Reviewerly/exHarmony huggingface dataset:
An Innovative application for code generation of mathematical equations and problem solving

Mathematics is the cradle of all creations, without which the world cannot move an inch. Mathematical functions are ‘extensively’ used in physics, ‘structurally’ used in graphics, ‘practically’ used in civil engineering, ‘potentially’ used in mechanical engineering and in many other fields as well. One fairly common difficulty faced by the engineers and scientists is to find the right function that solves their problem. This involves a lot of time consuming tasks. The idea proposed here is an android application that captures the mathematical expression using a built-in camera which produces a java code that can be utilized for solving the inputs of their needs. Along with the code, it also displays a basic plot of the function, which will be more helpful for selecting the apt solution for any problem. There are many applications in the market which can compute the result of a mathematical equation. But, this application will give an executable java code which can be used for further problem solving. So, the application is unique in its way and this is a new dimension of using image processing for code generation. This application supports polynomial, logarithmic, trigonometric and exponential functions up to four variables.
Learning to Suggest Hashtags

Twitter is one of the primary online social networks where users share messages and contents of interest to those who follow their activities. To effectively categorize and give audience to their tweets, users try to append appropriate hashtags to their short messages. However, the hashtags usage is very small and very heterogeneous and users may spend a lot of time searching the appropriate hashtags. Thus, the need for a system to assist users in this task is very important to increase and homogenize the hashtagging usage. In this chapter, the authors present a hashtag recommendation system on microblogging platforms by leveraging semantic features. Furthermore, they conduct a detailed study on how the semantic-based model influences the final recommended hashtags using different ranking strategies. Moreover, they propose a linear and a machine learning based combination of these ranking strategies. The experiment results show that their approach improves content-based recommendations, achieving a recall of more than 47% on recommending 5 hashtags.
Towards Building a Strong Transformer Neural Machine Translation System

Transformer model based on self-attention mechanism [17] has achieved state-of-the-art in recent evaluations. However, it is still unclear how much room there is for improvement of the translation system based on this model. In this paper we further explore how to build a stronger neural machine system from four aspects, including architectural improvements, diverse ensemble decoding, reranking, and post-processing. Experimental results on CWMT-18 Chinese $$\leftrightarrow $$ English tasks show that our approach can consistently improve the translation performance of 2.3–3.8 BLEU points than the strong baseline. Particularly, we find that ensemble decoding with a large number of diverse models is crucial for significant improvement.
Social Sustainability of Online Instant Messaging Platform—WhatsApp

We present an analysis of Social Sustainability of online communication platform "WhatsApp". We formulated a definition for a Socially Sustainable System in the context of online instant messaging platforms. Based on the definition, we performed a thorough study on WhatsApp. A set of factors were identified that affect the social sustainability of WhatsApp. The factors and the frequency of their recurrence were used to determine the perceived importance of the stages of interaction (based on Gulf of Evaluation and Gulf of Execution) in terms of social sustainability. It was found that "Goal Formation" is perceived to be the most important stage followed by "Action Specification", "Evaluation" and "Perception". A set of tangible artifacts that influence the system usage were also identified. We can move towards designing sustainable communication platforms by analyzing each stage of the interaction based on our study and by taking logical decisions as a designer in order to ensure a healthy and liveable online community.
Defect Prediction in Software Using Predictive Models Based on Historical Data

Nowadays, there are many methods and good practices in software engineering that aim to provide high quality software. However, despite the efforts of software developers, there are often defects in projects, the removal of which is often associated with a large financial effort and time. The article presents an example approach to defect prediction in IT projects based on prediction models built on historical information and product metrics collected from various data repositories.
TencentFmRD Neural Machine Translation System

This paper describes the Neural Machine Translation (NMT) system of TencentFmRD. Our systems are neural machine translation systems trained with our original system TenTrans. TenTrans is an improved NMT system based on Transformer self-attention mechanism. In addition to the basic settings of Transformer training, TenTrans uses multi-model fusion techniques, multiple features reranking, different segmentation models and joint learning. Finally, we adopt some data selection strategies to fine-tune the trained system. Our English $$\leftrightarrow $$ Chinese and Mongolian $$\rightarrow $$ Chinese systems achieve a stable performance improvement.
Round efficient computationally secure multi-party computation revisited

In this work, we consider the problem of secure multi-party computation (MPC) with n parties where at most t are under the control of a computationally bounded adversary given the constraint t < n/2. We aim to design a round efficient protocol by minimizing the number of rounds in which the broadcast primitive is involved. In this setting, the previous best protocol can be attributed to Katz-Koo (EUROCRYPT 2007) which is set in the offline-online paradigm (where the parties generate preprocessing data during the offline phase to lighten the computation in the online phase). Their online phase is a constant round protocol with no invocations of broadcast, while the offline phase protocol needs total 29 rounds with a broadcast invocation in one round. Our work improves the round complexity of their offline phase protocol, by running in 4 rounds, with only a single broadcast round. Additionally, we also improve the communication complexity of the offline phase protocol by a factor of Ω(n3). As a technical contribution, we present the first two round computationally-secure verifiable secret-sharing (VSS) scheme that invokes broadcast in only one round.
A Lean and Devops Approach to Teach Lean Software Development

This paper describes application of lean methodology in IT education in a context of an undergraduate course on “Lean Software Development” with a full devops pragmatics in mind. Strong connection between software development and delivery processes can be build on top of established lean practices. Which means that implementation of end-to-end automation by devops approach needs good understanding of lean principles and mindset. The course exposes students to the core concepts underneath lean development in software engineering, beyond myths and legends, emphasizing how it relates to the general principles of Lean Development. The principles behind Lean Management are crucial for the students, but often even among senior managers lean is confused with the application of some lean practices, which actually can be applied in any context even if they have been conceived inside a lean organization. So the goal of course is to understand the core of lean to the point of being able to understand its applicability in new software development environment. To achieve this goal, our paramount idea has been to get the students to “feel” what a lean approach is, therefore, we have decided to articulate the class in a series of activities that aim at bringing lean into the class.
Variational Bayesian Exploration-Based Active Sarsa Algorithm

We proposed an improved variational Bayesian exploration-based active Sarsa (VBE-ASAR) algorithm, which tries to balance the exploration and exploitation dilemma, and speeds up the convergence rate. First, in the learning process, variational Bayesian method is adopted to measure the information gain, which is used as an exploration factor to construct an internal reward function for heuristic exploration. In addition, before the learning process, in order to improve the exploration performance, transfer learning is used to initialize the value function, where Bisimulation metric is introduced to measure the distance between two states from the source MDP and the target MDP, respectively. Finally, we apply the proposed algorithm to the cliff walking problem, and compare with the Sarsa algorithm, the Q-Learning algorithm, the VFT-Sarsa algorithm and the Bayesian Sarsa (BS) algorithm. Experimental results show that the VBE-ASAR algorithm has a faster learning rate.
Mixed paradigms in Italo-Romance

This paper advocates a morphological approach to the phenomenon of mixed paradigms attested in a wide range of Italo-Romance varieties (cf. Loporcaro 2001, 2007, 2014; Manzini and Savoia 2005, among others). In these varieties, two auxiliary verbs, habere and esse, alternate within one and the same paradigm. As a result, such mixed paradigms exhibit various patterns which can range from morphosyntactically motivated to apparently unmotivated distributions (‘morphomic’). Starting from the notion of ‘inflectional periphrasis’ (cf., e.g. Brown 2012 et al.), under which auxiliary verb constructions can be accommodated, and from the notion of ‘lexical splits’ (cf. Corbett 2013, 2015, 2016), I describe the attested splits induced by such intraparadigmatic auxiliary alternations. Following Bonami (2015) and Štichauer (2016, 2018), I introduce a typology of such splits and I provide examples from the rich array of Italo-Romance data drawn mainly from Manzini and Savoia (2005). I conclude with a brief discussion of the historical origin of mixed paradigms arguing that the commonly accepted explanation (Bentley and Eythórsson 2001) is in need of further verification.
Aesthetics-Guided Graph Clustering With Absent Modalities Imputation

Accurately clustering Internet-scale Internet users into multiple communities according to their aesthetic styles is a useful technique in image modeling and data mining. In this paper, we present a novel partially supervised model which seeks a sparse representation to capture photo aesthetics. It optimally fuzes multi-channel features, i.e., human gaze behavior, quality scores, and semantic tags, each of which could be absent. Afterward, by leveraging the KL-divergence to distinguish the aesthetic distributions between photo sets, a large-scale graph is constructed to describe the aesthetic correlations between users. Finally, a dense subgraph mining algorithm which intrinsically supports outliers (i.e., unique users not belong to any community) is adopted to detect aesthetic communities. The comprehensive experimental results on a million-scale image set grabbed from Flickr have demonstrated the superiority of our method. As a byproduct, the discovered aesthetic communities can enhance photo retargeting and video summarization substantially.
A non-contact interactive stereo display system for exploring human anatomy

Stereoscopic display based on Virtual Reality (VR) can facilitate clinicians observing 3 D anatomical models with the depth cue which lets them understand the spatial relationship between different anatomical structures intuitively. However, there are few input devices available in the sterile field of the operating room for controlling 3 D anatomical models. This paper presents a cost-effective VR application for stereo display of 3 D anatomical models with non-contact interaction. The system is integrated with hand gesture interaction and voice interaction to achieve non-contact interaction. Hand gesture interaction is based on Leap Motion. Voice interaction is implemented based on Bing Speech for English language and Aitalk for Chinese language. A local database is designed to record the anatomical terminologies organized in a tree structure, and provided to the speech recognition engine for querying these uncommon words. Ten participants were asked to practice the proposed system and compare it with the common interactive manners. The results show that our system is more efficient than the common interactive manner and prove the feasibility and practicability of the proposed system used in the sterile field.
Criteria of morphometric analysis of a daily load profile

Analysis of electrical loads is crucial for a proper operation and control of electrical energy sources and planning and design of electrical power systems in terms of optimum capacity of the electricity generation. A typical daily load profile significantly varies over the 24-hour day and requires levelling actions which can be advised from the detailed analysis of the profile. This paper discusses the principles and implementation of morphometric analysis for a daily load profile evaluation using three criteria: roundness, compactness, and elongation. In order to conduct the morphometric analysis, the daily load profile represented as a time series has to be converted into a polygon of a particular form in a radar chart. The criteria for the profile analysis are based on geometrical interpretation of the shape of the polygon. The criterion roundness assesses the maximum and minimum loads of the profile and are related to the ratio of the inner circle of the polygon to the outer circle. The criterion compactness is based on the polygon perimeter and its inner area. The criterion elongation is defined as a relationship between the length of perpendicular to main axis of the polygon and the length of the main axis. The examples of the load profiles represented as a regular polygon and the case study have been used to demonstrate implementation of the analysis using the roundness, compactness, and elongation. It has been shown that the analysis using the morphometric criteria can be effectively applied for the detailed assessment of the load profiles.
Solar Radiation Measurement and Solar Radiometers

An instrument able to measure electromagnetic radiation, in its different forms and spectral ranges, is called a radiometer. This chapter focuses on the radiometers used for sensing solar radiation and on the measurements of different components and types of solar irradiance. As a simple classification, we will distinguish between broadband and spectral (narrowband) sensors. First, the fundamentals of physical sensors used to measure solar radiation are briefly described. Then, importance about calibration methods and uncertainty, as well as the structure of the traceability chain in the magnitude of solar irradiance, are presented. Next, solar radiometers and measurement techniques are described, starting from direct radiation in Earth’s surface, global irradiance in horizontal and tilted surfaces, diffuse irradiance, and finally another kind of radiation sensor. This structure is not casual but follows a path similar to that of the traceability chain, starting from the more accurate to the less accurate instruments. There are two additional sections devoted to the measurement of the spectral distribution of irradiance and to the measurement of aerosol contents in the atmosphere by using filter radiometers.
Distinguishability of adversarial examples

Machine learning models can be easily fooled by adversarial examples which are generated from clean examples with small perturbations. This poses a critical challenge to machine learning security, and impedes the wide application of machine learning in many important domains such as computer vision and malware detection. From a unique angle, we propose to investigate two important research questions in this paper: Are adversarial examples generated by different methods distinguishable from clean examples? Are adversarial examples generated by different methods distinguishable from each other? These two questions concern the distinguishability of adversarial examples. Answering them will potentially lead to a simple yet effective approach, termed as defensive distinction in this paper under the formulation of multi-label classification, for protecting against adversarial examples. We design and perform experiments using the MNIST and CIFAR-10 datasets to investigate these two questions, and obtain positive results demonstrating the strong distinguishability of adversarial examples.
From Words to Graphs, and Back

In 1918, Heinz Prüfer discovered a fascinating relationship between labelled trees with n vertices and words of length $$n-2$$ over the alphabet $$\{1,2,\ldots ,n\}$$ . Since the discovery of the Prüfer code for trees, the interplay between words and graphs has repeatedly been explored and exploited in both directions. In the present paper, we review some of the many results in this area and discuss a number of open problems related to this topic.
Head pose estimation using deep multitask learning

Head pose estimation (HPE) plays a vital role in the field of human–computer interaction and remains a challenging task due to individual differences. To mitigate this issue, we propose a simple yet effective deep multitask learning framework for joint HPE and face verification (FV), where FV acts as an auxiliary task to boost the performance of HPE. The framework comprises a backbone net, data separate module, and two branches for HPE and FV, respectively. Considering other regions beyond those of the face provides useful information for HPE while FV should focus only on face regions, two kinds of regions, head and face, which share common feature representations in the backbone net, are separated by the data separate module and then fed into the appropriate branches. Kullback–Leibler divergence loss and L2-constrained softmax loss are connected to the end of the HPE branch and FV branch, respectively, to optimize the architecture. The proposed method is validated on three publicly available datasets: Pointing04, CAS-PEAL-R1, and CMU multi-PIE. The experimental results demonstrate that our method surpasses the state of the art, showing up to a 16.38% improvement on the famous benchmark, Pointing04. The best accuracies we report on these datasets are 89.53%, 99.74%, and 99.72%, respectively.
Design of Diploma Supplement for Finding Students Themselves and for Improving their Academic Outcomes

Tokyo City University has designed a Diploma Supplement to assure quality of student skills at graduation. The Diploma Supplement is a radar chart that measures six areas (core literacy, core competency, language ability, core academic ability, specialist academic ability, and specialist competency) and is accompanied by qualitative evaluations. There is also a Pre-Diploma Supplement that is issued to students at the end of each academic year so that students learn to implement the PDCA cycle themselves and to find ways to improve their academic output.
Monitoring of Professionally Important Qualities of Students in the Conditions of Electronic Information and Educational Environment

The article deals with the issues related to the pedagogical monitoring technology used in the system of vocational education to identify the success of the development of professionally important qualities of students. Attention is drawn to the fact that the monitoring provides the teacher and the student the opportunity to compare the planned and actual results, to identify hidden reserves and guidelines for successful work, on the basis of which it is possible to outline a program of further action. It is indicated that the monitoring technologies, such as testing, reveal not only the level of development of professionally important qualities of the student, but also their compliance with specifi c standards obtained by analyzing the methods of performing a number of special tasks. Positive trends in educational situations in the electronic information and educational environment entail positive changes in the success of the development of professionally important qualities of students.
The Formation of a Situation of Success As a Method of Improving the Quality of Training of Students of Secondary Vocational Education in Accordance with International Standards

The article outlines the main priorities for the development of secondary vocational education in the regions of the Siberian Federal district. The author investigates the criteria infl uencing the improvement of the quality of training of students of secondary vocational education (SVE), in accordance with international standards, attempts to obtain an objective assessment of the quality of training carried out by professional educational organizations through detailed monitoring.
Risks and Societal Implications of Identity Theft

Transactions that involved identity theft are becoming increasingly popular in today’s society. Identity theft causes not only a violation of privacy for the victim, but also raises the possibility of increased stress for the victim and potential financial and/or legal consequences. Due to this, it is important to understand the nature and extent of the problem in detail so that novel identity management systems may be developed and eventually accepted. The focus of current exploratory research is to understand the spread and consequences of identity theft and fraud in Europe. A census representative on-line survey was carried out in Austria, France, Germany, Italy, Spain, the United Kingdom, and, for comparative purposes, in the United States of America. The research found that 25–30% of the adult population in the surveys countries experienced some form of misuse or attempted misuse of their personal information within the past three years. Extrapolating from the initial results, it is expected that around 100 million European citizens have dealt or experienced misuse of their personal information within the last 3 years and close to 40 million EU citizens had, because of the misuse of their personal information, incurred significant personal consequences ranging from debt collection to legal problems. Government issued electronic identity cards for on-line transactions are needed for electronic authentication and signatures, and their use in the private and public sector should become more widespread. Mobile ID can also service as a convenient and secure alternative to more traditional electronic identity cards.
Predictions and Modeling Energy Consumption for IT Data Center

Recent statistics of energy consumption by Cloud datacenter show the DCs consumes more and more energy each year that created big challenge in Cloud research. IT industry is keenly aware of the need for Green Cloud solutions that save energy consumption in Cloud DCs. A great deal of attention has been paid to minimize energy consumption in cloud datacenter. However, to understand the relationships between running tasks and energy consumed by hardware we need to propose mathematical models of energy consumption. The models of energy consumption can be help as to saving energy. Both researchers aim to proposed mechanism for energy consumption. In this paper, we analyzed the relationships between Cloud system manager and energy consumption. This paper aims at proposing and designing energy consumption models with mechanism of prediction energy.