Reviewerly/exHarmony huggingface dataset:
An IoT Based Intruder and Smoke Monitoring System

Burglar and fire are the two dangerous life events. An IoT based intruder and smoke monitoring system is proposed to protect us from these events. The proposed system is a low cost and energy efficient alternative to the existing systems. It is an integrated system that can detect and monitor theft and fire. The system consists of hardware and software parts. The hardware parts have two separated subsystems, smoke and intruder subsystem. The smoke subsystem uses an ESP8266 Arduino board with a smoke sensor (MQ2) and a buzzer. The intruder subsystem uses an ESPIno32CAM board with an ultrasonic sensor (HC-SR04). The software parts consist of three applications that are web, android, and LINE application. Both subsystems share the same web, android, and LINE application. When a smoke is detected by the sensor, the alarm sounds. The smoke subsystem sends a notification to the web and LINE application and the gas concentration level is sent to MySQL database. When an intruder is detected by the sensor, the intruder subsystem captures his photo and sends it to the LINE application for notification. The system also has the ability to recognize faces so that when a homeowner is detected by the sensor, no notifications are displayed in LINE and web application. Web application shows an alert message when there is an intruder and a smoke. It also shows a graph of gas level values. Android application can turn on and off the device in the system.
Research on Human Motion Recognition Method Based on Hybrid Convolution Neural Network-Hidden Markov Model

This paper proposes a human motion recognition method based on hybrid Convolutional Neural Network-Hidden Markov Model (CNN-HMM). The main idea is to embed the posterior probability of CNN into the observation probability matrix of HMM. In order to verify the effectiveness of this method, a motion library including one standard motion posture and five non-standard motion postures is established. The experimental data are obtained by combining with the wearable motion capture suit Perception Neuron 2.0 (PN 2.0). The accuracy of leg-lifting is identified and verified by data preprocessing and data segmentation. The results show that the proposed hybrid CNN-HMM method can distinguish the non-standard motion posture from the standard motion postures with a high recognition rate of 97%.
Study on Health Level Evaluation of Higher Education Based on AHP-NBM Fuzzy Comprehensive Evaluation Model

The development level of higher education is an important symbol of a country's comprehensive national strength and development potential, and its health level is related to the quality of higher talent training and scientific and technological competitiveness, etc. However, there is no unified evaluation system for the health level of higher education in all countries in the world. This paper adopts the social and economic development level and scale of higher education, higher education funds investment and output representative data related to higher education, perfect the predecessors to establish evaluation index system of the health level of higher education, adopt AHP - NBM - research method of fuzzy comprehensive evaluation model (ANFCE) to select the seven countries of health level for calculation and analysis, and the stability of the model is evaluated. The results are as follows: (1) The established evaluation system contains 37 indicators, which can scientifically and reasonably evaluate the health level of higher education around the world. (2) AHP-NBM-Fuzzy Comprehensive Evaluation Model (ANFCE) was used to calculate the scores of higher education health level in seven countries, and it was found that China had the lowest score and was at a fragile level. The sensitive index parameters were obtained by partial least square regression analysis, and the sensitive index parameters were adjusted. (3) China should strengthen the construction of higher education infrastructure, increase investment in higher education, and appropriately reduce the emphasis on the process of higher education and its development performance. (4) The model established in this paper is of reference significance for the improvement of higher education.
A Utility-Optimized Mechanism for Private Data Aggregation

With the era of big data, it becomes ubiquitous to aggregate and analyze data from millions of users. However, privacy is a critical issue in data aggregation, since data contributed by users may disclose sensitive information about individuals. Local differential privacy (LDP) is emerging as the de facto concept for private data aggregation without any reliance on trusted data aggregator or third-parties. In LDP mechanisms, each user locally perturbs his raw data employing a randomized mechanism and sends the perturbed version to the data collector. In this paper, we study the problem of privacy-preserving average aggregation for count value and propose a novel local differential private mechanism: Bit Frequency Mechanism (BFM). We provide both theoretical and experimental analyses of the proposed mechanism, specifically, the experimental results demonstrate that our mechanism can averagely reduce 50% of -error compared to existing state-of-the-art approaches.
A Study of Direct and Indirect Encoding in Phenotype-Genotype Relationships

This paper examines phenotype and genotype mappings that are biologically inspired. These types of coding are used in evolutionary computation. Direct and indirect encoding are studied. The determination of genotype and phenotype relationships and the connection to genetic algorithms, evolutionary programming and biology are examined in the light of newer advances. The NEAT and HyperNEAT algorithms are applied to the 2D Walker [] problem of an agent learning how to walk. Results and findings are discussed, and conclusions are given. Indirect coding did not improve the situation. This paper shows that indirect coding is not useful in every situation.
Theoretical Foundations of Fractal Electrotechnic. Fractal Elements and its Properties

The article is devoted to the foundations of the theory of fractal electric circuits, which generalizes the traditional theory of circuits to the case of the connection between currents and voltages of elements by derivatives of arbitrary order, including fractional ones. The dependence of the active and reactive components of the complex of the impedance of the fractal element on the degree of the derivative is investigated. The analysis of properties of ideal fractal elements is carried out. Based on this analysis, it was concluded that the traditional inductor, resistor and capacitor are special cases of a fractal element in the basis of powers of derivatives (1, 0, -1). It is shown that, unlike traditional ideal elements, a fractal element can have unusual properties, such as the dependence of active resistance on frequency or the presence of negative active resistance. A family of frequency characteristics of a fractal element is constructed depending on the degree of the derivative. The results obtained in the article can be the basis for studying the properties of fractal electric circuits in various operating modes.
INTERACTIVE TECHNOLOGIES IN PROJECT-BASED TRAINING

The work examines the virtual Miro board for organizing project-based training of students.
PSYCHOLOGICAL MEMORY FORGETTING MODEL USING LINEAR DIFFERENTIAL EQUATION ANALYSIS

To solve the problem of insufficient combination of neural mechanism and psychological mechanism in the study of human brain cognitive memory activity, in this exploration, the memory forgetting model in psychology is analyzed and constructed by fractional order model of linear differential equation. The study of memory in cognitive psychology is combined with the neurophysiological mechanism of memory. At the same time, for the stability of long-term memory, a long-term memory neural network model (LTMNNS) is proposed. The validity and accuracy of the model are tested by the recognition of memory eigenvectors. Numerical test and simulation results show that when the difference between any initial values of the system is not more than [Formula: see text], the corresponding system solution difference is not more than [Formula: see text] in the interval [Formula: see text]. Reasoning correctness of fractional order computation model of memory neural network is verified. The LTMNNS algorithm in this exploration guarantees a high number of completed tasks, and the average number of failed tasks is less than MemNNs, DNC and LSTM algorithm. Compared with other models, the proposed LTMNNS has better generalization ability and higher stability.
Automated Teller Machine-Based Voting System

Voting is a critical element of any election which involves the processes of electing leaders or representatives into positions of authority in a democratic system of government. In most developing countries of the world, this process is usually marred with challenges of confidentiality, integrity, availability and auditability such as falsification of results, identity theft, theft of ballot boxes, multiple voting problems, over voting, and electoral fraud. This paper presents a framework for Automated Teller Machine-based voting system that solves the aforementioned challenges of the current voting system by using the existing Automated Teller Machines and debit cards issued for voting. Going further to implement the solution proposed in this paper will enhance and guarantee the credibility of the electoral processes and show a true reflection of the wishes of the people.
Intelligent Virtual Environments with Assessment of User Experiences

Virtual reality (VR) is a powerful modern medium. The advent of low-cost head-mounted display (HMD) devices made this technology accessible at large and featured VR with possibilities to monitor interactions and user’s motion. However, due to lack of real-time feedback mechanism at present, the level of intelligence for virtual environments is still not sufficient to assist the experience and make group or individual assessments towards VR based applications. In this paper, we present our findings related to the problem of real-time feedback that focus on behavioral data by employing the novel feedback mechanism. Virtual-world coordinates, motions and interactions are tracked and captured in real-time while the user experiences particular application. Captured data is investigated to target the issue of complementing VR applications with features derived from real-time behavioral analysis. In our experiment, we also use collected data and provide a methodology to predict virtual-location by the nonlinear auto-regressive neural network with exogenous inputs (NARX). Results suggest employed neural network model is suitable for performing prediction which can be used to obtain a virtual environment with adaptive intelligence.
Reinforced Background Aware Correlation Filter with Adaptive Weighting Strategy for RGB-T Tracking

Nowadays, the fusion tracking based on visible and thermal infrared image (called RGB- T) has been developed faster and faster. However, the existing fusion tracking methods cannot have a higher tracking success rate. In order to solve this problem, we propose a reinforced background aware correlation filtering method, which is a strategy of fusion first and then tracking. This method first converts the thermal image into a single-channel image, and then uses the grayscale information to determine the degree of pixel difference between the target and the overall environment. Next, an adaptive weighting decision is made on the two images, and finally the background aware correlation filtering method is applied to the fusion image to realize the fusion tracking. Numerous experiments on the RGBT234 data set show that our proposed fusion tracking method has better performance.
Low-Temperature Attacks Against Digital Electronics: A Challenge for the Security of Superconducting Modules in High-Speed Magnetic Levitation (MagLev) Trains

This work examines volatile memory modules as ephemeral key storage for security applications in the context of low temperatures. In particular, we note that such memories exhibit a rising level of data remanence as the temperature decreases, especially for temperatures below 280 Kelvin. Therefore, these memories cannot be used to protect the superconducting modules found in high-speed Magnetic Levitation (MagLev) trains, as such modules most often require extremely low temperatures in order to provide superconducting applications. Thus, a novel secure storage solution is required in this case, especially within the oncoming framework concept of the internet of railway things, which is partially based on the increasing utilisation of commercial off-the-shelf components and potential economies of scale, in order to achieve cost efficiency and, thus, widespread adoption. Nevertheless, we do note that volatile memory modules can be utilised as intrinsic temperature sensors, especially at low temperatures, as the data remanence they exhibit at low temperatures is highly dependent on the ambient temperature, and can, therefore, be used to distinguish between different temperature levels.
Activity Simulation from Signals

Sensor-based human activity recognition technology has been used for estimating human action based on the sensor data. In this paper, we propose a new paradigm to render the human activity on a screen instead of classifying the activity among the activity labels. We could built this mockup of a simulator, combining our previous translation tool between signals [2] with the motion rendering systems [3]. We faced two problems which decrease the simulation ability a lot. We proposed two algorithms to increase the performance of this simulator in this preliminary work.
Extended Zernike invariants backed with complex-valued integral images for detection tasks

We consider the problem of computationally fast extraction of rotationally invariant features for object detection procedures based on the sliding window. The central point of interest are Zernike moments (ZMs), which can serve to generate rotationally invariant features. However, the definition-based computations of ZMs are linear with respect to the number of pixels in the image window, and therefore are too slow to be applied directly. The method, we have proposed, computes each feature in constant-time — O(1), and thereby does not depend on the number of pixels. This is owed to a suitable construction of complex-valued integral images that we perform prior to the detection procedure. The approach can be referred to extraction of well known Haar-like features (HFs), but our actual computations are more intricate, and obviously the HFs are not rotationally invariant. This paper constitutes a follow-up study and a refinement of our former research. We enrich our initial ideas by proposing an extended space of Zernike invariants backed with integral images. This feature space includes not only the moduli of Zernike moments but also real and imaginary parts of suitable moment products. We demonstrate the rotational invariance property of these products and provide all necessary algorithmic details, such as: a suitable indexation scheme, feature extraction procedures, and feature counts. Finally, we present a road sign detection experiment confirming that the extended space of features contributes to more accurate detection of objects regardless of their rotation.
Backdoor Filter: Mitigating Visible Backdoor Triggers in Dataset

Deep neural networks have achieved outstanding performance on decision-making tasks. Still, it is vulnerable to the backdoor attack because of lacking transparency, which results in unexpected results for the input with a trigger. Backdoors can remain hidden in neural networks for a long time, and the networks implanted with backdoors can still make correct judgments when receiving normal inputs. The most common way to implant backdoors into neural networks is to perform trigger inserting and label modifying in the training dataset so that the trained models naturally carry backdoors. This paper proposes a novel defense method against neural network backdoor attacks, referred to as the backdoor filter. The defense is deployed on the dataset used for model training: It first blurs the dataset images and then performs sharpening operations on the obtained processed intermediate parts. This processed dataset is then used as input to the neural network for model training. We use three different types of backdoor attacks to validate our approach, evaluating the prediction accuracy and the attack's success rate after our processing. Experiments show that our approach can significantly reduce the success rate of the corresponding attacks and effectively defend against backdoor attacks.
Optical cooperative effects of multiemitters in a one-dimensional (1D) dense array

We theoretically explore cooperative effects of equally spaced multiemitters in a 1D dense array driven by a low-intensity probe field propagating through a 1D waveguide by modeling the emitters as point-like coupled electric dipoles. We calculate the collective optical spectra of a number of 1D emitter arrays with any radiation-retention coefficient η using both exact classical-electrodynamics and mean-field-theory formalisms. We illustrate cooperative effects of lossless 1D emitter arrays with η = 1 at the emitter spacings, which are displayed by steep edges accompanied by a deep minimum and Fano resonances in the plots of transmissivities as a function of the detuning of the incident light from the emitter resonance. Numerical simulation of the full width of such optical bandgaps reveals that cooperativity between emitters is greater in a small array of size N ≤ 8 than in a larger one of size N > 8. For a lossy 1D emitter array in which the radiation retention coefficient is equal to or less than 0.1 the transmissivity obtained by exact-electrodynamics scheme exhibits no bandgap structures, being in good agreement with the mean-field-theory result. We propose that a 1D multiemitter array may work as a nanoscale filter blocking transmission of light with a frequency in the range of optical bandgaps.
Large-Sized Tablet-Based Live Mobile Learning System with a Large Whiteboard Area

Most existing live mobile distance learning systems lack support for the simultaneous view of both a presentation slide and a whiteboard, which makes it difficult for students to understand a lecture. Thus, we had previously developed a tablet-based live learning system with a whiteboard shown together with a presentation slide on a 9.7-inch tablet. However, the limited size of the tablet and the small size ratio of the whiteboard area still made it difficult for an instructor to draw a sophisticated figure that can help students to understand a difficult subject. Therefore, we propose a large-sized tablet-based live learning system with a large size ratio of a whiteboard area that allows students to watch lecture video, chat, presentation slide with annotation, and figures being drawn on a whiteboard area simultaneously on a 12.9-inch tablet in real time. The results of our preliminary evaluation suggest that students’ perception of the proposed system is significantly better than our previous one in terms of lecture understanding, sophistication of a lecture, and the feedback to a question.
Cognitive Heuristic Computation of Regional Culture Based on Latent Factor Algorithm

The cultivation of high-quality talents is mainly through universities. High quality talents should not only keep improving in scientific knowledge, but also develop in all aspects including music. Public music education in Colleges and universities is the most important part of improving the quality of college students and plays an increasingly prominent role. Therefore, this paper studies the content and teaching system of public music education in Colleges and Universities Based on the regional music culture under the latent factor algorithm.
Early Results from Automating Voice-based Question-Answering Services Among Low-income Populations in India

Question-answering systems where users can ask questions based on emergent needs which are then answered by experts or peers, have emerged as an important information seeking modality on digital platforms. Automating this process has been an active area of research since many years, to identify relevant answers from pre-existing question-answer databases. We report on the feasibility of running automated question-answering systems in the context of rural and less-literate users in India, accessed through IVR (Interactive Voice Response) systems. We use commercial speech recognition APIs to convert audio questions asked by users into their equivalent transcripts in real time, in Hindi, and use deep-learning based architectures to retrieve corresponding candidate answers which are instantly played to the users. We report several insights from an earlier phase of running question-answering programmes through a manual operation, to how it was transitioned to an automated setup, and document the user experiences during this journey.
A Multi-scale Capsule Network for Improving Diagnostic Generalizability in Breast Cancer Diagnosis Using Ultrasonography

Recently, deep learning has shown promising results in medical image processing. However, computer-aided diagnosis (CAD) systems based on deep learning still struggle for the real-world deployment, due to its low generalizability and reliability. It is essential to improve the generalization performance to enable them to be used routinely in clinical practice. In this paper, we propose a capsule network with a multi-scale setting to achieve better generalization performance in the differential diagnosis of breast tumors using ultrasonography. The proposed network utilizes a Gaussian pyramid to learn multi-scale features of breast tumors and dynamic routing to improve its robustness against image quality with severe noises. To evaluate the generalizability of the proposed method, we collected breast ultrasound images from 4 different hospitals and used one dataset from 1 hospital as a train set and the rest as external validation sets. We compared the classification performance with other networks, which were employed for the ultrasound diagnosis in previous studies, on the external validation sets. We also conducted additional experiments: feature space visualization and robustness evaluation study with respect to the image noise. Our model showed better classification results than other networks, such as GoogLeNet and Inception-v3, in the external validation. Experimental results also indicate that the proposed network can learn more robust and noise-invariant features from breast ultrasound imaging.
Degradation Reconstruction Loss: A Perceptual-Oriented Super-Resolution Framework for Multi-downsampling Degradations

Recent years have witnessed the great success of deep learning-based single image super-resolution (SISR) methods. However, most of the existing SR methods assume that low-resolution (LR) images are purely bicubic downsampled from high-resolution (HR) images. Once the actual degradation is not bicubic, their outstanding performance is hard to maintain. Although several SR methods have super-resolved LR images with multiple blur kernels and noise levels, they still follow the bicubic downsampling assumption. To address this issue, we propose a novel degradation reconstruction loss (DRL) to capture the degradation-wise differences between HR images and SR images based on a degradation simulator. By involving the proposed degradation simulator and the loss, a perceptual-oriented SR framework for multi-downsampled images is formed. Extensive experimental results demonstrate that our method outperforms the state-of-the-art perceptual-oriented SR methods on both multi-downsampled datasets and bicubic downsampled datasets.