Reviewerly/exHarmony huggingface dataset:
The Reality of the Globalizing World and Shaping the Professional Identity of an Individual in the World of “Boundaryless” Careers

Global transformations of the contemporary world, to which almost all aspects of social existence are subject, seem to be inevitable and their complexity difficult to grasp. On a micro and macro scale, new opportunities and prospects emerge, but also fears and anxieties, which are the contours of the society of the future. The qualitative specificity of new transformations in the relationship between the global and the local, society and the individual, and the links between them, is not without significance for the multi-contextual changes in the world of work, which place new demands upon employees. This vision implies definitely new ways of interpreting the world and the quality of judgments on the condition of contemporary man - including man as a “manager” of his own career. In a knowledge-based economy, which in turn makes the orientation of modern society towards knowledge a reality, the key element is the development of the careers of its members and investing in a career “portfolio”. Commitment to career development is characterized by the development of personal career goals, attachment to, identification with and involvement in these goals. Changes in the world of “borderless” careers update the problem of proactive behavior of individuals pursuing their careers. The distinguishing feature of proactive career planning, management and management is the awareness of being a subject who performs actions in the desired direction and influences the surrounding reality by initiating changes. Involvement in an internally defined career may be an important source of the crystallization of the (professional) identity of a subject, which seems necessary for the development of his or her career in the world of “borderless” careers in the reality of the globalizing world. Identity is formed in the context of social and cultural influences undergoing a permanent change in reality. Identity styles determined by social-cognitive processes refer to individual preferences in the processing of information about the subjective self, in decision making, in the choice of strategy for constructing or avoiding crystallization of one’s identity and the quality of career decisions in the world of “borderless careers”. In a situation where no a priori career scenario guarantees success, an investment in a career identified as “owned” by an entity becomes a necessity and forces its proactive planning and management.
A Dialog-Based Tutoring System for Project-Based Learning in Information Systems Education

This chapter discusses the design of a dialog-based intelligent tutoring system for the domain of Business Information Systems education. The system is designed to help students work on group projects, maintain their motivation, and provide subtle hints for self-directed discovery. We analyze the domain of Business Information Systems—which we find to be “ill-defined” in the sense that e.g. multiple conflicting solutions may exist and be acceptable for a given task. Based on an extensive collection of requirements derived from previous work, we propose a solution that helps both groups find solutions and individuals reflect on these solutions. This combination ensures that not only the group’s result is valid, but also that all group members reach the defined learning goals. We show how the complexity of the domain can be captured in a rather simple way via constraint-based engineering and how machine learning can help map student utterances to these constraints. We demonstrate the intended working principles of the system with some example dialogs and some first thoughts about backend implementation principles.
A Novel Adaptive Attention Model for Image Captioning

Abstract Image captioning is an extremely challenging work, which involves both computer vision and natural language processing. However, in recent years, with the introduction and improvement of various new technologies, it has made a great advancement. In this paper, a novel attention-based image caption model is proposed. We use Resnet harnessed by outstanding performance in convolutional neural network, as the encoder. For the decoding, LSTM network equipping with fitness of dealing with problems highly related to time series, is adopted. At each time step, the attention mechanism guides the language model to synthesize context vectors adaptively. The experimental results show that the model has good performance in various quantitative metrics as well as in human evaluation.
The genetic algorithm census transform: evaluation of census windows of different size and level of sparseness through hardware in-the-loop training

Abstract Stereo correspondence is a well-established research topic and has spawned categories of algorithms combining several processing steps and strategies. One core part to stereo correspondence is to determine matching cost between the two images, or patches from the two images. Over the years several different cost metrics have been proposed, one being the Census Transform (CT). The CT is well proven for its robust matching, especially along object boundaries, with respect to outliers and radiometric differences. The CT also comes at a low computational cost and is suitable for hardware implementation. Two key developments to the CT are non-centric and sparse comparison schemas, to increase matching performance and/or save computational resources. Recent CT algorithms share both traits but are handcrafted, bounded with respect to symmetry, edge lengths and defined for a specific window size. To overcome this, a Genetic Algorithm (GA) was applied to the CT, proposing the Genetic Algorithm Census Transform (GACT), to automatically derive comparison schemas from example data. In this paper, FPGA-based hardware acceleration of GACT, has enabled evaluation of census windows of different size and shape, by significantly reducing processing time associated with training. The experiments show that lateral GACT windows produce better matching accuracy and require less resources when compared to square windows.
Skeleton geometric transformation for human action recognition using convolutional neural networks

In this paper we present a methodology for understanding human actions. We try to compensate for viewpoint changes, by applying geometric transformations to 3D skeletal joint information. More specifically, motion information regarding human skeletal joints is pre-processed to create 2D image representations. Then a DST transformation is applied, to transform them to the spectral domain. Convolutional Neural Networks are then used for classification. We evaluate our approach in actions that may be used in an ambient assisted living scenario.
Performing Gesture and Time via an Emergent Database

This paper presents distinct modes of engaging with and structuring time, temporality and gesture as explicit units of semantic information. The work presents the newest iteration of a long-standing approach to instrumental system design and performance in which input gesture, sonic gesture and intermediate layers of information are stored as richly-structured "temporal semantic units" in a per-formable database, which can be queried via embodied action in order to be used in improvised performance contexts. This includes the mapping of temporal envelopes and the on-line training of machine learning algorithms. The history and lineage of ideas leading up to this current work are presented, as well as the novel system architecture and distinct modes of interaction that are composed from the perspective of the composer/performer engaged in the practice of electroacoustic improvisation.
Hybrid Violin Performance

My augmented violin is a personal instrument consisting of an acoustic violin, realtime signal processing, a custom sensor glove, and a violin shoulder rest embedded with voice coils for haptic feedback. Feature construction in software is not model-based but built by abductive experimentation---continuously refined by trial and error. My approach to movement analysis is not based on classifying formalized styles of bowing but on continuous tracking of non-tokenized movement and gesture. These techniques are co-developed with novel sonification methods ad libitum, conditioning the performance media without schematizing possible gestures a priori. Furthermore, ongoing development of the instrument, which includes novel haptic feedback elements, effectively symmetrizes sensory feedforward and feedback paths---the enactive loop between action and perception---yielding refined instrumental dynamics, yet the signal processing decisions entail no claim to universality or scientific validity. I improvise with this instrument in live performance, unfurling its possibilities and resingularizing my technique.
Digital Implementation of OS-ELM for Data Classification in Real-Time

Field-programmable gate array (FPGA) has been used as a very effective hardware platform in different research area as it enhances the efficiency of the embedded module. The accessibility of minimized, fast circuitry for a artificial neural networks (ANNs) is the most important and utmost necessity for many critical applications. In this paper, a single layer feed-forward neural network (SLFN) named as online sequential extreme learning machine (OS-ELM) is conferred and realized in digital platform for real-world data classification. The digital employment of OS-ELM supports to form an efficient hardware unit for data classification in real-time as the classifier has high learning speed and promising accuracy. Finally, the digital architecture of OS-ELM is implemented on a Virtex-5 FPGA hardware platform to validate the feasibility, efficacy, and vitality of the proposed classifier in real-time.
Agent-Based Mixed Reality Environments in Healthcare: The Smart Shock Room Project

Nowadays, the impressive development of smart technologies allows for designing novel kind of Personal Digital Assistant Agents (PDAA) supporting healthcare professionals in their individual and cooperative activities. Such technologies can have a disruptive impact in supporting those pathologies where the enhancement of both physician and the environment could be useful to reduce patient care times and to offer to physician new ways to access information and to be assisted by smart agents. This demo aims to present the prototype of the Smart Shock Room project. The project's purpose is to design and develop an innovative environment where smart technologies (e.g. mobile and pervasive computing, Mixed Reality, Vocal Assistants) can revolutionise the management of critical time-dependent pathologies (i.e. traumas) within the hospital emergency department.
Ergonomics in Mobile Fingerprint Recognition Systems: A User Interaction Evaluation

Fingerprint recognition represents the most installed recognition modality in modern mobile devices. Currently, there are thousands and thousands of smartphones equipped with fingerprint sensors; the type of sensor and its position on the device changes depending on the model. The fingerprints sensors are typically positioned in the home button (front side), even if sensors embedded on the backside or on the lateral side are equally common. In this paper, we research how the ergonomics of the mobile fingerprint systems influences the user experience and consequently the outcome of the identification. For this reason, we assessed the user interaction with a fingerprint recognition system installed in 3 smartphones (differentiated by the position of their fingerprint sensors). Data collected during the experiment let us analyse how the position of the sensors changes the recognition system’s performance and the user feelings while completing a biometric authentication.
Exploring the Design of History-Enriched Floor Interfaces for Asynchronous Navigation Support

Environmental cues influence our spatial behaviour when we explore unfamiliar spaces. Research particularly shows that the presence/actions of other people affects our navigation decisions. Here we examine how such social information can be integrated digitally into the environment to support navigation in indoor public spaces. We carried out a study (n=12) to explore how to represent traces of navigation behaviour. We compared 6 floor visualisations and examined how they affect participants' navigational choices. Results suggest that direct representations such as footprints are most informative. To investigate further how such visualisation could work in practice, we implemented an interactive floor system and used it as probe during one-to-one design sessions (n=26). We particularly focused on four design challenges: the overall visual representation, representation of multiple people, designing more prominent visualisations and the incorporation of non-identifying information. Our results provide insights for designers looking to develop history-enriched floor interfaces.
The Hasse Principle

The Hasse principle asks the natural question: if a polynomial equation has non-trivial solutions in R and in Q p for every prime p, can one deduce that it also has solutions in Q? For quadratic forms, the answer is encouraging, but for more general situations this is only a “principle”, which may be verified or not.
The "opaque panopticon"

Making the State transparent to citizens has been recognized as a sine qua non for good governance since the last two centuries. The pervasive use of Information and Communication Technologies (ICTs) in recent decades, by the State as well as the citizens, has led to a new-found optimism in finally making governments transparent to citizens. Yet, such enthusiasm rarely leads to a sustainable impact on citizen-State relations. In this paper, we look at the artefacts, i.e. the websites, and apps and the interaction of citizens with these artefacts to understand the barriers to the transparency of the State. Based on a year-long ethnography of India's livelihood program, the Mahatma Gandhi National Rural Employment Guarantee Act (MGNREGA), we find that the web portals are not appreciative of citizen's context and needs. Specifically, we notice three veils of opacity - the formal-legible form of transparency, the inaccessibility of information due to design, and finally the content of the information which dissuades citizens from seeing the state functioning transparently. Rather than enabling the citizens to hold the State actors to account, the opacity of the intended panopticon leads to disempowering the citizens of their rights, ultimately having them exit the program rather than enhancing their voice. We conclude by arguing for the need to recentre discussions on e-transparency towards the citizens.
Chapter 7. Navigating the academic job market

An important goal for most PhD students is to procure gainful employment. Although PhD students receive a great deal of training in the areas of critiquing, conducting, and publishing research, the process of searching, applying for, and securing an academic job is one that is often shrouded in mystery. This chapter attempts to shed some light on this arduous and stressful task, and in doing so make it less mysterious. Although the chapter is written with PhD students in mind, it may also be helpful for early career scholars with limited experience on the job market. Regardless of your individual background/circumstance, training, and previous job-searching experiences (or lack thereof), the goal is a shared one: To navigate the academic job market effectively and obtain one or more job offers. In this chapter, we cover three key phases in the job search process, including preparing for the job market (as early as possible), starting the search (approximately one year before one hopes to be employed), and excelling in job interviews. In each section, we highlight key aspects of the preparation stage, and provide specific steps an aspiring academic can take. It is our experience that few reasonably comprehensive resources exist that provide insight into the entire process of securing an academic job (see Kelsky, 2015). Furthermore, few resources offer the joint perspective(s) of new (i.e., Avizia and Kris) and seasoned (i.e., Graham) scholars who have successfully obtained academic jobs in the field. A particular goal of this chapter is to provide such a resource within the particular context of the field of applied linguistics.
Performance Analysis of Start-Step-Stop Codes and Gopala-Hemachandra Codes2 (GH-2(n)) Ascompression Algorithms on Text Files

Abstract Data compression was born as a solution to human needs for digital data. This research compares two compression algorithms, namely the Start-Step-Stop code algorithm and Gopala-Hemachandra codes 2 (GH -2 (n)) algorithm in the case of text file compression. compression ratio (CR), space savings (SS), bit rate and running time are calculated in the test as consideration to compare the advantages of both algorithms. Tests carried out using homogeneous strings, heterogeneous strings and artificial corpus. In homogeneous string compression, both algorithms have the same advantages because the value of CR, SS and bit rate are the same for each number of strings used. From the time of compression, Start-Step-Stop algorithm faster than the Gopala-Hemachandra algorithm. Testing of heterogeneous strings found that the Start-Step-Stop algorithm is superior to the Gopala-Hemachandra algorithm because it can save more storage space.
Design and Implementation of Wi-Fi-Trusted Authentication System Based on Blockchain

This paper first studies the operation mode and security mode of the traditional centralized Wi-Fi-shared network and summarizes the problems of centralized Wi-Fi-shared network. On the basis of studying the working principle and technical characteristics of blockchain, a P2P network sharing model based on blockchain is proposed. Based on studying the working principle and technical characteristics of blockchains, a Wi-Fi network sharing model based on blockchains is proposed, and then blockchains are constructed. It can improve the utilization rate of Wi-Fi resources, convenience of using Wi-Fi resources, security of Wi-Fi-shared network and cost of Wi-Fi-shared operating platform.
On using Euler’s Factorization Algorithm to Factor RSA Modulus

Abstract The RSA public key cryptosystem was among the first algorithms to implement the Diffie-Hellman key exchange protocol. At the core of RSA’s security is the problem of factoring its modulus, a very large integer, into its prime factors. In this study, we show a step-by-step tutorial on how to factor the RSA modulus using Euler’s factorization algorithm, an algorithm that belongs to the class of exact algorithms. The Euler’s factorization algorithm is implemented in Python programming language. In this experiment, we also record the relation between the length of the RSA moduli and its factorization time. As a result, this study shows that the Euler’s factorization algorithm can be used to factor small modulus of RSA, the correlation between the factoring time and the size of RSA modulus is directly proportional, and better selection of some Euler’s parameters may lead to lower factoring time.
STL-FNN: An Intelligent Prediction Model of Daily Theft Level

Theft is a long-standing crime against property, which exists alongside the development of private ownership. If the time pattern of thefts could be found out well, police officers can take preventive measures in advance to control the occurrence of theft crimes. However, thieves are smart enough to pick targets randomly. This makes it difficult for police to predict accurately when and where the offenders would commit crimes. In this paper, STL-FNN is proposed based on a seasonal trend decomposition procedure based on loess (STL) and full-connected neural network (FNN),which is designed to predict daily theft level in order to find out when criminals are most likely to commit crimes. The empirical case of prediction of daily theft level in City B shows that the STL-FNN model is better than the other five traditional models for prediction of the long-term sequence (365 days). This model is expected to have high potential application value in the schedule of anti-theft activities planned by polices.
Objective Video Quality Assessment Based On SURF Feature Matching

The significant consumption of video data over limited band- width, mostly compromises in video quality. Video quality assessment (VQA) algorithms aim to estimate the quality of a distorted video data. For this, the scores are created in such a way that agrees with the quality judgments aligned to human visual perception. To measure the quality of the video, we need to compare the subjective score and objective score. We proposed novel work on SURF feature-based VQA. SURF is used to compare the features in the original video against features of the distorted video. A mechanism using weighted features is illustrated to provide a better quality assessment. The subjective score is available in many image databases. Ex. LIVE, IPVL, and CSIQ database. An objective score is obtained by using formulas, and it is used to find a correlation between subjective score and objective score. The efficacy of the proposed method is evaluated against state-of-the-art VQA algorithms. Our method is observed to be consistent with the best VQA results.
Using RGB filters and a circularly polarized system to measure the deflection radius of optically anisotropic-polymeric substrates with a sub-grayscale resolution

Transparent optically anisotropic substrates (TOASs) are a crucial component in flexible electronics. Moreover, knowing the deflection radii of TOASs is essential to estimate the residual stresses of the thin films deposited on them. This paper proposes a novel measurement method to measure the deflection radii of polyimide (PI) substrates. Red, green, and blue filters and a circularly polarized system were used in the proposed method to investigate the influence of measurement precision on the wavelength of light adopted. Moreover, the image averaging technique was adopted to significantly reduce the noise in the fringe. Thus, the proposed method could be performed using only one set of image information and the measurement resolution could be improved to the sub-grayscale order. The experimental results reveal that red light can be adopted to precisely measure the deflection radii of PI substrates (error of 2.90%) However, large errors are caused when green light is used (22.35%). The experimental results reveal that blue light cannot be used to obtain any interferometric fringe patterns to calculate the deflection radius.
Should You Use a Vote Module for Sentiment Classification of Online Social Text?

In this work, we conduct a study where we compare the usage of a single classifier and the usage of a majority vote system composed of multiple classifiers. Each classifier was created using machine learning techniques and trained with real data. For the domain, we considered textual expressions present on online social networks, which can be volatile in characters count. This work seeks to prove two hypothesis: (1) the usage of a vote module that considers the output of an odd number of classifiers, will address the advantages characteristics of each classifier while mitigating their disadvantages; (2) the usage of a vote system will enable classifiers to correctly classify new labels that were not defined in the training process (like classifying neutral sentiment in addition to positive and negative). Our vote module is composed by a Naïve Bayes, a Logistic Regression, and a Support Vector Machine classifier. The tests that we conducted consider the online social textual content that varies in the character counting and our results suggests that there is no need for a vote system when considering the online social content, like comments, that is typically informal and do not surpass the count of 500 characters.