Reviewerly/exHarmony huggingface dataset:
Recognizing Tiny Faces

Objects are naturally captured over a continuous range of distances, causing dramatic changes in appearance, especially at low resolutions. Recognizing such small objects at range is an open challenge in object recognition. In this paper, we explore solutions to this problem by tackling the fine-grained task of face recognition. State-of-the-art embeddings aim to be scale-invariant by extracting representations in a canonical coordinate frame (by resizing a face window to a resolution of say, 224x224 pixels). However, it is well known in the psychophysics literature that human vision is decidedly scale variant: humans are much less accurate at lower resolutions. Motivated by this, we explore scale-variant multiresolution embeddings that explicitly disentangle factors of variation across resolution and scale. Importantly, multiresolution embeddings can adapt in size and complexity to the resolution of input image on-the-fly (e.g., high resolution input images produce more detailed representations that result in better recognition performance). Compared to state-of-the-art "one-size-fits-all" approaches, our embeddings dramatically reduce error for small faces by at least 70% on standard benchmarks (i.e. IJBC, LFW and MegaFace).
The Use of Static Bayesian Networks for Situational Modeling of National Economy Competitiveness

The article is devoted to the use of Bayesian networks to assess the impact of a complex of regional economic indicators on the competitive ability of the national economy. The paper shows that Bayesian networks (BN) allow us to identify the probabilistic dependence of the structure of economic indicators, which also include innovation and investment indicators of the regions, which can be analyzed in more detail using observations and sensitivity analysis of BN. In this study, it is shown how the multidimensional nature of the relationship with economic, investment-innovative and competitiveness indicators can be used as a decision support tool for politicians in the formation of strategic recommendations to improve the country's economic level.
Why Squashing Functions in Multi-Layer Neural Networks

Most multi-layer neural networks used in deep learning utilize rectified linear neurons. In our previous papers, we showed that if we want to use the exact same activation function for all the neurons, then the rectified linear function is indeed a reasonable choice. However, preliminary analysis shows that for some applications, it is more advantageous to use different activation functions for different neurons - i.e., select a family of activation functions instead, and select the parameters of activation functions of different neurons during training. Specifically, this was shown for a special family of squashing functions that contain rectified linear neurons as a particular case. In this paper, we explain the empirical success of squashing functions by showing that the formulas describing this family follow from natural symmetry requirements.
Japanese Recipe Interpretation for Motion Process Generation of Cooking Robot

This paper presents a method to generate actual motions and its procedure for cooking robot equipped one robotic arm by interpreting Japanese letter recipes posted on general cooking-website for human-cooking. The cooking-recipes obtained from those website includes not only the material of cooking and the procedure of the cooking but also several redundancies such as impressions of the author. Furthermore the recipe includes a pre-processing method, but it is not written explicitly. Therefore, words in the recipe are classified, encoded and rearranged properly then motion codes which are suitable for the movements of the arm and procedures in according to the intention of the author are generated. The feasibility of the method was confirmed by simulator and actual machine experiments.
Energy-Saving Resource Allocation in Cloud Data Centers

As the demands of cloud computing intensify, more facilities running power-intensive servers are necessary for cloud data centers. Such data center servers consume a large amount of energy, resulting in high operating costs. Therefore, high energy consumption has become a growing problem for cloud data centers. This paper proposes two energy-saving resource allocation algorithms that take into account several energy efficiency factors: resource allocation policies, power management techniques, and power models for better energy management in cloud data centers. These two algorithms are implemented on CloudSim toolkit and evaluated with real-world workload data. By the experimental evaluation of these algorithms for competitive analysis of energy consumption, we found that this work contributes to save the energy consumption in cloud data centers.
Finding Possible Promoter Binding Sites in DNA Sequences by Sequential Patterns Mining With Specific Numbers of Gaps

Identifying motifs in promoter regions is crucial to our understanding of transcription regulation. Researchers commonly use known promoter features in a variety of species to predict promoter motifs. However the results are not particularly useful. Different species rarely have similar features in promoter binding sites. In this study, we adopt sequence analysis techniques to find the possible promoter binding sites among different species. We sought to improve the existing algorithm to suit the task of mining sequential patterns with specific number of gaps. Moreover, we discuss the implementation of proposed method in a distributed environment. The proposed method finds the transcription start sites (TSS) and extracts possible promoter regions from DNA sequences according to TSS. We derived the motifs in the possible promoter regions, while taking into account the number of gaps in the motifs to deal with unimportant nucleotides. The motifs generated from promoter regions using the proposed methodology were shown to tolerate unimportant nucleotides. A comparison with known promoter motifs verified the efficacy of the proposed method.
Weighted Mean Squared Deviation Feature Screening for Binary Features

In this study, we propose a novel model-free feature screening method for ultrahigh dimensional binary features of binary classification, called weighted mean squared deviation (WMSD). Compared to Chi-square statistic and mutual information, WMSD provides more opportunities to the binary features with probabilities near 0.5. In addition, the asymptotic properties of the proposed method are theoretically investigated under the assumption log p = o ( n ) . The number of features is practically selected by a Pearson correlation coefficient method according to the property of power-law distribution. Lastly, an empirical study of Chinese text classification illustrates that the proposed method performs well when the dimension of selected features is relatively small.
Improve Understanding and Dissemination of Disaster Management and Climate Change by Using Knowledge Management Systems

Abstract In Law Number 24 Year 2007 concerning Disaster Management, it is stated that the central government and regional governments are responsible for implementing disaster management, with an emphasis on preparedness and mitigation in dealing with natural disasters, as well as based on the provisions of article 1 of Presidential Regulation No. 46 of 2008 concerning the National Council on Climate Change that climate change is a change in average conditions of climate and / or climate diversity from one period of time to another as a result of human activity. However, the socialization of these two matters is not regulated in existing laws and regulations, so that the information dissemination plan and dissemination of the information must be carried out to the community itself. In line with developments in technology and information systems that have entered the era of media convergence, the acceleration of the process of spreading government programs can also be done by using this technology. The SECI method model (Socialization, Externalization, Combination, Internalization) was developed through implementation based on the ‘Knowledge Management System Cycle’ theory, so that a knowledge management system was formed. The results of research on community preparedness in the application of Knowledge Management Systems (KMS) for natural disaster preparedness and mitigation can be used to support the dissemination and dissemination of disaster management mitigation, global warming, and community-based climate change, including performance measurement of success.
Distributed fine-tuning of CNNs for image retrieval on multiple mobile devices

The high performance of mobile devices has enabled deep learning to be extended to also exploit its strengths on such devices. However, because their computing power is not yet sufficient to perform on-device training, a pre-trained model is usually downloaded to mobile devices, and only inference is performed on them. This situation leads to the problem that accuracy may be degraded if the characteristics of the data for training and those for inference are sufficiently different. In general, fine-tuning allows a pre-trained model to adapt to a given data set, but it has also been perceived as difficult on mobile devices. In this paper, we introduce our on-going effort to improve the quality of mobile deep learning by enabling fine-tuning on mobile devices. In order to reduce its cost to a level that can be operated on mobile devices, a light-weight fine-tuning method is proposed, and its cost is further reduced by using distributing computing on mobile devices. The proposed technique has been applied to LetsPic-DL, a group photoware application under development in our research group. It required only 24 seconds to fine-tune a pre-trained MobileNet with 100 photos on five Galaxy S8 units, resulting in an excellent image retrieval accuracy reflected a 27–35% improvement.
A Hybrid Demosaicking Algorithm for Area Scan Industrial Camera Based on Fuzzy Edge Strength and Residual Interpolation

Area scan cameras are widely used in industrial manufacturing for quality controlling, production monitoring, or defect inspection. The area scan cameras are normally integrated in a single sensor covered by the Bayer color filter array for RGB signal capturing, and the raw image with one channel for each pixel is obtained in one exposure. Therefore, it is significant to reconstruct the other two channels at each pixel which is called demosaicking. In this article, one hybrid demosaicking algorithm is proposed based on fuzzy edge strength and residual interpolation (RI) method. We first demosaic the G channel with one edge-directed filter and refine the high textured details by using fuzzy edge strength interpolation. Then, the RI is employed to calculate R and B channels, where G is used as the guide image for computing residuals. In the experiment, we evaluate our proposed method with nine prevalent interpolation-based algorithms on the IMAX and Kodak image datasets. The experimental results demonstrate that the proposed algorithm outperform the others by synthesizing the performance in peak signal-to-noise ratio, structural similarity, visual comparison, and computational time.
Approximate Multiply-Accumulate Array for Convolutional Neural Networks on FPGA

Convolutional Neural Networks (CNNs) have been widely used in many computer applications. The growth in deep neural networks and machine learning applications has resulted in the state-of-the-art in CNN architectures becoming more and more complex. Millions of multiply-accumulate (MACC) operations are needed in this kind of processing. To deal with these massive computing requirements, accelerating CNNs on FPGAs has become a viable solution for balancing power efficiency and processing speed. In this paper, we propose an approximate high-speed implementation of the convolution stage of a CNN computing architecture, the Approximate Multiply-Accumulate Array. Compared with the traditional multiply-accumulate operation, this implementation converts multiplications into additions and systolic accumulate operations. A key feature is the logarithmic addition with iterative residual error reduction stages which, in principle, allows to trade off power, area and speed with accuracy through for specific data using different configurations. Here, we present experiments where we configure the approximate multiplier in different ways, changing number of iteration stages as well as the bit width of the data and investigate the impact on overall accuracy. In this paper we present initial experiments evaluating the architecture's error using random input data, and Sobel Edge detection is used to investigate the proposed architecture with regard to its use in image-processing CNNs. The experimental results show that the proposed approximate architecture is up to 10.7% faster than a competitive FPGA implementation of an exact multiplier when running the convolution kernel over a test image, and that residual errors after two iterations reach 1.6% for 8-bit inputs and 0.001% for 12-bit inputs on average, based on 10,000 random samples.
Detection of genuine N-Qubit W state, GHZ state and Twin-Fock state via Quantum Fisher information

We study the entanglement detection of three classes of state in the white noise model: N-qubit W state, Greenberger-Horne-Zeilinger state and Twin-Fock state. Taking advantage of Quantum Fisher information, we first present how to witness M-separable W state (M≤N) in the ideal situation, and then derive the criteria for detecting genuine W state, Greenberger-Horne-Zeilinger state and Twin-Fock state in the noisy model. Meanwhile, the bounds for M-separable W state, k-partite entangled GHZ state and k-partite entangled Twin-Fock state are also investigated. The results show that with the increase of the number of particles, the requirement of visibility V for genuine multipartite entanglement becomes more and more stringent, while it is relaxed for partial entanglement. Specially, an extremum value of VWN=1/3 is found in the limit of large N.
Cultural Tourism Web Service via Augmented Reality for Public Relations in Prachuapkhirikhan Province

This research aims to develop a Cultural Tourism Web Service via Augmented Reality for Public Relations in Prachuapkhirikhan Province and to evaluate the performance of the system the satisfaction of tourists about the system. The Cultural Tourism Web Service System was developed using WordPress program. The cultural tourist attractions in Prachuapkhirikhan province was presented via Augmented Reality. A questionnaire for evaluating system performance was considered by three experts choosing by the purposive sampling method, and a questionnaire for examining the satisfaction with the system was carried out by 400 tourists selected using a random sampling method. The statistical data analysis was used to calculate the mean and standard deviation. The results indicated that the developed system with an Augmented Reality web service is able to present information concerning cultural tourism in a new and interesting platform. The overall system performance showed a high level, and the tourist satisfaction with the system exhibited the highest level.
Image and Depth Estimation with Mask-Based Lensless Cameras

Mask-based lensless cameras replace the lens by placing a fixed mask on top of an image sensor. These cameras can potentially be very thin and even flexible. Recently, it has been demonstrated that such mask-based cameras can recover light intensity and depth information of a scene. Existing depth recovery algorithms either assume that the scene consists of a small number of depth planes or solve a sparse recovery problem over a large 3D volume, and lose robustness to complicated scenes consisting of varying depth surface. In this paper, we propose a new approach for depth estimation based on alternating gradient descent algorithm that jointly estimates the continuous depth map and light distribution of a scene. The computational complexity of the algorithm scales linearly with the spatial dimension of the imaging system. We present simulation results on image and depth reconstruction for standard 3D test scenes. The comparison between the proposed algorithm and other method shows that our algorithm is faster and more robust for natural scenes with a large range of depths.
Application of Summarization and Sentiment Analysis in the Tourism domain

Text summarization helps in reducing the size of a text while preserving its information content. Text Summarization can be derived as shortening the source text in to a version that it's information content and overall meaning is preserved. It is very difficult for human beings to manually summarize large documents of text. These Text summarization methods can be classified into two broader methods such as extractive summarization and abstractive summarization. The prior method consists of selecting important sentences; paragraphs etc. from the source document and make a brief version of it by combining them. The latter method consists of understanding the overall meaning embedded in the original text and re-generates the content briefly. Sentiment analysis on the other hand is the process of computationally identifying and categorizing opinions expressed in text to clarify someone's' attitude towards a topic is positive or else negative or even neutral. In this paper, a Survey of Summarization and sentiment analysis and their application in the tourism domain has been presented.
Results between STEM and non-STEM Teaching for Integral Learning in Primary School Children in Lima (Peru)

This paper presents a comparative analysis of two private primary schools located in Lima Norte (north of Lima, Peru), in one of them students are educated under the STEM methodology and in the other no. The objective of this comparison is to determine if the STEM favors the development of integral learning skills (critical thinking, communication, collaboration, creativity and research). For this, an investigation of quantitative, exploratory and descriptive approach was carried out. The sample is made up of 52 students; of them, 27 (51.9%) were educated with STEM methodology and 25 (48.1%) were not. The results revealed that students with STEM present high means and significant differences both in critical thinking (mean= 4.78, p= 0.008; <; 0.05), in communication and collaboration (mean= 4.52, p= 0.001; <; 0.05), and in creativity and research (mean= 4.93,p= 0.000; <; 0.05).
Atlas: A First Step Toward Multipath Validation

As an indispensable feature for future secure Internet, path validation verifies whether packets follow specified paths. Existing solutions, however, cannot apply to multipath routing with practical efficiency. Multipath routing may proliferate an exponential scale path choice and the source may not know which path will be followed by a packet as a priori knowledge. In this paper, we design and implement Atlas as the first protocol for efficient multipath validation. It makes a leap in efficiency by two newly proposed techniques—hierarchical validation and tagged pruning. Hierarchical validation divides multipath into non-overlapping segments. We need to compute the path credential for each segment only once no matter how many paths it may co-locate. Furthermore, tagged pruning labels each segment with a unique tag. A router can directly identify the credential field to validate and delete credentials of unused paths. This further accelerates validation and saves bandwidth. Furthermore, we explore two efficiency enhancements—low-level credential elimination and used credential elimination—to improve Atlas scalability. We validate the practicality and applicability of Atlas over a recent topology measurement of Internet2’s IP Network. To validate the performance of Atlas and the enhancements, we implement Atlas using the Click modular router. Experiment results show that compact Atlas headers enable large-scale multipath validation without breaching the MTU limit. Atlas thus invigorates multipath validation practicality.
Deep Neural Networks with Batch Speaker Normalization for Intoxicated Speech Detection

Alcohol intoxication can affect people both physically and psychologically, and one's speech will also become different. However, detecting the intoxicated state from the speech is a challenging task. In this paper, we first implement the baseline model with ComParE feature and then explore the influence of the speaker information on the intoxication detection task. Besides, we apply a ResNet18 based model to this task. The model contains three parts: a representation learning subnetwork with Deep Residual Neural Network(ResNet) of 18-layer, a global average pooling(GAP) layer and a classifier of 2 fully connected layers. Since we cannot perform speaker z-normalization on the variant-length feature input, we employ the batch z-normalization to train the proposed model. It also achieves similar improvement like applying the speaker normalization to the baseline method. Experimental results show that speaker normalization on baseline model and batch z-normalization on ResNet18 based model provides 4.9% and 3.8% improvement respectively. The results show that speaker normalization can improve the performance of both the baseline model and the proposed model.
Empowering Women Through Access to Information: The Sustainability of a Community Informatics Project in Bangladesh

Community informatics projects are designed to provide access to information and communication technologies (ICT) that will enable communities to meet their goals. Although their aim is to foster the development of skills that will contribute to the wellbeing of the communities, the sustainability of the formats in which the information is provided to the community groups is rarely taken into consideration during project development. This paper reports on how the challenge of ensuring the continuity of access to information is being addressed in the context of a community informatics project in rural Bangladesh. The PROTIC project has been working since 2015 in three remote villages of Bangladesh at empowering women working in agriculture by providing them with mobile phones and access to agricultural information through SMS, mobile applications and a dedicated call centre. In the last stage of the project, the Bangladeshi telecommunication partner is developing an application to store all the SMS that were sent to the participants during the course of the project and is making plans to keep the information up-to-date after the end of the project and to charge a subscription fee for access to it. However, the participants themselves, conscious of the fragility of digital formats, have taken steps to preserve the information that they found useful in more durable analogue formats. This paper discusses these two initiatives and argues that the continuity of access to information should be planned for from the beginning in community informatics projects in developing countries.