Miracle-dz/UniFAR-FTUs huggingface dataset:
CharaCtEriZation of staPhyloCoCCal CassEttE ChroMosoME MEC tyPEs in MEthiCillin-rEsistant sTAPHyLOCOCCUs INTERMEDIUs strains isolatEd froM dogs

sCCmec types in mecA-positive s. intermedius strains were characterized by using multiplex PCR method described previously by Oliveira and Lencastre [11]. A total of 100 preliminary identified S. intermedius isolates were tested to determine their species identify and mecA gene encoding methicillin resistance. The duplex PCR assay was able to identity all the strains carring mecA gene (17%). The study showed that 4% strains identified as S. intermedius based upon their phenotypic properties do not yielded a 16s rRNA gene amplification product, indicating staphylococci other than s. intermedius. sCCmec type IIIC was identified in 15 strains (88%), type IB was found only in one strain. One strain was not typable by this technique.
IMPLEMENTING A CSP SOLVER FOR SUDOKU

The most natural formal description of a Sudoku puzzle is to express it as a constraint satisfaction problem. The board presents the user with a number of cells, some already preassigned, and asks to fill in symbols from a finite domain such that each puzzle segment (row, column, or block) does not contain twice the same symbol. The näıve approach to solve a Sudoku is to try out potential candidate entries for the cells; if a conflict is detected, we backtrack and try out a different value. For Sudoku this is a bad choice: there exists a number of constraint propagation techniques which can solve most Sudokus without performing any search. In this paper we report on the implementation of a CSP solver for Sudokus, that integrates standard backtracking search with specialized constraint propagation techniques for Sudoku.
The Monsoon Shock in Rural Nepal

Monsoon rainfall is a key driver of economic life in rural Nepal as well as a major source of income variability. In this paper, the authors use a newly collected 3-year panel data set, representative of rural Nepal, merged with global monthly precipitation data to investigate the nature of the monsoon shock and to quantify household vulnerability to it. The authors find that the impact of the monsoon shock is concentrated in communities where water-intensive paddy dominates wet season cultivation and, coincidently, where groundwater irrigates dry season cultivation. In these communities, household size, area cultivated, agricultural and non-agricultural income, and household per capita food consumption measured nine months after the wet season harvest, all decline in response to a negative monsoon rainfall shock. A one standard deviation fall in monsoon precipitation is estimated to reduce total income by 3.8 percent and lead to a 0.8 percent drop in food consumption for the average rural household, but these figures rise to 11.5 percent and 3.3 percent, respectively, for households in the most paddy-intensive communities. These results have implications for social protection policies, especially in the lowlands of Nepal.
Content-Based Network Influence Probabilities: Extraction and Application

The diffusion of information and influence in networks shapes ideas, habits, and behaviors. Several diffusion control methods have been proposed to harness, maximize, limit, or direct such diffusion processes in desirable ways. State-of-the-art algorithms for prescriptive fine-grained diffusion control rely on simple models, most prominently the Independent Cascade (IC) model, rather than on advanced machine learning approaches. The simplicity of such models can be an advantage. Yet, to exploit this advantage, one needs not only well-designed algorithms, but also a powerful model-training framework that yields well-informed models. Unfortunately, much research effort has been devoted to algorithm design, while the development of techniques for informing the underlying model has been largely neglected. We propose a new content analysis workflow that derives realistic IC model parameters for diffusion control algorithms. We rely on a log of user text messages to derive a measure of similarity among those messages, and therefrom calculate the probability that one node influences another. We evaluate our model in terms of its predictive power and apply it to two representative diffusion control problems under the IC model. Our results showcase the capacity of our methods to make correct predictions, and provide the first, to our knowledge, study of diffusion control problems with a real-world probability model.
A viable system perspective on cluster development

There is disconsensus between/within theory and practice on how and why clusters work, with scholars responding to such face validity through various lens and arguments. Correspondingly, we sought to respond from a viable system perspective vis-à-vis the viable system model (VSM). Our analysis mapped content from public information to identify the viability issues of health clusters, while the process of analysis and reflection informed a second research question on how VSM can be applied to cluster development. Overall, 5 viability issues were identified. These ranged from the need for clarity on substance questions to dysfunctional system-functions. However, the process of analysis surfaced several limitations to the VSM, and points to potential synthesis with competition, co-creation, and constraints theories. To avoid ‘Beer goggles’, a viable system perspective should be used in tandem with aligning strategic functions by location, delivery, and overall competitiveness.
A Structured Approach for Rapid Identification of Fault-Sensitive Nets in Analog Circuits

The traditional body of literature on analog testing deals with propagation of faults to the output nets of the circuit. Often the set of detectable faults remains unsatisfactory because suitable stimuli cannot be found for propagating certain faults to the output. Existing technology supports capturing of the state of internal nets of a circuit, thereby enhancing the scope of detecting faults by observing their effect on internal nets. This approach is feasible only if the number of internal nets probed by the built-in test structure is very few. This paper presents a structured approach that identifies the sensitive nets, namely a well chosen small subset of internal nets that are affected by these faults. We utilize the speed of DC analysis and some common behavioral aspects of analog signals to find out this subset. We report dramatic improvement in fault coverage on several circuits including benchmarks.
Improving the quality of designing electromechanical energy converters for aircrafts by using a system-analytical modeling with the application of intelligent information technologies

This paper presents a model describing the primary magnetic field in the air gap of electromechanical energy converters with high-coercivity permanent magnets for aircrafts. It was obtained by using solutions of the Laplace partial differential equations in Cartesian coordinates. The verification method of this analytical model is presented and different software packages were used. The discrepancy between the solutions is in their two-dimensional and three-dimensional formulation in Cartesian and cylindrical coordinates is revealed. It is shown that the proposed analytical three-dimensional model is an effective interdisciplinary tool for the design of electromechanical energy converters with high-coercivity permanent magnets.
Work-in-Progress: Design Space Exploration of Multi-Task Processing on Space Shared FPGAs

High level synthesis frameworks, such as OpenCL, allow effective design space exploration by scaling of resource allocation via simple to use tunable parameters. The same process can be supported in multi-task processing but long synthesis time hinders system analysis and resource management optimization. This work proposes a methodology for simulation of multi-task processing on FPGA. In doing so, it also supports static spatial partitioning of resources along with a simulator to evaluate this approach. The simulator is based on a multidimensional resource fitting model for spatial evaluation and a machine learning based model for memory access. The results show that the simulator has an accuracy of at least 94.5% on average for throughput evaluation while allowing system design evaluation against various parameters.
Semi-supervised recurrent complex-valued convolution neural network for polsar image classification

This paper presents a novel semi-supervised terrain classification method of polarimetric synthetic aperture radar (PolSAR) image based on complex-valued convolution neural network (CV-CNN). Our proposed method only needs a small number of labeled samples to achieve good classification results. First, a Wishart classifier is used to find highly reliable samples in PolSAR data. Then, a new semi-supervised deep recurrent CV-CNN (RCVCNN) classification model has been proposed to improve PolSAR image classification accuracy and effectively solve network overfitting. Finally, a real PolSAR dataset is used to verify the effectiveness of our algorithm. Compared with the other three state-of-the-art methods, the proposed one show improvements in accuracy and better consistency.
On Embedding Backdoor in Malware Detectors Using Machine Learning

Researching for malware detection using machine learning is becoming active. However, conventional detection techniques do not consider the impact of attacks on machine learning, which has become complicated in recent years. In this research, we focus on data poisoning attack, which is one of the typical attacks on machine learning, and aim to clarify the influence of attacks on malware detection technology. Data poisoning attack is an attack method that intentionally manipulates the predicted result of a learned model by injecting poisoning data into training data, and by applying this, it is possible to embed a backdoor that induces mis-prediction of only specific input data. In this paper, we first propose an attack framework for backdoor embedding that prevents detection of only specific types of malware by data poisoning attack. Next, we will describe a method to generate poisoning data efficiently while avoiding attack detection by solving the optimization problem. Furthermore, we take malware detection technology using logistic regression and show the effectiveness of the our method through evaluation experiments using two datasets.
Class Size Variance Minimization to Metric Learning for Dish Identification

The objective of metric learning is to search a suitable metric for measuring distance or similarity between samples. Usually, it aims to minimize the distance between samples of same class and maximizes the distance between samples of different classes. However, most metric learning methods do not consider the sizes of classes, which may cause negative impact on the performance in classification since the size of a cluster is usually ignored in the distance comparison. In this work, we propose a triplet loss with variance constraint. Our method focuses not only on the distances between samples but also on the sizes of classes. The size difference between classes is also minimized in our objective function. The experimental results confirm that our method outperforms the one without the class size variance.
EEG-based Motion Task for Healthy Subjects Using Time Domain Feature Extraction: A Preliminary Study for Finding Parameter for Stroke Rehabilitation Monitoring

Nowadays, Stroke has been the second most cause of deaths in the world after Ischaemic heart disease. Rehabilitation of stroke patients after the attack is still the most effective way of restoring the patients to normal. However, most of the rehabilitation methods are done manually. In most of stroke rehabilitation programs, the evaluation procedures are still done using visual observation by clinicians. Considering that background, this study is the preliminary stage in preparing stroke rehabilitation monitoring by using EEG. Since EEG has been used widely for studying the human motion and human control especially in the neural system, applying EEG for stroke rehabilitation monitoring and evaluation would be a great solution because the assessment of the rehabilitation progress can be quantified in a better way. Eleven healthy subjects performing specific motion tasks: baseline (no motion), finger motion, grasping and elbow-flexion, the EEG is then recorded and extracted. Statistical parameters were calculated to get the EEG pattern such as mean and mean absolute value (MAV). From the data analysis, we found that during motion, the value of MAV was tended to decrease in low beta bands. We also found that the maximum amplitude of relaxing or no motion (MAR) is higher than the maximum amplitude of the movement (MAM) in the low beta band both C3 and C4 channel.
Relation Classification for Indonesian Music News Sentences

Relation classification (RC) for music domain consists of single-label, and multilabel classification problems. This research aims to build relation classifiers for various relation classification tasks on Indonesian music news sentences. Using Convolutional Neural Network as main architecture, different oversampling techniques for unbalanced datasets, and binary relevance method for handling multilabel classification, it achieves performance better than baseline methods SVM and shallow neural network, with average 0.85 for F1 score (micro) and 0.74 for F1 score (macro) for five different forms of relations (artist-artist, artist-album, artist-genre, artist-concert, and artist-song). This research also produces 14,541 annotated RC relations, 18,924 annotated named entities, and 1,241,428 unlabeled sentences in Indonesian music domain.
Depressive Symptoms and Functional Impairments Extraction From Electronic Health Records

This study aims to extract symptom profiles and functional impairments of major depressive disorder from electronic health records (EHRs). A chart review was conducted by three annotators on 500 discharge notes randomly selected from a medical center in Taiwan to compile annotated corpora for nine depressive symptoms and four types of functional impairment. Named entity recognition techniques including the dictionary-based approach. , a conditional random field model, and deep learning approaches were developed for the task of recognizing depressive symptoms and functional impairments from EHRs. The results show that the average micro-F-measures of the supervised learning approaches in extracting depressive symptoms is almost perfect (>0.90) but less accurate for the extraction of functional impairment.
Improving the Quality of the Conversational Datasets through Extensive Semantic Analysis

As more and more datasets become available their utilization in different applications increases in popularity. Their volume and production rate, however, means that their quality and content control is in most cases non-existing, resulting in many datasets that contain inaccurate information of low quality. Especially in the field of conversational assistants, where the datasets come from many heterogeneous sources with no quality assurance, the problem is aggravated. We present here an integrated platform that creates task-and topic-specific conversational datasets to be used for training conversational agents. The platform explores available conversational datasets, extracts information based on semantic similarity and relatedness, and applies a weight-based score function to rank the information based on its value for the specific task and topic. The finalized dataset can then be used for the training of an automated conversational assistance over accurate data of high quality.
Pre-Collision Warning and Recommendation System for Assistant Driver using Least Square Support Vector Machine and Fuzzy Logic

1.25 million people lose their lives due to road accidents every year. To deal with this problem, some innovative transportation are proposed to avoid road accidents, one of them is giving alert and recommendation for each vehicle which is well known as Pre-Collision Warning. According to many researches that have been conducted before, the performance of pre-collision warning can be improved by considering not only speed, but also the traffic sign and the distance to other objects. Furthermore, after predicting the danger, the system needs to give the speed recommendation and its action. This research conducts Least Square – Support Vector Machine (LS-SVM) to predict the danger and give the speed recommendation using Fuzzy Logic. The experiment shows that the best performance is achieved by LS-SVM according to the value of 98.71% for accuracy, 98.60% for precision, and 99.18% for recall.
The IMU and Bend Sensor as a Pointing Device and Click Method

A pointing device is one of the important interfaces between human and computer. A mouse is usually used as a pointing device; however, people with physical impairments who can not use a mouse due to its operation should use a table. One candidate for solving this problem is using an upper-arm movement combined with an inertial measurement unit sensor. The upper-arm gestures would be mapped and used to manipulate the mouse cursor on the monitor display. The ‘clicking’ action will be accomplished using a bending sensor attached on the opposite upper-arm. This study evaluates the combination of the IMU and bend sensor (IMU+Bend) as a substitute for the mouse. The evaluation is based on ISO/TS 9241-411: Ergonomics of human-system standard. The results mentioned that the use of IMU is a promising way to emulate the movement of cursor. However, the usage of bend sensor is uncomfortable when it is used as a clicking method. The throughput of the proposed is 1.75 bps, and qualitative results show that the mean of comfort is 4.85 on a Likert scale ranged from 1 to 7.
Passivity-Based Control of Hybrid Energy Storage System with Common Battery and Modular Multilever DC-DC Converter-Based Supercapacitor Packs

Hybrid energy storage systems (HESS) are often used in stand-alone power supply systems in order to conform fluctuations of electric energy generation and its consumption. In this work, bidirectional power supply system with battery/supercapacitor HESS based on a new topology of a two-way modular multilevel DC-DC converter of triangular structure was proposed. This topology is marked by high-boost and at the same time sufficiently simple due to the non-isolated configuration. Conducted investigation shows that only one SC pack must have a sufficient capacitance, but other SC packs may be low capacity. The capacitances of these SC packs depend from the power of one module of multilevel DC-DC converter and from the allowable error of DC bus voltage regulation. Because of significant nonlinearity and interconnection of the boost DC-DC converters, the passivity based control (PBC) approach was used. Effectiveness of the obtained control lows was demonstrated by the results of conducted simulation.
A Multilevel Gate Driver of SiC mosfets for Mitigating Coupling Noise in Bridge-Leg Converter

Silicon carbide (SiC) devices can significantly increase power efficiency, temperature reliability, power capacity, and power density because of their outstanding characteristics. Nevertheless, in the bridge-leg configuration of a power converter, the high-speed switching capability of SiC <sc>mosfet</sc>s is often restricted by the coupling noise between high-side <sc>mosfet</sc> and low-side <sc>mosfet</sc>. The coupling noise caused by high <inline-formula><tex-math notation="LaTeX">${dv/dt}$</tex-math></inline-formula> and <inline-formula><tex-math notation="LaTeX">${di/dt}$</tex-math></inline-formula> in switching transient will increase the switching loss and the risk of <sc>mosfet</sc> breakdown. In order to completely take advantage of the superior feature of SiC <sc>mosfet</sc>s, a novel multilevel gate driver is proposed in this article to mitigate the coupling noise effectively. Compared to the conventional gate drivers, the proposed gate driver adds a capacitor, an auxiliary <sc>mosfet</sc>, a Zener diode, and two resistors to regulate the turn-<sc>off</sc> voltage bias felicitously. In this article, the operation principle of the proposed gate driver is first introduced, to the best of our knowledge. Moreover, to obtain the circuit parameters accurately, a series of formulas have also been derived by equivalent circuit models. A 230-V/4.6-A synchronous buck converter based on SiC <sc>mosfet</sc>s is successfully experimented to verify this proposed gate driver.
Varying Resolution Pipelined ADC for Direct IF Sampling Receiver in Wireless Sensor Networks

Cheap and efficient sensor nodes aid in the development of the technology needed for IoT and wireless sensor networks. A critical component in these systems is the RF transceiver and it has been shown that lowering the power of the ADC significantly reduces the overall power consumption of wireless sensor nodes. This work presents a pipelined ADC with adaptive resolution based on the received signal for an IF-sampling receiver to reduce the power consumption of a wireless sensor node. Based on the modeling of a low-power wide-area network (LPWAN) transceiver with an output SNR of −3dB, the ADC needed only 6, 8, and 10 bits of resolution to properly decode a message signal, whose amplitude varies with a distance of 1–15 km based on propagation loss assuming constant transmit power. The pipelined ADC was composed of 10 different stages which were turned on or off based on the resolution needed, as determined by the detected amplitude of the message signal. Stages 1–2 and stages 1–4 were turned off for 8-bit and 6-bit configurations, respectively. The pipelined ADC was designed using a 65nm CMOS process with 1V supply and the power consumption of the 10, 8, and 6 bit states were 23mW, 16.5mW, and 13mW, respectively, with a 1Vpp differential full-scale voltage for all configurations. The static performance of the ADC was tested in the schematic level using a 600mVpp slow ramp and achieved an INL of within 0.875, 0.25, and 0.125 LSB for 10, 8, and 6 bit configurations, respectively. Dynamic tests were also done using a 600mVpp 10MHz sinusoidal input and an ENOB of 9.3, 7.9, and 5.8 bits were achieved for 10, 8, and 6 bit configurations, respectively.
Food supply chain decision optimization with quality information structure

Current food accidents occur repeatedly. Quality risk has become increasingly prominent. In the food supply chain, in view of the retailer’s risk preference, quality information has affected the strategy of the supply chain members profoundly. In our study, an ordering decision model is developed with the conditional value at risk criterion, analyzing the impact of quality information gathering strategy when the retailer is risk-averse. The research shows when the quality risk aversion retailer and the per unit ordering cost are high, the average order quantity has a positive correlation with the accuracy of information structure.
Attack-aware Lightpath Provisioning in Elastic Optical Networks with Traffic Demand Variations

This work considers lightpath provisioning in elastic optical networks with traffic demand variations while accounting for the impact of jamming attacks. Traffic requests are modeled based on their variation in traffic and a number of network traffic scenarios are pre-computed. Variations in traffic are used in order to design the network with improved resilience to jamming attacks in the general case, without considering a specific traffic matrix. The mathematical formulation and heuristic algorithms proposed in this work jointly consider the routing and spectrum allocation problem for the pre-computed network scenarios, aiming to minimize the required lightpath reallocations and the number of wavelength selective switches (WSSs) placed at specific network nodes/ports so as to minimize the impact of jamming attacks. Performance results demonstrate the benefits of this approach in terms of required lightpath reallocations, number of WSSs, as well as computational time required for dynamically reconfiguring the network when considering traffic demand variations.