Reviewerly/exHarmony huggingface dataset:
Photon number resolvability of multi-pixel superconducting nanowire single photon detectors using a single flux quantum circuit

Superconducting nanowire single-photon detectors (SNSPDs) are typical switching devices capable of detecting single photons with almost 100% detection efficiency. However, they cannot determine the exact number of incident photons during a detection event. Multi-pixel SNSPDs employing multiple read-out channels can provide photon number resolvability (PNR), but they require increased cooling power and costly multi-channel electronic systems. In this work, a single-flux quantum (SFQ) circuit is employed, and PNR based on multi-pixel SNSPDs is successfully demonstrated. A multi-input magnetically coupled DC/SFQ converter (MMD2Q) circuit with a mutual inductance M is used to combine and record signals from a multi-pixel SNSPD device. The designed circuit is capable of discriminating the amplitude of the combined signals in accuracy of Φ 0 / M with Φ 0 being a single magnetic flux quantum. By employing the MMD2Q circuit, the discrimination of up to 40 photons can be simulated. A 4-parallel-input MMD2Q circuit is fabricated, and a PNR of 3 is successfully demonstrated for an SNSPD array with one channel reserved for the functional verification. The results confirm that an MMD2Q circuit is an effective tool for implementing PNR with multi-pixel SNSPDs.
Experimental Investigation on the Analysis of Three Data Imputation Methods for Cervical Cancer Risk Prediction Algorithms

Missing data values in medical datasets are a recurring problem, usually attributed to input errors, lack of proper documentation and patient’s privacy concerns. This hinders the work of researchers in conducting predictive analysis on these datasets. Cervical cancer risk prediction algorithms employ machine learning, and deep learning algorithms to identify patients at the risk of cervical cancer. Currently researched algorithms for predicting the risk of cervical cancer incorporate various data imputation methods to address missing values, however the effectiveness of these techniques in improving the performance of these algorithms is often not investigated in detail. This paper details an experimental investigation on the performance of three commonly utilised data imputation techniques employed in two cervical cancer risk prediction algorithms based on a Multilayer Perceptron Network (MLPN) and a Random Forest classifier (RF). Findings reveal that for the algorithm based on an RF classifier, mean substitution will yield a high accuracy but does not improve the classification accuracy. For the algorithm based on an MLPN, either mean, mode or median substitution will improve the classification accuracy. The use of effective data imputation methods can improve the classification accuracy of cervical cancer risk prediction algorithms, and in turn improve the diagnosis of the disease.
Performative poetry as program, programmed poetry as performance

Innovative forms of poetry such as language art and experimental poetry have adopted intersemiotic processes and linguistic constraints in their codification, promoting rereading and transgression of technical apparatuses from the techno-capitalist societies. In addition, new inventive forms of digital language art, such as electronic poetry, have integrated generativity, indeterminacy and combinatorics in their programming, stimulating the reinvention and subversion of computing mechanisms from the networked societies. Through the appropriation of media, poetry articulates creative strategies that question the interface between arts and languages, forms and mechanisms. The experimental attitude of performative poetry presents itself as a program. Similarly, using the potentialities of the computer as a semiotic machine, programmed poetry can be regarded as performative, producing unique, unrepeatable events. Poetry and performance can thus be regarded as floating entities, promoting the transition from a fixed order to an economy of circulations. This scenario fuels the discussion concerning the connections between poetry, performance and program, supporting our reflections through the presentation and description of different examples of poetic intervention in the Portuguese context, which use programming and coding to reconfigure the awareness of language and mediation.
PERENCANAAN PENGUKURAN DAYA TERBANGKIT PADA SISTEM PEMBANGKIT LISTRIK TENAGA SURYA ATAP DENGAN KWH EKSPOR IMPOR PADA AREA PERUMAHAN HOMENESTY RESIDENCE SINGKAWANG PROVINSI KALIMANTAN BARAT

Electrical energy is important for all people especially for residential sector,offices,and industrial area. To generate this electricity there are several types of plants, which is solar power plant can maximize the use of clean energy,reduce of using fuel, and optimalize fuel by using renewable energy the utilization of natural gas and new energy. Along development of increasing capacity of noncommercial photovoltaic power energy system, there are named rooftop photovoltaic. Many PLN customers have using this system in last June 2019 from residential sector,business sector, and industrial area,and then government. Rooftop photovoltaic have benefit for reducing regular bills of PLN customers with the calculation of export import kWh required in the Minister of Energy and Mineral Resources Regulation No. 49 of 2018, and give the community to maximize using renewable energy. in this planning of rooftop photovoltaic at Homenesty Residential based on 36 house type with capacity 1,9 kWp which supply energy 2.764,464 kWh and save PLN billing until Rp 2.636.557,84 and 45 house type with capacity 3,04 kWp which supply energy 4.648,679 kWh and save PLN billing until Rp 4.433.593,96
Baby Expression Recognition System Design and Implementation Based on Deep Learning

With the increasing demand for social security, the performance of expression recognition are getting important for our social life. However, current expression recognition technology has a poor performance in accuracy and speed, especially in the challenge of baby expression recognition. In this paper, we propose a method for baby expression recognition, and design and implement baby expression recognition system bases on a deep learning model. In the system, we build a convolutional neural network model and train a baby expression dataset. During the method, we use a forward propagation neural network, which can loads a picture directly and output the recognition result. The method also provides various functions of baby expression recognition. At last, the experiment shows that our method can balance the requirements of accuracy and speed perfectly.
A Study on Awareness towards Digital Literacy Among Women Entrepreneur in Virudhunagar, Tamilnadu.

Digital literacy is a knowledge about usage of digital sources. Digital literacy defined as skill based on technology. Digitalisation in India increases transparency in transaction and it enables the entrepreneur to carry out their business activity using technology. Women entrepreneur is a great economic source in India. Digital knowledge among women entrepreneur is essential to sustain in competitive market. In this study 110 women entrepreneur in virudhunagar has been selected for this study using simple random sampling method. Data collected with help of interview schedule. SPSS package is used to analyse data. Demographical detail of the respondent has significant relationship between awareness level towards digital sources.
Attentive fine-tuning of Transformers for Translation of low-resourced languages @LoResMT 2021

This paper reports the Machine Translation (MT) systems submitted by the IIITT team for the English→Marathi and English⇔Irish language pairs LoResMT 2021 shared task. The task focuses on getting exceptional translations for rather low-resourced languages like Irish and Marathi. We fine-tune IndicTrans, a pretrained multilingual NMT model for English→Marathi, using external parallel corpus as input for additional training. We have used a pretrained Helsinki-NLP Opus MT English⇔Irish model for the latter language pair. Our approaches yield relatively promising results on the BLEU metrics. Under the team name IIITT, our systems ranked 1, 1, and 2 in English→Marathi, Irish→English, and English→Irish respectively. The codes for our systems are published1 .
SideControl: Controlled Open-domain Dialogue Generation via Additive Side Networks.

Transformer-based pre-trained language models boost the performance of open-domain dialogue systems. Prior works leverage Transformer-based pre-trained language models to generate texts with desired attributes in two general approaches: (1) gradient-based methods: updating all latent representations of pre-trained models with gradients from attribute models; (2) weighted-decoding methods: re-ranking beam candidates from pre-trained models with attribute functions. However, gradient-based methods lead to high computation cost and can easily get overfitted on small training sets, while weighted-decoding methods are inherently constrained by the low-variance high-bias pre-trained model. In this work, we propose a novel approach to control the generation of Transformer-based pre-trained language models: the SideControl framework, which leverages a novel control attributes loss to incorporate useful control signals, and is shown to perform well with very limited training samples. We evaluate our proposed method on two benchmark open-domain dialogue datasets, and results show that the SideControl framework has better controllability, higher generation quality and better sample-efficiency than existing gradient-based and weighted-decoding baselines.
The Great Acceleration

From the first transistor to deep learning networks, the rise of computing power over the last 50 years has been so phenomenal it's changed everything from the way we communicate to how our applian...
The Role of Electronic Publications in Socialization of Information in Turkey

As a result of the development of technology and the advancement of the internet and communication technologies, computer and internet has started to be used effectively in every sphere of our lives after 1990, the widespread use of the internet and the WWW (World Wide Web), have brought about a new type of publishing termed as “electronic publishing”. At the beginning of 1995, electronic publishing started to take an essential step in the life of press and publishing. Thus, the internet has become the most critical tool for offering information to people. Electronic publishing has introduced new regulations in education, trade, information centers, and many social activities of life. As a result of the use of communication technologies in every segment of society, individuals have begun to show the tendency to use the electronic form of resources. Many institutions prefer to use electronic resources rather than the print version in Turkey, in the information centers, and aimed at benefit from the advantages of electronic resources. Newspapers, journals, and encyclopaedias began to be published electronically; the electronic resource that the society first applied to as the primary source of access to information became indispensable. In this study, the period which started to be used the Internet from 1993 to present in Turkey, it was evaluated that the purpose of electronic production publishing, the reason for using them, its undertaken role to be delivered of information and field of influence in Turkey. In this study, the chronological and the description methods were used, and also the current situation was analyzed with the statistical data.
Understanding Guided Image Captioning Performance across Domains

Image captioning models generally lack the capability to take into account user interest, and usually default to global descriptions that try to balance readability, informativeness, and information overload. We present a Transformer-based model with the ability to produce captions focused on specific objects, concepts or actions in an image by providing them as guiding text to the model. Further, we evaluate the quality of these guided captions when trained on Conceptual Captions which contain 3.3M image-level captions compared to Visual Genome which contain 3.6M object-level captions. Counter-intuitively, we find that guided captions produced by the model trained on Conceptual Captions generalize better on out-of-domain data. Our human-evaluation results indicate that attempting in-the-wild guided image captioning requires access to large, unrestricted-domain training datasets, and that increased style diversity (even without increasing the number of unique tokens) is a key factor for improved performance.
Responding to cybercrime: Perceptions and need of Australian police and the general community

This research examines perceptions of cybercrime among police officers, community members and cybersecurity experts in Australia. It examines how these groups conceptualise the problem of cybercrime and where there is disagreement, as well as identifying opportunities for improvement to both officer capabilities and community awareness about cybercrime risk and prevention. The research draws from a survey of police officers based in Queensland, New South Wales and Tasmania; a survey of adult members of the general Australian community; and a focus group with cybersecurity experts. The research suggests that police officers and community members are engaged in an ongoing negotiation about their responsibilities in cybercrime investigations and prevention programs. It affirms that an officer’s preparedness to investigate cybercrime correlates with their levels of education, previous training, and degree of professional exposure to cybercrime investigations. It suggests that life experiences and sociodemographic characteristics influence perceptions. of cybercrime among both police officers and community members, and notes disagreement between police officers and community members about the public’s perception of risk related to cybercrime. Overall, the research advocates several recommendations for improving understandings of and responses to cybercrime in Australia
Work-in-Progress: Improving Fault Tolerance of DNNs through Weight Remapping based on Gaussian Distribution

In this paper, we approach to improve the fault tolerance of Deep Neural Networks (DNNs) for safety-critical artificial intelligent applications. We propose to remap the range of 32-bit float to weights to reduce the influence of invalid weights caused by bit-flip faults. From preliminary experiments, we observe that weakening bit-flip faults which make positive weights larger can help to improve the reliability of DNNs. Then, we propose a gaussian distribution based mapping method to prevent weights from being influenced by bit-flip faults, in which a novel function is formulated to remap the relation between 32-bit float and the values of weights. Extensive experiments demonstrate that our approach can improve the accuracy of VGG16 from 13.5% to 80.5%, which is better than the other six tolerance approaches of DNNs.
Incorporating pedagogical theory into VR to teach civil engineering

Purpose – The end of the Renaissance period in Europe saw civil engineering (CE) tuition develop from a master-apprentice relationship to the current classroom-based learning environment. This evolution allowed expertise to develop, but also created a situation where students could graduate from an engineering course without ever spending time on a construction site. The implementation of virtual field trips utilising virtual reality (VR) in CE education is a crucial development that can provide experiential learning to address this and can facilitate the consolidation of abstract theories into tangible competences. VR is uniquely able to solve a fundamental CE education problem: once a structure has been completed it is often impossible to see how it was built; hence, how can you explain the construction process to a student?Design/methodology/approach – This research used the opportunity of a new campus library being built to record the construction stages. Researchers visited the site multiple times (starting from the construction of the building foundations), using both an Insta360 Pro stereoscopic camera and the GoPro Fusion to take photos and videos of the construction process, respectively. GoPro Fusion Studio was used to process the videos and the Insta360 Pro Stitcher was used to process photos. Unfortunately, many pedagogical VR projects do not result in worthwhile educational experiences for the user because attention is spent on the VR aspect of the project due to its novelty. By its nature, utilising VR as a didactic tool facilitates experiential learning, but this project will incorporate discovery learning and situated cognition to develop students’ understanding of the construction process by being able to both (virtually) move around the construction site, and move backwards and forwards through the chronological constructional sequence. In addition to its pedagogical value, this learning resource will be made available to all students in future years. Findings – The use of VR in education is becoming increasingly common, but the explicit pedagogy utilised by these environments is rarely obvious or stated. This research draws upon current VR education discussions (Johnston et al., 2018) and explores the development of a VR environment with a pedagogical context. The goal is to create a VR learning environment for students that has pedagogical value and is not only of interest for its novelty value. Originality/value/implications – The development of the resource draws upon the pedagogical frameworks of discovery learning (Bruner, 1961) and situated cognition (Lave & Wenger, 1991). A unique aspect of this research is the implementation of pedagogic theories into a VR environment to create an educational resource.
PENERAPAN MODEL EXAMPLE NON EXAMPLE UNTUK MENINGKATKAN KETERAMPILAN MENULIS TEKS BERITA PADA SISWA KELAS VIII-A SMP SWASTA IMELDA MEDAN TAHUN PELAJARAN 2018/2019

This study aims to describe the application of an example non example model to improve news text writing skills. The implementation of this research is based on the problems that exist in learning to write news text on students who are still relatively low. The method used in this study is descriptive qualitative with a class action approach that describes the skills of writing news texts to students in a classical manner and the skills of writing news texts through example non exampled models in the form of implementing two cycles consisting of planning, action, observation, and reflection . The subject of this study was conducted with purposive sample on 32 students of class VIII-A Imelda Private Middle School in Medan. The results showed that students' news text writing skills improved after applying the example non example model. Viewed from the percentage of classical completeness of students in the first cycle 53.12% increased in the second cycle 87.5%. So that this study can be concluded that the application of the example non exampled model can improve news text writing skills in class VIII-A Imelda Private Middle School in Medan.
Design Principle and Method of Lightweight Block Cipher Diffusion Layer

Recently, due to the development and popularization of communication and computing technologies, new technologies have emerged one after another. The Internet of Things (IoT) is a state-of-the-art technology that is applicable to all fields. The rapid development of the IoT seriously threatens the security of restricted devices. Lightweight block ciphers, as the main security measures for restricted devices, have attracted the attention of more and more researchers. The diffusion layer is an important part of the lightweight block cipher. Its design not only affects the security of lightweight block ciphers, but also has an important impact on its efficiency in hardware and software implementation. In this paper, we research and analyze the design principle and method of the lightweight block cipher diffusion layer. In addition, combined with the characteristics of lightweight cipher and its application environment, we point out that the diffusion layer of lightweight cipher is worthy of further research in the future.
SAR Remote Sensing Image Ship Detection Method NanoDet Based on Visual Saliency

In the Synthetic Aperture Radar (SAR) remote sensing image, ships are visually significant targets on the sea surface. Because they are made of metal, thus the backscatter is strong, while the sea surface is smooth and the backscatter is weak. However, the large-width SAR remote sensing image has a complicated sea background, and the features of various ship targets are quite different. To solve this problem, a SAR remote sensing image ship detection model called NanoDet is proposed. NanoDet is based on visual saliency. First, the image samples are divided into various scene categories using an automatic clustering algorithm. Second, differentiated saliency detection is performed for images in various scenes. Finally, the optimized lightweight network model, NanoDet, is used to perform feature learning on the training samples added with the saliency maps, so that the system model can achieve fast and high-precision ship detection effects. This method is helpful for the real-time application of SAR images. The lightweight model is conducive to hardware transplantation in the future.This study conducts experiments based on the public data set SSDD and AIR-SARship-2.0, and the experiments results verify the effectiveness of our approach.
신병교육시「Why 캠페인」효과성에 관한 연구 :육군훈련소 훈련병을 중심으로

The purpose of this study is to verify the effectiveness of the core values(inner motives, creativity, service values) enhancement through an application of「Why campaign」. For this, we analyzed the effects by AHP. And we verified the change of the core values through the T-test. In addition, we confirmed effectiveness of the learning stage by questionnaire analysis, chi-square test and cross tabulation analysis. Last, we verified probabilities of passing through the chi-square test. As a result of this study, core values were improved 1.99 times compared to past. In addition, experts and the trainee group got positive effect on each stage of learning. And we found that the grenade, individual battle and TI&E were improved by「Why campaign」. This study proved that「Why campaign」has a positive effect in improving the inner motives, creativity, and service value of new recruits. Based on this result, We wish to extend「Why campaign」to other recruits unit so that the new recruits can have the worth of military service.
Zero Time Waste: Recycling Predictions in Early Exit Neural Networks

The problem of reducing processing time of large deep learning models is a fundamental challenge in many real-world applications. Early exit methods strive towards this goal by attaching additional Internal Classifiers (ICs) to intermediate layers of a neural network. ICs can quickly return predictions for easy examples and, as a result, reduce the average inference time of the whole model. However, if a particular IC does not decide to return an answer early, its predictions are discarded, with its computations effectively being wasted. To solve this issue, we introduce Zero Time Waste (ZTW), a novel approach in which each IC reuses predictions returned by its predecessors by (1) adding direct connections between ICs and (2) combining previous outputs in an ensemble-like manner. We conduct extensive experiments across various datasets and architectures to demonstrate that ZTW achieves a significantly better accuracy vs. inference time trade-off than other recently proposed early exit methods.
БЎЛҒУСИ ПЕДАГОГЛАРДА ЎЗ УСТИДА ИШЛАШ ФАОЛИЯТИ КАСБИЙ КОМПЕТЕНТЛИКНИ ОШИРИШ МЕЗОНИ СИФАТИДА

The item focuses on the content of the phenomenon of professional competence, which is considered very important for the practical activities of future teachers, as well as the specificity of practical activities with a set of motivational actions of the teacher in their field