Reviewerly/exHarmony huggingface dataset:
Artificial Intelligence's Prospects And Uses In Hospitality Industry: Literature Review

The Accompanying Thesis Evaluates The Present Reception Level And Shows The Potential Effect Of Man-Made Reasoning Systemsin The Movement And Tourism Industry. The Focus Of The Work Venture Lies On Current Ai Applications Such As Visit Bots Or Robots And Their Usage Along The Explorer Venture. The Assessment Of The Present Appropriation Is Based On An Assortment Of Use Cases. The Effect Assessment Is Based On Master Discussions And Opinions. In The Two Cases The Results Of Outsider Studies Are Also Included. The Purpose Of The Work Is To Give The Administration And Owners A Direction How To Deal With Computerized Reasoning In Their Movement And Tourism Business.
Enhancement of MRI Prostate Image Using Histogram Equalization Techniques

In this cutting edge world, Medical image processing in computerized field needs a compelling MRI image modality with less commotion and improved contrast of image. This is conceivable by utilizing image enhancement methodology. Image enhancement is referenced as a system of changing or altering image so as to make it progressively sensible for explicit applications and is utilized to enhance or improve contrast proportion, splendor of image, expel clamor from image and make it less hard to perceive. The purpose behind inclining toward Medical Resonance Imaging (MRI) is that it is a mind boggling medical technology which gives more useful information regarding malignancy, stroke and various another ailments. It helps the doctors to distinguish the diseases more adequately. MRI has exceptionally low difference proportion. To improve the contrast of MRI image, we utilized Histogram equalization technique. In which, Histogram Equalization, Local Histogram Equalization, Adaptive Histogram Equalization and Contrast Limited Adaptive Histogram Equalization techniques were used and it is pondered.
Vertical surface contact with a Micro Air Vehicle

In this work, we present a novel design for vertical surface contact using a two degree of freedom robotic arm attached to a Micro Air Vehicle. To achieve this, we propose a controller based on a Gain-Scheduled Proportional–Integral–Derivative approach. In previous works, the Gain-Scheduled Proportional–Integral–Derivative method was used to control the attitude of the Micro Air Vehicle, thus mitigating the perturbations induced by the movement of the arm. The novel approach of this work focuses on the achievement of an automatized full-contact with a rigid vertical surface using a Micro Air Vehicle with a robotic arm. We have improved the capabilities of the Gain-Scheduled Proportional–Integral–Derivative control to consider the inherent issues of approximating to a flat structure in order to carry out an aerial interaction task successfully. For the Micro Air Vehicle’s position feedback, a motion capture system is used in this work. A paintbrush attached to the end effector of the arm is used to draw over a whiteboard surface to show the full contact of the aerial manipulator. A distance sensor is added to the on-board sensors to measure the distance between the vertical surface and the system to ensure a correct distance and achieve a safe contact. Experimental testing results show that the controller can maintain a stable flight with sufficient accuracy to complete the aerial interaction tasks.
Juggling dynamics

With complex throwing patterns of multiple objects, jugglers seemingly defy human limits of reaction time and throwing accuracy.
Headspeak: Morse code Based Head Gesture To Speech Conversion Using Intel Realsense™ Technology

Advancement in technology has ensured better and more intuitive Human Computer Interaction (HCI). However, technological leap has not been able to bridge the gap for individuals with disability, particularly people with different impairments. Work has been done towards translating various sign languages and lip reading, but not much work is identified towards head gesture recognition and head gesture to speech conversion. Paralyzed patients, with ability to only move their head and eye find it rather difficult to communicate and interact with machines. This work presents a novel and efficient technique to map restricted head movements to text format using face and emotion detection capabilities of Intel RealSense SDK using Morse Code Mapping. The work maps UP and DOWN position of the head to DASH and DOT symbols of Morse code for an EYE_BLINK event trigger and SMILE emotion to convert the sequence of Morse symbols to an English character. The new language called HeadSpeak effectively reduces the pain for generating head gestures through 3D face position based gesture detection.The system can produce an average of 19 characters par minute, gesture error rate of .03% and average 4.5 words per minute.
Evidence

The idea of evidence intersects with the physical reenactment both before and after the performance itself. The theoretical pivot point for a consideration of what constitutes evidence and what are the conditions and the manner in which it is produced in relationship to reenactment, is the work of philosopher and archaeologist, R. G. Collingwood. Fictional detectives and actual criminal investigators alike resort to reenactment to produce evidence. Based on the concept of context-dependent memory, reenactments have long been firmly established in the field of crime detection. Exponential advances in computer-generated imagery used to recreate scenarios in three-dimensional virtual reality mean that the production of evidence by living history is migrating to hyper-reality. However, the process is the same: generating evidence by reenacting past events. A capacious definition was stultified early in the 19th century when Leopold von Ranke’s assertion that mining the archive for sources would shackle evidence to empirical data and present it as historical truth.
Windowing approach for face recognition using the spatial-temporal method and artificial neural network

Face recognition (FR) is getting a lot of attention for a good reason in the field of research and making a big impact in areas such as computer vision and human-computer interaction. This paper proposes a FR model based on the windowing technique using discrete cosine transform (DCT), average covariance and artificial neural network (ANN). The windowing technique is used to divide the whole image into 4 × 4, 8 × 8 and 16 × 16 size of windows. The DCT is applied to each window to acquire DCT coefficients. The average covariance is calculated for the obtained DCT coefficient matrix. The calculation of an average covariance decreases the original size of the image by around 97%. The network is created, trained and tested to assess the performance of the network using nine standard face databases. Experimental results indicate that the proposed model achieves a higher recognition rate with a reduced number of features and computational intricacy compared with conventional methods.
3D Multi-Parameter Geological Modeling and Knowledge Findings for Mo Oxide Orebodies in the Shangfanggou Porphyry–Skarn Mo (–Fe) Deposit, Henan Province, China

The Shangfanggou Mo–Fe deposit is a typical and giant porphyry–skarn deposit located in the East Qinling–Dabie molybdenum (Mo) polymetallic metallogenic belt in the southern margin of the North China Block. In this paper, three-dimensional (3D) multi-parameter geological modeling and microanalysis are used to discuss the mineralization and oxidation transformation process of molybdenite during the supergene stage. Meanwhile, from macro to micro, the temporal–spatial–genetic correlation and exploration constraints are also established by 3D geological modeling of industrial Mo orebodies and Mo oxide orebodies. SEM-EDS and EPMA-aided analyses indicate the oxidation products of molybdenite are dominated by tungsten–powellite at the supergene stage. Thus, a series of oxidation processes from molybdenite to tungsten–powellite are obtained after the precipitation of molybdenite; eventually, a special genetic model of the Shangfanggou high oxidation rate Mo deposit is formed. Oxygen fugacity reduction and an acid environment play an important part in the precipitation of molybdenite: (1) During the oxidation process, molybdenite is first oxidized to a MoO2·SO4 complex ion and then reacts with a carbonate solution to precipitate powethite, in which W and Mo elements can be substituted by complete isomorphism, forming a unique secondary oxide orebody dominated by tungsten–powellite. (2) Under hydrothermal action, Mo4+ can be oxidized to jordisite in the strong acid reduction environment at low temperature and room temperature during the hydrothermal mineralization stage. Ilsemannite is the oxidation product, which can be further oxidized to molybdite.
Elastic-Link for Binarized Neural Networks

Recent work has shown that Binarized Neural Networks (BNNs) are able to greatly reduce computational costs and memory footprints, facilitating model deployment on resource-constrained devices. However, in comparison to their full-precision counterparts, BNNs suffer from severe accuracy degradation. Research aiming to reduce this accuracy gap has thus far largely focused on specific network architectures with few or no 1 × 1 convolutional layers, for which standard binarization methods do not work well. Because 1 × 1 convolutions are common in the design of modern architectures (e.g. GoogleNet, ResNet, DenseNet), it is crucial to develop a method to binarize them effectively for BNNs to be more widely adopted. In this work, we propose an “Elastic-Link” (EL) module to enrich information flow within a BNN by adaptively adding real-valued input features to the subsequent convolutional output features. The proposed EL module is easily implemented and can be used in conjunction with other methods for BNNs. We demonstrate that adding EL to BNNs produces a significant improvement on the challenging large-scale ImageNet dataset. For example, we raise the top-1 accuracy of binarized ResNet26 from 57.9% to 64.0%. EL also aids con-vergence in the training of binarized MobileNet, for which a top-1 accuracy of 56.4% is achieved. Finally, with the integration of ReActNet, it yields a new state-of-the-art result of 71.9% top-1 accuracy.
FedKG: Model-Optimized Federated Learning for Local Client Training with Non-IID Private Data

In recent years, people&#x2019;s privacy issues are being challenged, federated learning (FL) is proposed. Traditional FL aggregates the gradient parameters (FedAvg) sent by each client on the server, which causes the passed parameters to be proportional to the size of client model. We refine the client model to reduce parameters and communication efficiency, but the important prerequisite for model refinement is that the training data set meets a similar structure. In order to reduce the impact of cross-client devices, we use generative adversarial networks (GAN) expansion and pre-processing data. We propose a new method that combines knowledge distillation and GAN to reduce communication loss and improve model performance. Experiments show that compared with FedAvg, the accuracy score of our method is improved by 9.76% in smaller iterations. When the amount of passed parameters is reduced by 72.19%, the accuracy score is also improved by 2.20%.
Research on Multi-modal Teaching of Graduate Students Academic English Writing Based on Information Technology

In the process of innovating the English teaching system, multi-modal teaching is very representative and promotes the digital and three-dimensional development of English teaching. The teaching of academic English writing is of great practical significance for graduate students to carry out academic research and express their academic views independently. In the traditional teaching of graduate students' Academic English writing, the content of the teaching is relatively single, and the teaching process lacks interaction, which affects the continuous improvement of graduate students' Academic English writing. Taking the application of information technology as the research background, this paper puts forward the teaching strategy of multi-modal academic English writing for graduate students.
Decentralization and Architecture of Blockchain Technology

In this chapter, we have described the necessity of decentralization in the current business market, which still resorts to a centralized system. It includes the methods of decentralization which include disintermediation which removes the intermediaries from the centralized system to provide complete decentralization, and competition which gives partial decentralization. A comparative analysis of centralized, distributed, and decentralization is conducted. However, it is also essential to measure the correctness of the decentralization once it is adopted by the application or platform. For that, a decentralization index has been formulated using Proof of Work (PoW) and Proof of Stake (PoS). The subsequent section discusses the benefits and challenges of decentralization in blockchain technology. Then in Notion of distributed consensus, the working of consensus algorithms with permissionless protocol is discussed. Lastly, this chapter involved a case study on blockchain-based decentralized supply chain management.
Neutrosophic Logic and Its Scientific Applications

The scientific term neutrosophy was first coined by Florentin Smarandache a few years ago. The origins, attribute, extent of neutralities and their interactions with other ideational spectra, and indeterminacy are all investigated in this discipline of study. Neutrosophic logic, a group of many-valued systems which can be regarded as an extension of fuzzy logic, is one of the new theories based on the fundamental principles of neutrosophy. Neutrosophy logic is a new branch of logic that addresses the shortcomings of fuzzy and classical logic. Some of the disadvantages of fuzzy relations are failures to handle inconsistent information and the high processing cost of completing a non-linear program. In neutrosophic sets, truthfulness and falsity are independent, whereas in intuitionistic fuzzy sets, it is dependent. The neutrosophic logic has the ability to manipulate both incomplete and inconsistent data. So, there is a need for research into the use of neutrosophic logic in different domains from medical treatment to the role of a recommender system using new advanced computational intelligent techniques. In this study, we are discussing about basic concepts of neutrosophic logic, fuzzy logic’s drawbacks and advantages of using neutrosophic logic, and also the comparison between neutrosophic logic, intuitionistic and interval-valued fuzzy systems, and classical logic on different factors like uncertainty and vagueness.
The Problems of Learning Media at University Amid Covid 19 Post New Normal in Indonesia

The Problems of Learning Media at University Amid Covid 19 Post New Normal in Indonesia. The aim of this research is necessary to be observed since Covid-19 has forced the education world to change its learning media. This study is conducted to equip the minimum research about learning amid pandemic Covid-19 specifically in Indonesian University. Meanwhile, this research focused on The Problems Of Learning At University Amid Covid-19 specifically at post-new normal. The new normal is a behavior change to run normal activities by complying with the health protocol for preventing the Covid-19 transmission.This research is field research. Interviewees in this research are postgraduate students who had undergone the learning in normal era at undergraduateâ€™s program, and students conduct the postgraduate at new normal. at least, students discover what are the differences between those situations. The data collection method utilized interviews, observation, and documentation. Analysis which is utilized by a researcher is a model developed by Miles and Huberman. The steps consist of Data Reduction, Data Display, and Conclusion Drawing and Verification.The researcher has found some facts from this research such as learning at University amid covid-19 using the learning media that is commonly used like Whatsapp Group, Zoom Meeting, Google Meet, Google Classroom, Youtube, E-mail, Telegram. Besides, specific media used for learning in Indonesian University is not only common media but also using specific media that created by his own University. Second, using learning media amid Covid-19 post new normal shown several obstacles such as Lack of Understanding, Efficiency, High Cost, Environment Support, Media, Technologically backward.
About Graph Sums

Summary In this article the sum (or disjoint union) of graphs is formalized in the Mizar system [4], [1], based on the formalization of graphs in [9].
Diagnose Pest and Disease of Black Orchid Plant Using Naive Bayes Method

Coelogyne pandurata or better known by Kalimantan Black Orchid. This plant is an epiphytic orchid that is attached to other plants but is not dangerous. This orchid is one of Kalimantan&#x0027;s endemic plants that require human intervention to save its sustainability. Black Orchid Plants are very susceptible to various pests and diseases; therefore, applications are needed that can provide insight to ornamental plant farmers. This study uses the Naive Bayes methods in the application of Expert Systems to diagnose pests and diseases of Black Orchid plants. There are 24 types of symptoms and nine types of diagnostic results. The results of this study are expected to provide a solution for cultivating Black Orchid plants so that they can find out the symptoms of pests and plant diseases early on. Nine diagnostic results consist of 4 types of pests and five types of disease. This application has a Mean Absolute Percentage Error (MAPE) value of 2.456&#x0025;.
Less Is More: Accelerating Faster Neural Networks Straight from JPEG

Most image data available are often stored in a compressed format, from which JPEG is the most widespread. To feed this data on a convolutional neural network (CNN), a preliminary decoding process is required to obtain RGB pixels, demanding a high computational load and memory usage. For this reason, the design of CNNs for processing JPEG compressed data has gained attention in recent years. In most existing works, typical CNN architectures are adapted to facilitate the learning with the DCT coefficients rather than RGB pixels. Although they are effective, their architectural changes either raise the computational costs or neglect relevant information from DCT inputs. In this paper, we examine different ways of speeding up CNNs designed for DCT inputs, exploiting learning strategies to reduce the computational complexity by taking full advantage of DCT inputs. Our experiments were conducted on the ImageNet dataset. Results show that learning how to combine all DCT inputs in a data-driven fashion is better than discarding them by hand, and its combination with a reduction of layers has proven to be effective for reducing the computational costs while retaining accuracy.
Webly-Supervised Fine-Grained Recognition with Partial Label Learning

The task of webly-supervised fine-grained recognition is to boost recognition accuracy of classifying subordinate categories (e.g., different bird species) by utilizing freely available but noisy web data. As the label noises significantly hurt the network training, it is desirable to distinguish and eliminate noisy images. In this paper, we propose two strategies, i.e., open-set noise removal and closed-set noise correction, to both remove such two kinds of web noises w.r.t. fine-grained recognition. Specifically, for open-set noise removal, we utilize a pre-trained deep model to perform deep descriptor transformation to estimate the positive correlation between these web images, and detect the open-set noises based on the correlation values. Regarding closed-set noise correction, we develop a top-k recall optimization loss for firstly assigning a label set towards each web image to reduce the impact of hard label assignment for closed-set noises. Then, we further propose to correct the sample with its label set as the true single label from a partial label learning perspective. Experiments on several webly-supervised fine-grained benchmark datasets show that our method obviously outperforms other existing state-of-the-art methods.
A novel method using camera traps to record effectiveness of artificial perches for raptors

Agricultural areas can benefit from the reduction of rodents by raptors, yet many croplands and pastures do not provide adequate perching structures needed by raptors to hunt effectively.Many artificial raptor perches have been constructed as a solution to this deficiency, however, monitoring the benefits of these perches has proved challenging.We developed a method using artificial perches and camera traps mounted on poles that allows for 24-hour monitoring of perch utilization.We tested the new method in an agricultural area in northern California and demonstrated its ability to facilitate accurate species identification and to quantify raptor use and activity.Three of the six raptor species observed at the site utilized the artificial perches: American kestrels (Falco sparverius), red-tailed hawks (Buteo jamaicensis), and red-shouldered hawks (B.lineatus).We did not document any rodent predation events from the perches; but we did observe American kestrels using perches to hunt for invertebrates.Overall, we found that using camera traps mounted on poles can successfully monitor artificial perches and can be easily used to study the effectiveness of hunting perches for raptors in agricultural areas.
Multi-Exit Semantic Segmentation Networks

Semantic segmentation arises as the backbone of many vision systems, spanning from self-driving cars and robot navigation to augmented reality and teleconferencing. Frequently operating under stringent latency constraints within a limited resource envelope, optimising for efficient execution becomes important. At the same time, the heterogeneous capabilities of the target platforms and the diverse constraints of different applications require the design and training of multiple target-specific segmentation models, leading to excessive maintenance costs. To this end, we propose a framework for converting state-of-the-art segmentation CNNs to Multi-Exit Semantic Segmentation (MESS) networks: specially trained models that employ parametrised early exits along their depth to i) dynamically save computation during inference on easier samples and ii) save training and maintenance cost by offering a post-training customisable speed-accuracy trade-off. Designing and training such networks naively can hurt performance. Thus, we propose a novel two-staged training scheme for multi-exit networks. Furthermore, the parametrisation of MESS enables co-optimising the number, placement and architecture of the attached segmentation heads along with the exit policy, upon deployment via exhaustive search in <1 GPUh. This allows MESS to rapidly adapt to the device capabilities and application requirements for each target use-case, offering a train-once-deploy-everywhere solution. MESS variants achieve latency gains of up to 2.83x with the same accuracy, or 5.33 pp higher accuracy for the same computational budget, compared to the original backbone network. Lastly, MESS delivers orders of magnitude faster architectural customisation, compared to state-of-the-art techniques.