Video anomaly detection (VAD) has long been studied as a crucial problem in public security and crime prevention. In recent years, weakly-supervised VAD (WVAD) has attracted considerable attention due to their easy annotation process and promising research results. While existing WVAD methods mainly tackle static datasets, the possibility that the domain of data can vary has been neglected. To adapt such domain shift, the continual learning (CL) perspective is required because otherwise additional training using only newly incoming data could easily cause performance degradation for previous data, i.e., forgetting. Therefore, we propose a brand-new approach, called Continual Anomaly Detection with Ensembles (CADE) that is the first work combining CL and WVAD viewpoints. Specifically, CADE uses the Dual-Generator (DG) to address data imbalance and label uncertainty in WVAD. We also found that forgetting exacerbates the "incompleteness" where the model becomes biased towards certain anomaly modes, leading to missed detections of various anomalies. To address this, we propose a Multi-Discriminator (MD) ensemble that captures missed anomalies in past scenes due to forgetting, using multiple models. Extensive experiments show that CADE significantly outperforms existing VAD methods on the common multi-scene VAD datasets, such as the ShanghaiTech, UCF-Crime, and Charlotte Anomaly Dataset.

In classic supervised learning, once a model is deployed in an application, it is fixed. No updates will be made to it during the application. This is inappropriate for many dynamic and open environments, where unexpected samples from unseen classes may appear. In such an environment, the model should be able to detect these novel samples from unseen classes and learn them after they are labeled. We call this paradigm Autonomous Learning after Model Deployment (ALMD). The learning here is continuous and involves no human engineers. Labeling in this scenario is performed by human co-workers or other knowledgeable agents, which is similar to what humans do when they encounter an unfamiliar object and ask another person for its name. In ALMD, the detection of novel samples is dynamic and differs from traditional out-of-distribution (OOD) detection in that the set of in-distribution (ID) classes expands as new classes are learned during application, whereas ID classes is fixed in traditional OOD detection. Learning is also different from classic supervised learning because in ALMD, we learn the encountered new classes immediately and incrementally. It is difficult to retrain the model from scratch using all the past data from the ID classes and the novel samples from newly discovered classes, as this would be resource- and time-consuming. Apart from these two challenges, ALMD faces the data scarcity issue because instances of new classes often appear sporadically in real-life applications. To address these issues, we propose a novel method, PLDA, which performs dynamic OOD detection and incremental learning of new classes on the fly. Empirical evaluations will demonstrate the effectiveness of PLDA.

Sense of agency (SoA) is the perceived control over one’s actions and their consequences, and through this one feels responsible for the consequent outcomes in the world. We analyze the far-reaching implications of a two-pronged knowledge on SoA and its impact on human-AI interactions. We argue that although there are interesting research efforts for an AI to inherently possess SoA, they are still sparse, constrained in scope and present unclear immediate benefit to the design of AI-enabled systems. We also argue that the knowledge on how human SoA is affected by an AI that is perceived to possess a sense of control presents more immediate benefit to AI, in particular, to eliciting positive human attitudes toward AI. Third, and lastly, we argue that research efforts for an AI to adapt to the dynamic changes of human SoA are practically non-existent primarily due to the difficulty of modeling, inferring and adaptively responding to human SoA in complex natural settings. We proceed by first delving deep into the influential and recent theoretical underpinnings of SoA, and discuss its conceptual reach in different disciplines and how it is applied in real-world research. We organize a substantial part of our paper to put forward and elucidate our three argumentative points while supported by evidence in the literature.

This paper studies the challenging continual learning (CL) setting of Class Incremental Learning (CIL). CIL learns a sequence of tasks consisting of disjoint sets of concepts or classes. At any time, a single model is built that can be applied to predict/classify test instances of any classes learned thus far without providing any task related information for each test instance. Although many techniques have been proposed for CIL, they are mostly empirical. It has been shown recently that a strong CIL system needs a strong within-task prediction (WP) and a strong out-of-distribution (OOD) detection for each task. However, it is still not known whether CIL is actually learnable. This paper shows that CIL is learnable. Based on the theory, a new CIL algorithm is also proposed. Experimental results demonstrate its effectiveness.

Existing research on task incremental learning in continual learning has primarily focused on preventing catastrophic forgetting (CF). Although several techniques have achieved learning with no CF, they attain it by letting each task monopolize a sub-network in a shared network, which seriously limits knowledge transfer (KT) and causes over-consumption of the network capacity, i.e., as more tasks are learned, the performance deteriorates. The goal of this paper is threefold: (1) overcoming CF, (2) encouraging KT, and (3) tackling the capacity problem. A novel technique (called SPG) is proposed that soft-masks (partially blocks) parameter updating in training based on the importance of each parameter to old tasks. Each task still uses the full network, i.e., no monopoly of any part of the network by any task, which enables maximum KT and reduction in capacity usage. To our knowledge, this is the first work that soft-masks a model at the parameter-level for continual learning. Extensive experiments demonstrate the effectiveness of SPG in achieving all three objectives. More notably, it attains significant transfer of knowledge not only among similar tasks (with shared knowledge) but also among dissimilar tasks (with little shared knowledge) while mitigating CF.

Knowledge graphs (KGs) are useful information sources to make machine learning efficient with human knowledge. Since KGs are often incomplete, KG completion has become an important problem to complete missing facts in KGs. Whereas most of the KG completion methods are conducted on a single KG, multiple KGs can be effective to enrich embedding space for KG completion. However, most of the recent studies have concentrated on entity alignment prediction and ignored KG-invariant semantics in multiple KGs that can improve the completion performance. In this paper, we propose a new multiple KG embedding method composed of intra-KG and inter-KG regularization to introduce KG-invariant semantics into KG embedding space using aligned entities between related KGs. The intra-KG regularization adjusts local distance between aligned and not-aligned entities using contrastive loss, while the inter-KG regularization globally correlates aligned entity embeddings between KGs using multi-view loss. Our experimental results demonstrate that our proposed method combining both regularization terms largely outperforms existing baselines in the KG completion task.

Continual learning (CL) learns a sequence of tasks incrementally. There are two popular CL settings, class incremental learning (CIL) and task incremental learning (TIL). A major challenge of CL is catastrophic forgetting (CF). While a number of techniques are already available to effectively overcome CF for TIL, CIL remains to be highly challenging. So far, little theoretical study has been done to provide a principled guidance on how to solve the CIL problem. This paper performs such a study. It first shows that probabilistically, the CIL problem can be decomposed into two sub-problems: Within-task Prediction (WP) and Task-id Prediction (TP). It further proves that TP is correlated with out-of-distribution (OOD) detection, which connects CIL and OOD detection. The key conclusion of this study is that regardless of whether WP and TP or OOD detection are defined explicitly or implicitly by a CIL algorithm, good WP and good TP or OOD detection are necessary and sufficient for good CIL performances. Additionally, TIL is simply WP. Based on the theoretical result, new CIL methods are also designed, which outperform strong baselines in both CIL and TIL settings by a large margin.

Sense of agency (SoA) is the subjective experience that one’s own volitional action caused an event to happen. This experience has cast light to understanding fundamental aspects of human behavior, which includes regulating actions during goal pursuit. Due to its many facets, investigating SoA has proved to be a strong challenge, compelling theorists and experimentalists to develop various paradigms to analyze it. While investigations on SoA have primarily focused on simple tasks that probe basic self-agency capacity awareness, and were carried out mostly under controlled laboratory settings over short experiment durations, we investigated this feeling of control in a complex, natural setting where participants performed daily their goal-directed tasks. More importantly, however, we investigated the SoA construct in a multidimensional way, i.e., simultaneously investigating its pre-reflective and reflective, local and general, and dynamic nature, as well as how individual differences moderated its influence on goal pursuit. We collected over 5,000 data points from 43 participants on their daily perceptions of self-agency and pursuance of healthy eating for more than a month outside the confines of a lab using a smartphone app that we designed. We present our analyses and insights that emerged from our empirical results on how the many facets of SoA impacted in various ways the pursuance of the goal. To our knowledge, we are the first to study the SoA construct in this manner, and we posit our method can be used for an intelligent system to enhance a human counterpart’s SoA for self-driven persuasion to follow through the goal.

Combinatorial interaction testing is an efficient software testing strategy. If all interactions among test parameters or factors needed to be covered, the size of a required test suite would be prohibitively large. In contrast, this strategy only requires covering t-wise interactions where t is ty pically very small. As a result, it becomes possible to significantly reduce test suite size. Locating arrays aim to enhance the ability of combinatorial interaction testing. In particular, (1¯,t) -locating arrays can not only execute all t-way interactions but also identify, if any, which of the interactions causes a failure. In spite of this useful property, there is only limited research either on how to generate locating arrays or on their minimum sizes. In this paper, we propose an approach to generating minimum locating arrays. In the approach, the problem of finding a locating array consisting of N tests is represented as a Constraint Satisfaction Problem (CSP) instance, which is in turn solved by a modern CSP solver. The results of using the proposed approach reveal many (1¯,t) -locating arrays that are smallest known so far. In addition, some of these arrays are proved to be minimum.

Combinatorial interaction testing is known to be an efficient testing strategy for computing and information systems. Locating arrays are mathematical objects that are useful for this testing strategy, as they can be used as a test suite that permits fault localization as well as fault detection. In this application, each row of an array is used as an individual test. This paper proposes an algorithm for constructing locating arrays with a small number of rows. Testing cost increases as the number of tests increases; thus the problem of finding locating arrays of small sizes is of practical importance. The proposed algorithm uses simulated annealing, a meta-heuristic algorithm, to find locating array of a given size. The whole algorithm repeatedly executes the simulated annealing algorithm with the input array size being dynamically varied. Experimental results show (1) that the proposed algorithm is able to construct locating arrays for problem instances of large sizes and (2) that, for problem instances for which nontrivial locating arrays are known, the algorithm is often able to generate locating arrays that are smaller than or at least equal to the known arrays. Based on the results, we conclude that the proposed algorithm can produce small locating arrays and scale to practical problems.

Thanks to the recent popularity of GPS-enabled mobile phones, modeling people flow or population dynamics is attracting a great deal of attention. Advances in methods where regular population patterns with respect to factors such as holidays or weekdays are extracted have provided successful results in irregularity detection. With large-scale crowded events such as fireworks, it is crucial that there be enough time to take countermeasures against the irregular congestion, i.e., irregularity prediction. It remains a tough challenge to predict population from GPS trace logs with existing methods.