Miracle-dz/UniFAR-FTUs huggingface dataset:
Definition and description of anatomical restoration in vertebral compression fractures – feasibility and assessment method

Introduction. So far, there is no evidence of anatomical reduction of the fractured vertebral body prior to its stabilization with current techniques for VCF treatment (vertebroplasty, balloon kyphoplasty). Although vertebral height restoration has been demonstrated, fractured endplates reduction is limited. Considering that the vertebra as a part of a joint, vertebral endplates deserve to be treated like any other weight-bearing joint to avoid further complica- tions (disc collapse, progressive kyphosis, facet joint arthritis). Materials and method. The first step of the procedure consisted in placing two titanium expandable implants just beneath the fracture through a trans- pedicular approach in the vertebral body. Then, the implants were expanded to push backward the fractured endplates in a more anatomical position. The instrumentation we used has been designed to meet this need to perfectly control the direction and the amount of implant expansion until the desired fracture reduction has been reached. To finish, the implants were let in place while PMMA cement was injected to avoid any loss of correction. To assess the capability of this new procedure to achieve an anatomical reduction of the fracture, a new assessment method based on CTscans 3D reconstructions was developed. Results. The three cases presented here are demonstrating the ability of this new procedure to reduce any type of VCF (biconcave, crush and wedge) by applying controlled direct forces onto the broken endplates. Vertebral kyphosis angles were reduced by 59.8% on average and vertebral endplates fractures were reduced (up to 6.5 mm). Conclusions. This procedure associated with this 3D assessment method have shown the possibility to reduce both vertebral kyphosis angle and fractured endplates. Further studies are needed to confirm those results on more important cohorts and on the long-term.
Deep Learning Anomaly Detection Using Edge AI Deep Learning Anomaly Detection Using Edge AI

Deep learning anomaly detection is an evolving field with many real-world applications. As more and more devices continue to be added to the Internet of Things (IoT), there is an increasing desire to make use of the additional computational capacity to run demanding tasks. The increase in devices and amounts of data flooding in have led to a greater need for security and outlier detection. Motivated by those facts, this thesis studies the potential of creating a distributed anomaly detection framework. While there have been vast amounts of research into deep anomaly detection, there has been no research into building such a model in a distributed context. In this work, we propose an implementation of a distributed anomaly detection system using the TensorFlow library in Python and three Nvidia Jetson AGX Xavier deep learning modules. The key objective of this study is to determine if it is practical to create a distributed anomaly detection model without a significant loss of accuracy on classification. We then present an analysis of the performance of the distributed system in terms of accuracy and runtime and compare it to a similar system designed to run on a single device. The results of this study show that it is possible to build a distributed anomaly detection system without a significant loss of accuracy using TensorFlow, but the overall runtime increases for these trials. This proves that it is possible to distribute anomaly detection to edge devices without sacrificing accuracy, and the runtime can be improved with further research.
THE ROLE OF GOVERNMENT POLICY IN DEVELOPING THE ISLAMIC MICROFINANCE INSTITUTIONS PERFORMANCE IN INDONESIA

In building a successful Islamic Microfinance Institutions (IMFIs) performance, many factors must be considered based on a multi-element point of view. This study aims to build key factors affecting IMFIs performance using multi-perspective and multi-criteria methods. This study uses an analytic network process (ANP) to calculate the degree of influence given by the IMFIs performance criteria and factors. Primary and secondary data collection techniques are used to collect critical data for this research. The primary data are collected through questionnaires and interviews. A purposive sampling technique to collect primary data is used to the 26 local BPRS, 11 BMT including Java, Sumatra, and Sulawesi as Indonesia's three biggest islands. The study results find that internal and external management aspects are two main problems affecting IMFIs. Five main solutions to increase their financing are regulatory, management, institutions, competencies, and partnerships.
for Grammar-Based Grounded Lexicon Learning

In this section, we will present and discuss the domain-specific languages (DSLs) we use for two domains: visual reasoning and language-guided navigation. We will further introduce the neurosymbolic module we have designed for executing programs in these two domains. Overall, each DSL contains a set of types and a set of deterministic modules that have been manually designed for realizing necessary operations in these domains. However, in contrast to realizing them as we do in standard programming languages (with for-loops and if-conditions), we will be using tensor operations (e.g., tensor additions and multiplications) to realize them so that the output of each program is differentiable with respect to all of its inputs.
MEDYM: An Architecture for Content-based Publish-Subscribe Service Networks

Designing a distributed architecture for content-based publish-subscribe service networks is challenging for two reasons: first, communication in such system is guided by the content of publications and subscriptions rather than addresses or network locations; second, while a publication often matches subscriptions from multiple locations, existing group-based multicast techniques such as IP multicast or application-layer multicast are not readily applicable due to the highly diversified patterns of interests. We propose an architectural approach that combines two orthogonal aspects: Match Early, and DY namic Multicast. We call this a MEDYM architecture, and we compare it with two major existing design approaches: Content-based Forwarding and Channelization. In MEDYM, event publications are matched as close as possible to publishers, to obtain the locations of servers that have matching subscriptions; then, a multicast route to the matching servers is computed dynamically, to best suit the heterogeneous event traffic patterns. We present methods to efficiently implement the dynamic multicast routing. We evaluate the MEDYM architecture using detailed simulations as well as a prototype deployment on the PlanetLab test bed. Experimental results show that the MEDYM approach significantly improves storage, computation and network efficiency compared to existing approaches, and is resilient, scalable, and extensible.
Evaluation of excavation motion sequence for hydraulic excavators based on extraction of excavation style and phase

In recent times, research on highly efficient construction has gained increasing popularity. During construction with hydraulic excavators, the sequence of excavation motions significantly affects the duration and precision of the work; therefore, it should be as efficient as possible. Because automation is still difficult, the ability improvement support system of nonskilled operators is required. However, currently, there exists no effective method for evaluating or planning the excavation motion sequence. This study proposes a method for evaluating excavation motion sequences based on a pattern recognition technique. First, the data array of hydraulic excavator motions is divided into unit excavation motions, characterized using a clustering method. Second, the patterns are divided into excavation phases with similar excavation motion patterns. Finally, the excavation motion sequence is evaluated by calculating the transition frequency between excavation phases. The result shows that the proposed approach can classify the skill level of the operator with accuracy of 0.8. Therefore, the frequency of excavation phase transitions can be used as a useful indicator for evaluating excavation motion sequences.
Wideband Rydberg atom-based receiver for amplitude modulation radio frequency communication

Based on Autler–Townes splitting and AC Stark shifts, we present a Rydberg atom-based receiver for determining the amplitude modulation (AM) frequency among a wideband carrier range utilizing a cesium atomic vapor cell. To verify this approach, we measured the signal-to-noise ratio and the data capacity with a 10 kHz AM frequency in the carrier range from 2 GHz to 18 GHz. Without changing the lasers, the working band can be easily extended to a higher range by optimizing the feed antenna and experimental configurations.
RENTAKA: A Novel Machine Learning Framework for Crypto-Ransomware Pre-encryption Detection

—Crypto ransomware is malware that locks its victim’s file for ransom using an encryption algorithm. Its popularity has risen at an alarming rate among the cyber community due to several successful worldwide attacks. The encryption employed had caused irreversible damage to the victim’s digital files, even when the victim chose to pay the ransom. As a result, cybercriminals have found ransomware a lucrative and profitable cyber-extortion approach. The increasing computing power, memory, cryptography, and digital currency advancement have caused ransomware attacks. It spreads through phishing emails, encrypting sensitive data, and causing harm to the designated client. Most research in ransomware detection focuses on detecting during the encryption and post-attack phase. However, the damage done by crypto-ransomware is almost impossible to reverse, and there is a need for an early detection mechanism. For early detection of crypto-ransomware, behavior-based detection techniques are the most effective. This work describes RENTAKA, a framework based on machine learning for the early detection of crypto-ransomware. The features extracted are based on the phases of the ransomware lifecycle. This experiment included five widely used machine learning classifiers: Naïve Bayes, kNN, Support Vector Machines, Random Forest, and J48. This study proposed a pre-encryption detection framework for crypto-ransomware using a machine learning approach. Based on our experiments, support vector machines (SVM) performed with the best accuracy and TPR, 97.05% and 0.995, respectively.
Neuropharmacological Activities of Abies pindrow Aerial Parts in Mice

The methanol extract (200 or 400 mg/kg, p.o.) and ethyl acetate fraction (25 or 50 mg/kg, p.o.) of A. pindrow aerial parts were screened for anticonvulsant, antidepressant, locomotor, hypnotic and antistress activities. The methanol extract (ME) and ethyl acetate fraction (EAF) could not reduce duration of MES-induced tonic extensor phase with respect to the standard drug, phenytoin (20 mg/kg, i.p.). Both ME and EAF showed significant reduction of time spent in immobile state in forced swim test and did not stimulate locomotion in an open field model, thereby confirming their specific antidepressant activity. In cold swim test, ME and EAF showed antistress activity comparable to diazepam (1 mg/kg, i.p.). None of the test doses of ME and EAF could significantly increase duration of sleep in mice as compared to the control group. Phytochemical screening of ME and EAF showed presence of flavonoids as major class of phytoconstituents.
Towards a plant pathologies detection solution

Agriculture is very important in Africa. But farmers are dealing with many plants pathologies that keep agriculture from developing and boosting the economy. Current pathologies diagnosis based on human screening is time consuming and expensive, while computer vision-based models are efficiently promising.That is why we address the problem of identification of the pathologies affecting agricultural crops with computer vision tools. Though the high variability of symptoms due to the age of infected tissues, genetic differences, and light conditions in trees reduces the precision of detection, we propose a method to achieve our goal.
Digital-Media-Based Interaction and Dissemination of Traditional Culture Integrating Using Social Media Data Analytics

This paper adopts the approach of the Internet fusion digital media to conduct in-depth research and analysis on the interaction and communication of traditional culture. This structural information can be used to express and describe the Internet requirements. With the increasing demand for the Internet applications, the number of objects in the network and the complexity of the structure are also growing, so we can design suitable clustering algorithms to obtain more reasonable network structure information by using different semantic information in the heterogeneous information network. Firstly, the caching scheme is transformed into a backpack problem, and the storage scheme is obtained recursively by using dynamic programming, after which the computational task is optimized based on this storage scheme by using the whale algorithm to make it the least costly. The exact balance between the connotation essence of traditional culture and the emotional resonance in modern life has not yet been found, and the visual language belonging to cultural aesthetics under the combination of traditional and modern aesthetics needs to be explored for a long time. From an objective point of view, the digital art communication of cultural elements still suffers from five problems: lack of communication cultivation and stratification of target audiences, low quality of transcription of digital artworks, unbalanced economic and cultural values, uneven development of digital art communication forms, and low degree of Internet adaptation. A ranking algorithm based on fusing multiple meta-paths is proposed to better reflect the ranking values of different object distributions of the Internet data in heterogeneous information networks, which can obtain better clustering, while better clustering can be calculated to obtain more reasonable rankings, and finally, the types of the Internet-oriented applications are divided into multiple reasonable clusters. The effects of selecting different meta-paths on the clustering algorithm and the effects of fused meta-paths versus single meta-paths on the clustering results are given in the experimental results section. Also compared with other clustering algorithms, the I-RankClus algorithm can better achieve the data clustering classification for the Internet application scenarios.
DRAM variable retention time

A DRAM bit has variable retention time (VRT) when the memory cell leakage, which determines how long a cell can retain information, varies with time. This paper reports on a study of VRT in cells from 4Mbit and 16 Mbit DRAM chips produced by a variety of manufacturers and in a number of technologies including trench capacitor and stacked capacitor cells. VRT cells were found on all chips. This paper describes the detection and characterization of two classes of VRT cells: 2-state and multi-state VRT cells, and presents a model containing 4 activation energies and 3 pre-factors that describes the temperature dependence of retention times and transition rates for the 2-state VRT cells. A description of new test techniques and the analysis needed to detect and study VRT are provided as well as examples from typical 4 Mbit chips. <<ETX>>
Crack Texture Feature Identification of Fiber Reinforced Concrete Based on Deep Learning

Structural cracks in concrete have a significant influence on structural safety, so it is necessary to detect and monitor concrete cracks. Deep learning is a powerful tool for detecting cracks in concrete structures. However, it requires a large quantity of training samples and is costly in terms of computational time. In order to solve these difficulties, a deep learning target detection framework combining texture features with concrete crack data is proposed. Texture features and pre-processed concrete data are merged to increase the number of feature channels in order to reduce the demand of training samples for the model and improve training speed. With this framework, concrete crack detection can be realized even with a limited number of samples. To accomplish this aim, self-made steel fiber reinforced concrete crack data is used for comparison between our framework and those without texture feature mergence or pre-processed concrete data. The experimental results show that the number of parameters that need to be fitted in the model training and training time can be correspondingly reduced and the detection accuracy can also be improved.
Application of the discrete linear chirp transform (DLCT) to estimate the parameters of multicomponent LFM signals

This paper is focused on method to estimate the parameters of multicomponent linear frequency modulation (LFM) signals. These nonstationary signals, which are often referred to as ”chirp”, are encountered in many fields such as communication, vibration analysis, radar systems. The presented method, which is based on the discrete linear chirp transform (DLCT), permits the chirp parameters to be precisely estimated. Its high performance, which was proven by the simulation results, coupled with its simplicity, makes this method useful for many applications. Streszczenie. W artykule przedstawiono metodę estymacji parametrów wieloskładnikowych sygnałów z liniową modulacją częstotliwości. Z tego typu sygnałami mamy do czynienia w takich dziedzinach jak telekomunikacja, analiza drgań, systemy radarowe. Przedstawiona metoda, bazująca na DLCT (ang. Discrete linear chirp transform), pozwala na oszacowanie parametrów wspomnianych sygnałów. Jej wysoka skuteczność, potwierdzona wynikami symulacji, w połączeniu z prostotą, czyni metodę użyteczną w wielu zastosowaniach. (Zastosowanie transformacji DLCT do estymacji wartości parametrów wieloskładnikowych sygnałów LFM).
Validity and reliability of a unique aerobic field test for estimating VO2max among basketball players

This study aimed at developing and validating an innovative field test for measuring the aerobic capacity of basketball players during games. Such capacity is necessary for recovering from high frequency anaerobic actions such as sprinting and continuing to perform well. To recover, the body must rebuild its creatine phosphate reserve and emit accumulated phosphate in very short periods of time. The participants included 21 male basketball players on an elite youth league in Israel, aged 16.4 years on average. In addition to participating in the proposed test (Yo-Yo Recovery Test for Basketball Players) twice (test/re-test), the players also performed three previously validated tests (Bruce Protocol Stress Test, Yo-Yo Intermittent Recovery Level 1 Test, and Yo-Yo Endurance Test). For each test, the players’ time and distance covered were documented, as were their maximum oxygen consumption and heartrate during recovery, and their perceived level of exertion. Our findings indicate the validity and reliability of the proposed aerobic field test for basketball players. Moreover, the test requires shorter times and distances for obtaining results than the other three tests. As such, this tool could be highly beneficial for basketball coaches in creating optimal training programs and game plans for each individual player and for the entire team.
A List-Ranking Framework Based on Linear and Non-Linear Fusion for Recommendation from Implicit Feedback

Although most list-ranking frameworks are based on multilayer perceptrons (MLP), they still face limitations within the method itself in the field of recommender systems in two respects: (1) MLP suffer from overfitting when dealing with sparse vectors. At the same time, the model itself tends to learn in-depth features of user–item interaction behavior but ignores some low-rank and shallow information present in the matrix. (2) Existing ranking methods cannot effectively deal with the problem of ranking between items with the same rating value and the problem of inconsistent independence in reality. We propose a list ranking framework based on linear and non-linear fusion for recommendation from implicit feedback, named RBLF. First, the model uses dense vectors to represent users and items through one-hot encoding and embedding. Second, to jointly learn shallow and deep user–item interaction, we use the interaction grabbing layer to capture the user–item interaction behavior through dense vectors of users and items. Finally, RBLF uses the Bayesian collaborative ranking to better fit the characteristics of implicit feedback. Eventually, the experiments show that the performance of RBLF obtains a significant improvement.
Global Resource Allocation for High Throughput and Low Delay in High-Density VANETs

Medium access control (MAC) plays a crucial role in ensuring proper operation in vehicular ad hoc networks (VANETs). However, existing solutions cannot meet the strict requirements of high throughput and low latency in high-density scenarios. In this paper, we propose a novel MAC protocol to meet such demands for VANETs. The roadside unit (RSU) pattern and the dual-transceiver manner are adopted to assign channel resources for all packets. The striking features of our approach are twofold: the time slot allocation based on global vehicle information and the consideration of different delay requirements of different applications. First, we design a time slot exchange mechanism, which avoids any transmission conflict between two adjacent RSUs, to reduce channel resource waste. Then, we design a packet weight allocation mechanism, by which the packets of any network edge area are assigned higher priorities to further reduce channel resource waste. Moreover, we devise a packet quality evaluation mechanism, by which our multi-objective problem is transformed into a single-objective problem. In addition, we devise a greedy branch-and-bound algorithm to address the multiple-knapsack problem, which is transformed into a single-knapsack problem. Extensive simulation results show the advantages of our approach in terms of throughput and latency.
Um Sistema Adaptativo de Detecção e Reação a Ameaças

Atacantes criam novas ameaças e constantemente mudam seu comportamento para enganar os sistemas de segurança atuais. Aliás, as ameaças são detectadas em dias ou semanas, enquanto uma contramedida deve ser imediatamente efetuada para evitar ou reduzir prejuízos. Este artigo propõe um sistema adaptativo de detecção de ameaças que possui um esquema baseado em Redes Definidas por Software (SDN) para realizar contramedidas. As contribuições do trabalho são: i) a detecção e prevenção de ameaças analisando uma sequência de apenas cinco pacotes de cada fluxo; ii) o desenvolvimento de algoritmos de detecção treinados em tempo real, com comportamento adaptativo; iii) o imediato acionamento de contramedidas sem esperar o fim do fluxo; e iv) o efetivo bloqueio de ameaças mesmo em cenários nos quais o endereço do pacote IP é mascarado. Um esquema baseado na tecnologia SDN efetua o monitoramento da sequência de cinco pacotes e o rápido bloqueio do ataque ainda na origem, evitando que recursos de rede sejam desperdiçados. Os resultados obtidos mostram uma alta acurácia na detecção de ameaças, mesmo com o comportamento da rede variando com o tempo.