Reviewerly/exHarmony huggingface dataset:
An Evaluation Method of Dynamic Camouflage Effect Based on Multifeature Constraints

Most of the existing camouflage effect evaluation methods are for static images, and the evaluation methods have problems of singularity and subjectivity.Therefore, this paper takes the camouflage of moving objects in video as the research object and proposes a comprehensive camouflage effect evaluation method based on multifeature constraints.This method has two parts: the Homo-F (homography transformation and optical flow) target detection module and the camouflage effect evaluation module.The former uses the optical flow method to correct the target detection results obtained by the homography transformation.The latter performs statistical analysis on the target detection results and the feature information of the neighborhood background and describes the effect of camouflage from multiple angles such as the degree of target fusion, the repetition rate and the target detection stability in the video sequence.The experimental results of the comprehensive camouflage evaluation of moving targets show that the proposed method can objectively and accurately evaluate moving targets with different levels of camouflage, which verifies the reliability and effectiveness of the method.
Research and Design of Automatic Navigation System for Agricultural Machinery Based on GPS

Agricultural machinery automatic navigation technology has been widely used at home and abroad and it is an important practice of agriculture intelligent operation. This paper conducts research by combining GPS technology. According to the characteristics of agricultural mechanization, the paper studies GPS at home and abroad, machine vision, and inertial navigation unit, such as the laser navigation positioning sensor research. Considering the existing experimental conditions, the author chooses GPS for navigation and positioning sensors.
Statistical Analysis of Solar Position Calculation Algorithms: SPA and Grena 1-5

Photovoltaic systems have been explored as a solution to meet the growing demand for electricity from a clean and renewable source. To improve the performance of photovoltaic systems, a widely used alternative is the implementation of solar trackers. The strategy of sun-tracking most used in photovoltaic power plants with solar trackers applies algorithms to calculate the sun position. This work presents a statistical analysis of six solar position calculation algorithms: Solar Position Algorithm (SPA) and Grena 1-5. The algorithm with the lowest solar vector error was the SPA.
Short‐text feature expansion and classification based on nonnegative matrix factorization

In this paper, a non-negative matrix factorization feature expansion (NMFFE) approach was proposed to overcome the feature-sparsity issue when expanding features of short-text. First, we took the internal relationships of short texts and words into account when segmenting words from texts and constructing their relationship matrix. Second, we utilized the Dual regularization non-negative matrix tri-factorization (DNMTF) algorithm to obtain the words clustering indicator matrix, which was used to get the feature space by dimensionality reduction methods. Thirdly, words with close relationship were selected out from the feature space and added into the short-text to solve the sparsity issue. The experimental results showed that the accuracy of short text classification of our NMFFE algorithm increased 25.77%, 10.89%, and 1.79% on three data sets: Web snippets, Twitter sports, and AGnews, respectively compared with the Word2Vec algorithm and Char-CNN algorithm. It indicated that the NMFFE algorithm was better than the BOW algorithm and the Char-CNN algorithm in terms of classification accuracy and algorithm robustness.
A Privacy Risk Assessment Model for Medical Big Data Based on Adaptive Neuro-Fuzzy Theory

Information leakage in the medical industry has become an urgent problem to be solved in the field of Internet security. However, due to the need for automated or semiautomated authorization management for privacy protection in the big data environment, the traditional privacy protection model cannot adapt to this complex open environment. Although some scholars have studied the risk assessment model of privacy disclosure in the medical big data environment, it is still in the initial stage of exploration. This paper analyzes the key indicators that affect medical big data security and privacy leakage, including user access behavior and trust, from the perspective of users through literature review and expert consultation. Also, based on the user’s historical access information and interaction records, the user’s access behavior and trust are quantified with the help of information entropy and probability, and a definition expression is given explicitly. Finally, the entire experimental process and specific operations are introduced in three aspects: the experimental environment, the experimental data, and the experimental process, and then, the predicted results of the model are compared with the actual output through the 10-fold cross verification with Matlab. The results prove that the model in this paper is feasible. In addition, the method in this paper is compared with the current more classical medical big data risk assessment model, and the results show that when the proportion of illegal users is less than 15%, the model in this paper is more superior in terms of accuracy and recall.
AI Acceleration with RISC-V for Edge Computing

Convolutional neural networks (CNNs) have been successfully applied to many Al applications and even demonstrate beyond-human capability in some cases. By implementing CNNs on edge devices, less energy dissipation, higher security, and lower latency can be achieved. In this talk, 1 will present a design framework that optimizes the dataflow of CNN by leveraging the data reuse. Memory access times can be minimized through proper memory partitioning and allocation. The proposed methodology is demonstrated by a system with an Al accelerator and a RISC-V core.
Secure Deduplication for Images using Blockchain

Lot of efforts and struggle has been done by the storage administrators to handle the spiraling data like images, audios and videos. Among different data present, images are the stringent data which has to be taken care regarding storage. Most of cloud storage servers demand high cost for storage and also burden backup efficiency. Cloud storage being cost effective have more chance of storing the duplicate images. Instead of finding ways to store multiple copies of data, removal of duplicate data can be achieved by using a technology called data deduplication. Data Deduplication is one of the most popular emerging methods which ensure the storage efficiency by removing the redundant data and storing the unique data only. On the other hand, while storing the valuable image into the centralized server it is necessary to authorize the data being uploaded. This authorization of uploaded data can be accomplished by decentralized system called Blockchain, using which one can authorize the data uploaded and claim for the ownership of the data, thus providing the copyrights of the user. Here in this paper, data deduplication is integrated with Blockchain and implemented which makes the image storage efficient, effective and more secured.
Military Applications of Artificial Intelligence

Artificial intelligence (AI) is one of the core technological innovations of future that can play an important role in making important changes in the world, in the transformation and development of modern society. AI technology has entered a new period of rapid development. At present, it is recognized as the most likely to change the future world of disruptive technology. Many countries in the world have raised the level of developing AI in the national strategy, not only from the policy guidance and strategic planning but also from funding and budget level to give strong support. To lay a foundation of the future military application of AI, we have to thoroughly analyzed the application of AI in this field and found the key points of the development of it.
Spatial-Intensity Transform GANs for High Fidelity Medical Image-to-Image Translation

Despite recent progress in image-to-image translation, it remains challenging to apply such techniques to clinical quality medical images. We develop a novel parameterization of conditional generative adversarial networks that achieves high image fidelity when trained to transform MRIs conditioned on a patient’s age and disease severity. The spatial-intensity transform generative adversarial network (SIT-GAN) constrains the generator to a smooth spatial transform composed with sparse intensity changes. This technique improves image quality and robustness to artifacts, and generalizes to different scanners. We demonstrate SIT-GAN on a large clinical image dataset of stroke patients, where it captures associations between ventricle expansion and aging, as well as between white matter hyperintensities and stroke severity. Additionally, SIT-GAN provides a disentangled view of the variation in shape and appearance across subjects.
A persuasive chatbot using a crowd-sourced argument graph and concerns

Chatbots are versatile tools that have the potential of being used for computational persuasion where the chatbot acts as the persuader and the human agent as the persuadee. To allow the user to type his or her arguments, as opposed to selecting them from a menu, the chatbot needs a sufficiently large knowledge base of arguments and counterarguments. And in order to make the user change their current stance on a subject, the chatbot needs a method to select persuasive counterarguments. To address this, we present a chatbot that is equipped with an argument graph and the ability to identify the concerns of the user argument in order to select appropriate counterarguments. We evaluate the bot in a study with participants and show how using our method can make the chatbot more persuasive.
Peningkatan Aktivitas dan Hasil Belajar Siswa pada Mata Pelajaran Biologi Melalui Model Pembelajaran Make and Match Berbasis Media Karu Pintar

This study aims to improve student activity and learning outcomes in biology subjects through the "Make and Match" learning model with Smart Card media. This research method uses classroom action research. This research was conducted on 21 students of class X-ATU-1 SMK Muhammadiyah 1 Batu. The research instrument used tests, interviews, and observations. The data analysis technique used qualitative and quantitative descriptive analysis. The results of this study indicate an increase in activity and student learning outcomes in the post test cycle I and post test cycle II. It can be seen from the observation that student activity has increased in each cycle, namely 73% in cycle I and increased again to 93% in cycle 2, students become more active in participating in learning with smart card-based make and match models and complete student learning outcomes. and student learning outcomes also experienced an increase in each cycle, namely 62.5% of students completed with an average score of 68.5 in the post-test cycle I, and increased again to 82% of students completed with an average score of 83 in the post-test cycle II.
Doing Away with “Database:” Exploring Medical Library Website Design

Library users can become confused by library jargon, especially the term “databases.” This study utilized a combination of end-user surveys and library website usability testing to determine if library users at an academic health sciences library were, in fact, using different terminology to refer to the library’s database collection. This study was devised to probe students' information needs and nomenclature for library resources and identify different ways of organizing information on the library’s website outside of the traditional database page and “Quick Links.” The study yielded valuable insight into how our users are identifying information resources that will allow us to improve website usability.
Variance error of multi-classification based anomaly detection for time series data

For abnormal detection of time series data, the supervised anomaly detection methods require labeled data. While the range of outlier factors used by the existing semi-supervised methods varies with data, model and time, the threshold for determining abnormality is difficult to obtain, in addition, the computational cost of the way to calculate outlier factors from other data points in the data set is also very large. These make such methods difficult to practically apply. This paper proposes a framework named LSTM-VE which uses clustering combined with visualization method to roughly label normal data, and then uses the normal data to train long short-term memory (LSTM) neural network for semi-supervised anomaly detection. The variance error (VE) of the normal data category classification probability sequence is used as outlier factor. The framework enables anomaly detection based on deep learning to be practically applied and using VE avoids the shortcomings of existing outlier factors and gains a better performance. In addition, the framework is easy to expand because the LSTM neural network can be replaced with other classification models. Experiments on the labeled and real unlabeled data sets prove that the framework is better than replicator neural networks with reconstruction error (RNN-RS) and has good scalability as well as practicability.
Performance Analysis of AES, Blowfish and Rijndael: Cryptographic Algorithms for Audio

Normally people share data like text, image, audio or video with others using a computer network. When these media are transmitting through the computer network, it faces various security problems for any form of data: text, image, audio or video. Information security is a challenging job while data transmission; that is why cryptography is badly required in network security. Audio encryption is very important nowadays because most of the transaction is in the form of audio. There are various algorithms available for encryption–decryption symmetric as well as asymmetric. Comparison of the performance for selected algorithms (Blowfish, AES and Rijndael) is the purpose of this research. The result shows that Blowfish performs better than AES and Rijndael.
3D Path Planning for UAV with Improved Double Deep Q-Network

Unmanned aerial vehicle (UAV) has been widely used in civil and military fields due to its advantages such as zero casualties, low cost and strong maneuverability. Path planning in 3D obstacle environment is one of the fundamental capabilities of UAV for mission performing. In this paper, we propose a 3D path planning algorithm to learn a target-driven end-to-end model based on an improved double deep Q-network (DQN), where a greedy exploration strategy is applied to accelerate learning. The model takes target and obstacle message as input, and moving command of UAV as output. It can realize path planning successfully for UAV in 3D complex environment. Besides, the experimental results show that improved double DQN has better convergence speed compared with DQN and double DQN.
Face Recognition from Surveillance Using Sequnetial CNN-Model

Nowadays there is lot of research going on face recognition and identification, with the increase in computational power working on images became easy and many complex and deep neural networks are implemented on images. There are many applications which updates automatic attendance and face recognition, we just wanted to extend to super market problem. This paper solves the problem of identification of customer and employees in super market. We have used deep convolution network and Siamese network to train images to classify in to customers, important customers and employees from cameras. This also compares the results from different models.
Characterizing Deterministic-Prover Zero Knowledge

Randomness is typically thought to be essential for zero knowledge protocols. Following this intuition, Goldreich and Oren (Journal of Cryptology 94) proved that auxiliary-input zero knowledge cannot be achieved with a deterministic prover. On the other hand, positive results are only known in the honest-verifier setting, or when the prover is given at least a restricted source of entropy. We prove that removing (or just bounding) the verifier’s auxiliary input, deterministic-prover zero knowledge becomes feasible: Together with the result of Goldreich and Oren, this characterizes when deterministic-prover zero knowledge is feasible. We also demonstrate the necessity of strong assumptions, by showing that deterministic prover zero knowledge arguments for a given language imply witness encryption for that language. We further prove that such arguments can always be collapsed to two messages and be made laconic. These implications rely on a more general connection with the notion of predictable arguments by Faonio, Nielsen, and Venturi (PKC 17).
Estimation Method of the Didactic Complexity of the School Textbooks on Various Disciplines

The article discusses the problem of evaluating the differential didactic complexity (DDC) of educational texts, which characterizes the difficulty of their perception and assimilation by pupil. It is shown that DDC is determined by: 1) the density of semantic information, depending on the abstraction degree of the terms used and their presence in the pupil’s thesaurus; 2) the complexity level of mathematical, chemical and other formulas; 3) the structural complexity of the text, depending on the average length of its constituent words and sentences. Multiplying the DDC of the text by its volume, you can find the integral didactic complexity of the text. For the evaluation of the textbook DDC expert selects one page fragments of text randomly, identifies the key concepts, “measures” their average information content, determines the share of formulas and their average complexity. In this case, the classification of concepts according to the abstraction degree is used, which takes into account the occurrence of a particular word in the thesaurus of a preschool, fifth-grader, ninth-grader and school graduate. The structural complexity of the text is also taken into account, depending on the average length of words and sentences. The analysis of textbooks for school graduates has shown that the most difficult disciplines to understand are biology, physics, chemistry, mathematics. As a result of evaluating computer science textbooks for 3rd, 5th, 9th and 11th grades it was found that their semantic information density and differential semantic complexity monotonically increase from 5.3 to 8.1 and from 5.7 to 10.4 respectively.
Visualization of Search Results of Large Document Sets

When presented with many search results, finding information or patterns within the data poses a challenge. This paper presents the design, implementation and evaluation of a visualization enabling users to browse through voluminous information and comprehend the data. Implemented with the JavaScript library Data Driven Documents (D3), the visualization represents the search as clusters of similar documents grouped into bubbles with the contents depicted as word-clouds. Highly interactive features such as touch gestures and intuitive menu actions allow for expeditious exploration of the search results. Other features include drag-and-drop functionality for articles among bubbles, merging nodes, and refining the search by selecting specific terms or articles to receive more similar results. A user study consisting of a survey questionnaire demonstrated that in comparison to a standard text-browser for viewing search results, the visualization performs commensurate or better on most metrics.
Impact of One- and Two-axis Solar Tracking on Techno-Economic Viability of On-Grid PV Systems: Case of Burnoye-1, Kazakhstan

Kazakhstan is currently committed to develop its renewable energy resources. In 2012, the government introduced a low-carbon energy strategy to reduce the production of air pollutants, including anthropogenic CO2e, and to increase the share of clean energy up to 50% of total consumption by 2050. As a contribution to this strategy, this paper presents a comparison in techno-economic performance of three scenarios. The differentiation will be between currently implemented fixed-slope on-grid PV power plants in Kazakhstan and proxy similar solar parks using one- or two-axis solar tracking systems. This paper aims to determine to what extent the more effective, but more expensive tracking systems might be a suitable standard in future PV power stations in the country. For this purpose, the existent fixed-slope 50 MWp Burnoye-1 commercial solar power plant located in Jambyl region, Kazakhstan, is used as a benchmark. As expected, solar panels with tracking systems produce more electricity year-round compared to those with fixed slopes. In comparison, one- and two-axis tracking systems led almost to the same amount of electricity export to the grid. Furthermore, despite the proxy PV power stations with one- and two-axis tracking technology could reduce around 10 ktCO2e emissions per year. This 25 to 29% and 33 to 33% ratio of extra-cost to extra-energy production, respectively, made both tracking scenarios not economically competitive compared to fixed panels. Nevertheless, if a tracking system has to be considered to further reduce GHG emissions and add extra annual electricity generation using current installed capacity of Burnoye-I, our results demonstrated that one-axis tracking should be preferred above two-axis system.
Non-autoregressive Neural Machine Translation with Distortion Model

Non-autoregressive translation (NAT) has attracted attention recently due to its high efficiency during inference. Unfortunately, it performs significantly worse than the autoregressive translation (AT) model. We observe that the gap between NAT and AT can be remarkably narrowed if we provide the inputs of the decoder in the same order as the target sentence. However, existing NAT models still initialize the decoding process by copying source inputs from left to right, and lack an explicit reordering mechanism for decoder inputs. To address this problem, we propose a novel distortion model to enhance the decoder inputs so as to further improve NAT models. The distortion model, incorporated into the NAT model, reorders the decoder inputs to close the word order of the decoder outputs, which can reduce the search space of the non-autoregressive decoder. We verify our approach empirically through a series of experiments on three similar language pairs (En\(\Rightarrow \)De, En\(\Rightarrow \)Ro, and De\(\Rightarrow \)En) and two dissimilar language pairs (Zh\(\Rightarrow \)En and En\(\Rightarrow \)Ja). Quantitative and qualitative analyses demonstrate the effectiveness and universality of our proposed approach.
A Generic Variable Inputs Quantum Algorithm for 3-SAT Problem

The Grover's algorithm can find an item satisfying certain properties in a search space of size N with circuit complexity $O\left( {\sqrt N } \right)$, where the circuit are formed by the G operator one by one in series. The huge circuit scale is the main obstacle to the practical application of the Grover's algorithm. In this paper, we present a generic variable inputs quantum algorithm by time-space tradeoff to reduce the scale of the quantum circuit. We combine the proposed algorithm in the paper with the Schöning's algorithm to solve the 3-SAT problem which is a NP-complete problem. Compared with previous algorithms, as the input variables n of 3-SAT problem increases, the advantages of our algorithm is obvious.