Greece

Advancements in Long Short Term Memory (LSTM) Net- works have shown remarkable success in various Natural Language Gen- eration (NLG) tasks. However, generating sequence from pre-specified lexical constraints is a new, challenging and less researched area in NLG. Lexical constraints take the form of words in the language model’s out- put to create fluent and meaningful sequences. Furthermore, most of the previous approaches cater this problem by allowing the inclusion of pre- specified lexical constraints during the decoding process, which increases the decoding complexity exponentially or linearly with the number of constraints. Moreover, some of the previous approaches can only deal with single constraint. Additionally, most of the previous approaches only deal with single constraints. In this paper, we propose a novel neu- ral probabilistic architecture based on backward-forward language model and word embedding substitution method that can cater multiple lexical constraints for generating quality sequences. Experiments shows that our proposed architecture outperforms previous methods in terms of intrinsic evaluation.

AIAI 2020

Backward-Forward Sequence Generative Network for Multiple Lexical Constraints

word embedding.

lexical constraints

natural language generation

recurrent neural networks

language models

[![](https://assets.underline.io/uploads/markdown_image/1/image/81a3c0317d24f663b49b996875024d45.png)](https://aclanthology.org/volumes/2022.coling-1/)



### THE CONFERENCE WE KNOW AND WE WANT.
**COLING**, the International Conference on Computational Linguistics, is one of the premier conferences for the natural language processing and computational linguistics.

First established in 1965, the biennial COLING conference is held in diverse parts of the globe and attracts participants from both top-ranked research centers and emerging countries. Today, the most important developments in our field are taking place not only in universities and academic research institutes but also in industrial research departments including tech-startups. COLING provides opportunities for all these communities to showcase their exciting discovery.

In fall of 2022, COLING will be held in Gyeongju in a hybrid format. All participants can either present at the venue site or join virtually. As more people get vaccinated, we are happy to provide safer environments for our colleagues. We believe that COLING 2022 will be one of the conferences, free from the pandemic. The hybrid format gives presenters and sponsors a valuable opportunity to promote their companies in both an online and in-person venue. For the first time in a long time, customers can interact with their sponsor's products first-hand. The online venue, too, gives sponsors the chance to network with those unable to attend the in-person session.

The hybrid format ultimately lends itself to greater exposure for our sponsors: COLING2022 will let you reach more potential partners and customers than ever before!<br><br><br>

To gain access to this event page you are required to register.<br> Find more information and pay the registration fee on event the organizer’s website **[https://coling2022.org/reg](https://coling2022.org/reg)**

COLING 2022

COLING, the International Conference on Computational Linguistics, is one of the premier conferences for the natural language processing and computational linguistics.

technical paper

AIAI 2020 (16th International Conference on Artificial Intelligence Applications and Innovations) should be held in Halkidiki, Greece. Due to current Covid-19 situation the conference will be held virtually. Underline will host a repository of video talks and preserve them for the future. Videos will be released on Underline one day after the conference.

As the requirements for automatic speech recognition are continually increasing, the demand for accuracy and efficiency is also of particular interest. In this paper, we present most of the well-known Automated Speech Recognition systems(ASR), and we benchmark three of them, namely the IBM Watson, Google, and Wit, using the WER, Hper, and Rper error metrics. The experimental results show that Google's automatic speech recognition performs better among the three systems. We intend to extend the benchmarking both to include most of the available Automated Speech Recognition systems and increase our test data.

Α Benchmarking of IBM, Google and Wit Automatic Speech Recognition Systems

Traffic load classification for Smart Cities based on weather data collected by sensors. Comparison and evaluation of predictability for periods throughout year.

Using Classification for Traffic Prediction in Smart Cities

Due to the surge of interest in online retailing, the use of credit cards has been rapidly expanded in recent years. Stealing the card details to perform online transactions, which is called fraud, has also seen more frequently. Preventive solutions and instant fraud detection methods are widely studied due to critical financial losses in many industries. In this work, a Gradient Boosting Tree (GBT) model for the real-time detection of credit card frauds on the streaming Card-Not-Present (CNP) transactions is investigated with the use of different attributes of card transactions. Numerical, hand-crafted numerical, categorical and textual attributes are combined to form a feature vector to be used as a training instance. One of the contributions of this work is to employ transaction aggregation for the categorical values and inclusion of vectors from a character level word embedding model which is trained on the merchant names of the transactions. The other contribution is introducing a new strategy for training dataset generation employing the sliding window approach in a given time frame to adapt to the changes on the trends of fraudulent transactions. In the experiments, the feature engineering strategy and the automated training set generation methodology are evaluated on the real credit card transactions.

An Adaptive Approach on Credit Card Fraud Detection using Transaction Aggregation and Word Embeddings

With the advent of social media, concepts such as forecasting and nowcast-ing became part of the public debate. Past successes include predicting elec-tion results, stock prices and forecasting events or behaviors. This work aims at using Twitter data, related to songs and artists that appeared on the top 10 of the Billboard Hot 100 charts, and perform sentiment analysis on the col-lected tweets, to predict the charts in the future. Our goal is to investigate the relation between the number of mentions of a song and its artist, as well as the semantic orientation of the relevant posts and its performance on the subsequent chart. The problem was approached via regression analysis, which estimated the difference between the actual and predicted positions and moderate results. We also focused on forecasting chart ranges, namely the top 5, 10 and 20. Given the accuracy and F-score achieved and com-pared to previous research, our findings are deemed satisfactory, especially for predicting the top 20.

Using Twitter to Predict Chart Position for Songs

The emerging 5G networks promises more throughput, faster, and more reliable services, but as the network complexity and dynamics increases, it becomes more difficult to troubleshoot the systems. Vendors are spending a lot of time and effort on early anomaly detection in their development cycle and majority of the time is spent on manually analyzing system logs. While main research in anomaly detection uses performance metrics, anomaly detection using functional behaviour is still lacking in depth analysis. In this paper we show how a boosted ensemble of Long Short Term Memory classifiers can detect anomalies in the 5G Radio Access Network system logs. Acquiring system logs from a live 5G network is difficult due to confidentiality issues, live network disturbance, and problems to repeat scenarios. Therefore, we perform our evaluation on logs from a 5G test bed that simulate realistic traffic in a city. Our ensemble learns the functional behaviour of an application by training on logs from normal execution time. It can then detect deviations from normal behaviour and also be retrained on false positive cases found during validation. Anomaly detection in RAN shows that our ensemble called BoostLog, outperforms a single LSTM classifier and further testing on HDFS logs confirms that BoostLog also can be used in other domains. Instead of using domain experts to manually analyse system logs, BoostLog can be used by less experienced trouble shooters to automatically detect anomalies faster and more reliable.

Boosted Ensemble Learning for Anomaly Detection in 5G RAN

The presentation depicts the work reported in the paper #95 that has been accepted in the conference AIAI 2020. The paper suggests a real-time online shopper behavior prediction system which predicts the visitor's shopping intent as soon as the website is visited. To do that, we rely on session and visitor information and we investigate naive Bayes classifier, C4.5 decision tree and random forest. Furthermore, we use oversampling to improve the performance and the scalability of each classifier. The results show that random forest produces significantly higher accuracy and F1 Score than the compared techniques.

Real-time prediction of online shoppers’ purchasing intention using random forest

Products with short life cycles are becoming increasingly common in many industries due to higher levels of competition, shorter product development time and increased product diversity. Accurate demand forecasting of such products is crucial as it plays an important role in driving efficient business operations and achieving a sustainable competitive advantage. Traditional forecasting methods are inappropriate for this type of products due to the highly uncertain and volatile demand and the lack of historical sales data. It is therefore critical to develop different forecasting methods to analyse the demand trend of these products. This paper proposes a new data mining approach based on the incremental k-means clustering algorithm and the RULES-6 rule induction classifier for forecasting the demand of short life cycle products. The performance of the proposed approach is evaluated using real data from one of the leading Egyptian companies in IT ecommerce and retail business, and results show that it has the capability to accurately forecast demand trends of new products with no historical sales data.

Demand Forecasting of Short Life Cycle Products Using Data Mining Techniques

The more complex data are, the higher the number of possibilities to extract partial information from those data. These possibilities arise by adopting different analytic approaches. The heterogeneity among these approaches and in particular the heterogeneity in results they produce are challenging for follow-up studies, including replication, validation and translational studies. Furthermore, they complicate the interpretation of findings with wide-spread relevance. Here, we take the example of statistical epistasis networks derived from genome-wide association studies with single nucleotide polymorphisms as nodes. Even though we are only dealing with a single data type, the epistasis detection problem suffers from many pitfalls, such as the wide variety of analytic tools to detect them, each highlighting different aspects of epistasis and exhibiting different properties in maintaining false positive control. To reconcile different network views to the same problem, we considered 3 network aggregation methods and discussed their performance in the context of epistasis network aggregation. We furthermore applied a latent class method as best performer to real-life data on inflammatory bowel disease (IBD) and highlighted its benefits to increase our understanding about IBD underlying genetic architecture.

Network aggregation to enhance results derived from multiple analytics

finElink is a recommendation system that provides guidance to French innovative companies with regard to their financing strategy through public funding mechanisms. Analysis of financial data from former funding recipients partially feeds the recommendation system. Financial company data from a representative French population are reduced and projected onto a two-dimensional space with Uniform Manifold Approximation and Projection, a manifold learning algorithm. Former French funding recipients' data are projected onto the two-dimensional space. Their distribution is non-uniform, with data concentrating in one region of the projection space. This region is identified using Density-based Spatial Clustering of Applications with Noise. Applicant companies which are projected within this region are labeled potential funding recipients and will be suggested the most competitive funding mechanisms.

Manifold learning for innovation funding: identification of potential funding recipients

We present a novel justification for why k-means clusters should be considered as (hyper)ball-shaped ones. We show that the clusters must be ball-shaped to attain motion-consistency. If clusters are ball-shaped, one can derive conditions under which two clusters attain the global optimum of k-means. 
We show further that if the gap is sufficient for perfect separation, then an incremental k-means is able to discover perfectly separated clusters. This is in conflict with the impression left by an earlier publication by Ackerman and Dasgupta. The proposed motion-transformations can be used to the new labeled data for clustering from existing ones.

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