent all possible branches of Alice's behaviour. This paper proposes an extension of pomsets, named branching pomsets, with a branching structure that can represent Alice's behaviour using 2 + 2 + 2 ordered actions. We encode choreographies as branching pomsets and show that the pomset semantics of the encoded choreographies are bisimilar to their operational semantics.
The augmented reality usage for learning of disappearing historical monuments

The study is devoted to the development of a mobile application for the reconstruction in augmented reality of disappearing objects of cultural and historical heritage on the example of the Monzhene?s castle, also known as the Kessler?s estate. The importance of creating an archive of such digital twins - 3D-models for objects of cultural heritage that have been practically destroyed to the ground, the reconstruction of which is difficult, as well as for preserving the history and culture of ancestors, is noted. The effectiveness of using the developed application based on augmented reality technology in the educational process is confirmed by the results of a survey of 17 schoolchildren who used the application in the lessons of the history of their native land.
Optimizations of a Multi-Agent System for a Real-World Warehouse Problem

Abstract In recent years, many warehouses applied mobile robots to move products from one location to another. We focus on a traditional warehouse where agents are humans, and they are engaged with tasks to navigate to the next destination one after the other. The possible destinations are determined at the beginning of the daily shift. Our real-world warehouse client asked us to minimize the total wage cost, and to minimize the irritation of the workers because of conflicts in their tasks. We define a heuristic for the optimizations for splitting the orders into warehouse carts, defining the sequence of the products within the carts, and the assignment of the carts to workers. We extend Multi-Agent Path Finding (MAPF) solution techniques. Furthermore, we have implemented our proposal in a simulation software, and we have run several experiments. According to the experiments, the make-span and the wage cost cannot be reduced with the heuristic optimization, however the heuristic optimization considerably reduces the irritation of the workers. We conclude our work with a guideline for the warehouse.
Learning From Visual Demonstrations via Replayed Task-Contrastive Model-Agnostic Meta-Learning

With the increasing application of versatile robotics, the need for end-users to teach robotic tasks via visual/video demonstrations in different environments is increasing fast. One possible method is meta-learning. However, most meta-learning methods are tailored for image classification or just focus on teaching the robot what to do, resulting in a limited ability of the robot to adapt to the real world. Thus, we propose a novel yet efficient model-agnostic meta-learning framework based on task-contrastive learning to teach the robot what to do and what not to do through positive and negative demonstrations. Our approach divides the learning procedure from visual/video demonstrations into three parts. The first part distinguishes between positive and negative demonstrations via task-contrastive learning. The second part emphasizes what the positive demo is doing, and the last part predicts what the robot needs to do. Finally, we demonstrate the effectiveness of our meta-learning approach on 1) two standard public simulated benchmarks and 2) real-world placing experiments using a UR5 robot arm, significantly outperforming current related state-of-the-art methods.
Peer Incentive Reinforcement Learning for Cooperative Multiagent Games

Social learning, especially social incentives, is extremely important for humans to achieve a high level of coordination. Inspired by this, we introduce this concept into cooperative multi-agent reinforcement learning (MARL), to implicitly address the credit assignment problem and promote the inter-agent direct interactions for cooperations among agents in cooperative multi-agent games. In this paper, we propose a novel Intrinsic Reward method with Peer Incentives (IRPI) based on actor-critic policy gradient. This method can enable agents to incentivize each other for their cooperations through using causal influence among them. Specifically, a novel intrinsic reward mechanism is innovatively designed to empower each agent the ability to give positive or negative rewards to other peer agents&#x0027; actions through considering the causal influence of the other agents on it. The mechanism is realized by a feed-forward neural network through utilizing causal influence between the agents. The causal influence of one agent on another is inferred via counterfactual reasoning using the joint action-value function in MARL. The quality of the influence is assessed via counterfactual reasoning using the individual value function in MARL. Simulations are carried out on two popular multi-agent game testbeds: Starcraft II Micromanagement and Multi-Agent Particle Environments. Simulational results demonstrate that the proposed IRPI can enhance cooperations among the agents to achieve better performance compared with a number of state-of-the-art MARL methods in a variety of cooperative multi-agent games.
Graph Convolution RPCA With Adaptive Graph

Principal component analysis (PCA) is warmly welcomed in dimensionality reduction and its applications. Due to the high sensitivity of PCA to outliers, a series of PCA methods are proposed to enhance the robustness of PCA. Besides, the representation ability of the existing PCA methods has limitations as well. To enhance the robustness and representation ability of robust PCA, we elaborate a novel Graph Convolution Robust PCA method (GRPCA) to incorporate the manifold structure into PCA. It constructs a sparse graph based on the local connectivity structure of samples. Graph auto-encoder is utilized to solve the robust PCA problem under the low-rank and sparse constraints. With the dual-decoder, GRPCA learns the low-dimensional embeddings that reconstruct the manifold structure and low-rank approximation simultaneously. Furthermore, since the graph suffers from misconnection triggered by occlusions, the local connectivity structure of low-dimensional embeddings is utilized to modify the graph. Our proposed method excels in both the clustering of low-dimensional embeddings and the low-rank recovery. Lastly, extensive experiments conducted on six real-world datasets demonstrated the efficiency and superiority of the proposed GRPCA.
DFAT: Dynamic Feature-Adaptive Tracking

During target tracking process, the state of the target is usually unpredictable. In theory, it is often beneficial to automatically assign suitable features to describe the specific target in each frame. Inspired by this, in this paper, we propose a novel dynamic feature-adaptive tracking framework (DFAT) which automatically assigns appropriate features to the consecutive frames during tracking process to boost the tracking performance. To implement DFAT, a large pool consisting of trackers/experts based on correlation filtering (CF) is constructed which is called candidate pool (CandPool). The diversity of the experts lies in their feature configurations and we call them candidate experts (CandExp). In this way, different features can be assigned for continuously changed scenarios and the target. Then to assign suitable experts, for each frame, we design the dynamic tracking process as the following three steps: (1) Several experts which are called executive experts (ExeExp) are selected from the CandPool according to CandExps’ past performance. (2) The ExeExps generate the tracking results and the performance of them are evaluated via a novel evaluation mechanism. (3) The selection rate of each CandExp in the CandPool is updated according to the performance evaluation and the final tracking result is selected. To better evaluate the CandExp, we propose two novel criteria: (1) content similarity weighted intra-evaluation, and (2) response confidence based self-evaluation. Compared with traditional post-event ensemble trackers that use fixed experts, the proposed method learns to dynamically assign appropriate ExeExps selected from a large CandPool which leads to adaption to different cases. Moreover, overfitting caused by fixed experts can also be mitigated via dynamic tracking. Experiments on both public available general and satellite videos based data sets demonstrate the superiority of the proposed method.
A Smart Virtual Tutor with Facial Emotion Recognition for Online Learning

The main aim of our work is to determine whether a virtual tutor can effectively deal with student queries in online distance learning environments. We adopted a Machine Learning (ML) approach to devise a prototype of a smart autonomous tutor which can understand queries and provide responses. Furthermore, with the use of the Web Camera, the virtual tutor can identify the emotion of the student when reading the provided response. This helps the virtual tutor to assess the appropriateness of the provided response. Students can type or use voice commands to ask a question and the response is also provided in both text and voice. The approach is mainly based on the application of the Recurrent Neural Network (RNN) with the Long Short Term Memory Algorithm (LSTM) to determine the query of the user. Emotion detection is based on the Convolutional Neural Network (CNN) approach. Our experiments demonstrate testing accuracy of around 87% for query detection and around 62% for emotion detection.
Design of Multiple Share Creation with Optimal Signcryption based Secure Biometric Authentication System for Cloud Environment

Biometric authentication plays a vital role in the cloud computing (CC) environment to accomplish security and privacy. Several kinds of biometrics are used for the authentication process, particularly, the fingerprint is a commonly used biometric, which accomplishes maximum security. But it can be easily stolen, unintentionally revealed, or difficult to remember. Therefore, biometric authentication techniques are needed and an encryption process is included to improve the security of the authentication process. In this aspect, this paper proposes a multiple share creation with an optimal signcryption-based biometric authentication system (MSCOS-BAS- FLNN- PeSeOA) for the cloud environment. The proposed model enables the creation of multiple shares of the input biometric image (fingerprint) and encrypts them for a secure authentication system. In addition, the encryption of individual shares takes place with an optimal signcryption system where the key generation process is carried out using the penguin search optimization algorithm (PeSeOA). Finally, the functional link neural network (FLNN) model is applied for the biometric authentication verification process. A wide range of simulation analyses are performed to highlight the supreme efficacy of the MSCOS-BAS- FLNN- PeSeOA technique. The experimental outcomes pointed out the hopeful performance of the MSCOS-BAS- FLNN- PeSeOA technique over the recent techniques.
Towards Generalizing the Information Theory for Neural Communication

Neuroscience extensively uses the information theory to describe neural communication, among others, to calculate the amount of information transferred in neural communication and to attempt the cracking of its coding. There are fierce debates on how information is represented in the brain and during transmission inside the brain. The neural information theory attempts to use the assumptions of electronic communication; despite the experimental evidence that the neural spikes carry information on non-discrete states, they have shallow communication speed, and the spikes' timing precision matters. Furthermore, in biology, the communication channel is active, which enforces an additional power bandwidth limitation to the neural information transfer. The paper revises the notions needed to describe information transfer in technical and biological communication systems. It argues that biology uses Shannon's idea outside of its range of validity and introduces an adequate interpretation of information. In addition, the presented time-aware approach to the information theory reveals pieces of evidence for the role of processes (as opposed to states) in neural operations. The generalized information theory describes both kinds of communication, and the classic theory is the particular case of the generalized theory.
Some metaheuristic algorithms for solving multiple cross-functional team selection problems

We can find solutions to the team selection problem in many different areas. The problem solver needs to scan across a large array of available solutions during their search. This problem belongs to a class of combinatorial and NP-Hard problems that requires an efficient search algorithm to maintain the quality of solutions and a reasonable execution time. The team selection problem has become more complicated in order to achieve multiple goals in its decision-making process. This study introduces a multiple cross-functional team (CFT) selection model with different skill requirements for candidates who meet the maximum required skills in both deep and wide aspects. We introduced a method that combines a compromise programming (CP) approach and metaheuristic algorithms, including the genetic algorithm (GA) and ant colony optimization (ACO), to solve the proposed optimization problem. We compared the developed algorithms with the MIQP-CPLEX solver on 500 programming contestants with 37 skills and several randomized distribution datasets. Our experimental results show that the proposed algorithms outperformed CPLEX across several assessment aspects, including solution quality and execution time. The developed method also demonstrated the effectiveness of the multi-criteria decision-making process when compared with the multi-objective evolutionary algorithm (MOEA).
Learned Design of a Compressive Hyperspectral Imager for Remote Sensing by a Physics-Constrained Autoencoder

Designing and optimizing systems by end-to-end deep learning is a recently emerging field. We present a novel physics-constrained autoencoder (PyCAE) for the design and optimization of a physically realizable sensing model. As a case study, we design a compressive hyperspectral imaging system for remote sensing based on this approach, which allows capturing hundreds of spectral bands with as few as four compressed measurements. We demonstrate our deep learning approach to design spectral compression with a spectral light modulator (SpLM) encoder and a reconstruction neural network decoder. The SpLM consists of a set of modified Fabry–Pérot resonator (mFPR) etalons that are designed to have a staircase-shaped geometry. Each stair occupies a few pixel columns of a push-broom-like spectral imager. The mFPR’s stairs can sample the earth terrain in along-track scanning from an airborne or spaceborne moving platform. The SpLM is jointly designed with an autoencoder by a data-driven approach, while spectra from remote sensing databases are used to train the system. The SpLM’s parameters are optimized by integrating its physically realizable sensing model in the encoder part of the PyCAE. The decoder part of the PyCAE implements the spectral reconstruction.
Vologan: Adversarial Domain Adaptation for Synthetic Depth Data

We present VoloGAN, an adversarial domain adaptation network that translates synthetic RGB-D images of a high-quality 3D model of a person, into RGB-D images that could be generated with a consumer depth sensor. This system is especially useful to generate high amount training data for single-view 3D reconstruction algorithms replicating the real-world capture conditions, being able to imitate the style of different sensor types, for the same high-end 3D model database. The network uses a CycleGAN framework with a U-Net architecture for the generator and a discriminator inspired by SIV-GAN. We use different optimizers and learning rate schedules to train the generator and the discriminator. We further construct a loss function that considers image channels individually and, among other metrics, evaluates the structural similarity. We demonstrate that CycleGANs can be used to apply adversarial domain adaptation of synthetic 3D data to train a volumetric video generator model having only few training samples.
Hand Gesture Recognition and Biometric Authentication Using a Multi-day Dataset

Hand-gesture recognition (HGR) is one of the major applications of electromyography (EMG), specifically for controlling functional prosthetic hands. Recently, another application i.e. the EMG-based biometrics has found growing research interest due to its potential of addressing some conventional biometric limitations. It has been observed that for EMG-based applications, the translation of laboratory research to real-life applications suffers from two major limitations: 1) a small subject pool, and 2) limited to single-session data recordings. In this study, forearm, and wrist EMG data were collected from 43 participants over three different days with long separation (Days 1, 8, and 29) while they performed static hand/wrist gestures. The HGR evaluation resulted in a mean AUC of 0.948 ± 0.018 and 0.941 ± 0.021 for forearm data and wrist data, respectively. The biometric evaluation resulted in a mean EER of 0.028 ± 0.007 and 0.038 ± 0.006 for forearm data and wrist data, respectively. These results were comparable to the widely used Ninapro database DB2. The large-sample multi-day dataset would facilitate further research on EMG-based HGR and biometric applications.
An Improved YOLOX for Detection in Urine Sediment Images

In clinical medicine, the detection of human urine sediment is usually a basic test. The indicators of test can effectively analyze whether the patient has the disease. The traditional method of chemical, physical and microscopic analysis of samples artificially is time-consuming and inefficient. With the development of deep learning technology, many detectors now can replace traditional manual work and play a good detection effect. So it is of great value to put deep learning technology into the medical field. Usually, the Urine Microscopic Image has challenges for research, in which many detectors can not detect the cells well due to their small scale and heavy overlap occlusion. Therefore, we propose a novel detector for the detection of urine sediment in this paper. Firstly, considering the friendliness of YOLOX to the small objects, we adopt the framework from the YOLOX. Secondly, we add spatial, channel and position attention to enhance the feature information to achieve more accurate detection results. Then, the better Giouloss is also applied to make a better regression of the bounding box. Finally, the experimental results show that our improved model based on YOLOX achieves $$44.5\%$$ $$A P_{50-90}$$ and $$80.1\%$$ $$A P_{50}$$ on the public dataset Urine Microscopic Image Dataset, which is far better than other detectors.