Reviewerly/exHarmony huggingface dataset:
Noisy Guesses

We consider the problem of guessing a random, finite-alphabet, secret n-vector, where the guesses are transmitted via a noisy channel. We provide a single-letter formula for the best achievable exponential growth rate of the ρ-th moment of the number of guesses, as a function of n. This formula exhibits a fairly clear insight concerning the penalty due to the noise. We describe two different randomized schemes that achieve the optimal guessing exponent. One of them is fully universal in the sense of being independent of source (that governs the vector to be guessed), the channel (that corrupts the guesses), and the moment power ρ. Interestingly, it turns out that, in general, the optimal guessing exponent function exhibits a phase transition when it is examined either as a function of the channel parameters, or as a function of ρ: as long as the channel is not too distant (in a certain sense to be defined precisely) from the identity channel (i.e., the clean channel), or equivalently, as long ρ is larger than a certain critical value, ρ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">c</sub> , there is no penalty at all in the guessing exponent, compared to the case of noiseless guessing.
SlideKey: Impact of In-depth Previews for a Predictive Text Entry Method

Last decade has seen the democratization of small sensitive devices. But text entry solutions remain faithful to the azerty or qwerty layout or to the 12-key mobile phone keypad. We propose SlideKey which is based on FOCL. It uses a linear keyboard whose layout changes according to probabilities. Key selection is operated with the four directional keys. SlideKey integrates a preview of the two-next layouts to ease user to planify his next interactions. Preliminary tests show a not significant gain in performance when the preview is used.
Experiencing English: A Textbook Evaluation on the Cultural Loads

This research aimed at investigating the cultural contents implied in an English textbook. A qualitative method was used in this research which analyzed this English textbook. The data source of this research was the English textbook for junior high school students entitled “Experiencing English”. The instrument used for collecting data was documentation in the form of textbook. The data were analyzed by using Byram (1994) framework to investigate the aspects of culture. In this research, the nine aspects that have to be included in the EFL textbook were analyzed. The result showed that stereotypes and national identity was more frequently used in “Experiencing English” textbook. This aspect represents the target culture stereotypes and national identity commonly focused on the British and American cultures. In the other words, “Experiencing English” textbooks frequently used target language culture. Notwithstanding this fact, the source/local culture values as drafted in the 2013 curriculum was still implied in the textbook as intended to enable the students to reflect their own culture in English.
Visual preferences prediction for a photo gallery based on image captioning methods

The paper considers a problem of extracting user preferences based on their photo gallery. We propose a novel approach based on image captioning, i.e., automatic generation of textual descriptions of photos, and their classification. Known image captioning methods based on convolutional and recurrent (Long short-term memory) neural networks are analyzed. We train several models that combine the visual features of a photograph and the outputs of an Long short-term memory block by using Google's Conceptual Captions dataset. We examine application of natural language processing algorithms to transform obtained textual annotations into user preferences. Experimental studies are carried out using Microsoft COCO Captions, Flickr8k and a specially collected dataset reflecting the user’s interests. It is demonstrated that the best quality of preference prediction is achieved using keyword search methods and text summarization from Watson API, which are 8 % more accurate compared to traditional latent Dirichlet allocation. Moreover, descriptions generated by trained neural models are classified 1 – 7 % more accurately when compared to known image captioning models.
Face Liveness Detection Using Eulerian Video Magnification and SIFT Algorithm

In this paper, face liveness detection method which is based on very short video is proposed. Firstly, spatial and temporal video processing is performed by using Eulerian video magnification. Next, the matching pairs between frames are extracted from the original video. Then, the paper constructs the feature histogram from the color difference between two pixels according to SIFT matching pairs. Finally, we use SVM classifier to determine the liveness of the subject. The experimental results show that the detection accuracy is 95% and the liveness detection can be completed in less than one second without any specific user action.
Discriminative Segmentation Tracking Using Dual Memory Banks

Existing template-based trackers usually localize the target in each frame with bounding box, thereby being limited in learning pixel-wise representation and handling complex and non-rigid transformation of the target. Further, existing segmentation tracking methods are still insufficient in modeling and exploiting dense correspondence of target pixels across frames. To overcome these limitations, this work presents a novel discriminative segmentation tracking architecture equipped with dual memory banks, i.e., appearance memory bank and spatial memory bank. In particular, the appearance memory bank utilizes spatial and temporal non-local similarity to propagate segmentation mask to the current frame, and we further treat discriminative correlation filter as spatial memory bank to store the mapping between feature map and spatial map. Without bells and whistles, our simple-yet-effective tracking architecture sets a new state-of-the-art on the VOT2016, VOT2018, VOT2019, GOT-10K and TrackingNet benchmarks, especially achieving the EAO of 0.535 and 0.506 respectively on VOT2016 and VOT2018. Moreover, our approach outperforms the leading segmentation tracker D3S on two video object segmentation benchmarks DAVIS16 and DAVIS17. The source code will be released at this https URL.
A Novel IM Sync Message-Based Cross-Device Tracking

Cybercrime is significantly growing as the development of internet technology. To mitigate this issue, the law enforcement adopts network surveillance technology to track a suspect and derive the online profile. However, the traditional network surveillance using the single-device tracking method can only acquire part of a suspect’s online activities. With the emergence of different types of devices (e.g., personal computers, mobile phones, and smart wearable devices) in the mobile edge computing (MEC) environment, one suspect can employ multiple devices to launch a cybercrime. In this paper, we investigate a novel cross-device tracking approach which is able to correlate one suspect’s different devices so as to help the law enforcement monitor a suspect’s online activities more comprehensively. Our approach is based on the network traffic analysis of instant messaging (IM) applications, which are typical commercial service providers (CSPs) in the MEC environment. We notice a new habit of using IM applications, that is, one individual logs in the same account on multiple devices. This habit brings about devices’ receiving sync messages, which can be utilized to correlate devices. We choose five popular apps (i.e., WhatsApp, Facebook Messenger, WeChat, QQ, and Skype) to prove our approach’s effectiveness. The experimental results show that our approach can identify IM messages with high <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M1"><mml:msub><mml:mrow><mml:mi>F</mml:mi></mml:mrow><mml:mrow><mml:mn>1</mml:mn></mml:mrow></mml:msub></mml:math>-scores (e.g., QQ’s PC message is 0.966, and QQ’s phone message is 0.924) and achieve an average correlating accuracy of 89.58% of five apps in an 8-people experiment, with the fastest correlation speed achieved in 100 s.
UDPipe at EvaLatin 2020: Contextualized Embeddings and Treebank Embeddings

We present our contribution to the EvaLatin shared task, which is the first evaluation campaign devoted to the evaluation of NLP tools for Latin. We submitted a system based on UDPipe 2.0, one of the winners of the CoNLL 2018 Shared Task, The 2018 Shared Task on Extrinsic Parser Evaluation and SIGMORPHON 2019 Shared Task. Our system places first by a wide margin both in lemmatization and POS tagging in the open modality, where additional supervised data is allowed, in which case we utilize all Universal Dependency Latin treebanks. In the closed modality, where only the EvaLatin training data is allowed, our system achieves the best performance in lemmatization and in classical subtask of POS tagging, while reaching second place in cross-genre and cross-time settings. In the ablation experiments, we also evaluate the influence of BERT and XLM-RoBERTa contextualized embeddings, and the treebank encodings of the different flavors of Latin treebanks.
Dual Learning for Dialogue State Tracking

In task-oriented multi-turn dialogue systems, dialogue state refers to a compact representation of the user goal in the context of dialogue history. Dialogue state tracking (DST) is to estimate the dialogue state at each turn. Due to the dependency on complicated dialogue history contexts, DST data annotation is more expensive than single-sentence language understanding, which makes the task more challenging. In this work, we formulate DST as a sequence generation problem and propose a novel dual-learning framework to make full use of unlabeled data. In the dual-learning framework, there are two agents: the primal tracker agent (utterance-to-state generator) and the dual utterance generator agent (state-to-utterance genera-tor). Compared with traditional supervised learning framework, dual learning can iteratively update both agents through the reconstruction error and reward signal respectively without labeled data. Reward sparsity problem is hard to solve in previous DST methods. In this work, the reformulation of DST as a sequence generation model effectively alleviates this problem. We call this primal tracker agent dual-DST. Experimental results on MultiWOZ2.1 dataset show that the proposed dual-DST works very well, especially when labelled data is limited. It achieves comparable performance to the system where labeled data is fully used.
Asynchronous Distributed Optimization with Randomized Delays

In this work, we study asynchronous finite sum minimization in a distributed-data setting with a central parameter server. While asynchrony is well understood in parallel settings where the data is accessible by all machines, little is known for the distributed-data setting. We introduce a variant of SAGA called ADSAGA for the distributed-data setting where each machine stores a partition of the data. We show that with independent exponential work times -- a common assumption in distributed optimization -- ADSAGA converges in $\tilde{O}\left(\left(n + \sqrt{m}\kappa\right)\log(1/\epsilon)\right)$ iterations, where $n$ is the number of component functions, $m$ is the number of machines, and $\kappa$ is a condition number. We empirically compare the iteration complexity of ADSAGA to existing parallel and distributed algorithms, including synchronous mini-batch algorithms.
Research on Speed Control Strategy of Permanent Magnet Synchronous Motor Based on BP-SSO-PID Algorithm

In order to improve the control performance of permanent magnet synchronous motor (PMSM), firstly, this paper analyses the characteristics of traditional PID controller and PID controller based on BP neural network. Aiming at the problems of large overshoot and weak anti-interference, this paper studies a BP-SSO-PID controller based on spider clustering algorithm to optimize the parameters of BP neural network. Then, by establishing the mathematical model of PMSM and compiling the S-function function block, this paper sets up the speed control model of PMSM based on BP-SSO-PID algorithm in MATLAB. The results of experiment are compared with traditional PID controller and neural network PID controller's results. At last, it proves that the new method can effectively improve the overshoot, improve the response of the system and enhance the anti-interference of the PMSM.
Splicing Forgery Localisation using Colour Illumination Inconsistencies

In the digital imaging era, people used to deliberately distort images or videos for fun or misleading others. Image splicing is one of the methods of manipulation by copying an image from one photograph and pasting it into another. Typically, those two photographs were captured in different environments from various image sources. In this paper, we proposed a simple statistical-based learning-free approach to reveal this type of splicing forgeries using illumination inconsistencies with the assumption that the original images may have uniform illumination. The image first segmented into irregular objects as superpixels and colour illumination is estimated for each superpixel using greyness index in rg-chromaticity space. For each pair of superpixels, the dissimilarity is then estimated. A superpixel region growing algorithm is proposed to extract automatically all the tampered superpixels to localise the spliced region without human involvement. The results of the experiment show that the proposed method effectively localises splicing forgery than the state of art.
Regaining Sight of Humanity on The Roadway towards Automation

A primary goal of the auto industry is to revolutionize transportation with autonomous vehicles. Given the mammoth nature of such a target, success depends on a clearly defined balance between technological advances, machine learning algorithms, physical and network infrastructure, safety, standards and regulations, and end-user education. Unfortunately, technological advancement is outpacing the regulatory space and competition is driving deployment. Moreover, hope is being built around algorithms that are far from reaching human-like capacities on the road. Since human behaviors and idiosyncrasies and natural phenomena are not going anywhere anytime soon and so-called edge cases are the roadway norm, the industry stands at a historic crossroads. Why? Because human factors such as cognitive and behavioral insights into how we think, feel, act, plan, make decisions, and problem-solve have been ignored. Human cognitive intelligence is foundational to driving the industry&#x2019;s ambition forward. In this paper I discuss the role of the human in bridging the gaps between autonomous vehicle technology, design, implementation, and beyond.
A Study on Petrochemical Engineering Knowledge

This study is based on the characteristics of petrochemical engineering; in it, the basic properties and characteristics of petrochemical engineering knowledge are described. The petrochemical engineering knowledge system structure and knowledge cluster are mainly introduced. The study is conducted from a philosophical point of view to analyze and develop a conception on the practice of petrochemical engineering knowledge along with the development of its industry, and forms a comprehensive, clear-cut, and well-structured knowledge system.
The Role of Absorptive Capacity and Firm Openness Strategies on Innovation Performance

Openness and absorptive capacity are both based on the notion that organizations can use external knowledge to increase their innovation outcome. The objective of this paper is to investigate the joint effect of openness strategies, firms' absorptive knowledge capacity, and the innovation performance of SMEs in a developing country. By using structural equation modeling (SEM) through PLS, the results show that internal practices and means for attracting external knowledge improve innovation performance and indicate that absorptive capacity mediates open search breadth and partially open search depth to affect innovation performance. These results highlight the additional nature of absorptive capacity and reinforce the knowledge of scholars in particularly developing countries.
Improving Commissioning and Qualification Delivery Using Last Planner® System

This study evaluates the implementation of Last Planner® System (LPS) in the Commissioning and Qualification (C&Q) phase of a pharmaceutical construction project utilising the Engineering, Procurement, Construction Management and Validation (EPCMV) delivery model.C&Q is the ultimate and most critical phase of capital project execution however, the importance of this phase is often underestimated as it commonly accounts for only 3-5% of project costs.The study utilised a mixed-method, qualitative, action-research approach and highlights the challenges to the introduction of LPS in C&Q, project execution issues, and improvements to the existing planning process.Introducing planning metrics like Planned Percent Complete (PPC) to the weekly C&Q planning process resulted in increased stability over the 40-week implementation period.However, the greatest benefits emerged from weekly collection and examination of the Reasons for Non-Completion (RNC) of task data allowing the identification and implementation of improvement mitigations.Other key findings include enhanced delivery in the form of greater collaboration, increased visibility of workflow, and the resulting productivity, schedule alignment, safety, cost, and client value-add benefits from the implementation.Clients should adopt Lean thinking and practices to provide added value on capital projects and should mandate LPS implementation across the entire project, end to end, as opposed to individual phases.Future studies should examine LPS extension to planning the entire project.
User Experience in Kiosk Application for Traceability of Fishery Products

Nowadays it is very important to understand the origins of most products because there is an increased product quality challenge and awareness from the customers about environmental and social aspects. Food traceability information must reach customers where they need to choose which products to buy. One of the best ways to provide this traceability information for the customers is to add kiosks on the points of sale of those products. This paper describes the development of Fish products traceability kiosk user interface and experience, in the scope of ValorMar R&D project, that will allow all kiosk devices in a supermarket, shop or other type of point of sale, to consult an online RESTfull API about the traceability of fish product lots. At the end of this development stage, a System Usability Scale questionnaire has been deployed to test the usability of this kiosk application, which was well accepted by the majority of the participants.
Constructions of full diversity $D_n$-lattices for all $n$

We construct some families of full diversity rotated Dn-lattices via ℤ-modules for any n≥3. We show that ℤ-modules known in previous works to obtain rotated ℤn-lattices with n an odd integer are ideals and we find a sufficient condition for such ideals to be principal ideals. We also present bounds and formulas for the minimum product distance of ℤn and Dn restricted to some conditions.
Analytical and Simple Form of Shrinkage Functions for Non-Convex Penalty Functions in Fused Lasso Algorithm

In some circumstances, the performance of machine learning (ML) tasks are based on the quality of signal (data) that is processed in these tasks. Therefore, the pre-processing techniques, such as reconstruction and denoising methods, are important techniques in ML tasks. In reconstructed (estimated) method, the fused lasso algorithm with non-convex penalty function is an efficient method when the signal corrupted by additive white Gaussian noise (AWGN) is considered. Therefore, this paper proposes new shrinkage functions for non-convex penalty functions, modified arctangent and exponential models, in fused lasso formulation. A lot of works present the shrinkage function for arctangent penalty function. Unfortunately, there is no closed-form solution. The numerical solution is required for shrinkage function of this penalty function. However, the analytical solution is derived in this paper. Moreover, the shrinkage function of modified exponential penalty function is proposed. This shrinkage function obtains from simple iterative method, fixed-point algorithm. We demonstrate the proposed methods through simulations with standard one-dimensional signals contaminated by AWGN. The proposed techniques are compared with traditional estimation methods, such as total variation (TV) and wavelet denoising methods. In experimental results, our proposed methods outperform several exiting methods both visual quality and in terms of root mean square error (RMSE). In fact, the proposed methods can better preserve the feature of noise-free signal than the compared methods. The denoised signals produced by the proposed methods are less smooth than the denoised signals produced by the compared methods.
Blockchain for enterprise

Blockchain for enterprise is on its way to pave the way for the future. Many organizations like IBM are trying to invest in an enterprise blockchain platform and enhance their business model. Enterprise blockchains mainly focus on the features of enterprise-grade and solve the issues that the industry faces. All the enterprise blockchain is specially equipped to meet with all organizational demands. Some important features of blockchain that enterprise can utilize are decentralized nature and ensures P2P network, immutability for no corruption, greater transparency increases responsibility, the cheaper cost will save money, the faster network increases efficiency, etc. In this chapter, we have described the need for blockchain technology in the future industry.
Design and Application of Swarm Intelligence Algorithm in Teaching Quality Evaluation System of Normal Mathematics

According to the specific situation of the teaching work of ordinary colleges and universities, combined with the specific requirements of teaching quality, for the purpose of cultivating high-quality talents, swarm intelligence algorithm theory is introduced into the teaching quality evaluation system, and the mathematical structure of the quantum behavior particle swarm optimization algorithm is used to establish the mathematical model. Among them, the application of intelligent algorithm in the practice of evaluation method has broadened the new ideas and measures for the reform and development of normal university education, promoted the teachers to further clarify their responsibilities, and created a good atmosphere for everyone to care about teaching quality.