While both the database and high-performance computing (HPC) communities utilize lossless compression methods to minimize floating-point data size, a disconnect persists between them. Each community designs and assesses methods in a domain-specific manner, making it unclear if HPC compression techniques can benefit database applications or vice versa. With the HPC community increasingly leaning towards in-situ analysis and visualization, more floating-point data from scientific simulations are being stored in databases like Key-Value Stores and queried using in-memory retrieval paradigms. This trend underscores the urgent need for a collective study of these compression methods' strengths and limitations, not only based on their performance in compressing data from various domains but also on their runtime characteristics. Our study extensively evaluates the performance of eight CPU-based and five GPU-based compression methods developed by both communities, using 33 real-world datasets assembled in the Floating-point Compressor Benchmark (FCBench). Additionally, we utilize the roofline model to profile their runtime bottlenecks. Our goal is to offer insights into these compression methods that could assist researchers in selecting existing methods or developing new ones for integrated database and HPC applications.

The existence of an anomaly detection method that is optimal for all domains is a myth. Thus, there exists a plethora of anomaly detection methods which increases every year for a wide variety of domains. But a strength can also be a weakness; given this massive library of methods, how can one select the best method for their application? Current literature is focused on creating new anomaly detection methods or large frameworks for experimenting with multiple methods at the same time. However, and especially as the literature continues to expand, an extensive evaluation of every anomaly detection method is simply not feasible. To reduce this evaluation burden, we present guidelines to intelligently choose the optimal anomaly detection methods based on the characteristics the time series displays such as seasonality, trend, level change concept drift, and missing time steps. We provide a comprehensive experimental validation and survey of twelve anomaly detection methods over different time series characteristics to form guidelines based on several metrics: the AUC (Area Under the Curve), windowed F-score, and Numenta Anomaly Benchmark (NAB) scoring model. Applying our methodologies can save time and effort by surfacing the most promising anomaly detection methods instead of experimenting extensively with a rapidly expanding library of anomaly detection methods, especially in an online setting.

The existence of an anomaly detection method that is optimal for all domains is a myth. Thus, there exists a plethora of anomaly detection methods which increases every year for a wide variety of domains. But a strength can also be a weakness; given this massive library of methods, how can one select the best method for their application? Current literature is focused on creating new anomaly detection methods or large frameworks for experimenting with multiple methods at the same time. However, and especially as the literature continues to expand, an extensive evaluation of every anomaly detection method is simply not feasible. To reduce this evaluation burden, we present guidelines to intelligently choose the optimal anomaly detection methods based on the characteristics the time series displays such as seasonality, trend, level change concept drift, and missing time steps. We provide a comprehensive experimental validation and survey of twelve anomaly detection methods over different time series characteristics to form guidelines based on several metrics: the AUC (Area Under the Curve), windowed F-score, and Numenta Anomaly Benchmark (NAB) scoring model. Applying our methodologies can save time and effort by surfacing the most promising anomaly detection methods instead of experimenting extensively with a rapidly expanding library of anomaly detection methods, especially in an online setting.

As Intelligent Virtual Agents (IVAs) increase in adoption and further emulate human personalities, we are interested in how humans apply relational strategies to them compared to other humans in a service environment. Human-computer data from three live customer service IVAs was collected, and annotators marked all text that was deemed unnecessary to the determination of user intention as well as the presence of multiple intents. After merging the selections of multiple annotators, a second round of annotation determined the classes of relational language present in the unnecessary sections such as Greetings, Backstory, Justification, Gratitude, Rants, or Expressing Emotions. We compare the usage of such language in human-human service interactions. We show that removal of this language from task-based inputs has a positive effect by both an increase in confidence and improvement in responses, as evaluated by humans, demonstrating the need for IVAs to anticipate relational language injection. This work provides a methodology to identify relational segments and a baseline of human performance in this task as well as laying the groundwork for IVAs to reciprocate relational strategies in order to improve their believeability.

The dimensionality of traditional text representation is large, but the underlying text data is sparse. This makes text clustering a very challenging task. Using language models and deep contextualized representations is promising in many Natural Language Processing (NLP) tasks. However, some task-specific guidance is necessary to adapt language models to a novel domain or to particular downstream tasks. We present an empirical study of a pipeline for semi-supervised text clustering tasks. Our proposed method utilizes a small number of labeled samples to fine-tune pre-trained language models. This fine-tuning step adapts the language models to produce task-specific contextualized representations, improving the performance of downstream text clustering tasks. We evaluate two clustering algorithms using the output of three different language models on six real-world text mining tasks to demonstrate to what extent this pipeline can improve text clustering accuracy and the amount of labeled samples needed for improvement. Our experiments show that for topic mining in novel domains or surfacing the intentions of abstracts, language models begin to produce better task-specific representations using a labeled subset as small as 0.5% of the task data. On the other hand, to find topics in domains that are overlapping with pre-training corpora, language models need labeled subsets closer to 1.0% of the task data to overcome the catastrophic forgetting problem. Further experiments show the downstream clustering accuracy gain begins to slow down or plateau if language models are fine-tuned with more than 5% of the task data. There is a trade-off between the desired downstream clustering quality and the cost for labeling and fine-tuning language models. Fine-tuned with 2.5% of the task data, our approach matches or exceeds the current state-of-the-art for several clustering tasks and provides baseline results for two novel clustering tasks. These results provide solid guidance to utilize powerful language models for text clustering and information retrieval practitioners.

Internet topology reflects economic and political constraints that change over time. Although autonomous systems (AS) topology has been measured and modeled for many years, focusing primarily on economic relationships, earlier studies have not quantified how topology is changing with respect to nation-state boundaries. National boundaries are natural points of control for surveillance, censorship, tariffs and data localization. This paper introduces a measure, national choke-point potential (NCP), to characterize how a country's AS topology is organized in terms of BGP paths that can carry traffic across international borders. To study country-level chokepoints, we developed BGP-SAS, an open source, cross platform, efficient set of tools for simulating BGP routing and calculating national chokepoint measures. We use these tools to assess how AS topologies have changed over a ten-year span, finding significant variability among countries, with some increasing their chokepoint potential and others remaining constant, fluctuating, and in some cases declining. Overall, however, most national Internet boundaries have either become more pronounced or remained constant, despite new infrastructure buildouts and increased Internet usage. When compared to independent measures of Internet freedom, we find statistically significant relationships between NCP and Internet freedom.

We investigate the occurrence of user restatement when there is no apparent error in Intelligent Virtual Assistant (IVA) understanding in a multimodal customer service environment. We define several classes of response medium combinations and use various statistical measures to determine how the combination of medium and linguistic complexity impacts the user's apparent willingness to accept their query result. Through analysis on 3; 000 sessions with a live customer service IVA deployed on an airline company website and mobile application, we discover that as more media are involved in a response, user restatements increase. We also determine which linguistic complexity measures should be minimized for every response class in order to improve user comprehension.

Causal structure learning from time series data is a major scientific challenge. Existing algorithms assume that measurements occur sufficiently quickly; more precisely, they assume that the system and measurement timescales are approximately equal. In many scientific domains, however, measurements occur at a significantly slower rate than the underlying system changes. Moreover, the size of the mismatch between timescales is often unknown. This paper provides three distinct causal structure learning algorithms, all of which discover all dynamic graphs that could explain the observed measurement data as arising from undersampling at some rate. That is, these algorithms all learn causal structure without assuming any particular relation between the measurement and system timescales; they are thus rate-agnostic. We apply these algorithms to data from simulations. The results provide insight into the challenge of undersampling.