Building semantic user profile for Polish web news portal

We present our research at Onet, the largest Polish news portal, aimed at constructing meaningful user profiles that are most descriptive of their interests in the context of the media content they browse. We used two distinct state-of-the-art numerical text-representation techniques: LDA topic modeling and Word2Vec word embeddings. We trained our models on the corpora of articles in Polish and compare them with a baseline model built on a general language corpora.We compared the performance of algorithms on two distinct tasks - similar articles retrieval and users gender classification. Our results show that the choice of text representation depends on the task - Word2Vec is more suitable for text comparison, especially for short texts such as titles. In the user profiling task, the best performance was obtained with a combination of features: topics from the article text and word embeddings from the title

On modeling cognitive and affective factors in legal decision-making

In recent years, many empirical studies of legal decision-making process have shown that it incorporates many cognitive, affective, and supra-legal factors. Our goal is to design artificial intelligence systems that model these aspects of legal decision-making. Our vision is to implement a kind of legal assistant that can be used by lawyers and judges to run through different scenarios and produce arguments for different, and possibly contradictory, decisions. We propose a multi-agent blackboard architecture for such an assistive system, employing some insights from our previous work on a context-aware recommender system

Incorporating human dimension in autonomous decision-making on moral and ethical issues

As autonomous systems are becoming more and more pervasive, they often have to make decisions concerning moral and ethical values. There are many approaches to incorporating moral values in autonomous decision-making that are based on some sort of logical deduction. However, we argue here, in order for decision-making to seem persuasive to humans, it needs to reflect human values and judgments. Employing some insights from our ongoing research using features of the blackboard architecture for a context-aware recommender system, and a legal decision-making system that incorporates supra-legal aspects, we aim to exploreif this architecture can also be adapted to implement a moral decision-making system that generates rationales that are persuasive to humans. Our vision is that such a system can be used as an advisory system to consider a situation from different moral pers pectives, and generate ethical pros and cons of taking a particular course of action in a given context

A bias detection tree approach for detecting disparities in a recommendation model’s errors

Many of the current recommendation systems are considered to be blackboxes that are tuned to optimize some global objective function. However, their error distribution may differ dramatically among different combinations of attributes, and such algorithms may lead to propagating hidden data biases. Identifying potential disparities in an algorithm’s functioning is essential for building recommendation systems in a fair and responsible way. In this work, we propose a model-agnostic technique to automatically detect the combinations of user and item attributes correlated with unequal treatment by the recommendation model. We refer to this technique as the Bias Detection Tree. In contrast to the existing works in this field, our method automatically detects disparities related to combinations of attributes without any a priori knowledge about protected attributes, assuming that relevant metadata is available. Our results on five public recommendation datasets show that the proposed technique can identify hidden biases in terms of four kinds of metrics for multiple collaborative filtering models. Moreover, we adapt a minimax model selection technique to control the trade-off between the global and the worst-case optimizations and improve the recommendation model’s performance for biased attributes

The impact of housing cooperative bankruptcy on the status of persons entitled under ownership rights to premises. Selected comments on a change in the Supreme Court’s position

The article presents an extremely important shift in the judicature by Poland’s Supreme Court as regards the assessment of the consequences of the transformation of the cooperative ownership right to a residential unit into the right of independent ownership of a residential unit in the wake of the housing cooperative’s bankruptcy proceedings. Originally, the Supreme Court held that a mortgage that encumbered the cooperative's real estate at the time of the transformation encumbers ex lege the resulting right of the independent ownership of a residential unit. Not until 2019 did the Supreme Court abandon this controversial position, and the ultimate change in its judicature is supported with a wide array of critical underlying motives. The author approves of the recently adopted judicial trajectory, yet with a proviso that the resolution of problems it pertains to be not left to the judicature only, as it requires the definitive intervention of the legislator

Meta‐User2Vec model for addressing the user and item cold‐start problem in recommender systems

The cold-start scenario is a critical problem for recommendation systems, especially in dynamically changing domains such as online news services. In this research, we aim at addressing the cold-start situation by adapting an unsupervised neural User2Vec method to represent new users and articles in a multidimensional space. Toward this goal, we propose an extension of the Doc2Vec model that is capable of representing users with unknown history by building embeddings of their metadata labels along with item representations. We evaluate our proposed approach with respect to different parameter configurations on three real-world recommendation datasets with different characteristics. Our results show that this approach may be applied as an efficient alternative to the factorization machine-based method when the user and item metadata are used and hence can be applied in the cold-start scenario for both new users and new items. Additionally, as our solution represents the user and item labels in the same vector space, we can analyze the spatial relations among these labels to reveal latent interest features of the audience groups as well as possible data biases and disparities

A Blackboard System for Generating Poetry

We present a system to generate poems based on the information extracted from input text such as blog posts. Our design uses the blackboard architecture, in which independent specialized modules cooperate during the generation process by sharing a common workspace known as the blackboard. Each module is responsible for a particular task while generating poetry. Our implementation incorporates modules that retrieve information from the input text, generate new ideas, or select the best partial solutions. These distinct modules (experts) are implemented as diverse computational units that make use of lexical resources, grammar models, sentiment-analyzing tools, and languageprocessing algorithms. A control module is responsible for scheduling actions on the blackboard. We argue that the blackboard architecture is a promising way of simulating creative processes because of its flexibility and compliance with the Global Workspace Theory of mind.The main contribution of this work is the design and prototype implementation of an extensible platform for a poetry-generating system that may be further extended by incorporating new experts as well as some existing poetrygenerating systems as parts of the blackboard architecture. We claim that this design provides a powerful tool for combining many of the existing efforts in the domain of automatic poetry generation

Administracja, zarządzanie i handel zagraniczny w warunkach integracji. Materiały konferencyjne - Handel Zagraniczny

Ze wstępu: "Obrady w sekcji H - „Handel zagraniczny” - prowadzone w ramach Międzynarodowej Konferencji Naukowej „Administracja, zarządzanie i handel zagraniczny w warunkach integracj i” dotyczyły szeroko rozumianej wymiany handlowej i współpracy gospodarczej Polski z innymi krajami, zarówno z punktu widzenia gospodarki, jak i poszczególnych przedsiębiorstw. Prezentowane w naszej sekcji referaty dotyczyły w głównej mierze problematyki ujednolicania polskich przepisów w zakresie prowadzenia działalności w szeroko pojętym handlu zagranicznym w celu dostosowania ich do regulacji obowiązujących w Unii Europejskiej."(...

Effect of Hydrocortisone on Mortality and Organ Support in Patients With Severe COVID-19: The REMAP-CAP COVID-19 Corticosteroid Domain Randomized Clinical Trial.

Importance: Evidence regarding corticosteroid use for severe coronavirus disease 2019 (COVID-19) is limited. Objective: To determine whether hydrocortisone improves outcome for patients with severe COVID-19. Design, Setting, and Participants: An ongoing adaptive platform trial testing multiple interventions within multiple therapeutic domains, for example, antiviral agents, corticosteroids, or immunoglobulin. Between March 9 and June 17, 2020, 614 adult patients with suspected or confirmed COVID-19 were enrolled and randomized within at least 1 domain following admission to an intensive care unit (ICU) for respiratory or cardiovascular organ support at 121 sites in 8 countries. Of these, 403 were randomized to open-label interventions within the corticosteroid domain. The domain was halted after results from another trial were released. Follow-up ended August 12, 2020. Interventions: The corticosteroid domain randomized participants to a fixed 7-day course of intravenous hydrocortisone (50 mg or 100 mg every 6 hours) (n = 143), a shock-dependent course (50 mg every 6 hours when shock was clinically evident) (n = 152), or no hydrocortisone (n = 108). Main Outcomes and Measures: The primary end point was organ support-free days (days alive and free of ICU-based respiratory or cardiovascular support) within 21 days, where patients who died were assigned -1 day. The primary analysis was a bayesian cumulative logistic model that included all patients enrolled with severe COVID-19, adjusting for age, sex, site, region, time, assignment to interventions within other domains, and domain and intervention eligibility. Superiority was defined as the posterior probability of an odds ratio greater than 1 (threshold for trial conclusion of superiority >99%). Results: After excluding 19 participants who withdrew consent, there were 384 patients (mean age, 60 years; 29% female) randomized to the fixed-dose (n = 137), shock-dependent (n = 146), and no (n = 101) hydrocortisone groups; 379 (99%) completed the study and were included in the analysis. The mean age for the 3 groups ranged between 59.5 and 60.4 years; most patients were male (range, 70.6%-71.5%); mean body mass index ranged between 29.7 and 30.9; and patients receiving mechanical ventilation ranged between 50.0% and 63.5%. For the fixed-dose, shock-dependent, and no hydrocortisone groups, respectively, the median organ support-free days were 0 (IQR, -1 to 15), 0 (IQR, -1 to 13), and 0 (-1 to 11) days (composed of 30%, 26%, and 33% mortality rates and 11.5, 9.5, and 6 median organ support-free days among survivors). The median adjusted odds ratio and bayesian probability of superiority were 1.43 (95% credible interval, 0.91-2.27) and 93% for fixed-dose hydrocortisone, respectively, and were 1.22 (95% credible interval, 0.76-1.94) and 80% for shock-dependent hydrocortisone compared with no hydrocortisone. Serious adverse events were reported in 4 (3%), 5 (3%), and 1 (1%) patients in the fixed-dose, shock-dependent, and no hydrocortisone groups, respectively. Conclusions and Relevance: Among patients with severe COVID-19, treatment with a 7-day fixed-dose course of hydrocortisone or shock-dependent dosing of hydrocortisone, compared with no hydrocortisone, resulted in 93% and 80% probabilities of superiority with regard to the odds of improvement in organ support-free days within 21 days. However, the trial was stopped early and no treatment strategy met prespecified criteria for statistical superiority, precluding definitive conclusions. Trial Registration: ClinicalTrials.gov Identifier: NCT02735707