Reviewerly/exHarmony huggingface dataset:
Conversational Agents for Insurance Companies: From Theory to Practice

Advances in artificial intelligence have renewed interest in conversational agents. Additionally to software developers, today all kinds of employees show interest in new technologies and their possible applications for customers. German insurance companies generally are interested in improving their customer service and digitizing their business processes. In this work we investigate the potential use of conversational agents in insurance companies theoretically by determining which classes of agents exist which are of interest to insurance companies, finding relevant use cases and requirements. We add two practical parts: First we develop a showcase prototype for an exemplary insurance scenario in claim management. Additionally in a second step, we create a prototype focusing on customer service in a chatbot hackathon, fostering innovation in interdisciplinary teams. In this work, we describe the results of both prototypes in detail. We evaluate both chatbots defining criteria for both settings in detail and compare the results and draw conclusions for the maturity of chatbot technology for practical use, describing the opportunities and challenges companies, especially small and medium enterprises, face.
Proposal and Assessment of a De-Identification Strategy to Enhance Anonymity of the Observational Medical Outcomes Partnership Common Data Model (OMOP-CDM) in a Public Cloud-Computing Environment: Anonymization of Medical Data Using Privacy Models

Background De-identifying personal information is critical when using personal health data for secondary research. The Observational Medical Outcomes Partnership Common Data Model (CDM), defined by the nonprofit organization Observational Health Data Sciences and Informatics, has been gaining attention for its use in the analysis of patient-level clinical data obtained from various medical institutions. When analyzing such data in a public environment such as a cloud-computing system, an appropriate de-identification strategy is required to protect patient privacy. Objective This study proposes and evaluates a de-identification strategy that is comprised of several rules along with privacy models such as k-anonymity, l-diversity, and t-closeness. The proposed strategy was evaluated using the actual CDM database. Methods The CDM database used in this study was constructed by the Anam Hospital of Korea University. Analysis and evaluation were performed using the ARX anonymizing framework in combination with the k-anonymity, l-diversity, and t-closeness privacy models. Results The CDM database, which was constructed according to the rules established by Observational Health Data Sciences and Informatics, exhibited a low risk of re-identification: The highest re-identifiable record rate (11.3%) in the dataset was exhibited by the DRUG_EXPOSURE table, with a re-identification success rate of 0.03%. However, because all tables include at least one “highest risk” value of 100%, suitable anonymizing techniques are required; moreover, the CDM database preserves the “source values” (raw data), a combination of which could increase the risk of re-identification. Therefore, this study proposes an enhanced strategy to de-identify the source values to significantly reduce not only the highest risk in the k-anonymity, l-diversity, and t-closeness privacy models but also the overall possibility of re-identification. Conclusions Our proposed de-identification strategy effectively enhanced the privacy of the CDM database, thereby encouraging clinical research involving multiple centers.
Exploring Text Specific and Blackbox Fairness Algorithms in Multimodal Clinical NLP

Clinical machine learning is increasingly multimodal, collected in both structured tabular formats and unstructured forms such as free text.We propose a novel task of exploring fairness on a multimodal clinical dataset, adopting equalized odds for the downstream medical prediction tasks.To this end, we investigate a modality-agnostic fairness algorithmequalized odds post processing -and compare it to a text-specific fairness algorithm: debiased clinical word embeddings.Despite the fact that debiased word embeddings do not explicitly address equalized odds of protected groups, we show that a text-specific approach to fairness may simultaneously achieve a good balance of performance and classical notions of fairness.We hope that our paper inspires future contributions at the critical intersection of clinical NLP and fairness.
The number of the non‐full‐rank Steiner triple systems

The $p$-rank of a Steiner triple system $B$ is the dimension of the linear span of the set of characteristic vectors of blocks of $B$, over GF$(p)$. We derive a formula for the number of different Steiner triple systems of order $v$ and given $2$-rank $r_2$, $r_2<v$, and a formula for the number of Steiner triple systems of order $v$ and given $3$-rank $r_3$, $r_3<v-1$. Also, we prove that there are no Steiner triple systems of $2$-rank smaller than $v$ and, at the same time, $3$-rank smaller than $v-1$. Our results extend previous work on enumerating Steiner triple systems according to the rank of their codes, mainly by Tonchev, V.A.Zinoviev and D.V.Zinoviev for the binary case and by Jungnickel and Tonchev for the ternary case.
Practice of using interactive technologies for bachelor of applied computer science training during the COVID-19 pandemic

Introduction. The relevance of the topic is due to the requirements of the new Federal State Educational Standard (FSES) in the direction of training bachelor's degree 09.03.03 «Applied Informatics», which assumes that university graduates have universal, general professional and professional competencies. Among the general professional competencies there is ICT-competence, which consists in the competent use of modern information technologies. The object of the research is interactive technologies used in the conditions of distance education. The subject of the research is the degree of student satisfaction with the methods of interactive learning in the context of distance education, organized in connection with the prevention of the spread of the new coronavirus infection (COVID-19). Scientific novelty – innovations in the education sector, innovative approaches to support the continuity of education and learning during the coronavirus pandemic are identified and systematized. Materials and Methods. Analysis of domestic and foreign literature, pedagogical experiment, survey of subjects of the educational process, methods of comparison, analysis and synthesis. Results. In the course of a survey to establish the level of student satisfaction with distance learning using interactive technologies during the coronavirus pandemic, the following results were obtained: about 77-85% of students believe that electronic courses in LMS are enough to organize distance learning, accordingly, there is no need to introduce additional forms of distance learning; 75% of the surveyed students believe that it is not necessary to tie webinars and online lectures to the curriculum. Discussions and Conclusions. For the organization of distance learning, each teacher individually chooses educational services and platforms, giving preference to those that are more accessible in terms of cost of use, have advanced functionality and a convenient interface. It can be noted that during the implementation of educational activities in a distance format in the direction of training «Applied Informatics» during the coronavirus pandemic, in addition to Moodle, Webex, Zoom, Skype were actively used.
Converting video classification problem to image classification with global descriptors and pre‐trained network

Motion history image (MHI) is a spatio‐temporal template that temporal motion information is collapsed into a single image where intensity is a function of recency of motion. Also, it consists of spatial information. Energy image (EI) based on the magnitude of optical flow is a temporal template that shows only temporal information of motion. Each video can be described in these templates. So, four new methods are introduced in this study. The first three methods are called basic methods. In method 1, each video splits into N groups of consecutive frames and MHI is calculated for each group. Transfer learning with fine‐tuning technique has been used for classifying these templates. EIs are used for classifying in method 2 similar to method 1. Fusing two streams of these templates is introduced as method 3. Finally, spatial information is added in method 4. Among these methods, method 4 outperforms others and it is called the proposed method. It achieves the recognition accuracy of 92.30 and 94.50% for UCF Sport and UCF‐11 action data sets, respectively. Also, the proposed method is compared with the state‐of‐the‐art approaches and the results show that it has the best performance.
Empirical Evidence during the Implementation of an Educational Chatbot with the Electroencephalogram Metric

A student's attention status during a digital conversation can differ when talking to an intelligent agent, that is, a chatterbot interactive.There is evidence that brain waves have different oscillations during the study.A virtual platform with an artificial intelligence chatterbot was explored in this study.During the study, participants were asked to use the Neurosky Mindwave sensor to assess their attention levels in real time.Statistical analyses of the results revealed significant effects on the participants' attention levels.The chatbot was associated with dominant proportions of delta and theta brain waves in didactic content.Conversation via social interaction chatbot triggered dominant gamma brain waves.
The Influence of AI-Based Chatbots and Their Design on Users’ Trust and Information Sharing in Online Loan Applications

Based on recent advances in Artificial Intelligence (AI), chatbots are now increasingly offered as an alternative source of customer service.For their uptake user trust in critical.However, little is known about how these interfaces fundamentally influence trust perceptions.In particular, it's unclear what exactly causes perceptual differences -the change towards a conversational interface or the usage of anthropomorphic design elements.In this study, an online experiment with 160 participants was conducted to examine the differential effects of conversational interaction and anthropomorphism on trust in the interface or the provider within the context of online loan applications.The results show that both treatment conditions affect trust in the interface and the provider by increasing perceptions of social presence.Meanwhile, trust in the interface significantly effects the intention to share information, while trust in the provider has no effect on behavioral intention.
Reflection Separation via Multi-bounce Polarization State Tracing

Reflection removal from photographs is an important task in computational photography, but also for computer vision tasks that involve imaging through windows and similar settings. Traditionally, the problem is approached as a single reflection removal problem under very controlled scenarios. In this paper we aim to generalize the reflection removal to real-world scenarios with more complicated light interactions. To this end, we propose a simple yet efficient learning framework for supervised image reflection separation with a polarization-guided ray-tracing model and loss function design. Instead of a conventional image sensor, we use a polarization sensor that instantaneously captures four linearly polarized photos of the scene in the same image. Through a combination of a new polarization-guided image formation model and a novel supervised learning framework for the interpretation of a ray-tracing image formation model, a general method is obtained to tackle general image reflection removal problems. We demonstrate our method with extensive experiments on both real and synthetic data and demonstrate the unprecedented quality of image reconstructions.
An automated guided logistics robot for pallet transportation

Purpose This study aims to present an automated guided logistics robot mainly designed for pallet transportation. Logistics robot is compactly designed. It could pick up the pallet precisely and transport the pallet up to 1,000 kg automatically in the warehouse. It could move freely in all directions without turning the chassis. It could work without any additional infrastructure based on laser navigation system proposed in this work. Design/methodology/approach Logistics robot should be able to move underneath and lift up the pallet accurately. Logistics robot mainly consists of two sub-robots, like two forks of the forklift. Each sub-robot has front and rear driving units. A new compact driving unit is compactly designed as a key component to ensure access to the narrow free entry of the pallet. Besides synchronous motions in all directions, the two sub-robots should also perform synchronous lifting up and laying down the pallet. Logistics robot uses a front laser to detect obstacles and locate itself using on-board navigation system. A rear laser is used to recognize and guide the sub-robots to pick up the pallet precisely within ± 5mm/1o in x-/yaw direction. Path planning algorithm under different constraints is proposed for logistics robot to obey the traffic rules of pallet logistics. Findings Compared with the traditional forklift vehicles, logistics robot has the advantages of more compact structure and higher expandability. It can realize the omnidirectional movement flexibly without turning the chassis and take zero-radius turn by controlling compact driving units synchronously. Logistics robot can move collision-free into any pallet that has not been precisely placed. It can plan the paths for returning to charge station and charge automatically. So it can work uninterruptedly for 7 × 24 h. Path planning algorithm proposed can avoid traffic congestion and improve the passability of the narrow roads to improve logistics efficiencies. Logistics robot is quite suitable for the standardized logistics factory with small working space. Originality/value This is a new innovation for pallet transportation vehicle to improve logistics automation.
PSO-based group-oriented crow search algorithm (PGCSA)

Purpose The purpose of this paper is to modify the crow search algorithm (CSA) to enhance both exploration and exploitation capability by including two novel approaches. The positions of the crows are updated in two approaches based on awareness probability (AP). With AP, the position of a crow is updated by considering its velocity, calculated in a similar fashion to particle swarm optimization (PSO) to enhance the exploiting capability. Without AP, the crows are subdivided into groups by considering their weights, and the crows are updated by conceding leaders of the groups distributed over the search space to enhance the exploring capability. The performance of the proposed PSO-based group-oriented CSA (PGCSA) is realized by exploring the solution of benchmark equations. Further, the proposed PGCSA algorithm is validated over recently published algorithms by solving engineering problems. Design/methodology/approach In this paper, two novel approaches are implemented in two phases of CSA (with and without AP), which have been entitled the PGCSA algorithm to solve engineering benchmark problems. Findings The proposed algorithm is applied with two types of problems such as eight benchmark equations without constraint and six engineering problems. Originality/value The PGCSA algorithm is proposed with superior competence to solve engineering problems. The proposed algorithm is substantiated hypothetically by using a paired t-test.
Light interaction with extended quantum systems in dispersive media

We study a two-level quantum system embedded in a dispersive environment and coupled with the electromagnetic field. We expand the theory of light-matter interactions to include the spatial extension of the system, taken into account through its wavefunctions. This is a development beyond the point-dipole approximation. This ingredient enables us to overcome the divergence problem related to the Green tensor propagator. Hence, we can reformulate the expressions for the spontaneous emission rate and the Lamb shift. In particular, the inclusion of the spatial structure of the atomic system clarifies the role of the asymmetry of atomic states with respect to spatial inversion in these quantities.
Combined Intuition and Rationality Increases Software Feature Novelty for Female Software Designers

Overcoming society's complex problems requires novel solutions. Applying different cognitive styles can promote novelty when designing software aimed at these problems. Through an experiment with 80 software design practitioners, we found that female practitioners who had a preference for more than one cognitive style (intuition and rationality) produced the most novel software features of all participants.
PLA-JPDA for Indoor Multi-Person Tracking Using IR-UWB Radars

Impulse-Radio Ultra-WideBand (IR-UWB) radar is of great interest due to its high range resolution. In indoor environments, IR-UWB radars are used for multi-person tracking. Besides, the Joint Probabilistic Data Association (JPDA) has been designed to associate tracks with persons. However, when multiple persons are wandering close to each other, multipath clutter and track coalescence pose challenges to the tracking task. This paper proposes the Path-Loss-based Adaptive JPDA (PLA-JPDA) for indoor multi-person tracking using IR-UWB radars. The PLA-JPDA suppresses multipath clutter according to the distance-dependent path-loss of radar signals. Moreover, the PLA-JPDA adjusts the validation gate based on measurements and modifies the weights related to association probability. Experiments are conducted in an indoor environment with three IR-UWB radars tracking two persons to validate the proposed approach. In the experiments, two persons wander towards each other along parallel lines. The average root-mean-square-error (RMSE) of tracking with the proposed PLA-JPDA is 0.14 meters. It is 18.7% and 13.7% of the average RMSEs of tracking with the JPDA and the Nearest Neighbor JPDA (NN-JPDA), respectively. The PLA-JPDA also outperforms the JPDA and the NN-JPDA in terms of Optimal Sub-Pattern Assignment (OSPA) distance. The source codes and experimental data of this paper are published at https://github.com/qhybupt/PLA-JPDA.
A Machine Learning Approach to Career Path Choice for Information Technology Graduates

Enterprises rely more and more on well-qualified and highly specialized IT professionals. Although the increasing availability of IT jobs is a good indicator for IT graduates, they nonetheless may find themselves confused about the most appropriate career for their future. In this paper, a recommendation system called CareerRec is proposed, which uses machine learning algorithms to help IT graduates select a career path based on their skills. CareerRec was trained and tested using a dataset of 2255 employees in the IT sector in Saudi Arabia. We conducted a performance comparison between five machine learning algorithms to assess their accuracy for predicting the best-suited career path among 3 classes. Our experiments demonstrate that the XGBoost algorithm outperforms other models and gives the highest accuracy (70.47%).
Discipline vs guidance: comparison of visual engagement approaches in immersive virtual environments

Immersive virtual environments (IVEs) have been extensively investigated for applications in education and man-power training because of the benefits of immersion-driven experiences as immersion becomes a factor that can both accelerate and hamper learning depending on the user's area of focus, which supports the importance of engagement. In this paper, two fundamental approaches to visual engagement in IVE are compared: discipline and guidance. The approaches aim to foster the learner's engagement to predefined area to be focused by either subtracting visual stimuli (discipline) or appending visual indicators pointing to the area (guidance). The experimental results showed no significant improvement in memory recall accuracy and time. However, the guidance group showed superior performances in usability metrics. Interestingly, a significant difference was found in the objective measure of the participants' gaze pattern revealing that the discipline makes the user's gaze consistent and stable.
Political Depolarization of News Articles Using Attribute-Aware Word Embeddings

Political polarization in the US is on the rise. This polarization negatively affects the public sphere by contributing to the creation of ideological echo chambers. In this paper, we focus on addressing one of the factors that contributes to this polarity, polarized media. We introduce a framework for depolarizing news articles. Given an article on a certain topic with a particular ideological slant (eg., liberal or conservative), the framework first detects polar language in the article and then generates a new article with the polar language replaced with neutral expressions. To detect polar words, we train a multi-attribute-aware word embedding model that is aware of ideology and topics on 360k full-length media articles. Then, for text generation, we propose a new algorithm called Text Annealing Depolarization Algorithm (TADA). TADA retrieves neutral expressions from the word embedding model that not only decrease ideological polarity but also preserve the original argument of the text while maintaining grammatical correctness. We evaluate our framework by comparing the depolarized output of our model in two modes, fully-automatic and semi-automatic, on 99 stories spanning 11 topics. Based on feedback from 161 human testers, our framework successfully depolarized 90.1% of paragraphs in semi-automatic mode and 78.3% of paragraphs in fully-automatic mode. Furthermore, 81.2% of the testers agree that the non-polar content information is well-preserved and 79% agree that depolarization does not harm semantic correctness when they compare the original text and the depolarized text. Our work shows that data-driven methods can help to locate political polarity and aid in the depolarization of articles.
Object Detection Made Simpler by Eliminating Heuristic NMS

We show a simple NMS-free, end-to-end object detection framework, of which the network is a minimal modification to a one-stage object detector such as the FCOS detection model [Tian et al. 2019]. We attain on par or even improved detection accuracy compared with the original one-stage detector. It performs detection at almost the same inference speed, while being even simpler in that now the post-processing NMS (non-maximum suppression) is eliminated during inference. If the network is capable of identifying only one positive sample for prediction for each ground-truth object instance in an image, then NMS would become unnecessary. This is made possible by attaching a compact PSS head for automatic selection of the single positive sample for each instance (see Fig. 1). As the learning objective involves both one-to-many and one-to-one label assignments, there is a conflict in the labels of some training examples, making the learning challenging. We show that by employing a stop-gradient operation, we can successfully tackle this issue and train the detector. On the COCO dataset, our simple design achieves superior performance compared to both the FCOS baseline detector with NMS post-processing and the recent end-to-end NMS-free detectors. Our extensive ablation studies justify the rationale of the design choices.