Cómputo con palabras para la evaluación de pares estudiantiles en presentaciones orales

Peer assessment in an oral presentation can motivate and give more sense of responsibility to students. In recent years, various methods have been proposed to evaluate peers. In this paper, a novel peer online assessment method is proposed for oral presentation using perceptual computing. The output of the proposed system can be a numerical score for the overall assessment of a student in the presentation, which allows comparison and ranking of student performance. Furthermore, a linguistic evaluation that describes the student's performance is obtained from the system. A case study has been conducted to show the effectiveness of the proposed method; then the results are analyzed and reviewed.La evaluación por pares en una presentación oral puede motivar y dar más sentido de responsabilidad a los estudiantes. En los últimos años, se han propuesto varios métodos para evaluar a los pares. En este artículo, se propone un método novedoso de evaluación en línea entre pares para la presentación oral utilizando la computación perceptiva. El resultado del sistema propuesto puede ser una puntuación numérica para la evaluación general de un estudiante en la presentación, que permite comparar y clasificar el desempeño del estudiante. además, del sistema se obtiene una evaluación lingüística que describe el desempeño del alumno. Se ha realizado un estudio de caso para mostrar la efectividad del método propuesto, luego se analizan y revisan los resultados

Cómputo con palabras para la evaluación de pares estudiantiles en presentaciones orales

La evaluación por pares en una presentación oral puede motivar y dar más sentido de responsabilidad a los estudiantes. En los últimos años, se han propuesto varios métodos para evaluar a los pares. En este artículo, se propone un método novedoso de evaluación en línea entre pares para la presentación oral utilizando la computación perceptiva. El resultado del sistema propuesto puede ser una puntuación numérica para la evaluación general de un estudiante en la presentación, que permite comparar y clasificar el desempeño del estudiante. además, del sistema se obtiene una evaluación lingüística que describe el desempeño del alumno. Se ha realizado un estudio de caso para mostrar la efectividad del método propuesto, luego se analizan y revisan los resultado

Performance analysis in text clustering using k-means and k-medoids algorithms for Malay crime documents

Few studies on text clustering for the Malay language have been conducted due to some limitations that need to be addressed. The purpose of this article is to compare the two clustering algorithms of k-means and k-medoids using Euclidean distance similarity to determine which method is the best for clustering documents. Both algorithms are applied to 1000 documents pertaining to housebreaking crimes involving a variety of different modus operandi. Comparability results indicate that the k-means algorithm performed the best at clustering the relevant documents, with a 78% accuracy rate. K-means clustering also achieves the best performance for cluster evaluation when comparing the average within-cluster distance to the k-medoids algorithm. However, k-medoids perform exceptionally well on the Davis Bouldin index (DBI). Furthermore, the accuracy of k-means is dependent on the number of initial clusters, where the appropriate cluster number can be determined using the elbow method

A Review on Fuzzy - AHP technique in Environmental Impact Assessment of Construction Projects, India

There are several countries today using procedures for Environmental impact assessment (EIA) is based on a series of mathematical techniques which attempt to localize, describe and assess the positive and negative effects that any human activity has on our environment, generally causing it to deteriorate. The environmental impact assessment (EIA) of projects requires the evaluation of the effects of very diverse actions on a number of different environmental factors, the uncertainty and inaccuracy being inherent in the process of allocating values to environmental impacts carried out by a panel of experts, stakeholders and affected population. The application of the fuzzy Logic and AHP technique can be helpful in identification of the risk associated with construction or developing project and improves the study of EIA. Fuzzy is one of the characteristics of human thoughts for which fuzzy sets theory is an effective tool for fuzziness. A fuzzy logic knowledge-based approach can be used for the environmental impact assessment study of the different construction projects. The review article highlights the role of Fuzzy AHP logic method in EIA of different construction projects, fuzzy logic modeling - software for fuzzy EIA, fuzzy numbers and steps of fuzzy methods as well as reveals that how fuzziness can be determined by applying fuzzy logic method in construction projects

Artificial Intelligence — An Enabler of Naval Tactical Decision Superiority

The article of record as published may be located at https://doi.org/10.1609/aimag.v40i1.2852Artificial intelligence, as a capability enhancer, offers significant improve- ments to our tactical warfighting advantage. AI provides methods for fus- ing and analyzing data to enhance our knowledge of the tactical environment; it provides methods for generating and assessing decision options from multidi- mensional, complex situations; and it provides predictive analytics to identify and examine the effects of tactical courses of action. Machine learning can improve these processes in an evolution- ary manner. Advanced computing tech- niques can handle highly heterogeneous and vast datasets and can synchronize knowledge across distributed warfare assets. This article presents concepts for applying AI to various aspects of tacti- cal battle management and discusses their potential improvements to future warfare

The Combatant’s Stance: Autonomous Weapons on the Battlefield

Do Autonomous Weapon Systems (AWS) qualify as moral or rational agents? This paper argues that combatants on the battlefield are required by the demands of behavior interpretation to approach a sophisticated AWS with the “Combatant’s Stance”—the ascription of mental states required to understand the system’s strategic behavior on the battlefield. However, the fact that an AWS must be engaged with the combatant’s stance does not entail that other persons are relieved of criminal or moral responsibility for war crimes committed by autonomous weapons. This article argues that military commanders can and should be held responsible for perpetrating war crimes through an AWS regardless of the moral status of the AWS as a culpable or non-culpable agent. In other words, a military commander can be liable for the acts of the machine independent of what conclusions we draw from the fact that combatants—even artificial ones—must approach each other with the combatant’s stance. This article argues that the basic framework for this liability was established at Nuremberg and subsequent tribunals—both of which focused on how a criminal defendant can be responsible for allowing a metaphorical “machine”—such as a concentration camp—to commit an international crime. The novelty in this technological development is that the law must shift from dealing with the metaphor of the “cog in the machine” to a literal machine. Nonetheless, this article also concludes that there is one area where international criminal law is ill suited to dealing with a military commander’s responsibility for unleashing an AWS that commits a war crime. Many of these cases will be based on the commander’s recklessness and unfortunately international criminal law has struggled to develop a coherent theoretical and practical program for prosecuting crimes of recklessness

The Combatant\u27s Stance: Autonomous Weapons on the Battlefield

Do Autonomous Weapon Systems (AWS) qualify as moral or rational agents? This paper argues that combatants on the battlefield are required by the demands of behavior interpretation to approach a sophisticated AWS with the “Combatant’s Stance” — the ascription of mental states required to understand the system’s strategic behavior on the battlefield. However, the fact that an AWS must be engaged with the combatant’s stance does not entail that other persons are relieved of criminal or moral responsibility for war crimes committed by autonomous weapons. This article argues that military commanders can and should be held responsible for perpetrating war crimes through an AWS regardless of the moral status of the AWS as a culpable or non-culpable agent. In other words, a military commander can be liable for the acts of the machine independent of what conclusions we draw from the fact that combatants — even artificial ones — must approach each other with the combatant’s stance. The basic framework for this liability was established at Nuremberg and subsequent tribunals — both of which focused on how a criminal defendant can be responsible for allowing a metaphorical “machine” — such as a concentration camp — to commit an international crime. The novelty in this technological development is that the law must shift from dealing with the metaphor of the “cog in the machine” to a literal machine. Nonetheless, this article also concludes that there is one area where international criminal law is ill suited to dealing with a military commander’s responsibility for unleashing an AWS that commits a war crime. Many of these cases will be based on the commander’s recklessness and unfortunately international criminal law has struggled to develop a coherent theoretical and practical program for prosecuting crimes of recklessness

Human-Machine Collaborative Optimization via Apprenticeship Scheduling

Coordinating agents to complete a set of tasks with intercoupled temporal and resource constraints is computationally challenging, yet human domain experts can solve these difficult scheduling problems using paradigms learned through years of apprenticeship. A process for manually codifying this domain knowledge within a computational framework is necessary to scale beyond the ``single-expert, single-trainee" apprenticeship model. However, human domain experts often have difficulty describing their decision-making processes, causing the codification of this knowledge to become laborious. We propose a new approach for capturing domain-expert heuristics through a pairwise ranking formulation. Our approach is model-free and does not require enumerating or iterating through a large state space. We empirically demonstrate that this approach accurately learns multifaceted heuristics on a synthetic data set incorporating job-shop scheduling and vehicle routing problems, as well as on two real-world data sets consisting of demonstrations of experts solving a weapon-to-target assignment problem and a hospital resource allocation problem. We also demonstrate that policies learned from human scheduling demonstration via apprenticeship learning can substantially improve the efficiency of a branch-and-bound search for an optimal schedule. We employ this human-machine collaborative optimization technique on a variant of the weapon-to-target assignment problem. We demonstrate that this technique generates solutions substantially superior to those produced by human domain experts at a rate up to 9.5 times faster than an optimization approach and can be applied to optimally solve problems twice as complex as those solved by a human demonstrator.Comment: Portions of this paper were published in the Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI) in 2016 and in the Proceedings of Robotics: Science and Systems (RSS) in 2016. The paper consists of 50 pages with 11 figures and 4 table

EVALUATING ARTIFICIAL INTELLIGENCE METHODS FOR USE IN KILL CHAIN FUNCTIONS

Current naval operations require sailors to make time-critical and high-stakes decisions based on uncertain situational knowledge in dynamic operational environments. Recent tragic events have resulted in unnecessary casualties, and they represent the decision complexity involved in naval operations and specifically highlight challenges within the OODA loop (Observe, Orient, Decide, and Assess). Kill chain decisions involving the use of weapon systems are a particularly stressing category within the OODA loop—with unexpected threats that are difficult to identify with certainty, shortened decision reaction times, and lethal consequences. An effective kill chain requires the proper setup and employment of shipboard sensors; the identification and classification of unknown contacts; the analysis of contact intentions based on kinematics and intelligence; an awareness of the environment; and decision analysis and resource selection. This project explored the use of automation and artificial intelligence (AI) to improve naval kill chain decisions. The team studied naval kill chain functions and developed specific evaluation criteria for each function for determining the efficacy of specific AI methods. The team identified and studied AI methods and applied the evaluation criteria to map specific AI methods to specific kill chain functions.Civilian, Department of the NavyCivilian, Department of the NavyCivilian, Department of the NavyCaptain, United States Marine CorpsCivilian, Department of the NavyCivilian, Department of the NavyApproved for public release. Distribution is unlimited