Reviewerly/exHarmony huggingface dataset:
Fast Regression Convolutional Neural Network for Visual Crowd Counting

This paper presents an improved convolutional neural network (CNN) architecture for accurate visual crowd counting in crowd images. Comprehensive analysis on the performance and inference speed of the network model are also presented. Multi-column convolutional neural network (MCNN) for visual crowd counting through predicted density map is widely used in previous works, however this method has limitation in predicting a quality density map. Instead, the proposed network is constructed by using the powerful CNN layers, dense layers, and one regressor node with whole image-based inference. Therefore, it is less computationally intensive and inference speed can be increased. Experiments have been conducted on Mall dataset. Moreover, benchmarking on different CNN architectures have been conducted. The proposed network shows promising counting accuracy and reasonable inference speed against the existing state-of-art approaches.
AI-based Air-to-Surface Mission Planning using Predictive Launch Acceptability Region Approach

In this paper, a dynamic air-to-surface mission planning strategy based on artificial intelligence (AI) is proposed for targets of opportunity in order to guide the pilot to follow the shortest and safest trajectory by taking advantage of recent advances in military technologies like predictive launch acceptability region (LAR) approach and High Speed 1760. All of the surface targets of an air-to-surface mission are usually planned and loaded to aircraft before the mission. However, sometimes pilots may be suggested to destroy some unanticipated targets which are unplanned. In that case, pilots can be obliged to deviate from the waypoints of the planned mission in order to accomplish the new unplanned task and this can pose a great danger since there could be many potential threats around theater of war. In proposed method, surface threats and predictive launch acceptability region queries are modeled. Then, probabilistic roadmap algorithm with Dubins distance is applied to produce a waypoint trajectory which makes possible to reach closest goal state. The proposed method is proven by constituting a realistic simulation environment based on ROSplane considering mechanical and environmental factors. In total 25 flight simulations, the maximum observed deviation and the mean deviation from a waypoint are calculated as 11.1 meters and 2.2 meters respectively in a 5000m x 5000m x 300m space. Therefore, the results show that the proposed method can dynamically generate waypoint trajectories by using predictive launch acceptability region queries, which are safe and possible to follow.
Formation of ic-competence of the future master of public administration

The article is substantiated the practical aspects of the formation of IC-competence of the future master of public administration (for students of the second (master's) level of higher education of the educational-professional program 281 Public administration and administration. State educational policy) in terms of informatization of higher education; the role of Google services (for example, Google Classroom, Google Docs &amp; SpreadSheets, Google Forms, Google Calendar) in the formation of IC-competence of the future master of public administration is revealed; their characteristics are outlined: 1) saving money on the purchase of software; 2) reducing the need for specialized classrooms, as in any classroom you can organize a modern educational process using laptops and wireless network; 3) the ability to perform many activities, monitor and evaluate student achievement online; 4) openness of the educational environment for those who teaches (teacher) and those who studied (student); examples of pedagogical using of elements of Google services for the formation of IС-competence of the future master of public administration in higher education institutions in the process of studying the discipline (educational component) "Management of the IT-structure of the educational institution".
Simple and Efficient Algorithm for Drone Path Planning

Unmanned aerial vehicles, or drones, have gained a lot of popularity due to their large number of applications in surveillance, aerial photography, 3D mapping, search and rescue operations, and shipping and delivery of goods. A critical task in the deployment of drones is the computation of effective flying paths that allow drones to reach their destinations while avoiding obstacles and minimizing the amount of energy that they need to consume. We present a novel path planning algorithm that is not only faster and uses less memory than other existing path planning algorithms, but it also produces shorter paths. All these performance metric improvements lead to a more energy efficient path planning algorithm for autonomous drones.
Blockchain-based privacy protection scheme for smart park multi-energy fusion system

In order to alleviate the increasing pressure on the environment and resources, the energy Internet that supports the efficient use of renewable energy has attracted widespread attention, and the energy system of the smart park led by "energy + Internet" has become an important part of the realization of the energy Internet. In the multi-energy fusion system of the smart park, the power grid obtains nearly real-time power consumption data on the user side through smart meters, so as to draw a portrait of the power consumption of the entire park and formulate dispatching strategies. However, this near-real-time electricity consumption data easily reveals the user's electrical appliance usage status, exposing the user's home behavior. This paper proposes a block chain-based privacy protection scheme for the smart park multi-energy fusion system. Through the distributed gradient descent algorithm, the grid can obtain the load change function of the park without knowing the power consumption of each user. behavior. In order to solve the security bottleneck in the process of load function prediction—the vulnerability of the central node, we adopted block chain technology to achieve decentralization, and through an improved block consensus algorithm to encourage users to actively participate, and improve the security of the entire system At the same time, the efficient operation of the energy-using system is guaranteed.
Research on Teaching Reform of “Motor and Drive”Based on Matlab Simulation

“Motor and Drive” is a professional basic course with strong theory and wide application in practical engineering. Its theory is abstract and formula is complex, which is considered by teachers and automation students as one of the professional courses difficult to teach and learn. In order to better teaching and learning, teaching reform is provided. The teaching content is optimized, the teaching methods, experimental methods and assessment methods are reformed based on matlab simulation. It is helpful to improve the teaching quality, stimulate students' learning enthusiasm and solve practical problems. The teaching reform foundation of “motor and drive” is provided in this paper.
Theories of Meaning for the Internet of Things

Abstract In this chapter, we consider the theoretical foundations for representing knowledge in the Internet of Things context. Specifically, we consider (1) the model-theoretic semantics (i.e., extensional semantics ), (2) the possible-world semantics (i.e., intensional semantics ), (3) the situation semantics , and (4) the cognitive/distributional semantics . Given the peculiarities of the Internet of Things, we pay particular attention to (a) perception (i.e., how to establish a connection to the world), (b) intersubjectivity (i.e., how to align world representations), and (c) the dynamics of world knowledge (i.e., how to model events). We come to the conclusion that each of the semantic theories helps in modeling specific aspects, but does not sufficiently address all three aspects simultaneously.
Cooperative multiple task assignment using cluster method and bidirectional particle swarm optimization

The cooperative multiple task assignment problem with heterogeneous UAVs demands different UAVS to executed multiple tasks on each target obeying peculiar order of task type. This is a typical NP-hard problem. However, the deadlock situation makes solving process in trouble without appropriate task execution order. In this paper, the precedent targets order is given by cluster method to avoiding the deadlock situation and the bidirectional particle swarm optimization (BPSO) is applied for assigning heterogeneous UAVs to accomplish each task of each target. Then simulation experiments are given to demonstrate the feasibility of BPSO. What’s more, the robust performance and optimality are better than random search algorithm by the result of Monte Carlo simulations.
Designing an Inventory Database Software Suitable for Small Business

COVID-19's detection and the rapid dissemination pace have led to many governments' decisive action. The lockout of large parts of society and economic life has arrived as an exogenous shock to many economic actors, not least innovative startups. The study illustrates an entrepreneur who faces problems with inventory management and shows the variations between the organization's existing system and the proposed database system. At the end of this paper, a wireframe design is created to assist small entrepreneurs in having more accessible communication with their customers.
Genetic Dropout: An Application to Handwritten Indic Script Classification

Inverted dropout is one of the extensively used methods applied as a regularization technique in neural network. It randomly drops off neurons during the training process with a particular probability distribution. This naive way of dropping random neurons without taking into account the contribution of the neuron towards decision making process has motivated us to propose Genetic Dropout, which uses masks evolved using Genetic Algorithm such that during the training process, attention is given to those neurons which are better performers. The effectiveness of Genetic Dropout is assessed on the problem of handwritten script classification from document images, and the results are compared with a simple feed forward neural network with or without inverted dropout. The results show a significant improvement in classification accuracy compared to a simple feed forward network while applied on both text line-level and word-level handwritten multi-scrip datasets. The source code of the present work is available at: https://github.com/saikat021/Genetic-Dropout.
An Extension of the FuzzyR Toolbox for Non-Singleton Fuzzy Logic Systems

Recent years have seen a surge in interest in non-singleton fuzzy systems. These systems enable the direct modelling of uncertainty affecting systems' inputs using the fuzzification stage. Moreover, recent work has shown how different composition approaches to modelling the interaction between the non-singleton input and the antecedent fuzzy sets enable the efficient handling of uncertainty without requiring changes in a system's rule base, with benefits both in terms of performance and interpretability. As thus far few current software toolkit support non-singleton fuzzy systems, this paper presents an extension of the FuzzyR toolbox, which is a freely available R package on CRAN, for non-singleton fuzzy logic systems. The updated toolbox enables a non-singleton model to be conveniently built from scratch, or for existing singleton fuzzy logic systems built using FuzzyR to be converted easily. Predefined operations include fuzzification of crisp inputs (e.g. into Gaussian membership functions), and a variety of composition approaches for computing rules' firing-strengths, based on the standard, centroid-based, and similarity-based methods. It is also possible to include user-defined options for these abovementioned methods, without the need to modify (or update) the FuzzyR toolbox itself. In this paper, detailed introductions for the new non-singleton features of the toolkit are presented, complete with code samples in R to facilitate adoption both within and beyond the community. Further, the paper presents a series of validation experiments, replicating a recent empirical analysis of non-singleton fuzzy logic systems in the context of time-series prediction with different levels of noise.
<i>SeUpdate</i>: Secure Encrypted Data Update for Multi-User Environments

Searchable Symmetric Encryption (SSE) is a key tool for secure data processing. To date, most of the SSEs were studied alone, while an SSE supporting update operations over encrypted data remained a challenging problem due to various statistical attacks and multi-user environments. In this article, we propose <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SeUpdate</i> , the first SSE scheme that simultaneously achieves keyword search and controlled update over encrypted data, with flexible read (search) and write (update) access control policies among multiple users. In <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SeUpdate</i> , users do not need to share secret keys and a single query enables one to efficiently search all his authorized data. We formally define a security model, and prove our scheme have both forward and backward security. We note that the write permission of an SSE is realized for the first time. We further extend the basic scheme with dynamic access policy update and support of a large number of files. We also implement <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SeUpdate</i> and some related work. The theoretical and experimental analyses demonstrate our scheme and its extension are practical and efficient.
DCST: a data-driven color/spatial transform-based image coding method

Block-based discrete cosine transform (DCT) and quantization matrices on YCbCr color channels play key roles in JPEG and have been widely used other standards in last three decades. In this work, we propose a new image coding method, called DCST. It adopts data-driven color transform and spatial transform based on statistical properties of pixels and machine learning. To match with the data-driven forward transform, we propose a method to design the quantization table based on human visual system (HVS). Furthermore, to efficiently compensate the quantization error, a machine learning based optimal inverse transform is proposed. Performance of our new design is verified using Kodak image dataset based on libjpeg. Our pipeline outperforms JPEG with a gain of 0.5738 in BD-PSNR (or a reduction of 9.5713 in BD-rate) range from 0.2 to 3bpp.
Language choices as audience design strategies in Chinese multilingual speakers’ Wechat posts

This current study reports three multilingual Chinese students’ audience design strategies on a populated Social Networking Site (SNS), WeChat. Considering the importance of audiences in shaping multilingual speakers’ language choice (Bell, A. (1984). Language style as audience design. Language in Society, 13(2), 145–204) and the potential hazard of conflated audiences in social media, as well as the comparative lack of research on Wechat which has different technical affordances from the well-researched SNS, Facebook, this article aims to shed new light on how multilingual speakers harness their linguistic repertoire to cope with the “context collapse” (Marwick, A. E., &amp; Boyd, D. (2011). I tweet honestly, I tweet passionately: Twitter users, context collapse, and the imagined audience. New Media &amp; Society, 13(1), 114–133) in semi-public SNS. Data collection and analysis follow an online ethnography method consisting of 588 initial posts from participants’ WeChat Moments during 1 year and semi-structured interviews. Theoretically informed by Androutsopoulos’s (2014b; Languaging when contexts collapse: Audience design in social networking. Discourse, Context &amp; Media, 4–5, 62–73) audience specification framework, the findings reveal that although the default language in both online and offline interactions is Chinese, Wechat users have a very high sensitivity to different patterns of Chinese–English code-switching and grammatical and lexical choices in English. Meanwhile, Chinese multilingual speakers usually developed highly nuanced audience design strategies to target or partition specific groups of audience, which are far more complicated than the audience design strategies found in previous research on Facebook.
Sedimentological and palynological assessment of three wells in eastern Dahomey Embayment, southwestern Nigeria, West Africa

Integrated studies were carried out on one hundred and eleven (111) ditch samples retrieved from three wells drilled in Lagos area within the Dahomey Embayment, coastal southwestern Nigeria. The purpose of this study is to determine the provenance, heavy mineral assemblage(s), establish the lowstand - highstand episodes, Quaternary palaeoenvironment and date the encountered lithofacies which constitute sandstones, siltstones with thin beds of lignite. The textural parameters show that the sandstones are medium to fine grained with the lignite consisting of plant fragment remains. Petrographic analyses showed abundant detrital minerals with average modal composition of quartz (Q = 90%), feldspar (F = 2%) and rock fragments (RF = 4%). The QFR ternary plots revealed that the sandstones are quartz arenites of continental block provenance origin deposited under humid climatic setting. This lithofacies has undergone medium distance range of transportation and have been subjected to mechanical and chemical weathering prior deposition. The heavy mineral assemblages and the ratios of Zircon-Tourmaline (ZTR index) in the wells suggest matured sediments. The Apatite: Tourmaline (ATi) ratios showed variations within the studied wells indicating differences in rate of weathering. It is evident that the periods of residence on the floodplain despite similar provenances and fluctuations in ratios, suggest sea level changes. The fluctuations in apatite: tourmaline ratios with respect to weathering are attributed to rapid changes in climatic conditions during the floodplain residence. The high ATi values in sandstone were deposited during relative sea level highstand (transgression), while sandstones with low ATi were deposited during lowstand (regression) in Nigeria and along the West African region. The abundance species of Rhizophora sp., Avicennia africana, Acrostichu mauruem, Laevigatosporite sp., Combretaceae sp., Mitrogynaciliata, Sapotaceae sp., Disopyros sp., Elaeis guineensis and Alchronea spp. indicated depositional transition from mangrove to fresh water swamp due to the episodic highstand and lowstand regimes. The abundance of Podocarpus milianjanus and Retitricolporites hians and the absence of Miocene diagnostic forms suggest late Pleistocene (Tarantian = 0.012–0.126 Ma) deposition.
Kelayakan Instrumen Evaluasi Buku Sekolah Elektronik (BSE) Bahasa Indonesia Siswa SMA Kelas XI

Curriculum development is directed at the condition of society in shaping the younger generation. The development of the curriculum also has implications for textbooks. Textbooks are an element of supporting educational facilities and infrastructure. This study aims to describe the feasibility of the Evaluation Instrument in the Indonesian Electronic School Book for SMA class XI students of the 2017 revision of the 2013 Curriculum. This research uses a qualitative approach with the method of Content Analysis. Data collection techniques are using literature study techniques and document techniques. The sample of this research was carried out purposively by determining the criteria according to the research objectives. The sample of this research is an evaluation instrument in the Indonesian language BSE of high school students class XI 2013 revision of the 2017 Curriculum. The data of this research are the feasibility of the evaluation instrument in terms of material aspects and language aspects. The percentage of eligibility level is done by dividing the number of scores obtained by the number of total criteria score then multiplied by one hundred. The results showed that the feasibility of the evaluation instrument in terms of the material aspect was 78.8% and in terms of the language aspect was 84.7%. Based on the research results of the BSE Indonesian evaluation instrument, Class XI high school students are categorized as feasible but must be accompanied by other learning resources. Such as the Internet, Print Media, etc. The results of this study can be used as one of the foundations for teachers to study and consider student textbooks, especially the feasibility of evaluation instruments.
Low-Back-Action RF SQUID Readout for a Josephson Flux Qubit Measurement

Superconducting qubits demonstrate good perspectives as scalable blocks for quantum information processing. These systems can be individually readout, addressed, and controlled, making them some of the most attractive thin-film qubits. Quantum measurements should be performed in order to readout the final state of flux qubits. To observe the dynamics of such systems, one requires a fast magnetometer with exceptional sensitivity and low back-action. We address the problem of reducing the back-action of an RF SQUID readout circuit, which may significantly destroy a Josephson flux qubit in the case of the circuits integration. The main sources of such back-action in conventional RF SQUID and DC SQUID circuits are low-ohmic shunting resistors ensuring high damping. The advantage of flux qubits is the presence of an optimal bias point at which, in the first-order approximation, the device is immune to fluctuations in readout and control lines. Nonetheless, even such systems have characteristics limited by a second-order dissipation. The dissipation can be eliminated by using an “ideal parametric readout” based on an RF SQUID acting in non-hysteretic adiabatic mode. In this paper, we propose an RF SQUID in a non-hysteretic regime as a fast, sensitive, and scalable readout system with low back-action for measurements of a single MW photon counter based on a flux qubit.
Training compression artifacts reduction network with domain adaptation

Compression artifact removal is imperative for more visually pleasing contents after image and video compression. Recent works on compression artifact reduction network (CARN) assume that the same or similar quality of images would be employed for both training and testing, and, accordingly, a model needs a quality factor as a prior to accomplish the task successfully. However, the possible discrepancy will degrade performance substantially in a target if the model confronts a different level of distortion from the training phase. To solve the problem, we propose a novel training scheme of CARN to take an advantage of domain adaptation (DA). Specifically, we assign an image encoded with a different quality factor as a different domain and train a CARN using DA to perform robustly in another domain of a different level of distortion. Experimental results demonstrate that the proposed method achieves superior performance on DIV2K, BSD68, and Set12.
Public Opinion Analysis of Weibo Comments Based on Crawler and SVM

In order to predict the trend of public opinion in Weibo news, this paper proposes a public opinion analysis method based on the combination of crawler and SVM. Firstly, word2vec model is used to train the sample, and the results are used to train SVM. Then, according to the popular news comments on Weibo, crawler is used to get the news, Jieba is used to segment the words into the model to judge, and hierarchical vector machine is used to judge the emotion. Finally, based on the statistical data to judge the trend of public opinion. The experimental results show that the test result is relatively more accurate and effective for public opinion analysis.