Miracle-dz/UniFAR-FTUs huggingface dataset:
Network Defense is Not a Game

Research seeks to apply Artificial Intelligence (AI) to scale and extend the capabilities of human operators to defend networks. A fundamental problem that hinders the generalization of successful AI approaches -- i.e., beating humans at playing games -- is that network defense cannot be defined as a single game with a fixed set of rules. Our position is that network defense is better characterized as a collection of games with uncertain and possibly drifting rules. Hence, we propose to define network defense tasks as distributions of network environments, to: (i) enable research to apply modern AI techniques, such as unsupervised curriculum learning and reinforcement learning for network defense; and, (ii) facilitate the design of well-defined challenges that can be used to compare approaches for autonomous cyberdefense. To demonstrate that an approach for autonomous network defense is practical it is important to be able to reason about the boundaries of its applicability. Hence, we need to be able to define network defense tasks that capture sets of adversarial tactics, techniques, and procedures (TTPs); quality of service (QoS) requirements; and TTPs available to defenders. Furthermore, the abstractions to define these tasks must be extensible; must be backed by well-defined semantics that allow us to reason about distributions of environments; and should enable the generation of data and experiences from which an agent can learn. Our approach named Network Environment Design for Autonomous Cyberdefense inspired the architecture of FARLAND, a Framework for Advanced Reinforcement Learning for Autonomous Network Defense, which we use at MITRE to develop RL network defenders that perform blue actions from the MITRE Shield matrix against attackers with TTPs that drift from MITRE ATT&CK TTPs.
Shadow Generation for Composite Image in Real-world Scenes

Image composition targets at inserting a foreground object into a background image. Most previous image composition methods focus on adjusting the foreground to make it compatible with background while ignoring the shadow effect of foreground on the background. In this work, we focus on generating plausible shadow for the foreground object in the composite image. First, we contribute a real-world shadow generation dataset DESOBA by generating synthetic composite images based on paired real images and deshadowed images. Then, we propose a novel shadow generation network SGRNet, which consists of a shadow mask prediction stage and a shadow filling stage. In the shadow mask prediction stage, foreground and background information are thoroughly interacted to generate foreground shadow mask. In the shadow filling stage, shadow parameters are predicted to fill the shadow area. Extensive experiments on our DESOBA dataset and real composite images demonstrate the effectiveness of our proposed method. Our dataset and code are available at https://github.com/bcmi/Object-Shadow-Generation- Dataset-DESOBA.
Reduction scheme for coupled Dirac systems

We analyze a class of coupled quantum systems whose dynamics can be understood via two uncoupled, lower-dimensional quantum settings with auxiliary interactions. The general reduction scheme, based on algebraic properties of the potential term, is discussed in detail for two-dimensional Dirac Hamiltonian. We discuss its possible application in description of Dirac fermions in graphene or bilayer graphene in presence of distortion scattering or spin–orbit interaction. We illustrate the general results on the explicit examples where the involved interactions are non-uniform in space and time.
Towards Causal Models for Adversary Distractions

Automated adversary emulation is becoming an indispensable tool of network security operators in testing and evaluating their cyber defenses. At the same time, it has exposed how quickly adversaries can propagate through the network. While research has greatly progressed on quality decoy generation to fool human adversaries, we may need different strategies to slow computer agents. In this paper, we show that decoy generation can slow an automated agent's decision process, but that the degree to which it is inhibited is greatly dependent on the types of objects used. This points to the need to explicitly evaluate decoy generation and placement strategies against fast moving, automated adversaries.
Machine vision detection to daily facial fatigue with a nonlocal 3D attention network

Fatigue detection is valued for people to keep mental health and prevent safety accidents. However, detecting facial fatigue, especially mild fatigue in the real world via machine vision is still a challenging issue due to lack of non-lab dataset and well-defined algorithms. In order to improve the detection capability on facial fatigue that can be used widely in daily life, this paper provided an audiovisual dataset named DLFD (daily-life fatigue dataset) which reflected people’s facial fatigue state in the wild. A framework using 3D-ResNet along with non-local attention mechanism was training for extraction of local and long-range features in spatial and temporal dimensions. Then, a compacted loss function combining mean squared error and cross-entropy was designed to predict both continuous and categorical fatigue degrees. Our proposed framework has reached an average accuracy of 90.8% on validation set and 72.5% on test set for binary classification, standing a good position compared to other state-of-the-art methods. The analysis of feature map visualization revealed that our framework captured facial dynamics and attempted to build a connection with fatigue state. Our experimental results in multiple metrics proved that our framework captured some typical, micro and dynamic facial features along spatiotemporal dimensions, contributing to the mild fatigue detection in the wild.
Game Theory to Study Interactions between Mobility Stakeholders

Increasing urbanization and exacerbation of sustainability goals threaten the operational efficiency of current transportation systems and confront cities with complex choices with huge impact on future generations. At the same time, the rise of private, profit-maximizing Mobility Service Providers leveraging public resources, such as ride-hailing companies, entangles current regulation schemes. This calls for tools to study such complex socio-technical problems. In this paper, we provide a game-theoretic framework to study interactions between stakeholders of the mobility ecosystem, modeling regulatory aspects such as taxes and public transport prices, as well as operational matters for Mobility Service Providers such as pricing strategy, fleet sizing, and vehicle design. Our framework is modular and can readily accommodate different types of Mobility Service Providers, actions of municipalities, and low-level models of customers' choices in the mobility system. Through both an analytical and a numerical case study for the city of Berlin, Germany, we showcase the ability of our framework to compute equilibria of the problem, to study fundamental tradeoffs, and to inform stakeholders and policy makers on the effects of interventions. Among others, we show tradeoffs between customers' satisfaction, environmental impact, and public revenue, as well as the impact of strategic decisions on these metrics.
Tackling Variabilities in Autonomous Driving

The state-of-the-art driving automation system demands extreme computational resources to meet rigorous accuracy and latency requirements. Though emerging driving automation computing platforms are based on ASIC to provide better performance and power guarantee, building such an accelerator-based computing platform for driving automation still present challenges. First, the workloads mix and performance requirements exposed to driving automation system present significant variability. Second, with more cameras/sensors integrated in a future fully autonomous driving vehicle, a heterogeneous multi-accelerator architecture substrate is needed that requires a design space exploration for a new form of parallelism. In this work, we aim to extensively explore the above system design challenges and these challenges motivate us to propose a comprehensive framework that synergistically handles the heterogeneous hardware accelerator design principles, system design criteria, and task scheduling mechanism. Specifically, we propose a novel heterogeneous multi-core AI accelerator (HMAI) to provide the hardware substrate for the driving automation tasks with variability. We also define system design criteria to better utilize hardware resources and achieve increased throughput while satisfying the performance and energy restrictions. Finally, we propose a deep reinforcement learning (RL)-based task scheduling mechanism FlexAI, to resolve task mapping issue. Experimental results show that with FlexAI scheduling, basically 100% tasks in each driving route can be processed by HMAI within their required period to ensure safety, and FlexAI can also maximally reduce the breaking distance up to 96% as compared to typical heuristics and guided random-search-based algorithms.
Discovering an Aid Policy to Minimize Student Evasion Using Offline Reinforcement Learning

High dropout rates in tertiary education expose a lack of efficiency that causes frustration of expectations and financial waste. Predicting students at risk is not enough to avoid student dropout. Usually, an appropriate aid action must be discovered and applied in the proper time for each student. To tackle this sequential decision-making problem, we propose a decision support method to the selection of aid actions for students using offline reinforcement learning to support decision-makers effectively avoid student dropout. Additionally, a discretization of student's state space applying two different clustering methods is evaluated. Our experiments using logged data of real students shows, through off-policy evaluation, that the method should achieve roughly 1.0 to 1.5 times as much cumulative reward as the logged policy. So, it is feasible to help decision-makers apply appropriate aid actions and, possibly, reduce student dropout.
Disentangled Face Identity Representations for joint 3D Face Recognition and Expression Neutralisation

In this paper, we propose a new deep learning-based approach for disentangling face identity representations from expressive 3D faces. Given a 3D face, our approach not only extracts a disentangled identity representation but also generates a realistic 3D face with a neutral expression while predicting its identity. The proposed network consists of three components; (1) a Graph Convolutional Autoencoder (GCA) to encode the 3D faces into latent representations, (2) a Generative Adversarial Network (GAN) that translates the latent representations of expressive faces into those of neutral faces, (3) and an identity recognition sub-network taking advantage of the neutralized latent representations for 3D face recognition. The whole network is trained in an end-to-end manner. Experiments are conducted on three publicly available datasets showing the effectiveness of the proposed approach.
Diverse and Specific Clarification Question Generation with Keywords

Product descriptions on e-commerce websites often suffer from missing important aspects. Clarification question generation (CQGen) can be a promising approach to help alleviate the problem. Unlike traditional QGen assuming the existence of answers in the context and generating questions accordingly, CQGen mimics user behaviors of asking for unstated information. The generated CQs can serve as a sanity check or proofreading to help e-commerce merchant to identify potential missing information before advertising their product, and improve consumer experience consequently. Due to the variety of possible user backgrounds and use cases, the information need can be quite diverse but also specific to a detailed topic, while previous works assume generating one CQ per context and the results tend to be generic. We thus propose the task of Diverse CQGen and also tackle the challenge of specificity. We propose a new model named KPCNet, which generates CQs with Keyword Prediction and Conditioning, to deal with the tasks. Automatic and human evaluation on 2 datasets (Home & Kitchen, Office) showed that KPCNet can generate more specific questions and promote better group-level diversity than several competing baselines. 1
Outcome-Driven Reinforcement Learning via Variational Inference

While reinforcement learning algorithms provide automated acquisition of optimal policies, practical application of such methods requires a number of design decisions, such as manually designing reward functions that not only define the task, but also provide sufficient shaping to accomplish it. In this paper, we view reinforcement learning as inferring policies that achieve desired outcomes, rather than as a problem of maximizing rewards. To solve this inference problem, we establish a novel variational inference formulation that allows us to derive a well-shaped reward function which can be learned directly from environment interactions. From the corresponding variational objective, we also derive a new probabilistic Bellman backup operator and use it to develop an off-policy algorithm to solve goal-directed tasks. We empirically demonstrate that this method eliminates the need to hand-craft reward functions for a suite of diverse manipulation and locomotion tasks and leads to effective goal-directed behaviors.
Cycloplegic Effects on the Cylindrical Components of the Refraction

It is important to predict which astigmatic patients require separate refraction for near vision. This study compared cylindrical components changes by cyclopentolate 1% for the low and high amount of astigmatism. The right eyes of 1014 healthy individuals (307 males and 707 females) with cylindrical refractive power more than −0.5 diopter on autorefractometer were selected. Both male and female patients in the age range of 17–45 years were refracted before and after cycloplegia, using 1% cyclopentolate. All volunteers were classified into 2 subgroups including the lower astigmatism group (−2.25 to −0.50) and the higher astigmatic group (−2.50 to over). Alpines' method was used to compare the effect of cycloplegic drop on cylindrical power. The mean age in the lower astigmatism group (29.58; 95% CI: 29.18 to 29.99 years) was not significantly different from the higher astigmatic group (29.85; 95% CI: 29.07 to 30.62) and there were no significant differences in gender between these two groups (P=0.54). Differences between wet and dry refraction in J0 (−0.03; 95% CI:−0.06 to −0.008) and J45 (−0.03; 95% CI:−0.06 to −0.01) were significant only in the higher astigmatic group. Axis changes by the cycloplegic drop in the lower astigmatism group were 3.51 (CI: 3.22 to 3.81) and axis changes by the cycloplegic drop in the higher astigmatism group were 2.21 (CI: 1.73 to 2.49). In patients with a lower amount of astigmatism (−2.25 to −0.50), additional near subjective refraction could be done for precise determination of axis and in patients with a higher amount of astigmatism (−2.50 to over), near subjective refraction might be done for precise determination of power.
Improving the Quality of Life of Family Caregivers of People with Alzheimer's Disease through Virtual Communities of Practice: A Quasiexperimental Study

Caring for a person with dementia burdens family caregivers, and there is a close negative relationship between this burden and their quality of life (QoL). Research suggests that caregivers' main needs are information and training about the disease and support from others experiencing the same situation, and Internet interventions hold considerable promise for meeting these needs. Virtual communities of practice (VCoPs) are Internet frameworks to share knowledge where members collaborate and achieve a sense of trust in the community. This paper seeks to evaluate the impact of participating in a VCoP (developed through an App) on the QoL of caregivers to people with Alzheimer's. Results show QoL before and after the intervention changed significantly. The impact of VCoP on caregivers' overall QoL is moderated by age and relation with the person with Alzheimer's, specifically those over 65, and spouses. VCoPs allow interaction and knowledge sharing among caregivers which provide them mainly with information and support from peers helping them to meet their needs. Furthermore, caregivers' QoL did not decrease when their relative deteriorated functionally, which could be due to the participation in VCoP. Although we found significant pre- and post differences in caregivers' health literacy, we must report the ambiguous result that this variable only impacts on QoL's physical domain. Participants also reported that they had a positive experience because the App was perceived to be a useful tool, because they could manage their own participation and they met peers and felt less lonely. Results suggest that participation in a VCoP impacts positively on caregivers' QoL.
CT Texture Analysis for Preoperative Identification of Lymphoma from Other Types of Primary Small Bowel Malignancies

Objectives To explore the application of computed tomography (CT) texture analysis in differentiating lymphomas from other malignancies of the small bowel. Methods Arterial and venous CT images of 87 patients with small bowel malignancies were retrospectively analyzed. The subjective radiological features were evaluated by the two radiologists with a consensus agreement. The region of interest (ROI) was manually delineated along the edge of the lesion on the largest slice, and a total of 402 quantified features were extracted automatically from AK software. The inter- and intrareader reproducibility was evaluated to select highly reproductive features. The univariate analysis and minimum redundancy maximum relevance (mRMR) algorithm were applied to select the feature subsets with high correlation and low redundancy. The multivariate logistic regression analysis based on texture features and radiological features was employed to construct predictive models for identification of small bowel lymphoma. The diagnostic performance of multivariate models was evaluated using receiver operating characteristic (ROC) curve analysis. Results The clinical data (age, melena, and abdominal pain) and radiological features (location, shape, margin, dilated lumen, intussusception, enhancement level, adjacent peritoneum, and locoregional lymph node) differed significantly between the nonlymphoma group and lymphoma group (p < 0.05). The areas under the ROC curve of the clinical model, arterial texture model, and venous texture model were 0.93, 0.92, and 0.87, respectively. Conclusion The arterial texture model showed a great diagnostic value and fitted performance in preoperatively discriminating lymphoma from nonlymphoma of the small bowel.
Limited Adherence to Growth Hormone Replacement in Patients with Traumatic Brain Injury

Background Growth hormone deficiency is a recognized consequence of traumatic brain injury. The aim of this study was to determine adherence to human growth hormone therapy among patients with traumatic brain injury compared with patients with hypothalamic or pituitary disease. Methods A retrospective chart review of patients with traumatic brain injury referred for growth hormone stimulation testing since December 2013. Within the same electronic medical record, patients who were started on human growth hormone for aetiologies other than traumatic brain injury were reviewed. Adherence to therapy at 1-year followup was compared. Results Of the patients with traumatic brain injury, 12/23 (52%) returned for follow-up at 1 year to continue human growth hormone treatment, whereas 11/23 (48%) did not return at 1 year. Amongst the patients with non-traumatic brain injury: 25/29 (86%) continued human growth hormone treatment, vs 4/29 (14%) who did not return. A higher proportion of patients with non-traumatic brain injury continued human growth hormone treatment; x2 (1, n = 52)p = 7.238, p = 0.007. Conclusion There may be differences in the patient-perceived benefits of human growth hormone between these patient populations. However, it is important to consider the potential influences of cognitive and psychosocial dysfunction that can occur in patients with brain injuries. LAY ABSTRACT Traumatic brain injuries can damage the pituitary gland and cause hormone deficiencies. Growth hormone deficiency is the most common hormone deficiency in patients with head injuries. These patients have adverse symptoms and poor quality of life, but it is not known whether these issues can be reversed or improved by growth hormone replacement therapy. This study investigated how many patients who started on growth hormone therapy after brain injury continued the treatment long-term. This allowed us to estimate whether the patients perceived such treatment to be beneficial. More than half (52%) of the patients with traumatic brain injury chose to continue growth hormone therapy for one year or longer. However, while some patients with brain injury may experience benefit from growth hormone replacement, it is possible that not all patients find growth hormone therapy helpful.
Circulating Prokineticin 2 Levels Are Increased in Children with Obesity and Correlated with Insulin Resistance

Objective Prokineticin 2 (PK2) has been shown to regulate food intake, fat production, and the inflammation process, which play vital roles in the pathogenesis of obesity. The first aim of this study was to investigate serum PK2 levels in children with obesity and normal-weight children. The second aim was to compare the levels of PK2 between children with obesity, with and without nonalcoholic fatty liver disease (NAFLD). Methods Seventy normal-weight children and 91 children with obesity (22 with NAFLD) were recruited. Circulating PK2, IL-6, and TNF-α were measured by enzyme-linked immunosorbent assays. Anthropometric and biochemical measurements related to adiposity, lipid profile, and insulin resistance were examined for all participants. Results Serum PK2 was significantly higher in children with obesity than in the normal-weight controls. Circulating PK2 levels were not different between the patients with and without NAFLD. Circulating PK2 was positively correlated with BMI, BMI z-score, insulin, glucose, HOMA-IR, total cholesterol, low-density lipoprotein cholesterol, alanine aminotransferase, and gamma-glutamyl transpeptidase. Binary logistic regression revealed that the odds ratios for obesity were significantly elevated with increasing PK2. Conclusions PK2 was strongly associated with obesity, and it may also be related to metabolic disorders and insulin resistance. This trial is registered with ChiCTR2000038838.