Miracle-dz/UniFAR-FTUs huggingface dataset:
Supervised Paragraph Vector: Distributed Representations of Words, Documents and Class Labels

While the traditional method of deriving representations for documents was bag-of-words, they suffered from high dimensionality and sparsity. Recently, many methods to obtain lower dimensional and densely distributed representations were proposed. Paragraph Vector is one of such algorithms, which extends the word2vec algorithm by considering the paragraph as an additional word. However, it generates a single representation for all tasks, while different tasks may require different representations. In this paper, we propose a Supervised Paragraph Vector, a task-specific variant of Paragraph Vector for situations where class labels exist. Essentially, Supervised Paragraph Vector uses class labels along with words and documents and obtains corresponding representations with respect to the particular classification task. In order to prove the benefits of the proposed algorithm, three performance criteria are used: interpretability, discriminative power, and computational efficiency. To test interpretability, we find words that are close and far to class vectors and demonstrate that such words are closely related to the corresponding class. We also use principal component analysis to visualize all words, documents, and class labels at the same time and show that our method effectively displays the related words and documents for each class label. To evaluate discriminative power and computational efficiency, we perform document classification on four commonly used datasets with various classifiers and achieve comparable classification accuracies to bag-of-words and Paragraph Vector.
Worst-Case Innovation-Based Integrity Attacks With Side Information on Remote State Estimation

In this paper, we study the worst-case consequence of innovation-based integrity attacks with side information in a remote state estimation scenario where a sensor transmits its measurement to a remote estimator equipped with a false-data detector. If a malicious attacker is not only able to compromise the transmitted data packet but also able to measure the system state itself, the attack strategy can be designed based on the intercepted data, the sensing data, or alternatively the combined information. Surprisingly, we show that launching attacks using the combined information are not always optimal. First, we characterize the stealthiness constraints for different types of attack strategies to avoid being noticed by the false-data detector. Then, we derive the evolution of the remote estimation error covariance in the presence of attacks, based on which the worst-case attack policies are obtained by solving convex optimization problems. Furthermore, the closed-form expressions of the worst-case attacks are obtained for scalar systems and the attack consequences are compared with the existing work to determine which strategy is more critical in deteriorating system performance. Simulation examples are provided to illustrate the analytical results.
Analysis of Negative-Capacitance Germanium FinFET With the Presence of Fixed Trap Charges

In this paper, the effect of negative-capacitance (NC) on germanium FinFET (GeFinFET) with the presence of fixed trap charges (<inline-formula> <tex-math notation="LaTeX">${N}_{\text {ftc}}$ </tex-math></inline-formula>) has been studied. The analysis of various parameters of NCGeFinFET has shown that the positive trap charges improve the device characteristics whereas the negative trap charges (NTC) degrade the performance of devices. Therefore, we have optimized properties of ferroelectric (FE) to suppress the degradation caused by NTC. Furthermore, the impact of <inline-formula> <tex-math notation="LaTeX">${N}_{\text {ftc}}$ </tex-math></inline-formula> has been studied on the output characteristics of NCGeFinFET inverter, and it has been shown that the degradation due to NTC reduces by optimizing the FE properties. It has also been demonstrated that NCGeFinFET exhibits excellently improved characteristics as compared to GeFinFET.
AESA radar and its application

Active electronically scanned array technology, with its unique degree of operational capability which is on the verge of increasing the performance of modern radar systems. AESA technology is particularly adventurous in the fighter radars. The performance, efficiency, life cycle cost makes it special. The development of packaging technology such as GaN, MMIC, the MMIC architecture are the recent trends which makes the further advancement in this sector.
Verilog Code Generation Scheme from Signal Language

Signal is a high-level declarative data flow language and has been successfully used for the design and implementation of reactive safety-critical embedded systems. The verified automatic code can be generated for various languages (C, C++, and Java) from the open source Signal framework (Polychrony Toolset). However, open source tool for automatic code generation from Signal language to Hardware Description Languages (HDLs) is not available. In this paper, we present a methodology of code generation from Signal language to Verilog. Verilog code is generated from the transformed Signal program based on guard's hierarchy and their associated sub-graph. Hardware structure is by default concurrent in nature; therefore, it is well-suited to generate code from Signal to HDL. It will provide more options for the designer when some part or complete application is required to be implemented in hardware.
Sub-1-dB and Wideband SiGe BiCMOS Low-Noise Amplifiers for  $X$ -Band Applications

In this paper, a design methodology for SiGe HBT-based low-noise amplifiers (LNAs) is proposed that can be utilized for both sub-1-dB noise figure (NF) and wide bandwidth as an alternative to the conventional simultaneous input noise and power matching technique. This paper focuses on the removal of the series base inductor and the inclusion of an output matching network as a design parameter that can be used to achieve both sub-1dB NF and wide bandwidth. The effects of the mentioned design parameters on NF and bandwidth are described and analyzed, including the comparison of results with the conventional design technique. To demonstrate the validity of the analysis and impact of utilizing base-to-collector capacitance on LNA performance metrics, two different LNAs implemented in a 0.13- $\mu \text{m}$ SiGe technology. The first LNA achieves a lower than 1-dB NF with 26-dB gain at 8.5 GHz, and the second LNA exhibits wideband operation, with a better than 10-dB return losses in the range of 8–35 GHz and lower than 3-dB NF from 6 to 20 GHz. The sub-1-dB LNA reaches −17.3 dBm of input-referred compression point (IP1dB), with a power consumption of 62 mW and an area of 0.795 mm2. The wideband LNA achieves 17.6 dB of peak gain with a 48.5-mW power consumption and 0.69-mm2 chip area. To the best of our knowledge, this paper achieves the best NF performance in the literature utilizing a SiGe technology, while the wideband LNA exhibits the best operational bandwidth, together with a reasonable low-noise performance.
Application of Adaptive Filters in Sensor Array Processing for Underwater Applications

This paper aims to apply adaptive filtering algorithms in sensor array processing for underwater surveillance and detection systems with linear arrays. Adaptive filters are iterative in nature and continuously update the filter taps to minimize the error signal based upon certain criterion. Adaptive filters are used in beamforming to place the main beam in the direction of desired signal and place nulls in the direction of unwanted signals. Adaptive filters are also used in spectral analysis of acoustic signals for background noise cancellation. This paper evaluates the performance of three adaptive filtering algorithms with respect to sensor array design parameters i.e. number of sensors, sensor spacing, number of incident signals and their angular separation. The three adaptive filtering algorithms namely least mean square (LMS), normalized LMS (NLMS), and sample matrix inversion (SMI) have been compared in the context of linear array beamforming. The paper provides detailed discussions on the simulation results of these adaptive algorithms in terms of convergence speed, beamwidth, null depths and maximum side lobe levels. In addition the paper illustrates a practical example of applying adaptive filters in underwater sensor array processing systems for detection of target’s acoustic signatures.
Supporting the Momentum Training Algorithm Using a Memristor-Based Synapse

Despite the increasing popularity of deep neural networks (DNNs), they cannot be trained efficiently on existing platforms, and efforts have thus been devoted to designing dedicated hardware for DNNs. In our recent work, we have provided direct support for the stochastic gradient descent (SGD) training algorithm by constructing the basic element of neural networks, the synapse, using emerging technologies, namely memristors. Due to the limited performance of SGD, optimization algorithms are commonly employed in DNN training. Therefore, DNN accelerators that only support SGD might not meet DNN training requirements. In this paper, we present a memristor-based synapse that supports the commonly used momentum algorithm. Momentum significantly improves the convergence of SGD and facilitates the DNN training stage. We propose two design approaches to support momentum: 1) a hardware friendly modification of the momentum algorithm using memory external to the synapse structure, and 2) updating each synapse with a built-in memory. Our simulations show that the proposed DNN training solutions are as accurate as training on a GPU platform while speeding up the performance by <inline-formula> <tex-math notation="LaTeX">$886\times $ </tex-math></inline-formula> and decreasing energy consumption by <inline-formula> <tex-math notation="LaTeX">$7\times $ </tex-math></inline-formula>, on average.
The Average-Case Complexity of Counting Cliques in Erdős-Rényi Hypergraphs

The complexity of clique problems on Erdős-Rényi random graphs has become a central topic in average-case complexity. Algorithmic phase transitions in these problems have been shown to have broad connections ranging from mixing of Markov chains and statistical physics to information-computation gaps in high-dimensional statistics. We consider the problem of counting k-cliques in s-uniform Erdős-Rényi hypergraphs G(n, c, s) with edge density c and show that its fine-grained average-case complexity can be based on its worst-case complexity. We prove the following: •Dense Erdős-Rényi hypergraphs: Counting k-cliques on G(n, c, s) with k and c constant matches its worst-case complexity up to a polylog(n) factor. Assuming ETH, it takes n^Ω(k) time to count k-cliques in G(n, c, s) if k and c are constant. • Sparse Erdős-Rényi hypergraphs: When c = Θ(n^-α), for each fixed α our reduction yields different average-case phase diagrams depicting a tradeoff between runtime and k. Assuming the best known worst-case algorithms are optimal, in the graph case of s = 2, we establish that the exponent in n of the optimal running time for k-clique counting in G(n, c, s) is ωk/3 - C α (k/2) + O_k, α (1), where ω/9 ≤ C ≤ 1 and ω is the matrix multiplication constant. In the hypergraph case of s ≥ 3, we show a lower bound at the exponent of k - α (k/s) + O_k, α (1) which surprisingly is tight against algorithmic achievability exactly for the set of c above the Erdős-Rényi k-clique percolation threshold. Our reduction yields the first known average-case hardness result on Erdos-Renyi hypergraphs based on a worst-case hardness assumption. We also analyze several natural algorithms for counting k-cliques in G(n, c, s) that establish our upper bounds in the sparse case c = Θ(n^-α).
Denial of Human Rights: We Must Change the Paradigm of Dementia Care

ABSTRACT In far too many instances treatment of persons with dementia has reflected a fundamental denial of basic human rights. At times, these individuals are treated worse than the treatment of animals when the five basic freedoms of animals, described by Pachana in her editorial, are implemented. A number of such examples of dehumanizing (and “de-animalizing”) persons with dementia are presented. A case is made for the position that this is the direct result of the “medicalization” of dementia and “Alzheimer Disease. ” This has led to the disenfranchisement of persons with dementia and their caregivers regarding the treatment of dementia, while medical “expertise” has led to a paradigm of learned helplessness while waiting for “the cure. ” While the medicalization of dementia has been a financial success in terms of funding failed researcher to find a cure, it has been a catastrophe for the quality of life of persons with dementia and their caregivers. It is time to take control of the treatment of dementia back, and especially to listen to the voices of persons with dementia. It is time to take action NOW – to become disruptive to the current paradigm. The emperor and his cure have no clothes. We deserve better. We must make this change in paradigm our mission, to demand it, and to accept nothing less. Power to the people.
Multimodal Mild Depression Recognition Based on EEG-EM Synchronization Acquisition Network

In this paper, we used electroencephalography (EEG)-eye movement (EM) synchronization acquisition network to simultaneously record both EEG and EM physiological signals of the mild depression and normal controls during free viewing. Then, we consider a multimodal feature fusion method that can best discriminate between mild depression and normal control subjects as a step toward achieving our long-term aim of developing an objective and effective multimodal system that assists doctors during diagnosis and monitoring of mild depression. Based on the multimodal denoising autoencoder, we use two feature fusion strategies (feature fusion and hidden layer fusion) for fusion of the EEG and EM signals to improve the recognition performance of classifiers for mild depression. Our experimental results indicate that the EEG-EM synchronization acquisition network ensures that the recorded EM and EEG data require that both the data streams are synchronized with millisecond precision, and both fusion methods can improve the mild depression recognition accuracy, thus demonstrating the complementary nature of the modalities. Compared with the unimodal classification approach that uses only EEG or EM, the feature fusion method slightly improved the recognition accuracy by 1.88%, while the hidden layer fusion method significantly improved the classification rate by up to 7.36%. In particular, the highest classification accuracy achieved in this paper was 83.42%. These results indicate that the multimodal deep learning approaches with input data using a combination of EEG and EM signals are promising in achieving real-time monitoring and identification of mild depression.
A Framework for Heterogeneous Many-core SoCs Generation

This work presents a framework for heterogeneous many-core SoCs generation, which comprises a flexible EDA (Electronic Design Automation) framework and a many-core model for heterogeneous SoCs. The framework together with the many-core model supports the integration of processors, network interfaces, routers, and peripherals. The hardware model is cycleaccurate, with a SystemC model to speed up simulation time and a VHDL model enabling prototyping in FPGAs devices. The framework provides a rich set of graphical debugging tools enabling an easy and intuitive understanding of computation and communication events happening at runtime. The coupled integration of the platform model to the EDA framework makes the many-core well suited to be employed in research and teaching. As case-study, we provide an evaluations addressing the many-core generation, simulation, and debugging.
CNN Learning for Image Processing: Center of Mass versus Genetic Algorithms

This paper presents a comparative performance analysis of two learning algorithms developed for the use in Cellular Neural Networks (CNN): the Center of Mass Algorithm, a back-propagation like technique, and an adaptation of the Genetic Algorithm. Both methods are applied for the training of a CNN built with Full Signal Range (FSR) cells, for the implementation of several well-known bipolar functions of image processing. Performance parameters such as total execution time, number of CNN runs and success rate are assessed in order to provide guidelines for the learning method choice.
Investigation on Contribution of Inductance Harmonics to Torque Production in Multiphase Doubly Salient Synchronous Reluctance Machines

This paper investigates the contribution of each order inductance harmonic to the torque (both average torque and torque ripple) of multiphase doubly salient synchronous reluctance machines (DS-SRMs). Such machines are similar to switched reluctance machines but supplied with sine wave currents. The investigations in this paper are as follows. First, a general analytical torque model based on Fourier series analysis of inductances has been built for machines with different phase numbers, slot/pole number combinations, and also winding configurations. The instantaneous torque for DS-SRMs with any given phase number can then be accurately predicted. Using such model, contribution of each order inductance harmonic to torque can be investigated separately. It is found that the torque ripple frequency of the DS-SRM only depends on phase number. For example, for an m-phase machine, there will be m $\times $ kth-order torque ripple if mod(mk, 2) = 0, where m is the phase number and k is a natural number. This paper also explains why certain phase numbers inherently produce lower torque ripple than others. The findings in this paper provide a future direction for potential torque ripple reduction methods either from machine design or advanced control. The simulations have been validated by experiments using a six-phase DS-SRMs.
An Entropy-Based Solution for Identifying Android Packers

By using wrapping techniques, malicious developers can build a packed app (or packer) to deceive the basic analysis layer and successfully publish it through Android‘s application distribution service (such as Google Play) before being detected by an in-depth analysis process. Although a thorough dynamic analysis can help in identifying packers, it is impossible to analyze mass numbers of apps in a short time. Regarding static analysis, most of the existing research on identifying packers depends on the signature of packers such as files or directories. In this paper, we propose a model for detecting packers through static analysis of various contents in an Android Package file. By applying entropy-based algorithms in conjunction with common classification solutions, our model can provide output about whether a target app has the potential to be a packer or not through prediction values. Compared with previous solutions with absolute results, the relative results could increase the likelihood of identifying variants from known packers. The results of our experiment have shown that our model can identify potential packers with high accuracy. Our proposed model can help improve the detection of potential packers as well as contribute new research directions for detecting packer by static analysis.
Low Noise Front-End and ADC for Real-Time ECG System in CMOS Process

This paper presents the design and experimental results of a digital acquisition system based on a chopper-stabilized Instrumentation Amplifier with Common-Mode feedback for CMRR enhancement. Chopping techniques are used to remove both offset and flicker noise, detrimental effects characteristic of pure CMOS processes. A second-order, discrete-time, single-bit Sigma-Delta ADC with CIFB structure is used to convert the signal into the digital domain where it can be processed in real time to diagnose and report urgencies. Measurements on a 0. 6μm process have shown that the input CMRR is boosted by 71dB when the feedback is closed through the patient. The input referred integrated noise for the overall system within the ECG band frequencies of 0.1Hz to 400Hz (including the quantization noise) is 4. 2μVP, below the recommended maximum detection error of 10. 0μVP.
Feasibility and Economical Analysis of Pumped Hydro Storage System for STES Campus, Lonavala

Electrical energy utilization and involvement of renewable energy sector is increasing day by day. For rapidly increasing renewable sector necessity of storage device is becoming very important. Various energy storage systems are available for storing the electricity. Pumped hydro storage system (PHS) is one of the most promising storage systems for storage of bulk power. Due to geographical conditions and naturally availability of water during rainy days, it is feasible to implement a PHS unit on Sinhgad Technical Education Society (STES) campus, Lonavala. The already available water storage tank can store water which can generate electricity at about 180 KW. Technical and economical calculations are done to ensure the feasibility of the PHS unit at Lonavala campus and presented in this paper.
Column2Vec: Structural Understanding via Distributed Representations of Database Schemas

We present Column2Vec, a distributed representation of database columns based on column metadata. Our distributed representation has several applications. Using known names for groups of columns (i.e., a table name), we train a model to generate an appropriate name for columns in an unnamed table. We demonstrate the viability of our approach using schema information collected from open source applications on GitHub.
Closed-form preintegration methods for graph-based visual–inertial navigation

In this paper, we propose a new analytical preintegration theory for graph-based sensor fusion with an inertial measurement unit (IMU) and a camera (or other aiding sensors). Rather than using discrete sampling of the measurement dynamics as in current methods, we derive the closed-form solutions to the preintegration equations, yielding improved accuracy in state estimation. We advocate two new different inertial models for preintegration: (i) the model that assumes piecewise constant measurements; and (ii) the model that assumes piecewise constant local true acceleration. Through extensive Monte Carlo simulations, we show the effect that the choice of preintegration model has on estimation performance. To validate the proposed preintegration theory, we develop both direct and indirect visual–inertial navigation systems (VINSs) that leverage our preintegration. In the first, within a tightly coupled, sliding-window optimization framework, we jointly estimate the features in the window and the IMU states while performing marginalization to bound the computational cost. In the second, we loosely couple the IMU preintegration with a direct image alignment that estimates relative camera motion by minimizing the photometric errors (i.e., image intensity difference), allowing for efficient and informative loop closures. Both systems are extensively validated in real-world experiments and are shown to offer competitive performance to state-of-the-art methods.