Femicides in Latin America

Femicide is the killing of a woman by a man solely on the account of her gender. It is the ultimate form of gender violence that includes sexual, mental, or physical suffering. It derives from a distorted sense of ownership, hatred, and entitlement from men. Femicides can stem from violent relationships; however, they can be targeted despite having no connection at all to their perpetrator. It has become an indisputable problem throughout Latin America without an end in sight. The purpose of this work is to introduce and identify the underlying factors that contribute to the occurrence of femicides within Latin America as well as to propose a solution. The solution consists of developing a gender violence preventative curriculum that will be used as a tool to decrease femicides. The methodology consists of analyzing official data reports, surveys, expert statements, and real-life testimonies in conjunction with current news and events such as protests and marches. The results demonstrated that femicides in Latin America are the product of a sexist culture. Additionally, the findings indicate that advocates and supporters no longer trust the government or their laws and demand change through social movements that promote awareness

Asking More Informative Questions for Grounded Retrieval

When a model is trying to gather information in an interactive setting, it benefits from asking informative questions. However, in the case of a grounded multi-turn image identification task, previous studies have been constrained to polar yes/no questions, limiting how much information the model can gain in a single turn. We present an approach that formulates more informative, open-ended questions. In doing so, we discover that off-the-shelf visual question answering (VQA) models often make presupposition errors, which standard information gain question selection methods fail to account for. To address this issue, we propose a method that can incorporate presupposition handling into both question selection and belief updates. Specifically, we use a two-stage process, where the model first filters out images which are irrelevant to a given question, then updates its beliefs about which image the user intends. Through self-play and human evaluations, we show that our method is successful in asking informative open-ended questions, increasing accuracy over the past state-of-the-art by 14%, while resulting in 48% more efficient games in human evaluations

Graphical user interface development for streamline testing of concentrator cards

Master of ScienceDepartment of PhysicsYurii MaravinThe Large Hadron Collider (LHC) at CERN has provided vital information to aid in understanding how particles interact. One of the latest achievements was the discovery of the Higgs boson in 2012 by the CMS and ATLAS collaborations at CERN. The Higgs boson was the missing piece of the standard model (SM) of particle physics. However, there are still phenomena that are not explained by the SM that lead researchers to believe there are particles predicted by new theories beyond the SM. Some of these particles can be produced in rare events at the LHC. The upgrade of the LHC for high luminosity operations, so called High Luminosity-Large Hadron Collider (HL-LHC), will allow interactions at higher energy and higher rate. This will result in producing more events of these rare processes and particles, and hopefully, lead to discoveries beyond the SM. The MIP timing detector (MTD) is one of the novel installments in the CMS detector for the HL-LHC operation phase that will allow for precision timing. Concentrator cards (CCs) will be placed in the MTD to communicate between the detector and the data acquisition system by sending timing signals between them. Hundreds of CCs will be assembled and tested at Kansas State University. This thesis discusses the development of a graphical user interface (GUI) that will improve the CC testing process. It will discuss the structure and application of the different modes of the testing process, along with future developments

Preventable Error Reduction Leadership Strategies of Nurse Managers in a Hospital Setting

AbstractPreventable medical errors in the healthcare industry account for hundreds of thousands of patient deaths annually. Nurse managers strive to develop strategies to reduce incidences of preventable medical error and increase patient safety in their organization to improve performance and reduce harm in the healthcare industry. Grounded in the complex adaptive systems theory, the purpose of this qualitative single case study was to explore strategies nurse managers use to reduce the rate of preventable medical errors among employees. The participants comprised 6 nurse managers who successfully used strategies to lower the rate of preventable errors in a healthcare facility in Southern California. Data were collected by using semistructured interviews and by performing document reviews, such as organizational performance reports. Four themes emerged from the analysis: staff empowerment, communication and collaboration, standardized processes, and accountability. A key recommendation includes nurse managers using staff empowerment to effect a personalization of care to reduce preventable medical errors effectively. The implications for positive social change due to the reduction of preventable medical errors include improved quality of care received by patients and increased perception of healthcare safety among the community. With improved perceived safety, the public will be more willing to use the health care facilities

PANCETTA: Phoneme Aware Neural Completion to Elicit Tongue Twisters Automatically

Tongue twisters are meaningful sentences that are difficult to pronounce. The process of automatically generating tongue twisters is challenging since the generated utterance must satisfy two conditions at once: phonetic difficulty and semantic meaning. Furthermore, phonetic difficulty is itself hard to characterize and is expressed in natural tongue twisters through a heterogeneous mix of phenomena such as alliteration and homophony. In this paper, we propose PANCETTA: Phoneme Aware Neural Completion to Elicit Tongue Twisters Automatically. We leverage phoneme representations to capture the notion of phonetic difficulty, and we train language models to generate original tongue twisters on two proposed task settings. To do this, we curate a dataset called PANCETTA, consisting of existing English tongue twisters. Through automatic and human evaluation, as well as qualitative analysis, we show that PANCETTA generates novel, phonetically difficult, fluent, and semantically meaningful tongue twisters.Comment: EACL 2023. Code at https://github.com/sedrickkeh/PANCETT

EUREKA: EUphemism Recognition Enhanced through Knn-based methods and Augmentation

We introduce EUREKA, an ensemble-based approach for performing automatic euphemism detection. We (1) identify and correct potentially mislabelled rows in the dataset, (2) curate an expanded corpus called EuphAug, (3) leverage model representations of Potentially Euphemistic Terms (PETs), and (4) explore using representations of semantically close sentences to aid in classification. Using our augmented dataset and kNN-based methods, EUREKA was able to achieve state-of-the-art results on the public leaderboard of the Euphemism Detection Shared Task, ranking first with a macro F1 score of 0.881

Simulator study to determine the wind speed limitation for a Passenger Cruise Vessel entering and berthing in the Port of Hambantota, Sri Lanka

Sri Lanka is being promoted as a tourist destination and the port of Hambanthota is having visits from Passengers. The port of Hambanthota in Sri Lanka is on the busy main east-west sea route. It has become necessary to determine if a Passenger Cruise Ship can be berthed safely in differing monsoonal wind conditions. Before a ship is manoeuvred into a port in the real sense, simulator trials are necessary to be carried out to see if the ship can be manoeuvred safely in varying conditions with all available resources. Simulator trials have been recognized as a cost-effective way to determine the safe limitations and prevent mishaps. The CINEC simulator research team successfully created the simulator environment of the port of Hambanthota as a part of the research under AHEAD operations of the ministry of high education funded by the world bank. As per the analysis of the results of simulator trials, it has been concluded that the wind speed should be limited to 20 knots for safe berthing mainly due to the berthing tug power used

PINEAPPLE: Personifying INanimate Entities by Acquiring Parallel Personification data for Learning Enhanced generation

A personification is a figure of speech that endows inanimate entities with properties and actions typically seen as requiring animacy. In this paper, we explore the task of personification generation. To this end, we propose PINEAPPLE: Personifying INanimate Entities by Acquiring Parallel Personification data for Learning Enhanced generation. We curate a corpus of personifications called PersonifCorp, together with automatically generated de-personified literalizations of these personifications. We demonstrate the usefulness of this parallel corpus by training a seq2seq model to personify a given literal input. Both automatic and human evaluations show that fine-tuning with PersonifCorp leads to significant gains in personification-related qualities such as animacy and interestingness. A detailed qualitative analysis also highlights key strengths and imperfections of PINEAPPLE over baselines, demonstrating a strong ability to generate diverse and creative personifications that enhance the overall appeal of a sentence.Comment: Accepted to COLING 2022; official Github repo at https://github.com/sedrickkeh/PINEAPPL

Internal Auditors’ Position, Recognition and Independence in Ghana: Evidence from State Owned Enterprises

The paper focused on exploring the level of internal auditors’ (IAs) placement/position and recognition/respect and the implications of their independence. The paper employs data from the various state-owned enterprises in Ghana. This paper was conceived from the call to strengthen IAs’ independence to facilitate their effectiveness. Three objectives were developed from the problem statement. Quantitative analytical procedure was used coupled with survey design. Mean scale and regression analysis was adopted in this paper. It was revealed that the threat to IAs’ placement is high resulting in low perceived placement. It also found that the level of recognition of IAs is threatened as evidence by low level of recognition. The inferential statistics indicated that IAs’ placement and recognition significantly determine the level of IAs’ independence. It is recommended that IA units should be restructured both by legislation and in practice to cure the lapses in IAs’ placement and recognition in order to improve IAss’ independence in state-owned enterprises