Miracle-dz/UniFAR-FTUs huggingface dataset:
Investigation of FlexAlgo for User-driven Path Control

This paper examines the Flexible Algorithm (FlexAlgo) for its potential to enable user-driven path control in intra-domain Segment Routing (SR) enabled networks. FlexAlgo is a relatively new approach to intra-domain routing that allows multiple custom algorithms to coexist within a single domain. This capability has the potential to provide users with greater control over the paths their data takes through a network. The research includes a thorough investigation of the FlexAlgo approach, including an examination of its underlying techniques, as well as a practical implementation of a FlexAlgo-based solution. We depict performed experiments where we implemented FlexAlgo in three different scenarios. We also present how we developed an automated tool for users to control traffic steering using preferred metrics and constraints. The results of this investigation demonstrate the capabilities of FlexAlgo as a means of enabling user-driven path control and therefore increase security and trust of users towards the network.
"Smart Parking Navigator": An IoT Fog-Based System for Realtime Car Parking Detection

Obtaining a parking spot is a significant obstacle for many citizens. With the massive increase in the number of vehicles in crowded cities, finding an unoccupied parking slot may consume time and effort and result in more traffic jams. Travelers often utilize conventional methods to locate a parking space near their destination. They navigate the entire area to locate the nearest open space. The mission is indeed aggravating and difficult. Due to these issues, "Smart Parking Navigator," an IoT fog-based smart parking system, is developed to facilitate searching available roadside parking spaces. The system monitors a predetermined parking space and provides users with real-time data about the occupancy of slots. The system employs image processing techniques, object detection algorithms, and OpenCV to determine whether a parking space is occupied or vacant. A Raspberry Pi camera connected to a Raspberry Pi 4 computer is used for real-time detection. Each parking space's status is stored in a Firebase Realtime Database. A mobile application for Android has been developed to assist clients in locating available parking. The application is connected to the database to retrieve the real-time status of each position. Once the user launches the application, the interface will visualize the condition of each parking slot. The solution is a cost-effective alternative to specialized and costly systems that has the potential for future commercial development.
Analysis of Arctic Buoy Dynamics using the Discrete Fourier Transform and Principal Component Analysis

Sea-Ice drift affects various global processes including the air-sea-ice energy system, numerical ocean modelling, and maritime activity in the polar regions. Drift has been investigated via various technologies ranging from satellite based systems to ship or ice-borne processes. This paper analyses the dynamics of sea-drift in the Arctic over 2019–2021 by Fourier Analysis and Principal Component Analysis of displacement data generated from the drift tracks of Ice-Tethered Profilers. We show that the frequency characteristics of drift support the notion that it is a function of both slowly varying processes, and higher frequency, random, forcing. In addition, we show that displacement data features high correlation between deployment locations and, consequently, suggest that there is scope for the optimisation of profiler deployment locations and for the reduction in number of instruments required to capture the displacement characteristics of drift.
Testing Database Systems via Differential Query Execution

Database Management Systems (DBMSs) provide efficient data retrieval and manipulation for many applications through Structured Query Language (SQL). Incorrect implementations of DBMSs can result in logic bugs, which cause SELECT queries to fetch incorrect results, or UPDATE and DELETE queries to generate incorrect database states. Existing approaches mainly focus on detecting logic bugs in SELECT queries. However, logic bugs in UPDATE and DELETE queries have not been tackled. In this paper, we propose a novel and general approach, which we have termed Differential Query Execution (DQE), to detect logic bugs in SELECT, UPDATE and DELETE queries of DBMSs. The core idea of DQE is that different SQL queries with the same predicate usually access the same rows in a database. For example, a row updated by an UPDATE query with a predicate φ should also be fetched by a SELECT query with the same predicate φ, If not, a logic bug is revealed in the target DBMS. To evaluate the effectiveness and generality of DQE, we apply DQE on five production-level DBMSs, i.e., MySQL, MariaDB, TiDB, CockroachDB and SQLite. In total, we have detected 50 unique bugs in these DBMSs, 41 of which have been confirmed, and 11 have been fixed. We expect that the simplicity and generality of DQE can greatly improve the reliability of DBMSs.
How Do Developers' Profiles and Experiences Influence their Logging Practices? An Empirical Study of Industrial Practitioners

Logs record the behavioral data of running programs and are typically generated by executing log statements. Software developers generally carry out logging practices with clear intentions and associated concerns (I&Cs). However, I&Cs may not be properly fulfilled in source code as log placement - specifically determination of a log statement's context and content - is often susceptible to an individual's profile and experience. Some industrial studies have been conducted to discern developers' main logging I&Cs and the way I&Cs are fulfilled. However, the findings are only based on the developers from a single company in each individual study and hence have limited generalizability. More importantly, there lacks a comprehensive and deep understanding of the relationships between developers' profiles and experiences and their logging practices from a wider perspective. To fill this significant gap, we conducted an empirical study using mixed methods comprising questionnaire surveys, semi-structured interviews, and code analyses with practitioners from a wide range of companies across a variety of industrial domains. Results reveal that while developers share common logging I&Cs and conduct logging practices mainly in the coding stage, their profiles and experiences profoundly influence their logging I&Cs and the way the I&Cs are fulfilled. These findings pave the way to facilitate the acceptance of important logging I&Cs and the adoption of good logging practices by developers
Expanding Edge Computing deeper into Beyond 5G Radio Access Networks

Relevant services envisaged for beyond 5G(B5G) systems, such as extended reality and holographic communications, present stringent user experience requirements with high computational and communication demands. While edge computing aims to address the computation requirements by offloading the computational tasks to edge servers close to the user, the communication will leverage the technologies developed for 5G New Radio together with an unprecedented level of network densification. This paper advocates for deploying relays equipped with edge computing capabilities. The potentials of this approach for B5G are identified and a system model is presented to characterize both computational and communications perspectives. Based on this, results are provided to show the benefits and limitations of the proposed approach from a system-level perspective.
Research on the Application of Long Short-Term Memory Neural Network in Power Load Forecasting

term power load forecasting plays a crucial role in power grid scheduling arrangement and power market trading. The more accurate the forecast is, the better the utilization rate of power generation equipment and the effectiveness of economic dispatch can be improved. However, traditional load forecasting methods have limitations in predicting accuracy, making it difficult to achieve more precise load prediction results. Artificial intelligence forecasting methods such as ANN model and GPR model cannot process the time-series load data effectively, while RNN often encounters gradient disappearance or gradient explosion during the processing of time-series data. Therefore, this paper proposes a short-term power load forecasting model based on long short-term memory neural networks, and compares its forecasting results with those of RT, GPR, and ANN algorithms. The results demonstrate that the proposed model has lower error indices and higher forecasting accuracy in comparison with other traditional forecasting models.
Controlling particle properties in YBa 2 Cu 3 O 7 − δ nanocomposites by combining PLD with an inert gas condensation system

The critical current density J c in YBa 2 Cu 3 O 7 − δ thin films, which limits their application in external magnetic fields, can be enhanced by the introduction of artificial pinning centers such as non-superconducting nanoparticles inducing additional defects and local strain in the superconducting matrix. To understand the correlation between superconductivity, defect structures and particles, a controlled integration of particles with adjustable properties is essential. A powerful technique for the growth of isolated nanoparticles in the range of 10 nm is dc-magnetron sputtering in an inert gas flow. The inert gas condensation (IGC) of particles allows for an independent control of both the particle diameter distribution and the areal density. We report on the integration of such gas-phase-condensed HfO 2 nanoparticles into pulsed laser deposited (PLD) YBa 2 Cu 3 O 7 − δ thin film multilayers with a combined PLD-IGC system. The particles and the structure of the multilayers are analyzed by transmission electron microscopy on cross-sectional FIB lamellae. As a result of the IGC particle implementation, randomly as well as biaxially oriented BaHfO 3 precipitates are formed in the YBa 2 Cu 3 O 7 − δ thin films. With as few as three interlayers of nanoparticles, the pinning force density is enhanced in the low-field region.
Compatibility Issue Detection for Android Apps Based on Path-Sensitive Semantic Analysis

Android API-related compatibility issues have be-come a severe problem and significant challenge for app devel-opers due to the well-known Android fragmentation issues. To address this problem, many effective approaches such as app-based and API lifetime-based methods have been proposed to identify incompatible API usages. However, due to the various implementations of API usages and different API invoking paths, there is still a significant weakness of existing approaches, i.e., introducing a massive number of false positives (FP) and false negatives (FN). To this end, in this paper, we propose PSDroid, an automated compatibility detection approach for Android apps, which aims to reduce FPs and FNs by overcoming several technical bottlenecks. Firstly, we make substantial efforts to carry out a preliminary study to summarize a set of novel API usages with diverse checking implementations. Secondly, we construct a refined API lifetime database by leveraging a semantic resolving analysis on all existing Android SDK frameworks. Based on the above two key phases, we design and implement a novel path-sensitive semantic approach to effectively and automatically detect incompatibility issues. To demonstrate the performance, we compared with five existing approaches (i.e., FicFinder, ACRYL, CIDER, IctAPIFinder, and CID) and the results show that PSDroid outperforms existing tools. We also conducted an in-depth root cause analysis to comprehensively explain the ability of PSDroid in reducing FPs and FNs. Finally, 18/30 reported issues have been confirmed and further fixed by app developers.
Electromagnetic Design of an Inductive Wireless Power Transfer System for Endoscopic Capsule

The power and energy demand for the instrumentation implemented in endoscopy capsules is such that a power supply from electrochemical cells is inappropriate. In this paper the electromagnetic design of the Wireless Power Transfer (WPT) system of the autonomous multimodal implantable endoscopic capsule designed within the project AUTOCAPSULE is firstly described. In particular, the design of the inductive link between the transmitting and the receiving coils is presented by showing the main involved parameters. The choice of wire and number of turns for transmitting and receiving coils is discussed by taking into account the power transferred to the load and the design constraints on available space and tissue absorption. The problem of patient exposure to nonionizing electromagnetic fields is treated with a 3-D FEM simulation. The WPT conceived system allows to satisfy the project requirements, i.e., a power transfer to the end-user of 150 mW at a distance up to 15 cm, showing as well compliance with SAR regulations.
Link Quality Estimation in Wireless Software Defined Network with a Reliable Control Plane

The Industrial Internet of Things (IIoT) has now emerged for many industrial applications. However, many critical applications require high reliability and bounded end-to-end latency. Fortunately, scheduled wireless networks such as IEEE 802.15. 4-TSCH try to orchestrate finely the transmissions. In particular, the Software-Defined Networking (SDN) paradigm is promising to concentrate the intelligence of the network in a controller and to simplify the functions achieved by the motes. SDN controller relies on link quality information of wireless devices to construct the routing topology of the network and to provision adequate radio resources for each link. We propose here an accurate link quality estimation scheme based on the SDN-TSCH, as an SDN solution for IEEE 802.15. 4-TSCH network. We exploit the centralized view of the SDN controller to transmit packets of link quality estimation in a collision-free manner. Moreover, we investigate the performance of the control plane of SDN-TSCH through different shared, dedicated, and hybrid (mix of shared and dedicated cells) approaches. We figured out that the dedicated control plane provides high reliability and fast convergence for wireless SDN networks.
Linear Coding for AWGN Channels With Noisy Output Feedback via Dynamic Programming

The optimal coding scheme for Additive White Gaussian noise (AWGN) channels with noisy output feedback has been unknown for several decades. The best-known linear scheme is by Chance and Love, where the coefficients of the linear scheme are numerically optimized based on unique observations. In this paper, we introduce a new class of linear coding schemes, called sequential linear schemes, where the encoder sequentially updates a linear state process based on feedback. We then derive the optimal scheme within this class, in a closed form, by formulating a novel Markov decision process and solving it via dynamic programming. We demonstrate that our scheme outperforms the Chance-Love scheme for channels with noisy feedback and coincides with the Shalkwijk-Kailath scheme for channels with noiseless feedback. This problem is an instance of decentralized control without any common information and, to the best of our knowledge, the first such scenario where we can derive analytical solutions using dynamic programming.
Identification of IoT Devices Using A Multiple Transformers Single Estimator (MTSE) Learning Pipeline

Adopting a malicious IoT device connected to a company’s network is a hazard to the enterprise. Firms must be able to distinguish between legitimate devices connected to their network and those that are threats. It is essential to comprehend the device’s properties and recognize it once it has been added to a network. Various machine learning algorithms for device identification have been introduced for this task, including Support vector machines, Adaboost classifiers, Logistic regression, KNeighbors classifiers, decision trees classifiers, XGBoost, Gradient boosting classifiers, and Random forests. However, there is a research gap in identifying the best-performing classification pipeline that adapts to the features and types of the associated data in the device identification problem while providing high recognition accuracy. For this purpose, we propose a Multiple Transformers Single Estimator (MTSE) Learning pipeline that captures the best-performing feature sets and the best-performing machine learning algorithm for the recognition problem. The MTSE uses transformers and estimators; the transformers filter or modify the data. The estimator learns and builds a recognition model from the data. Experimental results through different transformers and estimators achieved an accuracy of 89.50% compared to the existing classification methods.
Software Defined Wide Area Networks: Current Challenges and Future Perspectives

Software Defined Wide Area Network (SD-WAN) is rapidly becoming an attractive solution for enterprise networks as it offers several benefits such as cost efficiency, increased bandwidth, and improved application performance. However, SDWAN also brings new challenges that must be addressed for effective deployment (i.e. openness, interoperability, network automation, monitoring, QoS guarantees, scalability and security). In this paper, we highlight the criticalities of this technology and analyze the solutions proposed by the state of the art. We then present a scalable framework based on distributed Reinforcement Learning agents for guaranteeing availability and QoS to business applications. We believe that our work provides valuable insights into the opportunities and challenges of SD-WAN technology and offers new perspectives for future research in this area.
A New K Best Sphere Decoder in 16 × 16 MIMO System using Deep Learning Algorithm

In this paper, a new $\boldsymbol{K}$ best sphere decoder is proposed for a $16\times 16$ MIMO receiver, in which the radius is determined by a deep learning mechanism using neural networks. The modified $\boldsymbol{K}$ best sphere decoders are having performances very close to maximum likelihood, with significantly reduced computational complexities as compared to that of the conventional detection techniques. The significant improvement in the achieved results is because of the deep learning algorithm's ability to limit the number of codewords at the receiver. The efficiencies of the resultant deep learning-based sphere decoder are evaluated in terms of spectral efficiency and symbol error rate. The effectiveness of the proposed algorithm is supported through the simulations carried on MATLAB software for $16\times 16$ MIMO systems, using 16 QAM modulation.
Tilted Pentagon with Rectangular Slotted Patch Two-Port MIMO Antenna for 28GHz 5G mm-wave band Applications

A microstrip antenna with a tilted pentagon shape etched with an optimized rectangular slot is presented to radiate electromagnetic energy in a 28 GHz millimeter-wave band of 5G wireless communication technology. $12\times 12\text{mm}$ sized Roger RT/5880-type substrate with height $\mathrm{h}=0.254$ mm is used having dielectric constant $(\in_{T})$ of 2.2 and loss tangent(tan $\delta$) of 0.0009. The design was initiated with a compact conventional microstrip antenna followed by the slotted patch for the 28 GHz band. The slot was introduced in the patch to improve the matching as well as the impedance bandwidth for the 28 GHz band. The single element is then converted to dual-port multiple input-multiple outputs (MIMO) by placing the elements adjacently without any decoupling structure providing isolation of more than 21dB in the frequency range of 27.7GHz-28.2GHz making it applicable for satellite communication, security, and multimedia. The tilted pentagon MIMO antenna provides a gain of 10dBi along with good diversity performance parameters as Envelope Correlation Coefficient(ECC) is less than 0.013, Diversity Gain(DG) is greater than 9.999 dB, Total Active Reflection Coefficient(TARC) is less than zero dB, and Channel Capacity Loss(CCL) of less than 0.4b/s/Hz.
Energy Efficient Routing Methods in Wireless Body Area Networks: A Brief Study

In these days, the concept of WBAN is widely used that is also known as Wireless Body Area Network. This kind was created once wireless sensor networks reached a particular level of development and maturity. This was probably caused by the significant advancement in wireless comfort scientific knowledge and little electronic component parts. Undoubtedly, this type of research is receiving a lot of attention these days because of its potential applications in the real world, particularly in the field of healthcare. Today, a simple sensor can be attached to a person's body to record various mental factors. This sensor can then transmit the information it collects to another device, which can then capture more significant behaviours. As WBANs typically rely on battery-powered sensors, optimizing energy consumption is essential to ensure the longevity of the sensors' battery life and the overall efficiency of the network. In order to increase energy efficiency and lower the power consumption of the sensors themselves, we examine the most efficient routing protocols utilized in WBAN in this work.
MULTI-OUTPUT LEAST SQUARES TSVR

In order to improve the regression accuracy of MLS-SVR presented by Xu et al. [10] with a fast training algorithm, this paper proposes a new regression method named as multi-output least squares twin SVR (MLS-TSVR) for multi-output problems. The main advantage of the proposed method is that the relations among output weight vectors are considered as a whole rather than decomposed in MLS-SVR. Experimental results show that MLS-TSVR has a good property on regression accuracy
A Robot Hand with Capacitive Tactile Sensor for Object Recognition using Support Vector Machine

Touch sensing is essential for humans to perceive objects. Tactile sensing is also important for robot hand manipulation as it provides information on surface properties and interactive forces at contact points between robot fingers and an object. This paper presents a robot hand grasping system equipped with a novel capacitive tactile sensor. The sensor is integrated with a high-precision digital-analog hybrid chip based on a novel R-SpiNNaker architecture. According to the tactile dataset obtained from this system, object recognition is performed by the support vector machine (SVM) algorithm. The results show that superior performance is achieved by SVM with kernel of radial basis function, with accuracy of 98.6%.
BFTDETECTOR: Automatic Detection of Business Flow Tampering for Digital Content Service

Digital content services provide users with a wide range of content, such as news, articles, or movies, while monetizing their content through various business models and promotional methods. Unfortunately, poorly designed or unpro-tected business logic can be circumvented by malicious users, which is known as business flow tampering. Such flaws can severely harm the businesses of digital content service providers. In this paper, we propose an automated approach that discov-ers business flow tampering flaws. Our technique automatically runs a web service to cover different business flows (e.g., a news website with vs. without a subscription paywall) to collect execution traces. We perform differential analysis on the execution traces to identify divergence points that determine how the business flow begins to differ, and then we test to see if the divergence points can be tampered with. We assess our approach against 352 real-world digital content service providers and discover 315 flaws from 204 websites, including TIME, Fortune, and Forbes. Our evaluation result shows that our technique successfully identifies these flaws with low false-positive and false-negative rates of 0.49% and 1.44%, respectively.