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Large Language Models (LLMs) and Generative Pre-trained Transformers (GPTs) are reshaping the field of Software Engineering (SE). Existing LLM-based multi-agent systems have successfully resolved simple dialogue tasks. However, the potential of LLMs for more complex tasks, such as automated code generation for large and complex projects, have been explored in only a few existing works. This paper introduces CodePori, a novel model designed to automate code generation for extensive and complex software projects based on natural language prompts. We employ LLM-based multi-AI agents to handle creative and challenging tasks in autonomous software development. Each agent engages with a specific task, including system design, code development, code review, code verification, and test engineering. We show in the paper that CodePori is able to generate running code for large-scale projects, completing the entire software development process in minutes rather than hours, and at a cost of a few dollars. It identifies and mitigates potential security vulnerabilities and corrects errors while maintaining a solid code performance level. We also conducted an evaluation of CodePori against existing solutions using HumanEval and the Massively Multitask Benchmark for Python (MBPP) benchmark. The results indicate that CodePori improves upon the benchmarks in terms of code accuracy, efficiency, and overall performance. For example, CodePori improves the pass@1 metric on HumanEval to 87.5% and on MBPP to 86.5%, representing a clear improvement over the existing models. We also assessed CodePori's performance through practitioner evaluations, with 91% expressing satisfaction with the model's performance.

We introduce FindingEmo, a new image dataset containing annotations for 25k images, specifically tailored to Emotion Recognition. Contrary to existing datasets, it focuses on complex scenes depicting multiple people in various naturalistic, social settings, with images being annotated as a whole, thereby going beyond the traditional focus on faces or single individuals. Annotated dimensions include Valence, Arousal and Emotion label, with annotations gathered using Prolific. Together with the annotations, we release the list of URLs pointing to the original images, as well as all associated source code.

The new paradigm of finetuning-as-a-service introduces a new attack surface for Large Language Models (LLMs): a few harmful data uploaded by users can easily trick the finetuning to produce an alignment-broken model. We conduct an empirical analysis and uncover a \textit{harmful embedding drift} phenomenon, showing a probable cause of the alignment-broken effect. Inspired by our findings, we propose Vaccine, a perturbation-aware alignment technique to mitigate the security risk of users finetuning. The core idea of Vaccine is to produce invariant hidden embeddings by progressively adding crafted perturbation to them in the alignment phase. This enables the embeddings to withstand harmful perturbation from un-sanitized user data in the finetuning phase. Our results on open source mainstream LLMs (e.g., Llama2, Opt, Vicuna) demonstrate that Vaccine can boost the robustness of alignment against harmful prompts induced embedding drift while reserving reasoning ability towards benign prompts. Our code is available at \url{//github.com/git-disl/Vaccine}.

We investigate the constant-depth circuit complexity of the Isomorphism Problem, Minimum Generating Set Problem (MGS), and Sub(quasi)group Membership Problem (Membership) for groups and quasigroups (=Latin squares), given as input in terms of their multiplication (Cayley) tables. Despite decades of research on these problems, lower bounds for these problems even against depth-$2$ AC circuits remain unknown. Perhaps surprisingly, Chattopadhyay, Tor\'an, and Wagner (FSTTCS 2010; ACM Trans. Comput. Theory, 2013) showed that Quasigroup Isomorphism could be solved by AC circuits of depth $O(\log \log n)$ using $O(\log^2 n)$ nondeterministic bits, a class we denote $\exists^{\log^2(n)}FOLL$. We narrow this gap by improving the upper bound for many of these problems to $quasiAC^0$, thus decreasing the depth to constant. In particular, we show: - MGS for quasigroups is in $\exists^{\log^2(n)}\forall^{\log n}NTIME(\mathrm{polylog}(n))\subseteq quasiAC^0$. Papadimitriou and Yannakakis (J. Comput. Syst. Sci., 1996) conjectured that this problem was $\exists^{\log^2(n)}P$-complete; our results refute a version of that conjecture for completeness under $quasiAC^0$ reductions unconditionally, and under polylog-space reductions assuming EXP $\neq$ PSPACE. - MGS for groups is in $AC^{1}(L)$, improving on the previous upper bound of P (Lucchini & Thakkar, J. Algebra, 2024). - Quasigroup Isomorphism belongs to $\exists^{\log^2(n)}AC^0(DTISP(\mathrm{polylog},\log)\subseteq quasiAC^0$, improving on the previous bound of $\exists^{\log^2(n)}L\cap\exists^{\log^2(n)}FOLL\subseteq quasiFOLL$ (Chattopadhyay, Tor\'an, & Wagner, ibid.; Levet, Australas. J. Combin., 2023). Our results suggest that understanding the constant-depth circuit complexity may be key to resolving the complexity of problems concerning (quasi)groups in the multiplication table model.

State-of-the-Art (SotA) hardware implementations of Deep Neural Networks (DNNs) incur high latencies and costs. Binary Neural Networks (BNNs) are potential alternative solutions to realize faster implementations without losing accuracy. In this paper, we first present a new data mapping, called TacitMap, suited for BNNs implemented based on a Computation-In-Memory (CIM) architecture. TacitMap maximizes the use of available parallelism, while CIM architecture eliminates the data movement overhead. We then propose a hardware accelerator based on optical phase change memory (oPCM) called EinsteinBarrier. Ein-steinBarrier incorporates TacitMap and adds an extra dimension for parallelism through wavelength division multiplexing, leading to extra latency reduction. The simulation results show that, compared to the SotA CIM baseline, TacitMap and EinsteinBarrier significantly improve execution time by up to ~154x and ~3113x, respectively, while also maintaining the energy consumption within 60% of that in the CIM baseline.

Despite the Internet's continued growth, it increasingly depends on a small set of service providers to support Domain Name System (DNS) and web content hosting. This trend poses many potential threats including susceptibility to outages, failures, and potential censorship by providers. This paper aims to quantify consolidation in terms of popular domains' reliance on a small set of organizations for both DNS and web hosting. We highlight the extent to which a set of relatively few platforms host the authoritative name servers and web content for the top million websites. Our results show that both DNS and web hosting are concentrated, with Cloudflare and Amazon hosting over $30\%$ of the domains for both services. With the addition of Akamai, Fastly, and Google, these five organizations host $60\%$ of index pages in the Tranco top 10K, as well as the majority of external page resources. These trends are consistent across six different global vantage points, indicating that consolidation is happening globally and popular organizations can influence users' online experience across the world.

Artificial Intelligence (AI) has witnessed remarkable growth, particularly through the proliferation of Deep Neural Networks (DNNs). These powerful models drive technological advancements across various domains. However, to harness their potential in real-world applications, specialized hardware accelerators are essential. This demand has sparked a market for parameterizable AI hardware accelerators offered by different vendors. Manufacturers of AI-integrated products face a critical challenge: selecting an accelerator that aligns with their product's performance requirements. The decision involves choosing the right hardware and configuring a suitable set of parameters. However, comparing different accelerator design alternatives remains a complex task. Often, engineers rely on data sheets, spreadsheet calculations, or slow black-box simulators, which only offer a coarse understanding of the performance characteristics. The Abstract Computer Architecture Description Language (ACADL) is a concise formalization of computer architecture block diagrams, which helps to communicate computer architecture on different abstraction levels and allows for inferring performance characteristics. In this paper, we demonstrate how to use the ACADL to model AI hardware accelerators, use their ACADL description to map DNNs onto them, and explain the timing simulation semantics to gather performance results.

Background. The Expected Value of Sample Information (EVSI) measures the expected benefits that could be obtained by collecting additional data. Estimating EVSI using the traditional nested Monte Carlo method is computationally expensive but the recently developed Gaussian approximation (GA) approach can efficiently estimate EVSI across different sample sizes. However, the conventional GA may result in biased EVSI estimates if the decision models are highly nonlinear. This bias may lead to suboptimal study designs when GA is used to optimize the value of different studies. Therefore, we extend the conventional GA approach to improve its performance for nonlinear decision models. Methods. Our method provides accurate EVSI estimates by approximating the conditional benefit based on two steps. First, a Taylor series approximation is applied to estimate the conditional benefit as a function of the conditional moments of the parameters of interest using a spline, which is fitted to the samples of the parameters and the corresponding benefits. Next, the conditional moments of parameters are approximated by the conventional GA and Fisher information. The proposed approach is applied to several data collection exercises involving non-Gaussian parameters and nonlinear decision models. Its performance is compared with the nested Monte Carlo method, the conventional GA approach, and the nonparametric regression-based method for EVSI calculation. Results. The proposed approach provides accurate EVSI estimates across different sample sizes when the parameters of interest are non-Gaussian and the decision models are nonlinear. The computational cost of the proposed method is similar to other novel methods. Conclusions. The proposed approach can estimate EVSI across sample sizes accurately and efficiently, which may support researchers in determining an economically optimal study design using EVSI.

Natural Language Processing (NLP) has been revolutionized by the use of Pre-trained Language Models (PLMs) such as BERT. Despite setting new records in nearly every NLP task, PLMs still face a number of challenges including poor interpretability, weak reasoning capability, and the need for a lot of expensive annotated data when applied to downstream tasks. By integrating external knowledge into PLMs, \textit{\underline{K}nowledge-\underline{E}nhanced \underline{P}re-trained \underline{L}anguage \underline{M}odels} (KEPLMs) have the potential to overcome the above-mentioned limitations. In this paper, we examine KEPLMs systematically through a series of studies. Specifically, we outline the common types and different formats of knowledge to be integrated into KEPLMs, detail the existing methods for building and evaluating KEPLMS, present the applications of KEPLMs in downstream tasks, and discuss the future research directions. Researchers will benefit from this survey by gaining a quick and comprehensive overview of the latest developments in this field.

Within the rapidly developing Internet of Things (IoT), numerous and diverse physical devices, Edge devices, Cloud infrastructure, and their quality of service requirements (QoS), need to be represented within a unified specification in order to enable rapid IoT application development, monitoring, and dynamic reconfiguration. But heterogeneities among different configuration knowledge representation models pose limitations for acquisition, discovery and curation of configuration knowledge for coordinated IoT applications. This paper proposes a unified data model to represent IoT resource configuration knowledge artifacts. It also proposes IoT-CANE (Context-Aware recommendatioN systEm) to facilitate incremental knowledge acquisition and declarative context driven knowledge recommendation.