Searching for how Epistemic Power is Made, Appropriated, Circulated, and Challenged

In the 2020 Prague Virtual Conference of the Society for Social Studies of Science (4S), Sharon Traweek was awarded the society’s John D. Bernal Prize jointly with Langdon Winner. The Bernal Prize is awarded annually to individuals who have made distinguished contributions to the field of STS. Prize recipients include founders of the field of STS, along with outstanding scholars who have devoted their careers to the understanding of the social dimensions of science and technology. This is an edited transcription, which accompanies the full audio file also available in this issue of the journal. The interview supplements the text of Traweek’s 2020 Bernal lecture. In this interview, Traweek discusses her research, academic career, the many influences on her life, and her thoughts on STS—in the past and in the future.

LSBR Speech Steganalysis Based on Percent of Equal Adjacent Samples

Wide utilization of audio files has attracted the attention of cyber-criminals to employ this media as a cover for their concealed communications. As a countermeasure and to protect cyberspace, several techniques have been introduced for steganalysis of various audio formats, such as MP3, VoIP, etc. The combination of machine learning and signal processing techniques has helped steganalyzers to obtain higher accuracies. However, as the statistical characteristics of a normal audio file differ from the speech ones, the current methods cannot discriminate clean and stego speech instances efficiently. Another problem is the high numbers of extracted features and analysis dimensions that drastically increase the implementation cost. To tackle these, this paper proposes the Percent of Equal Adjacent Samples (PEAS) feature for single-dimension least-significant-bit replacement (LSBR) speech steganalysis. The model first classifies the samples into speech and silence groups according to a threshold which has been determined through extensive experiments. It then uses an MLP classifier to detect stego instances and determine the embedding ratio. PEAS steganalysis detects 99.8% of stego instances in the lowest analyzed embedding ratio — 12.5% — and its sensitivity increases to 100% for the ratios of 37.5% and above.

Sophisticated and modernized library running system with OCR algorithm using IoT

An internet of things (IoT) is an exclusive method, were its impact on the enactments of human life is very trendy. This research on library control system operates on the basis of IoT and optical character recognition (OCR) algorithm rules and its training. A closed-circuit television (CCTV) watched mechanism is created to control the book issuing and returning phenomenon via tag studying system in the library. In this proposed work text file is converted into an audio file. This audio file is being played and the contents of the book can be heard via the headset. This unique function of the OCR helps blind people. Now a days OCR widely focused in machine processes such as machine transformation, text to speech extraction and text data mining. It utilized in various area of research in artificial intelligence, computer vision and pattern recognition. Using OCR to scan the damaged book in the library converted into pdf format the book gets new life and sharing the contents to multiple readers. In this paper aims to implement IoT based library management system to maintaining books in digital format.

НАВЧАЛЬНИЙ ПРИКЛАД МАСКУВАННЯ ІНФОРМАЦІЇ В АКУСТИЧНОМУ СИГНАЛІ

The paper presents a steganographic method of hiding textual information in an audio file. Hiding is implemented by a program in Python. The introduction of individual letters of the text into the sound is carried out by the method of «the least significant bit». The program can be used for both educational and practical purposes. The commonly used wave library was used to work with sound files. It is not a library specialized for cryptographic and steganographic needs. Its use and the conciseness of the program code makes it possible to visualize the mechanism of hiding information in the classroom and demonstrate in the process of creating a program its debugging and testing. It is also important for educational purposes that working within the library allows you to see the state of an empty and filled audio container at the level of individual bits. To assess the practical value of the program, it was tested with texts of different lengths and with sound containers of different grades. In particular, the sound of a tuning fork, the sound of a guitar string, classical music, rap, jazz, and an audiobook were used. The experiment showed the correct reproduction of texts. It was found that if you listen carefully to the «pure sound» of the tuning fork, when the container is overloaded with information, suspicions of a text bookmark may arise. A text bookmark in the sound, in which the volume, tempo and frequency change quickly, does not reveal the suspicion of a possible bookmark. However, if the party who intercepted the masked message has guesses about how to bookmark the text, then the text is easily removed. Therefore, the use of the program for practical purposes requires additional manipulations in the code, in particular related to the order of text input and the choice of location. Additional text encryption is also desirable. Analysis of sound and its manipulation at the level of individual bits also has educational value in the sense that it gives an idea of the noise level, the magnitude of the useful physical signal and the sensitivity of the human ear. Key words: Python language, steganography, hiding information, masking information in an audio file, educational example.

Link Wray

This piece focuses on the musical and genre innovations of early rock n' roll guitarist, Link Wray. Given his Native American ancestry and physical disability, Wray is a difficult figure to place, though his material and embodied methods of making music prefigured other, more famous artists. The audio file reflecting on Wray suggests a more proper scope of his influence in an attempt to decolonize both the history of rock music as well as methods of multimodal and sound composition.

Securing Audio Using AES-based Authenticated Encryption with Python

The focus of this research is to analyze the results of encrypting audio using various authenticated encryption algorithms implemented in the Python cryptography library for ensuring authenticity and confidentiality of the original contents. The Advanced Encryption Standard (AES) is used as the underlying cryptographic primitive in conjunction with various modes including Galois Counter Mode (GCM), Counter with Cipher Block Chaining Message Authentication Code (CCM), and Cipher Block Chaining (CBC) with Keyed-Hashing for encrypting a relatively small audio file. The resulting encrypted audio shows similarity in the variance when encrypting using AES-GCM and AES-CCM. There is a noticeable reduction in variance of the performed encodings and an increase in the amount of time it takes to encrypt and decrypt the same audio file using AES-CBC with Keyed-Hashing. In addition, the corresponding encrypted using this mode audio spans a longer duration. As a result, AES should either have GCM or CCM for an efficient and reliable authenticated encryption integration within a workflow.

Simulation of LMS based adaptive noise cancellation using Labview

The audio signals processed in the signal measurement systems are inevitably susceptible to unwanted noise which significantly affects the quality of the signal and the overall performance of the signal communication systems. Due to its' random and unpredictable nature, the amount of noise in signals has proven to be a significant issue in designing these systems and recently has been a trending research topic. In this regard, the active noise cancellation method has proven to be an effective technique for eliminating the noise effects on signal processing. The concept of active noise cancellation is based on the application of adaptive filters and algorithms proposed to reduce the signal corruption and distortion caused by the noise due to the principle of destructive interference. In this paper a simulation model of active noise reduction technique using the LMS (Least Mean Square) algorithm in Labview is presented. The purpose of the work is to investigate the noise cancellation effect on a recorded audio file in terms of analyzing the audio file before and after filtering out the noise by using the LMS algorithm and discuss the results thereof.

ON THE VERGE OF THE PROFESSIONAL HALF-CENTURY WITH THE GLORY

The article tells about the history of the Anthem of Neurology, publishes words, notes and provides a link to the audio file (https://youtu.be/zVAVNAukUOA). The author of the words is Oleksiy Onoprienko. Composer is Volodymyr Ilemsky. The men’s choir of the G. Veryovka Ukrainian National Honored Academic Folk Choir sings. Keywords: neurology, Anthem of Neurology, The men’s choir of the G. Veryovka Ukrainian National Honored Academic Folk Choir sings.

Dementia risks identified by vocal features via telephone conversations: A novel machine learning prediction model

Due to difficulty in early diagnosis of Alzheimer’s disease (AD) related to cost and differentiated capability, it is necessary to identify low-cost, accessible, and reliable tools for identifying AD risk in the preclinical stage. We hypothesized that cognitive ability, as expressed in the vocal features in daily conversation, is associated with AD progression. Thus, we have developed a novel machine learning prediction model to identify AD risk by using the rich voice data collected from daily conversations, and evaluated its predictive performance in comparison with a classification method based on the Japanese version of the Telephone Interview for Cognitive Status (TICS-J). We used 1,465 audio data files from 99 Healthy controls (HC) and 151 audio data files recorded from 24 AD patients derived from a dementia prevention program conducted by Hachioji City, Tokyo, between March and May 2020. After extracting vocal features from each audio file, we developed machine-learning models based on extreme gradient boosting (XGBoost), random forest (RF), and logistic regression (LR), using each audio file as one observation. We evaluated the predictive performance of the developed models by describing the receiver operating characteristic (ROC) curve, calculating the areas under the curve (AUCs), sensitivity, and specificity. Further, we conducted classifications by considering each participant as one observation, computing the average of their audio files’ predictive value, and making comparisons with the predictive performance of the TICS-J based questionnaire. Of 1,616 audio files in total, 1,308 (81.0%) were randomly allocated to the training data and 308 (19.1%) to the validation data. For audio file-based prediction, the AUCs for XGboost, RF, and LR were 0.863 (95% confidence interval [CI]: 0.794–0.931), 0.882 (95% CI: 0.840–0.924), and 0.893 (95%CI: 0.832–0.954), respectively. For participant-based prediction, the AUC for XGboost, RF, LR, and TICS-J were 1.000 (95%CI: 1.000–1.000), 1.000 (95%CI: 1.000–1.000), 0.972 (95%CI: 0.918–1.000) and 0.917 (95%CI: 0.918–1.000), respectively. There was difference in predictive accuracy of XGBoost and TICS-J with almost approached significance (p = 0.065). Our novel prediction model using the vocal features of daily conversations demonstrated the potential to be useful for the AD risk assessment.