Reviewerly/exHarmony huggingface dataset:
IOT-Based Smart Irrigation Systems

Internet of Things (IOT) is a network-based technology that consists of hardware devices for automobiles, buildings, home automation, and added items that are embedded with hardware and software which provide network connectivity. IOT based agriculture technology is used to create a high range of production efficiency. IOT-based techniques are of great significance in the modern era, which makes it a little bit complicated to deal with security issues which are quite complex. IOT based application products have been developed and explored by various researchers using various algorithms. The model proposed is aimed to develop a smart irrigation system which could offer farms a perfect atmosphere. A system that was used to collect, process, and store disseminated data in the form of information required to execute the farm's operational functions is called a Farm Management Information Systems.
dpVAEs: Fixing Sample Generation for Regularized VAEs

Unsupervised representation learning via generative modeling is a staple to many computer vision applications in the absence of labeled data. Variational Autoencoders (VAEs) are powerful generative models that learn representations useful for data generation. However, due to inherent challenges in the training objective, VAEs fail to learn useful representations amenable for downstream tasks. Regularization-based methods that attempt to improve the representation learning aspect of VAEs come at a price: poor sample generation. In this paper, we explore this representation-generation trade-off for regularized VAEs and introduce a new family of priors, namely decoupled priors, or dpVAEs, that decouple the representation space from the generation space. This decoupling enables the use of VAE regularizers on the representation space without impacting the distribution used for sample generation, and thereby reaping the representation learning benefits of the regularizations without sacrificing the sample generation. dpVAE leverages invertible networks to learn a bijective mapping from an arbitrarily complex representation distribution to a simple, tractable, generative distribution. Decoupled priors can be adapted to the state-of-the-art VAE regularizers without additional hyperparameter tuning. We showcase the use of dpVAEs with different regularizers. Experiments on MNIST, SVHN, and CelebA demonstrate, quantitatively and qualitatively, that dpVAE fixes sample generation for regularized VAEs.
A TIRELESS RESEARCHER (ON THE BIRTH ANNIVERSARY OF R.N. SULEYMANOVA)

No
Optimal Tilt Angle For Getting Maximum Energy Produced By Pv Panel By Utilizing Clear Sky And Array Performance Models

In this paper, we analysed and implement clear sky and array performance models to achieve maximum electrical energy produced from the photovoltaic (PV) panel. The selected models just not only include location dependent parameters but also include environmental factors such as Linke Turbidity (include aerosols, absorption due to assorted gases, Rayleigh scattering), cloud cover, albedo, perceptible water vapour. 250 W PV panel was used as a reference to check the output electrical energy in a given location by inputting the latitude and longitude of a location. It is concluded that 27° facing south is the optimized tilt angle for every locations having latitude at north direction and longitude at east direction. By taking the example of location Nagpur India (21.14° N, 79.08° E), a difference of only 0.35 % is reported when comparing the computed electrical energy with actual electrical energy that have been acquired by using pyranometers, pyrheliometers and illuminance meters installed by the National Renewable Energy Laboratory (NREL). So, a good approximation of electrical energy can be computed by combining clear sky and array performance models.
Covid-19: contact tracing apps and privacy concerns

The 1918-20 flu pandemic, also known as the influenza or Spanish flu pandemic and caused by the influenza A virus resulting in 17-50 million estimated deaths, was one of the deadliest pandemics eve ...
Pengembangan Teknologi E-Marketplace Untuk Analisis Usaha Peternak Ayam Lokal di Kotawaringin Timur

East Kotawaringin Regency is one of the regencies in Central Kalimantan Province, Indonesia. The problems faced today are the marketing of local chicken livestock, the lack of information on local chicken farms and discussion media among breeders in the district and the management of transactions and reports that still use manual and even traditional recording which results in frequent loss of data. So, how to create an information system that can make it easier for customers to place orders via the Website without coming directly to the market and manage transaction data and reports in a systemized manner. To solve this problem, we need a Web-based E-Matketplace. To create this web-based E-Matketplace, we need an editor software for Macromedia Dreamweaver CS4 programming language, PHP programming language, and MySql database
Knowledge and Library Services: About Us: About Us

Our Whipps Cross and Newham libraries are open from 10.00 to 16.00 Monday to Friday. There are also knowledge hubs (computer rooms) at Whipps Cross and Newham which are accessible 24/7.
ScreenPlay: A Topic-Theory-Inspired Interactive System

ScreenPlay is a unique interactive computer music system (ICMS) that draws upon various computational styles from within the field of human–computer interaction (HCI) in music, allowing it to transcend the socially contextual boundaries that separate different approaches to ICMS design and implementation, as well as the overarching spheres of experimental/academic and popular electronic musics. A key aspect of ScreenPlay’s design in achieving this is the novel inclusion of topic theory, which also enables ScreenPlay to bridge a gap spanning both time and genre between Classical/Romantic era music and contemporary electronic music; providing new and creative insights into the subject of topic theory and its potential for reappropriation within the sonic arts.
LibGuides: Argent, Jasmine: Evaluating Resources

XYZ Vibrant Library has a number of useful resources to help find information on your family history.
Algorithmic Phenomena in Nature:an Explanation Model

The trend of algorithmic modeling of the phenomena of the surrounding world is&nbsp;gaining strength at the present time. The idea of a computational Universe has been put forward and is actively developing. All its processes are algorithmic in&nbsp;nature. In this regard, it is of interest to analyze the relationship between phys­ical processes described in the usual terms of the laws of nature and physical processes of an algorithmic nature. Algorithmic processes are analyzed using a&nbsp;number of examples. Distinctive features are: 1) a negligible probability of ac­cidentally meeting them in nature; 2) the division into steps; 3) a sequence of steps require the presence of a control (cybernetic) structure, a subject of action; 4) the functioning of the subject of action can be described anthropomorphically, as following the rules of the form “If A, perform d to start the physical process&nbsp;f”; 5) “breakdown” of the control structure leads to a violation of the al­gorithm, but not to a violation of the laws of nature. An algorithm in physical na­ture is a&nbsp;sequence of physical processes that are initiated by a control structure that acts as the subject of an action. Such understanding of a physical algorithm covers both living and non-living nature. In the human world, we find algorithms in&nbsp;various skills to operate intelligently with objects and create artifacts with al­gorithmic characteristics.
Slope One Meets Neighbourhood: Revisiting Slope One Predictor in Collaborative Filtering

Collaborative filtering (CF) frameworkShaw, Rabi inPatra, Bidyut Kumar recommendation is a very popular technique for providing personalized recommendation. Slope one predictor is a model-based CF which has received good attention from researchers and practitioners. In this paper, we revisit the slope one predictor to incorporate strong features of neighbourhood-based CF into it for providing personalized recommendation to users. Preliminary results with two real-world datasets are very promising. Proposed technique outperforms original slope one and its performance is at par with a variant of slope one introduced recently.
Digital Information System for Attack Reduction in Industry 4.0

The knowledge center is a Web-based framework intended to help individuals remotely share data. The knowledge center offers an assortment of devices and adornments that empower prompt services. Knowledge centers must execute different techniques by which the members can communicate and trade data. Knowledge is mined, mapped, and assessed inside the Knowledge Center. Integrating cloud technologies and storage access networks will provide cloud clients and providers access to confidential data in cloud storages as well as in data warehouses. The whole information dwells over a worldwide arrangement of neighborhood organized assets, empowering the data and information to be gotten to just through a protected channel of the cloud condition. Cloud services and resources experience security and protection issues because of the possible attacks and internal/external threats. The data server farm framework has the facility to monitor the latest creation of data, the live changes of data from common libraries to advanced data server farm, and rebuild the data.
IMPROVING STUDENTS’ VOCABULARY ACHIEVEMENT THROUGH FLASHCARD AT THE SECOND GRADE OF SMPN 31 BANDAR LAMPUNG

Vocabulary is important for the learners to learn a language, especially English as a foreign language. Having lack of vocabulary mastery is a serious obstacle to communicate either understanding spoken or written communication. The objective of this research is to find whether there is improvement of students’ vocabulary achievement after being taught by using flashcard. This research deals with the use of visual aid, especially pictures which is called flashcards. The design of this research is one group pretest and posttest design. The population of the research was the second grade students of SMPN 31 Bandar Lampung in academic year of 2018/2019 and the sample was class 8A taken randomly. The instrument used for collecting the data was vocabulary test in form of multiple choice on the pretest and posttest. The data in form of scores were analyzed using tabulation and simple descriptive statistic. The result of pretest and posttest was compared to see whether there was significant improvement. SPSS 16.0 was used to test the significance level of improvement. It showed that the significance level was less than 0.05. It means that the result of teaching and learning process before and after using the flashcard is different, the result of posttest is higher than that of pretest. It can be inferred that the use of flashcard in vocabulary teaching and learning process is effective enough to improve students’ vocabulary achievement. Therefore, it can be concluded that flashcard can be used to help students’ vocabulary improve their vocabulary achievement. Keywords: teaching vocabulary, flashcard, vocabulary achievement
Analysis of the value-motivational sphere of employees of internal affairs bodies in the forecast of the success of professional adaptation

The article discusses the results of an empirical study of the value-motivational sphere of employees of internal affairs bodies, which allow predicting the success of professional adaptation. A comparison of the indicators of groups of adapted and non-adapted individuals revealed a fundamentally different level of personal adaptive potential, communicative potential, behavioral regulation and moral level. It has been established that the value-motivational profile of employees with a high level of personal adaptive potential is represented by higher indicators of internal motivation, values of service to society, normative behavior and professional achievements. In the field of socio-psychological adaptability, these are indicators of moral normativeness and the ability to constructive communication, in the motivational sphere - an orientation to success and achievement. The features of the value consciousness of a successfully adapted personality are established, expressed in the priorities of serving society, moral and business value orientation and professionalism.
DEVELOPMENT OF DIALECTIC THINKING IN THE SPACEOF LANGUAGE AND CULTURE STUDY IN HIGHER EDUCATION INSTITUTION

The article discusses the problems of language competence development in the context of applying structural-dialectical approach. The author uses the method of constructing dialectical tasks which form the space of opportunities for the use of dialectical mental actions.
Multi-user network paves the way towards a quantum internet

Researchers in the UK and Austria have demonstrated secure information exchange between multiple users spaced around a city.
LibGuides: Solar Energy Technology: Finding Websites

These Solar Energy Technology resources shine bright! This guide covers AC/DC currents, electricity basics, solar energy, solar power & power plants, solar panels, and other solar energy resources.
Recognition Method and Evaluation of Traffic Signs Based on Capsule Network

In the process of recognizing traffic signs with traditional convolutional neural networks, the discarding of the target coordinate frame by the maximum pooling layer makes it impossible to detect the instantiated parameters, such as the pose and the position of object features in real-world scenarios. To solve this problem, a CapsNet algorithm for the recognition of traffic signs based on the Bagging integration is proposed in this paper. Using CapsNet as the base classifier, a novel traffic sign recognition algorithm based on the Bagging integration framework was constructed. By setting an adaptive oversampling rate, the SMOTE algorithm was used to oversample the minority class samples to reduce the imbalance in the samples. Algorithms such as Multi-Scale CNN, Committee of CNN & MLP were selected under the same testing environment to compare and analyze the recognition accuracy of the German traffic sign recognition benchmark (GTSRB) and the multi-view dataset after the affine transformation. In the meantime, by using GTSRB as the training set and the sampled dataset of LISA and Belgium TS as the testing set, the generalization capability of the proposed algorithm was investigated. The experimental results showed that, compared to the optimum public mode, the accuracy of the traffic sign recognition model proposed in this paper was 99.07% and the recognition accuracy on the multi-view traffic sign dataset was 77.58%, which is equivalent to a 7.21% enhancement in addition to a good generalization capability.
ANALYSIS OF ICT COMPANIES’ NEEDS FOR THE ENHANCEMENT OF ICT HIGHER EDUCATION AT MASTER LEVEL: THE CASE OF KAZAKHSTAN

The correspondence between ICT master programmes and requirements of ICT companies is shaped via needs analysis. The research aim: The aim of the present article is to analyse the empirical results of the study of needs of ICT companies, underpinning the elaboration of implications on ICT master programmes. The primary methods used: Exploratory research has been implemented within the present work. The representatives of four ICT companies, namely CISCO, KAIB, Kaspersky Lab and Soft Com, took part in the focus group interview organised at International Information Technology University, Almaty, Kazakhstan, in October 2019. A brief description of the main research results: The theoretical findings of the present research allow concluding that ICT companies and the universities belong to the local or regional community. The structuring content analysis allows identifying the areas of universities-companies cooperation, namely academic staff training, students’ training, provision of universities with modern equipment, joint elaboration of the content of ICT master programmes, organisation of joint laboratories including virtual laboratories, ensuring practice and internships for students, etc. The summarising content analysis highlights the need in cooperation between universities and companies to strengthen the sustainable development of the digital economy in the region. The novelty of the present work is formulated in the implications on ICT master programmes. Directions of further research are formulated.
On the structure of division rules

We consider the problem of dividing one unit of an infinitely divisible object among a finite number of agents. We provide a characterization of all single-peaked domains on which the uniform rule is the unique division rule satisfying efficiency, strategy-proofness, and equal treatment of equals (ETE). We also provide a class of division rules satisfying these properties on the remaining single-peaked domains. Next, we consider non single-peaked domains and provide a characterization of all such domains on which the uniform rule satisfies efficiency, strategy-proofness, and ETE. We also show that under some mild richness conditions the uniform rule is the unique rule satisfying the mentioned properties on these domains. Finally, we provide a class of division rules satisfying efficiency, strategy-proofness, and ETE on the remaining non single-peaked domains. We conclude the paper by providing a wide range of applications to justify the usefulness of our results.
Advancing Temporal Modeling and Heterogeneous Data Analysis for Digital Health

Author(s): Meng, Yiwen | Advisor(s): Arnold, Corey W; Speier, William F | Abstract: Recent development in electronic medical devices or systems has realized the effective collection and documentation of patients’ health in real time. To date, the potential clinical impact of this healthcare data has not been fully realized. Specifically, patients’ health data is heterogenous and sparse in nature, as it is composed of various modalities and is collected on different scales. In addition, processing this data efficiently in a temporal manner to take advantage of its sequential structure remains a barrier for medical records. This dissertation attempts to overcome these challenges by developing machine learning models to classify patient reported outcome (PRO) scores from activity tracker data and predict depression diagnoses based on data from patients’ historical electronic health records (EHR). A temporal model based on hidden Markov models (HMM) is first proposed to classify PRO scores in various categories from human vital signs collected from Fitbit activity trackers. This approach is able to combine various vital signs on difference scales in a single model that tracks changes in PRO scores over time. Second, several end-to-end machine learning models were built to aggregate multimodal EHR data in a single model. A novel hierarchical embedding method achieved superior performance for predicting depression diagnosis, which lays a foundation for addressing the heterogeneity and sparsity of EHR data. Third, an innovative bidirectional sequence learning model with a transformer architecture was developed for representation learning on high dimensional EHR data, demonstrating significantly improved performance over the traditional forward-only method. Finally, methods to improve the interpretability of the aforementioned models have been developed, which is a critical step before clinical deployment. Relative feature importance factors are determined for each vital sign collected from the Fitbit and attention weights are found for each data modality in the sequential EHR data. Extensive experiments and results have demonstrated the effectiveness of these proposed methods. This dissertation provides methodologies that advance modeling and understanding of digital health datasets, which lays the foundation to construct clinical decision support systems in this domain which could potentially lead to early disease detection and intervention.
Applications of Near-Term Quantum Computers

Author(s): Huggins, William James | Advisor(s): Whaley, Katherine B. | Abstract: Quantum computers exist today that are capable of performing calculations that challenge the largest classical supercomputers. Now the great challenge is to make use of these devices, or their successors, to perform calculations of independent interest. This thesis focuses on a family of algorithms that aim to accomplish this goal, known as variational quantum algorithms, and the challenges that they face. We frame these challenges in terms of two kinds of resources, the number of operations that we can afford to perform in a single quantum circuit, and the overall number of circuit repetitions required. Before turning to our specific contributions, we provide a review of the electronic structure problem, which serves as a central example for our work. We also provide a self-contained introduction to the field of quantum computing and the notion of a variational quantum algorithm.We begin the main body of the thesis by studying a generalization of the standard unitary coupled cluster ansatz. We present a sparse version of unitary coupled cluster and show that it can accurately represent the ground and excited states of small molecular systems using fewer quantum gates than the full unitary coupled cluster. We then introduce a strategy for representing molecular ground states as linear combinations of parameterized wavefunctions, allowing for a tradeoff between the number of operations required for each circuit and the number of circuit repetitions. We provide circuit primitives that allow for the efficient measurement of the required matrix elements between these wavefunctions. Subsequently, we show how the cost of estimating the energy of a quantum chemical wavefunction on a near-term quantum computer can be dramatically reduced by using a factorization of the two-electron integraltensor. Furthermore, we explain how this measurement strategy helps mitigate against errors during state preparation and measurement. We then present a Monte Carlo version of a classical algorithm for calculating the partition function of two-dimensional lattice models that shows a similar kind of tradeoff between two resources, albeit in a different context. Finally, we show how ideas based on tensor networks can inform the design of quantum circuits for machine learning tasks. We argue that this application is especially tolerant to noise and present numerical data substantiating this argument. We conclude by explaining explicitly how our work addresses the problems of the limited resources available to near-term quantum computers and offering some optimism about the future of the field.
CNN-Based Traffic Volume Video Detection Method

Traffic volume detection is the base for traffic management and even smart traffic construction. This paper proposes a method based on convolutional neural networks (CNN). Considering the camera always being fixed during traffic volume detection, a shallow residual neural network (ResNet) model is proposed in this paper, which uses road video data to train model parameters and extract vehicles feature. After training, this paper uses the model to identify the vehicles and a core correlation filter is proposed to track the target. Finally, the traffic volume count method is determined by judging whether the target passes through the region of interest (ROI). Compared with other traffic volume detection methods, this method is more suitable for classifying and counting vehicles in free flow because of its reliability and light weight. The experiment shows that the model has the recognition accuracy of 95.83% and the effective count rate is 88.37%.