Synthetic data generation (SDG) can be used to augment an existing dataset or create a new dataset with statistical characteristics similar to the original. SDG for tabular data is challenging because of the need to model both continuous and categorical features and their correlations. multiple approaches for tabular SDG use generative adversarial networks (GAN) or variational autoencoders (VAEs). Generally, GAN-based architectures create high-quality samples but have greater difficulty modeling the distribution of the target dataset. VAE-based approaches accurately model the data distribution but sometimes produce lower-quality samples. In this study, we propose T-VAE-GAN, a novel solution for tabular SDG. Our approach hierarchically combines GANs and VAEs to enable the generation of high-quality samples while ensuring that the overall feature distribution is highly similar to that of the original dataset. Extensive evaluation on a large number of datasets shows that our approach either outperforms or achieves comparable results to leading approaches while also being more computationally efficient.

Dynamic temporal graphs evolve over time, adding and removing nodes and edges between time snapshots. The tasks performed on such graphs are diverse and include detecting temporal trends, finding graph-to-graph similarities, and graph visualization and clustering. For all these tasks, it is necessary to embed the entire graph in a low-dimensional space by using graph-level representations instead of the more common node-level representations. This embedding requires handling the appearance of new nodes over time as well as capturing temporal patterns of the entire graph. Most existing methods perform temporal node embeddings and focus on different methods of aggregating them for a graph-based representation. In this work, we propose an end-to-end architecture that captures both the node embeddings and their influence in a structural context during a specific time period of the graph. We present GraphERT (Graph Embedding Representation using Transformers), a novel approach to temporal graph-level embeddings. Our method pioneers the use of Transformers to seamlessly integrate graph structure learning with temporal analysis. By employing a masked language model on sequences of graph random walks, together with a novel temporal classification task, our model not only comprehends the intricate graph dynamics but also unravels the temporal significance of each node and path. This novel training paradigm empowers GraphERT to capture the essence of both the structural and temporal aspects of graphs, surpassing state-of-the-art approaches across multiple tasks on real-world datasets.

Recent trajectory optimization methods for offline reinforcement learning ($R L$) define the problem as one of conditional-sequence policy modeling. One of these methods is Decision Transformer (DT), a Transformer-based trajectory optimization approach that achieved competitive results with the current state-of-the-art. Despite its high capabilities, DT underperforms when the training data does not contain full trajectories, or when the recorded behavior does not offer sufficient coverage of the states-actions space. We propose Feedback Decision Transformer (FDT), a data-driven approach that uses limited amounts of high-quality feedback at critical states to significantly improve DT’s performance. Our approach analyzes and estimates the Q-function across the states-actions space, and identifies areas where feedback is likely to be most impactful. Next, we integrate this feedback into our model, and use it to improve our model’s performance. Extensive evaluation and analysis on four Atari games show that FDT significantly outperforms DT in multiple setups and configurations.

Basketball is one of the most popular types of sports in the world. Recent technological developments have made it possible to collect large amounts of data on the game, analyze it, and discover new insights. We propose a novel approach for modeling basketball games using deep reinforcement learning. By analyzing multiple aspects of both the players and the game, we are able to model the latent connections among players' movements, actions, and performance, into a single measure - the Q-Ball. Using Q-Ball, we are able to assign scores to the performance of both players and whole teams. Our approach has multiple practical applications, including evaluating and improving players' game decisions and producing tactical recommendations. We train and evaluate our approach on a large dataset of National Basketball Association games, and show that the Q-Ball is capable of accurately assessing the performance of players and teams. Furthermore, we show that Q-Ball is highly effective in recommending alternatives to players' actions.

The enrichment of tabular datasets using external sources has gained significant attention in recent years.Existing solutions, however, either ignore external unstructured data completely or devise dataset-specific solutions.In this study, we proposed Few-Shot Transformer based Enrichment (FeSTE), a generic and robust framework for the enrichment of tabular datasets using unstructured data.By training over multiple datasets, our approach is able to develop generic models that can be applied to additional datasets with minimal training (i.e., few-shot).Our approach is based on an adaptation of BERT, for which we present a novel finetuning approach that reformulates the tuples of the datasets as sentences.Our evaluation, conducted on 17 datasets, shows that FeSTE is able to generate high quality features and significantly outperform existing fine-tuning solutions.

Binary code similarity detection (BCSD) has important applications in various fields such as vulnerabilities detection, software component analysis, and reverse engineering. Recent studies have shown that deep neural networks (DNNs) can comprehend instructions or control-flow graphs (CFG) of binary code and support BCSD. In this study, we propose a novel Transformer-based approach, namely jTrans, to learn representations of binary code. It is the first solution that embeds control flow information of binary code into Transformer-based language models, by using a novel jump-aware representation of the analyzed binaries and a newly-designed pre-training task. Additionally, we release to the community a newly-created large dataset of binaries, BinaryCorp, which is the most diverse to date. Evaluation results show that jTrans outperforms state-of-the-art (SOTA) approaches on this more challenging dataset by 30.5% (i.e., from 32.0% to 62.5%). In a real-world task of known vulnerability searching, jTrans achieves a recall that is 2X higher than existing SOTA baselines.

Multi-objective task scheduling (MOTS) combines the task of scheduling with the need to optimize multiple-and possibly contradicting-constraints. A challenging extension of this problem occurs when every individual task is a multiobjective optimization problem by itself. While deep reinforcement learning (DRL) has been successfully applied to complex sequential problems, its application to the MOTS domain has been stymied by two challenges. The first challenge is the inability of the DRL algorithm to ensure that every item is processed identically regardless of its position in the queue. The second challenge is the need to manage large queues, which results in large neural architectures and long training times. In this study we present MERLIN, a robust, modular and near-optimal DRL-based approach for multi-objective task scheduling. Our approach addresses both aforementioned challenges while also being more efficient and easier to train. Extensive evaluation on multiple queue sizes show that MERLIN outperforms multiple well-known scheduling algorithms by a large margin (≥ 22%).

The current trends of cloud computing in general, and serverless computing in particular, affect multiple aspects of organizational activity. Organizations of all sizes are transitioning parts of their operations off-premise in order to reduce costs and scale their operations more efficiently. The field of network security is no exception, with many organizations taking advantage of the distributed and scalable cloud environment. Since the charging model for serverless computing is "pay as you go" (i.e., payment per action), a reduction in the number of required computations translates into significant cost savings. This understanding is also relevant to the field of malware detection, where organizations often deploy multiple types of detectors to increase detection accuracy. In this study, we utilize deep reinforcement learning to reduce computational costs in the cloud by selectively querying only a subset of available detectors. We demonstrate that our approach is not only effective both for on-premise and cloud-based computing architectures, but that applying it to serverless computing can reduce costs by an order of magnitude while maintaining near-optimal performance.

Temporal dynamic graphs are graphs whose topology evolves over time, with nodes and edges added and removed between different time snapshots. Embedding such graphs in a low-dimensional space is important for a variety of tasks, including graphs' similarities, time series trends analysis and anomaly detection, graph visualization, graph classification, and clustering. Despite the importance of the temporal element in these tasks, existing graph embedding methods focus on capturing the graph's nodes in a static mode and/or do not model the graph in its entirety in temporal dynamic mode. In this study, we present tdGraphEmbed, a novel temporal graph-level embedding approach that extend the random-walk based node embedding methods to globally embed both the nodes of the graph and its representation at each time step, thus creating representation of the entire graph at each step. Our approach was applied to graph similarity ranking, temporal anomaly detection, trend analysis, and graph visualizations tasks, where we leverage our temporal embedding in a fast and scalable way for each of the tasks. An evaluation of tdGraphEmbed on five real-world datasets shows that our approach can outperform state-of-the-art approaches used for graph embedding and node embedding in temporal graphs.

Automatic machine learning (AutoML) aims to automate the different aspects of the data science process and, by extension, allow non-experts to utilize "off the shelf" machine learning solution. One of the more popular AutoML methods is the Tree-based Pipeline Optimization Tool (TPOT), which uses genetic programming (GP) to efficiently explore the vast space of ML pipelines and produce a working ML solution. However, TPOT's GP process comes with substantial time and computational costs. In this study, we explore TPOT's GP process and propose MetaTPOT, an enhanced variant that uses a meta learning-based approach to predict the performance of TPOT's pipeline candidates. MetaTPOT leverages domain knowledge in the form of pipelines pre-ranking to improve TPOT's speed and performance. Evaluation on 65 classification datasets shows that our approach often improves the outcome of the genetic process while simultaneously substantially reduce its running time and computational cost.

Automatic Machine Learning (AutoML) is an area of research aimed at automating Machine Learning (ML) activities that currently require the involvement of human experts. One of the most challenging tasks in this field is the automatic generation of end-to-end ML pipelines: combining multiple types of ML algorithms into a single architecture used for analysis of previously-unseen data. This task has two challenging aspects: the first is the need to explore a large search space of algorithms and pipeline architectures. The second challenge is the computational cost of training and evaluating multiple pipelines. In this study we present DeepLine, a reinforcement learning-based approach for automatic pipeline generation. Our proposed approach utilizes an efficient representation of the search space together with a novel method for operating in environments with large and dynamic action spaces. By leveraging past knowledge gained from previously-analyzed datasets, our approach only needs to generate and evaluate few dozens of pipelines to reach comparable or better performance than current state-of-the-art AutoML systems that evaluate hundreds and even thousands of pipelines in their optimization process. Evaluation on 56 classification datasets demonstrates the merits of our approach.

The widespread use of machine learning algorithms and the high level of expertise required to utilize them have fuelled the demand for solutions that can be used by non-experts. One of the main challenges non-experts face in applying machine learning to new problems is algorithm selection - the identification of the algorithm(s) that will deliver top performance for a given dataset, task, and evaluation measure. We present AutoGRD, a novel meta-learning approach for algorithm recommendation. AutoGRD first represents datasets as graphs and then extracts their latent representation that is used to train a ranking meta-model capable of accurately recommending top-performing algorithms for previously unseen datasets. We evaluate our approach on 250 datasets and demonstrate its effectiveness both for classification and regression tasks. AutoGRD outperforms state-of-the-art meta-learning and Bayesian methods.

One of the challenges of automating machine learning applications is the automatic selection of an algorithmic model for a given problem. We present AutoDi, a novel and resource-efficient approach for model selection. Our approach combines two sources of information: metafeatures extracted from the data itself and word-embedding features extracted from a large corpus of academic publications. This hybrid approach enables AutoDi to select top-performing algorithms both for widely and rarely used datasets by utilizing its two types of feature sets. We demonstrate the effectiveness of our proposed approach on a large dataset of 119 datasets and 179 classification algorithms grouped into 17 families. We show that AutoDi can reach an average of 98.8% of optimal accuracy and select the optimal classification algorithm in 49.5% of all cases.

High-quality, labeled data is essential for successfully applying machine learning methods to real-world text classification problems. However, in many cases, the amount of labeled data is very small compared to that of the unlabeled, and labeling additional samples could be expensive and time consuming. Co-training algorithms, which make use of unlabeled data to improve classification, have proven to be very effective in such cases. Generally, co-training algorithms work by using two classifiers, trained on two different views of the data, to label large amounts of unlabeled data. Doing so can help minimize the human effort required for labeling new data, as well as improve classification performance. In this article, we propose an ensemble-based co-training approach that uses an ensemble of classifiers from different training iterations to improve labeling accuracy. This approach, which we call vertical ensemble , incurs almost no additional computational cost. Experiments conducted on six textual datasets show a significant improvement of over 45% in AUC compared with the original co-training algorithm.

Feature generation is one of the challenging aspects of machine learning. We present ExploreKit, a framework for automated feature generation. ExploreKit generates a large set of candidate features by combining information in the original features, with the aim of maximizing predictive performance according to user-selected criteria. To overcome the exponential growth of the feature space, ExploreKit uses a novel machine learning-based feature selection approach to predict the usefulness of new candidate features. This approach enables efficient identification of the new features and produces superior results compared to existing feature selection solutions. We demonstrate the effectiveness and robustness of our approach by conducting an extensive evaluation on 25 datasets and 3 different classification algorithms. We show that ExploreKit can achieve classification-error reduction of 20% overall. Our codeis available at https://github.com/giladkatz/ExploreKit.

The query-performance prediction task is to estimate retrieval effectiveness with no relevance judgments. Pre-retrieval prediction methods operate prior to retrieval time. Hence, these predictors are often based on analyzing the query and the corpus upon which retrieval is performed. We propose a {\em corpus-independent} approach to pre-retrieval prediction which relies on information extracted from Wikipedia. Specifically, we present Wikipedia-based features that can attest to the effectiveness of retrieval performed in response to a query {\em regardless} of the corpus upon which search is performed. Empirical evaluation demonstrates the merits of our approach. As a case in point, integrating the Wikipedia-based features with state-of-the-art pre-retrieval predictors that analyze the corpus yields prediction quality that is consistently better than that of using the latter alone.

Today's organizations spend a great deal of time and effort on e‐mail leakage prevention. However, there are still no satisfactory solutions; addressing mistakes are not detected and in some cases correct recipients are wrongly marked as potential mistakes. In this article we present a new approach for preventing e‐mail addressing mistakes in organizations. The approach is based on an analysis of e‐mail exchanges among members of an organization and the identification of groups based on common topics. When a new e‐mail is about to be sent, each recipient is analyzed. A recipient is approved if the e‐mail's content belongs to at least one common topic to both the sender and the recipient. This can be applied even if the sender and recipient have never communicated directly before. The new approach was evaluated using the Enron e‐mail data set and was compared with a well known method for the detection of e‐mail addressing mistakes. The results show that the proposed approach is capable of detecting 87% of nonlegitimate recipients while incorrectly classifying only 0.5% of the legitimate recipients. These results outperform previous work, which reports a detection rate of 82% without reference to the false positive rate.

Decision trees have three main disadvantages: reduced performance when the training set is small, rigid decision criteria and the fact that a single "uncharacteristic" attribute might "derail" the classification process. In this paper we present ConfDTree - a post-processing method which enables decision trees to better classify outlier instances. This method, which can be applied on any decision trees algorithm, uses confidence intervals in order to identify these hard-to-classify instances and proposes alternative routes. The experimental study indicates that the proposed post-processing method consistently and significantly improves the predictive performance of decision trees, particularly for small, imbalanced or multi-class datasets in which an average improvement of 5%-9% in the AUC performance is reported.

Modern business activities rely on extensive email exchange. Various solutions attempt to analyze email exchange in order to prevent emails from being sent to the wrong recipients. However there are still no satisfying solutions; many email addressing mistakes are not detected and in many cases correct recipients are wrongly marked as potential addressing mistakes. In this paper we present a new approach for preventing emails addressing mistakes in organizations. The approach is based on analysis of emails exchange among members of the organization and the identification of groups based on common topics. Each member's topics are then used during the enforcement phase for detecting potential leakage. When a new email is composed and about to be sent, each email recipient is analyzed. A recipient is approved if the email's content belongs to at least one of the topics common to the sender and the recipient. We evaluated the new approach using the Enron Email dataset. Our evaluation results suggest that the new approach easily copes with email recipients that have no previous direct connection with the sender.

One important challenge in the field of recommender systems is the sparsity of available data. This problem limits the ability of recommender systems to provide accurate predictions of user ratings. We overcome this problem by using the publicly available user generated information contained in Wikipedia. We identify similarities between items by mapping them to Wikipedia pages and finding similarities in the text and commonalities in the links and categories of each page. These similarities can be used in the recommendation process and improve ranking predictions. We find that this method is most effective in cases where ratings are extremely sparse or nonexistent. Preliminary experimental results on the MovieLens dataset are encouraging.

Electrical dispersion compensation equalizer is a key and cost-effective element in optical communication on-off-keying systems in the presence of chromatic dispersion. Here, for the first time, to the best of the author's knowledge, an analytical solution is established for the electrical equalizer coefficients in an optical communication system. The solution is based on minimum mean-square error criterion. The analytical results show a perfect match with computer simulation. In addition BER performance comparison with the adaptive least mean square (LMS) method reveals that the analytical solution performs better due to LMS excess mean-square error.

In this paper we introduce a nonlinear equalizer using the radial basis function (RBF) network with decision feedback equalizer (DFE) for electronic dispersion compensation in optical communication systems with on-off-keying and a direct detection receiver. The RBF method introduces a non-linear equalization technique suitable for optical communication direct detection systems that include nonlinear transformation at the photodetector. A bit error rate performance comparison shows that the RBF with DFE out performs the RBF without DFE and achieves similar results provided by maximum likelihood sequence estimator.

Electrical dispersion compensation equalizer is a key and cost-effective element in optical communication OOK systems in the presence of chromatic dispersion. Here, for the first time, to the best of the authors' knowledge, an analytical solution is established for the electrical equalizer coefficients in an optical communication system. The solution is based on minimum mean-square error criterion. The analytical results show a perfect match with computer simulation. In addition BER performance comparison with the adaptive least mean square (LMS) method reveals that the analytical solution performs better due to LMS excess mean-square error.

We study the performances of several electrical dispersion compensation (EDC) equalizers in the presence of chromatic dispersion and polarization mode dispersion for optical coherent- and direct-detection on–off keying systems. The EDCs that are analyzed include decision-feedback equalizer, linear equalizer, and maximum-likelihood sequence estimator (MLSE). We present an inclusive quantitative analysis of the performance difference between the various techniques. The MLSE gives a good indication of the best possible performance.

We study the performances of several electrical dispersion compensation (EDC) equalizers in the presence of chromatic dispersion (CD) and polarization mode dispersion (PMD) for optical coherent and direct detection on-off keying systems. The EDCs that are analyzed include the decision-feedback equalizer, linear equalizer, and maximum-likelihood sequence estimator (MLSE). We present an inclusive quantitative analysis of the performance difference between the various techniques. The MLSE gives a good indication of the best possible performance