Miracle-dz/UniFAR-FTUs huggingface dataset:
Harmonic Relationship between Preferred Tempi and Heart Rate

Since people tend to prefer musical tempi ranging from 70 to 100 beats per minute, cyclic physiological response like heart rate is considered one of the factors affected by tempo preference. The present study aimed to examine the relationship between preferred tempi and heart rate. Subjects were instructed to find their favorite tempi while their heart rates were being measured. The presented tone stimulus was 440 Hz, 60 dB pressure of pure tone, and the presented tempo varied from 10 to 300 cycles per minute. The relationship between heart rate and preferred tempo was examined to calculate the density function of ratios of preferred tempi to heart rate. This density function indicated that preferred tempi were distributed mostly one, one and a half, and two times as fast as heart rate. This finding indicated preferred tempi had a simple harmonic relationship to heart rate.
Application Service Providers and SMEs: Further Research Needed.

This paper reports from an pilot study about the application service provider (ASP) phenomenon. The purpose of this paper is to raise some further research questions in the subject of ASP. In the paper some empirical findings are reported and compared with literature about ASP, IT outsourcing, inter-organizational relationship, and strategic network. The IT outsourcing literature focuses primarily on large organizations and with an underlying assumption that, IT outsourcing is when a large organization outsource an internal IT-department, IT-system or IT-function. An ongoing trend is the development of a new organization referred to as ASP. With ASP collaboration in IT outsourcing gets a new dimension. These “new” IT outsourcers play a significant part as intermediaries between sub-contractors and customer by supporting external clients with information and communication technologies (ICTs) and ICT services. The focus of the paper is, what role does ASPs play in collaboration. There is a discussion on how collaboration is done, or could be done through an ASP. Within this subject there are at least two areas that are interesting: ASP in itself and the effects of using an ASP. This paper gives short descriptions of 1) ASP and IT outsourcing, 2) interorganizational relationship, and 3) strategic networks. The ASP is described in terms of what it is, motives for small and medium sized enterprises (SMEs) joining an ASP, and which relationships there are between them. Following this, there is a description of a case organization, describing the difficulties that the organization experiences in the development and management of the relationships in an ASP situation. Finally, based on the literature review and the empirical findings some further research issues are proposed.
Concurrent tries with efficient non-blocking snapshots

We describe a non-blocking concurrent hash trie based on shared-memory single-word compare-and-swap instructions. The hash trie supports standard mutable lock-free operations such as insertion, removal, lookup and their conditional variants. To ensure space-efficiency, removal operations compress the trie when necessary. We show how to implement an efficient lock-free snapshot operation for concurrent hash tries. The snapshot operation uses a single-word compare-and-swap and avoids copying the data structure eagerly. Snapshots are used to implement consistent iterators and a linearizable size retrieval. We compare concurrent hash trie performance with other concurrent data structures and evaluate the performance of the snapshot operation.
A Novel Approach to Memory Disorder Diagnose Using Deep Learning Integration and Adaptive Artificial Approaches

Alzheimer’s illness is an untreatable brain condition that gradually erodes cognitive function, early identification can greatly lessen effects. In light of the lack of skilled doctors, the computerized identification of the illness becomes increasingly crucial since it lessens the workload on doctors and improves the accuracy in making the diagnostic. Using segmentation MRI scans, the condition must be precisely diagnosed by a thorough examination of particular brain problem tissues. The collection of pictures taken for dementia detected using MRI scans is divided into four categories: mildly informal (896 pictures), moderately informal (64 photos), not informal (3200 pictures), as well as extremely mildly informal (2240 pictures). The information available is seriously skewed. To solve this problem, we applied the adaptive artificial exceeding method. This method was used, and the information collected was matched. In order to test the effectiveness of the designs, a combination of VGG16 as well as Efficient Net was utilised to identify Alzheimer’s disease using both unbalanced and equal information. This method was used, and the information collected was normalised. The suggested approach merged numerous algorithms' projections to create a team approach that extracted intricate and subtle trends within the information at hand. Both algorithms' input and output were merged to create a combined approach, which was subsequently transferred to additional levels to create a more reliable prediction. In this project, we suggested a combination of EfficientNet-B2 and VGG-16 to accurately detect the illness at its earliest stages. Using two databases that are accessible to the public, studies were conducted. According to the findings of the study, the suggested technique obtained 97.85% efficiency and 99.64% (Area Under Curve) for multiple classes information.
Tone Analysis of Regional Articles in English-Language Newspapers Based on Recurrent Neural Network Bi-LSTM

This publication dwells upon the study of the human language processing techniques use, namely sentimental analysis to identify attitudes in English-language newspapers articles. General information about the development of human language has been presented. A brief description of analogues and the use of machine learning to carry out sentimental analysis of the text have been described. The section of analysis processes gives information about the sequence of program processes and the tools used. The statistics of neural network tasks with different functions of activators has been presented. The publication provides a control example of a demo version of the product, shows the main scenario of use. The classifier is needed to classify the vector that corresponds to the entered text. Each application in the program corresponds to one classifier, which is presented as a neural network model. The neural network has the following parameters: an embedding layer (with an input dimension of 541830, which corresponds to the size of the dictionary, an output vector with a dimension of 100, the length of the input data - 40) and a Bi-LSTM layer (intended to use a recursive Bi-LSTM neural network with the number of units 128). There is also the output fully connected 2-dimension and activator function - softmax, the Adam feedback function and the categorical cross entropy loss function and the accuracy metric. The maximum and minimum values for precision and loss have been found for the functions softmax (0.9117; 0.2138), relu (0.5002; 0.6134) and sigmoid (0.9681; 0.078) according to the training sample. The maximum accuracy values for the softmax, relu and sigmoid functions have been also found according to the test selection (0.8232; 0.4993; 0.8236). It is highlighted that the accuracy of the softmax, sigmoid function increases on the training sample, whereas the accuracy of the sigmoid function is the greatest, and the loss is the least. The accuracy of the softmax, sigmoid function is provided on the test sample, but the accuracy rates are not lower than 80%. The accuracy of the relay function is significantly lower than the other two, both on the test and on the training selection. Also, the accuracy indicators are decreasing. In the result, the losses for the softmax and sigmoid functions are similar: they are given for the third sample, and increase for the test sample, but to a small extent.
Design of Radio Frequency Power Amplifier Module at C-Band for Data Communication Link

This paper presents the design of a radio frequency power amplifier module for the C-band data communication link. The power stage is designed using a gallium nitride device in the hybrid microwave integrated circuit technology to achieve good efficiency and linearity. The designed matching network achieves the matching loads obtained from load-pull analysis, while keeping harmonics short at the intrinsic current generator reference plane of the device. The driver amplifier and gain stages are also optimally chosen to obtain high gain of the entire module without deteriorating the linearity. This power amplifier module operates from 4.6–5.0 GHz while delivering maximum output power upto 43.8 dBm in measurement. The measured maximum drain efficiency of the designed power stage is 53.2% at saturation with an EVM of 6% and adjacent channel power ratio of more than 30 dBc.
Thiopurine methyltransferase genotype and the toxicity of azathioprine in Japanese.

OBJECTIVE Thiopurine S-methyltransferase (TPMT) is a cytosolic enzyme that preferentially catalyzes the S-methylation of aromatic and heterocyclic sulfhydryl compounds, including Azathioprine (AZA). It has been reported that the level of AZA toxicity is dependent on the TPMT genotypes in Caucasian individuals; we thus investigated this relationship in Japanese. METHODS The TPMT genotype was determined using peripheral blood cell DNA obtained from 36 Japanese patients with rheumatic diseases who were treated with AZA, by polymerase chain reaction (PCR) technique. Duration of AZA administration, white blood cell counts before and after AZA administration, and side effects were investigated in each subject, and were compared between the patients with or without TPMT mutation. RESULTS The TPMT allelotype was TPMT*1/TPMT*1 in 33 (91.7%) and TPMT*1/TPMT*3C in 3 (8.3%) individuals. All 3 patients (100%) with the mutant TPMT allele (TPMT*3C) discontinued AZA treatment due to leucopenia while only 4 patients (12%) without mutant TPMT alleles showed leucopenia (p=0.0049, Fisher's exact test). However, leucopenia developed relatively late in patients with mutant TPMT. CONCLUSION The TPMT mutant allele, TPMT*3C, also exists in Japanese individuals, and the bone marrow toxicity of AZA is likely stronger in patients with this mutant allele.
Juggling the Effects of Latency: Software Approaches to Minimizing Latency in Dynamic Projector-Camera Systems

Projector-camera (pro-cam) systems afford a wide range of interactive possibilities, combining both natural and mixed-reality 3D interaction. However, the latency inherent within these systems can cause the projection to ? slip? from any moving target, so pro-cam systems have typically shied away from truly dynamic scenarios. We explore software-only techniques to reduce latency; considering the best achievable results with widely adopted commodity devices (e.g. 30Hz depth cameras and 60Hz projectors). We achieve 50% projection alignment on objects in free flight (a 34% improvement) and 69% alignment on dynamic human movement (a 40% improvement).
Early diagnosis of polycystic ovarian syndrome (PCOS) using machine learning: an ensemble learning approach

In recent years, women’s reproductive health has become a significant concern. Issues like preterm abortions, infertility, ovarian disorders, and declining fertility rates are prevalent. Polycystic Ovarian Syndrome (PCOS), a common reproductive disorder, often leads to infertility due to irregular cycles and elevated androgens. Despite its unclear cause and cure, early detection and intervention are vital. Researchers are exploring AI-based diagnostics to expedite diagnosis and mitigate clinical challenges. Our methodology revolves around leveraging non-invasive parameters to construct a feature vector optimized for machine learning algorithms. We utilize Principal Component Analysis (PCA) as a crucial step for dimensionality reduction, which streamlines the dataset’s representation while retaining vital information. To bolster the accuracy of PCOS diagnosis, we deploy a majority voting ensemble model that incorporates five base models. This ensemble approach not only enhances classification precision but also addresses issues like overfitting and model robustness, making it especially valuable when dealing with datasets of limited size. The accuracy, precision, f1-score, and recall for the suggested model are found to be 84.3%, 76.1%, 81%, and 84.3% respectively. Our research shows that our ensemble model performs better overall and across individual classes than the fundamental models. This development represents a substantial advancement in the field of PCOS diagnosis, showcasing the pivotal role of machine learning in enhancing diagnostic precision.
A Nonlinear Projection Based Human Emotion Recognition Approach Employing Face Point Data

In human-machine interaction (HMI), human emotion recognition plays a critical role. Facial expression could be used as a means to capture human emotions. A facial expression is a nonverbal communication that can take the shape of facial muscle postures, revealing an individual’s emotional conditions. In this work, a hybrid feature extraction methodology, comprising a particle swarm optimization (PSO) guided polynomial space projection and an uniform manifold approximation and projection (UMAP) technique, has been applied to the in-house Austro-Mongoloid sub-race based facial information dataset. Further, to evaluate the performance of the proposed method random forest classifier has been utilized to classify facial emotions like happiness, sadness, surprise, fear, and anger. After successful implementation of the proposed methodology a blind test accuracy of 98% has been achieved that outperforms the other competitive methods.
Achieving Robustness through the Integration of Language Production in Comprehension

Language comprehension in humans is very robust. With apparent ease, language users can grasp the meaning of utterances made in very bad acoustic conditions or containing grammatical errors. A key mechanism in achieving this robustness is the ability of the listener to predict what the speaker is likely to say. According to recent work in psycholinguistics, these predictions are made by covertly simulating the speaker while he is speaking and thus integrating production in comprehension. This article presents a model which makes use of this idea in order to enhance the robustness of language processing in intelligent systems, in particular to recover the intended meaning of utterances containing grammatical errors. The model is implemented in Fluid Construction Grammar and applied to a case study for the complex Dutch Verb Phrase.
Wearable Tactile Display of Landmarks and Direction for Pedestrian Navigation: A User Survey and Evaluation

This research investigates representation techniques for spatial and related information in the design of tactile displays for pedestrian navigation systems. The paper reports on a user survey that identified and categorized landmarks used in pedestrian navigation in the urban context. The results show commonalities of landmark use in urban spaces worldwide. The survey results were then used in an experimental study that compared two tactile techniques for landmark representation using one or two actuators. Techniques were compared on 4 measures: distinguishability, learnability, memorability, and user preferences. Results from the lab-based evaluation showed that users performed equally well using either technique to represent just landmarks alone. However, when landmark representations were presented together with directional signals, performance with the one-actuator technique was significantly reduced while performance with the two-actuator approach remained unchanged. The results of this ongoing research programme can be used to help guide design for presenting key landmark information on wearable tactile displays.
Passive Filter Design Techniques for a Three-Phase Four-Wire Inverter

Various approaches are explored for the analytical calculation and design of a passive filter for a four-wire three-phase voltage inverter. The outcomes of these analytical calculations are augmented by computer simulations, focusing on the filter’s effectiveness in eliminating higher harmonics when the static converter operates in inverter mode. This enables the identification of the most effective methodologies for passive filter calculation.
PyTPU: Migration of Python Code for Heterogenous Acceleration with Automated Test Generation

Software applications are increasingly built to take advantage of heterogeneous architectures as specialised hardware accelerators take centre stage in today’s computing environment. Tensor processing units (TPUs), the latest hardware addition, have demonstrated orders of magnitude improvement in computing efficiency over CPUs and GPUs for heterogeneous deep learning applications. However, despite the trend of incorporating heterogeneity and specialization in hardware, the creation of heterogeneous applications is confined to a handful of engineers. We propose a framework called PyTPU that takes Python code written in the PyTorch framework as input and automatically migrates it to its TPU compatible counterpart with test behaviour preservation and increased performance. First, PyTPU generates unit test cases to ensure test behavior compatibility. Second, using the abstract syntax tree and a manually curated exhaustive knowledge base, PyTPU migrates the original code to its TPU compatible version. Finally, PyTPU ensures the migrated code is readable and maintainable by adding necessary comments and conforming to PEP8 standard if needed. We evaluated PyTPU on four real-world Python applications with TPU v2 kernels. On average the migrated heterogeneous code is 19.103% faster than the original code while safeguarding test behavior preservation.
Microcantilever-Based Hydrogen Sensors: Fabrication, Response Dynamics, and Squeeze Film Damping Study

This study presents a comprehensive study on dynamic microcantilever-based hydrogen sensors, encompassing fabrication, experimental setup, and response analysis. Employing advanced surface micromachining techniques, these microcantilevers exhibit exceptional sensitivity. A measurement setup utilizing electrostatic actuation and a Laser Doppler Vibrometer, coupled with Zurich Instruments’ Ultra-High Frequency Lock-In (UHFLI) amplifier, enables real-time monitoring of frequency shifts. The investigation extensively explores how the squeeze film-damping effect impacts the behavior of microcantilevers under different concentrations of hydrogen gas. This research contributes insights into effectively analyzing the sensing behavior of the microcantilevers, even when influenced by the deteriorating squeeze film-damping effect. Furthermore, the study assesses the frequency stability of the device using the Allan deviation measurement. These findings showed an extraordinary capacity to detect exceedingly low concentrations of hydrogen (as low as 0.0005 %). The implications of these discoveries extend to the enhancement of sensor design, offering substantial promise for the realm of gas trace detection and seamless integration of this technology into sensor arrays.
Design space explorations of Hybrid-Partitioned TCAM (HP-TCAM)

Even though, TCAM provides search operation in a constant time, when compared with Static Random Access Memories (SRAMs), TCAMs have certain limitations such as low storage density, relatively slow access time, low scalability, complex circuitry, and expensive costs. Hence, the need for a TCAM architecture arises that can use SRAM (with additional logic) to behave like TCAM. This paper presents the idea of Hybrid-Partitioned, SRAM-based architecture (HP-TCAM), which provides the same functionality as TCAM. We implemented and analyzed an example design of 512 × 36 HP-TCAM on Xilinx FPGAs with its different design parameters. Energy/bit/search, as an important metric, for the design is 47.13 fJ on Virtex-7 FPGA. Furthermore, we have provided in detail, all the implementation results and power consumption for our designs.
Illuminating the Dark: Gaining Insights and Managing Risks with Dark Analytics

This research endeavors to explore the essence and significance of dark analytics, assessing its potential implications, advantages, inherent risks linked to handling dark data, and the possibilities it holds. Employing a comprehensive examination of secondary data, the study delves into research papers, expert perceptions within the industry, and company-released reports focused on dark data and analytics. Key findings encompass the surge in dark data volume, the evolving emphasis on dark analytics, pivotal sources such as unconventional unstructured data, IoT devices, and the deep web. Additionally, it scrutinizes data management in dark analytics, spotlighting implementation hurdles like infrastructure and legal complexities. Offering insights into both the merits and demerits of dark analytics, the research sheds light on how businesses can embrace a culture centered around this analytical approach, providing a holistic understanding of its adoption and its implications.
Neuroendocrine Synaptic Vesicles Are Formed In Vitro by Both Clathrin-dependent and Clathrin-independent Pathways

In the neuroendocrine cell line, PC12, synaptic vesicles can be generated from endosomes by a sorting and vesiculation process that requires the heterotetrameric adaptor protein AP3 and a small molecular weight GTPase of the ADP ribosylation factor (ARF) family. We have now discovered a second pathway that sorts the synaptic vesicle-associated membrane protein (VAMP) into similarly sized vesicles. For this pathway the plasma membrane is the precursor rather than endosomes. Both pathways require cytosol and ATP and are inhibited by GTPγS. The second pathway, however, uses AP2 instead of AP3 and is brefeldin A insensitive. The AP2-dependent pathway is inhibited by depletion of clathrin or by inhibitors of clathrin binding, whereas the AP3 pathway is not. The VAMP-containing, plasma membrane–derived vesicles can be readily separated on sucrose gradients from transferrin (Tf)-containing vesicles generated by incubating Tf-labeled plasma membrane preparations at 37°C. Dynamin- interacting proteins are required for the AP2-mediated vesiculation from the plasma membrane, but not from endosomes. Thus, VAMP is sorted into small vesicles by AP3 and ARF1 at endosomes and by AP2 and clathrin at the plasma membrane.
Query Expansion for Noisy Legal Documents

Abstract : The vocabulary of the TREC Legal OCR collection is noisy and huge. Standard techniques for improving retrieval performance such as content-based query expansion are ineffective for such document collection. In our work, we focused on exploiting metadata using blind relevance feedback, iterative improvement from the reference Boolean run, and the effects of using terms from different topic fields for automatic query formulation. This paper describes our methodologies and results.
Design of linux based transactional NAND flash memory file system

NAND flash memory is one of the most widely used storages because it is lightweight and non-volatile. However, existing NAND flash memory file systems such as JFFS2 and YAFFS do not provide transactional functionalities which allow users to use file operations more efficiently and guarantee safe and reliable file system operations.In this paper, we designed new NAND flash memory filesystem. RFFS uses an enhanced B-Tree for efficient block mapping techniques which shows significant performance benefits for large multimedia data with infrequent updates, makes it a flash memory file system more suitable for high-performance mobile devices.Furthermore, RFFS supports user-level transaction functionality to enable users to perform reliable file system operations and to make it easier to develop transaction supported user applications.