Reviewerly/exHarmony huggingface dataset:
An Enhanced Deep Hashing Method for Large-Scale Image Retrieval

Hashing-based image retrieval plays an important role in approximate nearest neighbor search because of its storage and retrieval efficiency. Efficient image features are of vital importance for image retrieval task. However, the features of images may not directly suitable for image retrieval due to the illumination or cluttered background in images. In this paper, we propose an enhanced deep hashing method for image retrieval to promote the retrieval accuracy, in which we adopt an enhanced feature module that selects the attractive areas in images. It jointly explores the hashing learning, enhanced feature module, and batch normalization module in a unified framework, which can guarantee the optimal compatibility of hash coding and feature learning. The proposed deep model contains three parts: (1) a convolutional sub-network consists of several convolutional-pooling layers and the proposed enhanced feature module; (2) a batch normalization module is utilized to control the quantization errors of hash codes at a moderate level; (3) a more comprehensive loss function is introduced to enhance the discriminative of image features and minimize the prediction errors of the learned hash codes. The experimental results on three datasets show that the proposed method can achieve competitive performance compared to other hashing approaches.
Validity Evaluation for the Data Used for Artificial Intelligence System

The data used to develop an AI system shall reflect the phenomena of the real-world sufficiently. This draws out a need to evaluate the validity of the data. This work has proposed a data validity evaluation method. To evaluate the data validity, the three steps of data preparation, data clustering, and validation are performed. To apply a data clustering algorithm is the main idea of the proposed data validity evaluation method. Analyzing the results from the data clustering in various perspectives leads us to evaluate the data validity of certain training data. Through a comparative study, the applicability of the proposed method is demonstrated.
Determination of the Initial Welding Point for Multi-pass Welding Based on Laser Vision

Most of the welding robots in practice are still teaching and playback mode. They are lack of the function to perceive the external environment, so it is difficult to carry out adaptive welding pass arrangement and position planning for medium thick plate. In this research, extraction procedure for feature points in laser stripe is presented. We get the weld groove profile with laser scanning. After processing the laser stripe with some image process methods, we get the two lowest feature points of the laser stripe including binarize image, medium filter for image, extracting the center of laser stripe. Then, two target feature points are extracted with linear fitting and distance formula. Finally, we convert the image coordinate value to the TCP coordinate of the robot according to the calibration matrixes. As a result, we restored the physical cross section of the weld bead, and can decide the initial welding position with the two feature points for every welding pass in the medium thick plates.
App Cost Estimation: Evaluating Agile Environments

Since the publication of the Agile Manifesto in 2001, the agile approach has had a vast influence on software development. Most agile methods break down a mobile application into small increments, which allow users to test single components and validate the value of an App. As a user satisfaction is mostly based on the validated values. However, implementing software replanning sessions for each increment allows an App to grow substantially. Over time, complexity such as in fighting bugs and fixing defects pile up, causing an increase in development costs. Since many cost estimation methods are not tailored for agile mobile development, only a few are partially applicable. With the current highly dynamic environment of Apps, this research identifies and evaluates the effectiveness of these methods to date. Key findings show a lack of appropriate and scientifically sound methods in app cost estimation. Therefore, this paper suggests new techniques in agile estimation. The main goal is to model a budgeting tool based on app decomposition, in both theoretical and practical approaches. The work aims to improve the accuracy in predicting development costs in mobile applications.
A Method of Intention Estimation for Human-Robot Interaction

Dynamics of human wrist play an important role in human-robot interaction. In this paper, we develop a novel method to classify the human wrist’s motion and to recognize its stiffness profile. In the proposed method, an integrated framework of linear discriminant analysis and extreme learning machine is developed to evaluate the intention of the wrist. Specifically, linear discriminant analysis is used to classify gestures of the wrist. Based on the result of classification, extreme learning method is use to construct a regression model of the stiffness. The experimental results are demonstrated the effectiveness of the proposed method.
Explicit Relation between Low-Dimensional LLL-Reduced Bases and Shortest Vectors

The Shortest Vector Problem (SVP) is one of the most important lattice problems in computer science and cryptography. The LLL lattice basis reduction algorithm runs in polynomial time and can compute an LLL-reduced basis that provably contains an approximate solution to the SVP. On the other hand, the LLL algorithm in practice tends to solve low-dimensional exact SVPs with high probability, i.e., >99.9%. Filling this theoretical-practical gap would lead to an understanding of the computational hardness of the SVP. In this paper, we try to fill the gap in 3,4 and 5 dimensions and obtain two results. First, we prove that given a 3,4 or 5-dimensional LLL-reduced basis, the shortest vector is one of the basis vectors or it is a limited integer linear combination of the basis vectors. In particular, we construct explicit representations of the shortest vector by using the LLL-reduced basis. Our analysis yields a necessary and sufficient condition for checking whether the output of the LLL algorithm contains the shortest vector or not. Second, we estimate the failure probability that a 3-dimensional random LLL-reduced basis does not contain the shortest vector. The upper bound seems rather tight by comparison with a Monte Carlo simulation.
The Use of Convolutional Neural Networks in Invariant Image Recognition

The paper investigates the issue of effective selection of methodological and mathematical bases for the implementation of an invariant image recognition system with regard to the requirements of solving problems of artificial vision using such system under the conditions typical for applied robotics, particularly, limited computational resources and the impossibility of optimizing the execution of algorithms due to the parallelization. It is shown that convolutional neural networks are the preferred basis for such systems. A model of such a system has been implemented, and its technical parameters and parameters for the efficiency of solving problems of invariant image recognition using such system have been investigated.
Karma Öğrenme Temelli Yabancı Dilde Bilimsel Makale Yazımını Öğrenme Modeli

Indonesian for Non-Native Speakers learning is mostly implemented in various universities in Indonesia through various programs. Along with the government's policy, various Indonesia universities encourage their students to write scientific articles for publication, both for proceedings and journals. Therefore, it is needed a learning model to write scientific articles. Generally, these learning activities have learners who come from the digital or Z generation. Referring to the characteristics of learners, learning activities to write scientific articles can be done based on blended learning. This research aims to discuss the theoretical studies which are needed in developing a model of learning to write scientific articles based on blended learning. In accordance with the purpose of writing, this research describes theoretical studies of learning models, second language writing, scientific articles, blended learning, and the development of blended learning.
RESTfulness of APIs in the Wild

REST is an architectural style, which has been around for quite some time and many web services claim to be RESTful, thus making REST a major buzzword in web applications. But are those web services really RESTful? This paper explains Richardson Maturity Model. Based on this model, an analysis of ten APIs is performed and their maturity levels are determined.
Formation of Secondary Specialized Library Education in Ukraine in the 1920s — 1950s

The article analyses characteristic features of the formation of system of secondary specialized library institutions in Ukraine in the 1920s — 1950s, as well as various professional courses and secondary technical schools that trained library specialists. The purpose of this article is to reveal the distinctive features, the main factors and stages of formation and development of secondary specialized library education in the Ukrainian SSR in the 1920s — 1950s, to characterize the varieties, features of creation and development of technical schools, which trained library specialists of mid-level management, especially library technical schools. The author used both general scientific research methods (description, comparison, analogy, deduction, induction, analysis, etc.) and historical (historical-comparative, historical-typological, historical-diachronic, chronological) methods. The author proves that in the second half of the 1930s, the country developed the system of secondary specialized library education, consisting both of training courses, retraining of specialists and also of a number of special educational institutions — various types of technical schools and other institutions where librarians were trained. In particular, there was formed the network of special library technical schools. Based on the analysis of historical, socio-political, cultural and other social factors, the author proposes periodization of formation and development of secondary special educational institutions, which trained library workers. Training of specialists in library technical schools and other secondary institutions became the basis for the formation of human resource of librarians of mid-level qualification for the different types of libraries (mainly for mass). The results and conclusions of this work can serve as a source material for further research on the history of secondary special library education; the material can be used in the special course lectures on the history of library education.
Perancancangan dan Simulasi Sistem Informasi Akademik Berbasis Wireless Application Protocol (WAP)

The Application Academic Information System based WAP is very needed in order to implement Information Technology into Teaching-Learning Process in University. By Using Emulator WAP as aid-equipment for simulating Academic Information System, will result an optimum output in developing Information System based WAP. Handphone as telecommunication equipment which is completed by WAP-GPRS facility can be used as an access Academic Information System through internet. Â Kata Kunci : WAP, Sistem Informasi Akademik, emulator, GPRS
Human Active Learning

Active machine learning (AML) is a popular research area in machine learning. It allows selection of the most informative instances in training data of the domain for manual labeling. AML aims to produce a highly accurate classifier using as few labeled instances as possible, thereby minimizing the cost of obtaining labeled data. As machines can learn from experience like humans do, using AML for human category learning may help human learning become more efficient and hence reduce the cost of teaching. This chapter is a review of recent research literature concerning the use of AML technique to enhance human learning and teaching. There are a few studies on the applications of AML to the human category learning domain. The most interesting study was by Castro et al., which showed that humans learn faster with better performance when they can actively select the informative instances from a pool of unlabeled data instead of random sampling. Although AML can facilitate object categorization for humans, there are still many challenges and questions that need to be addressed in the use of AML for modeling human categorization. In this chapter, we will discuss some of these challenges.
Subjective Image Fidelity Assessment: Effect of the Spatial Distance Between Stimuli

Understanding how we perceive visual quality is important in a range of applications such as streaming or cross-media reproduction. Despite current perception models showing high correlations with recorded mean opinion scores, the factors influencing visual quality are still not fully understood, particularly when it comes to memory. We designed and carried out a study to compare quality assessment for two different levels of reliance on visual short-term memory. We found that assessments based mostly on memory tend to be more positive for compression, blur or gamut mapping distortions. Our results further highlight the role of memory in subjective quality assessment and visual masking.
Design and Implementation of an Image Classifier using CNN

Surely there has been no time in history where the lived lives of people have changed more dramatically due to the emergence of Artificial Intelligence. Various research and development has taken place over the years on computer vision which is a branch of AI. AI disciplines like a vision system is applied in various fields like self driving cars, face detection by social media apps and law enforcement softwares, google lens and so on. This paper deals with design and implementation of an efficient way of training a GPU using python libraries to process and classify an image. The design is profoundly described along with its performance efficiency. The image processing techniques presented in this paper includes the concept of neural networks and deep learning.
A new approach to ROI based image compression using simple sliding window spatial filter

By reducing the file size, the number of images stored in the given amount of hard disk can be increased and the time required to send images over the internet has been reduced. Image compression is the common and adaptive method used for reducing all the chunks in our day to day life. There are many existing techniques and protypes available for compression. This paper deals with the special concepts on image compression using simple sliding window spatial filter or mean filter. The desired region can be selected using ROI from the image which can be processed for further compression of images. The proposed method uses DCT followed by the suitable encoding techniques. Various combinations of compression applied to come across the best one.
CUT AND GAMMA I: PROPOSITIONAL AND CONSTANT DOMAIN <b>R</b>

Abstract The main object of this article is to give two novel proofs of the admissibility of Ackermann’s rule ( γ ) for the propositional relevant logic R . The results are established as corollaries of cut elimination for systems of tableaux for R . Cut elimination, in turn, is established both nonconstructively (as a corollary of completeness) and constructively (using Gentzen-like methods). The extensibility of the techniques is demonstrated by showing that ( γ ) is admissible for RQ* ( R with constant domain quantifiers). The status of the admissibility of ( γ ) for RQ* was, to the best of the author’s knowledge, an open problem. Further extensions of these results will be explored in the sequel(s).
FootstepsMixer: A Tool to Express Multiple People’s Footsteps in a Footstep Transmission System for Awareness Support

Recently, the number of people living in single-person households has increased. Although the numbers of sensor-based services that monitor personal safety are increasing, they do not provide the feeling of living in a multi-person household. In this study, we attempt to realize a system that shares the sounds of a distant family household, thereby creating the impression that the family household is nearby. Using this system, we devised a method to express people’s activity in a remote location and evaluated the system experimentally. We considered multiple-footstep expression methods and based on experimental results, identified the most suitable method. Herein, we propose a “FootstepsMixer,” a tool that expresses the actions of multiple people using the optimal footstep expression method. With this tool, users can easily express footsteps in an exaggerated manner and can set the number of people and the maximum volume of each footstep. We also consider what type of motion expression can be realized using the proposed tool.
3D Shape Retrieval through Multilayer RBF Neural Network

3D object retrieval involves more efforts mainly because major computer vision features are designed for 2D images, which is rarely applicable for 3D models. In this paper, we propose to retrieve the 3D models based on the implicit parameters learned from the radial base functions that represent the 3D objects. The radial base functions are learned from the RBF neural network. As deep neural networks can represent the data that is not linearly separable, we apply multiple layers' neural network to train the radial base functions. Our feature can be applied to recover the 3D objects, which proves the effectiveness of our features in representing the 3D objects. Furthermore, the dimensionality of the learned feature is scalable, which leads to memory efficiency. Experiments demonstrate the accuracy of our feature in 3D model retrieval.
Development of a knowledge transfer support system for water treatment technology

Abstract The water supply must be safe and sustainable. This requires a regulatory compliance, selection of an optimal water treatment process, timely response to water quality incidents as well as maintaining internal skills and knowledge among utility personnel to keep appropriate operational standards in the long run. Water supply services in Japan face various challenges, including a shrinking revenue base due to a declining population as well as a mass retirement of the baby-boomer generation. The Japan Water Research Center conducted questionnaire surveys and based on our findings developed the ‘knowledge transfer support system for water treatment technology’ for utilities to maintain internal skills and knowledge in the face of shrinking labour forces in the water sector. The system consists of three components: the database on water treatment technology, learning program on water treatment technology, and the assessment tool for knowledge transfer on water treatment technology.
Using Auto-Encoder BiLSTM Neural Network for Czech Grapheme-to-Phoneme Conversion

The crucial part of almost all current TTS systems is a grapheme-to-phoneme (G2P) conversion, i.e. the transcription of any input grapheme sequence into the correct sequence of phonemes in the given language. Unfortunately, the preparation of transcription rules and pronunciation dictionaries is not an easy process for new languages in TTS systems. For that reason, in the presented paper, we focus on the creation of an automatic G2P model, based on neural networks (NN). But, contrary to the majority of related works in G2P field, using only separate words as an input, we consider a whole phrase the input of our proposed NN model. That approach should, in our opinion, lead to more precise phonetic transcription output because the pronunciation of a word can depend on the surrounding words. The results of the trained G2P model are presented on the Czech language where the cross-word-boundary phenomena occur quite often, and they are compared to the rule-based approach.
Fine-grained quantum computational supremacy

Output probability distributions of several sub-universal quantum computing models cannot be classically efficiently sampled unless some unlikely consequences occur in classical complexity theory, such as the collapse of the polynomial-time hierarchy. These results, so called quantum supremacy, however, do not rule out possibilities of super-polynomial-time classical simulations. In this paper, we study version of quantum supremacy that excludes some exponential-time classical simulations. First, we focus on two sub-universal models, namely, the one-clean-qubit model (or the DQC1 model) and the HC1Q model. Assuming certain conjectures in fine-grained complexity theory, we show that for any $a>0$ output probability distributions of these models cannot be classically sampled within a constant multiplicative error and in $2^{(1-a)N+o(N)}$ time, where $N$ is the number of qubits. Next, we consider universal quantum computing. For example, we consider quantum computing over Clifford and $T$ gates, and show that under another fine-grained complexity conjecture, output probability distributions of Clifford-$T$ quantum computing cannot be classically sampled in $2^{o(t)}$ time within a constant multiplicative error, where $t$ is the number of $T$ gates.
Efficient Answer-Annotation for Frequent Questions

Ground truth is a crucial resource for the creation of effective question-answering (Q-A) systems. When no appropriate ground truth is available, as it is often the case in domain-specific Q-A systems (e.g. customer-support, tourism) or in languages other than English, new ground truth can be created by human annotation. The annotation process in which a human annotator looks up the corresponding answer label for each question from an answer catalog ( $$\textsc {Sequential}$$ approach), however, is usually time-consuming and costly. In this paper, we propose a new approach, in which the annotator first manually groups questions that have the same intent as a candidate question, and then, labels the entire group in one step ( $$\textsc {Group}\text {-}\textsc {Wise}$$ approach). To retrieve same-intent questions effectively, we evaluate various unsupervised semantic similarity methods from recent literature, and implement the most effective one in our annotation approach. Afterwards, we compare the $$\textsc {Group}\text {-}\textsc {Wise}$$ approach with the $$\textsc {Sequential}$$ approach with respect to answer look-ups, annotation time, and label-quality. We show based on 500 German customer-support questions that the $$\textsc {Group}\text {-}\textsc {Wise}$$ approach requires 51% fewer answer look-ups, is 41% more time-efficient, and retains the same label-quality as the $$\textsc {Sequential}$$ approach. Note that the described approach is limited to Q-A systems where frequently asked questions occur.
Recognizing Chinese Texts with 3D Convolutional Neural Network

In this paper, we propose a deep learning system to localize and recognize Chinese texts in scenes with signage and road marks through 3D convolutional neural network. The proposed system adopts YOLO for detecting target location and exploits 3D convolutional neural network for recognizing the contents. The proposed design outperforms the existing designs based on LSTM and achieves real-time processing performance, which is feasible to be implemented on embedded platforms. The proposed system reaches over 90% accuracy in recognizing Chinese texts on bird's-eye viewing road marks in a self-driving vehicle equipped with a fisheye camera. In addition, this system can achieve 20 fps execution speed with NVIDIA DIGITS DevBox with 1080Ti GPU, which is fast enough for autonomous driving applications.