Reviewerly/exHarmony huggingface dataset:
A Facial Fatigue Expression Recognition Method Based on Sparse Representation on the Low-Resolution Image

In order to effectively improve the performance of facial fatigue expression recognition on the low-resolution image, a method of fatigue facial expression recognition based on Sparse Representation is proposed. At present, study on facial fatigue expression recognition is almost based on high-resolution, high-quality images. In the network environment, especially in the Internet of Things environment, the images of facial fatigue expression mostly are low-resolution images, in which the performance of facial fatigue expression recognition will degrade with image quality. In order to improve the recognition rate on the low-resolution image, a facial fatigue expression recognition method based on sparse representation on the low-resolution image is proposed. At the same time, in order to improve the credibility of the results of the experiment,Kendall’s coefficient of concordance method was used to construct the low-resolution facial fatigue expression database—TIREDFACE, whose words mean human’s tired-face. The TIREDFACE database consists of about 240 Image sequences of 10 university students. Size of all images is normalized to 92 × 112 pixels with 8-bit precision for grayscale values. Firstly, TIREDFACE database is set up, and then we exploit the discriminative nature of sparse representation to perform fatigue expression detection. Instead of using the generic dictionaries, we represent the test sample in an overcomplete dictionary whose base elements are the training samples themselves. Compressed sensing theory was used to solve its sparsest representation. With sufficient training samples for each fatigue status, including awake, slight fatigue and severe fatigue, it is done to represent the test samples as a linear combination of just those training samples from the same classification, and the sparse representations of the low resolution facial fatigue expression image of the identified test sample in the database TIREDFACE are given. After that, according to the sparsest representation solution, the low-resolution facial fatigue expression status classification is performed. In order to further investigate the advantages of this method, we compare this method with the linear classifier,the nearest neighbor (NN), support vector machine (SVM) and the nearest subspace (NS) on our database TIREDFACE. Experimental results show that the recognizing accuracy on low resolution facial fatigue expression images by the method of this paper is much higher than the linear classifier,the nearest neighbor (NN), support vector machine (SVM) and the nearest subspace (NS), etc. Therefore, the proposed method in this paper on the low-resolution facial fatigue expression recognition tasks is feasible.
WITHDRAWN: Privacy –Preserved in cloud based data sharing for energy management system using big data analytics

This article has been withdrawn: please see Elsevier Policy on Article Withdrawal (https://www.elsevier.com/about/our-business/policies/article-withdrawal). This article has been withdrawn as part of the withdrawal of the Proceedings of the International Conference on Emerging Trends in Materials Science, Technology and Engineering (ICMSTE2K21). Subsequent to acceptance of these Proceedings papers by the responsible Guest Editors, Dr S. Sakthivel, Dr S. Karthikeyan and Dr I. A. Palani, several serious concerns arose regarding the integrity and veracity of the conference organisation and peer-review process. After a thorough investigation, the peer-review process was confirmed to fall beneath the high standards expected by Materials Today: Proceedings. The veracity of the conference also remains subject to serious doubt and therefore the entire Proceedings has been withdrawn in order to correct the scholarly record.
Increase in level of detection of secret embedding into compressed JPEG-images based on selection of informative features

Abstract The research of informational content of features in the spatial and the frequency domains at steganalysis of the compressed JPEG-images is presented. Modern steganalysis methods, based on which the feature set has been created, are considered. As a method of informative features selection has been applied the greedy algorithm with an exception which has allowed reducing number of features in the set from 43 to 26. The experiments have shown that steganalysis on the basis of informative features allows increasing the accuracy of correct classification of initial images to 94%, of images with attachments by the PM1 and the Jsteg methods by 25 and 18 percent respectively. The issue of steganalysis of images with artificially created distortions is mentioned. The experiments showed that steganalysis based on informative features allow classifying correctly such images with the accuracy 96%.
Analysis of Spray Uniformity of Sprayers Based on Deep Belief Network

Abstract Improving spray quality has a important practical significance for controlling the drift of harmful substances, increasing the effective utilization rate of pesticides and reducing environmental pollution. The influence of factors on spray quality is analyzed, and the nozzle wear is regarded as one of the influencing factors for the first time in this paper. It also constructs the relationship between spray distribution uniformity and influencing factors Model by proposes a soft measurement method based on deep belief network. Besides, when the distribution coefficient of variation and another influencing factors is given, the pressure value corresponding to them can get by neural network inverse modeling method, And the pressure can be used as the controlled variable in the spraying process. Compared to BP, the DBN model has stronger nonlinear mapping capabilities, higher prediction accuracy, and high practical value.
Exploring the potential for age estimation using facial image sensing technology for postmortem investigation

In this study, we examine the potential for using face imaging sensing technology in place of a human forensic practitioner to estimate the age of cadavers. We used the age estimation software FieldAnalyst for Signage Ver. 6.0 AW32. To validate the usefulness of its age estimation for living subjects, images of 28 subjects were taken at three angles (+30°, 0°, and −30°) with respect to the horizontal plane, with their eyes open and closed. The highest positive correlation between mean the estimated age and the actual age (y = 1.02x − 0.35, and Spearman’s rank correlation coefficient of 0.78, P < 0.001) was obtained when the subjects had their eyes closed and the image was captured at an angle of 0°. The ages of 93% of the subjects were estimated within ±10 years of their actual ages. We then applied this procedure to 61 cadavers with their eyes closed. Facial images were taken at an angle of 0° with respect to the horizontal plane and used to estimate the ages of the cadavers. Although a positive correlation between the actual and mean estimated ages was obtained (y = 1.28x + 0.43, Pearson’s correlation coefficient of 0.69, P < 0.001), the mean estimated ages of only 39.3% of the subjects were within ±10 years of their actual ages. It appears that this technology is not accurate enough to use to determine the age of a cadaver. Therefore, medical inspectors with adequate knowledge and experience are still required for postmortem examination.
A Study on Framework Development and Augmented Reality Technological Factors Consumers’ Evaluation for Cultural and Creative Products

Following the rapid development of high-technology industries, digital applications have been applied in various technologies, bringing people a whole new daily living experience. Interactive technology has led to breakthrough innovations. Application of augmented reality (AR), in addition to people’s approaches to interact with the physical world, has changed industry owners’ methods in designing products. This reveals the criticality of designing products with AR technology and new forms of interactions. This study investigated the current status regarding the cultural and creative products in consumer markets and the effectiveness of incorporating AR technology into cultural and creative product design courses. Data were analyzed to clarify said effectiveness and confirm the feasibility of AR technology in cultural and creative product design. A survey was performed on the cultural and creative products in the National Palace Museum located in Taipei and those designed by university students majoring in design to explore the effect of AR technology employed in cultural and creative product design on consumers’ review of and purchase intentions for the products. The following conclusion was reached: (1) The scale constructed to evaluate the cultural and creative products designed using AR technology was feasible. (2) AR technology positively affected participants’ review of and purchase intentions for cultural and creative products. (3) The products created by students according to the teaching method employed exhibited business value of high interactivity. Future cultural and creative product design courses can employ AR technology in their principles and practices in training innovative talents that satisfy the market demand. The design principles developed thereof can also serve as a reference for industry owners in designing cultural and creative products.
Experimental demonstration of random walk by probability chaos using single photons

In our former work, we demonstrated chaotic oscillation induced in a nanoscale system consisting of quantum dots between which energy transfer occurs via optical near-field interactions. It is intriguing that the chaotic behavior is associated with probability. Indeed, we previously examined such oscillating probabilities via diffusivity analysis by constructing random walkers driven by chaotically driven bias. Herein, we experimentally implemented the concept of probability chaos using a single-photon source that was chaotically modulated by an electro-optical modulator that directly yielded random walkers via single-photon observations. An evident signature was observed in the ensemble average of the time-averaged mean square displacement.
Refining the CC-RDG3 Algorithm with Increasing Population Scheme and Persistent Covariance Matrix

The cooperative coevolution framework has been used extensively to solve large scale global optimization problems. Recently, the framework is used in CC-RDG3 where it uses recursive differential grouping and covariance matrix adaptation evolution strategies (CMA-ES). It was shown that the algorithm performs well on the CEC2013-LSGO benchmark functions. In this study, some modifications to the CC-RDG3 algorithm are proposed to improve performance. The modifications should be applied differently depending on the modality of the problem at hand.
An algorithm to estimate parameters and states of a nonlinear maneuvering target.

This paper investigates the problem of unknown input estimation such as acceleration, target class, and maneuvering target tracking using a hybrid algorithm. One of the challenges of unknown input estimation is that no effective method has been presented so far that could be applied to general cases. The available methods are ineffective when the range of variation of the unknown input parameter is large. Also, the issue of determining the system class could improve the performance of the tracking algorithms in many applications. Using the Bayesian theory, the posterior distribution functions of state and parameter could be obtained concurrently. In the proposed algorithm, Liu and West and multimode filters are used for unknown parameters’ estimation, and particle filter is used to estimate the posterior density function. Parameter estimation and mode determination could be used in the resampling phase to weight the particles in accordance with the target mode. The main advantage of the adaptive parameter estimation approach is its ability to provide a quick estimation of the abruptly changing parameters from noninformative prior knowledge and to do this for multiple unknown parameters. Simulation results show that the proposed algorithm performs better than the other input estimation and tracking methods.
Research on Application of Blockchain Technology in Distributed Energy Trading of Microgrid

The global ecological environment continues to deteriorate and traditional energy sources are facing exhaustion. The development and use of clean and sustainable renewable energy has become an important development trend in the energy industry. Distributed energy has the characteristics of high energy efficiency, flexible operation and good economy, and has gradually become an important source of power generation. At the same time, the characteristics of blockchain technology such as decentralization, distribution, detrust, non-tamperable data, and traceability are consistent with the needs of distributed energy transactions. Therefore, exploring distributed energy transactions based on blockchain can help to improve economic efficiency and renewable energy consumption rate and reduce credit costs. This paper introduces the overall situation of china's distributed energy development, explains the necessity of developing distributed energy market transactions, and analyzes the fit between blockchain technology and distributed transactions. Combing the principles of blockchain technology in detail, popularizing the application scenarios of blockchain technology in the energy industry. This paper proposes the application mechanism of distributed energy market-based transaction architecture based on blockchain, which has reference significance for the application of blockchain technology in distributed energy transactions in microgrid.
WMat algorithm based on Q-Learning algorithm in taxi-v2 game

Reinforcement learning is a framework in which an agent aims to optimize the sum of the rewards it receives from the environment around based on a specific policy. Although many researches have been done on this type of learning, it has not been specifically addressed to improve Q-table values and to increase the amount of reward received by the agent. One of the notable issues in this regard is the improvement of the results of the q-learning algorithm functions. Hence in order to improve the amount of received reward, a weighting matrix is proposed and defined for the set of possible actions of the agent. In this regard our method is capable of reducing the likelihood of behaviours consideration by the agent which leads to a notable decrease in the time of action choosing and as a result achieves improvement in the amount of reward and produces acceptable results in this criteria.
Optical Hash function based on multiple scattering media

Hash functions, which can extract message digest from input messages as output, play an important role in digital signature and authentication. Meanwhile, Hash functions are essential in many cryptographic protocols and regimes. With the research becoming more and more in depth, a series of Hash functions is proposed, such as MD series and SHA series. At the same time, the security analysis and attacks against Hash functions are carried out. The security of Hash functions is threatened. In this case, how to improve the security of the Hash functions becomes the primary concern. In this paper, an optical Hash function based on the interaction between light and multiple scattering media is proposed. Unlike most of the traditional Hash functions which are based on mathematical transformations or complex logic operations, this method innovatively takes advantage of the natural random scattering effect of multiple scattering media on coherently modulated light, and realizes the “confusion” and “diffusion” of modulated light, which satisfies the core functional requirement of the Hash function: one-way encoding/encryption with strong security. The photoelectric hybrid system designed by this method can effectively simulate the "compression function" in the Hash function. Combined with the Sobel filter with feature extraction function, the input data of arbitrary length can be compressed and encrypted into the output with a fixed length of 256-bit (Hash value). The principle of the proposed optical Hash function can be described as follows. 1) Two 8-bit images with a size of 16×16 pixels are loaded in SLM&lt;sub&gt;1&lt;/sub&gt; (amplitude-only spatial modulator) and SLM&lt;sub&gt;2&lt;/sub&gt; (phase-only spatial modulator) respectively. 2) The coherent wavefront is modulated by SLM&lt;sub&gt;1&lt;/sub&gt; and SLM&lt;sub&gt;2&lt;/sub&gt;, and then propagates on multiple scattering media. 3) A speckle pattern is recorded by CCD because of the confusion of multiple scattering media. 4) The features of the speckle pattern, which is extracted by Sobel filter, serve as the input of the next compression function. For the unpredicted and non-duplicated disorder multiple scattering media, it is tremendously difficult to determine the internal state of the multiple scattering media. Therefore, the proposed optical Hash function is considered to have a high security. A series of simulation results shows that the proposed optical Hash function has a good “avalanche effect” and “collision resistance”, and its security performance is comparable to that of the most widely used traditional Hash functions (MD5 and SHA-1).
Question Generalization in Conversation

The dialogue response generation system is one of important topics in natural language processing, but the current system is difficult to produce human-like dialogues. The responses proposed by the chat-bot are only a passive answer or assentation, which does not arouse the desire of people to continue communicating. To address this challenge, in this paper, we propose a question generalization method with three types of question proposing schemes in different conversation patterns. A probability-triggered multiple conversion mechanism is used to control the system to actively propose different types of questions. In experiments, our proposed method demonstrates its effectiveness in dialogue response generalization on standard dataset. In addition, it achieves good performance in subjective conversational assessment.
Deep Matrix Factorization on Graphs: Application to Collaborative Filtering

This work addresses the problem of completing a partially filled matrix incorporating metadata associated with the rows and columns. The basic operation of matrix completion is modeled via deep matrix factorization, and the metadata associations are modeled as graphs. The problem is formally modeled as deep matrix factorization regularized by multiple graph Laplacians. The practical problem of collaborative filtering is an ideal candidate for the proposed solution. It needs to predict missing ratings between users and items, given demographic data of users and metadata associated with items. We show that the proposed solution improves over the state-of-the-art in collaborative filtering.
A <scp>request for e‐voting system in South Africa</scp>: A <scp>case of</scp> 2019 <scp>national elections</scp>

On the 8th of May 2019, South African citizens in all nine provinces were granted with the opportunity to elect their preferred political party into power. Over million people exercised their right through a ballot voting system for political parties of their choice. However, not every citizen was satisfied with how the election process was coordinated and facilitated particularly in certain areas. This is triggered by the fact that rumors were circulating in various social media platforms imparting that some of the eligible voters were accused of multiple voting while some of the ballot papers were found outside certain voting stations. The paper requests the South African government to introduce an e‐voting system as a means to prevent the above‐mentioned challenges that are associated with the manual voting system. Consequently, using the diffusion of innovation theory, this paper argues that e‐voting system has several pros that may strengthen the credibility of elections by reducing the risk of double voting and spoilt ballot papers and it also acknowledge the 4IR as an advanced system to complement and be integrated into the e‐voting system to be more effective. The aim of this paper was achieved through the use of desktop content analysis approach to reflect on the pros and cons with specific focus to both e‐voting and manual system. The paper examined the pitfalls associated with the ballot voting system. In conclusion, some of the challenges that emerged in the 2019 general elections could be resolved through the adoption of e‐voting system in the long‐run.
A Comprehensive Review of Professional Network Impact on Education and Career

In their very beginnings, when social networks were solely used for leisure purposes, any action performed online had minimal effect on the real world lives of their members. This has very much changed in our modern world, where becoming an influencer on Instagram can substantially raise one's income, politics is done on Twitter, and an inappropriate video posted on YouTube can get one fired. Similarly, professional networks have changed the approach universities take to prepare their students, the mechanisms behind companies seeking expertise, and the way in which professionals land matching jobs. In the context of discussing the benefits and pitfalls of using such platforms, several points relating to data privacy are highlighted. Additionally, for a complete view of all analytics possibilities, a survey was conducted by looking over 24 research papers, summarising their findings, detailing the six generic research areas which were identified and speculating on what the future might hold.
Preparation of Poly[lactic-co-glycolic] Acid Nanospheres and Its Role in Hepatoma Cells

Poly[lactic-co-glycolic] acid (PLGA) targeting nanoparticles AFP/PLGA/Dt 386 , loaded with Dt 386 plasmid of diphtheria toxin gene, modified by Alpha fetoprotein (AFP) monoclonal antibody, is prepared. Its physical and chemical properties and its effect on HepG2 cells are studied. Firstly, Dt 386 expression plasmid pET11a/Dt 386 is constructed and PLGA nanoparticles are prepared by emulsion solvent evaporation (ESE). Scanning electron microscope (SEM) is used to observe its morphology. Laser Particle Sizer is used to measure the particle size. In addition, the encapsulation efficiency, drug loading and in vitro release rate of PLGA nanoparticles are measured. Carboxy fluorescein and rhodamine fluorescein are used to double label IgG/PLGA/Dt 386 and AFP/PLGA/Dt 386 nanospheres, respectively, the entry of nanospheres into HepG2 cells are observed at 3 h and 12 h. The effect of AFP/PLGA/Dt 386 nanospheres on the migration of HepG2 cells is examined by wounding healing assay. Transwell chamber experiment is used to detect the effect of AFP/PLGA/Dt 386 nanospheres on the invasion of HepG2 cells. MTT method is utilized to determine the inhibitory activity of nanoparticles on HepG2 cell proliferation. After treated with IgG/PLGA/Dt 386 and AFP/PLGA/Dt 386 nanoparticles for 48 hours, flow cytometry is used to detect the apoptosis rate and cell cycle of HepG2 cells in each group. The results show that the prepared nanospheres have regular morphology, flat surface, average particle size of 265.72±12.46 nm, zeta potential of −18.15 mV. The average entrapment efficiency and drug loading are 78.48±1.71% and 3.16±0.35%, respectively. The nanoparticles release slowly and stably in vitro . At the 10th day, the release rate reaches 75.13%. PLGA nanospheres can effectively protect DNA from nuclease degradation. The results show that AFP/PLGA/Dt 386 nanospheres have biological targeting effect and can be enriched in cells. AFP/PLGA/Dt 386 nanoparticles can significantly inhibit the migration, invasion and proliferation of HepG2 cells, and promote apoptosis.