Organizations are often reluctant to share defectlabeled source code because of privacy and intellectual property concerns, yet effective cross-project defect prediction (CPDP) depends on learning from diverse projects. This paper introduces FedCPDP, a federated and privacy-preserving framework that enables collaborative defect prediction without exchanging raw project data and without requiring a common feature schema. The approach employs feature hashing to unify heterogeneous metric spaces, integrates federated proximal optimization with representation alignment to mitigate distribution shift, and incorporates differential privacy mechanisms to safeguard sensitive updates. To further enhance robustness, FedCPDP explores adaptive server aggregation strategies (attention and graphbased) and seed ensembling, while addressing class imbalance through focal loss and threshold optimization. The framework is evaluated on thirty benchmark datasets across five CPDP families under leave-one-project-out validation, using both threshold-free and effort-aware metrics. Results show that FedCPDP improves F1 by up to 10-15% over FedAvg and 6-8% over FedDPI, while achieving 8-10% higherPoptunder effort-aware evaluation. These gains are statistically significant, confirming that FedCPDP consistently outperforms state-of-the-art methods while uniquely supporting heterogeneous features and offering explicit privacy guarantees. These findings establish FedCPDP as a practical step toward enabling secure and deployable defect prediction in multiorganizational settings.

Abstract Recognizing entities and their corresponding roles are important in human activity recognition. In light of recent advancements, the primary emphasis is recognizing the abstract activities involving person‐person interaction. The contribution of this work is proposing an architecture, which utilizes the knowledge of the human body parts coordinates in role detection of each individual. The network preprocesses the coordinates to build intra‐body and inter‐body features. The extracted features build the relationship between the interacting bodies and learn the temporal relation corresponding to each role using the human memory‐inspired hierarchical temporal memory. The model is tested on vague samples of mutual actions in the experimental work. The model is found robust in action and role recognition tasks and performed well per expectations.

We present a new optimization DECAF that optimizes recursive task parallel (RTP) programs by reducing the task creation and termination overheads. DECAF reduces the task termination (join) operations by aggressively increasing the scope of join operations (in a semantics preserving way), and eliminating the redundant join operations discovered on the way. Further, DECAF extends the traditional loop chunking technique to perform load-balanced chunking, at runtime, based on the number of available worker threads. This helps reduce the redundant parallel tasks at different levels of recursion. We also discuss the impact of exceptions on our techniques and extend them to handle RTP programs that may throw exceptions. We implemented DECAF in the X10v2.3 compiler and tested it over a set of benchmark kernels on two different hardwares (a 16-core Intel system and a 64-core AMD system). With respect to the base X10 compiler extended with loop-chunking of Nandivada et al. [26] (LC), DECAF achieved a geometric mean speed up of 2.14× and 2.53× on the Intel and AMD system, respectively. We also present an evaluation with respect to the energy consumption on the Intel system and show that on average, compared to the LC versions, the DECAF versions consume 71.2% less energy.

Energy-efficient compilation is an important problem for multi-core systems. In this context, irregular programs with task-parallel loops present interesting challenges: the threads with lesser work-loads ( non-critical -threads) wait at the join-points for the thread with maximum work-load ( critical -thread); this leads to significant energy wastage. This problem becomes more interesting in the context of multi-socket-multi-core (MSMC) systems, where different sockets may run at different frequencies, but all the cores connected to a socket run at a single frequency. In such a configuration, even though the load-imbalance among the cores may be significant, an MSMC-oblivious technique may miss the opportunities to reduce energy consumption, if the load-imbalance across the sockets is minimal. This problem becomes further challenging in the presence of mutual-exclusion, where scaling the frequencies of a socket executing the non-critical-threads can impact the execution time of the critical-threads. In this article, we propose a scheme (X10Ergy) to obtain energy gains with minimal impact on the execution time, for task-parallel languages, such as X10, HJ, and so on. X10Ergy takes as input a loop-chunked program (parallel-loop iterations divided into chunks and each chunk is executed by a unique thread). X10Ergy follows a mixed compile-time + runtime approach that (i) uses static analysis to efficiently compute the work-load of each chunk at runtime, (ii) computes the “remaining” work-load of the chunks running on the cores of each socket at regular intervals and tunes the frequency of the sockets accordingly, (iii) groups the threads into different sockets (based on the remaining work-load of their respective chunks), and (iv) in the presence of atomic-blocks, models the effect of frequency-scaling on the critical-thread. We implemented X10Ergy for X10 and have obtained encouraging results for the IMSuite kernels.