Miracle-dz/UniFAR-FTUs huggingface dataset:
Different Data, Different Modalities! Reinforced Data Splitting for Effective Multimodal Information Extraction from Social Media Posts

Recently, multimodal information extraction from social media posts has gained increasing attention in the natural language processing community. Despite their success, current approaches overestimate the significance of images. In this paper, we argue that different social media posts should consider different modalities for multimodal information extraction. Multimodal models cannot always outperform unimodal models. Some posts are more suitable for the multimodal model, while others are more suitable for the unimodal model. Therefore, we propose a general data splitting strategy to divide the social media posts into two sets so that these two sets can achieve better performance under the information extraction models of the corresponding modalities. Specifically, for an information extraction task, we first propose a data discriminator that divides social media posts into a multimodal and a unimodal set. Then we feed these sets into the corresponding models. Finally, we combine the results of these two models to obtain the final extraction results. Due to the lack of explicit knowledge, we use reinforcement learning to train the data discriminator. Experiments on two different multimodal information extraction tasks demonstrate the effectiveness of our method. The source code of this paper can be found in https://github.com/xubodhu/RDS.
KOJAK: A New Corpus for Studying German Discourse Particle ja

In German, ja can be used as a discourse particle to indicate that a proposition, according to the speaker, is believed by both the speaker and audience. We use this observation to create KoJaK, a distantly-labeled English dataset derived from Europarl for studying when a speaker believes a statement to be common ground. This corpus is then analyzed to identify lexical choices in English that correspond with German ja. Finally, we perform experiments on the dataset to predict if an English clause corresponds to a German clause containing ja and achieve an F-measure of 75.3% on a balanced test corpus.
Improving Jejueo-Korean Translation With Cross-Lingual Pretraining Using Japanese and Korean

Jejueo is a critically endangered language spoken on Jeju Island and is closely related to but mutually unintelligible with Korean. Parallel data between Jejueo and Korean is scarce, and translation between the two languages requires more attention, as current neural machine translation systems typically rely on large amounts of parallel training data. While low-resource machine translation has been shown to benefit from using additional monolingual data during the pretraining process, not as much research has been done on how to select languages other than the source and target languages for use during pretraining. We show that using large amounts of Korean and Japanese data during the pretraining process improves translation by 2.16 BLEU points for translation in the Jejueo → Korean direction and 1.34 BLEU points for translation in the Korean → Jejueo direction compared to the baseline.
Learning Hierarchy-Aware Quaternion Knowledge Graph Embeddings with Representing Relations as 3D Rotations

Knowledge graph embedding aims to represent entities and relations as low-dimensional vectors, which is an effective way for predicting missing links. It is crucial for knowledge graph embedding models to model and infer various relation patterns, such as symmetry/antisymmetry. However, many existing approaches fail to model semantic hierarchies, which are common in the real world. We propose a new model called HRQE, which represents entities as pure quaternions. The relational embedding consists of two parts: (a) Using unit quaternions to represent the rotation part in 3D space, where the head entities are rotated by the corresponding relations through Hamilton product. (b) Using scale parameters to constrain the modulus of entities to make them have hierarchical distributions. To the best of our knowledge, HRQE is the first model that can encode symmetry/antisymmetry, inversion, composition, multiple relation patterns and learn semantic hierarchies simultaneously. Experimental results demonstrate the effectiveness of HRQE against some of the SOTA methods on four well-established knowledge graph completion benchmarks.
Do Discourse Indicators Reflect the Main Arguments in Scientific Papers?

In scientific papers, arguments are essential for explaining authors’ findings. As substrates of the reasoning process, arguments are often decorated with discourse indicators such as “which shows that” or “suggesting that”. However, it remains understudied whether discourse indicators by themselves can be used as an effective marker of the local argument components (LACs) in the body text that support the main claim in the abstract, i.e., the global argument. In this work, we investigate whether discourse indicators reflect the global premise and conclusion. We construct a set of regular expressions for over 100 word- and phrase-level discourse indicators and measure the alignment of LACs extracted by discourse indicators with the global arguments. We find a positive correlation between the alignment of local premises and local conclusions. However, compared to a simple textual intersection baseline, discourse indicators achieve lower ROUGE recall and have limited capability of extracting LACs relevant to the global argument; thus their role in scientific reasoning is less salient as expected.
Automatic Assessment of Online Content Credibility by Measuring the Adherence to Journalistic Standards

This paper analyses the correlation between online news articles’ credibility and compliance with journalistic standards and ethics. Our main hypothesis is that news sources with low credibility adhere less to journalistic standards and ethics than high credibility sources. Additionally, the influence of web page layout factors is analysed. Our definition of metrics is based on the existing literature regarding journalistic best practices, as well as the list of credibility signals provided by the W3C Credible Web Community Group. News article credibility is assessed based on a score, which is calculated as a sum of linear subscores for the identified metrics divided in four credibility signal categories: Formality, Neutrality, Transparency and Layout. A curated dataset of 250 recent news items from known fake news sources, as well as 200 news items from established real news sources, is used in the testing phase. Although the comparison between real news and false news shows that, on average, the credibility score of genuine news is only 5% higher than that of fake news, the results do show more significant differences for certain subcategories and signals.
SKYE: More than a conversational AI

This paper presents how a virtual character was created through a conversational AI system that not only has an open-domain dialog system but also takes visual and audio inputs to create an interactive and interpersonal responses to users. The virtual character has its 3D figure with a particular voice and personality for the immersive AI experience. Multiple AI modules were integrated into the system to make human-like interaction including automatic speech recognition, Text-to-speech model, intelligent open-domain dialog system, and 3D game engine.
Real-Time Monitoring of Silences in Contact Center Conversations

Contact center conversations often contain segments with hold music, automatic-recorded-messages or pure silences, where neither the customer nor the agent is speaking. We refer to these segments as Conversational Silences [1]. These silences when continued beyond an acceptable level can negatively impact important contact center KPIs, like average handling time, agent efficiency, etc. and may lead to poor customer experience. As a result, it becomes imperative for contact centers to identify si-lences in conversations and define mechanisms to better handle them. In this paper, we propose a cascaded system consisting of an ASR engine, a silence detector block, a text classification layer and a heuristic engine to surface instances in calls where agents are missing the protocols to handle silences. This system is used to trigger alerts to agents in real time thus enabling them to course correct while being on call with the customer. More-over, these instances can also be surfaced to their supervisors so as to identify agents who are frequently missing these protocols and thereby design dedicated coaching sessions.
This paper is included in the Proceedings of the 16th USENIX Symposium on Operating Systems Design and Implementation.

Deep Learning Recommender Systems (DLRSs) need to up-date models at low latency, thus promptly serving new users and content. Existing DLRSs, however, fail to do so. They train/validate models offline and broadcast entire models to global inference clusters. They thus incur significant model update latency (e.g. dozens of minutes), which adversely affects Service-Level Objectives (SLOs). This paper describes Ekko, a novel DLRS that enables low-latency model updates. Its design idea is to allow model updates to be immediately disseminated to all inference clusters, thus bypassing long-latency model checkpoint, validation and broadcast. To realise this idea, we first design an efficient peer-to-peer model update dissemination algorithm . This algorithm exploits the sparsity and temporal locality in updating DLRS models to improve the throughput and latency of updating models. Further, Ekko has a model update scheduler that can prioritise, over busy networks, the sending of model updates that can largely affect SLOs. Finally, Ekko has an inference model state manager which monitors the SLOs of inference models and rollbacks the models if SLO-detrimental biased updates are detected. Evaluation results show that Ekko is orders of magnitude faster than state-of-the-art DLRS systems. Ekko has been deployed in production for more than one year, serves over a billion users daily and reduces the model update latency compared to state-of-the-art systems from dozens of minutes to 2.4 seconds.
On the Semantics of "null" in DMN: Undefined is not Unknown

Decision Model and Notation (DMN) is a formalism for the representation of knowledge about decision processes. It represents a set of decision rules in an easy-to-understand tabular format, called a decision table. In this paper, we argue that current formal semantics for DMN have certain limitations and we propose a novel formal semantics. Our semantics considers a decision table as a definition. The semantics consists of two components: the first component captures the meaning of one row (rule), and the second component aggregates the meaning of the rules into meaning for the whole table. By choosing the second component in different ways, the different “hit policies” of DMN (i.e., mechanisms for deciding what happens when multiple rows of the same table are applicable) can be represented. Our semantics can cope better with undefined and unknown values and provides a foundation for forms of reasoning different from deriving the output for a given set of inputs.
Improved Information-Theoretic Generalization Bounds for Distributed, Federated, and Iterative Learning

We consider information-theoretic bounds on expected generalization error for statistical learning problems in a networked setting. In this setting, there are K nodes, each with its own independent dataset, and the models from each node have to be aggregated into a final centralized model. We consider both simple averaging of the models as well as more complicated multi-round algorithms. We give upper bounds on the expected generalization error for a variety of problems, such as those with Bregman divergence or Lipschitz continuous losses, that demonstrate an improved dependence of 1/K on the number of nodes. These "per node" bounds are in terms of the mutual information between the training dataset and the trained weights at each node, and are therefore useful in describing the generalization properties inherent to having communication or privacy constraints at each node.
AddrMiner: A Comprehensive Global Active IPv6 Address Discovery System

Fast Internet-wide scanning is essential for network situational awareness and asset evaluation. However, the vast IPv6 address space makes brute-force scanning infeasible. Although state-of-the-art techniques have made effective attempts, these methods do not work in seedless regions, while the detection efficiency is low in regions with seeds. Moreover, the constructed hitlists with low coverage cannot truly represent the active IPv6 address landscape of the Internet. This paper introduces AddrMiner, a systematic and comprehensive global active IPv6 address probing system. We divide the IPv6 address space regions into three kinds according to the number of seed addresses to discover active IPv6 addresses from scratch, from few to many. For the regions with no seeds, we present AddrMiner-N , leveraging an organization association strategy to mine active addresses. It fills the gap of address probing in seedless regions and finds active addresses covering 86.4K IPv6 prefixes announced by BGP, accounting for 81.6% of the probed announced prefixes. For the regions with few seeds, we propose AddrMiner-F , utilizing a similarity matching strategy to probe active addresses further. The hit rate of active address probing is improved by 70%-150% compared to existing algorithms. Moreover, for the regions with sufficient seeds, we present AddrMiner-S to generate target addresses based on reinforcement learning dynamically. It nearly doubles the hit rate compared to the state-of-the-art algorithms. Finally, we deploy AddrMiner and discover 2.1 billion active IPv6 addresses, including 1.7 billion de-aliased active addresses and 0.4 billion aliased addresses, through continuous probing for 13 months. We would like to further open the door of IPv6 measurement studies by publicly releasing AddrMiner and sharing our data.
On the search for an intrinsic magnetic field at Venus

Magnetic field observations obtained by the Pioneer Venus orbiter at low altitude are now available for two sets of orbits in the Venus wake. Data from these 130 orbits are examined for possible surface correlated features or any intrinsic magnetic moment. No surface correlated magnetic fields are observed, but the threshold for the detectability of such fields at Venus is about an order of magnitude greater than at the moon. A surface feature of 10 deg extent would have to create an anomaly of at least 5 gammas at 200 km to be detected in the Pioneer Venus data. Using measurements averaged in 72 10 x 10 deg bins, a planetary magnetic dipole moment of 0.87 + or - 3.00 x 10 to the 21st gauss-cu cm is obtained. Thus the upper limit of the present day Venus moment is less than 4 x 10 to the -5th of the terrestrial moment.
Scientific Collaboration Network Views: A Brazilian Computer Science Graduate Programs Case

Scientific collaboration networks can present different views of researchers’ interactions. This work presents SCI-synergy, an online navigable artifact aiming to promote mechanisms and views of scientific collaboration networks. The artifact focuses on the researchers’ interaction in the co-authorship of publications considering intra- and interprogram relationships. SCI-synergy is developed upon the design science research paradigm using scientific publication data available on the large Digital Bibliography & Library Project (DBLP) repository. Official data from the Sucupira repository of six Brazilian graduate program members including Federal University of Minas Gerais (UFMG), State University of São Paulo (USP), Federal University of Rio Grande do Norte (UFRN), Federal University of Amazonas (UFAM), University of Brasília (UnB), and University of Vale do Rio dos Sinos (UNISINOS) is used. Data from these graduate programs illustrate the artifact usage regarding the scientific collaboration network of each program, how each researcher cooperates, and what relationship patterns exist in intra- and inter-programs views. We advocate that, even though it is necessary to consider data from each program’s history and current contextualization regarding politics, economics, and administration, the collaboration network views provided by SCI-synergy might help to understand collaboration network patterns.
This paper is included in the Proceedings of the 2022 USENIX Annual Technical Conference.

Recent persistent memory (PM) key-value (KV) stores adopt the log-structured approach to reap PM’s full potential. However, they fail to sustain high performance at high capacity utilization due to ine ffi cient compaction. The ine ffi ciency results from the unawareness of PM’s characteristics. This paper proposes P acman , an e ffi cient PM-aware compaction approach for log-structured KV stores on PM. P acman (1) o ffl oads reference search during compaction to service threads, so as to mitigate the onerous index traversal overhead, (2) leverages tagged pointer and DRAM-resident compaction information to avoid excessive PM accesses introduced by garbage collection, (3) redesigns the compaction pipeline based on the PM peculiarities to lower the persistence overhead, and (4) separates hot and cold objects in a lightweight manner to reduce PM data copying in compaction. We apply P acman to state-of-the-art PM-based log-structured KV stores and evaluate P acman using various benchmarks. Our evaluations show that P acman curtails the performance degradation at high capacity utilization, increases the compaction bandwidth by 2-4 × , and boosts the performance of the state-of-the-art systems by 1.5-1.8 × under write-intensive workloads.
This paper is included in the Proceedings of the 2022 USENIX Annual Technical Conference.

Replication is the basic mechanism for providing application-transparent reliability through fault tolerance. The design and implementation of replication mechanisms is particularly challenging for general multithreaded services, where high latency overhead is not acceptable. Most of the existing replication mechanisms fail to meet this challenge. RRC is a fully-operational fault tolerance mechanism for multiprocessor workloads, based on container replication. It minimizes the latency overhead during normal operation by addressing two key sources of this overhead: (1) it decouples the latency overhead from checkpointing frequency using a hybrid of checkpointing and replay, and (2) it minimizes the pause time for checkpointing by forking a clone of the container to be checkpointed, thus allowing execution to proceed in parallel with checkpointing. The fact that RRC is based on checkpointing makes it inherently less vulnerable to data races than active replication. In addition, RRC includes mechanisms that further reduce the vulnerability to data races, resulting in high recovery rates, as long as the rate of manifested data races is low. The evaluation includes measurement of the recovery rate and recovery latency based on thousands of fault injections. On average, RRC delays responses to clients by less than 400 µ s and recovers in less than 1s. The average pause latency is less than 3.3ms. For a set of eight real-world benchmarks, if data races are eliminated, the performance overhead of RRC is under 48%.
Combination of User and Venue Personality with Topic Modelling in Restaurant Recommender Systems

Recommender systems are popular information systems used to support decision makers’ information overload. However, despite their success in simple problems, such as music recommendation, they have been criticized of insufficient performance in highly complex domains, characterized by many parameters, such as restaurant recommendations. Recent research has acknowledged the importance of personality in influencing consumers’ choice, but recommendation methodologies do not exploit this in the restaurant recommendation problem. Hence, this work seeks to analyze the contribution of personality in combination with extracted topics from consumers’ electronic word of mouth (eWOM) to restaurant recommender systems. The paper utilizes a bi-directional transformer approach with a feed-forward classification layer for personality prediction, due to its improved performance in similar problems over other machine learning models. One issue with this approach is the handling of long text, such as narratives written by people of different personality types (labels). Thus, different long-text management methods are evaluated to find the one with best personality prediction performance. Two personality models are evaluated, namely the Myers-Briggs and Big Five, based on two labelled datasets that are utilized to generate two personality classifiers. In addition to customer personality, this work investigates the concept of venue personality estimated from personalities of users that visited a venue and liked it. Finally, the customer and venue personalities are used together with the topics discussed by customers to form the input to the extreme gradient boosting (XGBoost) models for predicting user ratings of restaurants. The performance of these models is compared to traditional collaborative filtering methods using various prediction metrics.
MT^2: Memory Bandwidth Regulation on Hybrid NVM/DRAM Platforms

Non-volatile memory (NVM) has emerged as a new memory media, resulting in a hybrid NVM/DRAM configuration in typical servers. Memory-intensive applications competing for the scant memory bandwidth can yield degraded performance. Identifying the noisy neighbors and regulating the memory bandwidth usage of them can alleviate the contention and achieve better performance. This paper finds that bandwidth competition is more severe on hybrid platforms and can even significantly degrade the total system bandwidth. Besides the absolute bandwidth, the competition is also highly correlated with the bandwidth type. Unfortunately, NVM and DRAM share the same memory bus, and their traffic is mixed together and interferes with each other, making memory bandwidth regulation a challenge on hybrid NVM/DRAM platforms. This paper first presents an analysis of memory traffic interference and then introduces MT2 to regulate memory bandwidth among concurrent applications on hybrid NVM/DRAM platforms. Specifically, MT2 first detects memory traffic interference and monitors different types of memory bandwidth of applications from the mixed traffic through hardware monitors and software reports. MT2 then leverages a dynamic bandwidth throttling algorithm to regulate memory bandwidth with multiple mechanisms. To expose such a facility to applications, we integrate MT2 into the cgroup mechanism by adding a new subsystem for memory bandwidth regulation. The evaluation shows that MT2 can accurately identify the noisy neighbors, and the regulation on them allows other applications to improve performance by up to 2.6× compared to running with unrestricted noisy neighbors.