Reviewerly/exHarmony huggingface dataset:
A novel distortion-tolerant speech encryption scheme for secure voice communication

The current increasing need for privacy-preserving voice communications is leading to new ideas for securing voice transmission. This paper refers to a relatively new concept of sending encrypted speech as pseudo-speech in the audio domain over digital voice communication infrastructures, like 3G cellular network and VoIP. This work presents a novel distortion-tolerant speech encryption scheme for secure voice communications over voice channels that combines the robustness of analog speech scrambling and elevated security offered by digital ciphers like AES-CTR. The system scrambles vocal parameters of a speech signal (loudness, pitch, timbre) using distance-preserving pseudo-random translations and rotations on a hypersphere of parameters. Next, scrambled parameters are encoded to a pseudo-speech signal adapted to transmission over digital voice channels equipped with voice activity detection. Upon reception of this pseudo-speech signal, the legitimate receiver restores distorted copies of the initial vocal parameters. Despite some deciphering errors, an integrated neural-based vocoder based on the LPCNet architecture reconstructs an intelligible speech. The experimental implementation of this speech encryption scheme has been tested by simulations and sending an encrypted signal over FaceTime between two iPhones 6 connected to the same WiFi network. Moreover, speech excerpts restored from encrypted signals were evaluated by a speech quality assessment on a group of about 40 participants. The experiments demonstrated that the proposed scheme produces intelligible speech with a gracefully progressive quality degradation depending on the channel noise. Finally, the preliminary computational analysis suggested that the presented setting may operate on high-end portable devices in nearly real-time.
Engineering the Neural Automatic Passenger Counter

Automatic passenger counting (APC) in public transportation has been approached with various machine learning and artificial intelligence methods since its introduction in the 1970s. It is mainly used for revenue sharing, which (in Germany alone) is in the billions annually and supply planning, which is essential for services of general interest. While equivalence testing is becoming more popular than difference detection (Student’s t-test), the former is much more difficult to pass to ensure low user risk. On the other hand, recent developments in artificial intelligence have led to algorithms that promise much higher counting quality (lower bias). However, gradient-based methods (including Deep Learning) typically run into local optima. In this work, we explore and exploit various aspects of machine learning to increase the reliability, performance, and counting quality of the Neural APC 3D depth video-based LSTM neural network. We perform a grid search with several fundamental parameters: the selection and size of the training set, which is similar to cross-validation, and the initial network weights and randomness during the training process. Using this experiment, we show how aggregation techniques such as ensemble quantiles can reduce bias, and we give an idea of the overall spread of the results. We utilize the test success chance, a simulative metric based on the empirical distribution. We also employ a post-training Monte Carlo quantization approach and introduce cumulative summation to turn counting into a stationary method and allow unbounded counts. All in all, our numerous additions provide a major quality increase to the NAPC.
Work In Progress: Safety and Robustness Verification of Autoencoder-Based Regression Models using the NNV Tool

This work in progress paper introduces robustness verification for autoencoder-based regression neural network (NN) models, following state-of-the-art approaches for robustness verification of image classification NNs.Despite the ongoing progress in developing verification methods for safety and robustness in various deep neural networks (DNNs), robustness checking of autoencoder models has not yet been considered.We explore this open space of research and check ways to bridge the gap between existing DNN verification methods by extending existing robustness analysis methods for such autoencoder networks.While classification models using autoencoders work more or less similar to image classification NNs, the functionality of regression models is distinctly different.We introduce two definitions of robustness evaluation metrics for autoencoder-based regression models, specifically the percentage robustness and un-robustness grade.We also modified the existing Imagestar approach, adjusting the variables to take care of the specific input types for regression networks.The approach is implemented as an extension of NNV, then applied and evaluated on a dataset, with a case study experiment shown using the same dataset.As per the authors' understanding, this work in progress paper is the first to show possible reachability analysis of autoencoder-based NNs.
The Ambient Assisted Working (AAW) concept

Assistive environments is a well established research area, particularly, the Ambient Assisted Living (AAL) paradigm, which relies in data and artificial intelligence to infer the user's actions and status, thus enabling assistive actions. In a very distinct environment – the manufacturing industry, we propose a twin concept – the Ambient Assisted Working (AAW), to provide assistance to the workers on the factory floor, using solutions with design principles similar to AAL systems. The Industry 4.0 (I4.0) Technical Assistance design principle, introduces the assistance concept in I4.0 solutions and the AAW builds on top of that design principle.
Physical Adversarial Attack on a Robotic Arm

Collaborative Robots (cobots) are regarded as highly safety-critical cyber-physical systems (CPSs) owing to their close physical interactions with humans. In settings such as smart factories, they are frequently augmented with AI. For example, in order to move materials, cobots utilize object detectors based on deep learning models. Deep learning, however, has been demonstrated as vulnerable to adversarial attacks: a minor change (noise) to benign input can fool the underlying neural networks and lead to a different result. While existing works have explored such attacks in the context of picture/object classification, less attention has been given to attacking neural networks used for identifying object locations, and demonstrating that this can actually lead to a physical attack in a real CPS. In this paper, we propose a method to generate adversarial patches for the object detectors of CPSs, in order to miscalibrate them and cause potentially dangerous physical effects. In particular, we evaluate our method on an industrial robotic arm for card gripping, demonstrating that it can be misled into clipping the operator’s hand instead of the card. To our knowledge, this is the first work to attack object locations and lead to an incident on human users by an actual system.
Accelerating Multi-Object Tracking in Edge Computing Environment with Time-Spatial Optimization

Multi-Object Tracking (MOT) task is a key issue in computer vision. It has capacious application prospects in survelliance, self-driving, and augmented reality. Benefit from the continuous progress of Deep Neural Network (DNN), the accuracy of the MOT algorithm has been significantly improved. Nevertheless, limited to computing power, achieving real-time DNN-based MOT is difficult in embedded systems. In reality, there are many wasteful and unnecessary computations in traditional frame-by-frame full-size video analysis. Therefore, in this paper, we propose a strategy that optimizing the execution of a traditional MOT pipeline in the dimension of time and space. In the temporal dimension, DNN only works in periodic keyframes while using a lightweight model for quickly generating results in the common frames. In the spatial dimension, we design an image density region discriminator to narrow down the input size of DNN. An edge device is introduced to perform end-edge collaborative computing to further accelerating the execution. Additionally, an end-edge parallel computing mechanism is designed that performing dynamic decisions based on the computing power and network environment between end and edge. Moreover, we rebuild the DNN model by TensorRT to optimize the model structure of DNN. By integrating the above approaches, the system can achieve 17.6 ~ 38.1 &#x00D7; speedup ratio, while with 3%~10.4% absolute tracking accuracy sacrifice and can be deployed in an unstable network environment.
Cloud Task Scheduling Based on Proximal Policy Optimization Algorithm for Lowering Energy Consumption of Data Center

As a part of cloud computing technology, algorithms for cloud task scheduling place an important influence on the area of cloud computing in data centers.In our earlier work, we proposed DeepEnergyJS, which was designed based on the original version of the policy gradient and reinforcement learning algorithm.We verified its effectiveness through simulation experiments.In this study, we used the Proximal Policy Optimization (PPO) algorithm to update DeepEnergyJS to DeepEnergyJSV2.0.First, we verify the convergence of the PPO algorithm on the dataset of Alibaba Cluster Data V2018.Then we contrast it with reinforcement learning algorithm in terms of convergence rate, converged value, and stability.The results indicate that PPO performed better in training and test data sets compared with reinforcement learning algorithm, as well as other general heuristic algorithms, such as First Fit, Random, and Tetris.DeepEnergyJSV2.0achieves better energy efficiency than DeepEnergyJS by about 7.814%.
Mind the data gap(s): Investigating power in speech and language datasets

Algorithmic oppression is an urgent and persistent problem in speech and language technologies.Considering power relations embedded in datasets before compiling or using them to train or test speech and language technologies is essential to designing less harmful, more just technologies.This paper presents a reflective exercise to recognise and challenge gaps and the power relations they reveal in speech and language datasets by applying principles of Data Feminism and Design Justice, and building on work on dataset documentation and sociolinguistics.
Effect of using Cloud Computing technology on the accounting job

The study aimed to demonstrate the importance of the concept of cloud accounting and its advantages over the accounting profession and compare it with traditional accounting systems, and to study the risks and downsides of adopting cloud computing technology, the study concluded that cloud computing technology affects the organization and measurement of accounting work by reducing the costs of the information technology infrastructure, which is getting more and more complex daily due to the huge changes that currently exist, such as the increase in the number of devices and the increase in the consumption of electrical energy. Significant cost savings and enhanced operational efficiency, where many companies have invested in computing capabilities and capabilities to improve their business and take advantage of the capabilities of cloud storage and database management, so it is necessary to use cloud computing technology, as it greatly reduces the costs of the company’s information system infrastructure, and the study recommended the need to rely on modern technologies at work accounting and preparing trained cadres ready to work with these techniques.
Distributed Timed Elastic Band (DTEB) Planner: Trajectory Sharing and Collision Prediction for Multi-Robot Systems

Autonomous navigation of mobile robots is a well studied problem in robotics. However, the navigation task becomes challenging when multi-robot systems have to cooperatively navigate dynamic environments with deadlock-prone layouts. We present a Distributed Timed Elastic Band (DTEB) Planner that combines Prioritized Planning with the online TEB trajectory Planner, in order to extend the capabilities of the latter to multi-robot systems. The proposed planner is able to reactively avoid imminent collisions as well as predictively resolve potential deadlocks among a team of robots, while navigating in a complex environment. The results of our simulation demonstrate the reliable performance and the versatility of the planner in different environment settings. The code and tests for our approach are available online.
Scalable Self-Sovereign Identity Architecture

We propose a scalable self-sovereign identity (S3I) system architecture that uses a hierarchically structured Byzantine fault tolerance layer of validating authorities and a permissioned blockchain to record and manage user identities. Users and service providers interact with the S3I system through nearby proxy nodes that store copies of the blockchain but do not participate in the consensus protocol. We identify different challenges of such a system and discuss design choices that allow those challenges to be addressed and ultimately resolved, with focus on networking issues such as performance and coverage. We also show preliminary performance evaluation results that confirm the validity of the proposed approach.
Course Quality Evaluation Based on Deep Neural Network

In this paper, we propose a deep model of course quality evaluation using deep neural network, which is simple and efficient to accurately evaluate the course quality. Specifically, we utilize multi-layer fully-connected (FC) layers with different activation functions to model the network. We perform a series of simulation experiments to evaluate the effect of FC layer number and activation functions. Simulation experiment results verify that the proposed model has excellent performance.
Face Age Estimation using Shortcut Identity Connection of Convolutional Neural Network

Depletion of skin and muscle tone has a considerable impact on the appearance of the face, which is constantly evolving.Algorithms necessitate a large number of aging faces for this purpose.Another popular deep learning technique is convolutional neural networks.In a recent study, many computer vision and pattern recognition problems have been successfully tackled using it.But these methods have architectural issues (e.g., the training process) that have a negative impact on their age estimation performance.As a result, a whole new approach is proposed in this research to address the issue.Using a convolutional neural network framework and resnet50 architecture, researchers were able to detect the age of a human face.This proposed shortcut identity connection strategy, which enables age estimation from the face image, has improved the success of the resnet50 architecture.To be able to tell a person's age from a picture of their face, it was important to know the characteristics of aging.As a result, the rhetorical classification method, which employs the resnet50 structure, is used to shift the face aging levels to a probability level.All of the 50 layers in the proposed residual network have a residual block that connects them.As far as face-aging databases go, ImageNet and FG-NET are both good choices for the proposed age estimation process.In the training session, the experiment results are 2.27 and 2.38, based mostly on the mean absolute error.The test accuracy results for the ImageNet dataset are 81.75% with the FG-NET dataset and 57% with the ImageNet dataset.
Directive Speech of Mata Najwa in <i>“Kontroversi Mas Menteri”</i> and Its Relevance to Indonesian Language Learning

This study aims to describe the forms of directive speech acts in the Mata Najwa episode "Kontroversi Mas Menteri" and their relevance to learning Indonesian.This research is qualitative descriptive.The data source was obtained from Mata Najwa's Youtube episode "Kontroversi Mas Menteri" in the form of oral speech.Researchers used listening and note-taking techniques in collecting data, while data analysis techniques employed pragmatic matching techniques.The data were then validated by triangulation of data sources.Based on the study results, it can be concluded that on Mata Najwa's Youtube account "Kontroversi Mas Menteri," it was found: (1) five directive speech acts: requesting, begging, asking, prohibiting, and ordering, (2) three speech acts requesting, two speech acts of begging, six speech acts of asking, two speech acts of prohibiting, and three speech acts of ordering, and (3) the research results are relevant to learning Indonesian in junior high school class VIII, with KD 4.13, concluding the contents, suggestions, invitations, directions, and considerations about various positive things about actual text problems from persuasive texts (environment, social conditions, and/or cultural diversity) heard and read.
Parsing the Hand Gesture of Traffic Police Officers by Using OpenPose

Nowadays, as self-driving cars become more and more popular, more features should be added to the whole function of self-driving cars.One of them should be the ability to parse the hand gestures of traffic police officers.To fully bring self-driving cars to reality, such a feature should be discussed and considered in their functionality.In recent decades, real-time 3D hand pose estimation combined with depth cameras has contributed to the successful launch of virtual reality and augmented reality applications.This essay focuses on using OpenPose to parse the hand gestures of traffic police officers based on the expected functionality of self-driving cars.Based on the given background, this article will use comparison to separate the traditional methods and the new method.The result generated by OpenPose is faster and more accurate (99% accuracy), so that it can be a choice for the future algorithm of recognition of hand gesture recognition of traffic officers in self-driving cars.
Investigating person-specific errors in chat-oriented dialogue systems

Creating chatbots to behave like real people is important in terms of believability.Errors in general chatbots and chatbots that follow a rough persona have been studied, but those in chatbots that behave like real people have not been thoroughly investigated.We collected a large amount of user interactions of a generation-based chatbot trained from largescale dialogue data of a specific character, i.e., "target person" and analyzed errors related to that person.We found that person-specific errors can be divided into two types: errors in attributes and those in relations, each of which can be divided into two levels: self and other.The correspondence with an existing taxonomy of errors was also investigated, and person-specific errors that should be addressed in the future were clarified.
One Wug, Two Wug+s Transformer Inflection Models Hallucinate Affixes

Data augmentation strategies are increasingly important in NLP pipelines for low-resourced and endangered languages, and in neural morphological inflection, augmentation by so called data hallucination is a popular technique.This paper presents a detailed analysis of inflection models trained with and without data hallucination for the low-resourced Canadian Indigenous language Gitksan.Our analysis reveals evidence for a concatenative inductive bias in augmented models-in contrast to models trained without hallucination, they strongly prefer affixing inflection patterns over suppletive ones.We find that preference for affixation in general improves inflection performance in "wug test" like settings, where the model is asked to inflect lexemes missing from the training set.However, data hallucination dramatically reduces prediction accuracy for reduplicative forms due to a misanalysis of reduplication as affixation.While the overall impact of data hallucination for unseen lexemes remains positive, our findings call for greater qualitative analysis and more varied evaluation conditions in testing automatic inflection systems.Our results indicate that further innovations in data augmentation for computational morphology are desirable.
Vocational College Teachers’ Professional Identity and Its Relationship with Social Support and Sense of Efficacy

An empirical research was conducted on 822 teachers from 10 vocational colleges in Jiangsu Province investigated through self-designed and revised questionnaires.Teachers have the highest professional identity in professional behavior and the lowest in professional reputation.There is a significant positive correlation between social support and professional identity, as well as professional identity and personal teaching efficacy, while there is no significant correlation between general teaching efficacy and other variables.Social support has a direct and positive predictive power to professional identity, so as professional identity to personal teaching efficiency.Social support has an indirect positive predictive power to personal teaching efficacy, in which professional identity plays a completely intermediary role.The construction of social support system from the personal and environmental aspects can promote the sense of professional identity and teaching efficacy, which ultimately benefits the personnel training in vocational colleges.
RARTS: An Efficient First-Order Relaxed Architecture Search Method

Differentiable architecture search (DARTS) is an effective method for data-driven neural network design based on solving a bilevel optimization problem. Despite its success in many architecture search tasks, there are still some concerns about the accuracy of first-order DARTS and the efficiency of the second-order DARTS. In this paper, we formulate a single level alternative and a relaxed architecture search (RARTS) method that utilizes the whole dataset in architecture learning via both data and network splitting, without involving mixed second derivatives of the corresponding loss functions like DARTS. In our formulation of network splitting, two networks with different but related weights cooperate in search of a shared architecture. The advantage of RARTS over DARTS is justified by a convergence theorem and an analytically solvable model. Moreover, RARTS outperforms DARTS and its variants in accuracy and search efficiency, as shown in adequate experimental results. For the task of searching topological architecture, i.e., the edges and the operations, RARTS obtains a higher accuracy and 60% reduction of computational cost than second-order DARTS on CIFAR-10. RARTS continues to out-perform DARTS upon transfer to ImageNet and is on par with recent variants of DARTS even though our innovation is purely on the training algorithm without modifying search space. For the task of searching width, i.e., the number of channels in convolutional layers, RARTS also outperforms the traditional network pruning benchmarks. Further experiments on the public architecture search benchmark like NATS-Bench also support the preeminence of RARTS.
Developmental Negation Processing in Transformer Language Models

Reasoning using negation is known to be difficult for transformer-based language models.While previous studies have used the tools of psycholinguistics to probe a transformer's ability to reason over negation, none have focused on the types of negation studied in developmental psychology.We explore how well transformers can process such categories of negation, by framing the problem as a natural language inference (NLI) task.We curate a set of diagnostic questions for our target categories from popular NLI datasets and evaluate how well a suite of models reason over them.We find that models perform consistently better only on certain categories, suggesting clear distinctions in how they are processed. 1