Miracle-dz/UniFAR-FTUs huggingface dataset:
A Fuzzing Data Generation Method Based on Conditional Generative Adversarial Networks

Testing network protocols is a challenging task, and fuzzing, as one of the mainstream methods in this field, has demonstrated its practical value and validity. However, the complex syntax and state transition of network protocols impose limitations on existing approaches when testing stateful and proprietary protocols, significantly impacting their applicability and efficiency. In this paper, we propose a testing case generation method guided by real-time message traffic, which can not only learn the format of protocol messages and reconstruct semi-valid test cases that conform to the syntax but also respond correctly based on the current state of the protocol. Experimental results demonstrate that our approach provides more effective support for industrial network protocol fuzz testing, compared with several existing methods.
Extracting Actionable Cyber Threat Intelligence from Twitter Stream

Actionable cyber threat intelligence is vital for effective defense. In practice, indicators of compromises (IP addresses or domains) are used to alert potential malicious activities. However, such alerts lack important context for defenders to take effective actions. For example, given an alert concerning an IP address, a defender wants to know what type of malicious act (e.g., phishing website vs. C2) was observed associated with it. What technical manifestations (e.g., modification of a particular registry key) have been observed from the attacker using this IP address? Such context information helps defenders prioritize alerts and quickly determine whether a system has been compromised. Much of such context information exists in real-time information-sharing systems such as messaging apps and forums. This paper describes an approach to extract technical manifestations of attacks from tweets. We have compared our results with the performance of the GPT-3.5- Turbo model and text-embedding-ada-002 model of OpenAI and also created an open-source project to make our tools and data available for the cybersecurity research community [9].
Energy Consumption Estimation of Mobile Networks’ Base Stations Due to Traffic Change

Mobile operators are facing strong challenges regarding the environmental impact of their operations. The International Telecommunications Union (ITU) considered energy consumption as the prime source of Carbon dioxide (CO2) emissions for the whole Information and Communications Technology (ICT) sectors. ITU encouraged all ICT sectors to commit their efforts in order to control their energy consumption and consider renewable energy sources. By the year 2050, CO2 emissions should be fully neutralized as per the Paris climate agreement. The energy consumption of the Radio Access Network (RAN) represents almost 80% of the total mobile network energy consumption. RAN mainly consists of a large number of distributed Base Stations (BSs). There are some factors that affect the total amount of energy consumption of those BSs such as adding more Base Stations (BSs), upgrading existing ones, and the amount of carried traffic. The number of BSs is increasing rapidly to satisfy the growing demand for radio capacity and coverage. Moreover, upgrading BSs to the next generations like 5G is causing a considerable shift in energy consumption for existing BSs. However, the impact of the incremental traffic on BSs energy consumption is difficult to estimate or even measure, unlike the other factors. The objective of this study is to build a model that can estimate the amount of BSs energy consumption due to changes in traffic. This model will help mobile operators to predict the expected BSs energy consumption considering their forecasted traffic growth on existing mobile networks. Both voice and data traffic changes were considered in this model. Historical data of a large sample of 1,011 BSs were used to generate a linear model describing the relationship between energy consumption and both data & voice traffic using multiple regression techniques. This model was then adapted to predict energy consumption on a network scale by considering only the forecasted change in traffic along with the latest historical actual energy data. This adaptation was validated over a cluster of 1,600 BSs and showed maximum absolute error of 3%. Moreover, the inclusion of operators’ historical energy data in the adapted model would make it more universal for use by different mobile operators with different BSs configurations.
Fractional MQAM: Analysis of Ergodic Capacity and Flexible PAPR Under AWGN Channels

This paper presents a system of symbol modulation called fractional M-ary quadrature amplitude modulation (F-MQAM). F-MQAM provides flexibility between conventional MQAM and phase shift keying (PSK). Firstly, it is shown that MQAM has a rectangular structure, which is the same family as amplitude PSK (APSK). In the F-MQAM, several sets of constellations are designed to convey information to the intended recipient where total sum symbols increase due to the multiplicity of sets. Each set of the F-MQAM constellations consists of symbols whose amplitudes are a ratio to one another. Similar results are obtained when part of the incoming information bits are used to identify the constellation set, while the remaining bits are used to identify the symbols to be transmitted from the selected symbol set. At the receiver, both the symbol set and the symbol in the set are jointly detected. As an example, the design and simulation results validate that 8QAM constellation can be used to convey 5 bits. As expected, the peak-to-average power ratio (PAPR) is lowered in F-MQAM due to the reduced peak power levels due to the fractional amplitudes of the signal that are transmitted. Furthermore, it is observed that the ergodic capacity for F-MQAM is higher than the conventional phase shift keying modulation (PSK). In summary, results show that F-MQAM provides flexibility in the quality of service (QoS) tradeoffs.
Evaluating the Potential of Multispectral Sensor Networks based on Deployments in Wheat Fields

The reflection spectra of vegetation hold valuable information on the state of the plants. Analysing data from satellites, airplanes, and Unmanned Aerial Vehicles (UAVs) equipped with multispectral sensors has therefore become very popular in the last decades. While these systems offer global coverage in the case of satellites and high spatial resolution in the case of UAVs, these systems are not suitable for obtaining continuous, real-time measurements. With advances in miniaturization, Wireless Sensor Networks (WSNs) are on the brink of becoming capable to serve this purpose. While different kinds of sensors have been experimentally evaluated for agricultural WSNs, this has barely been done with multispectral sensors. Therefore, we share our results from two deployments of such WSNs. We provide comparisons with other sensing system, investigate the suitability for generating Normalized Difference Vegetation Index (NDVI) timeseries, and sneak a peek at the derivation of yield estimates. Index Terms—Multispectral Imaging, Wireless Sensor Networks, Precision Agriculture
Spatial Distributions and Changes of Aerosol Optical Depth over Eastern and Central China

Seasonal changes of aerosol optical depth over the Central and Eastern China,the Sichuan Basin,the Yangtze River Delta and the Pearl River Delta from MODIS data from March 2000 to February 2009 are analyzed and compared.It is found that in the 9 years the mean AOD over every region in all seasons has significant increasing trends,except for Sichuan Basin in autumn.The highest AOD occurs over the Yangtze River Delta in both annual and seasonal averages,and the increasing rate of annual mean is up to 1.82% per year.The AOD over Sichuan basin ranks second,and then is North China,the Pearl River Delta has the lowest annual average AOD.The fastest increase of AOD is in summer,the difference between spring and summer becomes smaller,and some areas,such as North China,AOD in summer is higher than that in spring.AOD data are the least and their growth rate are the lowest in winter.Thus,the gap between winter and other seasons becomes larger.The AOD over some regions in some seasons,such as over the North China in summer and autumn,the Sichuan Basin in spring,summer and autumn,and the Pear River Delta in autumn,over the period of 2006 to 2008 have continuous decreasing trends.These results will benefit the studies of regional climate change and air pollution variation.
Ultra-Wideband Terahertz 3D Imaging with Aspherical Telecentric f-θ Optics

In this contribution we report on the development of a mobile ultra-wideband frequency-modulated continuous-wave (FMCW) terahertz imaging device with an integrated f-θ lens for 3D image acquisition. The objective lens is designed to operate with the broad bandwidth of 1.65 THz of the optoelectronic FMCW radar. For the design of the f-θ lens, the optical properties of three different materials for lens fabrication were investigated using the optoelectronic FMCW radar.
Automatic Classification of C-SAM Voids for Root Cause Identification of Bonding Yield Degradation

Wafer-level direct bonding technology is a key process for the production of backside illuminated (BSI) CMOS image sensor (CIS). Usually, constant-depth mode scanning acoustic microscope (C-SAM) 300mm wafer images are acquired and defect size distribution is provided to monitor defects that degrade bonding yield. This acquisition technique is used as it is nondestructive and is able to capture the full wafer. Current solutions are not providing information detailed enough to identify the root cause of this degradation. A consequence is that most measurements and classification is currently done manually which take time and have reproducibility issues. In this paper, we propose a rule-based method for the classification of the defects and automatic segmentation of the defects to extract precise measurements depending on the type of defect. The classes of defects are precisely defined based on their origin in the process. All these information will allow to reduce the time to analyze the images and improve the precision and consistency of the analysis. These results will allow to identify more precisely the defects thus the causes of yield degradation during bonding process.
Challenging Weather for License Plate Detection: Impaired visibility

License plate detection is crucial for automatic toll collection, surveillance systems, and traffic management. This paper presents the findings of our investigation employing the YOLOv8 deep learning framework to detect license plates in challenging scenarios such as heavy haze, rain, snow, and sandstorms that severely limit camera operation and computer vision algorithms for traffic scene perception. Rain flow and ice reflections on roadways can cause enormous detection errors, which harm intelligent visual traffic systems. The major goal should be to achieve a high level of accuracy while maintaining real-time inference speed. To ascertain the dependability and robustness of the YOLOv8 system, a thorough assessment procedure was undertaken. Initially, a synthetic dataset was created to emulate challenging settings, such as the presence of haze and other meteorological phenomena, which are often encountered in real-life contexts. Thereafter, a simulation was conducted to evaluate the performance of the YOLOv8 model under poor weather conditions. Our method’s remarkable frame processing speed, reaching up to 70 frames per second on conventional hardware, renders it well-suited for real-time applications that need efficient and quick license plate identification. In comparison to established state-of-the-art methodologies, our approach demonstrates superior performance in terms of both accuracy and frame processing time, thereby representing a substantial advancement in the field of license plate detection. A variety of experiments have been undertaken on the CCPD dataset and its several subsets, including the Base, DB, Blur, FN, Rotate, Tilt, and Challenge datasets, which offer a diverse selection of license plate changes. The relevant CCPD subsets yield exceptional outcomes, with accuracies of 99.62%, 96.8%, 98.9%, 99.72%, 99.71%, 99.5%, and 99.1%, respectively.
IMPLEMENTATION OF J2000 HYDROLOGICAL MODEL IN THE WESTERN PART OF JAVA ISLAND, INDONESIA

Climate and land cover changes are currently taking place globally. The issues of climate changes are also noticed in the Jakarta, Indonesia. In Jakarta, climate changes are indicated by the increasing of air temperature by 1.2°C in the 20th century, more intense rainfall, long-term drought and sea level rise at a rate of 1-8mm/year. Extreme runoff due to high intensity rainfall and landuse changes causing Jakarta is vulnerable to inundation. Nearly every year the Jakarta is inundated during the wet season. Several river basins are flowing across the Jakarta city area. These are Ciliwung, Citarum and Cisadane. Citarum is the largest river basin in the western part of Java Island. Its area is 8058km2. The upper area of Citarum basin is located in Bandung, the capital city of West Java Province. In this study, the upper part of Citarum basin (UCB - upper Citarum basin) was selected as a representative basin for hydrological modelling study. A setup of hydrological modelling in UCB is the primary discussion in this paper. The total area of UCB is 1821km2 (23% of entire Citarum basin). The elevation ranges from 667 to 2222m. The slope is varying from 0-51° with 8° average. The landscape is dominated by agricultural with 60.7% of total landuse with 77.2% volcanic rocks of total geological condition and andosol soil type. The average annual rainfall is 2168mm. High rainfall typically occurs during November to April during the west monsoon so-called wet season. 21.4°C, 30.7°C, and 25.2°C are measured as an average minimum, maximum and mean air temperature in this area. A distributed hydrological model named J2000 was performed to reconstruct the hydrological dynamics in UCB. The inputs of the model are hydrometeorology data (precipitation, air temperature, humidity, wind speed, sunshine duration), a
PREDICTION OF BEACH CHANGES AROUND ARTIFICIAL REEF USING BG MODEL

On the Kaike coast, one of the twelve detached breakwaters was converted into an artificial reef. After the conversion, the tombolo behind the structure was reduced in size because of the generation of shoreward currents on the artificial reef, and severe scouring occurred at the opening between the artificial reef and the existing detached breakwater owing to the development of rip currents. To investigate this, the BG model (a 3-D model for predicting beach changes based on Bagnold’s concept) proposed by Serizawa and Uda was improved to include the effect of not only waves but also strong nearshore currents. Also, the increase in the depth of closure due to currents was considered in the model. The predicted and measured beach changes around an artificial reef were in good agreement.
High CMRR and Low Output Offset Current Mode Instrumentation Amplifier for Wearable Ballistocardiography Application

Continuous blood pressure monitoring necessitates the use of small wearable ballistocardiography (BCG) devices. This paper presents the design of a current mode instrumentation amplifier (CMIA) as an analog front-end in BCG applications. The circuit uses a differential implementation of an output current subtraction CMIA to achieve a high common mode rejection ratio (CMRR). In addition, chopper stabilization and a DC servo loop (DSL) are implemented to reduce the output offset and offset drift. The designed circuit achieved a minimum CMRR of 135.46 dB, a maximum output offset of 8.017 μV, and a maximum offset drift of 106.433 nV/°C. This results in a low percentage error for BCG signals at the output. In addition, the CMIA operates using a supply voltage of 1.1V, a gain of 47 dB, and a power consumption of 23.88 μW.
Effect of the Variation of Atmospheric Refractive Index on Signal Transmission for Digital Terrestrial Television in Jos, Nigeria

In this paper, the effect of the variation of atmospheric refractive index on digital terrestrial television signal is examined. Combined measurements of signal strength and atmospheric parameters - such as temperature, relative humidity and atmospheric pressure - were obtained using network analyzer and weather station respectively. Data obtained over the period of twelve months were processed and analyzed based on International Telecommunication Union (ITU) recommendations. Results obtained revealed mild variation in the atmospheric parameters resulting in the fluctuation of atmospheric refractive index, which in turn affects the transmitted digital terrestrial television (DTT) signal. A monthly summary of these variations are as thus: from January (63.82dBuVm), February (55.81 dBuV/m), March (59.99 dBuV/m), April (62.39 dBuV/m), May (56.31 dBuV/m), June (54.54 dBuV/m), July (49.66 dBuV/m), August (50.80 dBuV/m), September (51.64 dBuV/m), October (51.00 dBuV/m), November (56.34 dBuV/m) and December (60.51 dBuV/m). It was discovered that fluctuations in refractive index contribute to atmospheric interference effects such as fading and multipath propagation. This results in loss of signal strength and increased errors in signal reception, hence, the observed variation in received signal strength for all the months under review.
Terahertz Torsional Dynamics and Their Influence on Electron-Phonon Coupling in Organic Semiconductors

Terahertz vibrational dynamics are the primary cause of detrimental electron-phonon coupling in organic semiconducting crystals. This has made these materials prime candidates for study using terahertz vibrational spectroscopy. In this work, we highlight how terahertz torsional dynamics in thiophene-functionalized organic semiconductors play a major role on charge transport properties in these materials. Overall, this work highlights the ability to guide the rational design of new materials using crucial insight from terahertz time-domain spectroscopy studies.
MIMO Virtual Array Design for mmWave 4D-Imaging Radar Sensors

The recently developed 4D-Imaging mmWave MIMO radars have significant advantages over conventional radar sensors because they offer a large virtual array in both azimuth and elevation. However, the physical placement of transmit and receive antennas to achieve the desired virtual array, while considering cost and efficiency is not intuitive. In this paper, we propose an optimization framework for optimally placing the transmit and receive array physical elements. The proposed framework obtains a sequence of optimal placements based on the coordinate descent by defining a desired virtual array and number of transmit and receive antenna elements as the objective. The objective function is then monotonically minimized in each iteration of the proposed algorithm, converging to a solution that guarantees the desired number of transmit and receive antenna elements, while the obtained virtual array is as close to the desired virtual array as possible. The simulation results are validated by simulating the virtual array of multiple commercially available 4D-Imaging radar products.
Task Scheduling and Resource Allocation in Cloud Computing : A Review and Analysis

Effective resource allocation is a critical aspect of cloud computing, ensuring efficient utilization, cost-effectiveness, and optimal system performance. Ongoing research focuses on innovative techniques and algorithms to address resource allocation challenges in the evolving cloud computing landscape. This paper contributes to the field by conducting a survey and analysis of resource allocation algorithms and strategies. The authors aim to identify and compare various algorithms used in cloud computing resource allocation. Additionally, the paper provides a selection of integrated and scalable cloud environment tools, both open-source and cost- effective, for researchers in the field. These tools are analyzed and compared based on their features, offering valuable insights to cloud computing researchers.The study concludes that the fixed allocation approach suits predictable workloads, while dynamic allocation offers enhanced efficiency, resource utilization, and scalability. The hybrid system combines the benefits of cloud computing with stability and predictability. To effectively utilize simulators for resource allocation, it is crucial to define simulation goals, and evaluate and choose suitable simulators based on maturity, features, and community support. Scalability, compatibility, performance testing, reliability validation, limitations awareness, maintenance planning, and cost considerations are important factors to be considered.
Towards the Development of a Security Threat Identification Framework for UAV Information Infrastructure

Information sharing in emerging UAVs (Unmanned Aerial Vehicle) requires high speed and secure data infrastructure. This draws our attention to the several raised threats to the UAV device and information asset. There are several isolated threat modelling approaches that need to be evaluated and casted into a unified framework for threat modelling for information infrastructure for UAVs. This study addresses this research question, analyses the existing threat models and proposes a unified framework. The proposed framework focuses on data flows, threat points, threat levels, attack agents and threat vectors related to UAV information infrastructure. This paper further reports important insights and future directions about the usage and adaptation of this unified threat-modelling framework.
Mapping the DNA of Urban Neighborhoods: Clustering Longitudinal Sequences of Neighborhood Socioeconomic Change

The spatial pattern of longitudinal trends in neighborhood socioeconomic dynamics has long been implied by traditional urban models dating back to the Chicago School; however, empirical studies beyond the mapping of change between two points in time are surprisingly limited. This article introduces a methodology to the study of spatial–temporal patterns of neighborhood socioeconomic change. The approach first involves establishing discrete classes of neighborhoods following a k-means clustering procedure and then applies a sequential pattern mining algorithm to determine the similarity of longitudinal sequences. Sequences are then clustered to derive a typology of neighborhood trajectories. The method is employed in an empirical analysis of neighborhood change from 1970 to 2010 for all census tracts in the cities of Chicago and Los Angeles. In Chicago, this time period was marked by a sustained process of center city revitalization through two distinct upgrading processes, whereas in Los Angles, neighborhood upgrading largely came in the form of suburban upgrading. The spatial structure of neighborhood dynamics in Chicago resembled patterns described by Chicago School theorists, whereas the dynamics of Los Angeles deviated from this ordered regularity.
Ontology-based data management

Ontology-based data management aims at accessing and using data by means of an ontology, i.e., a conceptual representation of the domain of interest in the underlying information system. This new paradigm provides several interesting features, many of which have been already proved effective in managing complex information systems. On the other hand, several important issues remain open, and constitute stimulating challenges for the research community. In this talk we first provide an introduction to ontology-based data management, illustrating the main ideas and techniques for using an ontology to access the data layer of an information system, and then we discuss several important issues that are still the subject of extensive investigations, including the need of inconsistency tolerant query answering methods, and the need of supporting update operations expressed over the ontology.