Image steganography faces inherent trade-offs among embedding capacity, undetectability, and visual fidelity. To address this, we propose iSME, a novel GAN framework for image steganography with a Median-Enhanced Channel-Spatial (MECS) attention mechanism. The MECS module integrates channel attention and multi-scale spatial attention, enhancing feature representation while reducing embedding-induced distortion. The framework includes a generator (for embedding), an extractor (for recovery), and a discriminator based on XuNet, trained with the Wasserstein GAN objective for stability. Experiments on COCO and DIV2K datasets show that iSME outperforms SOTA methods (such as HiDDeN, SteganoGAN, CHAT-GAN) across 1–5 bits per pixel (BPP). Notably, at 5 BPP, it achieves PSNR of 41.93 (COCO) and 45.52 (DIV2K), with high SSIM and extraction accuracy (>91%), and stego images are nearly imperceptible. Ablation studies validate the critical role of MECS. This work provides a promising approach for high-capacity, secure, and high-fidelity image steganography.

Tests, as an essential artifact, should co-evolve with the production code to ensure that the associated production code satisfies specification. However, developers often postpone or even forget to update tests, making the tests outdated and lag behind the code. To predict which tests need to be updated when production code is changed, it is challenging to identify all related tests and determine their change probabilities due to complex change scenarios. This paper fills the gap and proposes a hybrid approach named COTE to predict code-to-test co-evolution. We first compute the linked test candidates based on different code-to-test dependencies. After that, we identify common co-change patterns by building a method-level dependence graph. For the remaining ambiguous patterns, we leverage a pre-trained language model which captures the semantic features of code and the change reasons contained in commit messages to judge one test’s likelihood of being updated. Experiments on our datasets consisting of 6,314 samples extracted from 5,000 Java projects show that COTE outperforms state-of-the-art approaches, achieving a precision of 89.0% and a recall of 71.6%. This work can help practitioners reduce test maintenance costs and improve software quality.

Python’s dynamic typing nature provides developers with powerful programming abstractions. However, many type-related bugs are accumulated in code bases of Python due to the misuse of dynamic typing. The goal of this article is to aid in the understanding of developers’ high-risk practices toward dynamic typing and the early detection of type-related bugs. We first formulate the rules of six types of risky dynamic typing-related practices (type smells for short) in Python. We then develop a rule-based tool named RUPOR, which builds an accurate type base to detect type smells. Our evaluation shows that RUPOR outperforms the existing type smell detection techniques (including the Large Language Models–based approaches, Mypy, and PYDYPE) on a benchmark of 900 Python methods. Based on RUPOR, we conduct an empirical study on 25 real-world projects. We find that type smells are significantly related to the occurrence of post-release faults. The fault-proneness prediction model built with type smell features slightly outperforms the model built without them. We also summarize the common patterns, including inserting type check to fix type smell bugs. These findings provide valuable insights for preventing and fixing type-related bugs in the programs written in dynamic-typed languages.

Smart homes contain diverse sensors and actuators controlled by IoT apps that provide custom automation. Prior works showed that an adversary could exploit physical interaction vulnerabilities among apps and put the users and environment at risk, e.g., to break into a house, an adversary turns on the heater to trigger an app that opens windows when the temperature exceeds a threshold. Currently, the safe behavior of physical interactions relies on either app code analysis or dynamic analysis of device states with manually derived policies by developers. However, existing works fail to achieve sufficient breadth and fidelity to translate the app code into their physical behavior or provide incomplete security policies, causing poor accuracy and false alarms.

With the rapid development of cloud computing technology, massive services and online information cause information overload. Collaborative Filtering (CF) is one of the most successful and widely used technologies in personalized recommendation system to deal with information overload. However, traditional CF recommendation algorithms go through high time cost and poor real-time performance when dealing with the large-scale behavior data. Moreover, most collaborative recommendation methods mainly focus on improving recommendation accuracy, while ignore privacy preservation. In addition, the recommendation results of traditional CF recommendation algorithms are often too single, which could not meet user's diverse requirements. To solve these problems, this paper proposes a privacy-aware collaborative recommendation algorithm based on local sensitive hash (LSH) and factorization techniques. First, LSH is adopted to determine nearest neighbor set of the target users, where a neighbor matrix for the target user can be generated. The matrix factorization technique is applied in the neighbor matrix to predict the missing ratings. Then the nearest neighbors can be determined based on the predicted ratings. Finally, predictions for the target user are made based on the neighborhood-based CF recommendation model and diversified recommendations are made for the target user. Experimental results show that the proposed algorithm can effectively improve the efficiency of recommendation on the premise of protecting the privacy of users.

Smart home systems are becoming increasingly popular. Software engineering of such systems hence becomes a prominent challenge. In this engineering paradigm, users are often interested in considering sensor states while they are performing various activities. Existing works have proposed initial efforts on incremental development method for activity-oriented requirements. However, there is no systematic way of ensuring reliability and security of such systems which may be developed by various developers and may execute in a complex environment. Some properties, especially those including timing constraints, need to be satisfied. In this paper, we introduce Actom, a framework for identification of activity-oriented requirements and runtime verification. Actom supports the development of the mapping between activities and required sensor readings (activity-sensor mapping). At runtime, Actom receives results of activity recognition and is able to trigger actuators to provide the required physical conditions for the activities, as determined by the activity-sensor mapping. Moreover, Actom continuously monitors whether activity-sensor mapping holds over a time period during the activity. We also discuss the evaluation plan to demonstrate the effectiveness and efficiency of Actom. The end product will be a systematic framework to facilitate the development of activity-oriented requirements and monitor properties with timing constraints to improve reliability and security.

While event logs generated by business processes play an increasingly significant role in business analysis, the quality of data remains a serious problem. Automatic recovery of dirty event logs is desirable and thus receives more attention. However, existing methods only focus on missing event recovery, or fall short of efficiency. To this end, we present Effa, a ProM plugin, to automatically recover event logs in the light of process specifications. Based on advanced heuristics including process decomposition and trace replaying to search the minimum recovery, Effa achieves a balance between repairing accuracy and efficiency.

Location Based Service (LBS) is one kind of ubiquitous applications whose functions are based on the locations of clients. The core of LBS is an effective positioning system. As wireless LAN (WLAN) costs less and is easy to access, using WLAN for indoor positioning has been widely studied recently. K nearest neighbors (KNN) is one of the basic deterministic fingerprint based algorithms and widely used for WLAN-based indoor positioning. However, KNN takes all the nearest K neighbors for calculating the estimated result, which could be improved if some selective work could be done to those neighbors beforehand. In this paper we propose a new scheme called "cluster filtered KNN" (CFK). CFK utilizes clustering technique to partition those neighbors into different clusters and chooses one cluster as the delegate. In the end, the final estimate can be calculated only based on the elements of the delegate. With experiments, we found that CFK does outperform KNN.