Reviewerly/exHarmony huggingface dataset:
CVTP3D: Cross-view Trajectory Prediction Using Shared 3D Queries for Autonomous Driving

Trajectory prediction with uncertainty is a critical and challenging task for autonomous driving. Nowadays, we can easily access sensor data represented in multiple views. However, cross-view consistency has not been evaluated by the existing models, which might lead to divergences between the multimodal predictions from different views. It is not practical and effective when the network does not comprehend the 3D scene, which could cause the downstream module in a dilemma. Instead, we predicts multimodal trajectories while maintaining cross-view consistency. We presented a cross-view trajectory prediction method using shared 3D Queries (XVTP3D). We employ a set of 3D queries shared across views to generate multi-goals that are cross-view consistent. We also proposed a random mask method and coarse-to-fine cross-attention to capture robust cross-view features. As far as we know, this is the first work that introduces the outstanding top-down paradigm in BEV detection field to a trajectory prediction problem. The results of experiments on two publicly available datasets show that XVTP3D achieved state-of-the-art performance with consistent cross-view predictions.
On Adversarial Robustness of Demographic Fairness in Face Attribute Recognition

Demographic fairness has become a critical objective when developing modern visual models for identity-sensitive applications, such as face attribute recognition (FAR). While great efforts have been made to improve the fairness of the models, the investigation on the adversarial robustness of the fairness (e.g., whether the fairness of the models could still be maintained under potential malicious fairness attacks) is largely ignored. Therefore, this paper explores the adversarial robustness of demographic fairness in FAR applications from both attacking and defending perspectives. In particular, we firstly present a novel fairness attack, who aims at corrupting the demographic fairness of face attribute classifiers. Next, to mitigate the effect of the fairness attack, we design an efficient defense algorithm called robust-fair training. With this defense, face attribute classifiers learn how to combat the bias introduced by the fairness attack. As such, the face attribute classifiers are not only trained to be fair, but the fairness is also robust. Our extensive experimental results show the effectiveness of both our proposed attack and defense methods across various model architectures and FAR applications. We believe our work could be strong baselines for future work on robust-fair AI models.
A Bibliometric Analysis of 25 years of Workplace Deviant Behavior Research

Workplace deviant includes any act that is significantly different from what is considered acceptable by prescribed norms. Deviant workplace conduct is not uncommon and is a regular phenomenon in the workplace. It can be found in all sorts of businesses and at all levels of management. Thus, there is a need for study in this area. The publication of workplace deviant behavior has grown steadily, and the trends have been increasing consistently. As a result, the study focuses on examining the scientific literature on workplace deviant behavior research. The purpose of this study is to evaluate the evolution of the concept. It also aims at analyzing which academics, countries, and journals are most interested in this area, the most relevant affiliations and collaborations, and which research has the greatest influence. Based on the “keywords” search results, the Scopus Database was used to compile literature on workplace behavior from 1997 to 2022. This study included 357 papers that were relevant for the analysis from the Scopus Database. The findings revealed that research into workplace deviant behavior has been strengthened during the last few years. This research draws upon findings and current information to give readers a glimpse into the future of workplace behavior research.
Korean College Students' English Writing Characteristics Based on Feedback from English Writing Clinics

The purpose of this study was to examine what kind of writing feedback the students received when they had a writing conference with a native English-speaking professor in English writing clinics, and to examine the characteristics of students' English writing from the perspective of errors. In addition, this study attempted to investigate how learners' English writing changed after they got the English writing conference. To do this, 402 pieces of student writing feedback checklists obtained through English writing clinics with native English-speaking professors were analyzed for frequency. As a result of the analysis, the total number of English writing clinic feedback provided by native English-speaking professors was 3631, with an average of about 9 per student. The frequency of language use was 1324(36.5%), the frequency of content and organization feedback was 946(26.1%), the frequency of task completion was 804(22.1%), and the frequency of mechanical errors was 557(15.3%). Feedback in language use related to grammar and vocabulary accounted for the most, followed by the feedback on content and organization for writing fluency. The characteristics of the students' English writing, as seen through the feedback received from the native English-speaking professors at the English writing clinic, were that the students generally wrote according to the subject, the length tended to be rather short, and there were minor mistakes in the use of capitalization and punctuation marks. The students still had a lot of lexical and grammatical errors, and in terms of content and organization, they tended to express proper nouns in Korean, and occasionally lacked the topic sentences as well as supporting details. Additionally, in order to find out whether students use the feedback given by the native English-speaking professors when revising their English writings, the scores on the students' first drafts of English writing and the revised drafts were compared and analyzed. The average English writing score of the revised writings was significantly higher than the score of the first drafts. In other words, the feedback offered by the native English-speaking professors during English writing clinics helped students to correct their English writing and led to the improvement of the quality of English writing, showing the usefulness of the English writing clinic.
Robust Image Ordinal Regression with Controllable Image Generation

Image ordinal regression has been mainly studied along the line of exploiting the order of categories. However, the issues of class imbalance and category overlap that are very common in ordinal regression were largely overlooked. As a result, the performance on minority categories is often unsatisfactory. In this paper, we propose a novel framework called CIG based on controllable image generation to directly tackle these two issues. Our main idea is to generate extra training samples with specific labels near category boundaries, and the sample generation is biased toward the less-represented categories. To achieve controllable image generation, we seek to separate structural and categorical information of images based on structural similarity, categorical similarity, and reconstruction constraints. We evaluate the effectiveness of our new CIG approach in three different image ordinal regression scenarios. The results demonstrate that CIG can be flexibly integrated with off-the-shelf image encoders or ordinal regression models to achieve improvement, and further, the improvement is more significant for minority categories.
Learning to Send Reinforcements: Coordinating Multi-Agent Dynamic Police Patrol Dispatching and Rescheduling via Reinforcement Learning

We address the problem of coordinating multiple agents in a dynamic police patrol scheduling via a Reinforcement Learning (RL) approach. Our approach utilizes Multi-Agent Value Function Approximation (MAVFA) with a rescheduling heuristic to learn dispatching and rescheduling policies jointly. Often, police operations are divided into multiple sectors for more effective and efficient operations. In a dynamic setting, incidents occur throughout the day across different sectors, disrupting initially-planned patrol schedules. To maximize policing effectiveness, police agents from different sectors cooperate by sending reinforcements to support one another in their incident response and even routine patrol. This poses an interesting research challenge on how to make such complex decision of dispatching and rescheduling involving multiple agents in a coordinated fashion within an operationally reasonable time. Unlike existing Multi-Agent RL (MARL) approaches which solve similar problems by either decomposing the problem or action into multiple components, our approach learns the dispatching and rescheduling policies jointly without any decomposition step. In addition, instead of directly searching over the joint action space, we incorporate an iterative best response procedure as a decentralized optimization heuristic and an explicit coordination mechanism for a scalable and coordinated decision-making. We evaluate our approach against the commonly adopted two-stage approach and conduct a series of ablation studies to ascertain the effectiveness of our proposed learning and coordination mechanisms.
Inducing Stackelberg Equilibrium through Spatio-Temporal Sequential Decision-Making in Multi-Agent Reinforcement Learning

In multi-agent reinforcement learning (MARL), self-interested agents attempt to establish equilibrium and achieve coordination depending on game structure. However, existing MARL approaches are mostly bound by the simultaneous actions of all agents in the Markov game (MG) framework, and few works consider the formation of equilibrium strategies via asynchronous action coordination. In view of the advantages of Stackelberg equilibrium (SE) over Nash equilibrium, we construct a spatio-temporal sequential decision-making structure derived from the MG and propose an N-level policy model based on a conditional hypernetwork shared by all agents. This approach allows for asymmetric training with symmetric execution, with each agent responding optimally conditioned on the decisions made by superior agents. Agents can learn heterogeneous SE policies while still maintaining parameter sharing, which leads to reduced cost for learning and storage and enhanced scalability as the number of agents increases. Experiments demonstrate that our method effectively converges to the SE policies in repeated matrix game scenarios, and performs admirably in immensely complex settings including cooperative tasks and mixed tasks.
Few-shot Classification via Ensemble Learning with Multi-Order Statistics

Transfer learning has been widely adopted for few-shot classification. Recent studies reveal that obtaining good generalization representation of images on novel classes is the key to improving the few-shot classification accuracy. To address this need, we prove theoretically that leveraging ensemble learning on the base classes can correspondingly reduce the true error in the novel classes. Following this principle, a novel method named Ensemble Learning with Multi-Order Statistics (ELMOS) is proposed in this paper. In this method, after the backbone network, we use multiple branches to create the individual learners in the ensemble learning, with the goal to reduce the storage cost. We then introduce different order statistics pooling in each branch to increase the diversity of the individual learners. The learners are optimized with supervised losses during the pre-training phase. After pre-training, features from different branches are concatenated for classifier evaluation. Extensive experiments demonstrate that each branch can complement the others and our method can produce a state-of-the-art performance on multiple few-shot classification benchmark datasets.
Black-Box Data Poisoning Attacks on Crowdsourcing

Understanding the vulnerability of label aggregation against data poisoning attacks is key to ensuring data quality in crowdsourced label collection. State-of-the-art attack mechanisms generally assume full knowledge of the aggregation models while failing to consider the flexibility of malicious workers in selecting which instances to label. Such a setup limits the applicability of the attack mechanisms and impedes further improvement of their success rate. This paper introduces a black-box data poisoning attack framework that finds the optimal strategies for instance selection and labeling to attack unknown label aggregation models in crowdsourcing. We formulate the attack problem on top of a generic formalization of label aggregation models and then introduce a substitution approach that attacks a substitute aggregation model in replacement of the unknown model. Through extensive validation on multiple real-world datasets, we demonstrate the effectiveness of both instance selection and model substitution in improving the success rate of attacks.
A Solution to Co-occurence Bias: Attributes Disentanglement via Mutual Information Minimization for Pedestrian Attribute Recognition

Recent studies on pedestrian attribute recognition progress with either explicit or implicit modeling of the co-occurence among attributes. Considering that this known a prior is highly variable and unforeseeable regarding the specific scenarios, we show that current methods can actually suffer in generalizing such fitted attributes interdependencies onto scenes or identities off the dataset distribution, resulting in the underlined bias of attributes co-occurence. To render models robust in realistic scenes, we propose the attributes-disentangled feature learning to ensure the recognition of an attribute not inferring on the existence of others, and which is sequentially formulated as a problem of mutual information minimization. Rooting from it, practical strategies are devised to efficiently decouple attributes, which substantially improve the baseline and establish state-of-the-art performance on realistic datasets like PETAzs and RAPzs.
Generalization Bounds for Adversarial Metric Learning

Recently, adversarial metric learning has been proposed to enhance the robustness of the learned distance metric against adversarial perturbations. Despite rapid progress in validating its effectiveness empirically, theoretical guarantees on adversarial robustness and generalization are far less understood. To fill this gap, this paper focuses on unveiling the generalization properties of adversarial metric learning by developing the uniform convergence analysis techniques. Based on the capacity estimation of covering numbers, we establish the first high-probability generalization bounds with order O(n^{-1/2}) for adversarial metric learning with pairwise perturbations and general losses, where n is the number of training samples. Moreover, we obtain the refined generalization bounds with order O(n^{-1}) for the smooth loss by using local Rademacher complexity, which is faster than the previous result of adversarial pairwise learning, e.g., adversarial bipartite ranking. Experimental evaluation on real-world datasets validates our theoretical findings.
Efficient Object Search in Game Maps

Video games feature a dynamic environment where locations of objects (e.g., characters, equipment, weapons, vehicles etc.) frequently change within the game world. Although searching for relevant nearby objects in such a dynamic setting is a fundamental operation, this problem has received little research attention. In this paper, we propose a simple lightweight index, called Grid Tree, to store objects and their associated textual data. Our index can be efficiently updated with the underlying updates such as object movements, and supports a variety of object search queries, including k nearest neighbors (returning the k closest objects), keyword k nearest neighbors (returning the k closest objects that satisfy query keywords), and several other variants. Our extensive experimental study, conducted on standard game maps benchmarks and real-world keywords, demonstrates that our approach has up to 2 orders of magnitude faster update times for moving objects compared to state-of-the-art approaches such as navigation mesh and IR-tree. At the same time, query performance of our approach is similar to or better than that of IR-tree and up to two orders of magnitude faster than the other competitor.
Independent Feature Decomposition and Instance Alignment for Unsupervised Domain Adaptation

Existing Unsupervised Domain Adaptation (UDA) methods typically attempt to perform knowledge transfer in a domain-invariant space explicitly or implicitly. In practice, however, the obtained features is often mixed with domain-specific information which causes performance degradation. To overcome this fundamental limitation, this article presents a novel independent feature decomposition and instance alignment method (IndUDA in short). Specifically, based on an invertible flow, we project the base features into a decomposed latent space with domain-invariant and domain-specific dimensions. To drive semantic decomposition independently, we then swap the domain-invariant part across source and target domain samples with the same category and require their inverted features are consistent in class-level with the original features. By treating domain-specific information as noise, we replace it by Gaussian noise and further regularize source model training by instance alignment, i.e., requiring the base features close to the corresponding reconstructed features, respectively. Extensive experiment results demonstrate that our method achieves state-of-the-art performance on popular UDA benchmarks. The appendix and code are available at https://github.com/ayombeach/IndUDA.
Learn and Sample Together: Collaborative Generation for Graphic Design Layout

In the process of graphic layout generation, user specifications including element attributes and their relationships are commonly used to constrain the layouts (e.g.,"put the image above the button''). It is natural to encode spatial constraints between elements using a graph. This paper presents a two-stage generation framework: a spatial graph generator and a subsequent layout decoder which is conditioned on the previous output graph. Training the two highly dependent networks separately as in previous work, we observe that the graph generator generates out-of-distribution graphs with a high frequency, which are unseen to the layout decoder during training and thus leads to huge performance drop in inference. To coordinate the two networks more effectively, we propose a novel collaborative generation strategy to perform round-way knowledge transfer between the networks in both training and inference. Experiment results on three public datasets show that our model greatly benefits from the collaborative generation and has achieved the state-of-the-art performance. Furthermore, we conduct an in-depth analysis to better understand the effectiveness of graph condition modeling.
Mean-Semivariance Policy Optimization via Risk-Averse Reinforcement Learning (Extended Abstract)

Keeping risk under control is often more crucial than maximizing expected rewards in real-world decision-making situations, such as finance, robotics, autonomous driving, etc. The most natural choice of risk measures is variance, while it penalizes the upside volatility as much as the downside part. Instead, the (downside) semivariance, which captures negative deviation of a random variable under its mean, is more suitable for risk-averse proposes. This paper aims at optimizing the mean-semivariance (MSV) criterion in reinforcement learning w.r.t. steady reward distribution. Since semivariance is time-inconsistent and does not satisfy the standard Bellman equation, the traditional dynamic programming methods are inapplicable to MSV problems directly. To tackle this challenge, we resort to Perturbation Analysis (PA) theory and establish the performance difference formula for MSV. We reveal that the MSV problem can be solved by iteratively solving a sequence of RL problems with a policy-dependent reward function. Further, we propose two on-policy algorithms based on the policy gradient theory and the trust region method. Finally, we conduct diverse experiments from simple bandit problems to continuous control tasks in MuJoCo, which demonstrate the effectiveness of our proposed methods.
Towards Hierarchical Policy Learning for Conversational Recommendation with Hypergraph-based Reinforcement Learning

Conversational recommendation systems (CRS) aim to timely and proactively acquire user dynamic preferred attributes through conversations for item recommendation. In each turn of CRS, there naturally have two decision-making processes with different roles that influence each other: 1) director, which is to select the follow-up option (i.e., ask or recommend) that is more effective for reducing the action space and acquiring user preferences; and 2) actor, which is to accordingly choose primitive actions (i.e., asked attribute or recommended item) to estimate the effectiveness of the director’s option. However, existing methods heavily rely on a unified decision-making module or heuristic rules, while neglecting to distinguish the roles of different decision procedures, as well as the mutual influences between them. To address this, we propose a novel Director-Actor Hierarchical Conversational Recommender (DAHCR), where the director selects the most effective option, followed by the actor accordingly choosing primitive actions that satisfy user preferences. Specifically, we develop a dynamic hypergraph to model user preferences and introduce an intrinsic motivation to train from weak supervision over the director. Finally, to alleviate the bad effect of model bias on the mutual influence between the director and actor, we model the director’s option by sampling from a categorical distribution. Extensive experiments demonstrate that DAHCR outperforms state-of-the-art methods.
Label Specific Multi-Semantics Metric Learning for Multi-Label Classification: Global Consideration Helps

In multi-label classification, it is critical to capitalize on complicated data structures and semantic relationships. Metric learning serves as an effective strategy to provide a better measurement of distances between examples. Existing works on metric learning for multi-label classification mainly learn one single global metric that characterizes latent semantic similarity between multi-label instances. However, such single-semantics metric exploitation approaches can not capture the intrinsic properties of multi-label data possessed of rich semantics. In this paper, the first attempt towards multi-semantics metric learning for multi-label classification is investigated. Specifically, the proposed LIMIC approach simultaneously learns one global and multiple label-specific local metrics by exploiting label-specific side information. The global metric is learned to capture the commonality across all the labels and label-specific local metrics characterize the individuality of each semantic space. The combination of global metric and label-specific local metrics is utilized to construct latent semantic space for each label, in which similar intra-class instances are pushed closer and inter-class instances are pulled apart. Furthermore, metric-based label correlation regularization is constructed to maintain similarity between correlated label spaces. Extensive experiments on benchmark multi-label data sets validate the superiority of our proposed approach in learning effective distance metrics for multi-label classification.