Miracle-dz/UniFAR-FTUs huggingface dataset:
Professional Connections within Analyst Teams and Forecast Accuracy

Understanding factors associated with the performance of the sell-side analyst is of great value in both theory and practice. We profile the crucial influence of professional connections among security analysts on generating earnings forecast in this study based on a dataset of analysts’ employment history obtained from the Securities Association of China and analysts’ history forecasts obtained from CSMAR. By estimating a multivariate linear regression model, we find that professional connections within analyst teams are positively associated with earnings forecast accuracy. The results of Fisher's Permutation test further confirm that the effect of professional connections on analyst forecast quality is stronger when firms covered are facing decreasing EPS. Our results still hold for additional analyses with an alternative measure for professional connections. In conclusion, this work confirms the significant role of professional connections in information sharing and mutual understanding among analysts.
Extending Loyalty Programs with BI Functionalities

Effective customer loyalty programs are essential for every company. Small and medium sized brick-andmortar stores, such as bakeries, butcher and flower shops, often share a common overarching loyalty program, organized by a third-party provider. Furthermore, these small shops have limited resources and often cannot afford complex BI tools. Out of these reasons we investigated how traditional brick-andmortar stores can benefit from an expansion of service functionalities of a loyalty card provider. To answer this question, we cooperated with a cross-industry customer loyalty program in a polycentric region. The loyalty program was transformed from simple card-based solution to a mobile app for customers and a webapplication for shop owners. The new solution offers additional BI services for performing data analytics and strengthening the position of brick-and-mortar stores. Participating shops can work together in order to increase sales and align marketing campaigns. Therefore, shopping data from 12 years, 55 shops, and 19,000 customers was analyzed.
A Design Methodology for Learning Analytics Information Systems: Informing Learning Analytics Development with Learning Design

The paper motivates, presents and demonstrates a methodology for developing and evaluating learning analytics information systems (LAIS) to support teachers as learning designers. In recent years, there has been increasing emphasis on the benefits of learning analytics to support learning and teaching. Learning analytics can inform and guide teachers in the iterative design process of improving pedagogical practices. This conceptual study proposed a design approach for learning analytics information systems which considered the alignment between learning analytics and learning design activities. The conceptualization incorporated features from both learning analytics, learning design, and design science frameworks. The proposed development approach allows for rapid development and implementation of learning analytics for teachers as designers. The study attempted to close the loop between learning analytics and learning design. In essence, this paper informs both teachers and education technologists about the interrelationship between learning design and learning analytics.
Gatekeepers Rather than Helpless: An Exploratory Investigation of Seniors' Use of Information and Communication Technology in Critical Settings

Reports and studies often show that seniors suffer disproportionately during disasters. Yet seniors’ handling of information and communication technology is largely overlooked. Based on a qualitative study comprising interviews and focus groups, this research shows important properties of seniors’ practices with information in critical settings. Seniors embody and tap into local knowledge, mingle offline and take online cues about emergency situations, and maintain trust towards institutions. We discuss the need to pursue and diversify investigation on this topic, as well as the role of seniors as gatekeepers rather than considering them helpless individuals. This research contributes to a better understanding of seniors’ use of digital resources by highlighting their role in information sharing in disaster settings. We pave the way for future research to inform policy making and support seniors’ survival in future disasters.
A Review on Consumer Health Information Technology Research in IS

While there is a rapid growth in the application of consumer health information technology (CHIT), its growth as an area of interest in IS research is still relatively slow. While there is great potential for research in this area, knowledge barriers to conducting CHIT research do exist. These include a lack of a clear definition of CHIT and lack of knowledge on the current state of CHIT research in IS. To overcome these barriers, we offer a definition of CHIT and then use that definition, together with the IT artifact perspective, to conduct a thematic analysis of CHIT research in the IS domain. We find that CHIT research spans all five IT views but to different degrees: nominal, proxy, and tool views are the most widely used perspectives. Based on our analysis, we suggest future research directions to enrich understanding of CHIT.
Formative Evaluation of Data-Driven Business Models - The Data Insight Generator

New technological developments such as Big Data or, the Internet of Things lead to exponentially increasing amounts of data created and stored by organizations. As a consequence, new data-driven business models (DDBMs) appear. These business models have special characteristics which need to be included in the business model development process. Thus, different methods and tools have emerged to support the development of DDBMs. One of these is the Data Insight Generator (DIG) which seeks to combine the key resource and value proposition of a DDBM. This paper comprises the application of the thinking-aloud method for a formative evaluation of the DIG. The contribution of this paper is twofold. First, the usability of the DIG is tested and implications for further development are derived. Second, the paper provides empirically-based insights into development of DDBM that facilitate the future development of such business models.
The Pursuit of Sustainable ICT4D: Lessons from Timor-Leste

Information and communication technology for development (ICT4D) initiatives often result in abandonment following a successful technical implementation. This article contributes to the literature on the sustainability of ICT4D projects by proposing a substantive theory of Cultivating Sustainability. A qualitative study, based on three ICT4D implementation cases in a least developed country, served to explore the question of how the sustainability of ICT4D could be enhanced. This paper suggests that sustainability needs to be cultivated proactively and continuously, from project initiation to benefit realization. The article indicates specific strategies that can help least developed countries achieving the long-lasting benefits donors and recipients anticipated.
Insights into Adolescent Online Conflict through Qualitative Analysis of Online Messages

Given adolescents’ widespread use of online messaging and social media, as well as the prevalence of cyberbullying, analyzing adolescents’ online messagebased communication topics and patterns is relevant to public health. To better describe conflict in adolescent online communication, this paper analyzes patterns of conflict in a dataset of adolescent online messages. We describe a qualitative methodology for analyzing these complex data, to expand understanding of adolescents’ online conversations, and to identify how best to categorize conflict within online media datasets. In this study, 14,239 messages from 20 adolescents in the Northeast United States (of which 1,911 were coded) were analyzed using thematic analysis. Several distinct kinds of conflict and responses were identified. Conflict was either direct or indirect, serious or non-serious; it most often was indirect and serious, referenced either insults or romantic contacts, and was frequently related to in-person fights. Coding relied on understanding both textual contexts and referents.
Visual Analysis for Spatio-Temporal Event Correlation in Manufacturing

The analysis of events with spatio-temporal context and their interdependencies is a crucial task in the manufacturing domain. In general, understanding this context, for example investigating error messages or alerts is important to take corrective actions. In the manufacturing domain, comprehending the relations of errors is often based on the technicians’ experience. Validation of cause-effect relations is necessary to understand if an effect has a preceding causality, e.g., if an error is the result of multiple issues from previous working steps. We present an approach to investigate spatio-temporal relations between such events. Based on a time-sensitive correlation measure, we provide multiple coordinated views to analyze and ﬁlter the data. In collaboration with an industry partner, we developed a visual analytics approach for error logs reported by machines that covers a multitude of analysis tasks. We present a case study based on real-world event logs of an assembly line with feedback from our industry partner’s domain experts. Furthermore, we discuss how our approach is applicable in other domains.
Determining Project Contingency Reserve Using a Fuzzy Arithmetic-Based Risk Analysis Method

Traditional techniques for estimating contingency reserve fail to capture subjective uncertainties and expert knowledge, and they rely on historical data. This paper proposes a fuzzy risk analysis model (FRAM) that uses fuzzy arithmetic to analyze risk and opportunity events and determine construction project contingency reserve. The FRAM allows experts to use natural language to assess the probability and impact of risk and opportunity events by employing linguistic scales represented by fuzzy numbers, thus addressing the data reliance problem of probabilistic methods. It enables experts to customize linguistic scales and fuzzy numbers for different project types and stages. The FRAM also deals with the challenges associated with deterministic approaches by addressing measurement imprecision and the subjective uncertainty of experts’ opinions. Moreover, the FRAM allows analysts to estimate contingency at different levels of confidence. This paper also illustrates Fuzzy Risk Analyzer© (FRA©), software that implements the fuzzy arithmetic procedure of the FRAM.
Towards Empowering Educators to Create their own Smart Personal Assistants

Despite a growing body of research about the design and use of Smart Personal Assistants such as Amazon’s Alexa or Google’s Assistant, little is known about their ability to help educators offering individual support in large-scale learning environments. Smart Personal Assistant ecosystems empower educators to develop their own agents without deep technological knowledge. The objective of this paper is to design and validate a method that helps educators to create Smart Personal Assistants for their learning environments. Using a design science research approach, we first gather requirements from students and educators as well as from information systems and education theory. Next, we create an alpha version of our method and evaluate it with a focus group before we instantiate our artifact in an everyday learning environment. The findings indicate that our method is able to empower educators to design Smart Personal Assistants that significantly improve students’ learning success.
Automatic Segmentation of Grammatical Facial Expressions in Sign Language: Towards an Inclusive Communication Experience

Nowadays, natural language processing techniques enable the development of applications that promote communication between humans and between humans and machines. Although the technology related to automated oral communication is mature and affordable, there are currently no appropriate solutions for visualspatial languages. In the scarce efforts to automatically process sign languages, studies on non-manual gestures are rare, making it difficult to properly interpret the speeches uttered in those languages. In this paper, we present a solution for the automatic segmentation of grammatical facial expressions in sign language. This is a low-cost computational solution designed to integrate a sign language processing framework that supports the development of simple but high value-added applications for the context of universal communication. Moreover, we present a discussion of the difficulties faced by this solution to guide future research in this area.
Creativity Loading - Please Wait! Investigating the Relationship between Interruption, Mind Wandering and Creativity

With the advancement of information technologies, routine tasks are increasingly supported by information systems, which is why ideation and creativity is becoming more and more important. We know from many anecdotes that creative ideas emerge when our mind is wandering instead of being focused on the task at hand. Yet, most information systems that are used for work-related purposes offer only little opportunities for task-unrelated thoughts. In contrast, current literature shows that most information technology is designed to keep our attention. In order to better understand the value of mind wandering, we propose an experimental design that incorporates interruptions that vary in their length with the objective to stimulate episodes of mind wandering and thus positively impact creativity. We provide initial insights on how the experiment should designed and discuss implications for future research.
Enhancement of Image Segmentation Using Morphological Operation

Segmentation of image play vital role in the area of image processing field there is wide use of segmentation of image in medical science. Image segmentation can be obtained by using various methods, but the drawback of most of the methods is that they use a high level language for coding In this paper we emphasises on graphical approach to solve the segmentation problem which is easy to understand this can be possible with the help of MATLAB simulink blocks which makes it very easy to handle with respect to other software. we use different methodology for better segmentation so that there is better visibility to find out a particular point where the dieses in the human body occur. The purpose of this paper is to present a comprehensive overview of different segmentation methodology. In this paper segmentation technique is defined using the edge detection and morphological operations and mathematical model is proposed both the cases and compare the result.
Crop identification using UAV image segmentation

Crop identification is important to precise agriculture, traditional survey is laboring intensive and classic remote sensing image processing methods are highly dependent on expertise, aiming at this problem, a U-Net based deep segmentation network is proposed for crop identification in UAV remote sensing image segmentation. First, we preprocess the UAV remote images of dataset for training. Then, we replace the transposed convolutional layer in U-Net upsampling block with sub pixel convolutional layer, to solve the problem that edge structure information lost and the low segmentation accuracy. The experiment result shows that, compared with original U-Net and other state of art segmentation model, the proposed method can improve the edge smoothness and pixel wise accuracy. The method uses sub pixel convolution layer for upsampling can be applied to other segmentation model, and the way uses segmentation model to identify the crop species can be extended to other remote sensing image processing filed.
Region of interest detection on the complex sea scenes based on visual saliency

The ship detection technology has important civil and military value. Aiming at the problem of low accuracy of ship detection in complex sea scenes, a candidate region detection algorithm of maritime ship based on FT and Scharr is proposed. The FT saliency model can effectively suppress the background noise and highlight the central region of the target, but the detection effect of small target is poor. Scharr edge detection operator can highlight the edges of all salient targets, but it is easy to introduce background noise. The saliency maps obtained by FT and Scharr is fused by gaussian mixture function, so as to highlight the salient target and suppress the background noise. Finally, the region of interest is obtained by region extraction method. The detection results show that the fusion model can effectively suppress the background noise of the sea surface, and highlight the ship targets of all sizes, and has good effect in different complex sea scenes.
An analytical method for investigations of acoustic phonons spectra in semiconductor ultra-thin flat films

An analytical approach for determine the acoustic phonon dispersion relation in the qusi-2D nanosystems such as flat ultra-thin flat films is represented there. Method is based on the representation the components of polarization vectors for all possible phonon branches in the film of finite thickness as appropriate Fourier series. This made it possible to establish the analytical dependencies of energy on the frequency for all branches of the acoustic phonon spectrum in ultra-thin films. Such results can allow an analytical investigations of the electron spectra transformations that caused by electrons interaction with acoustic phonons in the ultra-thin films by variation of their thicknesses.
Surface defects detection algorithm for a small sample of sealing rings for aerospace based on deep learning

When using visual image technology to detect surface defects of sealing rings for aerospace, traditional image processing algorithms are constrained by surface defects types, scale differences, illumination variations, and non-uniformities of surface image. As a result, the application of each algorithm is relatively limited. To solve the problems, a general deep learning algorithm for surface defects detection of sealing rings is proposed. Texture features of defects, gradient features of edges and contour features are selected as main features of surface defects of sealing rings differentiated from the background, and a new cascade modular backbone network is design for extraction of these features. The average aspect ratio of label box for defects among dataset of defect images of sealing rings is calculated and aspect ratio distribution is counted to obtain the proportional parameter for generating prior box. Based on the calculated proportional parameters of prior box and scale features of defects, multi-scale defects detection network is designed. Meanwhile, defects classification and position regression compound loss function are defined. In the end, weighting parameters of the network are updated on the basis of end-to-end training. Experimental result shows that the accuracy of proposed algorithm for defects detection reach 94.03% under the condition that the original defects sample images is 107 and confidence is 0.6. Compared with RefineDet network, the depth of backbone network of feature extraction increases by 78% while the scale of parameters reduces by 84.82% and the accuracy of defects detection reduces by 3.01%.
Recognition of alphanumeric characters using artificial neuron networks and MSER algorithm

The article presents a method of recognizing alphanumeric characters located in the image, based on a previously created database of patterns using neural networks. For this purpose the convolutional networks were used, which independently search for features that allow to distinguish characters in the image. A larger number of convolution layers allows us to recognize a greater number of features and thus to increase the probability of correctly recognized characters. The main purpose of the paper is to present software that recognizes the alphanumeric characters in images and to investigate the impact of the size of this database on the program's speed and character recognition efficiency. This software can also be used in more complex structures, such as automatic translators or as a computer reader. The calculation of the first program that recognizes single character and the second program that reads all the text from the image have been made in the MATLAB environment. The paper describes the components of this software, such as the learning subsystem and the character recognition subsystem. The results of the program were presented in the form of screenshots showing the results of the learning process and character recognition process. The speed of the software and the effectiveness of recognizing alphanumeric characters using the artificial neural networks and maximally stable extremal regions (MSER) algorithm are presented in the table and figures. Attention was also paid to the impact of the size of the database used to learn the network on the speed of calculations and recognition efficiency.
Flying point target tracking using infrared images

To improve the detection level and performance of designed infrared motion analysis system, a combined scheme is proposed to get long-term tracklet for flying small objects: 1) point object is efficiently detected via a fast background extraction, and an improved correlation filtering algorithm is utilized for possibly near object with much texture; 2) tracker is initialized and managed by estimating continuous motion using Kalman filter; 3) prior knowledge of object is further incorporated to remove false object in tracklet association process. Outdoor experiment proves the proposed techniques improve the accuracy for target objects, and it also extends the validness of our strategy for coming on orbit system.
Performance evaluation of SIFT under low light contrast

As an excellent method for extracting distinctive invariant features from images, SIFT (scale-invariant feature transform) can effectively resist affine transformation such as translation and rotation of images, and theoretically has better resistance to illumination changes [1]. However, in practical applications the performance of SIFT is always affected by the contrast reduction caused by illumination changes. In this paper, the performance of SIFT under different contrasts is systematically analyzed and evaluated, and a reasonable explanation is given for the reason of SIFT performance change under different illumination conditions. And a SIFT fast matching method based on contrast compression is proposed.
Remote multi-object detection based on bounding box field

This paper proposes a new irregular remote sensing object detection algorithm that different from the ROI or rotating BOX obtained by traditional one. The architecture is designed to jointly learn four bounding box corner points and their association via two branches of the same sequential prediction process. The algorithm predicts four key points of the object and their associated connection, Bounding Box Fields(BBF) via convolutional neural network(CNN), and thus obtains the detail spatial distribution of the objects. In order to improve the positioning accuracy of the key points, network architecture reduced Receptive Field from large to small stage by stage. It has achieved ROI free finally. In this method, the object detection problem is framed as CNN convolution point detection and bounding box field detection, it achieved the one stage object detection with high precision and high speed. We verified the effectiveness and efficiency of the algorithm through experiments, which proved that the new data structure could locate the object attitude and spatial direction more accurately in real time with strong practicability.