OpenRaiser/Intern-Atlas huggingface dataset:
Company2Vec -- German Company Embeddings based on Corporate Websites
Venue: arXiv cs.AI 2023
Year: 2023

With Company2Vec, the paper proposes a novel application in representation learning. The model analyzes business activities from unstructured company website data using Word2Vec and dimensionality reduction. Company2Vec maintains semantic language structures and thus creates efficient company embeddings in fine-granular industries. These semantic embeddings can be used for various applications in banking. Direct relations between companies and words allow semantic business analytics (e.g. top-n words for a company). Furthermore, industry prediction is presented as a supervised learning application and evaluation method. The vectorized structure of the embeddings allows measuring companies similarities with the cosine distance. Company2Vec hence offers a more fine-grained comparison of companies than the standard industry labels (NACE). This property is relevant for unsupervised learning tasks, such as clustering. An alternative industry segmentation is shown with k-means clustering on the company embeddings. Finally, this paper proposes three algorithms for (1) firm-centric, (2) industry-centric and (3) portfolio-centric peer-firm identification.
Closing star-free closure
Venue: arXiv cs.FL 2023
Year: 2023

We introduce an operator on classes of regular languages, the star-free closure. Our motivation is to generalize standard results of automata theory within a unified framework. Given an arbitrary input class $C$, the star-free closure operator outputs the least class closed under Boolean operations and language concatenation, and containing all languages of $C$ as well as all finite languages. We establish several equivalent characterizations of star-free closure: in terms of regular expressions, first-order logic, pure future and future-past temporal logic, and recognition by finite monoids. A key ingredient is that star-free closure coincides with another closure operator, defined in terms of regular operations where Kleene stars are allowed in restricted~contexts. A consequence of this first result is that we can decide membership of a regular language in the star-free closure of a class whose separation problem is decidable. Moreover, we prove that separation itself is decidable for the star-free closure of any finite class, and of any class of group languages having itself decidable separation (plus mild additional properties). We actually show decidability of a stronger property, called covering.
Data Cross-Segmentation for Improved Generalization in Reinforcement Learning Based Algorithmic Trading
Venue: arXiv cs.LG 2023
Year: 2023

The use of machine learning in algorithmic trading systems is increasingly common. In a typical set-up, supervised learning is used to predict the future prices of assets, and those predictions drive a simple trading and execution strategy. This is quite effective when the predictions have sufficient signal, markets are liquid, and transaction costs are low. However, those conditions often do not hold in thinly traded financial markets and markets for differentiated assets such as real estate or vehicles. In these markets, the trading strategy must consider the long-term effects of taking positions that are relatively more difficult to change. In this work, we propose a Reinforcement Learning (RL) algorithm that trades based on signals from a learned predictive model and addresses these challenges. We test our algorithm on 20+ years of equity data from Bursa Malaysia.
Adapting an ASR Foundation Model for Spoken Language Assessment
Venue: arXiv cs.CL 2023
Year: 2023

A crucial part of an accurate and reliable spoken language assessment system is the underlying ASR model. Recently, large-scale pre-trained ASR foundation models such as Whisper have been made available. As the output of these models is designed to be human readable, punctuation is added, numbers are presented in Arabic numeric form and abbreviations are included. Additionally, these models have a tendency to skip disfluencies and hesitations in the output. Though useful for readability, these attributes are not helpful for assessing the ability of a candidate and providing feedback. Here a precise transcription of what a candidate said is needed. In this paper, we give a detailed analysis of Whisper outputs and propose two solutions: fine-tuning and soft prompt tuning. Experiments are conducted on both public speech corpora and an English learner dataset. Results show that we can effectively alter the decoding behaviour of Whisper to generate the exact words spoken in the response.
How do software citation formats evolve over time? A longitudinal analysis of R programming language packages
Venue: arXiv cs.DL 2023
Year: 2023

Under the data-driven research paradigm, research software has come to play crucial roles in nearly every stage of scientific inquiry. Scholars are advocating for the formal citation of software in academic publications, treating it on par with traditional research outputs. However, software is hardly consistently cited: one software entity can be cited as different objects, and the citations can change over time. These issues, however, are largely overlooked in existing empirical research on software citation. To fill the above gaps, the present study compares and analyzes a longitudinal dataset of citation formats of all R packages collected in 2021 and 2022, in order to understand the citation formats of R-language packages, important members in the open-source software family, and how the citations evolve over time. In particular, we investigate the different document types underlying the citations and what metadata elements in the citation formats changed over time. Furthermore, we offer an in-depth analysis of the disciplinarity of journal articles cited as software (software papers). By undertaking this research, we aim to contribute to a better understanding of the complexities associated with software citation, shedding light on future software citation policies and infrastructure.
Pseudo Outlier Exposure for Out-of-Distribution Detection using Pretrained Transformers
Venue: arXiv cs.CL 2023
Year: 2023

For real-world language applications, detecting an out-of-distribution (OOD) sample is helpful to alert users or reject such unreliable samples. However, modern over-parameterized language models often produce overconfident predictions for both in-distribution (ID) and OOD samples. In particular, language models suffer from OOD samples with a similar semantic representation to ID samples since these OOD samples lie near the ID manifold. A rejection network can be trained with ID and diverse outlier samples to detect test OOD samples, but explicitly collecting auxiliary OOD datasets brings an additional burden for data collection. In this paper, we propose a simple but effective method called Pseudo Outlier Exposure (POE) that constructs a surrogate OOD dataset by sequentially masking tokens related to ID classes. The surrogate OOD sample introduced by POE shows a similar representation to ID data, which is most effective in training a rejection network. Our method does not require any external OOD data and can be easily implemented within off-the-shelf Transformers. A comprehensive comparison with state-of-the-art algorithms demonstrates POE's competitiveness on several text classification benchmarks.
ChatSpot: Bootstrapping Multimodal LLMs via Precise Referring Instruction Tuning
Venue: arXiv cs.CL 2023
Year: 2023

Human-AI interactivity is a critical aspect that reflects the usability of multimodal large language models (MLLMs). However, existing end-to-end MLLMs only allow users to interact with them through language instructions, leading to the limitation of the interactive accuracy and efficiency. In this study, we present precise referring instructions that utilize diverse reference representations such as points and boxes as referring prompts to refer to the special region. This enables MLLMs to focus on the region of interest and achieve finer-grained interaction. Based on precise referring instruction, we propose ChatSpot, a unified end-to-end multimodal large language model that supports diverse forms of interactivity including mouse clicks, drag-and-drop, and drawing boxes, which provides a more flexible and seamless interactive experience. We also construct a multi-grained vision-language instruction-following dataset based on existing datasets and GPT-4 generating. Furthermore, we design a series of evaluation tasks to assess the effectiveness of region recognition and interaction. Experimental results showcase ChatSpot's promising performance.
Variable Independence in Linear Real Arithmetic
Venue: arXiv cs.LO 2023
Year: 2023

Variable independence and decomposability are algorithmic techniques for simplifying logical formulas by tearing apart connections between free variables. These techniques were originally proposed to speed up query evaluation in constraint databases, in particular by representing the query as a Boolean combination of formulas with no interconnected variables. They also have many other applications in SMT, string analysis, databases, automata theory and other areas. However, the precise complexity of variable independence and decomposability has been left open especially for the quantifier-free theory of linear real arithmetic (LRA), which is central in database applications. We introduce a novel characterization of formulas admitting decompositions and use it to show that it is coNP-complete to decide variable decomposability over LRA. As a corollary, we obtain that deciding variable independence is in $ Σ_2^p $. These results substantially improve the best known double-exponential time algorithms for variable decomposability and independence. In many practical applications, it is crucial to be able to efficiently eliminate connections between variables whenever possible. We design and implement an algorithm for this problem, which is optimal in theory, exponentially faster compared to the current state-of-the-art algorithm and efficient on various microbenchmarks. In particular, our algorithm is the first one to overcome a fundamental barrier between non-discrete and discrete first-order theories. Formulas arising in practice often have few or even no free variables that are perfectly independent. In this case, our algorithm can compute a best-possible approximation of a decomposition, which can be used to optimize database queries by exploiting partial variable independence, which is present in almost every logical formula or database query constraint.
Agile Methodology in Online Learning and How It Can Improve Communication: A Case Study
Venue: arXiv cs.SE 2023
Year: 2023

This paper presents a study on using Agile methodologies in the teaching process at the university/college level during the Covid-19 pandemic, online classes. We detail a list of techniques inspired from software engineering Agile methodologies that can be used in online teaching. We also show, by analyzing students grades, that these Agile inspired techniques probably help in the educational process.
Surgical Action Triplet Detection by Mixed Supervised Learning of Instrument-Tissue Interactions
Venue: arXiv cs.CV 2023
Year: 2023

Surgical action triplets describe instrument-tissue interactions as (instrument, verb, target) combinations, thereby supporting a detailed analysis of surgical scene activities and workflow. This work focuses on surgical action triplet detection, which is challenging but more precise than the traditional triplet recognition task as it consists of joint (1) localization of surgical instruments and (2) recognition of the surgical action triplet associated with every localized instrument. Triplet detection is highly complex due to the lack of spatial triplet annotation. We analyze how the amount of instrument spatial annotations affects triplet detection and observe that accurate instrument localization does not guarantee better triplet detection due to the risk of erroneous associations with the verbs and targets. To solve the two tasks, we propose MCIT-IG, a two-stage network, that stands for Multi-Class Instrument-aware Transformer-Interaction Graph. The MCIT stage of our network models per class embedding of the targets as additional features to reduce the risk of misassociating triplets. Furthermore, the IG stage constructs a bipartite dynamic graph to model the interaction between the instruments and targets, cast as the verbs. We utilize a mixed-supervised learning strategy that combines weak target presence labels for MCIT and pseudo triplet labels for IG to train our network. We observed that complementing minimal instrument spatial annotations with target embeddings results in better triplet detection. We evaluate our model on the CholecT50 dataset and show improved performance on both instrument localization and triplet detection, topping the leaderboard of the CholecTriplet challenge in MICCAI 2022.
Dead Man's PLC: Towards Viable Cyber Extortion for Operational Technology
Venue: arXiv cs.CR 2023
Year: 2023

For decades, operational technology (OT) has enjoyed the luxury of being suitably inaccessible so as to experience directly targeted cyber attacks from only the most advanced and well-resourced adversaries. However, security via obscurity cannot last forever, and indeed a shift is happening whereby less advanced adversaries are showing an appetite for targeting OT. With this shift in adversary demographics, there will likely also be a shift in attack goals, from clandestine process degradation and espionage to overt cyber extortion (Cy-X). The consensus from OT cyber security practitioners suggests that, even if encryption-based Cy-X techniques were launched against OT assets, typical recovery practices designed for engineering processes would provide adequate resilience. In response, this paper introduces Dead Man's PLC (DM-PLC), a pragmatic step towards viable OT Cy-X that acknowledges and weaponises the resilience processes typically encountered. Using only existing functionality, DM-PLC considers an entire environment as the entity under ransom, whereby all assets constantly poll one another to ensure the attack remains untampered, treating any deviations as a detonation trigger akin to a Dead Man's switch. A proof of concept of DM-PLC is implemented and evaluated on an academically peer reviewed and industry validated OT testbed to demonstrate its malicious efficacy.
Fitted avatars: automatic skeleton adjustment for self-avatars in virtual reality
Venue: arXiv cs.HC 2023
Year: 2023

In the era of the metaverse, self-avatars are gaining popularity, as they can enhance presence and provide embodiment when a user is immersed in Virtual Reality. They are also very important in collaborative Virtual Reality to improve communication through gestures. Whether we are using a complex motion capture solution or a few trackers with inverse kinematics (IK), it is essential to have a good match in size between the avatar and the user, as otherwise mismatches in self-avatar posture could be noticeable for the user. To achieve such a correct match in dimensions, a manual process is often required, with the need for a second person to take measurements of body limbs and introduce them into the system. This process can be time-consuming, and prone to errors. In this paper, we propose an automatic measuring method that simply requires the user to do a small set of exercises while wearing a Head-Mounted Display (HMD), two hand controllers, and three trackers. Our work provides an affordable and quick method to automatically extract user measurements and adjust the virtual humanoid skeleton to the exact dimensions. Our results show that our method can reduce the misalignment produced by the IK system when compared to other solutions that simply apply a uniform scaling to an avatar based on the height of the HMD, and make assumptions about the locations of joints with respect to the trackers.
Design and Analysis of Pairing-Friendly Elliptic Curves for Cryptographic Primitives
Venue: arXiv cs.CR 2023
Year: 2023

Elliptic curve cryptography (ECC) is a remarkable mathematical tool that offers the same level of security as traditional public-key cryptography (PKC) with a significantly smaller key size and lower computational requirements. The use of pairing on elliptic curves has emerged as a vibrant field of research that provides enhanced security measures for the next generation of cryptographic systems. This thesis explores using ECC and Pairing-Based Cryptosystems (PBC) as effective mathematical tools for achieving high-security levels with minimal key size and computation cost. Specifically, the research aims to analyze Pairing-Friendly Elliptic Curves (PF-EC) and their practicality in resource-constrained environments. It proposes solutions to some of the limitations of existing applications of pairing-based cryptography. The thesis begins by presenting a comprehensive framework for constructing PF-EC and evaluating the practical security of several families of pairing-friendly curves. The study then explores the limitations of Identity-Based Encryption (IBE), a recognized application of pairing-based cryptography. It proposes mechanisms to address issues such as key escrow, secure key issuing, user slandering, and key abusing problems. The proposed solutions include an Escrow-Free Identity-Based Encryption (EF-IBE) scheme secured against confidentiality and an Escrow-Free Identity-Based Signature (EF-IBS) scheme that is forgeable and secure.
On the Existence of Envy-Free Allocations Beyond Additive Valuations
Venue: arXiv cs.GT 2023
Year: 2023

We study the problem of fairly allocating $m$ indivisible items among $n$ agents. Envy-free allocations, in which each agent prefers her bundle to the bundle of every other agent, need not exist in the worst case. However, when agents have additive preferences and the value $v_{i,j}$ of agent $i$ for item $j$ is drawn independently from a distribution $D_i$, envy-free allocations exist with high probability when $m \in Ω( n \log n / \log \log n )$. In this paper, we study the existence of envy-free allocations under stochastic valuations far beyond the additive setting. We introduce a new stochastic model in which each agent's valuation is sampled by first fixing a worst-case function, and then drawing a uniformly random renaming of the items, independently for each agent. This strictly generalizes known settings; for example, $v_{i,j} \sim D_i$ may be seen as picking a random (instead of a worst-case) additive function before renaming. We prove that random renaming is sufficient to ensure that envy-free allocations exist with high probability in very general settings. When valuations are non-negative and ``order-consistent,'' a valuation class that generalizes additive, budget-additive, unit-demand, and single-minded agents, SD-envy-free allocations (a stronger notion of fairness than envy-freeness) exist for $m \in ω(n^2)$ when $n$ divides $m$, and SD-EFX allocations exist for all $m \in ω(n^2)$. The dependence on $n$ is tight, that is, for $m \in O(n^2)$ envy-free allocations don't exist with constant probability. For the case of arbitrary valuations (allowing non-monotone, negative, or mixed-manna valuations) and $n=2$ agents, we prove envy-free allocations exist with probability $1 - Θ(1/m)$ (and this is tight).
STRAPPER: Preference-based Reinforcement Learning via Self-training Augmentation and Peer Regularization
Venue: arXiv cs.LG 2023
Year: 2023

Preference-based reinforcement learning (PbRL) promises to learn a complex reward function with binary human preference. However, such human-in-the-loop formulation requires considerable human effort to assign preference labels to segment pairs, hindering its large-scale applications. Recent approache has tried to reuse unlabeled segments, which implicitly elucidates the distribution of segments and thereby alleviates the human effort. And consistency regularization is further considered to improve the performance of semi-supervised learning. However, we notice that, unlike general classification tasks, in PbRL there exits a unique phenomenon that we defined as similarity trap in this paper. Intuitively, human can have diametrically opposite preferredness for similar segment pairs, but such similarity may trap consistency regularization fail in PbRL. Due to the existence of similarity trap, such consistency regularization improperly enhances the consistency possiblity of the model's predictions between segment pairs, and thus reduces the confidence in reward learning, since the augmented distribution does not match with the original one in PbRL. To overcome such issue, we present a self-training method along with our proposed peer regularization, which penalizes the reward model memorizing uninformative labels and acquires confident predictions. Empirically, we demonstrate that our approach is capable of learning well a variety of locomotion and robotic manipulation behaviors using different semi-supervised alternatives and peer regularization.
ActorLens: Visual Analytics for High-level Actor Identification in MOBA Games
Venue: arXiv cs.HC 2023
Year: 2023

Multiplayer Online Battle Arenas (MOBAs) have garnered a substantial player base worldwide. Nevertheless, the presence of noxious players, commonly referred to as "actors", can significantly compromise game fairness by exhibiting negative behaviors that diminish their team's competitive edge. Furthermore, high-level actors tend to engage in more egregious conduct to evade detection, thereby causing harm to the game community and necessitating their identification. To tackle this urgent concern, a partnership was formed with a team of game specialists from a prominent company to facilitate the identification and labeling of high-level actors in MOBA games. We first characterize the problem and abstract data and events from the game scene to formulate design requirements. Subsequently, ActorLens, a visual analytics system, was developed to exclude low-level actors, detect potential high-level actors, and assist users in labeling players. ActorLens furnishes an overview of players' status, summarizes behavioral patterns across three player cohorts (namely, focused players, historical matches of focused players, and matches of other players who played the same hero), and synthesizes key match events. By incorporating multiple views of information, users can proficiently recognize and label high-level actors in MOBA games. We conducted case studies and user studies to demonstrate the efficacy of the system.
Two Tales of Platoon Intelligence for Autonomous Mobility Control: Enabling Deep Learning Recipes
Venue: arXiv cs.AI 2023
Year: 2023

This paper presents the deep learning-based recent achievements to resolve the problem of autonomous mobility control and efficient resource management of autonomous vehicles and UAVs, i.e., (i) multi-agent reinforcement learning (MARL), and (ii) neural Myerson auction. Representatively, communication network (CommNet), which is one of the most popular MARL algorithms, is introduced to enable multiple agents to take actions in a distributed manner for their shared goals by training all agents' states and actions in a single neural network. Moreover, the neural Myerson auction guarantees trustfulness among multiple agents as well as achieves the optimal revenue of highly dynamic systems. Therefore, we survey the recent studies on autonomous mobility control based on MARL and neural Myerson auction. Furthermore, we emphasize that integration of MARL and neural Myerson auction is expected to be critical for efficient and trustful autonomous mobility services.