Reviewerly/exHarmony huggingface dataset:
Inconspicuous Perception: The Solution for the Neglected!

Building a web application is the main aim and focus that connects all the orphanages and the NGOs to one website. There are numerous shelters contemporaneously accessible that are as of now working for the prospering of road kids or Orphaned, Abandoned, Surrendered (OAS) youngsters yet it is highly unlikely that crosses over any barrier between the road kids, OAS, and the orphanages. The web application works in the way of the utilization of the logistic regression algorithm alongside image analysis and recognition to characterize the kid experienced living in the city or an OAS classification into either a Juvenile or Juvenile Delinquent. The prototype also takes care of the lost and found category. Despite the arrangement of the child and the lost and found category, there is one more part of the medical diagnosis. By taking the child’s data as the input, the web application will produce the result, wherein the necessary steps would be taken. The result would be either a Juvenile child or a Juvenile Delinquent. After taking a looking at the contemporaneous situation, thus advances a proposed solution “Inconspicuous Perception”—by the name itself “Inconspicuous” signifies something imperceptible or misty and “Perception” means the ability to become aware of something.
Readiness to Use Augmented Reality Solutions in Small and Medium Enterprises in Poland: A Survey

The article discusses the results of work to assess the readiness of Polish industrial companies to apply augmented reality technology in practice. As part of the work, a tool in the form of a survey was prepared, available online as a digital survey, and a mobile application. The survey contains several dozen questions divided into sections, and not only the technical readiness of a given company is assessed, but also the potential area of application of the Augmented Reality technique in its activities such as training, support for production and production-related processes and support for machine operation and maintenance. In addition to the development and implementation of the tool, it was also verified at the enterprises from the Wielkopolska region and neighboring regions. Survey results can be used by those enterprises to help them get ready to introduce Augmented Reality in their work. Gathered data alongside its analysis are presented in this paper.
Bridging the Digital Divide: Telephone Tutoring at the Center.

As many academic support centers transitioned from in-person to online from March 2020 onward, center professionals were looking for technology that would assist them to do the job right. In one Midwest, private, comprehensive university, students had already self-selected the phone as their answer to seeking writing assistance before the pandemic arrived. This essay will share findings from that study in the writing center as well as theorize why phone conversations make good sense in academic support centers. Writing centers and communication centers should adopt phone tutoring with online formats as a means to fully and justly connect with students, ensuring that diverse voices are heard and included and to enable all students to have an equitable experience amidst the digital divide.
Research on the Cultivation of Innovative Entrepreneurship Ability of Business English majors under the Background of Internet

With the development of social economy and Internet technology, the demand for business English professionals has improved significantly. Business English major is mainly to train applied high-quality technical talents for the society, so we should pay attention to the practical ability of the students. Under the background of the era of “Internet”, business English education should pay attention to the cultivation of students’ innovative and entrepreneurial ability, and explore more ways to cultivate the ability of combining innovative entrepreneurial practice with business English teaching.
Channel-Modulated Multibranch Convolutional Neural Network

Depth-wise convolution has become very popular recently, owing to research on efficient convolutional networks. However, it performs convolution in each channel separately, therefore, decreases the representational power of features. In this thesis, we take a systematic research on the architecture and shortcomings of depth-wise convolution. We observe that combining the benefits of multibranch depth-wise operation, shared channel modulation, and feature fusion brings us considerable improvements on both computation accuracy and convolution efficiency. This simple yet effective idea enables us to propose a novel channel-modulated multibranch convolution (CMMB-Conv). In our approach, we first use a multibranch depth-wise operation on the input feature maps to increase the channel width. Next, we gather the spatial information of feature maps from multiple branches through Max and Average Pooling layers. Followed by this, a shared hidden neural perceptron is employed to modulate the inter-channel relationship of the feature maps. Finally, we concatenate the multibranch-modulated feature maps and fuse them by using the point-wise convolution. Compared with feature maps extracted by the depth-wise separable convolution, the feature maps resulting from our CMMBConv have strong representation capability, improving the accuracy of existing MobileNets on both ImageNet and CIFAR classification. Extensive experimental results on the ImageNet2012 have shown that our CMMBConv can jointly improve accuracy and efficiency. Specifically, the Top-1 accuracy is increased by 3.3% to 73.91% in contrast with that of the depth-wise convolution. Meanwhile, the number of parameters and floating-point operations are reduced by 29.02% and 30.77%, respectively, compared with the standard convolution.
WISE: a computer system performance index scoring framework

Abstract The performance levels of a computing machine running a given workload configuration are crucial for both users and providers of computing resources. Knowing how well a computing machine is running with a given workload configuration is critical to making proper computing resource allocation decisions. In this paper, we introduce a novel framework for deriving computing machine and computing resource performance indicators for a given workload configuration. We propose a workload/machine index score (WISE) framework for computing a fitness score for a workload/machine combination. The WISE score indicates how well a computing machine is running with a specific workload configuration by addressing the issue of whether resources are being stressed or sitting idle wasting precious resources. In addition to encompassing any number of computing resources, the WISE score is determined by considering how far from target levels the machine resources are operating at without maxing out. Experimental results demonstrate the efficacy of the proposed WISE framework on two distinct workload configurations.
Design of Social Media-based Virtual Tour Through Personas

Coronavirus Disease 2019 (COVID-19) has been proven to spread faster than Flu viruses. Thus, as a form of safety precautions, the government throughout nations has to implement health protocols, such as physical distancing and nationwide lockdown. All activities, including work and school, have to be performed from home to minimize the virus's spreading, which triggers the influx of internet use. So do the Satellite Technology Center of the National Institute of Aeronautics and Space of Indonesia (LAPAN) uses the internet and social media to keep their open house activities well functional and has since turned it into a virtual tour. This research addresses the design of this virtual tour through personas. The personas are constructed following the literature study approach's design thinking method and followed by an online questionnaire shared with particular respondents. This research conduces a solution for a virtual tour by utilizing the virtual reality technology and social media platform that can be used by students conveniently. This study also showed how most of the kids nowadays already get used to technology and social networks to earn a lot of information fast and easy. Utilizing social media and the internet will be the solution to this pandemic situation. For not making teenagers get bored inside their house, providing this virtual tour might help them to keep learning in a fun way with the internet they are using.
Two Step Framework on Indoor Occupant Counting

Knowing the number of occupants of different zones in buildings, is crucial for intelligent buildings and building energy managing system. In this paper, a two-step framework on indoor occupant counting is proposed, where the region of interest (ROI) of the target image is obtained based on a cascade classifier in the first step, and the number of indoor occupant is estimated through classifying and recognizing the ROI area based on deep learning technologies in the second step. Finally, this system framework have been implemented and real experiments are conducted to verify its effectiveness. Results show that the average of more than 91% accuracy rate on the occupant number detection can be achieved by the partly background elimination, and the binary absence or presence prediction accuracy can reach more than 93%.
A hierarchical stacking extreme learning machine for multi-classification

Extreme learning machine (ELM) is an emerging single hidden layer feedforward neural network learning, whose hidden node parameters are randomly generated, and the output weights are computed by linear regression algorithms. This paper proposes a hierarchical stacking framework for ELM (HS-ELM), which is characterized by encoding the input features layer by layer with ELM, and the encoded features are passed forwards to realize the stacking of features. The network is divided into two parts, a stacking autoencoder and a final classifier. Stacking autoencoder allows inheritance of the original features without attenuating as the number of hidden layers increases. In the stacking autoencoder, a sparse training method based on LI norm constraints is used to make the network more compact. In addition, the bias and activation functions of the stacking autoencoder are appropriately streamlined to further reduce the training time of the network. The extracted features are finally classified using an ELM parallel structure with input to output layer connection. Experiments on three widely used classification datasets show that the proposed learning framework has a universal learning ability and obtains similar or higher accuracy than the existing state-of-the-art hierarchical ELM method.
On methodological approaches to develop educational programs in the school-university system taking into account continuity

The article objective is to define the basis of educational program continuity of higher pedagogical education and general secondary education in conditions of the secondary education renewal. Research Methodology. The authors carry out analytical and comparative study, analysis of philosophical, psychological, pedagogical, scientific and methodological literature, normative legal documents, forecasting, and synthesis. Results. The paper defines methodological approaches determining the basis of continuity of educational programs in the school-higher educational institution system; structural and substantive features of educational curricula of secondary education and higher education in the Republic of Kazakhstan; methodological basis of educational program design. To preserve and improve continuity in training teachers in natural science, higher educational institutions are offered recommendations to develop educational programmes and modular curricula based on the existing program study and analysis. The aurhors reveal didactic principles of ensuring continuity of educational programs of secondary and higher pedagogical education, and draw up a matrix for determining the content of education. Scientific novelty. The study is the first systematic work to ensure continuity of natural and scientific pedagogical education in the school-university system in frames of updating the content of secondary education. Practical importance. To improve the quality of training future teachers in natural science, recommendations are developed to improve the content of educational programmes based on continuity, which are the foundation to develop theoretical and methodological bases for ensuring the continuity of natural science education.
An Active and Adaptive Image Enhancement Method for Applications in Low-Light and Narrow Environment

The images in the low-light and narrow environment generally have the problems of non-uniform illumination, low contrast, and large random noise. Existing algorithms are mostly subject to the imaging effect of the original image, and are prone to failure in extremely weak light or strong reflective environments. This paper proposes an active and adaptive image enhancement method. First of all, this paper combines the distributed light source model and the difference in brightness between frames to complete active feedback fill light, which improves the quality of the input image. Then gamma correction, contrast limited adaptive histogram equalization and bilateral filter are designed to enhance image details and suppress noise amplification. Combined with fuzzy inference parameter self-adjustment method, algorithm adaptability is improved. Finally, through experimental verification, the method proposed in this paper can effectively enhance the images in the low-light and narrow environment.
Angle-based softmax loss for face verification

In the past, the design of the loss function for face verification mostly focused on adding different margins, ignoring the gradient of the loss function. Based on the gradient of different loss functions, we propose a loss function that has a better geometric interpretation and uses the angles between the feature vector and the weights of the fully connected layer as a variable to directly optimize the intra-class similarity and the inter-class similarity in this paper. Most importantly, our experiments show that the variable using angle as the loss function can have a more reasonable gradient and improve the accuracy of face recognition.
Control enhanced estimation of the driving strength in a superconducting circuit

In this paper, we consider the system where a superconducting qubit is coupled to a resonator acting as a harmonic oscillator. Unlike the existing work regarding estimation of the coupling strength or oscillator frequency, we aim to sense the amplitude of a microwave drive by utilizing the qubit as a quantum probe. Furthermore, in order to improve the estimation precision, we employ a periodic control protocol that is easy to be implemented in practice. Such improvement does not require any manipulation of the oscillator. The proposed strategy sheds light on a systematic study on estimation of the driving field in superconducting circuits by means of adding control.
Multi-Branch Network with Dynamically Matching Algorithm in Person Re-Identification

In person re-identification problem, learning distinctive features to differentiate one person from the others is one of the key factors to improve the result. In this paper, we propose the Multi-Branch Network with Dynamically Matching (MNDM) algorithm, which has a multi-branch deep network architecture consisting of three branches: one for learning global features, and two for local features. In the one branch learning local features, where the bounding box is split into horizontal stripes, the model applies a dynamically matching algorithm to efficiently matching these parts addressing the misalignment issue caused by imperfect detection bounding boxes. Several experiments are conducted on Market1501, CUHK03 and DukeMTMC. All the results that demonstrate the proposed model significantly outperforms the previous state-of-the-art.
Data Augmentation with Adversarial Autoencoders for the Clustering of Electromagnetic Interference Signals

The widespread application of telecommunication technologies urgently calls for an effective method to analyze the interferences in signals. To utilize machine learning algorithms to discern different types of EMI, several models have been proposed in the past, unfortunately almost all of which suffer from the scarcity of training data. In this paper, we propose a method to augment the training data set. With data generated by pre-trained adversarial autoencoders, models are enabled to perform better across multiple metrics.
Desain Kolam Retensi Berbantu Komputer di Cibuluh Kota Bogor

Water retention serves to control the surface runoff and conservation of rainwater in maintaining groundwater accumulation. This research aims to know the discharge of flood, know the volume of water to be accommodated in water retention, determine optimal water retention volume capacity, produce an effective design of water retention technical design, and produce a cost budget plan. Research conducted in Cibuluh Quarter, Tanah Baru Village, District Bogor Utara, Bogor City, West Java. Komulative flood discharge is 12.01 m3/sec. Capacity of Ciheuleut River is 9.38 m3/sec. Total capacity of water retention is 5272.52 m3. The Capacity development is 10481 m2. Water retention area that can be constructed is 4241 m2. The depth is 4 m so the capacity is 16964 m3. The structure is oval. The shortest diameter dimension is 50 m and the longest is 108 m. Total depth plan of 6 m is able to accommodate maximum 25446 m3 of water. Planned a type of sluice with width 1300 mm x height 1600 mm with a discharge of 4.89 m3/sec. It takes a total of 5 sluices on inlet and outlet. The cost required in the construction of Cibuluh Water Retention is Rp13.680.570.000,00.
Active Learning with Multi-granular Graph Auto-Encoder

Predictive modeling of networked data finds many real-world applications, such as fraud detection in social networks, drug discovery in biomedical networks, paper topic classification in citation networks, and so forth. Although the advanced machine learning approaches can help build reasonably accurate predictive models, their applicability is immensely hindered by the data labeling tasks, which are onerous, time-consuming, and error-prone. In this paper, we propose a novel active learning paradigm for networked data, named topology-and-content-aware (TACA) active learning, aiming to minimize the number of labels while achieving a desirable level of model accuracy. Overall, TACA advances existing works from two aspects: (1) TACA makes no assumption on the network property, whereas most existing works only perform effectively on a locally consistent network in which linked nodes are expected to share the same labels and (2) TACA generates queries without relying on model performance, thereby enjoying robust predictive results even when noises exist in the queried labels. Both theoretical and empirical evidences are presented, substantiating the effectiveness of and optimism our approach.
Fleet-Oriented Pattern Mining Combined with Time Series Signature Extraction for Understanding of Wind Farm Response to Storm Conditions

Offshore wind turbine installations are rapidly spreading around Europe and all over the world. These turbines are typically installed in large wind farms combining turbines of the same type. Farm owners target maximal performance of the farm in general and particularly predictability of behaviour. The latter is getting increasingly important since offshore wind farms are being managed more and more as conventional power plants driven by the electricity market supply and demand considerations. The context of zero subsidy farms exposes farm operators to fluctuations in electricity market prices. As such, deep understanding of farm behaviour is essential to come up with a good strategy to deal with these fluctuations. This paper focusses on the automated extraction of farm-wide response to storm conditions. The input data for the analysis are status logs and SCADA 1-second data. The status logs record the important turbine controller events. Typically, they consist of a number, a time of occurrence, and a time of de-activation. The number is linked to a detailed description. The SCADA data consists of time series of the most important sensors in the turbine: power produced, RPM, wind speed,… The advantage of the 1-sec data over the traditional 10-minute averages is that the dynamic event content is much more preserved. Data of several offshore wind farms is used in the analysis to have a solid dataset. In total, 5 years of data of more than 50 turbines is used. We show a novel farm-wide pattern mining approach that extracts events occurring for multiple turbines in the same time period. This allows us to identify those events that are predominantly driven by global wind excitations (e.g., gusts) or grid events (e.g., low voltage ride through). From the extracted events, we lift out the storm conditions. For these conditions, a further investigation of the time series data is done. Using event detection algorithms, we extract the signatures of the stop events that each turbine is performing from the time series data. We show that the extreme change in wind speed and wind direction leads to an excessive misalignment of the turbines in the farm, followed by a stop of those turbines. The extracted patterns are compared to the time signatures to show their correlation and complementarity. As such, the typical turbine response to this event is identified. This can serve as input for identification of novel controller approaches by the farm owner and turbine manufacturer to deal with this problem.
A study of prime numbers distribution based on support vector domain description

Prime numbers are fundamental to the most common type of asymmetric encryption used today, namely Diffie-Hellman key exchange, Rivest-Shamir-Adleman cryptosystems, ElGamal Cryptosystem, Elliptic curve cryptosystems, etc. The strength of primes lies in the fact that they are the ‘building blocks’ of mathematics and in addition there is infinity of them with no apparent regularity in their distribution. Given these characteristics, they were used to build efficient one way functions used in public key cryptography. This work aims at exploring sets of prime numbers with the objective of finding patterns in their structure. For this purpose an efficient supervised learning model called Support Vector Domain Description was used as an explorer. The experimental results have shown a good generalization capability of this model to describe new prime numbers.
Automatic Transcription and Captioning System for Bahasa Indonesia in Multi-Speaker Environment

Compared to the more established languages, such as English, Bahasa Indonesia, which is still considered a low-resource language, remains deficient in terms of communication-assisting technology development. This research paper proposes a new method for automatically transcribing simultaneous speech in Bahasa Indonesia. The proposed method could be used as an assistive tool in situations that involve simultaneous speech, such as online discussions and remote conferences. The proposed method uses pitch-aware gain-based speech separation to distinguish the speech between speakers, and a recurrent neural network (RNN) is used to generate a transcription of the speech. This method can detect and transcribe a mixed speech signal of up to three speakers and demonstrates enhanced performance in single-speaker situations compared to the baseline method.