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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.

Drug sales facilitated through digital communication on the surface web and on darknet cryptomarkets have increased during the past two decades. This has resulted in an increase in drug law enforcement efforts to combat these markets and a subsequent increase in judicial sentencing of people selling drugs online. The aim of this study was to analyze how Swedish courts describe sentenced sellers and how the courts apply case law.

The empirical material consists of 71 sentencing documents produced by Swedish courts in cases of online drug selling between January 1, 2010 and January 1, 2020. In total, 99 sentenced persons occur in the documents. Using a qualitative research design, the authors analyzed the material through thematic text analysis.

Overall, in their descriptions of online drug sale operations, the courts’ characterizations of the concepts of street capital and digital capital show a dichotomy. These forms of capital are situationally described as both aggravating and mitigating aspects in the application of case law, indicating that it may be fruitful to view both street and digital capital as resources used on contemporary drug markets in general.

Very little research exists into how judicial systems describe and perceive the developing phenomenon of online drug sales. Using a relatively large sample from a decade of sentencing, the authors provide an analysis of how Swedish courts view and valuate capital forms in the online drugs trade.

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.

According to the Indian Sign Language Research and Training Centre (ISLRTC), India has approximately 300 certified human interpreters to help people with hearing loss. This paper aims to address the issue of Indian Sign Language (ISL) sentence recognition and translation into semantically equivalent English text in a signer-independent mode.

This study presents an approach that translates ISL sentences into English text using the MobileNetV2 model and Neural Machine Translation (NMT). The authors have created an ISL corpus from the Brown corpus using ISL grammar rules to perform machine translation. The authors’ approach converts ISL videos of the newly created dataset into ISL gloss sequences using the MobileNetV2 model and the recognized ISL gloss sequence is then fed to a machine translation module that generates an English sentence for each ISL sentence.

As per the experimental results, pretrained MobileNetV2 model was proven the best-suited model for the recognition of ISL sentences and NMT provided better results than Statistical Machine Translation (SMT) to convert ISL text into English text. The automatic and human evaluation of the proposed approach yielded accuracies of 83.3 and 86.1%, respectively.

It can be seen that the neural machine translation systems produced translations with repetitions of other translated words, strange translations when the total number of words per sentence is increased and one or more unexpected terms that had no relation to the source text on occasion. The most common type of error is the mistranslation of places, numbers and dates. Although this has little effect on the overall structure of the translated sentence, it indicates that the embedding learned for these few words could be improved.

Sign language recognition and translation is a crucial step toward improving communication between the deaf and the rest of society. Because of the shortage of human interpreters, an alternative approach is desired to help people achieve smooth communication with the Deaf. To motivate research in this field, the authors generated an ISL corpus of 13,720 sentences and a video dataset of 47,880 ISL videos. As there is no public dataset available for ISl videos incorporating signs released by ISLRTC, the authors created a new video dataset and ISL corpus.

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.