Reviewerly/exHarmony huggingface dataset:
Spatial Pyramid-based Wavelet Embedding Deep Convolutional Neural Network for Semantic Segmentation

To reduce the amount of calculation and expand the receptive field, the deep convolutional neural network needs to downsample the feature map. In the process of downsampling, high-frequency information is discarded. This part of information plays an important role in the pixel-by-pixel recognition of the image. At the same time, due to the discarding of high-frequency information, subsequent upsampling is an ill-conditioned process, and high-frequency information cannot be restored. To solve this problem, wavelet embedding network is proposed. The structure proposed in this paper embeds the wavelet transform. In the process of reducing the feature map to enlarge the receptive field, high-frequency information will not be lost. The subsequent use of inverse wavelet transform to enlarge the feature map can avoid the loss of information to the greatest extent. At the same time, the spatial pyramid structure is used to build the network to better integrate the multi-scale information of the image. This paper makes predictions on large public data sets such as Cityscapes and Pascal VOC 2012, and compares with the current advanced methods to prove its effectiveness.
Gender Determination Using Data Mining Methods Through Measurements of Ascender and Descender Parts of Letters

Gender determination of given handwriting is crucial in various applications. In forensic, the reliable determination of the gender of handwriting helps forensic document examiners to narrow down the suspects at hand. However, high accuracy rates are needed to eliminate the possible errors. To this end, handwriting samples including 11 sentences containing the letters “b, d, f, g, h, k, t, y, p” at initial, medial, and end positions were collected from 50 female and 50 male participants. The ascender and descender parts of these letters were measured in millimeters, and statistically significant differences were found between the handwriting of the two genders. To increase the accuracy rates, Logistics Regression (LR), k-Nearest Neighbors (k-NN), Support Vector Machine (SVM), and Artificial Neural Network (ANN), the four most successful methods in machine learning studies were applied to the data and accuracy rates of 0.65, 0.60, 0.71, and 0.82 respectively. The obtained accuracy rates are relatively high with the methods used in the current study. With the addition of parameters such as age and hand in handwriting determination to gender parameter in further studies, it is believed that higher accuracy rates can be achieved in the determination of the owner of the handwriting in question with the model applied in this study.
Design of Proton Radiotherapy Room Based on Environmental Psychology

Proton radiotherapy is currently a more advanced tumor treatment method in radiotherapy, which can accurately eliminate tumor cells while reducing damage to surrounding healthy cells. However, anxiety and depression often occur in cancer patients during treatment. On the one hand, it will cause the displacement of the lesion and affect the effect of radiotherapy. On the other hand, negative psychology is not conducive to physical rehabilitation. In this paper, the principle of environmental psychology is used to guide the design of indoor environment of radiotherapy from the perspective of visual and auditory senses. Through the influence of environment on psychology, the purpose of calming the mood of patients and improving the therapeutic effect is achieved, and it provides support and reference for the design of humanized radiotherapy room space in the future.
A Novel Indexing Scheme Over Lattice of Cuboids and Concept Hierarchy in Data Warehouse

In data warehouse, lattice of cuboids is very important as it represents all the combination of dimensions for that particular business application. The size of the structure is high as for N dimensions, the total number of cuboids is 2N. If the dimensions maintain concept hierarchies, that results in more numbers of cuboids. Hence, the search time is quite high if the data warehouse maintains cuboids in the form of lattice. Here, a secondary index scheme is proposed over lattice of cuboids by analyzing the existing query set. A novel methodology is proposed to rank the dimensions based on the usage of the cuboids and corresponding dimensions. Secondary index is created based on these ranking and that improves the search time significantly. Both the case study and experimental results show the efficacy of the proposed method.
Replacing Common Picking Devices from Augmented Reality Scenarios at Warehouses by a Laser Projection System

The human worker will still play a major part in the factory of the future. The number of tasks to accomplish is constantly rising in modern production facilities and warehouses, whereas the shortage of skilled workers is increasing. As a result, fewer professionals must work alongside with many roustabouts at highly complex technical systems and in short clock cycles. Therefore, in this paper, we present a laser projection system as a new way of provision of information to guide the worker through the processes. Further, we demonstrate the usage of the system in a common logistic process and compare it with other established interaction modalities
Alternative Learning Particle Swarm Optimization for Aircraft Maintenance Technician Scheduling

In the particle swarm optimization (PSO), each particle updates its velocity depending on its own experience and the best location that the swarm has approached so far, but this also means that it has a high tendency to fall into local optimum. To solve this problem, a new algorithm called alternative learning particle swarm optimization (ALPSO) based on the center-learning mechanism is proposed in this paper, consisting of three alternative learning strategies, like random learning strategy (ALPSO-RLS), central learning strategy (ALPSO-CLS), and mixed learning strategy (ALPSO-MLS). In the experimental part, four benchmark functions are chosen to test the performance of ALPSO, compared with the standard PSO. Finally, ALPSO is applied to tackle the aircraft maintenance technician scheduling problem. The results show that both ALPSO-RLS and ALPSO-CLS perform better than PSO in this test scenario.
Deepfake Detection Using Multiple Feature Fusion

It is becoming increasingly easy to generate forged images and videos using pre-trained deepfake methods. The forged images and videos are difficult to distinguish with the human eye, posing security and privacy threats. This chapter describes a deepfake detection method that employs multiple feature fusion to identify forged images. An image is preprocessed to extract high-frequency features in the spatial and frequency domains. Two autoencoder networks are then used to extract the two features of the preprocessed image, following which the extracted features are fused. Finally, the fused feature is input to a classifier comprising three fully-connected layers and a max-pooling layer. The experimental results demonstrate that fusing two features yields an average detection accuracy of 98.62%, much better than using the features individually. The results also show that the method has superior accuracy and generalization capability than state-of-the-art deepfake detection methods.
Adaptive Differential Privacy for Language Model Training

Although differential privacy (DP) can protect language models from leaking privacy, its indiscriminative protection on all data points reduces its practical utility.Previous works improve DP training by discriminating private and non-private data.But these works rely on datasets with prior privacy information, which is not available in real-world scenarios.In this paper, we propose an Adaptive Differential Privacy (ADP) framework for language modeling without resorting to prior privacy information.We estimate the probability that a linguistic item contains privacy based on a language model.We further propose a new Adam algorithm that adjusts the degree of differential privacy noise injected to the language model according to the estimated privacy probabilities.Experiments demonstrate that our ADP improves differentially private language modeling to achieve good protection from canary attackers.
Philosophical Analysis of the Problem of Human Improvement in the Expansion of the Possibilities of Biotechnology

The paper examines the transformation of ethical norms governing the activities of “eugenicists” in the context of increasing opportunities for biotechnology. The main question is formulated as follows: whether the successes of science bring novelty to the “humanitarian” sphere of eugenics. Eugenic discourse since its formation in ancient times is characterized by repetitive structural elements, such as the thesis of the degeneration of man in the course of civilizational de­velopment, a reference to “scientific” knowledge and an appeal to power struc­tures as the force that should carry out eugenic transformations in society. It is also noted that at all times eugenic programs have represented a certain chal­lenge to public morality, since the improvement of the “human breed” reduces man to the position of an agricultural animal. The development of biotechnology has made it really possible to adjust the genome (for the first time since the birth of the eugenic idea). However, ethical standards have not changed significantly since the criticism of racial hygiene programs. The progress of biotechnology gives the impression that significant progress is taking place in the “humanitar­ian” part of eugenics. However, a balanced analysis shows that completely dif­ferent processes are taking place in this area. Thus, the turn towards the liberal project marks the abandonment of the central task of classical eugenics, namely the improvement of the species homo sapiens. Whereas the goal of liberal eugen­ics is to correct individual genomes, which cannot significantly affect the ge­nome of mankind. The use of digital technologies, in addition to the direct pur­pose of processing large amounts of data, also serves to blur responsibility for interfering with the human genome, since part of the decisions (for example, di­agnosis and determination of the treatment strategy) are transferred to machine intelligence. This circumstance shows that the search for new arguments in favor of (or against) eugenic policy will be appropriate and in demand.
Information Security Threat Assessment Using Social Engineering in the Organizational Context – Literature Review

Due to the value and diversity of data that organizations use and produce in their activity, it is extremely important to protect this asset. Security flaws can arise due to several factors and whenever it is difficult to access the desired information because of technological barriers. In this case, attacks are redirected to the exploitation of human beings vulnerabilities through various techniques. The objective of this work focuses on literature review, studying the underlying theme of Social Engineering, as it uses human trust, convincing someone of something fake, using various interactions and different vectors to gain access to private information. Design Science Research will support the research work due to the possibility of construction, evaluation, and subsequent validation of the artefact. The contribution of a framework proposal for preventing social engineering attacks in organizations and providing the best recommendations, guiding, and supporting the stakeholders in the selection and definition of controls that guarantee the security of organizational information and avoid possible attacks by Social Engineering. It is expected that the practical effects of the future work will result in a reduction in the number of attacks using Social Engineering, greater individual and collective preparation to deal with this problem and, over time, the incentive to the continued expansion of the adoption of these artefacts at the organizational level.
Another expression of the MacWilliams identities and its applications

<p style='text-indent:20px;'>Let <inline-formula><tex-math id="M1">\begin{document}$ \mathcal C $\end{document}</tex-math></inline-formula> be a maximum distance separable (MDS) linear code over a finite field <inline-formula><tex-math id="M2">\begin{document}$ \Bbb F_q $\end{document}</tex-math></inline-formula>. In this paper, we present a new formula of its weight distribution, which can be seen as another expression of the MacWilliams identities. Moreover, using the new formula, we obtain the <inline-formula><tex-math id="M3">\begin{document}$ b $\end{document}</tex-math></inline-formula>-symbol weight distribution of <inline-formula><tex-math id="M4">\begin{document}$ \mathcal C $\end{document}</tex-math></inline-formula> in the cases: <inline-formula><tex-math id="M5">\begin{document}$ b = 2 $\end{document}</tex-math></inline-formula> and <inline-formula><tex-math id="M6">\begin{document}$ b = 3 $\end{document}</tex-math></inline-formula>, respectively.
A Comparative Study on the Impact of Financial Development on Poverty in Different Countries

In recent years, in order to improve the current situation of global poverty, developed and developing countries have successively implemented financial poverty reduction policies, and the study of poverty from the perspective of financial development has attracted more and more attention.This paper aims to compare the similarities and differences of the impact of financial development level on poverty between developed and developing countries through empirical analysis.After combing the research literature on the impact of financial development on poverty at home and abroad, this paper uses the fixed effect model for regression analysis based on the panel data of five developing countries including China, Russia, Turkey, Indonesia and Thailand and five developed countries including Switzerland, Germany, Spain, France and Italy from 2005 to 2019.The results show that, on the whole, the improvement of financial development level in both developed and developing countries is conducive to poverty alleviation, and the marginal effect of financial development in developing countries is more significant.Finally, by comparing the empirical analysis conclusions of developed and developing countries, this paper puts forward policy suggestions according to China's national conditions.
Development of Deep Learning Algorithms, Frameworks and Hardwares

Abstract As the core algorithm of artificial intelligence, deep learning has brought new breakthroughs and opportunities to all walks of life. This paper summarizes the principles of deep learning algorithms such as Autoencoder (AE), Boltzmann Machine (BM), Deep Belief Network (DBM), Convolutional Neural Network (CNN), Recurrent Neural Network (RNN) and Recursive Neural Network (RNN). The characteristics and differences of deep learning frameworks such as Tensorflow, Caffe, Theano and PyTorch are compared and analyzed. Finally, the application and performance of hardware platforms such as CPU and GPU in deep learning acceleration are introduced. In this paper, the development and application of deep learning algorithm, framework and hardware technology can provide reference and basis for the selection of deep learning technology.
An Innovative Framework Based Algorithmic Approach for Object Detection

Object Detection techniques have been enumerably used in a variety of real-time applications such as robotics, crime investigation, transportation, etc. Object classification is an important task in computer vision and is the process of tagging objects into predefined and semantically significant classes using trained datasets. A framework for object detection using the Viola-Jone algorithm for object classification is proposed in this paper. The architecture of the framework encompasses image acquisition, image pre-processing, classification and extraction of objects, and computation of related measures. The experiment is performed on a dataset containing 120 images of human faces with different angles, poses, and light conditions. It is worth mentioning that the faces as objects are recognized successfully from the set of input images with a rate of 96.67%. Moreover, the objects on faces such as eyes, nose, and mouth are detected successfully with an average accuracy of 93.10%, 93.10%, and 90.80% respectively. The attributes/measures of these objects are vital for computer recognition and hence, the properties/measures of respective objects are computed at the end. This framework will be useful in several real-life applications; especially in criminal investigation applications for identification of persons/criminals and computer portrait designing, etc.
MARIUS GOLDHORN’UN “PARK” ROMANINDA ANA KARAKTER ARNOLD’UN BEDENSEL VE DİJİTAL ÇÖKÜŞÜ

Since the beginning of the 21st, near-body digital technologies consist of mobile internet-connected portable small devices. They are an inseparable part of daily life. Users have physically and psychologically an intimate relationship with them. Digital beings and the global network are an extension of users' bodies. Users lose their bodies' integrity and can’t have control over their bodies. Accordingly, the users' self loses its central location, and the self-being gets involved simultaneously in the physical and digital world. The existence of the self in the physical world falls into danger because the technological man of the digital age can't focus on himself and his life intensely. The self suffers from physical and psychological disorders. In the novel "Park" by Marius Goldhorn, the protagonist Arnold tries to have his being under these circumstances. The internet-connected digital devices have an important place in his daily life. His daily behaviors automatically become routine and unconscious because he busies himself permanently with the internet connected-digital devices. The boundaries between the physical world and the digital world are blurring. A hybrid type derived from an interaction between humans and machines, such as Arnold, emerges. He loses the basis of his physical being in the world and is dispersed in the nothingness of the digital world. His time perception decays. He has digital dementia and gets into a digital depression since he is generally unhappy. Consequently, he is detached physically and psychologically from life; in other words, he dies. He dies too digitally since he can't connect to the internet. His physical and digital decadence happens simultaneously.&nbsp
Data Distribution in Reliable and Secure Distributed Cloud Environment Using Hash-Solomon Code

Cloud storage technology has expanded in popularity and development in recent years. Cloud storage technology has emerged, and unstructured data has increased. Despite the increasing usage of cloud data storage and the many advantages it offers, there are still major concerns about cloud data, including security, reliability and secrecy. User data are retained on cloud servers under the current storage approach, where customers lose control of their data and are in danger of privacy. This means that consumers lose their capacity to manage their data and are exposed to the risk of data leakage. Traditional privacy systems typically rely on encryption technology; however, such solutions cannot survive an assault from the internal network architecture of a cloud server. To tackle this problem, we propose a three-layer, fog-based storage solution. The smart distribution of the data had to be done for the distribution of the data in three levels including the local server, cloud server and fog layer server. In this article, we analyse the usage of Hash-Solomon codes to build a stable and safe cloud storage architecture utilising the fog layer and the local server layer. We use this data distribution method in our final design of three-layer cloud storage protection technologies that are based on this Hash-Solomon code and fog computing.
A Method on Online Learning Video Recommendation Method Based on Knowledge Graph

The MOOC platforms usually recommend online course resources to users among a large number of courses on platform whose users are almost college students or students by selves. However, the entire course as the recommendation results may ignore student interests in some specific knowledge points, and so may reduce student learning interests. The recommendation not courses but online learning videos of course are researched. The connection between a single learning video and other entities is considered to construct learning video knowledge graph. Based on the end-to-end deep learning framework, a convolutional neural network KGCN-LV is applied in learning videos recommendation, which is integrated learning video knowledge graph. Experimental results on public data set MOOCCube show that the learning video recommendation method based on knowledge graph is up to 5% better than the traditional collaborative filtering method. The recommendation performance is effectively improved, and the recommendation effect is interpretable.
Implementing a Formal System

So far, we have been mostly using a set of formal systems to prove software correctness. In this final chapter, we show how to both create and use a formal system in order to be able to prove facts. For that, we will provide a minimal implementation of propositional logic, as described in Chapter 2 . For a more advanced implementation of a formal system, see [7].
Visual transfer for reinforcement learning via gradient penalty based Wasserstein domain confusion

It is pretty challenging to transfer learned policies among different visual environments.The recently proposed Wasserstein Adversarial Proximal Policy Optimization (WAPPO) attempts to overcome this difficulty by definitely learning a representation, which is sufficient to express the originating and the target domains simultaneously.Specifically, WAPPO uses the Wasserstein Confusion target function to force reinforcement learning (RL) agents to learn the mapping from visually different environments to domain-independent expressions, thereby achieving better domain adaptation performance in RL.However, WAPPO uses weight clipping to strengthen the Lipschitz continuity of the Wasserstein Confusion target function, which results in poor manifestation.In this paper, we present Gradient Penalty based Wasserstein Adversarial Proximal Policy Optimization (GPWAPPO), a new approach for the visual transfer in RL that learns to match the distributions of distilled characteristics between an originating domain and the objective domain.Specifically, we propose a new target function, Gradient Penalty-based Wasserstein Confusion (GPWC), which uses selective clipping weights to catch up with the gradient norm of the target function relative to its input.GPWAPPO is superior to the previous methods in visual transfer and triumphantly transfers strategies across Visual Cartpole and 16 OpenAI Procgen domains.