Miracle-dz/UniFAR-FTUs huggingface dataset:
Efficient Gauss-Seidel Precoding with Parallel Calculation in Massive MIMO Systems

A number of requirements for 5G mobile communication are satisfied by adopting multiple input multiple output (MIMO) systems. The inter user interference (IUI) which is an inevitable problem in MIMO systems becomes controllable when the precoding scheme is used. In this paper, the horizontal Gauss-Seidel (HGS) method is proposed as precoding scheme in massive MIMO systems. In massive MIMO systems, the exact inversion of channel matrix is impractical due to the severe computational complexity. Therefore, the conventional Gauss-Seidel (GS) method is used to approximate the inversion of channel matrix. The GS has good performance by using previous calculation results as feedback. However, the required time for obtaining the precoding symbols is too long due to the sequential process of GS. Therefore, the HGS with parallel calculation is proposed in this paper to reduce the required time. The rows of channel matrix are eliminated for parallel calculation inHGSmethod. In addition,HGSuses the ordered channel matrix to prevent performance degradation which is occurred by parallel calculation. The HGS with proper number of parallelly computed symbols has better performance and reduced required time compared to the traditional GS.
Innovation systems in the service economy : measurement and case study analysis

List of Contributors. Preface. 1. Introduction, Overview and Reprise J.S. Metcalfe, I. Miles. Part I: Conceptual Frameworks. 2. Distributed Innovation Systems and Instituted Economic Processes B. Andersen, et al. 3. Innovation as a Loosely Coupled System in Services J. Sundbo, F. Gallouj. 4. Recombination and the Production of Technological Knowledge: Some International Evidence C. Antonelli. Part II: Measuring Service Innovation. 5. Innovation, Measurement and Services: The New Problematique R. Coombs, I. Miles. 6. Rethinking Innovation Comparisons between Manufacturing and Services: The Experience of the CBR SME Surveys in the UK A. Hughes, E. Wood. 7. Service Innovation: What Makes It Different? Empirical Evidence from Germany B. Preissl. 8. Information Flows and Knowledge Creation in Knowledge-Intensive Business Services: Scheme for a Conceptualization C. Hipp. 9. Indicators of Manufacturing and Service Innovation: Their Strengths and Weaknesses A. Kleinknecht.
Distinct colony boundaries and larval discrimination in polygyne red imported fire ants (Solenopsis invicta)

Evaluating the factors that promote invasive ant abundance is critical to assess their ecological impact and inform their management. Many invasive ant species show reduced nestmate recognition and an absence of boundaries between unrelated nests, which allow populations to achieve greater densities due to reduced intraspecific competition. We examined nestmate discrimination and colony boundaries in introduced populations of the red imported fire ant (Solenopsis invicta; hereafter, fire ant). Fire ants occur in two social forms: monogyne (colonies with a single egg‐laying queen) and polygyne (colonies with multiple egg‐laying queens). In contrast with monogyne nests, polygyne nests are thought to be interconnected due to the reduced antagonism between non‐nestmate polygyne workers, perhaps because polygyne workers habituate the colony to an odour unique to Gp‐9b‐carrying adults. However, colony boundaries and nestmate discrimination are poorly documented, particularly for worker‐brood interactions. To delimit boundaries between field colonies, we correlated the exchange of a 15N‐glycine tracer dissolved in a sucrose solution with social form. We also evaluated nestmate discrimination between polygyne workers and larvae in the laboratory. Counter to our expectations, polygyne colonies behaved identically to monogyne colonies, suggesting both social forms maintain strict colony boundaries. Polygyne workers also preferentially fed larval nestmates and may have selectively cannibalized non‐nestmates. The levels of relatedness among workers in polygyne colonies was higher than those previously reported in North America (mean ± standard error: 0.269 ± 0.037). Our study highlights the importance of combining genetic analyses with direct quantification of resource exchange to better understand the factors influencing ant invasions.
AssistSR: Affordance-centric Question-driven Video Segment Retrieval

It is still a pipe dream that AI assistants on phone and AR glasses can assist our daily life in addressing our questions like “how to adjust the date for this watch? ” and “how to set its heating duration? (while pointing at an oven)”. The queries used in conventional tasks (i.e. Video Question Answering, Video Retrieval, Moment Localization) are often factoid and based on pure text. In contrast, we present a new task called Affordance-centric Question-driven Video Segment Retrieval (AQVSR). Each of our questions is an image-box-text query that focuses on affordance of items in our daily life and expects relevant answer segments to be retrieved from a corpus of instructional video-transcript segments. To support the study of this AQVSR task, we construct a new dataset called AssistSR. We design novel guidelines to create high-quality samples. This dataset contains 1.4k multimodal questions on 1k video segments from instructional videos on diverse daily-used items. To address AQVSR, we develop a straightforward yet effective model called Dual Multimodal Encoders (DME) that signiﬁcantly outperforms several baseline methods while still having large room for improvement in the future. Moreover, we present detailed ablation analyses. Our codes and data are available at https://github.com/StanLei52/ AQVSR .
Clinical Factors Implicated in Antibiotic Resistance in Helicobacter pylori Patients

Helicobacter pylori is a common gastric pathogen associated with multiple clinical syndromes, including cancer. Eradication rates of H. pylori remain suboptimal despite the progress made in the past few decades in improving treatment strategies. The low eradication rates are mainly driven by antibiotic resistance of H. pylori. Non-invasive molecular testing to identify patients with antibiotic-resistant H. pylori represents a promising therapeutic avenue, however this technology currently remains limited by availability, costs, and lack of robust validation. Moreover, there is insufficient evidence to demonstrate that resistance-testing-based treatment approaches are superior to appropriately designed empiric strategies. Consensus guidelines recommend use of proven locally effective regimens; however, eradication data are inconsistently generated in several regions of the world. In this review, we describe several clinical factors associated with increased rates of antibiotic resistant H. pylori, including history of previous antibiotic exposure, increasing age, female gender, ethnicity/race, extent of alcohol use, and non-ulcer dyspepsia. Assessment of these factors may aid the clinician in choosing the most appropriate empiric treatment strategy for each patient. Future study should aim to identify locally effective therapies and further explore the clinical factors associated with antibiotic resistance.
Enhanced Security for an IoT Devices in Cyber-Physical System against Cyber Attacks

Cyber physical systems (CPS) integrate the physical and numerical worlds. Number of computing system connected with wireless networks and also transmits sensitive data in secure manner of network. Those platforms, however, are vulnerable to cyber-attacks on people, equipment, and the ecosystem. The method of transmitting sensed data to controllers through a wireless transmission link, therefore increasing the attack surface, attracts cyber concerns in CPS. Intrusion detection systems (IDS) are well-known as powerful survival mechanisms, and machine learning (ML) approaches have lately been employed in their creation. The conventional ML-based IDS, which need a considerable computing capacity, do not work on IoT devices as computational resources such as memory, processing power and limited energy are restricted. The proposed work is to build and implement a lightweight, resource-restricted IDS-based appliance. Moreover, the presents IMPACT, a lightweight IDS model based on ML (Impersonation Attack detection). This is built on extensive object detection using a gradation sequential Support Vector Machine (SVM) to reduce the amount of variables via feature selection and extraction utilizing a C4.8 wrapper, mutual information (MI), and stacked autoencoder (SAE), to install and operate on resource-constrained devices. Using the Aegean Wi-Fi Intruder Dataset (AWID), the IMPACT taught to detect impersonator attacks. The suggested IMPACT model surpassed current state-of-the-art benchmark techniques, achieving 98.23% accuracy, 1.21% false alarm rate, and 97.65 % detection rate.
Behind the Tube: Exploitative Monetization of Content on YouTube

The YouTube video sharing platform is a prominent online presence that delivers various genres of content to society today. As the viewership and userbase of the platform grow, both individual users and larger companies have recognized the potential for monetizing this content. While content monetization is a native capability of the YouTube service, a number of requirements are enforced on the platform to prevent its abuse. Yet, methods to circumvent these requirements exist; many of which are potentially harmful to viewers and other users. In this paper, we present the ﬁrst comprehensive study on exploitative monetization of content on YouTube . To do this, we ﬁrst create two datasets; one using thousands of user posts from eleven forums whose users discuss monetization on YouTube , and one using listing data from ﬁve active sites that facilitate the purchase and sale of YouTube accounts. We then perform both manual and automated analysis to develop a view of illicit monetization exploits used on YouTube by both individual users and larger channel collectives. We discover six distinct exploits used to execute illicit content monetization on YouTube ; four used by individual users, and two used by channel collectives. Further, we identify real-world evidence of each exploit on YouTube message board communities and provide insight into how each is executed. Through this, we present a comprehensive view of illicit monetization exploits on the YouTube platform that can motivate future investigation into mitigating these harmful endeavors.
Design and Modeling of Ultra-Compact Wideband Implantable Antenna for Wireless ISM Band

This paper proposes a wideband ultra-compact implantable antenna for a wireless body area network (WBAN). The proposed patch antenna works in the industrial, scientific, and medical (ISM) bands. The proposed patch antenna with an ultra-compact size (5 × 5 × 0.26 mm3) was designed with 29% wide bandwidth (about 670 MHz). This wide bandwidth makes the antenna unaffected by implantation in different human body parts. The miniaturization process passed many steps by adding many slots with different shapes in the radiating element as well as in the ground plane. A 50 Ω coaxial feeding excites the antenna to maintain matching and low power loss. The specific absorption rate (SAR) was calculated for health considerations. The result was within the standard limits of IEEE organizations and the International Commission on Non-Ionizing Radiation Protection (ICNRP). The antenna was tested in tissues with multiple layers (up to seven layers) and at various depths (up to 29 mm). The link margin was calculated, and the proposed antenna enables 100 Kbps of data to be transferred over a distance of 20 m and approximately 1 Mbps over a distance of 7 m. The proposed antenna was fabricated and tested. The measured S11 parameters and the simulated results using the Computer Simulation Technology (CST Studio) simulator were in good agreement.
A Three-Transistor Energy Management Circuit for Energy-Harvesting-Powered IoT Devices

Energy harvesting (EH) provides a promising solution for powering distributed Internet of Things (IoT) devices. Due to the low-level and sporadic ambient energy supply, an EH-powered device should operate in an intermittent and energy-driven mode. Commercial voltage supervisors were not optimized for the EH scenario, making it difficult to satisfy all new demands. The conventional energy management (EM) circuit has a risk of locking up during the turn-ON transient; therefore, it might fail to power the IoT load device. Previous technologies have used a relatively large circuit to solve this problem. In this article, a concise discrete three-transistor EM (3T-EM) circuit is proposed. It can track stored energy, switch ON/OFF to the load device, and provide a regulated voltage output. These key functions are realized by utilizing a minimum number of components; therefore, power consumption and manufacturing cost are largely cut. The voltage thresholds and minimum input current are theoretically derived. In experiments, the ON/OFF thresholds can be adjusted accurately, as predicted by the theory. The 3T-EM circuit can ensure the correct operation when the input current is as low as $0.4~ \mu \text{A}$ . Control experiments also prove the effectiveness and performance of the 3T-EM circuit. The proposed 3T-EM circuit shows the characteristics of low cost, low power, inherent regulation, high voltage rating, and good predictability. It is a good candidate to perform the EM task in widely distributed EH-powered IoT devices.
Peranan Eksternal Audit Terhadap Pengendalian Manipulasi Laba Studi Kasus: Perusahaan BUMN dan Anak Perusahaan

Manipulasi laba telah berkembang menjadi isu dalam lingkungan bisnis saat ini. Karena hasil laba yang tinggi biasanya dilihat sebagai tanda keberhasilan, maka tidak sedikit perusahaan yang merasa diuntungkan jika dapat mengatur pencatatan laporan keuangan sehingga dapat menghasilkan laporan yang lebih baik dari yang sebenarnya terjadi. Situasi ini dapat mengaburkan penilaian para pemangku kepentingan, termasuk investor, kreditur, dan pejabat pemerintah. Oleh karena itu, penting bagi perusahaan untuk memiliki pengendalian yang efektif yang dapat mendeteksi dan mencegah praktik manipulasi laba di dalam perusahaan. Salah satu faktor terpenting dalam manipulasi data laba adalah peran auditor eksternal dalam berkomitmen untuk mengevaluasi laporan keuangan perusahaan dan memberikan pendapat atas keandalan dan kebenaran laporan keuangan tersebut. Selain itu, auditor harus berkomitmen penuh untuk mendeteksi dan melaporkan setiap penyimpangan yang muncul selama proses audit. Penelitian ini menggunakan metode Deskriptif Kualitatif Naturalistik. Hasil dari penelitian ini adalah terdapat pengaruh positif dari auditor eksternal terhadap pengendalian praktik manipulasi laba di perusahaan dan adanya perbedaan persepsi antara manajer keuangan dan auditor eksternal terkait pengendalian praktik manipulasi laba di perusahaan. Faktor-faktor internal dan eksternal yang mempengaruhi efektivitas pengendalian praktik manipulasi laba di perusahaan.
A Trajectory K-Anonymity Model Based on Point Density and Partition

As people's daily life becomes increasingly inseparable from various mobile electronic devices, relevant service application platforms and network operators can collect numerous individual information easily. When releasing these data for scientific research or commercial purposes, users' privacy will be in danger, especially in the publication of spatiotemporal trajectory datasets. Therefore, to avoid the leakage of users' privacy, it is necessary to anonymize the data before they are released. However, more than simply removing the unique identifiers of individuals is needed to protect the trajectory privacy, because some attackers may infer the identity of users by the connection with other databases. Much work has been devoted to merging multiple trajectories to avoid re-identification, but these solutions always require sacrificing data quality to achieve the anonymity requirement. In order to provide sufficient privacy protection for users' trajectory datasets, this paper develops a study on trajectory privacy against re-identification attacks, proposing a trajectory K-anonymity model based on Point Density and Partition (KPDP). Our approach improves the existing trajectory generalization anonymization techniques regarding trajectory set partition preprocessing and trajectory clustering algorithms. It successfully resists re-identification attacks and reduces the data utility loss of the k-anonymized dataset. A series of experiments on a real-world dataset show that the proposed model has significant advantages in terms of higher data utility and shorter algorithm execution time than other existing techniques.
Wit, Stupidity – from Peirce to Freud Uwe Wirth The Commens Encyclopedia The Digital Encyclopedia of Peirce Studies

: According to Kant, human stupidity reveals, a lack of “power of judgement”, or as Peirce might say, a lack of abductive competence that confuses the relevant and the irrelevant. Peirce anticipated this idea. He writes: “I have come to the conclusion that it is folly to attempt to set limits which human stupidity cannot overpass (CP 4.321). Apparently both, Semiosis and human Stupidity are unlimited. The only two possible reactions to this condition humain are either critical reasoning or laughing. It was Aristotle who first defined man as at the same time animal syllogans and as animal ridens: an animal that can reason and that can laugh. I want to highlight a particular aspect of this very general double determination, namely the relation between the Peircean logic of abduction and the Freudian “logic” of the comic.
Model Choice in Multi-City Time Series Studies of Air Pollution and Mortality

Multi-city time series studies of particulate matter (PM) and mortality and morbidity have provided evidence that daily variation in air pollution levels is associated with daily variation in mortality counts. These findings served as key epidemiological evidence for the recent review of the United States National Ambient Air Quality Standards (NAAQS) for PM. As a result, methodological issues concerning time series analysis of the relation between air pollution and health have attracted the attention of the scientific community. Critics have raised concerns about the adequacy of current model formulations. Time series data on pollution and mortality are generally analyzed using log-linear, Poisson regression models for overdispersed counts with the daily number of deaths as outcome, the (possibly lagged) daily level of pollution as a linear predictor, and smooth functions of weather variables and calendar time used to adjust for time-varying confounders. Investigators around the world have used different approaches to adjust for confounding, making it difficult to compare results across studies. To date, the statistical properties of these different approaches have not been comprehensively compared. To address these issues, we quantify and characterize model uncertainty and model choice in adjusting for seasonal and long-term trends in time series models of air pollution and mortality. First,we conduct a simulation study to compare and describe the properties of statistical methods commonly used for confounding adjustment. We generate data under several confounding scenarios and systematically compare the performance of the different methods with respect to the mean squared error of the estimated air pollution coefficient. Second, we apply and compare the modelling approaches to the National Morbidity, Mortality, and Air Pollution Study (NMMAPS) database which is comprised of daily time series of several pollutants, weather variables, and mortality counts covering the period 1987--2000 for the largest 100 cities in the United States. We find that the NMMAPS estimates for the national average effect of PM(10) at lag 1 on mortality are robust to the different strategies for adjusting for seasonal and long-term trends.
FedNAR: Federated Optimization with Normalized Annealing Regularization

Weight decay is a standard technique to improve generalization performance in modern deep neural network optimization, and is also widely adopted in federated learning (FL) to prevent overfitting in local clients. In this paper, we first explore the choices of weight decay and identify that weight decay value appreciably influences the convergence of existing FL algorithms. While preventing overfitting is crucial, weight decay can introduce a different optimization goal towards the global objective, which is further amplified in FL due to multiple local updates and heterogeneous data distribution. To address this challenge, we develop {\it Federated optimization with Normalized Annealing Regularization} (FedNAR), a simple yet effective and versatile algorithmic plug-in that can be seamlessly integrated into any existing FL algorithms. Essentially, we regulate the magnitude of each update by performing co-clipping of the gradient and weight decay. We provide a comprehensive theoretical analysis of FedNAR's convergence rate and conduct extensive experiments on both vision and language datasets with different backbone federated optimization algorithms. Our experimental results consistently demonstrate that incorporating FedNAR into existing FL algorithms leads to accelerated convergence and heightened model accuracy. Moreover, FedNAR exhibits resilience in the face of various hyperparameter configurations. Specifically, FedNAR has the ability to self-adjust the weight decay when the initial specification is not optimal, while the accuracy of traditional FL algorithms would markedly decline. Our codes are released at \href{https://github.com/ljb121002/fednar}{https://github.com/ljb121002/fednar}.