Large Law Firms Respond to Russia’s Invasion of Ukraine

As these words are being strung together, the civilian death toll continues to rise in Ukraine. As of March 14th, over 600 people have died and over 1,100 civilians have been wounded. In addition to those injured, over 2.8 million people have fled their homes in Ukraine, seeking refuge in Poland, Germany, Romania, Moldova, and Lithuania, to name a few. As Russia continues to attack Ukraine and war surges, the world is responding in a number of significant ways –– donations, spreading awareness across social media platforms, cross-border acceptance of refugees, pro bono legal assistance, protests against Russian war tactics. The international community has relentlessly used its voice, social media platforms, financial resources, and even opened their homes to provide safe harbor for refugees and denounce Russia’s deadly attacks on Ukraine. More specifically, the international legal community has taken a stand against Russian attacks in two distinct ways. First, many major law firms have shut down operations in Russia, closing offices and dropping Russian clients to comply with sanctions that have been imposed. Second, law firms and lawyers in their individual capacities have been providing pro bono legal services to Ukrainians to offset some of the deleterious effects of the war. This post was originally published on the Cardozo International & Comparative Law Review on May 18, 2022. The original post can be accessed via the Archived Link button above

Learning and Predicting Group Preference: Modeling Group Utility Function with Artificial Neural Networks

This paper reports on an empirical investigation into the ability of Artificial Neural Networks (ANN) in approximating group preference. Given the existence of a group utility function in a multi-criteria decision context, an ANN, as a universal function approximator, would be able to recognize group decision patterns, approximate the underlying functional relationship, and generalize over new cases. The merit of ANN over the traditional utility theory approach is in its ability to learn group preference without imposing strong assumptions on the functional form and behavior of decision patterns

On Learning and Predicting Preference with Artificial Neural Networks: Some Preliminary Results

This paper reports on an empirical investigation into the ability of Artificial Neural Network (ANN) technique in learning and predicting preference. ANNs were used to learn preference patterns of holistic judgments on a sample of multi-criteria decision alternatives defined by the orthogonal design. Then a comparative study was conducted with utility theory-based models and ANNs to predict decision makers’ choice. In all cases, the predictive ability of ANN was found to be as good as or better than those of utility theory-based models

Effectiveness of an Interactive Application to Assist Learning: A Test Case

Advances in computer technologies have made it possible to develop computer-aided learning tools for enhanced learning. Today, most researchers in the field of educational technology seem to be preoccupied with either the development of Artificial Intelligence applications or the representation of various learning theories such as constructivism by a computer program. The enthusiasm to develop technologically advanced learning tools resulted in technologies with limited application. The need to develop simple computer-based tools to assist instruction and demonstrate its effectiveness to enhance learning is paramount. Moreover, those tools need to be designed and integrated into a pedagogical framework. As a result, the instructor transforms into a content facilitator with altered needs. This paper presents the design and use of an interactive computer-aided learning tool for enhanced learning. Two participant groups were randomly selected. One group was allowed to use the interactive computer-aided tool prior to a test, while the second group was not. Performance of the groups was compared. Results revealed a higher mean test score for group one. The impact of the tool on test scores was found to be significant. The findings have direct implications on the design, development, testing and implementation of interactive computer-aided learning tools and on today\u27s transforming roles of educators and learners

Designing Optimal Knowledge Base for Integrated Neural Expert Systems

This paper reports on the design of an optimal knowledge base for integrated Artificial Neural Network (ANN) and Expert Systems (ES). In this system, an orthogonal plan is used to define an optimal set of examples to be taken from a problem domain. Then holistic judgments of experts on these examples will provide a training set for an ANN to serve as an initial knowledge base for the integrated system. Any counter-examples in generalization over the new cases will be added to the training set to retrain the network in order to enlarge its initial knowledge base

Managers' perceptions towards automation in manufacturing

Based on a survey of managers' perceptions, the major benefits and problems related to the adoption of new automated manufacturing systems have been identified. Variables related to cost justification are perceived to be less important than variables associated with improved flexibility and product quality in influencing a company's intention to raise its level of technological sophistication in the manufacturing area. Problems associated with employee resistance are perceived to be less influential in hampering firms to raise their level of manufacturing automation than variables pertaining to start up, financial justification, and system maintenance. Of several organizational context variables examined, only the type of industry within which the firm functions and the existence of unions in the firm appear to affect managers' perceptions of the benefits of automation. None of these moderating variables seems to influence managers' perceptions of problems related to the adoption of automated manufacturing systems.published_or_final_versio

Desarrollo e implementación de sistemas de información organizacionales-Factores humanos críticos de éxito relativos al usuario final

Es irrefutable que las contribuciones del usuario final (UF) son críticas para el éxito de los proyectos de desarrollo e implementación de sistemas de in-formación (SI). También lo es la necesidad de nuevas investigaciones, motivada por el número creciente de fracasos de este tipo de proyectos, que profundicen en dichas contribuciones. El objetivo de este trabajo fue analizar tres factores huma-nos del UF considerados claves y determinar su impacto en el éxito de los SI. Este estudio incluyó cuatro grandes empresas industriales relacionadas y se recogieron las percepciones de experimentados profesionales de SI y líderes usuarios. Un análisis cuantitativo es complementado con un análisis cualitativo, estimándose que los resultados aportan nuevo material a la teoría y a la práctica gerencial en las organizaciones

Retos de la Gerencia de TI para construir sistemas de información exitosos: aportes claves del usuario final

Por décadas y hasta el presente, numerosos continúan siendo los casos de SI fallidos, parcial o totalmente, causando grandes pérdidas en las organizaciones y planteando serios retos a los profesionales de las TI y a los niveles de Dirección. La literatura reconoce que los principales factores influyentes son más de orden social que tecnológico, dando un peso relevante al rol de usuario final y sugiriendo profundizar en su investigación, por considerarlo como el “stakeholder” clave para el éxito de un SI. Este estudio empírico ha buscado ampliar las subdimensiones y analizar el impacto de tres factores críticos de dicho rol desde la perspectiva del mismo usuario final, no hallados en la literatura sobre el tema. Se presentan los resultados de un análisis cuantitativo y otro cualitativo, complementándose ambos enfoques y dando las mayores cargas a los factores “conocimientos” y “participación” efectiva. Las conclusiones más significativas resaltan la relevancia de la gerencia del conocimiento así como medidas preventivas respecto a las subdimensiones analizadas para reducir los riesgos inherentes. Se estima que los resultad os aportan mayor detalle a la teoría y a la práctica sobre los SI en las organizaciones