Miracle-dz/UniFAR-FTUs huggingface dataset:
Distance-aware Hierarchical Data-collecting Path Selection for Mobile Sink in Sparse WSNs

A Mobile Sink (MS) is a device that can be used to collect data from sparsely deployed sensor nodes by visiting the sensor nodes in close proximity. Various existing schemes face the problem of bottlenecks during data transmission to the MS as only a few sensor nodes are chosen to transmit data. In this article, we propose the Distance-aware Hierarchical Data-collecting Path Selection (DHDP) algorithm that aims to determine optimal Rendezvous Points (RPs) using the Range bound Hierarchical Agglomerative Clustering (R-HAC) and check the cluster's quality based on Average Silhouette Width (ASW). Further, we exploit Ant Colony Optimization (ACO) to determine the optimal path for the mobile sink to follow. To reduce the energy consumption of sensor nodes, we appoint Collection Agents (CA) to transmit data directly to the mobile sink when the sensor node comes within transmission range. The extensive simulation indicates significant improvements in network lifetime and end-to-end data delivery latency compared to the state-of-the-art methods in sparse networks.
SHAP Interpretations of Tree and Neural Network DNS Classifiers for Analyzing DGA Family Characteristics

Domain Generation Algorithms (DGA’s) have been employed by botnet orchestrators for controlling infected hosts (bots), while evading detection by performing multiple DNS requests, mostly for non-existing domain names. With blacklists ineffective, modern DGA filtering methods rely on Machine Learning (ML). Emerging needs for higher intrusion detection accuracy lead to complex, non-interpretable black-box classifiers, thus requiring eXplainable Artificial Intelligence (XAI) techniques. In this paper, we utilize SHapley Additive exPlanation (SHAP) to derive model-agnostic, post-hoc interpretations on DGA name classifiers. This method is applied to binary supervised tree-based classifiers (e.g. eXtreme Gradient Boosting - XGBoost) and deep neural networks (Multi-Layer Perceptron - MLP) to assess domain name feature importance. SHAP visualization tools (summary, dependence, force plots) are used to rank features, investigate their effect on model decisions and determine their interactions. Specific interpretations are detailed for identifying names belonging to common DGA families pertaining to arithmetic, wordlist, hash and permutation based schemes. Learning and interpretations are based on up-to-date datasets, such as Tranco for benign and DGArchive for malicious names. Domain name features are extracted from dataset instances, thus limiting time-consuming and privacy-invasive database operations on historical data. Our experimental results demonstrate that SHAP enables explanations of XGBoost (the most accurate tree-based model) and MLP classifiers and indicates the characteristics of specific DGA schemes, commonly employed in attacks. In conclusion, we envision that XAI methods will expedite ML deployment in networking environments where justifications for black-box models are required.
ZeroCost-LLC: Shared LLCs at No Cost to WCL

ZeroCost-LLC (ZCLLC) is a shared inclusive lastlevel cache (LLC) architecture for predictable multicore platforms that does not incur additional cost to the worst-case latency (WCL) of memory requests when compared to the memory hierarchy without an LLC. Thus, the WCL remains the same as without an LLC in the memory hierarchy, but with the performance benefits of having an LLC, in the form of additional caching capacity. ZCLLC achieves this by eliminating all cache line invalidations, and proactively updating the main memory with cache lines to preserve an important vacancy invariant. Furthermore, ZCLLC does not impose any constraints on the way the LLC is used unlike other approaches such as LLC partitioning. Our analysis reveals that the WCL is 55.6%, 68.0%, and 80.2% lower, and the performance is 2.4%, 7.2%, and 25.6% better than the state-of-the-art LLC partition sharing techniques for 2, 4, and 8 cores, respectively.
Social Network Community Detection based on Inner-Community Information Diffusion Simulation Algorithm

Community detection based on information diffusion is a relatively niche direction. This paper proposes a social network community detection model based on the inner-community information diffusion simulation. The model uses the inner-community information diffusion algorithm adjusted on the naive infectious disease algorithm for node representation, including the dynamic spreading parameter and repeatable infected nodes. Model clusters nodes into communities according to their representation. Experiment results prove that our model outperforms previous related models on synthetic and real datasets. We verify the interpretability of the inner-community information diffusion representation algorithm meanwhile.
A Systematic Review of Facial Expression Detection Methods

Understanding emotions is one of the greatest capabilities of human beings, as it allows the understanding of facial expressions that facilitate the capture of important information about other individuals, which are used for the perception of mental or emotional states. Advances in Artificial Intelligence and Visual Computing, more specifically in Deep Learning with the advent of Artificial Neural Networks, have enhanced the ability of machines to infer human emotions through image analysis. This paper presents a Systematic Literature Review (SLR) with the purpose of researching, mapping and summarizing studies that address techniques or algorithms more efficiently. The convolutional neural network models analyzed in this review are based on deep learning with an emphasis on expression and microexpression recognition. The results suggest that database uses, with laboratory controlled images, combined with CNN’s such as VGG and ResNet, have excellent performances in their tests. For better understanding, we will detail and compare all the methods obtained in the review.
Design of a Fuzzy Type-2 Controller for 1-DOF Pitch Axis Twin Rotor System

This paper presents the design, manufacturing, modeling, and control of a novel 1-DOF pitch axis Twin Rotor system. An IMU sensor is used to precisely access the desired pitch angle of the system. A type-2 fuzzy controller is designed and implemented to achieve fast rise time and low overshoot, which are the desired response specifications for the system. In terms of overshoot and settling time, the performance of the type-2 fuzzy-PID controller is compared to that of the type-l fuzzy-PID controller and the conventional PID controller. The type-2 fuzzy-PID controller's performance is promising when compared to that of the other controllers, proving that it is useful for taming the Twin Rotor system's non-linearities.
Galvanic displacement deposition of Ag using citric acid for Cu-to-Cu hybrid bonding

This study reports Ag deposition for hybrid bonding using galvanic displacement deposition. The galvanic displacement method enables selective deposition only on Cu without any additional process. To uniform Ag layer, citric acid was added. Citric acid suppresses the Cu dissolution and stabilize the Cu surface. The morphologies of Ag layer were observed by scanning electron microscopy (SEM). The deposited Ag atoms has a property of interdiffusion with Cu during bonding process because of the difference of activation energy. Depth-profiling X-ray photoelectron spectroscopy (XPS) shows the interdiffusion between Ag and Cu, and the bonding properties improve with Ag layer.
A Degree Condition for Graphs to Have Fractional Factors

In this paper,a degree condition for a graph to have fractional factors is studied.The following result is obtained.Let k be an integer such that k1,and let G be a connected graph of order n with n4k-3,and minimum degreeδ(G)k.If G satisfies max{(d_G(u),d_G(v)}n/2 for each pair of nonadjacent vertices u,v∈V(G),then G has a fractional k-factor.Furthermore,we prove that the result is the best possible in some sense.
Computation Offloading with Reinforcement Learning for Improving QoS in Edge Computing Environments

A computation offloading scheme based on edge collaboration was proposed for smoothly handling high delay-sensitive tasks in the Internet of Things (IoT). However, it shows poor service time and load balancing due to resource limitations and the number of processed task restrictions of edge servers. To solve this problem, edge collaboration using Reinforcement Learning (RL) has been proposed. RL requires a lot of exploration to maximize the cumulative reward. In a distributed environment, RL has a problem that the agent does not have enough experience due to the data sparsity for learning. Also, training variation between agents is high in a distributed environment. In this paper, we propose computation offloading with reinforcement learning for improving Quality of Service (QoS) in edge computing environments. We select offloading target based on RL for minimizing the service time and maximizing the load balance. The proposed scheme determines the priority of experience and shares the experience to improve reward. The priority of experience is calculated by Temporal Difference (TD) error and a state value. Also, the sharing of experience is proceeded on the basis of the policy gradient of agents. Experimental results show that the proposed scheme achieves a better QoS through high reward compared to the existing schemes.
Ultra Low Energy Charge Trapping MOSFET With Neuro-Inspired Learning Capabilities

In this article, a synaptic semiconductor device and its crossbar have been demonstrated for real-time artificial intelligence (AI) applications. The proposed device is a dual gate, metal-oxide-semiconductor field-effect transistor (MOSFET) with charge trapping and de-trapping capabilities to emulate synaptic weight modulation. It achieves ultra-low energy consumption in the range few-tenth of femto-Joules and can be further reduced by adjusting device parameters. Moreover, to prove the potential of the proposed device as an analog AI accelerator, it was trained and tested for an ANN based underwater image enhancement algorithm on Underwater Image Enhancement Benchmark (UIEB) dataset. The results were compared to the state-of-arts which show that our proposed algorithm works better than most of the previous works on qualitative as well as quantitative grounds. The ultra-low energy consumption of the order of one-tenth of femto-Joules makes the proposed crossbar architecture (AI accelerator) a potential candidate for real-time data intensive AI/ML applications.
Research on patent recommendation method based on graph neural network

Intellectual property transactions have shown a strong growth momentum in recent years, but the patent transaction market has been plagued by the matching degree of consumers and sellers, resulting in frequent problems such as low patent transformation efficiency and poor transaction quality. This paper proposes a method of recommending patents to consumers by experts to improve the environment of patent transactions. Through the analysis of the past transaction information of the patent, the effective path information of the target is extracted. The graph neural network is used to describe the characteristics and semantics among experts, patents and consumers, and then capture the potential weight among them through the common attention mechanism, and then dynamically integrate them to predict the occurrence of recommendation behavior. The paper makes reasonable use of social information and expert information in the transaction, which significantly improves the rationality and accuracy of expert recommendation.
MARTEN: Measurement-Based Trustcheck for Constrained Networks

Due to the digitization more and more devices get connected and are also integrated into our private space. In SmartHomes/Buildings these devices are constrained and, thus, limited with computational capacities but are requested to measure different values (e.g., temperature or humidity) periodically and may trigger others (e.g., ventilation or shading). Due to their deployments and resources security support for communication is very limited calling for trustchecks in gen-eral. Thus, a Measurement-Based Trustcheck for Constrained Networks (MARTEN) was designed and developed in order to include a trustcheck on received measurements of a single node. With this solution trust can be more individually assigned and allows more fine-grained distinction compared to known node- or cluster-based approaches. Thus, MARTEN differs from existing trust- check approaches based on the reputation of reporting nodes or similar. It was evaluated in a SmartHome/Building scenario proving correct labelling of measurements and handling by the network owner (NO). Currently, the integration in an air mobility application is ongoing. We believe MARTEN is feasible here as well as it was designed in a modular structure and, thus, is adaptable with less overhead. Due to MARTEN's automation process the interaction with the NO is decreased to a minimum.
Boreas: A Cost-Effective Mitigation Method for Advanced Hotspots using Machine Learning and Hardware Telemetry

Managing advanced hotspots on modern microprocessors is a critical and worsening issue, affecting performance, product reliability, and device lifetime. Many thermal management techniques focus primarily on remaining below a critical temperature, and while they are successful to that end, they come with a significant performance cost. This cost stems from the large temperature guardbands that are needed to ensure device safety and correct IC operation. These guardbands must be sized to account for multiple factors including 1) control-loop latency, 2) thermal sensor delay, 3) thermal gradients inside timing paths that could result in timing violations, and 4) the instantaneous temperature delta between a temperature sensor and the true peak temperature on the IC. This work demonstrates the need for novel hotspot avoidance techniques that can react quickly and simultaneously account for each of these concerns in order to safely maximize performance. Recently introduced hotspot metrics–Hotspot-Severity and Maximum Local Temperature Difference (MLTD)–are used in order to allow model designers to have a single optimization target that accounts for each of these thermal concerns simultaneously. We present Boreas, a novel hotspot mitigation technique that uses a Machine Learning model implemented in an on-chip specialized hardware accelerator that leverages micro-architectural performance counters. Boreas outperforms existing thermal management techniques while remaining lightweight and well-suited for implementation in hardware. Even with a conservative thermal sensor delay, Boreas is able to predict severity with high precision, resulting in effective hotspot mitigation on unseen workloads. These machine learning models were, therefore, able to select a frequency that was 4.5% better than thermal only models on average, and up to 9.6% higher in the best case, while having the same reliability budget as the thermal models.
Nodes Grouping Genetic Algorithm for Influence Maximization in Multiplex Social Networks

Influence maximization (IM) aims to select a small number of seed users who can maximize the influence of information spread in social networks. The influence maximization problem in multiplex social networks considers the effects of overlapping users between different social networks on spreading the influence across networks. Since nodes in the network have different selection cost, the importance of a node cannot be determined only by the node's influence. This paper proposes a genetic algorithm using a novel node grouping strategy based on the node influence and selection cost, termed NGGA, for multiplex social networks. A node selection operation uses a shielding node set to realize a flexible search. Experimental results on three real multiplex networks demonstrate the effectiveness of the proposed algorithm.
A Copula-Based Linear Model of Coregionalization for Non-Gaussian Multivariate Spatial Data

We propose a new copula model for replicated multivariate spatial data. Unlike classical models that assume multivariate normality of the data, the proposed copula is based on the assumption that some factors exist that affect the joint spatial dependence of all measurements of each variable as well as the joint dependence among these variables. The model is parameterized in terms of a cross-covariance function that may be chosen from the many models proposed in the literature. In addition, there are additive factors in the model that allow tail dependence and reflection asymmetry of each variable measured at different locations and of different variables to be modeled. Permutation asymmetry of different variables can also be obtained, thus providing greater flexibility. The likelihood of the model can be obtained in a simple form and therefore the likelihood estimation is quite fast. We use simulation studies to demonstrate the wide range of dependence structures that can be generated by the proposed model with different parameters. We apply the proposed model to temperature and pressure data and compare its performance with the performance of a popular model from multivariate geostatistics.
Human Machine Co-adaptation using Co-Adaptive Policy Gradient Algorithm

The challenge of dynamic mapping and Human-Machine Co-adaptation (HMCo) is solved by the proposed Co-Adaptive Policy Gradient Algorithm (Co-PGA), a modified version of the Policy Gradient Algorithm (PGA). In this paper, the algorithm is applied to solve a problem where a human is using a joystick to control an object, but the joystick is twisted, which results in a mismatch between the human's desired movements and the machine's output. The results show that the algorithm is successful in compensating for the twisting mismatch by co-adapting to human behavior and determining the best policy to follow the moving target on the screen. The algorithm is model-free and works under the assumption that humans are rational in their behavior. The fact that there is no direct communication between the machine and the human is significant and emphasizes the efficiency of the suggested algorithm. The novel Co-PGA contributes significantly to the field of Human Machine Interaction (HMI) and has the potential to improve the performance of human-machine systems in a wide range of applications.
Stream: A Modeling Framework for Fine-grained Layer Fusion on Multi-core DNN Accelerators

To keep up with the ever-growing performance demand of DNN processing, specialized hardware (HW) accelerators are shifting towards multi-core architectures. Stream is the first open-source design space exploration (DSE) framework for co-optimization of HW architecture and fine-grained scheduling of such multi-core DNN accelerators. Stream supports finegrained layer fusion, to optimally trade-off energy, latency, and/or on-chip memory footprint for constrained edge devices. Validation against three SotA chips, together with a case study on seven HW architectures with different scheduling granularity, demonstrate the reliability and capabilities of Stream. Results show that high-level architectural decisions greatly impact HW efficiency under the fine-grained scheduling paradigm, reducing the energy-delay product from $2.4 \times$ for single-core architectures to up to $30 \times$ for heterogeneous multi-core architectures compared to traditional scheduling at layer granularity. Stream is open-source at github.com/ZigZag-Project/stream.
Coverage Study of a Sustainable IoT System for Industrial Applications

Internet of Things (IoT) devices are very useful to monitor information in industrial scenarios even in areas which have difficult or impossible access through traditional wired connections. Reliable wireless communications can be challenging in industrial environments, where the communications signal is often blocked due to the presence of industrial machinery, with possible intermittent and/or unpredictable blockages. This paper evaluates the coverage characteristics of a low-cost sustainable IoT deployment to monitor an industrial facility dedicated to the design and assembly of automation systems for the food industry. For this purpose, a measurement campaign has been carried out at different points of the industrial site using the aforementioned IoT system. The results show that the system is able to provide enough coverage around the whole area, with a reduced number of outages. Substantial differences in terms of received power and outages are observed when comparing the measurements in a totally empty area and an area with machinery and workers. Finally, the received power measurements have been fitted to a simplified pathloss model, showing a pathloss exponent value consistent with previously reported values for industrial environments.
A Dynamic Binding Method for Situation-aware IoT Services Targeting Proactive BPM

By leveraging IoT data, Business Process Management (BPM) systems can sense the physical world situation and make more accurate decisions proactively, but the current BPM systems lack the effective method to take good use of IoT data. In this paper, a situation-aware dynamic binding method for IoT services targeting proactive BPM is proposed to meet this requirement. The method first establishes a prediction model to predict the bindable IoT services under given situations by constructing an encoder-decoder structure network model using bi-directional gated recurrent units (Bi-GRU) and incorporating an attention mechanism. Then our method creates a Dynamic IoT Service Task (DIT) model for BPM. This enables the BPM system to integrate the prediction model with dynamic switching of IoT services and a parallel running architecture for multiple IoT services. A prototype system is developed and a case study is conducted to evaluate the performance of the method. The study focuses on the safety supervision scenario for the transportation of hazardous Liquefied Natural Gas (LNG) by sea. Results show that the prediction model proposed in this paper outperforms other models on multiple indicators. Also, the case study and experimental results verify the effectiveness of the method.
Extreamly Advanced Cu Interconnect with Selective ALD Barrier for High Performance Logic Device

In this paper, selective atomic layer deposition (ALD) TaN barrier process was described for advanced high performance logic device. Compared to conventional ALD deposited barrier, selective barrier adopted process showed 40% lower via resistance and comparable electrical healthiness at various via test structures. By applying this process, we achieved better chip performance and comparable chip yield using internal foundry product. This result demonstrates manufacturability of a selective barrier for advanced high performance logic device. Keywords—Cu; Barrierless, ALD TaN;
MultiSSE: Static Syscall Elision and Specialization for Event-Triggered Multi-Core RTOS

The implementation of static real-time control systems often allows for extensive compile-time optimizations based on RTOS-aware whole-program analyses. Previous work has shown the high optimization potential of control-flow aware static system-call tailoring, but is restricted to single-core systems due to the inherent problem of an exponentially growing analysis state in multicore settings. We present MultiSSE, a multi-core capable and RTOS-aware static whole-system analysis that makes such analyses also feasible on multi-core systems. MultiSSE exploits structural and optional timing information to analyze the core-level control flows as independently as possible from each other, synchronizing their states only when necessary. Thereby, MultiSSE provides means to realize compile-time deadlock detection, lock elision, and systemcall optimization also on multi-core systems. We evaluate our approach with synthetic benchmarks and a real-world quadrotor application. In all cases, we were able to optimize or even completely elide costly cross-core system calls and system objects.