Reviewerly/exHarmony huggingface dataset:
Are Two Heads Better Than One? An Exploration of Ambiguity in Crowd-Collected Language Decisions from the Phrase Detectives Game.

The online game-with-a-purpose Phrase Detectives (https://www.phrasedetectives.com) [1] has been collecting decisions about anaphoric coreference in human language for over 10 years (4 million judgements from 40,000 players). The game was originally designed to collect multiple valid solutions for a single task, which complicated aggregation but created a very rich (and noisy) dataset [2]. Analysis of the ambiguous player decisions highlights the need for understanding and resolving disagreement that is inherent in language interpretation. This talk will present some of the interesting cases of ambiguity found by the players of Phrase Detectives (a dataset that will be made available to the research community later this year [3]) and discuss the statistical methods we have been working on to harness crowds that disagree with each other [4, 5].
Quantum Cryptography: A Survey

This paper represents the overview of Quantum Cryptography. Cryptography is the art of secrecy and it is the use of quantum mechanical properties to perform cryptographic tasks. It is a way of securing the channel using quantum mechanics properties. There are so many examples of quantum cryptography but the most important example is Quantum Key Distribution, which provides a solution to the breaking of various popular public key encryption and signature schemes (e.g. RSA and ElGamal). This helps to solve the security problems and also makes the communication channel is more secure. There are so many advantages of quantum cryptography, one thing is that the quantum computer gives the quadratic speed up on the general problems and second thing is that the quantum cryptography lies in the fact it allows the completion of various cryptographic tasks. That is proven to be impossible using classical communication.
Robust Object Tracking against Self-occlusion Using Color and Depth Information

Visual object tracking techniques are widely required by many vision applications. The color-based particle filter is known as one of useful methods for robust object tracking. However, the conventional color-based particle filter has a problem that it is not robust against self-occlusion. Self-occlusion occurs when a part of a target object is hidden by itself from a camera. When the target object moves or rotates, a part of the target disappears because the self-occlusion occurs and other part appears because the self-occlusion is resolved. The conventional color-based particle filter often fails to follow such a change of the target's appearance due to self-occlusion during the tracking process. In this paper, we propose a novel method for robust object tracking against the self-occlusion. The proposed method is based on the color-based particle filter, and it also uses depth information obtained by an RGB-D camera. When the self-occlusion occurs and the target's appearance changes, the proposed method extracts a region for the target object in the input image by the graph cuts based on depth information. However, this process often includes unnecessary regions, especially when some objects are close to the target. Then, the proposed method distinguishes the region for the target from unnecessary ones by investigating expanse of colors around the target. Therefore, the target model is correctly updated and the robust tracking is achieved. In order to verify the effectiveness of the proposed method, we carried out an experiment to compare the proposed method with the conventional one. Experimental results show that the proposed method works well.
CALCULATION OF THE POWER OF A TWO-DIRECTIONAL DC/DC CONVERTER FOR AUTONOMOUS UNDERWATER APPARATUS

One of the ways to increase the efficiency of autonomous underwater vehicles is to use a DC electric power system. With the help of impulse control it is possible to achieve a stable voltage on consumers even with significant changes in the speed of the propeller rotation caused by waves, bottom relief, current, winds, course change, etc. Issues related to the simulation of DC-DC converters for autonomous underwater vehicles have been considered. The powers supplied from a DC power source to loading have been found. Possible ways of regulating the power and voltage at the output of the pre-driver are analyzed and performing the power and voltage adjustment by means of the phase angle to the inverting and rectifying bridges is proved to be expedient. At an angle of 90º, the current in the pulse transformer looks like a sinusoid in shape, and at the same time it is simple for piecewise linear approximation, which in turn makes it easy to determine the transmitted power over a period. Various variants of an output signal are presented under changing a phase angle of opening of IGBT-transistors. There have been illustrated oscillograms of the AC-DC converter and of the current type in the inductor. According to the calculations, the analytical solution was found to determine the transmitted power. A bi-directional DC converter was modeled using a Simulink package, which resulted in a voltage on the inductor and voltage on the load as a graphic dependence. The obtained dependences completely coincide with the theoretical analysis.
Automatic Detection of Early Gastric Cancer from Endoscopic Images Using Fast Discrete Curvelet Transform and Wavelet Transform Modulus Maxima

We propose an automatic detection method for early stage gastric cancer from endoscopic images that uses the fast discrete curvelet transform (FDCT) and wavelet transform modulus maxima (WTMM). First, we convert an original image to CIE L*a*b* color space and apply the dyadic wavelet transform (DYWT) to the a* component of the image. Next, we compute the WTMM using the high frequency components obtained by the DYWT and generate a binary image based on the WTMM. We then produce a composite image using the L* and a* components of the image and compute the curvelet coefficient by applying FDCT to the composite image. In addition, we apply the inverse curvelet transform to only the imaginary component of the curvelet coefficient and generate a binarized image by applying threshold processing to the obtained image. We divide the two binarized images into non-overlapped blocks and compute the average value of each block. The average value of the block corresponding to the abnormal portion tends to be smaller than that of the entire image. On the basis of these features, we describe in detail a method of detecting abnormal regions and show by our experimental results that the proposed method can detect the region in the endoscopic image that may indicate early stage gastric cancer.
Web-Based Gesture Recognition System for Controlling Heterogeneous IoT devices using Deep Learning

Most of the Internet-of-Things (IoT) devices are sensor based and controlled individually using mobile applications remotely. Users need to learn the application before controlling these IoT devices to accomplish a specific task. In this paper, we improve this interaction capability of users with IoT devices using Web-based Gesture Recognition and Deep Learning. Here, various gestures are used to control different IoT devices.
The Relationship Between the Representation of a IIR Digital Filter in the State Space and the Description by the Topological Matrix

When analyzing and synthesizing digital filters with different structures, their representation in the state space is most often used. But this is not the only way to describe the filter. A digital filter with an arbitrary structure is most adequately described by a topological matrix, the elements of which are transfer coefficients between nodes of a block diagram. This paper allows us to expand the capabilities of the mathematical model of a digital filter in the form of a topological matrix.
3D image processing using deep neural network

In 3D image processing field, many researches have been conducted, such as multiview image coding and data compression, view interpolation, coded aperture based light field acquisition, and light field display signal calculation. The challenge of these technologies is that they usually require heavy computation due to the large amount of data. In this paper, we report the results of some experiments where we replace these computation with deep neural network (DNN) and convolutional neural network (CNN). In some of the cases, DNN and CNN show better performance than conventional methods both in quality and calculation speed.
Enhancing Readability of Dyslexic Children by Machine Learning Techniques—A Survey

Dyslexia is type of disorder that occurs normally in children, and is also called as learning disability. Children who are having dyslexia, requires more time to read, write as compared to normal children. There are different kinds of disabilities which come under dyslexia like dysgraphia, and dyscalculia but this paper focuses on reading disability in children. Here, children between 8 and 10 years will be taught on most frequently used Hindi words. First, frequently used Hindi words will be created as a database and then the system is trained with those input words. Speech synthesis is applied to those words to get speech output from the trained system. Children will be given a set of words to read. If children are saying the wrong word three times then the system will give correct speech and train the children. This makes the process automatic and the system acts as a personal trainer to the dyslexic children. By doing this model, the children can learn in a more interactive manner and will remember the words as the system can show different pictures for the same word. The use of machine learning techniques makes this system more accurate and the system will be trained for speech recognition and phonetics of each alphabet and words in Hindi.
Examining the Impact of Exercise Tracking Data on Promoting Regular Exercise Among University Students

A smart bracelet accumulates exercise data like calories, step counts, and the length of exercise, etc. It is very convenient for a regular exerciser to follow up his exercise status or find out if he is exercising more than last month. The increasing data can serve as an incentive for consistent exercise. However, there isn’t much study in the extant literature if sports related devices can increase the behaviors of regular exercise. Based on the data smart bracelet collected, this study examined whether using a smart bracelet increases the users’ exercise behaviors and enhances their physical conditions. The study found that by using a smart bracelet, regular exercisers maintain their regular exercise pattern and non-regular exercisers enhanced their exercise performance.
Hybrid Decentralized Solution for Bitcoin Zero-Confirmation Transactions

The main attraction of cryptocurrency is the decentralized, append-only public ledger blockchain that records the transactions. The current challenge of bitcoin blockchain is to increase the throughput. Two-party or multi-party payment channel using multi-signature address formed a connected payment network to solve the issue of scaling that fascinated bitcoin community. The micropayment channel solution implicitly assumes that parties will continue to interact for long intervals. This assumes a long-lived relation between buyer and seller. Another major disadvantage of forcing participants to lock funds for long intervals. The micropayment channel must know about the parties beforehand.We address this issue and present a protocol that offers freedom to do micropayment transaction with anyone as per the need without any prior knowledge about the parties. The protocol gives an alternate secured path for zero-confirmation transactions. From the test cases, we have shown that buyer will get quick services from sellers and honest sellers will not lose their funds share.
Symbolic Encryption with Pseudorandom Keys

We give an efficient decision procedure that, on input two (acyclic) expressions making arbitrary use of common cryptographic primitives (namely, encryption and pseudorandom generators), determines (in polynomial time) if the two expressions produce computationally indistinguishable distributions for any cryptographic instantiation satisfying the standard security notions of pseudorandomness and indistinguishability under chosen plaintext attack. The procedure works by mapping each expression to a symbolic pattern that captures, in a fully abstract way, the information revealed by the expression to a computationally bounded observer. Our main result shows that if two expressions are mapped to different symbolic patterns, then there are secure pseudorandom generators and encryption schemes for which the two distributions can be distinguished with overwhelming advantage. At the same time if any two (acyclic) expressions are mapped to the same pattern, then the associated distributions are indistinguishable.
Reconstructing Attention with Dynamic Regularization

This paper presents an attention reconstructing method for encoder-decoder framework. As the framework has been widely used to sequence-to-sequence learning problems, attention mechanism plays a role of predicting where to attend at next step. However, whether the attention is accurate or not is not validated. In other words, the attention model lacks direct supervision. Therefore, the attention weights are reconstructed using ground-truth labels. Difference between original attention weights and reconstructed ones is added to cost function to provide weak supervision for attending process. In addition, the attention model should focus on different positions at different step. To enhance attention strength, a diversity regularization term which maximizes difference of attention weights at different steps is attached to cost function. The proposed approach is applied to image caption generation task. Experiments conducted on benchmark datasets, Flickr8k, Flickr30k and MSCOCO, demonstrate the effectiveness of our proposed approach.
The Effect of the Arab Spring Revolution on the Yemeni Economy

The economic and humanitarian conditions of the Yemeni population have been deteriorating. The variable that affects the Yemeni economy is the foreign exchange rate of the Yemeni currency. In 2014, one US Dollar equaled to 240 Yemeni Ryal. In 2018, one US Dollar equals 700 Yemeni Ryal. The massive leap of the value of the Yemeni Ryal against the US Dollar, paired with the stabilization of the public sector occupation salaries, deeply affected the quality of life of the Yemeni citizen. Furthermore, the leap of the Yemeni Ryal value leads to the increase of the merchandise prices as well as the price of public services. The decrease of the value of the Yemeni Ryal against the US Dollar is one variable that is assisting the levels of poverty in Yemen. However, this chapter believes that economic mismanagement and the foreign exchange rate are essential variables that explain the increase of poverty levels in Yemen.
A Proposal to Improve the Earned Value Management Technique Using Quality Data in Software Projects

Currently the Project Management Institute (PMI) estimates that approximately 25% of the world’s Gross Domestic Product (GDP) is spent on projects of various kinds and that about 16.5 million professionals are directly involved in project management worldwide. This volume of projects and changes in the world scenario, increasingly competitive, generate the need for faster results, with higher quality, lower costs and shorter deadlines. Among the main techniques for analyzing cost, time and scope performance, the Earned Value Management (EVM) technique is considered to be the most reliable. Several formulas derived from EVM’s measurements are available and have been studied over the past 15 years. However, EVM has a significant limitation regarding quality in its method. The technique is effective in providing cost and schedule related information but is still weak in taking the quality factor into account. The objective of this work is to improve the predictability of cost and schedule of software projects using the EVM technique, by adding quality variables and allowing EVM to integrate not only scope, schedule and cost but quality as well. This paper presents a proposal to enhance the EVM technique by integrating the quality component. The proposed technique is evaluated and compared to the traditional technique through different hypothesis tests, utilizing data from simulated projects. Hypotheses tests with 95% significance level were performed, and the technique was more accurate than the traditional EVM for the projection of the Cost Performance Index – CPI and the Schedule Performance Index – SPI.
Inverse formulas of length twelve parameterized orthogonal wavelets

Inverse parameterizations of length 12 orthogonal wavelet filters are presented, which allow to determine parameter values from filter coefficients. Its applicability is shown in a case of study of image processing where the optimization of five parameters is required. The parameterization of leng th N filters involves N2-1 parameters, and it is easier to optimize shorter filters once they explore a subset of the search space. Under this approach, the optimization of length 12 filters is accelerated based on a nested optimization of length 4, 6, 8, and 10 filters by exporting the best solutions from shorter to larger filters via inverse parameterizations. Experimental results support the success of the nested optimization when exploring the search space. The conclusions are that the use of the inverse formulas accelerates the convergence and that parameterized filters provide better results as their length increases and achieve a better performance than standard filters.
Extending QoM Taxonomy for Complex XML Schema Matching

This paper focuses on the quality of match relatedness measurements among XML schemas. The Quality of Match QoM, that is used to distinguish the “goodness” of one match metrics, is extended to evaluate the complex QoM of two given schemata by providing a composite qualitative and quantitative analysis techniques. The introduced taxonomy of schema matches as a weight-based match model is extending the QoM model from a syntactic matching on the node level, to a model that detects complex matches by integrating it with the semantic technologies, such as ontologies. The new equations that calculate these weights are derived and proved throughout practical examples.
An Analysis of Innovative Talents Training of Arts Education Under the Development of Creative Economy in the Guangxi Province

Guangxi arts education and the cultivation of innovative talents are far behind the speed of China’s economic growth. Hence, there are a long way to go to accelerate Guangxi arts education and the rapid development of innovative talents. This article aims to make the countermeasures of cultivating art creative talents to promote economic transformation in Guangxi.
Internet Routing and Non-monotonic Reasoning

Internet routing is the process of selecting paths across the Internet to connect the communicating hosts, it is unique in that path selection is jointly determined by a network of independently operated networks, known as domains or Autonomous Systems (ASes), that interconnect to form the Internet. In fact, the present routing infrastructure takes such an extreme position that it favors local autonomy—an AS can use arbitrary path preference to override the default shortest path policy, at the expense of potential global oscillation—a collection of AS preferences (policies) can fail to converge on a stable path, a path that is also the most preferred possible for every AS along the path. In this paper, we examine the route oscillation problem with non-monotonic reasoning. We observe that, in the absence of any AS specific policies, Internet routing degenerates into the monotonic computation of shortest path—a preferred (shorter) (super)path always extends another preferred (sub)path; But fully autonomous AS policies are non-monotonic—a path favored by one AS can be an extension of a less preferred path of a neighbor, to which an “upgrade” to a better path can cause this AS to downgrade to a less preferred path previously discarded. Based on this insight, we present an Answer Set Programming (ASP) formulation that allows for automatic oscillation detection. Our evaluation using the clingo ASP solver is promising: on realistic Internet topology and representative policies, clingo can detect anomalies within 35 s.
Fourℚ on ASIC: Breaking Speed Records for Elliptic Curve Scalar Multiplication

An ASIC cryptoprocessor for scalar multiplication (SM) on FourQ is proposed. By exploiting Karatsuba multiplication and lazy reduction techniques, the arithmetic units of the proposed processor are tailored for operations over quadratic extension field (F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</sub> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ). We also propose an automated instruction scheduling methodology based on a combinatorial optimization solver to fully exploit the available instruction-level parallelism. With the proposed processor fabricated by using a 65 nm silicon-on-thin-box (SOTB) CMOS process, we demonstrate that an SM can be computed in 10.1 μs when a typical operating voltage of 1.20V is applied, which corresponds to 3.66× acceleration compared to the conventional P-256 curve SM accelerator implemented on an ASIC platform and is the fastest ever reported. We also demonstrate that by lowering the supply voltage down to 0.32V, the lowest ever reported energy consumption of 0.327 μJ/SM is achieved.
RecBoard: A Web-based Platform for Recommendation System Research and Development

This paper introduces RecBoard, a unified web-based platform that facilitates researchers and practitioners to train, test, deploy, and monitor recommendation systems. RecBoard streamlines the end-to-end process of building recommendation systems by providing a collaborative user interface that automates repetitive tasks related to dataset management, model training, visualization, deployments, and monitoring. Our demo prototype demonstrates how RecBoard can empower common tasks in research and development. RecBoard will be open-sourced and publicly available upon publication.
Discussions on the Construction of Digital Campus in Higher Vocational Colleges

Building a digital campus is of great significance for higher vocational colleges to seize the commanding heights of education informatization and realize the leap-forward development of higher vocational education. Combining with the actual situation of the construction of digital campus of Changzhou College of Information Technology, the author discussed the relevant issues and matters which needs attention in the construction, and put forward some new ideas and overall plans for construction.