Miracle-dz/UniFAR-FTUs huggingface dataset:
Impact of Learning rate in the development of Multi-Layer Perceptron for flash flood assessment

Environmental and climatic changes produce adverse effects like flash floods. Flash floods are very common in many countries and they are very sudden. Sudden flash floods cause infrastructural losses and casualties. Usually high precipitation index, water color, wave’s pattern, temperature, wind velocity and environmental CO2 levels have been measured as a yardstick to determine the run-offs. Diversified methodologies have been adopted to determine the flash flood actual timings and other properties. Sensors based, radar based and satellite based observations were used to investigate the flash floods. Artificial intelligence based algorithms were developed to detect the flash floods timely with less false alarm rate. In this research paper multi-layer perceptron neural network with different learning rate has been proposed for the vigorous determination of flash floods with less false alarm rate. Variable learning rates have been adopted to achieve the higher accuracy objective function for the better results. Impact of changing the learning rate has been discussed to prove that appropriate learning rate must be used to reduce the error rate with in the links of neuron.
Activity Pattern Mining for Healthcare

Cerebellar Ataxia (CA) is a neurological disease with the symptom of poor coordination of movement and balance disorders. In clinical medicine, the heel-knee-shin test and the rapid alternating movements test are important basis for assessing CA. Based on the above tests, this paper presents a non-contact method and investigates the feasibility of this method for detecting CA. This body sensor networks uses wireless devices operating in the C-band frequency range to capture data of both types of tests without intrusiveness. The obtained data of tests contains massive useful information about human health which is really significant to subjects. But the information can be so subtle that people ignore the value hidden in it. So utility pattern mining (UPM) is used for the purpose of mining subjects’ activity pattern. We find that the subjects’ activity pattern differs greatly in amplitude information. We extracted the amplitude information that is helpful for analyzing the test’ results to determine whether the test is positive or negative. Then we use different kind of algorithms to classify the data samples. Among them, support vector machine (SVM) has the best classification effect on both tests. In the heel-knee-shin test, the coincidence rate ( $\pi$ ) is 98.7%, the sensitivity (Se) is 98.9% and the specificity (Sp) is 98.5%. In the rapid alternating movements test, the $\pi $ is 99.4%, Se is 99.8% and Sp is 99%. The experimental results show that this technique has the potential to open up new clinical opportunities for contactless and accurate CA monitoring in a patient-friendly and flexible environment.
Micro Tissue Assembly for Co-Culturing 3D Skeletal Muscle and Adipose Tissues

This paper proposes micro tissue assembly for co-culturing 3D skeletal muscle and adipose tissues. The adipocytes encapsulated in a microfiber were cultured in advance for maturation which accumulated significantly larger size of lipid droplets compared with conventional 2D dish culture. Then, we assembled a micro tissue by placing the microfiber-based adipose tissue on a PDMS substrate with myoblast-laden collagen solution covering on the top. The assembled micro tissue was then co-cultured for 5 days. We found that the skeletal muscle tissue fabricated in the micro tissue bundled up adipose tissue forming in-vivo like composition. Our skeletal muscle and adipose tissue assembly not only gives a promising outlook for the micro physiological system but also tools for development studies or the cultured meat industry.
Design of Buck Converter with Dead-time Control and Automatic Power-Down System for WSN Application

A buck converter design with an automatic power-down technique and dead-time control system intended for low power application such as a wireless sensor network is proposed. With an input voltage range of 1V to 1.2V, the buck converter regulated the output voltage at 0.8V. This buck converter operates in a pulse-width modulation technique at load current range of 1mA-100mA. The output voltage ripple measured is 7.5 m V with the peak efficiency is 94.98 %. The quiescent current $(\mathrm{I}_{\mathrm{q}})$ of this proposed design is about $5\mu \mathrm{A}$. The line and load regulation is 0.195 mV/V and 0.61mV/mA, respectively. The circuit core layout dimension is $179 \mu\mathrm{m}$ and $120\mu \mathrm{m}$ 65nm CMOS technology.
Are SMEs Ready for Industry 4.0 Technologies: An Exploratory Study of I 4.0 Technological Impacts

Fourth Industrial revolution, Industry 4.0- A rapidly growing world of digitization that opens the doorway for new era of automation plays a crucial role within the industry which requires a constant research and improvements to achieve and sustain themselves in current world of competitive market. Implementation of industry 4.0 technologies pioneer for a profit maximization with cost reduction and has even realized to show a significant refinement in improved sustainability. Global Market leading organisations are positioning themselves with world of digitisation to their end consumers utilising the digital marketing, e-commerce and customer experience virtually driving towards a completely new era of digital ecosystem. Supply chain management (SCM) within the small and medium enterprises (SME‘s) plays an important role in supporting the global manufacturing economies. SME's lacks the understanding and significance of transforming themselves with the extremely complex digital supply chain systems that may leads to losses if they don't embrace now for innovations and implement the latest technological advancements. This paper aims to examine the current trends and impacts of latest technologies within the SME's with the view of key factors of Industry 4.0.
Reinforcement Learning Guided Symbolic Execution

Symbolic execution is an indispensable technique for software testing and program analysis. Path-explosion is one of the key challenges in symbolic execution. To relieve the challenge, this paper leverages the Q-learning algorithm to guide symbolic execution. Our guided symbolic execution technique focuses on generating a test input for triggering a particular statement in the program. In our approach, we first obtain the dominators with respect to a particular statement with static analysis. Such dominators are the statements that have to be visited before reaching the particular statement. Then we start the symbolic execution with the branch choice controlled by the policy in Q-learning. Only when symbolic execution encounters a dominator, it returns a positive reward to Q-learning. Otherwise, it will return a negative reward. And we update the Q-table in Q-learning accordingly. Our initial evaluation results indicate that in average more than 90% of exploration paths and instructions are reduced for reaching the target statement compared with the default search strategy in KLEE, which shows the promise of this work.
Research on Parking Space Prediction Based on Long Short-Term Memory

The berth occupancy prediction is a key issue in the intelligent parking guidance information system. Current neural network-based prediction techniques will dramatically decrease in prediction accuracy as the prediction step size increases. In order to solve the problem that the prediction accuracy of berth occupancy decreases with the increase of step size, we propose a time series prediction model based on long short-term memory (LSTM) neural network. The model enhances the memory function of the network by controlling the forget gate, output gate and input gate, filtering out relatively unimportant information and reducing the sample dependence of the model, thus it improves the training efficiency. The model is tested by using real parking data. The test results show that the proposed model has certain advantages in the accuracy of long-term prediction.
Influence of the Source of Intention to Move on Readiness Potential of Brain Waves

Anticipating future events is an interesting topic discussed through generations. When it comes to human movement prediction a premotor brain activity, termed ‘readiness potential (RP), is highlighted. It is an upsurge in brain activity prior to a motor-related action which can be registered via scalp electroencephalography (EEG). In the current study we investigated the effect of self-generated intention to move versus externally triggered intention to move on readiness potential. This experiment was conducted on 12 subjects. EEG data was recorded under two major conditions; (1) subject makes a fist voluntarily and (2) subject makes a fist according to a random auditory stimulus. Simultaneous button presses were used as the markers to the points of physical movement. The occurrence of increase in brain activity relative to the physical movement was compared in both conditions. The results confirmed that there is a visible upsurge in the brain activity prior to motor activities which can be recognized as the readiness potentials. Results also showcased noteworthy differences in the behavior of RP, suggesting that the RP can get affected by the source of the intention to move. i.e. self-generated intention or externally triggered intention.
A Sector Combination Optimization Algorithm for the Multi-Depot Vehicle Routing Problem

This paper proposes a bilevel programming method to solve the MDVRP problem, the goal is to minimize the vehicle distribution distance. On the premise of meeting the capacity of the yard and the demand of customers, the upper layer assigns customers to the depots according to the nearest distance from the depot. The lower level solves the problem of single vehicle route planning respectively, assigning customers to vehicles and planning their access sequence. In the lower level, the sector combination optimization (SCO) algorithm is used to generate multiple initial solutions. With these solutions as the initial population of genetic algorithm and ant colony algorithm, the number of iterations can be reduced to achieve fast convergence. And because of the abundant initial solutions, the algorithm can avoid trapping in local optimum. The results show that the sector combination optimization algorithm makes the initial solutions more abundant while reducing the space of the problem.
Investigation of a Flexible Downstream Scheme for Sampling Rate and Bandwidth Reduction in Short Reach Communication Systems

We demonstrate a cost- and spectrum-effective orthogonal frequency-division multiple access passive optical network downstream scheme based on multi-band and modified sub-Nyquist sampling over transmission reach of 20km. The sampling rate and bandwidth of ADC are reduced effectively, and double spectrum efficiency is obtained. The aggregate bit rate achieves 50.4 Gb/s. The influence of bit resolution of ADC/DAC is investigated and 8-bit resolution is regarded as a reasonable choice. The performance of receiver sensitivity and nonlinearity tolerance is studied. A 20km PON downstream transmission with splitter ratio of 1:64 is considered and the FEC limit of $3.8\times 10 ^{-3}$ is met. The research results show that the proposed scheme is suitable for the short reach communication systems and could be regarded as a promising candidate for the system beyond NG-PON2.
An Experimental Study on Spatiotemporal Step Characteristics of Individuals Considering the Effect of Ship Heeling and Trim

Ship listing and motion affects individual movement pattern on board, thus individual walking behavior would be significantly different from those on level ground. To quantify the correlation between individual walking speed and gait parameters of individuals on board, we designed a ship corridor simulator, where we performed both fast and freely walking experiments considering the effect of ship heeling and trim. Results indicated that both step length and step frequency are positively linearly associated with individual walking speed with different degrees, with one exception that individuals could adopt quick and tiny steps in a much lower walking speed with even larger step frequency to keep balance when the trim-up or trim-down angle exceeds 20°. However, there is no direct relationship that could be observed between step width and individual walking speed, despite that walking with wider steps might weaken individual stability and increase fall risk. Moreover, though individual experimental subjects tend to walk along the right side due to cultural factors, the walking routes are more likely to be close to the handrail arranged on the opposite side for safety purpose when the trim-down angle exceeds 20° even though some of them did not use the handrail actually. This work would be a primary consideration in the development of evacuation models and the design of evacuation facilities on board.
Flexible ZnO Thin Film Surface Acoustic Wave Device for Flow Rate Measurement

In this work, we demonstrate a surface acoustic wave (SAW) based ZnO/Al flexible device for the application of flow rate measurement. The flexible nature of the Al foil allows it to better conform to curved surfaces, in which case the rigid devices are difficult to apply. The device reaches an equilibrium condition above room temperature when it is heated. While the surrounding gas flow changes this equilibrium state, and the new equilibrium temperature is determined by the flow rate. Attributed to the large coefficient of thermal expansion of aluminum, the device has a large temperature coefficient of frequency (TCF) of approximately 280 ppm/K, resulting in high sensitivity to gas flow. Computational fluid dynamics (CFD) simulation was performed to describe the flow field used in the experiment. With the advantages of flexibility and high sensitivity, the proposed device could be a competitive alternative for flow rate measurement on curved surfaces.
Behavior of magnetic properties of power transformers structural steel A36 at different temperatures

A36 structural steel is used in shipbuilding industry, pipes that transport gas and oil, offshore structures, other type of structures, as well as in electric equipment especially in the structural parts of power transformers such as tank walls, flitch plates, and frames. Its use in these applications has focused the research on the mechanical properties. Hence, this paper is aimed on the magnetic properties. The analysis is carried out for three different ranges of operating temperature. Using the experimental Epstein frame method, the behavior of magnetization and the specific losses curves are obtained. Moreover, with the data obtained and using the finite element method, the model of the zone where high current bushings are mounted on a transformer is obtained and the eddy current losses in the tank wall are calculated.
Predictive Model of the Annual Remuneration Budget Using Data Mining: Case Study

Data mining allows the extraction of valuable knowledge that is hidden in large databases that have not been explored and remains hidden stored in historical databases. There are several applications of data mining such as financial models and healthcare. The theoretical basis used and the KDD methodology that was applied are described; the database was analyzed to gradually optimize these models and finally the most optimal model was determined to make the projection of the salary budget as a whole; it presents the conclusions reached. This research presents a model to make projections of the total salary budget; using 3,000,000 records with historical data from 2009 to 2017. The work presented establishes a significant improvement in the methodology of trends and partial and total salary patterns. There is a big difference in precision and accuracy, with respect to manual projection processes; using data mining techniques such as linear regression and neural networks.
High-Accuracy Classification of Attention Deficit Hyperactivity Disorder With l2,1-Norm Linear Discriminant Analysis and Binary Hypothesis Testing

Attention Deficit Hyperactivity Disorder (ADHD) is a high incidence of neurobehavioral disease in school-age children. Its neurobiological diagnosis (or classification) is meaningful for clinicians to give proper treatment for ADHD patients. The existing ADHD classification methods suffer from two problems, i.e., insufficient data and noise disturbance. In this paper, a high-accuracy classification method is proposed by using brain Functional Connectivity (FC) as ADHD features, where an ${l_{2,1}}$ -norm Linear Discriminant Analysis (LDA) model and a binary hypothesis testing framework are effectively employed. In detail, we introduce a binary hypothesis testing framework to cope with insufficient data of ADHD database. The FCs of test data (without seeing its label) are used for training and thus affect the subspace learning of training data under binary hypotheses. On other hand, the ${l_{2,1}}$ -norm LDA model generates a subspace to represent ADHD features, aiming to overcome noise disturbance. By robustly learning ADHD features, the subspace energy difference between binary hypotheses becomes more discriminative. Thereby, the true hypothesis can be rightly estimated with its larger subspace energy, which provides reliable evidence to predict the label of test data. By the test on ADHD-200 database, it shows that our method outperforms other state-of-the-art methods with the significant average accuracy of 97.6%. Moreover, the corresponding result analysis with ADHD symptom score and the explanation of discriminative FCs between ADHD and healthy control groups are given, which further verifies the validity of our classification method.
A pilot study on the use of ultra‐deformable liposomes containing bleomycin in the treatment of equine sarcoid

The efficacy of a novel topical liposome‐encapsulated preparation of bleomycin (Bleosome) was studied in 118 clinical cases of equine sarcoid and efficacy was compared with two other standard conventional treatments, tazarotene and 5‐fluorouracil (5‐FU) as well as with the Bleosome in combination with each of these two conventional treatments. Treatments were arbitrarily assigned. Fifty‐two of the 118 sarcoids (44%) were resolved after 12 months but there were significant differences between treatment groups with the combinations of either 5‐FU and bleomycin, or tazarotene and bleomycin resulting in significantly superior resolution of 77 and 78% of the lesions, respectively. The preliminary results suggest that bleomycin in a liposomal carrier may be a useful treatment modality for superficial, diffuse and verrucose sarcoids. The treatment process was simple, and outcomes were functionally and cosmetically excellent. Liposome‐encapsulated bleomycin is economic compared to radiation and other treatment options and can be applied effectively by the owner under guidance. Further studies with different administration protocols and higher concentrations of bleomycin are warranted.
Temperature Robust Silicon Resonant Accelerometer with Stress Isolation Frame Mounted on Axis-Symmetrical Anchors

This paper presents a novel silicon resonant accelerometer (SRA) with a stress isolation frame mounted on axis-symmetrical anchors to improve the temperature performance. The stress-insensitive design prevents the thermal stress produced by the mismatch of the thermal expansion coefficients (CTE) of the heterogeneous materials in the device from transmitting to double-ended tuning forks (DETF), resulting in reducing the thermal sensitivity of SRA. The results show the scale factor is 516 Hz/g and the average nominal frequency of the two DETFs is ∼138.4 kHz with the closely matched temperature coefficients of frequency (TCF) 5.72 Hz/°C and 5.92 Hz/ °C, respectively. The bias thermal sensitivity and compensated stability are 0.42 mg/°C and 0.6 mg over the temperature range from −40 °C to 40 °C, which is competitive compared with previously reported results in literatures.
PointerLock: Protecting Function Pointers with Access Control on Page

Since function pointers are closely related to software control flow and they are writable, attackers often take function pointers as the targets. To protect the function pointers, we propose a novel solution called PointerLock. To avoid being tampered with by the attacker, PointerLock restricts the writing operation on the function pointers with the support of access control on the page. PointerLock centralizes the application's function pointers to a few pages, and these pages are only readable by default. Only when the application want to modify the pointer, PointerLock makes the page writable where the function pointer is located in. This reduces the time window of attacking the function pointer. To prevent an attacker from tampering with other function pointers on the same page as function pointers, PointerLock performs the necessary checks on the page before they are used again. The prototype system of PointerLock was built in Fedora 21 with a 3.17. 6 kernel. Two system calls are implemented in kernel space, and six interfaces are developed for users in the form of dynamic link libraries in user space. The tests show that PointerLock is capable of preventing the attacker from tampering with the function pointers, and it does not introduced an overheavy performance.
An Efficient FPGA implementation of Turbo Product Code decoder with single and double error correction

The paper presents FPGA implementation of turbo product code decoder with single and double error correcting BCH constituent codes that is capable of supporting high throughput and still maintains low complexity. The implementation is based on the Chase-Pyndiah algorithm, which exhibits a modular, simple structure with fine parallelism. Complexity reduction and pipelining for throughput and latency has been done through novel optimizations in submodules of TPC decoder. The resulting turbo decoder is implemented on a Xilinx Virtex-6 customized hardware. Performance comparison against third party IP cores is also presented,
A Partial Scrambling Overlapped Selected Mapping PAPR Reduction Scheme for OFDM/OQAM Systems

As a spectrum efficient multi-carrier modulation scheme, filter-bank based orthogonal frequency division multiplexing techniques using offset quadrature amplitude modulation (OFDM/OQAM) are investigated for future wireless communication systems, where a low-sidelobe spectrum is achieved by band-limiting each subcarrier while keeping their orthogonality. One of the technical issues in OFDM/OQAM is the suppression of the peak-to-average power ratio (PAPR). Since existing PAPR reduction schemes for traditional OFDM are not straightforwardly applicable to OFDM/OQAM systems, PAPR suppression for OFDM/OQAM is still a challenging issue. This paper proposes a partial scrambling overlapped selected mapping (PS-OSLM) technique for OFDM/OQAM systems, where a partial scrambling technique is applied to an overlapped SLM scheme which selects the best candidate sequence among possible overlapped candidate sequences so as to minimize the PAPR. It is verified by simulation that the proposed PS-OSLM effectively reduce PAPR of OFDM/OQAM signal without explicit side information while improving bit error rate (BER) performance compared with conventional schemes.