Search results

Exhibiting an unprecedented rate of advancement, technology’s progression over the past two decades has regrettably led to a disturbing increase in the distribution of child sexual abuse materials (CSAM) online. Compounded by the emergence of an underground cryptocurrency market, which serves as a primary distribution channel for these materials, the investigation and sanctioning of CSAM present a complex and unique set of challenges. The purpose of this study is to accurately diagnose the CSAM sentencing landscape and build a more comprehensive, evidence-based legal framework in penology.

The study collected and analyzed case details regarding CSAM sanctions in a database sourced from the US Department of Justice for 2020. Various factors were analyzed such as the victim’s age, offender typology and previous conviction, accompanied by an analysis of how these factors affect the sentence length.

The study found that the hierarchical agency-level interactions give insight into resource allocation prioritization, as well as confirming a close relationship between prior conviction history and sentence length, with the victim’s age inversely related to sentence length. Leveraging data-driven insights, the study paves the way for more targeted and effective sanctions, ultimately contributing to the broader goal of safeguarding children from online sexual exploitation.

The paper provides a critical analysis of the complex landscape surrounding CSAM distribution and judicial sentencing. By examining case details and leveraging data-driven insights, it offers valuable contributions to understanding the interplay between various factors such as victim age, offender typology and prior convictions on sentencing outcomes. This comprehensive approach not only sheds light on the dynamics of CSAM sanctions but also lays the groundwork for evidence-based legal frameworks in penology. Its originality lies in its nuanced examination of hierarchical agency interactions and its potential to inform more targeted interventions for safeguarding children from online exploitation.

This study aims to provide measurable information that evaluates a company’s ESG performance based on the conceptual connection between ESG, non-financial elements of a company and the UN Sustainable Development Goals (SDGs) for resolving global issues.

A novel data processing method based on the BERT is presented and applied to analyze the changes and characteristics of SDG-related ESG texts from companies’ disclosures over the past decade. Specifically, ESG-related sentences are extracted from 93,277 Form 10-K filings disclosed between 2010 and 2022 and the similarity between these extracted sentences and SDGs statements is calculated through sentence transformers. A classifier is created by fine-tuning FinBERT, a financial domain-specific pre-trained language model, to classify the sentences into eight ESG classes.

The quantified results obtained from the classifier reveal several implications. First, it is observed that the trend of SDG-related ESG sentences shows a slow and steady increase over the past decade. Second, large-cap companies relatively have a greater amount of SDG-related ESG disclosures than small-cap companies. Third, significant events such as the COVID-19 pandemic greatly impact the changes in disclosure content.

This study presents a novel approach to textual analysis using neural network-based language models such as BERT. The results of this study provide meaningful information and insights for investors in socially responsible investment and sustainable investment and suggest that corporations need a long-term plan regarding ESG disclosures.

This paper aims to investigate how congruent keywords are used in information security policies (ISPs) to pinpoint and guide clear actionable advice and suggest a metric for measuring the quality of keyword use in ISPs.

A qualitative content analysis of 15 ISPs from public agencies in Sweden was conducted with the aid of Orange Data Mining Software. The authors extracted 890 sentences from these ISPs that included one or more of the analyzed keywords. These sentences were analyzed using the new metric – keyword loss of specificity – to assess to what extent the selected keywords were used for pinpointing and guiding actionable advice. Thus, the authors classified the extracted sentences as either actionable advice or other information, depending on the type of information conveyed.

The results show a significant keyword loss of specificity in relation to pieces of actionable advice in ISPs provided by Swedish public agencies. About two-thirds of the sentences in which the analyzed keywords were used focused on information other than actionable advice. Such dual use of keywords reduces the possibility of pinpointing and communicating clear, actionable advice.

The suggested metric provides a means to assess the quality of how keywords are used in ISPs for different purposes. The results show that more research is needed on how keywords are used in ISPs.

The authors recommended that ISP designers exercise caution when using keywords in ISPs and maintain coherency in their use of keywords. ISP designers can use the suggested metrics to assess the quality of actionable advice in their ISPs.

The keyword loss of specificity metric adds to the few quantitative metrics available to assess ISP quality. To the best of the authors’ knowledge, applying this metric is a first attempt to measure the quality of actionable advice in ISPs.

In recent decades, the use of capital punishment has declined, but in its place, a ‘new death penalty’ has arisen: life without parole (LWOP), which is being used far more frequently and for more crimes than capital punishment ever was. Yet, LWOP has received far less scholarly attention than the death penalty. Because of its greater scale, assessing the effects of LWOP on crime has important policy implications and is a better test of extreme penalties. Existing studies of LWOP focus on humanitarian issues and ignore its potentially reciprocal relationship with crime. Therefore, we use available LWOP data to fill these gaps in the literature, using models specifically designed to control for potential reciprocal effects. The results indicate there is no reciprocal causation between LWOP and violent crime and, at best, LWOP’s impact on crime is small, temporary, and, most importantly, no greater than the impact of life with parole.

The purpose of this paper is to describe a new approach to sentence representation learning leading to text classification using Bidirectional Encoder Representations from Transformers (BERT) embeddings. This work proposes a novel BERT-convolutional neural network (CNN)-based model for sentence representation learning and text classification. The proposed model can be used by industries that work in the area of classification of similarity scores between the texts and sentiments and opinion analysis.

The approach developed is based on the use of the BERT model to provide distinct features from its transformer encoder layers to the CNNs to achieve multi-layer feature fusion. To achieve multi-layer feature fusion, the distinct feature vectors of the last three layers of the BERT are passed to three separate CNN layers to generate a rich feature representation that can be used for extracting the keywords in the sentences. For sentence representation learning and text classification, the proposed model is trained and tested on the Stanford Sentiment Treebank-2 (SST-2) data set for sentiment analysis and the Quora Question Pair (QQP) data set for sentence classification. To obtain benchmark results, a selective training approach has been applied with the proposed model.

On the SST-2 data set, the proposed model achieved an accuracy of 92.90%, whereas, on the QQP data set, it achieved an accuracy of 91.51%. For other evaluation metrics such as precision, recall and F1 Score, the results obtained are overwhelming. The results with the proposed model are 1.17%–1.2% better as compared to the original BERT model on the SST-2 and QQP data sets.

The novelty of the proposed model lies in the multi-layer feature fusion between the last three layers of the BERT model with CNN layers and the selective training approach based on gated pruning to achieve benchmark results.

The purpose of this study is to examine public perceptions and attitudes toward using artificial intelligence (AI) in the US criminal justice system.

The authors took a quantitative approach and administered an online survey using the Amazon Mechanical Turk platform. The instrument was developed by integrating prior literature to create multiple scales for measuring public perceptions and attitudes.

The findings suggest that despite the various attempts, there are still significant perceptions of sociodemographic bias in the criminal justice system and technology alone cannot alleviate them. However, AI can assist judges in making fairer and more objective decisions by using triangulation – offering additional data points to offset individual biases.

Other scholars can build upon the findings and extend the work to shed more light on some problems of growing concern for society – bias and inequality in criminal sentencing. AI can be a valuable tool to assist judges in the decision-making process by offering diverse viewpoints. Furthermore, the authors bridge the gap between the fields of technology and criminal justice and demonstrate how the two can be successfully integrated for the benefit of society.

To the best of the authors’ knowledge, this is among the first studies to examine a complex societal problem like the introduction of technology in a high-stakes environment – the US criminal justice system. Understanding how AI is perceived by society is necessary to develop more transparent and unbiased algorithms for assisting judges in making fair and equitable sentencing decisions. In addition, the authors developed and validated a new scale that can be used to further examine this novel approach to criminal sentencing in the future.

Question answering (QA) answers the questions asked by people in the form of natural language. In the QA, due to the subjectivity of users, the questions they query have different expressions, which increases the difficulty of text retrieval. Therefore, the purpose of this paper is to explore new query rewriting method for QA that integrates multiple related questions (RQs) to form an optimal question. Moreover, it is important to generate a new dataset of the original query (OQ) with multiple RQs.

This study collects a new dataset SQuAD_extend by crawling the QA community and uses word-graph to model the collected OQs. Next, Beam search finds the best path to get the best question. To deeply represent the features of the question, pretrained model BERT is used to model sentences.

The experimental results show three outstanding findings. (1) The quality of the answers is better after adding the RQs of the OQs. (2) The word-graph that is used to model the problem and choose the optimal path is conducive to finding the best question. (3) Finally, BERT can deeply characterize the semantics of the exact problem.

The proposed method can use word-graph to construct multiple questions and select the optimal path for rewriting the question, and the quality of answers is better than the baseline. In practice, the research results can help guide users to clarify their query intentions and finally achieve the best answer.

Whether automatically generated summaries of health social media can assist users in appropriately managing their diseases and ensuring better communication with health professionals becomes an important issue. This paper aims to develop a novel deep learning-based summarization approach for obtaining the most informative summaries from online patient reviews accurately and effectively.

This paper proposes a framework to generate summaries that integrates a domain-specific pre-trained embedding model and a deep neural extractive summary approach by considering content features, text sentiment, review influence and readability features. Representative health-related summaries were identified, and user judgements were analysed.

Experimental results on the three real-world health forum data sets indicate that awarding sentences without incorporating all the adopted features leads to declining summarization performance. The proposed summarizer significantly outperformed the comparison baseline. User judgement through the questionnaire provides realistic and concrete evidence of crucial features that remarkably influence patient forum review summaries.

This study contributes to health analytics and management literature by exploring users’ expressions and opinions through the health deep learning summarization model. The research also developed an innovative mindset to design summarization weighting methods from user-created content on health topics.

Over a decade since the Special Criminal Court (SCC) was established in Cameroon, hundreds of individuals have been indicted, tried and convicted. Sentences have been imposed, most of which include a term of imprisonment (principal punishment/penalty) and confiscation as accessory penalty or punishment. Research focus has not been directed at the sentences which, as argued in this paper, are inconsistent, incommensurate with the amounts of money stolen and a significant departure from the Penal Code. This paper aims to explore the aspect of sentencing by the SCC.

To identify, highlight and discuss the issue of sentencing, the paper looks at a blend of primary and secondary materials: primary materials here include but not limited to the judgements of the SCC and other courts in Cameroon and the Penal Code. Secondary materials shall include the works of scholars in the fields of criminal law, criminal justice and penal reform.

A few findings were made: first, the judges are inconsistent in the manner in which they determine the appropriate sentence. Second, in making that determination, the judges would have been oblivious to the prescripts in the Penal Code, which provides the term of imprisonment, and in the event of a mitigating circumstance, the prescribed minimum to be applied. Yet, the default imposition of an aggravating circumstance (being a civil servant) was not explored by the SCC. Finally, whether the sentences imposed are commensurate with the amounts of monies stolen.

This research unravels key insights into the functioning of the SCC. It advances the knowledge thereon and adds to the literature on corruption in Cameroon.

The prosecution and judges at the SCC should deepen their knowledge of Cameroonian criminal law, especially on the nature of liberty given to judges to determine within the prescribed range of the sentence to be imposed but also consider the existence of an aggravating factor – civil servant. They must also consider whether the sentences imposed befit the crime for which they are convicted.

The paper is an original contribution with new insights on the manner in which sentencing should be approached by the SCC.

The Scandinavian penal exceptionalism literature has focused largely on imprisonment but has yet to explore other aspects of the penal field in detail. This chapter provides an overview of the penal field in Norway and how community sanctions and measures have evolved within it. The author uses the work of Wacquant and Bourdieu to argue that there are three important levels within the Norwegian penal field: political, policy and practice. The author also discusses how drivers from the political and policy levels are affecting community-based penal practice. Using McNeill’s dimensions of mass supervision, the author discusses the implications of these changes for three less-explored aspects of punishment in Norway: the serving of short sentences at home on electronic monitoring, supervision of people under 18 and ‘punishment debt’ enforcement.