Miracle-dz/UniFAR-FTUs huggingface dataset:
Sudden Death

The Sudden Death expansion for Blood Bowl: Team Manager – The Card Game contains three new teams that belong to the Dark Sorcery Syndicate (DSS), a new subdivision of the Team Manager’s Union. This expansion adds new scoring opportunities with Cabalvision Contracts while keeping things unpredictable with Enchanted Balls. Additionally, this expansion adds new cards to your base game decks with new Staff Upgrades, Team Upgrades, Highlights, and Spike! Magazine cards.
Precoding Design for Key Generation in Near-Field Extremely Large-Scale MIMO Communications

This paper investigates an artificial randomness-based key generation scheme in near-field extremely large-scale multiple-input multiple-output (XL-MIMO) communications, aiming to overcome the limitations of static wireless chan-nels in generating secret keys. The proposed approach introduces artificial randomness via the pre coding vector, enabling legitimate users to generate secret keys while preventing eavesdropping. To maximize the secret key rate (SKR), the singular value decomposition (SVD) method is utilized to identify the legitimate subspace for generating secret keys and its orthogonal subspace to thwart eavesdropping. Additionally, a Dinkelbach method-based power allocation algorithm is designed to inject noise into both the legitimate subspace and its orthogonal subspace. Our simulation results validate the efficiency of the proposed key generation scheme, demonstrating its superiority over existing approaches.
Modulation Recognition of Aliasing Electromagnetic Signal Based on Improved Capsule Network

As the fifth-generation (5G) /the sixth-generation (6G) communication technologies evolve rapidly, the electromagnetic spectrum has become increasingly congested, resulting in signal aliasing interference. This poses a significant challenge for signal modulation identification. To effectively address this issue, we present a deep learning model that combines the attention based mechanism of Capsule Network (CapsNet) with Long and Short-term Memory Networks(ACLNet). Firstly, we transform the aliasing signal recognition problem into a multi-label classification task by adjusting the activation and loss functions of the output layer. Secondly, we incorporate an attention mechanism that enables ACLNet to automatically concentrate on the most relevant information for the task, thereby enhancing the model's performance and generalization. Furthermore, by leveraging the structural benefits of CapsNet and Long and Short-term Memory Networks(LSTM), ACLNet is better equipped to capture the signal's time-frequency characteristics and hierarchical structure. Finally, we adopt a dynamic threshold to determine output labels to adapt to the variations in SNR. The experimental results demonstrate that our proposed ACLNet exhibits significant recognition performance advantages in multi-signal aliasing scenarios.
Constructing an Annotated Story Corpus: Some Observations and Issues

This paper discusses our ongoing work on constructing an annotated corpus of childrens stories for further studies on the linguistic, computational, and cognitive aspects of story structure and understanding. Given its semantic nature and the need for extensive common sense and world knowledge, story understanding has been a notoriously difficult topic in natural language processing. In particular, the notion of story structure for maintaining coherence has received much attention, while its strong version in the form of story grammar has triggered much debate. The relation between discourse coherence and the interestingness, or the point, of a story has not been satisfactorily settled. Introspective analysis on story comprehension has led to some important observations, based on which we propose a preliminary annotation scheme covering the structural, functional, and emotional aspects connecting discourse segments in stories. The annotation process will shed light on how story structure interacts with story point via various linguistic devices, and the annotated corpus is expected to be a useful resource for computational discourse processing, especially for studying various issues regarding the interface between coherence and interestingness of stories.
Designing Social Good Semantic Computing Architectures for the Long Tail: Case Studies, Evaluation, and Challenges

Many real-world systems, especially systems characterized by high social activity (such as the Web), tend to obey power law distributions and thereby have a significant ‘long tail’. We argue that researching, developing and designing semantic computing systems for the long tail, especially dependent on inductive AI, constitutes an important class of problems, not least because the long tail is challenging both technically and socially. By its very nature, the long tail is irregular, testing the generalization capabilities of the state-of-the-art, especially in architectures and interfaces that are built on some form of machine learning or statistical inference (including large language models). As machine learning and generative AI continues to be integrated into more front facing systems, the issue of the long tail cannot be ignored by either the systems engineering or the AI communities. We present two case studies with important social consequences (fighting human trafficking online, and managing information effectively and in real-time during humanitarian crises) where semantic computing and AI platforms specifically designed to handle long-tail challenges find critical application, and sometimes with drastically different design choices compared to designing only for the short tail (with the main goal of maximizing average accuracy).
Using Crowd-Based Data Selection to Improve the Predictive Power of Search Trend Data

Large-scale data generated by crowds provide a myriad of opportunities for monitoring and modeling people's intentions, preferences, and opinions. A crucial step in analyzing such "Big Data" is identifying the relevant data items that should be provided as input to the modeling process. Interestingly, this important step has received limited attention in previous research. This paper proposes a novel crowdbased approach to this data selection problem: leveraging crowds to amplify the predictive capacity of search trend data (Google Trends). We developed an online word association task that taps into people's "thought-collection" process when thinking about a focal term. We empirically tested this method in two domains that have been used as test-beds for prediction. The method yields predictions that are equivalent or superior to those obtained in previous studies (using alternative data selection methods) and to predictions obtained using various benchmark data selection methods.
Fintech Approach to Real Estate Valuation in Vietnam Incorporating Land Pricing Legal Framework and Local Socio-Economic Characteristics

The growth potential of the real estate industry has led to a rise in financial activities associated with this sector. Consequently, property appraisal plays a crucial role in the real estate market. Although the demand for property valuation is growing, the current appraisal practices often incur high costs and time consumption. Recently, automated methods have been developed and gained considerable attention due to advancements in machine learning and big data. However, these methods frequently encounter challenges and instability when faced with fluctuations in the socio-economic landscape. Therefore, we propose a solution that utilizes legal foundations and socioeconomic characteristics reflected through geographical location, while also integrating them into a comprehensive mobile app solution.
Finger Photo Rescaling for Interoperability of Touchless and Touch-based Fingerprint Verification

Taking a finger photo image from a smartphone can replace capturing a fingerprint from a general touch-based sensor. Typically, fingerprint recognition systems require input fingerprint images with approximately 500 dots-per-inch (dpi) resolution. Capturing a finger photo with this specific resolution has to control the distance between a finger and a smartphone camera, which is not an easy task for general users. Fingerprint recognition systems require automatically rescaling a finger photo image to 500 dpi without user interaction. This work proposes two methods to rescale an input finger photo automatically. The first method directly estimates friction ridge frequencies from finger photos and slapped fingerprint images. The second method utilizes rearranged Fourier subbands to estimate both accurate frequencies. Finally, a 500 dpi rescaling ratio can be calculated from two estimated frequencies from both fingerprint types. Experiment results show that the second method can improve matching accuracy close to the brute force approach on the four-resolution subsets of the IIIDT ISPFDv2 dataset. The source codes were made publicly available.
A Traffic Prioritization Framework For Smart Home IoT Networks using Programmable Data Planes

The rapid growth of IoT provides a wide range of applicationsx, such as smart homes, healthcare, industry, etc. However, these applications generate various data that must be processed dynamically to ensure the desired QoS. Fortunately, recent advancements in data plane programmability provide some unique opportunities to dynamically process the loT traffic flow in a fine-grained manner. However, most research lacks of provide a solution to prioritize and schedule loT traffic flows in programmable switches. This paper proposes a dynamic prioritization technique for loT traffic flows by analyzing header field values granularly. Further, a weighted fair queue scheduling algorithm is designed in the programmable switch, which updates queue weights dynamically to reduce the delay for delay-sensitive loT applications. The experimental results show that the proposed solution prioritizes loT traffic flows 99.6% correctly. Additionally, the solution has achieved a throughput of 410.377Mbps for high-priority queues, and high-priority flows experienced a minimum delay of 1.84ms on average.
Compact Multiple-port Decoupled Antennas for MIMO Applications

Based on field valleys distribution, a compact multiple-port decoupled antenna is proposed for multi-input-multi-output (MIMO) applications. This antenna has five radiators, each enclosed by a hollow pentagonal dielectric decoupler. Due to this dielectric decoupler, a desired field valley distribution can be obtained, enabling high isolation among these five radiators. This antenna operates in the 5G FR1 band, with the isolation between ports exceeding 20 dB. For each port, there is a directional beam with an elevation angle of 45°, and all five independent beams with low ECCs can fully cover the entire azimuth plane. Moreover, the elevation angles of the main lobes can be controlled with different configuration parameters.
Ontogeny of the Immune System: γ/δ and α/β T Cells Migrate from Thymus to the Periphery in Alternating Waves

The embryonic thymus is colonized by the influx of hemopoietic progenitors in waves. To characterize the T cell progeny of the initial colonization waves, we used intravenous adoptive transfer of bone marrow progenitors into congenic embryos. The experiments were performed in birds because intravenous cell infusions can be performed more efficiently in avian than in mammalian embryos. Progenitor cells, which entered the vascularized thymus via interlobular venules in the capsular region and capillaries located at the corticomedullary junction, homed to the outer cortex to begin thymocyte differentiation. The kinetics of differentiation and emigration of the T cell progeny were analyzed for the first three waves of progenitors. Each progenitor wave gave rise to γ/δ T cells 3 d earlier than α/β T cells. Although the flow of T cell migration from the thymus was uninterrupted, distinct colonization and differentiation kinetics defined three successive waves of γ/δ and α/β T cells that depart sequentially the thymus en route to the periphery. Each wave of precursors rearranged all three TCR Vγ gene families, but displayed a variable repertoire. The data indicate a complex pattern of repertoire diversification by the progeny of founder thymocyte progenitors.
Multimodal Objects Categorization by Fusing GMM and Multi-layer Perceptron

Multi-object recognition resolves the need to instantaneously identify numerous things, which is essential for developing an in-depth understanding of the world of vision. Variations in object features, occlusions, scale, perspective, and intricate backgrounds make this task challenging. The practical strategy for categorizing objects into multiple categories using well-known benchmark datasets is presented in this work. The method extracts multi-object segmentation from difficult images using an advanced Gaussian mixture model (GMM) technique. A multi-layer perceptron (MLP) architecture that incorporates local descriptors and detectors optimizes the classification process. The method’s ability to handle concerns like object occlusion and spatial complexity is validated by extensive experiments using a well-known dataset, Corel 10k. The advised strategy overtakes cutting-edge techniques in terms of accuracy, with scores of 87.40% accuracy for the Corel 10k dataset.
A Novel Two-Step Bayesian Hyperparameter Optimization Strategy for DoS Attack Detection in IoT

Variations of Hyperparameter in Machine Learning (ML) algorithm effectively strikes the model's performance in terms of accuracy, loss, F1 score and many others. In the current study a two-step hyperparameter optimization approach is represented to analyse selected ML models' performance in detecting specific Denial of Service attacks in IoT. These attacks are Synchronization Flooding Attack at Transport layer, DIS Flooding attack and Sinkhole attack at Network layer. The two-step approach is a combination of Manual Hyperparameter tuning followed by Bayesian Optimization technique. The first stage manually analyses the hyperparameters of ML algorithms by considering the nature of the attack datasets. This technique is quite rigorous as it demands thorough analysis of the dependencies of the nature of datasets with hyperparameter types. At the same time this process is time consuming. The output of the first stage is the ranges of independent hyperparameter values that give maximum accuracy (minimum error rate). In the next stage Bayesian Hyperparameter tuning is used to specifically derive the single set of all hyperparameters values that give optimized accuracy faster than the BO. The input to the second stage is the ranges of individual hyperparameters that gave maximum accuracy in the first stage. The efficiency of the approach is depicted by comparative analysis of training time between the proposed and existing BO. NetSim simulator is used for generating attack datasets and Python packages are used for executing the two-step approach.
Estimating inbreeding coefficients from NGS data: Impact on genotype calling and allele frequency estimation

Most methods for next-generation sequencing (NGS) data analyses incorporate information regarding allele frequencies using the assumption of Hardy–Weinberg equilibrium (HWE) as a prior. However, many organisms including those that are domesticated, partially selfing, or with asexual life cycles show strong deviations from HWE. For such species, and specially for low-coverage data, it is necessary to obtain estimates of inbreeding coefficients (F) for each individual before calling genotypes. Here, we present two methods for estimating inbreeding coefficients from NGS data based on an expectation-maximization (EM) algorithm. We assess the impact of taking inbreeding into account when calling genotypes or estimating the site frequency spectrum (SFS), and demonstrate a marked increase in accuracy on low-coverage highly inbred samples. We demonstrate the applicability and efficacy of these methods in both simulated and real data sets.
Fault tolerant on‐board networks with priorities

We consider on‐board networks in satellites interconnecting entering signals (inputs) to amplifiers (outputs). The connections are made via expensive switches, each of which has four available links. The paths connecting inputs to outputs should be link‐disjoint. Some of the input signals, called priorities, must be connected to the amplifiers that provide the best quality of service (that is, to some specific outputs). In practice, amplifiers are prone to fail, and the faults cannot be repaired. Therefore, extra outputs have to be built into the network to ensure that every input can be routed to operational outputs. Given three integers, n, p, and f, we would like to design a low‐cost network (where the network cost is proportional to the total number of switches) such that it is possible to route all n inputs to n operational amplifiers, and to route the p priorities to the p best quality amplifiers for any set of f faulty and p best‐quality amplifiers. Let R(n, p, f) be the minimum number of switches of such a network. We prove here that $R(n,p,f)\leq{{n+f}\over{2}}\lceil\log_2p\rceil+{{5}\over{2}}(n-p)+g(f)$ with g a function depending only on f. We then compute R(n, p, f) exactly for a few small values of p and f. © 2005 Wiley Periodicals, Inc. NETWORKS, Vol. 47(1), 9–25 2006
Compact 5G n77 Bandpass Filter Utilizing LTCC Heterogeneous Integration Technology

paper presents a Low-Temperature Co-fired Ceramic (LTCC) bandpass filter for 5G N77 band communication applications. The filter utilizes a simple schematic diagram based on lumped components. It operates at a center frequency of 3.75GHz and incorporates two transmission zeros to enhance out-of-band suppression. The LTCC process is employed to stack two dielectric materials with dielectric constants of 8 and 45, respectively. By strategically distributing the lumped components across different dielectric layers, the circuit size is minimized, reducing mutual coupling effects. The proposed LTCC filter exhibits low insertion loss (<1dB) and excellent return loss (>16dB), making it suitable for wireless applications. With compact dimensions of only 1.5 × 1.3 × 0.39 mm3, this filter offers the advantages of small size and low loss, making it suitable for 5G N77 band wireless applications.
Trustworthy Federated Learning via Decentralized Consensus Under Communication Constraints

The advent of 6G is anticipated to bring advanced support for decentralized data processing, promoting the exploration of Federated Learning (FL). FL enables collaborative learning among distributed clients without direct access to raw data, offering benefits in communication efficiency and privacy preservation. However, some challenges hinder its widespread adoption in the 6G context, such as anomalous local models' packet loss due to communication resource limitations, and centralized design on data management. To address these obstacles, this work proposes a trustworthy architecture for supporting FL with the following three key contributions. First, our approach develops a robust model aggregation method by incorporating model analysis along with client reputation, withstanding abnormal models and enhancing system resilience. Second, it utilizes all received models, including those partially received because of packet loss, for accuracy optimization while ensuring fair contribution reputations for all participants. Third, our work customizes a consensus mechanism in Distributed Ledger Technology (DLT) for the proposed aggregation rule. This mechanism facilitates transparent and immutable records for data exchanges, and decentralizes the system. Our simulation demonstrates that the proposed architecture accurately identifies outlier models and utilizes incomplete models, enhancing global model accuracy by 13% compared to the method that averages over randomly selected fully-received models. Additionally, when benchmarked against the state-of-the-art Krum algorithm, our approach registers a 5% performance improvement.
Pre-Charge Strategy and Light-Load Voltage Balance Enhancement for DC/AC Flying Capacitor Converter

This paper introduces a control strategy that utilizes common mode current (CMC) for two objectives: 1) pre-charging a flying capacitor converter (FCC) with DC source, and 2) enhancing voltage balancing of flying capacitors under light load conditions. The methodology presented herein is applicable to existing PV, batter storage, and motor drive inverter systems that integrated with L-C filter. The proposed pre-charging process minimally impacts both load current and leakage current, which is a critical consideration for applications that sensitive to AC power quality. Two methods are proposed for CMC control: 1) common mode voltage (CMV) injection, and 2) switching frequency variation. These proposed methods are verified through computer simulations and experimental verification via laboratory testing using a prototype.
Automatic Door Closure System Based on Lighting Conditions

Chickens are social animals that prefer to be in the company of their flock during the day and seek shelter at night. Providing them with a safe and comfortable coop is essential for their well-being, but managing the door can be a tedious task, especially during the changing seasons when the light conditions vary. Automating the door opening based on light conditions can simplify this task, save time and effort, and ensure that your chickens have access to fresh air and sunlight during the day and a comfortable nesting place at night. In this work, the opening of the door to the chicken coop based on the light conditions will be solved. The device will consist of a main microcontroller that will handle all the functionality. A light intensity sensor will be connected to it, based on which the door will be opened or closed. This function will be provided by a stepper motor. In addition to this basic function, the device will also take temperature and send the data via Wi-Fi to a website where the data can be viewed. The power supply here will be solved using a solar panel. The goal is therefore to achieve independence and therefore independence from the power line.
Examining Low-Energy Data Aggregation with Linear Predictive Coding

This paper examines using linear predictive coding (LPC) to research an expansion of low-strength statistics, which include thermal and acoustic indicators. The author recognizes the application of linear prediction to time-based alerts, in which linear prediction can be used to reduce the dimensionality of the facts by doing away with redundancies. The authors use a -degree approach of (a) signal segmentation, which includes dividing the signal into overlapping time sections, and (b) linear prediction, which entails predicting the next sign price from a collection of previous signals. The authors evaluated the accuracy of their approach to the use of a spread of datasets, which include scientific indicators, audio recordings, and thermal recordings. The consequences confirmed that linear predictive coding is a valid technique to reduce the dimensionality of low-energy data and gain significant results. The authors concluded that linear predictive coding could effectively read and extract significant statistics from low-electricity information. Low-electricity information aggregation with linear predictive coding (LPC) has emerged as a promising method for lowering facts transfer over wireless networks. LPC can lessen the energy intake of user nodes using taking the benefit of temporal and statistical correlations of the information, as a consequence presenting utility-unique electricity financial savings. This paper examines the efficacy of LPC for low-energy data aggregation in a realistic context. We remember the actual-global information set parameters for which we derive the most effective LPC compression parameters. Simulation effects suggest that LPC can gain enormous energy financial savings compared to an uncompressed representation, demonstrating its ability as an energy-conscious data compression method.