Reviewerly/exHarmony huggingface dataset:
A Language-agnostic Framework for Mining Static Analysis Rules from Code Changes

Static analysis tools detect a wide range of code defects, including code quality issues, security vulnerabilities, operational risks, and best-practice violations. Creating and maintaining a set of high-quality static analysis rules that detect misuses of popular libraries and SDKs across multiple languages is challenging. One of the mechanisms for inferring static analysis rules is by leveraging frequently occurring bug-fix code changes in the wild that are committed by multiple developers and into different software repositories. The intuition is that code changes following a common pattern correspond to recurring mistakes, from which deriving best practices could likely be of high value and accepted by the community.Automating the process of mining and clustering code changes enables a scalable mechanism to source and generate best-practices rules. From a coverage standpoint, the rules are derived from real-world code changes, which ensures that popular libraries and application domains are accounted for.In this paper, we present a language-agnostic framework for mining and clustering code changes from software repositories using a graph-based representation dubbed MU (μ). Unlike language-specific ASTs, the MU representation generalizes across languages by modeling programs at a higher semantic level, which enables grouping of code changes that are semantically similar yet syntactically distinct. We have mined a total of 62 high-quality static analysis rules across Java, JavaScript, and Python from less than 600 code change clusters. These cover multiple libraries, including the AWS Java and Python SDKs, as well as libraries like pandas, React, Android libraries, Json parsing libraries, and many more. These rules are integrated into a cloud-based static analyzer, Amazon CodeGuru Reviewer. Developers have accepted 73% of recommendations from these rules during code review, which signifies the value of these rules to help improve developer productivity, make code secure, and improve code hygiene.
Code Compliance Assessment as a Learning Problem

Manual code reviews and static code analyzers are the traditional mechanisms to verify if source code complies with coding policies. However, they are hard to scale. We formulate code compliance assessment as a machine learning (ML) problem, to take as input a natural language policy and code, and generate a prediction on the code’s compliance, non-compliance, or irrelevance. Our intention for ML-based automation is to scale the development of Amazon CodeGuru, a commercial code analyzer. We explore key research questions on model formulation, training data, and evaluation setup. We obtain a joint code-text representation space (embeddings) which preserves compliance relationships via the vector distance of code and policy embeddings. As there is no task-specific data, we re-interpret and filter commonly available software datasets with additional pre-training and pre-finetuning tasks that reduce the semantic gap. We benchmarked our approach on two listings of coding policies (CWE and CBP). This is a zero-shot evaluation as none of the policies occur in the training set. On CWE and CBP respectively, our tool Policy2Code achieves classification accuracies of (59%, 71%) and search MRR of (0.05, 0.21) compared to CodeBERT with classification accuracies of (37%, 54%) and MRR of (0.02, 0.02). In a user study, 24% Policy2Code detections were accepted compared to 7% for CodeBERT. Policy2Code is considered a useful ML-based aid to supplement manual efforts.
Unmasking the shadows: Investigating cyber scams and its strategies for prevention

Cybercrime is mainly related to online fraud and encompasses various criminal activities through computers and online platforms, posing significant challenges in today's technologically advanced world. The case study presented herein investigates the use of unsolicited mass email messages as a prominent method employed by cybercriminals. These messages, disseminated via instant messaging, text messaging, or other digital messaging systems, serve as a deceptive means to defraud unsuspecting individuals. This study aims to enhance our understanding of the techniques, motives, and impact of online fraud. Furthermore, it highlights the attractiveness of online fraud to advertisers, who exploit its low operating costs beyond managing mailing lists. This aspect underscores the complex interplay between cybercriminals, their targets, and the broader digital ecosystem where illicit activities intertwine with legitimate platforms. By delving into the realm of cybercrime, this case study serves as a valuable resource for researchers and individuals seeking to understand the evolving nature of cyber threats. It emphasizes the importance of heightened awareness, robust cybersecurity measures, and the need for continuous adaptation to mitigate the risks posed by cybercriminals in an interconnected world.
Estimating the Components of a Mixture of Extremal Distributions under Strong Dependence

In this paper, we provide a method based on quantiles to estimate the parameters of a finite mixture of Fréchet distributions, for a large sample of strongly dependent data.This is a situation that appears when dealing with environmental data and there was a real need of such method.We validate our approach by means of estimation and goodness-of-fit testing over simulated data, showing an accurate performance.
Development and Evaluation of a Knowledge-Based Cyber-Physical Production System to Support Industrial Set-Up Processes Considering Ergonomic and User-Centered Aspects

Dynamic markets and constantly changing work practices are causing an increased number of industrial set-up operations on production machines in the wake of a growing demand for customized product requirements. Augmented reality (AR)-based cyber-physical production systems (CPPS) can be used to support complex and knowledge-intensive processes. Resting on a comprehensive ethnographic study, this topic was addressed to identify practices of machine operators in the course of set-up processes on forming or bending machines through a qualitative research approach. Subsequently, a set-up application for an AR-mediated head-mounted display was developed according to a user-centered design approach. For a holistic, objective and subject-related human factors analysis on the handling of AR-based CPPS in the context of assembly or set-up processes, ergonomic sub-studies were conducted. The research work advances the state of the art in the design of digital technologies or CPPS to support operators who are entrusted with set-up processes of industrial production machines.
Area-Efficient Intellectual Property (IP) Design of Advanced Encryption Standard

This brief proposes an area-efficient AES design approach considering both application-specific integrated circuits (ASIC) and field-programmable gate arrays (FPGA) implementation characteristics. This brief focuses on optimizing and analyzing the design approach of Subbytes and MixColumns, which take up the most significant portion of AES hardware area. Furthermore, this brief presents an area-efficient AES intellectual property (IP) design by analyzing the trade-off relationship between area and clock cycles based on the datapath variations. The proposed AES IPs were designed using Verilog HDL and synthesized using Samsung 28nm standard cell library for performance comparison. The proposed AES IPs show the advanced normalized area efficiency of 70% over the existing AES design with the same datapath. Furthermore, the Xilinx UltraScale+ KCU116 evaluation board (XCKU5P) was used for FPGA verification and performance analysis. As a result, the FPGA implementation results show up to 36% better look-up table (LUT) utilization efficiency than the Xilinx AES IP, and up to 17.9 times better than the existing AES implementation results.
${\sf FedRLChain}$: Secure Federated Deep Reinforcement Learning With Blockchain

This paper introduces <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math>${\sf FedRLChain}$</tex-math></inline-formula> , a novel framework for blockchain-based secure federated deep reinforcement learning, which allows users to securely and collaboratively train a Deep Reinforcement Learning (DRL) model by plugging appropriate aggregation and verification algorithms for specific problems. Unlike existing systems, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math>${\sf FedRLChain}$</tex-math></inline-formula> adopts (1) a novel verification algorithm to prevent malicious clients, (2) an aggregation weight scheme from preventing the global model from getting biased toward any client, and (3) a variant of traditional FedAverage algorithm to accelerate the convergence process. We perform a rigorous experimental evaluation of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math>${\sf FedRLChain}$</tex-math></inline-formula> considering the classic cart-pole problem, and we show a significant improvement in the number of epochs and time required for model convergence w.r.t. the state-of-the-art frameworks – DDQL, BAFFLE, and BASE-PIoT.
Position-Aware Voxel Aggregate Network for Two-Stage 3-D Object Detector

Voxel-based 3-D object detection methods have drawn extensive attention due to their efficiency and accuracy. However, effectively using the feature information of point clouds to improve the detection performance remains to be explored. In this article, we propose a novel two-stage object detector, called position-aware voxel aggregate network for two-stage 3-D object detector (PA-Det3D). First, in the region proposal network (RPN) stage, we introduce a cross-scale location attention module (CLM) that enables effective fusion of high-level semantic information and low-level spatial details at each location. Second, we introduce a secondary grouping module (SGM), which consists of two components: the position offset secondary grouping module (PSM) and the distance-enhanced module (DEM). The PSM predicts the relative positional offsets of querying voxels, allowing for dynamic adjustment of query positions to overcome distance limitations. The DEM, on the other hand, incorporates relative distance information to provide more explicit geometric properties and enhance robustness. Finally, we propose a consistency matching based on the Gaussian mixture model (CMG), which can adaptively adjust the threshold to establish teacher–student relationships, resulting in more stable supervision signals. Extensive experiment results on the KITTI and Waymo datasets demonstrate that our approach achieves competitive performance in 3-D bounding box detection, which verifies the effectiveness of our method.
Tail Recursion Transformation for Invertible Functions

Tail recursive functions allow for a wider range of optimisations than general recursive functions. For this reason, much research has gone into the transformation and optimisation of this family of functions, in particular those written in continuation passing style (CPS). Though the CPS transformation, capable of transforming any recursive function to an equivalent tail recursive one, is deeply problematic in the context of reversible programming (as it relies on troublesome features such as higher-order functions), we argue that relaxing (local) reversibility to (global) invertibility drastically improves the situation. On this basis, we present an algorithm for tail recursion conversion specifically for invertible functions. The key insight is that functions introduced by program transformations that preserve invertibility, need only be invertible in the context in which the functions subject of transformation calls them. We show how a bespoke data type, corresponding to such a context, can be used to transform invertible recursive functions into a pair of tail recursive function acting on this context, in a way where calls are highlighted, and from which a tail recursive inverse can be straightforwardly extracted.
Improving HCI on Cognition for Children with Intelligent UI/UX

Human-computer interaction (HCI) research focuses on the interactions between humans and computers in designing and using computer technology and making computers more user-friendly for humans. Every aspect of HCI is taken into account, including the user’s perspective and how they interact with the world and technology in general. However, computers’ interactions with children need slightly more attention and an innovative approach. This research focuses on intelligent UI/UX design approaches (considering kids’ psychology) for software and web apps that can effectively improve human-computer interaction for children. The study will provide an overview of the children’s expectations and needs as they interact with computers and the various approaches to improving the user interface and experience.
Present Causal Relationship Retrieval for Historical Analogy

The importance of studying history and applying this knowledge is widely recognized. In this study, we propose a search algorithm to promote historical analogy by immediately exploiting historical knowledge. Our algorithm outputs present causal relationships similar to the input past causal relationship. It first replaces temporal entities that are limited to certain periods with their entity types to enhance the connection between the causal relationships of the past and present. Subsequently, it measures causal similarities on a bipartite graph of the past and present causal relationships. Finally, the accuracy of the algorithm is evaluated on two datasets comprising present and past causal relationships, respectively. We found that the temporal entity replacement is effective in searching for causal relationships.
Differentially Private Streaming Data Release Under Temporal Correlations via Post-processing

The release of differentially private streaming data has been extensively studied, yet striking a good balance between privacy and utility on temporally correlated data in the stream remains an open problem. Existing works focus on enhancing privacy when applying differential privacy to correlated data, highlighting that differential privacy may suffer from additional privacy leakage under correlations; consequently, a small privacy budget has to be used which worsens the utility. In this work, we propose a post-processing framework to improve the utility of differential privacy data release under temporal correlations. We model the problem as a maximum posterior estimation given the released differentially private data and correlation model and transform it into nonlinear constrained programming. Our experiments on synthetic datasets show that the proposed approach significantly improves the utility and accuracy of differentially private data by nearly a hundred times in terms of mean square error when a strict privacy budget is given.
The Virtual Revival of the City of Saarlouis via a Multimodal Augmented/Mixed Reality Touristic HoloLens App

In this paper, we present our framework for Augmented and Mixed Reality touristic applications, which we are currently developing with the city of Saarlouis (Germany). It aims to provide tourists and visitors with a new augmented experience to discover the unique history of the city. Whereas most Augmented and Mixed Reality touristic applications are designed for a single user without any human city guide, our app, called SaAR-Louis, offers a more immersive touristic experience as it displays holographic information via Augmented and Mixed Reality on HoloLens/iPad in addition to the explanations of the tour guide. Moreover, thanks to geolocation and 3D scanning of the surroundings, our application provides tourists with the unique experience of discovering 3D reconstructed ancient/old buildings that no longer exist and offers multimodal interaction with a 3D Avatar that gives insights about the historic buildings. In the following, we will explain the app architecture, the pipeline of creating the 3D virtual revival of the city of Saarlouis, and the challenges behind designing, conceiving, implementing, and deploying the SaAR-Louis app.
Velluscinum: A Middleware for Using Digital Assets in Multi-agent Systems

Distributed Ledger Technologies (DLT) characteristics can contribute to several domains, such as Multi-agent Systems (MAS), facilitating the agreement between agents, managing trust relationships, and distributed scenarios. Some contributions to this integration are in the theoretical stage, and the few existing practical contributions have limitations and low performance. This work presents a MAS approach that can use digital assets as a factor of agreement in the relationship between cognitive agents using the Belief-Desire-Intention model. To validate the proposed methodology, we present the middleware Velluscinum that offers new internal actions to agents. The middleware was tested by adapting the Building-a-House classic example to cryptocurrency and agreements mediated by a distributed ledger.
Optimization of Complex Systems in Photonics by Multi-agent Robotic Control

The optimization of complex industrial systems represents a class of difficult problems, due to their embodiment in the physical world, and whose search spaces are disrupted, non-linear and potentially vast. Their parametrization relies on the combination of many variables, each change generally impacting the whole system. Mathematical approaches are limited by the fact that the models are too coarse or non-existent, and by the imprecision of the measurements and the machining of components of such systems. The action cost of the system calls into question population-based heuristics and swarm intelligence where each individual must be tested. We propose a multi-agent approach allowing a global/black-box modeling of the system in terms of input variables and objectives, as well as an agnostic and continuous adaptive optimization, based on sensor feedbacks from the running system.
Improved Cost-Metric for Nearest Neighbor Mapping of Quantum Circuits to 2-Dimensional Hexagonal Architecture

Quantum computing offers substantial speedup over conventional computing in solving certain computationally hard problems. The emergence of quantum computers in recent years has motivated researchers to develop design automation tools to map quantum circuits to such platforms. One major challenge is to limit the noise or computational error during gate operations; in particular, errors are higher when gates operate on non-neighbor qubits. A common approach to tackle this problem is to make the circuits Nearest-Neighbor (NN) compliant by inserting either Swap gates or CNOT templates. Reduction of gate overhead also becomes important as it serves to limit the overall noise and error. In some recent works, mapping of quantum circuits to hexagonal qubit architecture have been investigated. Hexagonal layout of qubits offers extended neighborhood that helps to reduce the number of Swap or additional CNOT gates required for NN-compliance. Existing approaches incur high gate overheads that can be reduced by improved gate mapping strategies with better cost metrics. The present work proposes one such approach using a priority-based cost metric. The proposed cost-metric is general and can be applied to any architectures; however, in this work we show its benefit for hexagonal architecture. Experiments on benchmark circuits confirm that the proposed method reduces gate overhead by $$29\%$$ over a very recent work based on greedy mapping.
Contact Tracing /Filiation Cost for COVID-19 in Türkiye

Aim: Aim of this study is to estimate direct and indirect cost of COVID-19 contact tracing (CT) for capital city of Türkiye. Materials and Methods: This is an economic study to estimate the cost for COVID-19 CT program from March 11, 2020 to March 11, 2021. This study was conducted in Pursaklar district of Ankara city. Direct and indirect cost were estimated for national level, the direct costs include fixture, salary of the team, consumption and car while indirect costs include nutritıon, transportation, communication, PPE, Food and others. Results: total direct cost was estimated as $ 69108,3; the highest cost was related to salary ($ 42204,0) followed by consumption ($15560,9), transportation ($5481,0) and fixture ($98,8) while indirect cost was estimated as $5763,6; the highest mean cost was related to transportation ($98,3) followed by shoes-clothes ($91,5), Vitamin-like supplement food ($65,7), food ($43,7), PPE ($34,1) and communication ($17,3) annually. Conclusion: The total cost for COVID-19 contact tracing program for the district was estimated to be $ 69108,3 of which, 91,7% ($ 63344,7) were due to direct cost while 8,3% ($ 5763,6) were generated by indirect cost. The annual national cost for CT team for 2020 was estimated to be 106,288,565.4 $.
Exploiting the Benefits of Clean Ancilla Based Toffoli Gate Decomposition Across Architectures

Elementary gate decomposition of larger Toffoli operations is often carried out using additional qubits (ancilla). The number of gates and the circuit depth vary in such transformation depending on the type of ancilla used (clean or dirty). The present Noisy Intermediate Scale Quantum (NISQ) devices have limited number of coherent qubits with faulty native operation support. Superconducting devices also have coupling restrictions or Nearest-Neighbor (NN) constraints, which require additional gates to map the transformed netlist for execution. While the mapping overhead is correlated with the number of 2-qubit gates and involved qubits, the fidelity of execution is inversely proportional to the number of gates and circuit depth. There is a tradeoff in devising the transpilation (i.e. low-level transformation and mapping) approach — dirty ancilla demands less qubits and overhead at the expense of more gates and depth as compared to clean ancilla, which involves less gates and depth at the expense of more qubits and overhead. This paper analyzes the disparity in gates, depth and qubits between: (i) the low-level transformation approaches without considering device coupling information, and (ii) the mapping schemes based on netlist transformation using a specific type of ancilla. We analyze the benefits of using NN-constraints at the transformation stage, and the impact of distributing clean ancilla across architectures. We have carried out experiments on IBM Q20 and Hexagonal Q20 architectures, which show improvements of 17% and 13% respectively in terms of number of gates.
Multi-agent Learning of Causal Networks in the Internet of Things

In Internet of Things deployments, such as a smart home, building, or city, it is of paramount importance for software agents to be aware of the causal model of the environment in which they operate (i.e. of the causal network relating actions to their effects and observed variables to each other). Yet, the complexity and dynamics of the environment can prevent to specify such model at design time, thus requiring agents to learn its structure at run-time. Accordingly, we introduce a distributed multi-agent protocol in which a set of agents, each with partial observability, cooperate to learn a coherent and accurate personal view of the causal network. We evaluate such protocol in the context of a smart home scenario and for a two-agents case, showing that it has superior accuracy in recovering the ground truth network.