Miracle-dz/UniFAR-FTUs huggingface dataset:
Early Development of New Technology-Based Firms: How Internal and External Resource Dimensions Impact and Structure the Firm

How do founders’ and firms’ business environment impact the early development of new technology-based firms (NTBFs)? In order to answer this question, this thesis explores how internal and external resource dimensions impact and structure the early development of NTBFs. NTBFs are known for their technological innovation and their abilities to boost economic growth and development. These new, young firms are vulnerable in their first years of development, and their development is dependent on resources related to the founders and external business environment. Impacts from these internal and external resource dimensions provide means for firms to develop. Explaining how these dimensions together influence the early development of NTBFs would broaden the perspective on these firms in their first years, clarifying the type of support required for these firms. Examining NTBFs both qualitatively and quantitatively, the thesis analysed the structuring of the early development and the impacts on it, including business- and innovation performance, and initial business models. Findings reveal that the early development is affected by internal resource dimensions, such as founders’ previous business experiences and relationships within their business environment, and by external resource dimensions such as the type of business networks and firm localisation. However, impacts from these internal or external resource dimensions differ over time depending on founders’ experiences, maturity, and self-trust, and hence one dimension or the other dominates the early development. The thesis contributes to the research on NTBFs and entrepreneurship, describing and analysing imprinting effects of the aforementioned dimensions, such as founders’ attitudes, on the firms’ early development. It also discussed how the external business environment becomes less influential on the firms’ development as the founders rely more on own decisions to do business.
The Covert Advertising Recognition and Effects (CARE) model: Processes of persuasion in native advertising and other masked formats

Abstract Covert advertisements, or those that utilize the guise and delivery mechanisms of familiar non-advertising formats, differ from other more direct forms of advertising in several ways that are important for understanding users’ psychological responses. Research across various covert advertising formats including various forms of sponsored editorial content, other native advertising formats, and product placement has shown that variation consumers’ persuasive responses to such messages is largely driven by whether they recognize that such messages are advertising at all. After reviewing the findings of empirical research into covert advertising effects, we present a model of covert advertising recognition effects (CARE) that outlines potential antecedents and processes underlying the recognition of covert advertising, and maps several pathways to persuasive outcomes that are contingent on advertising recognition and perceptions related to the information in and perceived presentation of the advertisement itself.
Analisis Prediksi Potensi Kebangkrutan Pt Indo Asia Sukses Dengan Model Zmijewski Dan Model Springate

This research aims to analyze potential bankruptcies of PT Indo Asia Sukses using Zmijewski and Springate method in period of 2016-2018. The data used is Primary data coming from the company. This research uses qualitative method with two data collections, those are documentation and theoritical study method. The result of research shows that: (1) According to Zmijewski X-Score, the company was in good condition with the negative result in 2016-2018. (2) According to Springate S-Score, the company was in good condition in 2016-2018, but the first quarter of 2017 showed the company had potential to get bankrupt. According to the result of research, it hoped that the company could improve the sales beacuse the research shows that low in sales could get the company bankrupt in first quarter of 2017 and the company could gain more awareness of financial if similar condition happening in the future.
Research on Adaptive UKF Algorithm in Integrated Navigation System

According to the characteristics of the combined navigation system and the adaptive UKF filtering algorithm, the navigation accuracy of the integrated navigation system is improved. Firstly, the algorithm of the UKF filtering algorithm is analyzed and the characteristics of the UKF algorithm are summarized, and the adaptive factor is introduced into the UKF filtering algorithm by the adaptive estimation principle, and the improved adaptive UKF algorithm is obtained, and the algorithm is applied to the integrated navigation system. Finally, the simulation calculation of the computer is carried out. The simulation results of adaptive UKF algorithm applied to integrated navigation system are improved, and the results are compared with the results of UKF algorithm. The experimental results show that the improved adaptive UKF can effectively suppress the influence of the initial value selection of the integrated navigation system, reduce the disturbance of the system state model, and effectively improve the precision of the integrated navigation. Keywords—component; adaptive estimation principle; Unscented Kalman Filter; integrated navigation
Inalienable Customer Capital, Corporate Liquidity, and Stock Returns

We develop a model in which customer capital depends on key talents' contribution and pure brand recognition. Customer capital guarantees stable demand but is fragile to financial constraints risk if retained mainly by talents, who tend to quit financially constrained firms, damaging customer capital. Using a proprietary, granular brand-perception survey, we construct a firm-level measure of the inalienability of customer capital (ICC) that captures the degree to which customer capital depends on talents. Firms with higher ICC have higher average returns, higher talent turnover, and more precautionary financial policies. The ICC-sorted long-short portfolio's spread comoves with financial constraints factor.
Résolution de problèmes de tournées avec synchronisation : applications au cas multi-échelons et au cross-docking

L’interconnexion croissante dans les systemes de transports a conduit a la modelisation de nouvelles contraintes, dites contraintes de synchronisation, dans les problemes de tournees de vehicules. Dans cette these, nous nous interessons a deux cas dans lesquels ce type de problematiques apparait. Dans un premier temps, nous proposons une methode heuristique pour un probleme a deux echelons rencontre pour la distribution de marchandises en ville. Dans un second temps, nous etudions l’integration d’un cross-dock dans des tournees de collectes et livraisons. Une premiere contribution a ce sujet concerne le probleme de tournees de vehicules avec cross-docking, et une seconde contribution integre, en plus, des contraintes de ressources au cross-dock dans le probleme de routage. Une methode pour un probleme de chargement 3D, etudie lors d’un stage doctoral en entreprise, est egalement presentee.
A self-adaptive subtraction algorithm for dynamic background video

This paper presents an effective background modeling method that incorporates adaptive mechanism for the dynamic background. Each pixel in the background model is defined by a history of the N most recent image values at each pixel. It then compares the model with the current pixel value to determine whether or not the pixel belongs to the background using the decision threshold. We design the Time-spatial dynamic feature (TSD feature) innovatively to describe the dynamic background. According to the TSD feature, the decision threshes can be adjusted adaptively with feedback loops that overcome global threshold influence for dynamic background. Updating the background model is essential in order to account for changes in the background, such as moving background objects and lighting changes. The update rate in the background model also can be adjusted adaptively with the background changes based on the TSD feature. The experimental results demonstrate that the proposed algorithm outperforms several state-of-the-art methods on dynamic background video sequences.
Value-Flow-Based Demand-Driven Pointer Analysis for C and C++

We present Supa, a value-flow-based demand-driven flow- and context-sensitive pointer analysis with strong updates for C and C++ programs. Supa enables computing points-to information via value-flow refinement, in environments with small time and memory budgets. We formulate Supa by solving a graph-reachability problem on an inter-procedural value-flow graph representing a program's def-use chains, which are pre-computed efficiently but over-approximately. To answer a client query (a request for a variable's points-to set), Supa reasons about the flow of values along the pre-computed def-use chains sparsely (rather than across all program points), by performing only the work necessary for the query (rather than analyzing the whole program). In particular, strong updates are performed to filter out spurious def-use chains through value-flow refinement as long as the total budget is not exhausted. We have implemented Supa on top of LLVM (4.0.0) together with a comprehensive micro-benchmark suite after a years-long effort (consisting of around 400 test cases, including hand-written ones and the ones extracted from real programs). We have evaluated Supa by choosing uninitialized pointer detection and C++ virtual table resolution as two major clients, using 24 real-world programs including 18 open-source C programs and 6 large CPU2000/2006 C++ benchmarks. For uninitialized pointer client, Supa achieves improved precision as the analysis budget increases, with its flow-sensitive (context-insensitive) analysis reaching 97.4 percent of that achieved by whole-program Sparse Flow-Sensitive analysis (SFS) by consuming about 0.18 seconds and 65 KB of memory per query, on average (with a budget of at most 10,000 value-flow edges per query). With context-sensitivity also considered, Supa becomes more precise for some programs but also incurs more analysis times. To further demonstrate the effectiveness of Supa, we have also evaluated Supa in resolving C++ virtual tables by querying the function pointers at every virtual callsite. Compared to analysis without strong updates for heap objects, Supa's demand-driven context-sensitive strong update analysis reduces 7.35 percent spurious virtual table targets with only 0.4 secs per query, on average.
Banks' Screening and the Leverage of Newly Founded Firms

Banks finance newly-founded firms extensively despite severe asymmetric information. Whereas the demand for credit usually follows from entrepreneurs' lack of liquidity, we ask why and how banks supply credit to new firms of unknown value. We propose a model of credit allocation in which, due to banks' screening, new firms with higher future value issue debt below capacity to avoid costly investment delay. Consistent with this prediction, new firms with a lower first leverage ratio (initial leverage) perform better ex post in a unique US representative sample. In the model and in the data, this negative association is more pronounced in lending markets in which banks have greater market power, and also stronger incentives to screen. Our results are robust to controlling for characteristics such as entrepreneurs' net worth, skin in the game, and education levels. We conclude that banks ensure that profitable new ventures develop.
Personalized Product Service Recommendation Based on User Portrait Mathematical Model

In the “Internet+” digital age, the use of customers’ information was not fully utilized when pushed the information. The traditional similarity exists some problems, such as incalculable, indistinguishable and high-level etc., it results in poor pertinence of personalized product services and a decline in the quality of recommendations. Concerning this shortcoming, this paper proposes a collaborative filtering recommendation algorithm based on the user's portrait model customer rating, and use the customer's rating results to make recommendations. To this end, a “user portrait” mathematical model was constructed, the similarity was improved by using a discrete-volume correlation theory and was weighted approaching to the users’ preference. It would be more accurate for “K” nearest neighbor set by similarity calculation. Furthermore, this paper recommends more suitable products to users. The experiment was conducted with the customer's sales data of Le Bee net Cosmetics, it shows that the method proposed in the paper improves the accuracy of the recommendation effectively and improves the recommendation quality to some extent.
Corpus-based learning of tonal expectations with expectation networks

ABSTRACT Although tonal expectations are an essential element of musical meaning in Western art music, it remains unclear whether the distinct melodic expectations associated with distinct scale degrees can be derived directly from musical corpora. Drawing on an analogy to neural learning, the current study outlines expectation networks, a novel and computationally simple method of measuring the expectation of encountering a given pitch followed in relatively close temporal proximity by another given pitch under a particular tonal system. Comparing expectation networks with listener ratings of asymmetrical pitch similarity and goodness of continuation between pitches reveals a large and highly significant effect, pointing to overlap between listeners’ judgments of tonal distance and corpus-derived expectations as to which pitches tend to be grouped close together in time.
A Prototype Design for a Virtual Hospice and Initial Usability Study

Hospice provides physical, social, emotional and spiritual care for people with life-shortening illness. Providing equitable access to services is becoming increasingly difficult for all hospices due to a rising number of people living longer with life-shortening illness and limited healthcare resources. Consequently, hospices are increasingly looking at ways of using technology to deliver services over a distance. This paper presents a prototype design for a web-based system (‘virtual hospice’) to improve access to services provided by Highland Hospice in the UK, and an initial usability study involving three elderly male patients. Participants completed most of the usability tasks, made positive comments, and would definitely or likely recommend the system to people who might benefit from it. The findings were translated into recommended changes to the virtual hospice, and may be helpful for other HCI designers and researchers working in this area.
Automatic image style transfer using emotion-palette

Palette-based color editing can form an intuitive interface through which novices can perform interesting color modifications. However, because such modifications involve pixel-level operations, considerable labor is needed to accomplish an image’s emotion transformation. In this paper, we propose a novel color-transferring framework for image based on palettes to evoke different emotions. The purpose is to use the emotion-related color combinations to change the picture's original color palette to obtain stylized images. For an input image, the optimized k-medoids algorithm is used to create a set of the most representative color palettes. The system search for the best color combinations from a predefined database according to the color palette characteristics of the input image. Finally, using the selected emotion-palette we conduct a color transfer. The novel method can transfer a wide range of image into a new color style depicting the required emotional impact.
JOB RECOMMENDATION USING FACEBOOK PERSONALITY SCORES

Facebook is one of the most popular social media sites that has become part of our lives. User-generated Facebook data are useful and can be used to gauge personality. However, previous studies did not use Facebook data for personality assessment and mapping for professional purposes. The current study mainly aims to identify personality features using user-generated content in Facebook. A computational score is created and a model is developed by utilizing these scores in job recommendations that match the personality of the user. The personality score of Facebook is benchmarked against the Big Five Inventory (BFI) test score to determine accuracy. The scores of Facebook personality scores and BFI test reached 93.1%. The findings of this study benefits job candidates, especially fresh graduates by assisting them in identifying a career that suits their personality. This study also helps create awareness among individuals by identifying the personality strengths and weaknesses through the use of Facebook information. This study can help employers find candidates who fit the needs of the company by gauging their personality through Facebook data.
Assembly Sequence Planning by Probabilistic Tree Transformation

Various types of computer systems including CAD/CAM systems have been introduced in machine industry. Some of the systems can handle assembly sequence planning, however it requires long time for planning. This paper proposes a method of generating assembly sequences efficiently. This method extracts some parts and/or subassemblies whose possibilities of being removed from a product are strong, and tests whether they can be removed without any geometric interference. By performing these operations repeatedly, the method generates a disassembly sequence of the product, and obtains an assembly sequence by reversing it. The extraction of some parts and/or subassemblies is performed, based on probabilistic tree transformation. The authors present a calculation example by using a software tool integrated with a CAD system.
Crossing a topological phase transition with a quantum computer

Quantum computers promise to perform computations beyond the reach of modern computers with profound implications for scientific research. Due to remarkable technological advances, small scale devices are now becoming available for use. One of the most apparent applications for such a device is the study of complex many-body quantum systems, where classical computers are unable to deal with the generic exponential complexity of quantum states. Even zero-temperature equilibrium phases of matter and the transitions between them have yet to be fully classified, with topologically protected phases presenting major difficulties. We construct and measure a continuously parametrized family of states crossing a symmetry protected topological phase transition on the IBM Q quantum computers. The simulation that we perform is easily scalable and is a practical demonstration of the utility of near-term quantum computers for the study of quantum phases of matter and their transitions.
PRAGMATIC FUNCTIONALITY OF PUNCTUATION ON TWITTER

OF THESIS PRAGMATIC FUNCTIONALITY OF PUNCTUATION ON TWITTER This work presents an analysis of punctuation use in computer-mediated communication (CMC); in particular, the present study aims to describe the pragmatic functions of nonstandard punctuation on Twitter, providing a corpus-driven overview of the distribution and frequency of nonstandard punctuation use, and an analysis of sampled tweets at the individual tweet level to estimate noise levels in the overall corpus. A survey was also conducted which aimed to identify user understanding of the affective content of nonstandard punctuation strings and to identify any possible effects of character repetition. Survey results indicate that linguistic content was the strongest indicator of affective understanding, type of punctuation (i.e., ? , ! , and combinations thereof) was a weaker indicator of some affective content, and repetition was not found to be significant. The study argues that certain string types, possibly defined by punctuation type and not count, have large indexical fields of pragmatic meaning available to them, which are bounded by context. In light of these observations, the study also proposes distinctions/categories of punctuation strings and their associated pragmatic meanings.
Spoofing and Anti-Spoofing with Wax Figure Faces

We have witnessed rapid advances in both face presentation attack models and presentation attack detection (PAD) in recent years. Compared to widely studied 2D face presentation attacks (e.g. printed photos and video replays), 3D face presentation attacks are more challenging because face recognition systems (FRS) is more easily confused by the 3D characteristics of materials similar to real faces. Existing 3D face spoofing databases, mostly based on 3D facial masks, are restricted to small data size and suffer from poor authenticity due to the difficulty and expense of mask production. In this work, we introduce a wax figure face database (WFFD) as a novel and super-realistic 3D face presentation attack. This database contains 2300 image pairs (totally 4600) and 745 subjects including both real and wax figure faces with high diversity from online collections. On one hand, our experiments have demonstrated the spoofing potential of WFFD on three popular FRSs. On the other hand, we have developed a multi-feature voting scheme for wax figure face detection (anti-spoofing), which combines three discriminative features at the decision level. The proposed detection method was compared against several face PAD approaches and found to outperform other competing methods. Surprisingly, our fusion-based detection method achieves an Average Classification Error Rate (ACER) of 11.73\% on the WFFD database, which is even better than human-based detection.
Representation stability, secondary stability, and polynomial functors.

We prove a general representation stability result for polynomial coefficient systems which lets us prove representation stability and secondary homological stability for many families of groups with polynomial coefficients. This gives two generalizations of classical homological stability theorems with twisted coefficients. We apply our results to prove homological stability for hyperelliptic mapping class groups with twisted coefficients, prove new representation stability results for congruence subgroups, establish secondary homological stability for groups of diffeomorphisms of surfaces viewed as discrete groups, and improve the known stable range for homological stability for general linear groups of the sphere spectrum.
Variational Tracking and Prediction with Generative Disentangled State-Space Models

We address tracking and prediction of multiple moving objects in visual data streams as inference and sampling in a disentangled latent state-space model. By encoding objects separately and including explicit position information in the latent state space, we perform tracking via amortized variational Bayesian inference of the respective latent positions. Inference is implemented in a modular neural framework tailored towards our disentangled latent space. Generative and inference model are jointly learned from observations only. Comparing to related prior work, we empirically show that our Markovian state-space assumption enables faithful and much improved long-term prediction well beyond the training horizon. Further, our inference model correctly decomposes frames into objects, even in the presence of occlusions. Tracking performance is increased significantly over prior art.