The study identified the impact of adherence to anti-money laundering procedures on the performance of banks operating in Yemen from many aspects, which is to identify the planning and design of anti-money laundering procedures on the performance of
banks operating in Yemen, and the organization of the responsible administrative unit, monitoring , follow-up and correction, and the policies and procedures imposed on banks. The money laundering crime is one of the most dangerous crimes facing the Yemeni economy in general and the banking sector in particular.
The study problem was identified with some questions, including what is the impact of commitment to anti-money laundering procedures on the performance of banks operating in Yemen.
The study relied on two variables, the independent variable, commitment to anti-money laundering procedures, and the dependent variable, the performance of banks according to the dimensions of the balanced scorecard. The study relied on the analytical descriptive approach as the most widely used method in human and social sciences studies, and the questionnaire was relied on as a main tool for collecting the necessary data for this study, and the sample size of the study was determined based on the sample size table of (Krejcie & Morgan)
A disproportionate stratified random sample was selected, and after completing statistical analyzes of the data collected through questionnaires in banks operating in Yemen, a number of results were reached, including that the level of commitment to anti-money laundering procedures in banks operating in Yemen in general was high, For all aspects and came in order (planning and designing anti-money laundering procedures, organizing the administrative unit responsible for money laundering procedures, monitoring, following up and correcting anti-money laundering procedures)
Banks have written policies and procedures for combating money laundering, and follow the international standards and regulations for combating money laundering and the instructions of the Central Bank of Yemen to formulate its policies and procedures.
Banks are obligated to submit their suspected anti-money laundering reports on time to the Financial Information Collection Unit. The policies and procedures manual is based on international standards,the updating of ban lists, and on local laws and instructions from control and supervision authorities.
Code-mixed language plays a crucial role in communication in multilingual societies. Though the recent growth of web users has greatly boosted the use of such mixed languages, the current generation of dialog systems is primarily monolingual. This in
crease in usage of code-mixed language has prompted dialog systems in a similar language. We present our work in Code-Mixed Dialog Generation, an unexplored task in code-mixed languages, generating utterances in code-mixed language rather than a single language that is more often just English. We present a new synthetic corpus in code-mix for dialogs, CM-DailyDialog, by converting an existing English-only dialog corpus to a mixed Hindi-English corpus. We then propose a baseline approach where we show the effectiveness of using mBART like multilingual sequence-to-sequence transformers for code-mixed dialog generation. Our best performing dialog models can conduct coherent conversations in Hindi-English mixed language as evaluated by human and automatic metrics setting new benchmarks for the Code-Mixed Dialog Generation task.
Endowing a task-oriented dialogue system with adaptiveness to user personality can greatly help improve the performance of a dialogue task. However, such a dialogue system can be practically challenging to implement, because it is unclear how user pe
rsonality influences dialogue task performance. To explore the relationship between user personality and dialogue task performance, we enrolled participants via crowdsourcing to first answer specified personality questionnaires and then chat with a dialogue system to accomplish assigned tasks. A rule-based dialogue system on the prevalent Multi-Domain Wizard-of-Oz (MultiWOZ) task was used. A total of 211 participants' personalities and their 633 dialogues were collected and analyzed. The results revealed that sociable and extroverted people tended to fail the task, whereas neurotic people were more likely to succeed. We extracted features related to user dialogue behaviors and performed further analysis to determine which kind of behavior influences task performance. As a result, we identified that average utterance length and slots per utterance are the key features of dialogue behavior that are highly correlated with both task performance and user personality.
Mikolov et al. (2013a) observed that continuous bag-of-words (CBOW) word embeddings tend to underperform Skip-gram (SG) embeddings, and this finding has been reported in subsequent works. We find that these observations are driven not by fundamental
differences in their training objectives, but more likely on faulty negative sampling CBOW implementations in popular libraries such as the official implementation, word2vec.c, and Gensim. We show that after correcting a bug in the CBOW gradient update, one can learn CBOW word embeddings that are fully competitive with SG on various intrinsic and extrinsic tasks, while being many times faster to train.
We introduce Classification with Alternating Normalization (CAN), a non-parametric post-processing step for classification. CAN improves classification accuracy for challenging examples by re-adjusting their predicted class probability distribution u
sing the predicted class distributions of high-confidence validation examples. CAN is easily applicable to any probabilistic classifier, with minimal computation overhead. We analyze the properties of CAN using simulated experiments, and empirically demonstrate its effectiveness across a diverse set of classification tasks.
Multilingual language models achieve impressive zero-shot accuracies in many languages in complex tasks such as Natural Language Inference (NLI). Examples in NLI (and equivalent complex tasks) often pertain to various types of sub-tasks, requiring di
fferent kinds of reasoning. Certain types of reasoning have proven to be more difficult to learn in a monolingual context, and in the crosslingual context, similar observations may shed light on zero-shot transfer efficiency and few-shot sample selection. Hence, to investigate the effects of types of reasoning on transfer performance, we propose a category-annotated multilingual NLI dataset and discuss the challenges to scale monolingual annotations to multiple languages. We statistically observe interesting effects that the confluence of reasoning types and language similarities have on transfer performance.
The research aimed to assess the performance of the private banking sector in Syrian Arab Republic during the period between 2010-2018 from a supervisory prospect of the Central Bank to determine the strengths and weaknesses of the banking sector in
Syrian Arab Republic. We gathered the statistical data issued by local banks (14 traditional and Islamic banks). The performance of the banks was evaluated separately using the CAMELS model, where the study showed average performance of the operating banks. The researchers deliberately used indicators of capital adequacy, asset quality, management efficiency, profitability, liquidity, and sensitivity to market risks. In addition, the researchers tried to study the effect of stock market risk on the performance of the banking sector by forming a portfolio of shares of banks operating in Syria. The results of the statistical analysis using the SPSS program, through the Discriminant Analysis test, showed the presence of an impact of a number of indicators on the performance of the banking sector. In the end, a model for evaluating the performance of the banking sector was obtained using a multiple regression model and the model is able to explain the performance of the banking sector by 88.4%.
Many state-of-art neural models designed for monotonicity reasoning perform poorly on downward inference. To address this shortcoming, we developed an attentive tree-structured neural network. It consists of a tree-based long-short-term-memory networ
k (Tree-LSTM) with soft attention. It is designed to model the syntactic parse tree information from the sentence pair of a reasoning task. A self-attentive aggregator is used for aligning the representations of the premise and the hypothesis. We present our model and evaluate it using the Monotonicity Entailment Dataset (MED). We show and attempt to explain that our model outperforms existing models on MED.
Recent work on entity coreference resolution (CR) follows current trends in Deep Learning applied to embeddings and relatively simple task-related features. SOTA models do not make use of hierarchical representations of discourse structure. In this w
ork, we leverage automatically constructed discourse parse trees within a neural approach and demonstrate a significant improvement on two benchmark entity coreference-resolution datasets. We explore how the impact varies depending upon the type of mention.
Transformer and its variants have achieved great success in natural language processing. Since Transformer models are huge in size, serving these models is a challenge for real industrial applications. In this paper, we propose , a highly efficient i
nference library for models in the Transformer family. includes a series of GPU optimization techniques to both streamline the computation of Transformer layers and reduce memory footprint. supports models trained using PyTorch and Tensorflow. Experimental results on standard machine translation benchmarks show that achieves up to 14x speedup compared with TensorFlow and 1.4x speedup compared with , a concurrent CUDA implementation. The code will be released publicly after the review.
