Miracle-dz/UniFAR-FTUs huggingface dataset:
Memomusic Version 2.0: Extending Personalized Music Recommendation with Automatic Music Generation

Music emotion experience is a rather subjective and personalized issue. Therefore, we previously developed a personalized music recommendation system called MemoMusic to navigate listeners to more positive emotional states based not only on music emotion but also on possible memories aroused by music. In this paper, we propose to extend MemoMusic with automatic music generation based on an LSTM network, which can learn the characteristic of a tiny music clip with particular Valence and Arousal values and predict a new music sequence with similar music style. We call this enhanced system MemoMusic Verison 2.0. For experiment, a new dataset of 177 music in MIDI format was collected and labelled using the Valence-Arousal model from three categories of Classical, Popular, and Yanni music. Experimental results further demonstrate that memory is an influencing factor in determining perceived music emotion, and MemoMusic Version 2.0 can moderately navigate listeners to better emotional states.
Reason Forecast with Bert on Test Result Alarm in Semiconductor Fab

The development of artificial intelligence in fab raised the opportunity of improving the product yield and quantity by predictive analytics. That improving the keen awareness of the knowledge of relationship between anomalous data and product yield can help engineers to find potential risk in wafer processing, which contributes to improving product yields. In this paper, we extracted a set of alarm records with confirmed reasons from engineers as data set. And we used an attention-based convolutional neural network with BERT as the embedding layer and added some more attentive features to find the alarm reason and the alarm influence. The result indicated that this resolution could help engineer find alarm reason timely.
Cache Replacement Algorithm Based on Dynamic Constraints in Microservice Platform

Distributed cache is one of the most important components in cloud computing and microservice systems. Adding cache components to the microservice system can significantly improve the concurrency and throughput of the whole microservice framework, and effectively promote the overall performance of the microservice system. However, traditional caching algorithm can easily lead to the imbalance of cache resource allocation in microservice system, thus affecting the performance of the microservice system. We study the basic architecture of cache in microservice platform, and propose the mathematical model of distributed cache and the probability distribution model of data access in microservice platform. Next, based on the above mathematical model, we design a cache replacement algorithm based on dynamic constraints in microservice platform. The algorithm can effectively solve a series of problems of traditional algorithms in microservice platform, making the algorithm more suitable for the environment of microservice architecture. We evaluate and verify the algorithm on the simulation software platform. Experimental results show that the algorithm meets the performance requirements of high cache hit rate, high concurrency and high throughput in the microservice platform, and is significantly better than the traditional cache algorithms.
Detecting Layered Bottlenecks in Microservices

We propose a method to detect both software and hardware bottlenecks in a web service consisting of microservices. A bottleneck is a resource that limits the maximum performance of the entire web service. Bottlenecks often include both software resources such as threads, locks, and channels, and hardware resources such as processors, memories, and disks. Bottlenecks form a layered structure since a single request can utilize multiple software resources and a hardware resource simultaneously. The microservice architecture makes the detection of layered bottlenecks challenging due to the lack of a uniform analysis perspective across languages, libraries, frameworks, and middle-ware.We detect layered bottlenecks in microservices by profiling numbers and status of working threads in each microservice and dependency among microservices via network connections. Our approach can be applied to various programming languages since it relies only on standard debugging tools. Nevertheless, our approach not only detects which microservice is a bottleneck but also enables us to understand why it becomes a bottleneck. This is enabled by a novel visualization method to show layered bottlenecks in microservices at a glance. We demonstrate that our approach successfully detects and visualizes layered bottlenecks in the state-of-the-art microservice benchmarks, DeathStarBench and Acme Air microservices. This enables us to optimize the microservices themselves to achieve a higher throughput per re-source utilization rate compared with simply scaling the number of replicas of microservices.
DiseSniper: A potato disease identification system based on the ResNet model

Potato is one of China's most promising high-production cash crops. However, the frequent occurrence of potato diseases and insect pests directly leads to a considerable crop reduction or low yield, restricting the agricultural economy's development. A variety of potato diseases and insect pests dramatically impact potatoes' quality and production and pose a significant threat to human health. Therefore, it is necessary to use image processing methods to detect potato leaves' growth state at early stages. This study mainly implements the accurate identification of potato blight through the analysis of RGB images and is divided into three parts: data set construction, model construction, and system development. First, this study collected the images of potato blight from the Internet as part of the data set. Blurred and unidentifiable images were removed during data set construction. Then we consulted agricultural experts on crop disease to label the disease types of potato disease image data to construct the labeled potato disease data set. Second, various image filtering algorithms were used to enhance the input image data. A potato blight recognition model was built based on deep learning algorithms. Specifically, the Restnet50 model was used to train the blight classification model. The convolution operation was performed on the input image of 3 channels containing 50 Conv2D functions to distinguish the types and the degree of diseases. Finally, the disease identification software, DiseSniper, was developed based on the trained model. The system was constructed by the agile development software engineering method. Specifically, the software's front end was designed using PyQT5 and implemented the algorithm in the software. Based on the image data set of potato leaves, the accuracy of the test data set reaches more than 98%.
OBJECT REMOVAL AND IMAGE RESTORATION USING IMAGE INPAINTING

— Digital inpainting is an image interpolation mechanism, which can automatically restore damaged or partially removed image. Most inpainting mechanisms use a singular resolution approach on the extrapolation or interpolation of pixels. We propose a multi-resolution algorithm, which can take into consideration the different levels of details. Robustness with respect to the shape of the manually selected target region is also demonstrated.
Area-aware optimization of XOR-AND Graph based on Reed-Muller logic expansion

XOR-AND Graph (XAG) is a logic representation method for Boolean functions. A major advantage of using XAG is that it is efficient for cryptography and security applications in which the number of AND gates relates to the multiplicative complexity. A Reed-Muller logic expansion is used in this paper to develop an area-aware optimization method for XAG. The logic functions can be represented in several ways by Reed-Muller logic based on different polarities. To find an area-efficient representation of the logic network, the cuts of the logic network are enumerated and then the mixed polarity conversion algorithm is utilized. When the representation is improved over the existing one, we replace the existing cut. Following the experimental results over EPFL benchmark suites, the proposed method can optimize XAG size. Compared with the best known results, we achieve a reduction of up to 50% and an average reduction of 14% in the number of XOR gates.
Simulation Analysis of a Wideband Antenna on a Drone

Wireless systems have been upgraded over the past several years and as a result, drone-based communications have found a wide range of applications. This paper presents the performance of a printed log-periodic antenna, which operates in the frequency range from 0.7 to 6.5 GHz when mounted on the top of a drone. The effect of a metallic mast and a base plate is considered in our analysis. From our simulations, it is observed that an improvement in realized gain of 1.6 dB is obtained over a frequency band of 2 to 6.5 GHz. All simulations are performed using CST Studio Suite.
Towards 6G: Deep Learning in Cell-Free Massive MIMO

Massive Multiple-Input-Multiple-Output (MIMO) technology is considered a crucial part of the fifth generation (5G) telecommunications systems. However, moving towards sixth generation (6G) wireless networks, novel solutions have to be incorporated into the current telecommunications' systems. Cell-free Massive MIMO and especially the user-centric approach, seems to be the most promising idea to this direction at this moment. Nevertheless, there are many open issues to be resolved. Deep Learning has been successfully applied to a wide range of problems in many different fields, including wireless communications. In this paper, a review of the state-of-the-art Deep Learning methods applied to Cell-free Massive MIMO communications systems is provided. In addition future research directions are discussed.
The Study of Sige Channel Formation for FDSOI

Si<inf>1-x</inf>Ge<inf>x</inf> channel is becoming one of the most promising materials to improve PMOS device performance. In this work, thickness relation between oxide and pre-dep Si<inf>1-x</inf>Ge<inf>x</inf> is proposed. Two step methods, which rapid thermal oxidation (RTO) combined with anneal (RTA), were used to form Si<inf>1-x</inf>Ge<inf>x</inf> channel for PMOS at FDSOI structure. The concentration and thickness of Si<inf>1-x</inf>Ge<inf>x</inf> were measured by SIMS and TEM. It was found that the thickness of Si<inf>1-x</inf>Ge<inf>x</inf> channel was decreased with increasing soak time and temperature. The profile of Ge gets better when bake time become longer. The uniformity of Si<inf>1-x</inf>Ge<inf>x</inf> was affected by pre-layers.
Analytical Optimization Method for VLSI Global Placement

Placement is one of the critical stages in the physical design of very large scale integrated circuits (VLSI), which has a significant impact on the performance of subsequent stages. Modern placement algorithms need to handle large-scale designs with millions of cells and complex placement constraints. Further, placement often includes three steps: global placement, legalization, and detailed placement. To a great extent, global placement affects the quality of the final placement and thus is considered a crucial part of the placement. Based on the recent research progress, this paper briefly introduces related optimization models and algorithms commonly used in VLSI global placement and discusses possible research directions.
Removing (Sub)Surface Defects Induced by Si Wafer Thinning Processes Enables High-Performance Backscattered Electron Detector

High-performance backscattered electron detectors (BSDs) with high sensitivity and low dark current require a high-quality wafer substrate surface. Currently, chemical-mechanical polishing (CMP) has become an essential method to obtain a polished flat wafer surface. However, during a CMP process, wafers are subjected to various defect sources attributed to slurries, polishing pads, and polishing machines. Therefore, post-CMP cleaning of a wafer is crucial in fabricating high-performance BSD detectors. In this work, four post-CMP cleaning experiments were taken to identify different (sub) surface defects and to develop proper approaches for such defects' removal. Minority carrier lifetimes of Si wafer before and after various surface-defect removal treatments were measured and found to increase significantly, especially after the surface passivation using amorphous silicon, with an increase from $26\ \mu\mathrm{s}$ to $202\ \mu\mathrm{s}$, indicating the successful recovery of Si wafer (sub) surface that was previously damaged by conventional wafer thinning processes.
Ransomware Detection and Classification Strategies

Ransomware uses encryption methods to make data inaccessible to legitimate users. To date a wide range of ransomware families have been developed and deployed, causing immense damage to governments, corporations, and private users. As these cyberthreats multiply, researchers have proposed a range of ransom ware detection and classification schemes. Most of these methods use advanced machine learning techniques to process and analyze real-world ransomware binaries and action sequences. Hence this paper presents a survey of this critical space and classifies existing solutions into several categories, i.e., including network-based, host-based, forensic characterization, and authorship attribution. Key facilities and tools for ransomware analysis are also presented along with open challenges.
Smart Edge Power Management to Improve Availability and Cost-efficiency of Edge Cloud

With the increased 5G deployments across the world, new use-cases are emerging in many new domains, such as autonomous vehicles, smart cities, smart grid and potentially the proliferation of augmented reality. Some of these applications require high availability, bandwidth and/or extremely low latency that depends on the applied service.Currently the cost of deployment of distributed edge nodes and its relation to the availability of power infrastructure is not well known. In this work, we demystify the cost of edge resources by proposing a cost estimation framework by considering the various existing edge related constraints, such as power grid and edge power node infrastructure. We consider Capital Expenditure (CAPEX) and Operational Expenditure (OPEX) as well as time value of money in relation to the Hardware (HW) redundancy and depreciation for edge cloud resource estimation. The cost of resources are made in relation to the local edge power infrastructure conditions for the applied services and required Service Level Agreement (SLA). The availability of application is estimated using Reliability Block Diagram (RBD) of the edge components including power and cooling systems.We propose a new method, called Smart Edge Power Management (SEPM), that includes identification of the relevant parameters and states of the edge power infrastructure and how to overcome the various edge power related constraints and to further improve the cost efficiency during operation. The performance and evaluation are made on country wide edge deployments for a mobile operator in Sweden. With our new proposed method SEPM, the cost efficiency of edge resources can be improved upto 10%.
Dissemination of bla KPC-2 by the Spread of Klebsiella pneumoniae Clonal Complex 258 Clones (ST258, ST11, ST437) and Plasmids (IncFII, IncN, IncL/M) among Enterobacteriaceae Species in Brazil (cid:1)

Dissemination of blaKPC-2 by the Spread of Klebsiella pneumoniae Clonal Complex 258 Clones (ST258, ST11, ST437) and Plasmids (IncFII, IncN, IncL/M) among Enterobacteriaceae Species in Brazil Leonardo Neves Andrade, Tânia Curiao, Joseane Cristina Ferreira, Juliana Mucedola Longo, Eduardo Carneiro Clímaco, Roberto Martinez, Fernando Bellissimo-Rodrigues, Aníbal Basile-Filho, Marco Antônio Evaristo, Pedro F. Del Peloso, Vanessa Bley Ribeiro, Afonso Luis Barth, Milena Cristina Paula, Fernando Baquero, Rafael Cantón, Ana Lúcia da Costa Darini, and Teresa M. Coque* Faculdade de Ciências Farmacêuticas de Ribeirão Preto, Universidade de São Paulo, Ribeirao Preto, Brazil; Servicio de Microbiología, Hospital Universitario Ramón y Cajal, Instituto Ramón y Cajal de Investigación Sanitaria (IRYCIS), and CIBER en Epidemiología y Salud Pública (CIBERESP), Madrid, Spain; Hospital das Clínicas da Faculdade de Medicina de Ribeirão Preto, Universidade de São Paulo, Ribeirao Preto, Brazil; Laboratório Richet, Rio de Janeiro, Brazil; Unidade de Microbiologia e Biologia Molecular, Serviço de Patologia Clínica, Hospital das Clínicas de Porto Alegre, Porto Alegre, Brazil; and Laboratório Santa Casa de Franca, Franca, Brazil
Modeling and Forecasting Stock Index Returns using Intermarket Factor Models : Predicting Returns and Return Spreads using Multiple Regression and Classication

The purpose of this thesis is to examine the predictability of stock indices with regression models based on intermarket factors. The underlying idea is that there is some correlation between past price changes and future price changes, and that models attempting to capture this could be improved by including information derived from correlated assets to make predictions of future price changes. The models are tested using the daily returns from Swedish stock indices and evaluated from a portfolio perspective and their statistical signicance. Prediction of the direction of the price is also tested by Support vector machine classication on the OMXS30 index. The results indicate that there is some predictability in the market, in disagreement with the random walk hypothesis.
Electronic Journal of Applied Statistical Analysis A interval based on modiﬁed trimmed standard deviation for the mean of positively skewed populations

In this study, we propose a robust conﬁdence interval which is adjustment of the Student-t conﬁdence interval based on the trimmed mean and the modiﬁed trimmed standard deviation for the mean of skewed populations. The proposed conﬁdence interval is compared with existing conﬁdence intervals in terms of coverage probability and average width. The simulation study showed that the proposed robust conﬁdence interval performed better than the others. Also, proposed conﬁdence interval has narrowest average width in all sample sizes. In addition to the simulation, two real data sets were analyzed to illustrate the ﬁndings of the simulation study and the simulation results were veriﬁed. Consequently, we recommend the conﬁdence interval based on trimmed mean and modiﬁed trimmed standard deviation to estimate the mean of positively skewed populations.
Effects of the 5-HT 2C receptor agonist CP809101 in the amygdala on reinstatement of cocaine-seeking behavior and anxiety-like behavior

Serotonin 2C receptor (5-HT 2C R) agonists attenuate reinstatement of cocaine-seeking behavior. These receptors are found throughout the limbic system, including the basolateral amygdala (BlA), which is involved in forming associations between emotional stimuli and environmental cues, and the central amygdala (CeA), which is implicated in the expression of conditioned responding to emotional stimuli. This study investigated whether 5-HT 2C Rs in the amygdala are involved in cue and cocaine-primed reinstatement of cocaine-seeking behavior. Rats were trained to self-administer cocaine (0.75 mg/kg, i.v.) which that was paired with light and tone cues, and then subsequently they underwent daily extinction training. Rats then received bilateral microinfusions of the 5-HT 2C R agonist CP809101 (0.01 – 1.0 μ g/0.2 μ l/side) into either the BlA or CeA prior to tests for cue or cocaine-primed (10 mg/kg, i.p.) reinstatement. Rats were also tested for CP809101 effects on anxiety-like behavior on the elevated plus-maze (EPM). Surprisingly, intra-BlA CP809101 had no effect on cue reinstatement, though it did increase anxiety-like behavior on the EPM. Intra-CeA infusions of CP809101 attenuated cocaine-primed reinstatement, an effect that was prevented with concurrent administration of the 5-HT 2C R antagonist SB242084 (0.1 μ g/0.2 μ l/side). CP809101 had no effect on cue reinstatement or anxiety-like behavior on the EPM. These ﬁ ndings suggest that 5-HT 2C Rs in the BlA modulate anxiety, whereas those in the CeA modulate incentive motivational effects induced by cocaine priming injections.
Biochemical and Molecular Actions of Nutrients Dietary Resveratrol Does Not Affect Intestinal Tumorigenesis in Apc Min/ (cid:1) Mice 1

To determine its effect on intestinal tumorigenesis and the protumorigenic COX pathway in Apc Min/ (cid:1) mice, resveratrol was administered as a powdered admixture in the diet at 0, 4, 20, or 90 mg/kg body weight for 7 wk. In two separate experiments, resveratrol did not affect intestinal tumor load. It was stable in the diet under experimental conditions, circulated in the plasma as the glucuronide-conjugated form and reached the tumors as evidenced by signiﬁcant decreases in PGE 2 levels. However, immunohistochemical staining of intestinal tumors revealed no changes in COX-2 expression. This study demonstrates that resveratrol consumed ad libitum in the diet, does not modify tumorigenesis in Apc Min/ (cid:1) mice. J. Nutr. 134: 5–10, 2004.
Induction of the nitrate assimilation nirA operon and protein-protein 1 interactions in the maturation of nitrate and nitrite reductases in the 2 cyanobacterium Anabaena

17 Nitrate is widely used as a nitrogen source by cyanobacteria, in which the nitrate assimilation 18 structural genes frequently constitute the so-called nirA operon. This operon contains the genes 19 encoding nitrite reductase ( nirA ), a nitrate/nitrite transporter (frequently an ABC-type 20 transporter; nrtABCD ), and nitrate reductase ( narB ). In the model filamentous cyanobacterium 21 Anabaena sp. strain PCC 7120, which can fix N 2 in specialized cells termed heterocysts, the 22 nirA operon is expressed at high levels only in media containing nitrate or nitrite and lacking 23 ammonium, a preferred nitrogen source. Here we examined the genes downstream of the nirA 24 operon in Anabaena and found that a small open reading frame of unknown function, alr0613 , 25 can be co-transcribed with the operon. The next gene in the genome, alr0614 ( narM ), showed 26 an expression pattern similar to that of the nirA operon implying correlated expression of narM 27 and the operon. An insertional mutant of narM failed to produce nitrate reductase activity, 28 consistently with the idea that NarM is required for maturation of NarB. Both narM and narB 29 mutants were impaired in nitrate-dependent induction of the nirA operon, suggesting nitrite as 30 an inducer of the operon in Anabaena . It has been previously shown that the nitrite reductase 31 protein, NirA, requires NirB, a protein likely involved in protein-protein interactions, to attain 32 maximum activity. Bacterial two-hybrid analysis confirmed possible NirA-NirB and NarB- 33 NarM interactions suggesting that development of both nitrite reductase and nitrate reductase 34 activities in cyanobacteria involves physical interaction of the corresponding enzymes with their 35 cognate partners, NirB and NarM, respectively.