Reviewerly/exHarmony huggingface dataset:
Semantic Image Segmentation by Dynamic Discriminative Prototypes

Semantic segmentation achieves significant success through large-scale training data. Meanwhile, few-shot semantic segmentation was proposed to segment image regions of novel classes through few labeled testing data. However, it ignores classes previously learned from training data. This paper proposes a new segmentation framework called segmentation by dynamic prototype (SDP), which can simultaneously segment the image regions of base classes learned from many training data and novel classes learned from a few testing data. SDP performs segmentation by searching for the nearest prototype of each pixel's features. Different prototypes are representative features for different classes. In testing, SDP dynamically constructs novel prototypes according to support images while maintaining base prototypes learned from training images. The main challenge of SDP is how to achieve intra-image compactness, intra-class compactness, and inter-class separability simultaneously in the feature space. To tackle these challenges, we first introduce a discriminative pixelwise feature and prototype training method to improve the above three types of feature discriminability. We then introduce a mask refinement process in testing, which refines support images' masks to extract more separable novel prototypes. In addition, we introduce a prototype adaptation process in testing, which allows all prototypes to adapt to query images to reduce the prototypes' intra-class variances. Our approach achieves state-of-the-art results for novel class segmentation against existing few-shot semantic segmentation methods on PASCAL-5 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</sup> and COCO-20 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</sup> benchmarks in combination with superior runtime efficiency. In addition, our method can maintain strong results in the segmentation of base classes.
Detecting the Background-Similar Objects in Complex Transportation Scenes

With the development of intelligent transportation systems, most human objects can be accurately detected in normal road scenes. However, the detection accuracy usually decreases sharply when the pedestrians are merged into the background with very similar colors or textures. In this paper, a camouflaged object detection method is proposed to detect the pedestrians or vehicles from the highly similar background. Specifically, we design a guide-learning-based multi-scale detection network (GLNet) to distinguish the weak semantic distinction between the pedestrian and its similar background, and output an accurate segmentation map to the autonomous driving system. The proposed GLNet mainly consists of a backbone network for basic feature extraction, a guide-learning module (GLM) to generate the principal prediction map, and a multi-scale feature enhancement module (MFEM) for prediction map refinement. Based on the guide learning and coarse-to-fine strategy, the final prediction map can be obtained with the proposed GLNet which precisely describes the position and contour information of the pedestrians or vehicles. Extensive experiments on four benchmark datasets, e.g., CHAMELEON, CAMO, COD10K, and NC4K, demonstrate the superiority of the proposed GLNet compared with several existing state-of-the-art methods.
Feature Selection Based on Dynamic Crow Search Algorithm for High-Dimensional Data Classification

High-dimensional biomedical data plays an important role in disease diagnosis and classification. However, a great amount of redundant, irrelevant or weak correlation features are prevalent in these datasets, which significantly deteriorate the capability of machine learning techniques. To address this issue, an improved Crow Search Algorithm (CSA) which is known as the Dynamic CSA (DCSA) is proposed to find the optimal feature subset in high-dimensional classification tasks. In DCSA, three modifications are investigated. Firstly, we propose a dynamic bi-level awareness probability to guide the transition between global search and local search. Consequently, the balance between exploration and exploitation is improved. Secondly, levy flight with a unfixed step length control parameter is employed as the global search and the random search mechanism of the original CSA is abandoned. Thirdly, a dynamic flight length strategy is adopted to enhance the local search and speed up convergence. All in all, DCSA is developed as a wrapper feature selection model, where KNN classifier is applied as the feature subset evaluator. The performance of DCSA is measured on seven high-dimensional biomedical datasets. Experimental results show that DCSA outperforms other state-of-the-art methods.
The Association between Stress and Night Eating Syndrome Among University Students

Background: Night Eating Syndrome (NES) is not only a common trait seen among the obese but is also present among people with psychological disorders such as depression, anxiety, and stress. If a correlation between Night Eating Syndrome and stress among young adults is identified, it may help prevent obesity and other comorbidities. Thus, this study investigates the relationship between stress and Night Eating Syndrome among Saudi students.A total of 331 healthy students from King Saud University, Riyadh, King Saudi Arabia, participated in the current study. A depression, anxiety and stress scale-21 items questionnaire was used to assess stress symptoms among the participants, and the Night Eating Questionnaire was used to assess eating patterns. Nineteen percent of the participants presented with Night Eating Syndrome symptoms, and 49.5% were above the cut-off point of stress. A significant positive association between stress and night eating syndrome (p=0.037) was seen. There was no association seen between Night Eating Syndrome and depression or anxiety. Further research, including different Saudi universities from different regions, should be conducted to confirm the association between NES and stress.
An application of Faster R-CNN for the detection and recognition of Ecuadorian traffic signs

This paper presents an application of Faster R-CNN in the development of a software for traffic signs recognition; i.e, this work implements an object detector based on Faster-RCNN with ZF-Net. The entire training and testing were developed on a database taken in urban environments from several cities in Ecuador. This dataset consists of 52 classes, collected in the various lighting environments (dawn, day, sunset and cloudy) from 6 am to 7 pm. After that, several experiments were carried out in real road driving conditions by using a technology platform, which consists of a vehicle for the implementation of driving assistance systems using Computer Vision and Artificial Intelligence.
Titrating effects of acoustic variability on context effects and psychometric function slopes in speech categorization

In temporal contrast effects (TCEs, also termed speaking rate normalization), a fast context sentence makes the onset of the subsequent target word sound longer, and vice versa. King, Sharpe, Shorey, and Stilp (2022 ASA) reported that variability in the talkers or lexical contents of context sentences each decreased TCE magnitudes. However, in that experiment, acoustic variability was not tightly controlled. Using the same stimuli as King et al., listeners completed three blocks, hearing one talker speaking the same sentence (1 Talker/1 Sentence) or 200 different sentences (1/200), or 200 different talkers speaking 200 different sentences (200/200). On each trial, listeners heard one (fast/slow) context sentence, then identified the subsequent target word as “deer” or “tier.” In one experiment, speaking rates were matched across 1/200 and 200/200 blocks; TCE magnitudes were comparable and psychometric slopes did not differ. In another experiment, all slow sentences were set to 2.67 syllables/sec and all fast sentences to 8 syllables/sec. Differences due to sentence variability (1/1 versus 1/200) were extinguished; TCE magnitudes were smaller and psychometric slopes were shallower in the 200/200 block. Thus, multiple levels of variability must be considered in how they shape speaking rate normalization. [Work supported by NIH R01DC020303.]
Round and round we go: Speech intelligibility and localization for in-the-round spaces

The directional characteristics of the human voice are a key theatrical effect in spoken word drama. Maintaining intelligibility and believable localization as performers move and reorient on the stage is a formidable design challenge for in-the-round spaces. We provide an overview of the design strategies for this theatrical form using some notable precedents. Referencing Chicago’s newest in-the-round theatre at Steppenwolf Theatre, we present the acoustic goals and the architecturally integrated design for speech acoustics. The presentation includes the key design parameters and the traditional and non-traditional intelligibility metrics used to evaluate the design options and the completed construction, along with subjective impressions of artists and audiences related to speech intelligibility and localization.
The Acentech acoustical checkout: Measuring and listening to completed performance spaces

Visiting a completed performance space is an invaluable learning opportunity. Our practice at Acentech has evolved to include approximately equal parts measurement and listening. We report relevant findings to the owner and design team; but often, much of the information that we collect is for our own internal research and development. This presentation will summarize our process for evaluating a completed performance space, including measurement, visualization, and analysis of spatial impulse responses, listening to and recording speech and music in the room, and listening to calibrated convolutions of measured spatial impulse responses with anechoic audio. Ideas for future analysis tools and related research efforts will also be discussed.
Szilard-Wigner distributions completely define physics and perceptions of concert halls

At the second joint meeting of the Acoustical Society of Japan and the Acoustical Society of America in 1988 Yamasaki and Itoh presented time-frequency measurements of several concert halls employing Fourier transforms on autocorrelations, the same device Szilard and Wigner had developed for quantum theory in the 1920s. In 1995, it was proved that resonant scattering in the body of good halls generates power spectra resolved in time and frequency, correctly presenting musical information emanating from the orchestra. In poor halls specular reflections from hard flat surfaces generate echoes encoding architectural information about size, shape, features of the hall, necessarily detracting from desired musical information. Yamasaki and Itoh also demonstrated that “reverberation-time” tells essentially nothing about properties, physical or perceptual, of concert halls; impulse responses, from which Szilard-Wigner distributions are computed, tell much more. Careful study of these matters explains why after the era of Wallace Sabine, when “reverberation-time” became the sine qua non of concert hall theory, design of fine concert halls became impossible.
What would a Webchuck Chuck?

Webchuck is a new platform for real-time web-based music synthesis. Combining expressive music programming power with the ubiquity of web browsers is a game changer, one which we've experienced in recent teaching at Stanford's CCRMA. The paper will describe how this has fulfilled a long-sought promise for accessible music making and simplicity of experimentation. The Chuck music programming language now runs anywhere a browser can and a link is all it takes. Sample-synchronous live coding alongside full-on studio functionality is what a Webchuck can Chuck, thanks to advances in the web audio API.
Experimental tests of free reed instrument simulations

Recent simulations using finite element software have been used to study the fluid dynamics, pressure acoustics, and structural mechanics couplings involved in self-sustained reed oscillation and reed-pipe coupling in free reed instruments. This paper reports on two experimental studies related to these simulations. One of these involves reeds of the type used in harmonicas and accordions. The results verify that the assumed dependence of sounding frequency on blowing pressure, which has long been used as a benchmark for a successful simulation is correct for blowing pressures close to those normally used in playing the instruments. At higher pressures, however, a variety of anomalous results can occur. The second study involves finite element models of pipes from the free-reed Asian khaen. Pipe simulations produce results for the input impedance of the pipe that agree very well with experimental measurements. A pressure acoustics model of the khaen pipe coupled to a free reed produces results that appear realistic for sound radiation but does not realistically capture the mechanism of the self-sustained oscillation of the reed driven by airflow. Measurements in the current study show that this model does provide approximate quantitative agreement with the predictions of the simulation.
A study of impedance of brass instruments and mouthpieces—Comparison of models and measurements

The impedance of a brass instrument has an important influence on the frequencies of the notes that can be played and on the timbre of the sound. The shape of the mouthpiece has various features, such as the cup volume and shape, opening diameter, and length, that determine the characteristics of the overall impedance of the instrument-mouthpiece combination. Brass instruments, and especially mouthpieces, are designed for specific purposes, and many brass players own several different horns or mouthpieces, and choose which to use depending on their particular musical requirements at the time. In order to investigate the relationship between the physical parameters of instruments and mouthpieces and the resulting impedance, brass instruments and mouthpieces have been modeled with transfer matrix techniques, and the results are compared with impedance measurements of the instruments alone, the mouthpieces alone, and combination of instruments and mouthpieces. Trumpets, flugelhorns, horns, trombones, and the corresponding mouthpieces have been used for this study. The mouthpiece-instrument combination has been investigated in terms of intonation, playability, and timbre. The question of whether (and why) some mouthpieces are more suited to certain instruments and certain playing styles is investigated as well as the effect of varying the physical parameters of mouthpieces and instruments.
Bifurcation behavior and underblowing on the bass crumhorn

The crumhorn is a double reed musical wind instrument with a narrow cylindrical bore and a characteristic hook-shaped curve near its open end. It is played by blowing air through a narrow slit into a windcap which surrounds the reed. Crumhorns were made in several sizes, from soprano to contrabass, and in the sixteenth and early seventeenth centuries were frequently employed in wind ensembles in Germany, Italy and the Netherlands. Since no lip control of the reed is possible, the instruments cannot be overblown, and the playing range of a standard crumhorn is limited to a major ninth. On the bass crumhorn, however, a sixteenth century source mentions a technique for extending the lower range of the instrument by up to a perfect fifth by reducing the blowing pressure below its normal level. In 2000, Gibiat and Castellengo investigated these underblown notes experimentally and associated them with period doubling cascades. In the present work, the bifurcation behavior of an artificially blown bass crumhorn has been studied experimentally, and the results are compared with bifurcation diagrams calculated using a continuation method.
Low-delay interactive rendering of virtual acoustic environments with extensions for distributed low-delay transmission of audio and bio-physical sensor data

In this study, we present a system that enables low-delay rendering of interactive virtual acoustics. The tool operates in the time domain based on a physical sound propagation model with basic room acoustic modelling and a block-wise update and interpolation of the environment geometry. During the pandemic, the tool was extended by low-delay network-transmission of audio and sensor data, e.g., from motion sensors or bio-physical sensors such as EEG. With this extension, distributed rendering of turn-taking conversations as well as ensemble music performances with individual head-tracked binaural rendering and interactive movement of directional sources is possible. Interactive communication requires a low time delay in sound transmission, which is particularly critical for musical communication, where the upper limit of tolerable delay is between 30 and 50 ms, depending on the genre. Our system can achieve latencies between 7 (dedicated local network) and 100 ms (intercontinental connection), with typical values of 25–40 ms. This is far below the delay achieved by typical video-conferencing tools and is sufficient for fluent speech communicationand music applications. In addition to a technical description of the system, we show here example measurement data of head motion behaviour in a distributed triadic conversation.
Observation of torsion on bowed strings

Research on bowed string motion focuses on transverse waves and not so much on torsional waves. These are believed to play only a minor role for stabilizing vibrations and no role for perception. Here, torsion is measured on both sides of the bow contact point for a variety of bridge-bow distances on a cello string. Electromagnetical and optical measurements correspond. Every periodic string release is preceded by a reverse torsional motion independent from bowing position or dynamics. Transverse and torsional motions are coupled and there are cases of stabilization, but also cases of perturbation or surrender. Structural and timing analyses of torsional waves suggest that the earlier concepts of differential slipping can be essentially confirmed. Other concepts cannot be confirmed, such as the Schelleng ripples, or are under question, such as the concept of subharmonics.
The Use Of Scrabble Game As A Teaching Medium To Improve Students’ Vocabulary Mastery

This study aims to examine the use of Scrabble game as a teaching medium to improve the vocabulary mastery of 2nd grade students of a Junior High School in West Nusa Tenggara Province, Indonesia. This research employed classroom action research (CAR). The subjects in this study were 27 2nd grade students. The data collection methods used in this research are observation, documentation, and tests. The results showed that the scrabble game medium was able to improve the vocabulary mastery of the students. This is evidenced by the increase in students' English vocabulary mastery in each cycle. In the first cycle the average value obtained was 62.37 (enough) out of 100 (very good) with a completeness percentage of 33% (unfinished) from 100% (completed) while in the second cycle the average value obtained by students increased from 62.37 (enough) to 77.37 (good) from 100 (very good) with the percentage of completeness reaching 70% (completed) from 100% (completed).
Arduino Based Sign Language Interpreter for Speech Impaired People

Our individual lives depend significantly on communication. Deaf and dumb people find it difficult to engage in normal conversations with other people. According to the World Health Organization, across the world 466 million people are affected (more than 5 percent of the world's population) and in that 34 million are kids. Consequently, they are unable to communicate efficiently with others. Sign language is the medium through which deaf people communicate, which involves the usage of gestures to express their feelings and emotions. A sign language gesture is a particular action of the hands, composed of a specific form. The main motive behind this device is to make disabled people feel confident in their communication and also to get rid of their fear or any kind of awkwardness while communicating with others. Five LDR sensors are used in such a way that each LDR sensor is fixed at the tip of glove finger. The gesture of the hand plays a vital role. The micro-controller decodes the gestures. The words which are fed are measured using each particular gesture (i.e., making entirely different finger positions) of LDR sensors. The system analyzes those gestures and synthesizes the display the message for common people to recognize. In this system, hand gloves are used through which the hand gestures are measured directly and accurately.
An effective framework for detecting the object from the video sequences by utilizing deep learning with hybrid technology

In recent technologies, object detection is considered as an effective tool for diagnosing the anonymous activities of a particular location. We can recognize the specific object from images and videos. Therefore, we can obtain essential information for developing a highly secure framework. The ODEF technique is developed for enhanced object detection and classification process. By utilizing the proposed technique, the anonymous activities in a specific region can be detected through the video. This technique detected the objects with bounding boxes; therefore, malicious activities can be shown in distinct visual. Then, the utilization of DRCNN technique provides better platform to classify the object.
Users’ risk and problems in digital wallets in India

Digitalization has crossed the boundaries and no nation is left over. India is one of them. The whole nation is in impact of technology in all the ways. The pattern of payments, education, work etc. has changed a lot. With the technology, people feel in an upgraded standard, but on the other hand some problems are also there. The present study is on understanding the risks and problems associated with digital wallets especially in India. Particularly during the COVID era, people have faced multiple issues. So it was learnt that physical touch must be avoided in all ways whether it is of living in different states of India. All age groups have been considered for the study and 400 respondents have been selected. Primary data has been collected and analyzed on SPSS.
ETHICAL ASPECTS OF ARTIFICIAL INTELLIGENCE IN BANKING

The following study aim to analyse the ethical concerns related to the use of Artificial Intelligence in banking. By using AI, many ethical issues were identified which includes the wrong credit scoring, sharing of wrong information, miss selling, and unauthorised trading in the banking. The AI applications and methods significantly changed and enhanced the entire financial and non-financial framework of the banking sectors but there is always a risk of the data losses which are associated with the artificial intelligence (AI). Due to the fact, that the new technologies can exploit threats in AI and machine learning systems. So, it can be recommended to establish a committee that will ensure ethical compliance and validate the human concerns regarding unsafe platforms. The organisation needs to think about preventing the potential harm caused by AI-assisted content creation. Hence, friendly, and ethical AI system is mandatory for the human existence.