Reviewerly/exHarmony huggingface dataset:
Learning Temporally Invariant and Localizable Features via Data Augmentation for Video Recognition

Deep-Learning-based video recognition has shown promising improvements along with the development of large-scale datasets and spatiotemporal network architectures. In image recognition, learning spatially invariant features is a key factor in improving recognition performance and robustness. Data augmentation based on visual inductive priors, such as cropping, flipping, rotating, or photometric jittering, is a representative approach to achieve these features. Recent state-of-the-art recognition solutions have relied on modern data augmentation strategies that exploit a mixture of augmentation operations. In this study, we extend these strategies to the temporal dimension for videos to learn temporally invariant or temporally localizable features to cover temporal perturbations or complex actions in videos. Based on our novel temporal data augmentation algorithms, video recognition performances are improved using only a limited amount of training data compared to the spatial-only data augmentation algorithms, including the 1st Visual Inductive Priors (VIPriors) for data-efficient action recognition challenge. Furthermore, learned features are temporally localizable that cannot be achieved using spatial augmentation algorithms. Our source code is available at https://github.com/taeoh-kim/temporal_data_augmentation.
A Combined Model of Blockchain, Price Intelligence and IoT for Reducing the Corruption and Poverty

Corruption of price hike is common and one of the major issues in the third world as well as in developing countries.Corruption and price hike issues are correlated with one another.A number of recent studies have been examined to establish the relationship between corruption and poverty.The people of low and middle-income countries cannot take proper medical treatment and buy nutritional food due to price hike and corruption.As a result, every year, many children and poor people have been suffering and dying from malnutrition.Blockchain is a leading technology which is prohibitive to data manipulation and an open distributed computation system where data transactions occur between two parties efficiently.Price Intelligence technology is used for price monitoring at the market level.This study presents a technological model where we have used blockchain, price intelligence and IoT approaches for monitoring the price transaction in the medical, banking and educational areas.This proposed model represents a technological architecture where different people and organizations are connected to the cloud through the internet and money transactions happened through blockchain approach.As a result, people of differing abilities and resources can detect the price hike and corruption and poor people can get priority for better service.Finally, we have suggested the potential impact of the proposed model and have explained how poor people can get better service by using this model.
Analysis and Research on Grouping Scheduling of Marine Verification Tasks Based on Dynamic Priority

Peng, F.; Zheng, L.; Gong, M., and Zhou, H., 2020. Analysis and research on grouping scheduling of marine verification tasks based on dynamic priority. In: Qiu, Y.; Zhu, H., and Fang, X. (eds.), Current Advancements in Marine and Coastal Research for Technological and Sociological Applications. Journal of Coastal Research, Special Issue No. 107, pp. 121-124. Coconut Creek (Florida), ISSN 0749-0208.With the development and utilization of marine resources in various countries, ocean verification task plays an important role in verification and ocean observation as a basic service guarantee work. Based on this, this paper first introduces the dynamic priority scheduling algorithm, then studies the theoretical basis of marine metrology verification task scheduling and the dynamic priority scheduling algorithm of marine verification task, and finally analyzes the advantages of the group scheduling algorithm of marine verification task based on dynamic priority.
Application of Blockchain-Based technology for Smart Transport System

The article discusses applications of the blockchainbased technology in a smart transport system (STS).We proposed to represent the urban transport system as a complex system with the interaction between some stakeholders, where each stakeholder has own purposes and opportunities and can resolve it with the blockchain-based technology.We proposed a method of assessing the investment risks of initial coin offer (ICO) for transport companies.We also have developed the algorithm of investment projects risk analysis in crypto technology sector.There are some examples of blockchain implementations in the smart transport systems, we made the comparative study of some blockchain-based transportation projects, and classification of investment risks in transport projects.
Communicative Codes of Visual Nature for Business Communications

The use of communication codes for efficiency communication programming (infogramming) is an innovative approach to soft support for management decisions.This approach was proposed by Richard H. Thaler and named Nudge.However, the author of this approach did not offer efficiency tools for the practical application of this innovation in supporting solutions.We offer an author's tool for the practical implementation of Nudgeinfographic, so to speak-information programming of decision makers to transform decisions from the irrational field of the mind to the rational one through communication codes of a visual nature.The practitioners require communication code to form such communications.They are certainly different for divers target environment.Their identification requires complex marketing research.Recently, a scientific and methodological discussion has been held on the relevance of the results obtained through sociological surveys as a method of marketing research.In our opinion these discussions lead to a fair conclusion about the low relevance of the results of opinion polls due to a number of factors affecting this parameter.The most convincing explanations about the traps that the researchers introduce the method of opinion polls are reflected in the work of recognized authorities in marketing research.An alternative to the methodology of the opinion poll is the in-depth interview method, which assumes, in contrast to the method of opinion polls, a qualitative study that answers the questions of "how", "in what way", etc.At the same time, qualitative research has its own traps, consisting in a small number of respondents.As a rule, this number is about 3-6 people.For this reason, many researchers do not consider the results of such studies to be practically valuable.In this regard, the authors of this article face the task of proposing a hybrid method that is quantitative in terms of the number of respondents and qualitative in terms of avoiding the traps of social surveys.The authors confirm the practical value of the proposed hybrid method by comparing the results of quantitative and qualitative research methods of the same target environment.The metaphor extraction methodology proposed by Gerald Zaltman (ZMET) was used in order to escape the traps of quantitative research methods.
Data Privacy in IoT Equipped Future Smart Homes

Smart devices are becoming inseparable from daily lives and are improving fast for providing intelligent services and remote monitoring and control. In order to provide personalized and customized services more personal data collection is required. Consequently, intelligent services are becoming intensely personal and they raise concerns regarding data privacy and security. In this paper data privacy requirements in a smart home environment equipped with Internet of Things are described and privacy challenges for data and models are addressed.
Compiling a Higher-Order Smart Contract Language to LLVM.

Scilla is a higher-order polymorphic typed intermediate level language for implementing smart contracts. In this talk, we describe a Scilla compiler targeting LLVM, with a focus on mapping Scilla types, values, and its functional language constructs to LLVM-IR. The compiled LLVM-IR, when executed with LLVM's JIT framework, achieves a speedup of about 10x over the reference interpreter on a typical Scilla contract. This reduced latency is crucial in the setting of blockchains, where smart contracts are executed as parts of transactions, to achieve peak transactions processed per second. Experiments on the Ackermann function achieved a speedup of more than 45x. This talk abstract is aimed at both programming language researchers looking to implement an LLVM based compiler for their functional language, as well as at LLVM practitioners.
Restoration of Dynamically Degraded Images in Displays

This chapter discusses the smearing of the displayed image is demonstrated by means of simulation by considering the appropriate temporal response for the imaging condition under analysis. It shows that the phosphor type as well as the magnitude and direction of the relative motion between the target and the sensor play significant roles in image degradation. Imaging faithfully in a dynamic environment is an important task for an electrooptical imaging system. An environment is referred to as dynamic when there exists a finite relative motion between the object and the image-gathering system. To comprehend the extent of image degradation in an electro-optical display, a particular image intensifier tube, such as that used in night vision goggles, a cathode-ray tube display under motion, and display under vibration are considered. To restore the degraded image, one needs to know the phosphor decay time constant and the magnitude and direction of relative velocity between the sensor and the target.
LibGuides: Getting Started @ the Bentley Library -- Online!: Research Help

Learn all of the resources and services available to you through the Bentley Library online - no visit to our library building required!
Improving face recognition by artificial neural network using principal component analysis

The face-recognition system is among the most effective pattern recognition and image analysis techniques. This technique has met great attention from academic and industrial fields because of its extensive use in detecting the identity of individuals for monitoring systems, security and many other practical fields. In this paper, an effective method of face recognition was proposed. Ten person's faces images were selected from ORL dataset, for each person (42) image with total of (420) images as dataset. Features are extracted using principle component analysis PCA to reduce the dimensionality of the face images. Four models where created, the first one was trained using feed forward back propagation learning (FFBBL) with 40 features, the second was trained using 50 features with FFBBL, the third was trained using the same features but using Elman Neural Network. For each person (24) image used as training set for the neural networks, while the remaining images used as testing set. The results showed that the proposed method was effective and highly accurate. FFBBL give accuracy of (98.33,97.14) with (40, 50) features respectively, while Elman gives (98.33, 98.80) for with (40, 50) features respectively.
LibGuides: Elementary Education: Children's Book Award Winners

This subject guide is designed to support the Rogers State University and Cameron University joint Elementary Education programs.
LibGuides: Getting Started @ the Bentley Library -- Online!: Textbooks & Course Reserves

Learn all of the resources and services available to you through the Bentley Library online - no visit to our library building required!
Optimal minimal Linear codes from posets

Recently, some infinite families of minimal and optimal binary linear codes were constructed from simplicial complexes by Hyun {\em et al.} We extend this construction method to arbitrary posets. Especially, anti-chains are corresponded to simplicial complexes. In this paper, we present two constructions of binary linear codes from hierarchical posets of two levels. In particular, we determine the weight distributions of binary linear codes associated with hierarchical posets with two levels. Based on these results, we also obtain some optimal and minimal binary linear codes not satisfying the condition of Ashikhmin-Barg.
Correction to: A unified view of density-based methods for semi-supervised clustering and classification

The article, A unified view of density-based methods for semi-supervised.
Implement a Portable Smart Device for Elder Health Care

The population structure of Taiwan is evolving toward aged society. The ratio of elderly population over total population is increasingly approaching to 1/4 or above, leading to the increasing demand of the aged care in Taiwanese society. Further the increasing the long-term care cost would have a great impact on National financial burden. As for the delimitation on the growth rate of the elder population, we would like to develop an auxiliary aid capable of reminding peopleâs daily exercise, as well as detecting the peopleâs aged degree. In this study we implement such an aid by a cost-effective, intelligent ring type sensor capable of measuring peopleâs essential tremor as well as recording peopleâs daily exercise/showing them up at a user friendly interface. Based on the state of the art sensor technology, the intelligent sensor comes out to be capable of recording aged peopleâs essential tremor spectrum via fast Fourier transform (FFT) and their daily exercise simultaneously. With gathering both of detecting information, the sensor would be transmitting the information in a real-time fashion toward peopleâs cellular phone via a Bluetooth interface. Then an application on the phone could evaluate the degree of the aging and show the daily sport records toward the medical staff at the remote site. In order encourage elderly people to exercise and prevent elderly people from falling accidentally, the device also has fall warning function. It is believed via such a surveillance on elder people and referring their daily sport records in a long term fashion, the phenomenon of population aging could be effectively improved.
Retrospective Analysis of Fine Needle Aspiration Cytology Procedure at Tertiary Care Rural Hospital

Breast lump is one of the commonest presentations of breast diseases. The diagnosis of breast diseases can be achieved like in other clinical conditions using patient history, physical examination and cytological investigation among others. A palpable breast lump is a common diagnostic problem to both general practitioners and surgeons. Fine needle aspiration cytology (FNAC) is a valuable tool and can be used to evaluate all palpable and nonpalpable mammographically evident breast lesions. It has become popular as a valuable tool in preoperative assessment of breast masses, and it shows high accuracy, sensitivity, and specificity. It has gained popularity due to its fast and easy approach, being inexpensive, and can be performed with little complications. Early definitive diagnosis is required for initiation of the treatment. FNAC procedure and cytological data of 46 female patients (14-70 years age group) with palpable breast lump were utilized in this study which had taken place over a period of one year between October 2018 to October 2019 in a tertiary care rural hospital. The final procedure was determined so as to maintain maximum accuracy and patient’s interest. Fibroadenoma and intraductal carcinoma were the most common diagnosed benign and malignant breast lesions. Malignant tumours were absent below age of 30 years. Benign proliferative breast disease was distributed among almost all age groups.
MULTIDIMENSIONAL INSTRUMENT OF EXAMINERS’ ATTITUDE TOWARDS KNOWLEDGE OF MALPRACTICE INDICATORS

In this study, the authors constructed and validated instrument to measure examiners’ disposition towards knowledge of examination malpractice indicators of West African Examinations Council (WAEC). Consequently, scale development research type of single-subject design was employed. The sample for the two phases consisted of nine hundred and sixty (960) WAEC mathematics examiners from 9 marking centres, which were drawn randomly from Osun, Ogun and Ondo State, Nigeria. The instrument used for data collection was Examiners’ Attitude Towards Knowledge of Malpractice Indicators Scale (EAKMIS). Data were analyzed using Principal Component Analysis (PCA), Exploratory Factor Analysis (EFA), Confirmatory Factor Analysis (CFA) and Ordinal Alpha. The results showed that the scale was reduced from twenty (20) to eleven (11) items across three (3) dimensions. Final compliance indices were: χ2 = 457.54, p = 0.01, RMSEA= 0.03, GFI = 0.96, AGFI = 0.96, TLI = 0.94, NFI = 0.90, SRMR= 0.01, CFI= 0.91 and IFI= 0.91. The ordinal alpha reliability index for the three (3) factors of EAKMIS was 0.79, while the reliability index for each of the subscale of the EAKMIS ranged from 0.85 to 0.93. It was recommended that there should be intensive training and re-training for all the examiners by the examining body where issues related to the detection of examination malpractice cases while marking would be discussed.
UIC Leaflet: Information in Stations – UIC Communications

UIC LEAFLET - 181 Scope of application In stations, passengers should have useful elements and information for their route in the station. These elements constitute the information chain. This…
Federated Doubly Stochastic Kernel Learning for Vertically Partitioned Data

In a lot of real-world data mining and machine learning applications, data are provided by multiple providers and each maintains private records of different feature sets about common entities. It is challenging to train these vertically partitioned data effectively and efficiently while keeping data privacy for traditional data mining and machine learning algorithms. In this paper, we focus on nonlinear learning with kernels, and propose a federated doubly stochastic kernel learning (FDSKL) algorithm for vertically partitioned data. Specifically, we use random features to approximate the kernel mapping function and use doubly stochastic gradients to update the solutions, which are all computed federatedly without the disclosure of data. Importantly, we prove that FDSKL has a sublinear convergence rate, and can guarantee the data security under the semi-honest assumption. Extensive experimental results on a variety of benchmark datasets show that FDSKL is significantly faster than state-of-the-art federated learning methods when dealing with kernels, while retaining the similar generalization performance.
Developing the Students' Reading Comprehension through Interactive Approaches by Using Short Stories (An Experimental Research at South Aceh Polytechnic)

&#x0D; &#x0D; &#x0D; &#x0D; This research was conducted to see the effectiveness of using short stories in teaching reading comprehension. The teaching model was used an interactive approach to be developed the reading skills of second semester students at the Department of Computer Engineering, South Aceh Polytechnic. The reading ability of students was explored and to be found the right teaching model, then the aspects of the ability to be seen was understanding the meaning of words, sentence intentions, information, problems, opinions, recommendations, sentence structure, analysis and communication and student responses after the application of the teaching model in the classroom. After treatment, there was an increase in students' reading comprehension skills especially in the experimental class after using short story teaching materials with interactive approach teaching models to obtain an average value of pre-test = 55, and post-test = 77.67. Whereas in the control class only got a pre-test value = 49,64 and post-test = 67,86. Then there was a significant difference in the scores of the two classes in the post-test scores (77,67-67,86) namely 9,81. Meanwhile, from questionnaire data, 87% of students responded very well and the remaining 13% responded well to the use of short stories with interactive approach teaching models. As well as answering enough options and less as much as 0%. From the responded above, which has helped develop student confidence, collaboration, self-ability and group work. Of course this research was far from perfection and needs to be improved in the future with other teaching methods, while still providing motivation to students to continue to be improved their learning, especially reading skills. So from the results of this simple study it can be recommended that, the use of short stories in teaching comprehension reading with interactive approach models got a significant increase in students' ability in reading comprehension than conventional learning models.&#x0D; &#x0D; &#x0D; &#x0D;
Quantum state preparation of spin eigenstates including the Dicke states with generalized all-coupled interaction in a spintronic quantum computing architecture

There has been an extensive development in the use of multi-partite entanglement as a resource for various quantum information processing tasks. In this paper we focus on preparing arbitrary spin eigenstates whose subset contain important entangled resources like Dicke states as well as some other sub-radiant states that are difficult to prepare. Leveraging on the symmetry of these states we consider uniform pairwise exchange coupling between every pair of qubits. Starting from a product state of a given spin eigenstate with a single qubit state, another spin eigenstate can be prepared using simple time evolutions. This expansion paves a deterministic approach to prepare arbitrary Dicke states in linear steps. We discuss an improvement in this cost building up on a previous work for W states deterministic preparation in logarithmic circuit depth. The modified algorithm requires several iterations of pumping spin angular momentum into the system and is akin to the amplitude amplification in Grover search. As a use case to demonstrate the proposed scheme, we choose a system of non-interacting static spin qubits connected to a ferromagnetic reservoir. The flying qubits emerging from the reservoir locally interact with static qubits successively, mediating an in-direct exchange interaction between all the pairs.
Sejarah PLTA Lamajan Pangalengan Sebagai Situs Peninggalan Belanda di Kabupaten Bandung Tahun 1925

This study aims to determine how the history of the Lamajan Hydroelectric Power in 1925, both in terms of the geographical, demographic conditions of Pangalengan and also the components that still exist in Lamajan. Lamajan is a Dutch hydroelectric power plant (PLTA) which was built in 1924 in Pangalengan, Bandung and has been operating since 1925. Lamajan has three generator units, the engine used by Lamajan supplied from the Dutch factories Heemaf and Smit Slikkerveer, initiated by V.H Willem Smith &amp; Co. and R.W.H. Hofstede Crull. The method used in this study is a qualitative method, namely by collecting data through literature and documentation. This method is carried out through four stages namely, heuristics, criticism, interpretation, and historiography. The results of this study show this hydroelectric power plant was built during the Dutch colonial era in 1920-1924 and operated in 1925. This power plant was initially built by a Dutch engineer named Willem Beyerinc K. for the electricity needs of sugar factories but over time was used to illuminate the area of Bandung and its surroundings, this power plant utilizes the flow of water from the Cisarua and Cisangkuy rivers.