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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.

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.

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.

Intent detection (ID) and slot filling (SF) are two important tasks in natural language understanding. ID is to identify the main intent of a paragraph of text. The goal of SF is to extract the information that is important to the intent from the input sentence. However, most of the existing methods use sentence-level intention recognition, which has the risk of error propagation, and the relationship between intention recognition and SF is not explicitly modeled. Aiming at this problem, this paper proposes a collaborative model of ID and SF for intelligent spoken language understanding called ID-SF-Fusion.

ID-SF-Fusion uses Bidirectional Encoder Representation from Transformers (BERT) and Bidirectional Long Short-Term Memory (BiLSTM) to extract effective word embedding and context vectors containing the whole sentence information respectively. Fusion layer is used to provide intent–slot fusion information for SF task. In this way, the relationship between ID and SF task is fully explicitly modeled. This layer takes the result of ID and slot context vectors as input to obtain the fusion information which contains both ID result and slot information. Meanwhile, to further reduce error propagation, we use word-level ID for the ID-SF-Fusion model. Finally, two tasks of ID and SF are realized by joint optimization training.

We conducted experiments on two public datasets, Airline Travel Information Systems (ATIS) and Snips. The results show that the Intent ACC score and Slot F1 score of ID-SF-Fusion on ATIS and Snips are 98.0 per cent and 95.8 per cent, respectively, and the two indicators on Snips dataset are 98.6 per cent and 96.7 per cent, respectively. These models are superior to slot-gated, SF-ID NetWork, stack-Prop and other models. In addition, ablation experiments were performed to further analyze and discuss the proposed model.

This paper uses word-level intent recognition and introduces intent information into the SF process, which is a significant improvement on both data sets.

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.

Aspect-based sentiment analysis (ASA) is a task of sentiment analysis that requires predicting aspect sentiment polarity for a given sentence. Many traditional techniques use graph-based mechanisms, which reduce prediction accuracy and introduce large amounts of noise. The other problem with graph-based mechanisms is that for some context words, the feelings change depending on the aspect, and therefore it is impossible to draw conclusions on their own. ASA is challenging because a given sentence can reveal complicated feelings about multiple aspects.

This research proposed an optimized attention-based DL model known as optimized aspect and self-attention aware long short-term memory for target-based semantic analysis (OAS-LSTM-TSA). The proposed model goes through three phases: preprocessing, aspect extraction and classification. Aspect extraction is done using a double-layered convolutional neural network (DL-CNN). The optimized aspect and self-attention embedded LSTM (OAS-LSTM) is used to classify aspect sentiment into three classes: positive, neutral and negative.

To detect and classify sentiment polarity of the aspect using the optimized aspect and self-attention embedded LSTM (OAS-LSTM) model. The results of the proposed method revealed that it achieves a high accuracy of 95.3 per cent for the restaurant dataset and 96.7 per cent for the laptop dataset.

The novelty of the research work is the addition of two effective attention layers in the network model, loss function reduction and accuracy enhancement, using a recent efficient optimization algorithm. The loss function in OAS-LSTM is minimized using the adaptive pelican optimization algorithm, thus increasing the accuracy rate. The performance of the proposed method is validated on four real-time datasets, Rest14, Lap14, Rest15 and Rest16, for various performance metrics.

English as a foreign langauge (EFL) students find it difficult to apply the theoretical knowledge they acquire on translation in the practical world. Therefore, this study explored if training in pretranslation techniques (PTTs) (syntactic parsing) as suggested by Almanna (2018) could improve the translation proficiency of Yemeni EFL students. Moreover, the study also assessed which of the PTTs the intervention helped to develop.

The study adopted a primarily experimental pre- and posttests research design, and the sample comprised of an intake class with 16 students enrolled in the fourth year, Bachelor in Education (B.Ed), Hadhramout University. Six participants were also interviewed to gather the students' perceptions on using PTTs.

Results showed that students' performance in translation developed significantly (Sig. = 0.002). All the six PTTs showed development, though subject, tense and aspect developed more significantly (Sig. = 0.034, 0.002, 0.001 respectively). Finally, the study reported students' positive perceptions on the importance of using PTTs before doing any translation tasks.

One of the recurrent errors that can be noticed in Yemeni EFL students' production is their inability to transfer the grammatical elements of sentences from L1 (Arabic) into L2 (English) or the visa versa. The researchers thought though translation is more than the syntactic transmission of one language into another, analyzing the elements of sentences using syntactic and semantic parsing can help students to produce acceptable texts in the target language. These claims would be proved or refuted after analyzing the experiment result of the present study.