Reviewerly/exHarmony huggingface dataset:
Giant FFTs for Sample-Rate Conversion

The audio industry uses several sample rates interchangeably, and high-quality sample-rate conversion is crucial.This paper describes a frequency-domain sample-rate conversion method that employs a single large ("giant") fast Fourier transform (FFT).Large FFTs, corresponding to the duration of a track or full-length album, are now extremely fast, with execution times on the order of a few seconds on standard commercially available hardware.The method first transforms the signal into the frequency domain, possibly using zero-padding.The key part of the technique modifies the length of the spectral buffer to change the ratio of the audio content to the Nyquist limit.For up-sampling, an appropriate number of zeros is inserted between the positive and negative frequencies.In down-sampling, the spectrum is truncated.Finally, the inverse FFT synthesizes a time-domain signal at the new sample rate.The proposed method does not result in surviving folded spectral images, which occur in some instances with timedomain methods.However, it causes ringing at the Nyquist limit, which can be suppressed by tapering the spectrum and by low-pass filtering.The proposed sample-rate conversion method is targeted to offline audio applications in which sound files need to be converted between sample rates at high quality.
Machine Learning-Assisted OFDM-Based DSRC Communication Systems

The automotive industry has developed Dedicated Short-Range Communication (DSRC) technology for specific Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) communication applications. However, the effectiveness of DSRC communication is highly dependent on other integrated standards for interoperability, which is still room for research, especially under high mobility. Deep learning has recently played a crucial role in boosting the system performance in 5G-and-beyond networks. This paper utilizes deep learning to improve the channel estimation quality of DSRC systems under time-variant and frequency-selective channels and applying a post filtering process to enhance the quality of reconstructed images. We consider an OFDM-based system where the propagation channels are roughly estimated at the receiver by a low-cost least squares method. Then, the channel estimation quality is enhanced by a data-driven approach exploiting supervised learning. Numerical results manifest the added benefits of deep learning for improving the channel estimation quality and boosting the Bit Error Ratio (BER) compared to the traditional estimation methods. Besides, a post-filter is necessary to remove artifacts and residual errors in recovered image data. Quantitatively, the support of deep learning improves the channel estimation quality by about 30%. At the same time, the post-filtering process enhances the reconstruction quality in terms of the Peak-Signal-to-Noise Ratio (PSNR) up to 4dB.
DOES SELF-ESTEEM INFLUENCE STUDENT ENGLISH PROFICIENCY TEST SCORES?

As one of Indonesia’s most prominent foreign languages, English is essential in education. The importance of English is reflected in the fact that it is taught from elementary school through university and is even used as a requirement for graduation or employment. Passing the campus-provided English Proficiency Test is a graduation requirement for Teknokrat University students. As a result, learning and mastering English is an important step for students.When it comes to teaching English in the classroom, there are various factors to consider. Every student has their own individuality. Different personalities result in varying levels of comprehension. Students’ personalities vary enormously, yet self-esteem is important in influencing individuals. In short, students’ personalities can differ based on their self-esteem. As a result, students’ personalities are greatly influenced by their sense of self, affecting their learning. This study aimed to see if there is a relationship between undergraduate students’ self-esteem and their English Language proficiency test at Teknokrat University. The computation results show that the correlation coefficient was .454. This study revealed a moderately positive relationship between students’ self-esteem and their EPT scores. Thus, the greater their self-esteem, the better their EPT score.Keywords: English Learning, English Proficiency Test and Self-esteem
THE USE OF SMALL-GROUP DISCUSSION TO IMRPOVE STUDENTS’ SPEAKING SKILL

Speaking skill plays an important role in communication. Speaking skill is needed not only in our daily language but also we need it to use it using an international language. Speaking skill is not easy, especiall for vocational high school. In addition, teacher needs to find the solution to help the students to master of it. Teacher should be creative and use elective strategy that still can supervise students individually, so students can learn and teacher can focus on the students even though in a group. To keep students from boredom while improving their speaking skill, teacher can use a small group in learning to invite students to talk more and critics more. Small group discussion is one of way for teacher and students to use it while the students learn how to improve their speaking skill and teachers easily to focus on students and help the teacher to give feedback for students individually. This research aimed at indentifiying students’ improvement on their speaking mastery after learning using small group discussion in the classroom. The participant of this study is students of senior high school in lampung province. Data gathered through questionnaire. The result of this research that using small group discussion in improving speaking skill to be effective, because in small group students more confidance to deliver their idea dan critize to think and matches the characteristics of children as a learners.
Double and Nothing: Understanding and Detecting Cryptocurrency Giveaway Scams

As cryptocurrencies increase in popularity and users obtain and manage their own assets, attackers are pivoting from just abusing cryptocurrencies as a payment mechanism, to stealing crypto assets from end users.In this paper, we report on the first large-scale analysis of cryptocurrency giveaway scams.Giveaway scams are deceptively simple scams where attackers set up webpages advertising fake events and promising users to double or triple the funds that they send to a specific wallet address.To understand the population of these scams in the wild we design and implement CryptoScamTracker, a tool that uses Certificate Transparency logs to identify likely giveaway scams.Through a 6-month-long experiment, CryptoScamTracker identified a total of 10,079 giveaway scam websites targeting users of all popular cryptocurrencies.Next to analyzing the hosting and domain preferences of giveaway scammers, we perform the first quantitative analysis of stolen funds using the public blockchains of the abused cryptocurrencies, extracting the transactions corresponding to 2,266 wallets belonging to scammers.We find that just for the scams discovered in our reporting period, attackers have stolen the equivalent of tens of millions of dollars, organizing large-scale campaigns across different cryptocurrencies.Lastly, we find evidence that attackers try to re-victimize users by offering fund-recovery services and that some victims send funds multiple times to the same scammers.
An OS-agnostic Approach to Memory Forensics

The analysis of memory dumps presents unique challenges, as operating systems use a variety of (often undocumented) ways to represent data in memory.To solve this problem, forensics tools maintain collections of models that precisely describe the kernel data structures used by a handful of operating systems.However, these models cannot be generalized and developing new models may require a very long and tedious reverse engineering effort for closed source systems.In the last years, the tremendous increase in the number of IoT devices, smart-home appliances and cloud-hosted VMs resulted in a growing number of OSs which are not supported by current forensics tools.The way we have been doing memory forensics until today, based on handwritten models and rules, cannot simply keep pace with this variety of systems.To overcome this problem, in this paper we introduce the new concept of OS-agnostic memory forensics, which is based on techniques that can recover certain forensics information without any knowledge of the internals of the underlying OS.Our approach allows to automatically identify different types of data structures by using only their topological constraints and then supports two modes of investigation.In the first, it allows to traverse the recovered structures by starting from predetermined seeds, i.e., pieces of forensics-relevant information (such as a process name or an IP address) that an analyst knows a priori or that can be easily identified in the dump.Our experiments show that even a single seed can be sufficient to recover the entire list of processes and other important forensics data structures in dumps obtained from 14 different OSs, without any knowledge of the underlying kernels.In the second mode of operation, our system requires no seed but instead uses a set of heuristics to rank all memory data structures and present to the analysts only the most 'promising' ones.Even in this case, our experiments show that an analyst can use our approach to easily identify forensics-relevant structured information in a truly OS-agnostic scenario.
Exploiting the Potential of Overlapping Cropping for Real-World Pedestrian and Vehicle Detection with Gigapixel-Level Images

Pedestrian and vehicle detection is widely used in intelligent assisted driving, pedestrian counting, drone aerial photography, and other applications. Recently, with the development of gigacameras, gigapixel-level images have emerged. The large field of view and high resolution provide global and local information, which enables object detection in real-world scenarios. Although existing pedestrian and vehicle detection algorithms have achieved remarkable success for standard images, their methods are not suitable for ultra-high-resolution images. In order to improve the performance of existing pedestrian and vehicle detectors in real-world scenarios, we used a sliding window to crop the original images to solve this problem. When fusing the sub-images, we proposed a midline method to reduce the cropped objects that NMS could not eliminate. At the same time, we used varifocal loss to solve the imbalance between positive and negative samples caused by the high resolution. We also found that pedestrians and vehicles were separable in size and comprised more than one target type. As a result, we improved the detector performance with single-class object detection for pedestrians and vehicles, respectively. At the same time, we provided many useful strategies to improve the detector. The experimental results demonstrated that our method could improve the performance of real-world pedestrian and vehicle detection.
Barriers and Facilitators of Ambient Assisted Living Systems: A Systematic Literature Review

Ambient Assisted Living Systems (AALSs) use information and communication technologies to support care for the growing population of older adults. AALSs focus on providing multidimensional support to families, primary care facilities, and patients to improve the quality of life of the elderly. The literature has studied the qualities of AALSs from different perspectives; however, there has been little discussion regarding the operational experience of developing and deploying such systems. This paper presents a literature review based on the PRISMA methodology regarding operational facilitators and barriers of AALSs. This study identified 750 papers, of which 61 were selected. The results indicated that the selected studies mentioned more barriers than facilitators. Both barriers and facilitators concentrate on aspects of developing and configuring the technological infrastructure of AALSs. This study organizes and describes the current literature on the challenges and opportunities regarding the operation of AALSs in practice, which translates into support for practitioners when developing and deploying AALSs.
Subverting Telegram’s End-to-End Encryption

Telegram is a popular secure messaging service with third biggest user base as of 2021. In this paper, we analyze the security of Telegram’s end-to-end encryption (E2EE) protocol in presence of mass-surveillance. Specifically, we show &gt;that Telegram’s E2EE protocol is susceptible to fairly efficient algorithm substitution attacks. While official Telegram clients should be protected against this type of attack due their open-source nature and reproducible builds, this could potentially lead to a very efficient state sponsored surveillance of private communications over Telegram, either on individuals through a targeted attack or massively through some compromised third-party clients. We provide an efficient algorithm substitution attack against MTProto2.0 — the underlying authenticated encryption scheme — that recovers significant amount of encryption key material with a very high probability with few queries and fairly low latency. This could potentially lead to a very efficient state sponsored surveillance of private communications over Telegram, either through a targeted attack or a compromised third-party app. Our attack exploits MTProto2.0’s degree of freedom in choosing the random padding length and padding value. Accordingly, we strongly recommend that Telegram should revise MTProto2.0’s padding methodology. In particular, we show that a minor change in the padding description of MTProto2.0 makes it subversion-resistant in most of the practical scenarios. As a side-effect, we generalize the underlying mode of operation in MTProto2.0, as MTProto-G, and show that this generalization is a multi-user secure deterministic authenticated encryption scheme.
Phonon coupling versus pure dephasing in the photon statistics of cooperative emitters

Realising scalable quantum networks requires a meticulous level of understanding and mitigating the deleterious effects of decoherence. Many quantum device platforms feature multiple decoherence mechanisms, often with a dominant mechanism seemingly fully masking others. In this paper, we show how access to weaker dephasing mechanisms can nevertheless be obtained for optically active qubits by performing two-photon coincidence measurements. To this end we theoretically investigate the impact of different decoherence mechanisms on cooperatively emitting quantum dots. Focusing on the typically dominant deformation-potential coupling to longitudinal acoustic phonons and typically much less severe additional sources of pure dephasing, we employ a numerically exact method to show that these mechanisms lead to very different two-photon coincidence signals. Moreover, surprisingly, the impact of the strongly coupled phonon environment is weak and leads to long-lived coherences. We trace this back to the superohmic nature of the deformation-potential coupling causing inter-emitter coherences to converge to a nonzero value on a short timescale, whereas pure dephasing contributions cause a complete decay of coherence over longer times. Our approach provides a practical means of investigating decoherence processes on different timescales in solid state emitters, and thus contributes to understanding and possibly eliminating their detrimental influences.
Voice Assistant Using Artificial Intelligence

Artificial intelligence is crucial to day-to-day life. science of computers defines AI research as the study of intelligent agents. Today, most people, intentionally or not, have turned to some form of computerized information processing technology. Artificial Intelligence (AI) is already changing our lifestyle. A device that senses its surroundings and performs actions that maximize the likelihood of achieving a goal. The input to a database can be a choose to of users and articles, and the output is a ruthless recommendation. Input can be verbally or textually submitted by the user within the system. This paper presents a new approach to intelligent search. Overall, there are many people around the world who use assistants. This paper describes the provocation of applying virtual assistant technology. The paper also introduces the application of virtual assistants that can help open up opportunities for humanity in various fields. Voice control is an important growing feature that will change people's lives. Voice assistants are available for laptops, desktops and mobile phones. Assistant is now available on all electronic devices. A voice assistant is a software agent that can interpret human speech and respond in machine language.
An App-Based Recommender System Based on Contrasting Automobiles

Product recommendation systems are essential for enhancing customer experience, and integrating them with mobile apps is crucial for improving usability and fostering user engagement. This study proposes a hybrid approach that utilizes comparative facts from pairwise comparison data and comparison lists, with association rules as the method to formulate the recommendation system. The study employs a dataset from the New-Cars Database app, comprising 30,867 vehicle comparisons made by 5327 users across 40 car brands and 870 cars from 30 January 2015 to 2 April 2015. Two metrics are developed to measure the system’s output under varying support and confidence thresholds. The findings suggest that adjusting the support and confidence values can improve the breadth and depth of product recommendations. In addition, the unit of analysis can affect the recommendation system’s output, with comparison lists supplementing and expanding the exploration of potential outcomes. The proposed hybrid approach aims to provide more reliable and comprehensive product recommendations by combining both approaches and has implications for both academic and managerial contexts by facilitating the development of effective recommendation systems.
Converting South African sign language to verbal

There is a significant population of hearing-impaired people who reside in South Africa; however, South African Sign Language (SASL) has not yet been recognized as South Africa's 12 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">th</sup> official language, resulting in slow uptake of this important language. Since most people do not know SASL, there is a need for gesture recognition systems that convert Sign Language (SL) to verbal and/or text to reduce the communication barriers between the hearing and the hearing-impaired. This study presents an application for gesture recognition in converting SASL to both a verbal format and a textual format. By using gesture recognition from a single wearable glove, hand gestures were quantified, categorized, and then converted into an auditory format and played on a speaker, as well as the equivalent textual information displayed on an LCD screen. The complete prototype consists of a wearable glove with a transmitter and an associated receiver box which were all designed to cost less than $150. The glove consists of five flex sensors that measure the handshape and an inertial measurement unit which measures the hand motion. The handshape and motion data are processed and wirelessly transmitted to a receiver box. This then displays the associated English character on an LCD while also playing the audio on a speaker. The SL converter can convert the 26 letters of the SASL manual alphabet with an overall accuracy of 69%, It can also convert common words and phrases, as well as proper names when fingerspelled.
LongEval: Longitudinal Evaluation of Model Performance at CLEF 2023

In this paper, we describe the plans for the first LongEval CLEF 2023 shared task dedicated to evaluating the temporal persistence of Information Retrieval (IR) systems and Text Classifiers. The task is motivated by recent research showing that the performance of these models drops as the test data becomes more distant, with respect to time, from the training data. LongEval differs from traditional shared IR and classification tasks by giving special consideration to evaluating models aiming to mitigate performance drop over time. We envisage that this task will draw attention from the IR community and NLP researchers to the problem of temporal persistence of models, what enables or prevents it, potential solutions and their limitations.
Large-scale basement mobilization endows the giant Carlin-type gold mineralization in the Youjiang Basin, South China: Insights from mercury isotopes

The metal source and genesis of hydrothermal mercury-rich gold metallogenic systems occurring far away from active continental margins remain puzzling. The Youjiang Basin of South China, where exists numerous Carlin-type gold deposits and some synmineralization hidden intrusions, is a natural laboratory to address this issue due to it was up to 1000 km inward from the active continental margins of South China when mineralization. Here, we use mass-independent fractionation of mercury isotope ratios (reported as ∆199Hg), which is predominantly generated during Hg photochemical reactions on Earth’s surface and has superiority of isotopic inheritance during hydrothermal processes, to address the metal source of the Youjiang Carlin-type gold deposits. Ore-associated sulfides from seven representative deposits display negative to near-zero ∆199Hg values (−0.29‰ to 0.04‰), which fall in between that of the regional Precambrian basement rocks (−0.21‰ to 0.06‰) and deep magmatic-hydrothermal systems (∼0‰), suggesting a binary mixing of Hg from these two sources. An isotope mixing model and mass balance calculations demonstrate that ∼1000 km3 of the basement rocks, which contributed to 86% of Hg budget, were leached and remobilized by magmatic-hydrothermal fluids and deep-circulating crustal fluids to endow the gold reserves of these deposits. Given that traditional S, Pb, C, and O isotopic data yielded indirect and ambiguous constraints on metal source due to their complex evolution processes and isotope fractionation during the fluids ascended. Our results, therefore, highlight the great advantage of using Hg isotope as a new tracer to understand metal sources of hydrothermal deposits.
Fully adaptive Bayesian algorithm for data analysis: FABADA

Abstract The discovery potential from astronomical and other data is limited by their noise. We introduce a novel non-parametric noise reduction technique based on Bayesian inference techniques, fully adaptive Bayesian algorithm for data analysis (FABADA) that automatically improves the signal-to-noise ratio of one- and two-dimensional data, such as astronomical images and spectra. The algorithm iteratively evaluates possible smoothed versions of the data, the smooth models, estimating the underlying signal that is statistically compatible with the noisy measurements. Iterations stop based on the evidence and the χ2 statistic of the last smooth model. We then compute the expected value of the signal as a weighted average of the whole set of smooth models. We explain the mathematical formalism and numerical implementation of the algorithm, and evaluate its performance in terms of the peak signal-to-noise ratio, the structural similarity index, and the time payload, using a battery of real astronomical observations. Our FABADA yields results that, without any parameter tuning, are comparable with standard image processing algorithms whose parameters have been optimized based on the true signal to be recovered, something that is impossible in a real application. On the other hand, state-of-the-art non-parametric methods, such as block-matching and three-dimensional filtering, offer slightly better performance at high signal-to-noise ratio, while our algorithm is significantly more accurate for extremely noisy data, a situation usually encountered in astronomy.