Recall that we present a unifying EOT framework for analyzing cooperative communication.

Quality: From the technical point of view, the paper seems to be well-prepared. In particular, the authors propose \gamma-heuristic theory for link prediction and show that many common heuristics can be expressed in a general formulation. They build their framework based on this theory and on the previous work (WLNM). However, I would like to touch on some points here: - In Line 115, there is no clear definition of the \textbf{h-order heuristic} although it is mentioned between lines 26 and 31. If \textbf{h-order heuristic} is defined as, for example, "a heuristic requiring to know up to h-hop neighborhood of the target nodes", then do we really need Theorem 1? - In lines 274 and 275, it is stated that "We run all experiments for 5 times and report the average".

To explore the vulnerability of deep neural networks (DNNs), many attack paradigms have been well studied, such as the poisoning-based backdoor attack in the training stage and the adversarial attack in the inference stage. In this paper, we study a novel attack paradigm, which modifies model parameters in the deployment stage. Considering the effectiveness and stealthiness goals, we provide a general formulation to perform the bit-flip based weight attack, where the effectiveness term could be customized depending on the attacker's purpose. Furthermore, we present two cases of the general formulation with different malicious purposes, i.e., single sample attack (SSA) and triggered samples attack (TSA). To this end, we formulate this problem as a mixed integer programming (MIP) to jointly determine the state of the binary bits (0 or 1) in the memory and learn the sample modification. Utilizing the latest technique in integer programming, we equivalently reformulate this MIP problem as a continuous optimization problem, which can be effectively and efficiently solved using the alternating direction method of multipliers (ADMM) method. Consequently, the flipped critical bits can be easily determined through optimization, rather than using a heuristic strategy. Extensive experiments demonstrate the superiority of SSA and TSA in attacking DNNs.

On Demand services, such as a ride sharing [1], coordination of multiply robots [2], user serving in MIMO networks [3] etc utilize management strategies in order to improve customer quality of service (QoS) requirements. The problem of shared resource utilization is very common in wireless networks [4] and becoming more important with more devices connected because of development of IoT and 5G. Usually such systems have multiply concurrent users awaiting serving and fewer number of workers resources available, along with switching costs from serving user to user (like trip for taxi driver from drop off of one user to pick up point of the next one). Real world systems are dynamic in nature with cause and effect information not being given and system behavior and QoS only being observed. Previous works developed different algorithmic or classical scheduling methods, where QoS is maintained via algorithm using some sort of priority index, like Proportional Fair [5], [3] or MLWDF [6]. This work focuses on reinforced learning applied to general formulation of user scheduling problem. A Q-learning based method is presented for maximizing customer QoS and compared to analytical strategies. A Q-learning approach is shown to improve QoS up to TODO% compared to baseline scenarios.

This paper presents a brief introduction to the key points of the Grey Machine Learning (GML) based on the kernels. The general formulation of the grey system models have been firstly summarized, and then the nonlinear extension of the grey models have been developed also with general formulations. The kernel implicit mapping is used to estimate the nonlinear function of the GML model, by extending the nonparametric formulation of the LSSVM, the estimation of the nonlinear function of the GML model can also be expressed by the kernels. A short discussion on the priority of this new framework to the existing grey models and LSSVM have also been discussed in this paper. And the perspectives and future orientations of this framework have also been presented.

We investigate the value of extending the completeness of a decision model along different dimensions of refinement. Specifically, we analyze the expected value of quantitative, conceptual, and structural refinement of decision models. We illustrate the key dimensions of refinement with examples. The analyses of value of model refinement can be used to focus the attention of an analyst or an automated reasoning system on extensions of a decision model associated with the greatest expected value.