Reviewerly/exHarmony huggingface dataset:
Identification of Bias Against People with Disabilities in Sentiment  Analysis and Toxicity Detection Models

Sociodemographic biases are a common problem for natural language processing, affecting the fairness and integrity of its applications. Within sentiment analysis, these biases may undermine sentiment predictions for texts that mention personal attributes that unbiased human readers would consider neutral. Such discrimination can have great consequences in the applications of sentiment analysis both in the public and private sectors. For example, incorrect inferences in applications like online abuse and opinion analysis in social media platforms can lead to unwanted ramifications, such as wrongful censoring, towards certain populations. In this paper, we address the discrimination against people with disabilities, PWD, done by sentiment analysis and toxicity classification models. We provide an examination of sentiment and toxicity analysis models to understand in detail how they discriminate PWD. We present the Bias Identification Test in Sentiments (BITS), a corpus of 1,126 sentences designed to probe sentiment analysis models for biases in disability. We use this corpus to demonstrate statistically significant biases in four widely used sentiment analysis tools (TextBlob, VADER, Google Cloud Natural Language API and DistilBERT) and two toxicity analysis models trained to predict toxic comments on Jigsaw challenges (Toxic comment classification and Unintended Bias in Toxic comments). The results show that all exhibit strong negative biases on sentences that mention disability. We publicly release BITS Corpus for others to identify potential biases against disability in any sentiment analysis tools and also to update the corpus to be used as a test for other sociodemographic variables as well.
Margin-Independent Online Multiclass Learning via Convex Geometry

We consider the problem of multi-class classification, where a stream of adversarially chosen queries arrive and must be assigned a label online. Unlike traditional bounds which seek to minimize the misclassification rate, we minimize the total distance from each query to the region corresponding to its correct label. When the true labels are determined via a nearest neighbor partition -- i.e. the label of a point is given by which of $k$ centers it is closest to in Euclidean distance -- we show that one can achieve a loss that is independent of the total number of queries. We complement this result by showing that learning general convex sets requires an almost linear loss per query. Our results build off of regret guarantees for the geometric problem of contextual search. In addition, we develop a novel reduction technique from multiclass classification to binary classification which may be of independent interest.
Transient force identification by use of high-speed digital holography

This paper discusses on a full and large field, high spatial resolution, contact-less, and non-intrusive approach for force retrieval at different excitation regimes of mechanical structures. The inverse problem based on the Force Analysis Technique is combined with the full-field measurement to identify the force applied to a structure, in terms of localization and quantification. In the transient regime, the proposed approach is able to retrieve the time dependence of the force and yields good agreement with that from a piezoelectric force sensor.
Development of cooperation in vocational education

The article is devoted to the scientific and applied research of the practice of improving vocational education in higher education institutions in cooperation of participants in relations in the field of education. The methods of interdisciplinary analysis of literature and normative legal documents, the method of analysis of pedagogical experience, general scientific methods of systematization, generalization, comparison, classification were applied. New programs of higher professional education have been developed and are being implemented to prepare bachelors and masters in the field of Linguistics at the Department of Foreign Languages of Professional Communication of Vladimir State University in cooperation with partner organizations based on the provisions of domestic pedagogy of cooperation enshrined in modern legislation. The current professional competencies of bachelors of the direction 45.03.02 - Linguistics, developed in accordance with the labor functions of the professional standard 04.015 "Specialist in translation", are presented. Indicators of their achievement are structured according to the skill levels indicated by the professional community. Particular importance is attached to the relevance of designing individual educational routes of students. A route model is proposed, which is based on various forms of development of cooperation in the field of vocational education in higher education. The results of the study show the need to develop cooperation that takes into account the interests and needs of all subjects of educational activity. In the practice of the department, it is implemented through joint training of bachelors with employers, obtaining additional qualifications by them, continuing their studies in a master's degree with the prospect of pedagogical activity.
Revisiting dequantization and quantum advantage in learning tasks

It has been shown that the apparent advantage of some quantum machine learning algorithms may be efficiently replicated using classical algorithms with suitable data access -- a process known as dequantization. Existing works on dequantization compare quantum algorithms which take copies of an n-qubit quantum state $|x\rangle = \sum_{i} x_i |i\rangle$ as input to classical algorithms which have sample and query (SQ) access to the vector $x$. In this note, we prove that classical algorithms with SQ access can accomplish some learning tasks exponentially faster than quantum algorithms with quantum state inputs. Because classical algorithms are a subset of quantum algorithms, this demonstrates that SQ access can sometimes be significantly more powerful than quantum state inputs. Our findings suggest that the absence of exponential quantum advantage in some learning tasks may be due to SQ access being too powerful relative to quantum state inputs. If we compare quantum algorithms with quantum state inputs to classical algorithms with access to measurement data on quantum states, the landscape of quantum advantage can be dramatically different. We remark that when the quantum states are constructed from exponential-size classical data, comparing SQ access and quantum state inputs is appropriate since both require exponential time to prepare.
Methods of Generating Time Series Image for detecting Anomalies in Time Series data

To detect anomalies in time series data, statistical techniques such as PCA and autoencoder are used, or anomalies are detected based on deep learning models such as RNN. It is difficult to expect good performance only with simple statistical techniques or RNN-based deep learning models because the environment and causes in which anomalies are recorded are not simple and various variables affect them. In this paper, we proposed a method of detecting anomalies using CNN-based deep learning model, a binary classification model of representative images, by imaging time series data. RP, GASF, GADF, and MAF algorithms were used as methods for imaging. All of time series image-based models showed equal or higher accuracy than conventional LSTM-based models, and among imaging-based CNN models, the method of imaging with MTF algorithm derived the highest accuracy.
FedHM: Efficient Federated Learning for Heterogeneous Models via  Low-rank Factorization

The underlying assumption of recent federated learning (FL) paradigms is that local models usually share the same network architecture as the global model, which becomes impractical for mobile and IoT devices with different setups of hardware and infrastructure. A scalable federated learning framework should address heterogeneous clients equipped with different computation and communication capabilities. To this end, this paper proposes FedHM, a novel federated model compression framework that distributes the heterogeneous low-rank models to clients and then aggregates them into a global full-rank model. Our solution enables the training of heterogeneous local models with varying computational complexities and aggregates a single global model. Furthermore, FedHM not only reduces the computational complexity of the device, but also reduces the communication cost by using low-rank models. Extensive experimental results demonstrate that our proposed \system outperforms the current pruning-based FL approaches in terms of test Top-1 accuracy (4.6% accuracy gain on average), with smaller model size (1.5x smaller on average) under various heterogeneous FL settings.
Instinct-Driven Engineering

Current Information engineering is focused on artificial world in engineering, in information processing, etc. But as the Real World is getting more and more complicated and complex, and the environments and situations come to change frequently, extensively and in an unpredictable manner, it becomes increasingly difficult to cope with such problems of large dimensions. As the curse of dimension tells us if the number f dimensions become too large, we cannot process the problem mathematically. But we can manage to cope with such changing Real World, although it becomes more and more difficult. Why can we manage to cope with such drastically changin Real World? It is because our instinct can process a tremendously large amount of data. So, instead to sticking to processing everything mathematically, we should think to use our instinct, when it becomes difficult to process mathematically. But even when we use instinct, it becomes more and more difficult to cope with the changes of the Real World. So, this paper proposes to develop a tool to support instinct to reduce its burden. Mahalanobis Distance-Pattern (MDP) approach is proposed as a tool in that direction. MDP is based on ordinal approach based Mahalanobis Distance and combine it with pattern. Thus, it enables instinct to make the most of qualitative evaluation, while the current engineering is primarily based on cardinal approach and only provide quantitative evaluation.
Performance evaluation of invariant-based inverse engineering by quantum  speed limit

Quantum speed limits for two time-evolved states are introduced and applied to overlap between true dynamics and approximate dynamics. In particular, we point out that the present idea is suitable for invariant-based inverse engineering, i.e., the worst case performance of invariant-based inverse engineering can be evaluated by using a designed time-evolved state. As demonstrations, we apply the present method to stimulated Raman adiabatic passage and quantum annealing, and then we find that the present idea brings valuable insight to control scheduling.
Alice in Passphraseland: Assessing the Memorability of Familiar  Vocabularies for System-Assigned Passphrases

Text-based secrets are still the most commonly used authentication mechanism in information systems. IT managers must strike a balance between security and memorability while developing password policies. Initially introduced as more secure authentication keys that people could recall, passphrases are passwords consisting of multiple words. However, when left to the choice of users, they tend to choose predictable natural language patterns in passphrases, resulting in vulnerability to guessing attacks. System-assigned authentication keys can be guaranteed to be secure, but this comes at a cost to memorability. In this study we investigate the memorability of system-assigned passphrases from a familiar vocabulary to the user. The passphrases are generated with the Generative Pre-trained Transformer 2 (GPT-2) model trained on the familiar vocabulary and are readable, pronounceable, sentence like passphrases resembling natural English sentences. Through an online user study with 500 participants on Amazon Mechanical Turk, we test our hypothesis - following a spaced repetition schedule, passphrases as natural English sentences, based on familiar vocabulary are easier to recall than passphrases composed of random common words. As a proof-of-concept, we tested the idea with Amazon Mechanical Turk participants by assigning them GPT-2 generated passphrases based on stories they were familiar with. Contrary to expectations, following a spaced repetition schedule, passphrases as natural English sentences, based on familiar vocabulary performed similarly to system-assigned passphrases based on random common words.
An Experimental Design Perspective on Model-Based Reinforcement Learning

In many practical applications of RL, it is expensive to observe state transitions from the environment. For example, in the problem of plasma control for nuclear fusion, computing the next state for a given state-action pair requires querying an expensive transition function which can lead to many hours of computer simulation or dollars of scientific research. Such expensive data collection prohibits application of standard RL algorithms which usually require a large number of observations to learn. In this work, we address the problem of efficiently learning a policy while making a minimal number of state-action queries to the transition function. In particular, we leverage ideas from Bayesian optimal experimental design to guide the selection of state-action queries for efficient learning. We propose an acquisition function that quantifies how much information a state-action pair would provide about the optimal solution to a Markov decision process. At each iteration, our algorithm maximizes this acquisition function, to choose the most informative state-action pair to be queried, thus yielding a data-efficient RL approach. We experiment with a variety of simulated continuous control problems and show that our approach learns an optimal policy with up to $5$ -- $1,000\times$ less data than model-based RL baselines and $10^3$ -- $10^5\times$ less data than model-free RL baselines. We also provide several ablated comparisons which point to substantial improvements arising from the principled method of obtaining data.
A safe distribution scheme of sensitive data based on full homomorphic encryption model

Given the problems of a large number of nodes, the huge demand for IP addresses and the poor security of communication between nodes in IPv6 wireless sensor networks, this paper proposes a homomorphic data encryption scheme based on the SSL protocol in IPv6 network. The proposed scheme is mainly to realise the secure distribution of secret file information between the server and the corresponding network nodes. Besides, in response to the security problems of the confidential file distribution system, this paper also proposes a new SSL-based security protocol. The proposed protocol can not only securely send and receive confidential files but also limit the client's operation on confidential files. Furthermore, the protocol uses an improved fully homomorphic encryption algorithm to operate on the ciphertext. The algorithm improves the efficiency and security of transmission between nodes by reducing the size and expanding the plaintext space. Experimental results and comparisons show that the proposed scheme has the advantages of better security and availability during data transmission than other schemes.
7th AI Driving Olympics: 1st Place Report for Panoptic Tracking

In this technical report, we describe our EfficientLPT architecture that won the panoptic tracking challenge in the 7th AI Driving Olympics at NeurIPS 2021. Our architecture builds upon the top-down EfficientLPS panoptic segmentation approach. EfficientLPT consists of a shared backbone with a modified EfficientNet-B5 model comprising the proximity convolution module as the encoder followed by the range-aware FPN to aggregate semantically rich range-aware multi-scale features. Subsequently, we employ two task-specific heads, the scale-invariant semantic head and hybrid task cascade with feedback from the semantic head as the instance head. Further, we employ a novel panoptic fusion module to adaptively fuse logits from each of the heads to yield the panoptic tracking output. Our approach exploits three consecutive accumulated scans to predict locally consistent panoptic tracking IDs and also the overlap between the scans to predict globally consistent panoptic tracking IDs for a given sequence. The benchmarking results from the 7th AI Driving Olympics at NeurIPS 2021 show that our model is ranked #1 for the panoptic tracking task on the Panoptic nuScenes dataset.
Quantum Interior Point Methods for Semidefinite Optimization

We present two quantum interior point methods for semidefinite optimization problems, building on recent advances in quantum linear system algorithms. The first scheme, more similar to a classical solution algorithm, computes an inexact search direction and is not guaranteed to explore only feasible points; the second scheme uses a nullspace representation of the Newton linear system to ensure feasibility even with inexact search directions. The second is a novel scheme that might seem impractical in the classical world, but it is well-suited for a hybrid quantum-classical setting. We show that both schemes converge to an optimal solution of the semidefinite optimization problem under standard assumptions. By comparing the theoretical performance of classical and quantum interior point methods with respect to various input parameters, we show that our second scheme obtains a speedup over classical algorithms in terms of the dimension of the problem $n$, but has worse dependence on other numerical parameters.
The Effect of Combinatorial Coverage for Neurons on Fault Detection in Deep Neural Networks

Many test adequacy metrics for deep neural net-works (DNNs) were proposed to measure the quality of the testing of deep learning system. The combinatorial coverage of neurons was proposed because it considers the influence of neurons in the same layer on neurons in the next layer. However, it is still inconclusive whether the combinatorial coverage of neurons is beneficial to the testing of DNNs. We conduct an empirical study on MNIST dataset to answer how the combinatorial coverage of neurons affects the fault detection in DNNs. The experimental results show a medium or strong correlation between the fixed-strength combinatorial coverage of neurons and the number of adversarial examples for DNNs. Such a results suggests that it is feasible to use the combinatorial coverage of neurons to guide the testing of DNNs.
Robust RDH Technique using Sorting and IPVO-based Pairwise PEE for Secure Communication

Nowadays, Reversible Data Hiding (RDH) is used extensively in information sensitive communication domains to protect the integrity of hidden data and the cover medium. However, most of the recently proposed RDH methods lack robustness. Robust RDH methods are required to protect the hidden data from security attacks at the time of communication between the sender and receiver. In this paper, we propose a Robust RDH scheme using IPVO based pairwise embedding. The proposed scheme is designed to prevent unintentional modifications caused to the secret data by JPEG compression. The cover image is decomposed into two planes namely HSB plane and LSB plane. As JPEG compression most likely modifies the LSBs of the cover image during compression, it is best not to hide the secret data into LSB planes. So, the proposed method utilizes a pairwise embedding to embed secret data into HSB plane of the cover image. High fidelity improved pixel value ordering (IPVO) based pairwise embedding ensures that the embedding performance of the proposed method is improved.
“A Deeper and More Conscious Silence”: Aurality in Thoreau and Hawthorne’s Journals and Later Works

ABSTRACT Both Hawthorne and Thoreau were observing silence, then creating literary works with fully-developed ideas on silence, at the same time and the same place. Both writers explored issues of silence as correlated to public spectacle, intimacy, inanimate objects, and the natural world. Twenty-first-century readers must reorient themselves to nineteenth-century notions of silence in studying their body of work. This essay attempts to synthesize their ideas together, starting with their early journals from the 1830s.
THE PLACE AND ROLE OF ARTIFICIAL INTELLIGENCE CHATBOTS IN ADULT EDUCATION AND TRAINING OF ADULT EDUCATORS

Artificial intelligence chatbots are becoming the new trend of technology in the education sector and competitively changing teaching and learning environments with innovations. Artificial intelligence is a field of science studying and producing machines that think, behave, and solve specific human problems. The study explained the place and role of artificial intelligence chatbots in adult education and the training of adult educators in Nigeria. The article also explored the likely challenges to limiting the successful implementation of artificial intelligence chatbots in adult education. The study concludes with recommendations for policy, practice, and future studies on artificial intelligence chatbots in adult education and the training of adult educators.
Player Modeling using Behavioral Signals in Competitive Online Games

Competitive online games use rating systems to match players with similar skills to ensure a satisfying experience for players. In this paper, we focus on the importance of addressing different aspects of playing behavior when modeling players for creating match-ups. To this end, we engineer several behavioral features from a dataset of over 75,000 battle royale matches and create player models based on the retrieved features. We then use the created models to predict ranks for different groups of players in the data. The predicted ranks are compared to those of three popular rating systems. Our results show the superiority of simple behavioral models over mainstream rating systems. Some behavioral features provided accurate predictions for all groups of players while others proved useful for certain groups of players. The results of this study highlight the necessity of considering different aspects of the player's behavior such as goals, strategy, and expertise when making assignments.
Deep Distilling: automated code generation using explainable deep  learning

Human reasoning can distill principles from observed patterns and generalize them to explain and solve novel problems. The most powerful artificial intelligence systems lack explainability and symbolic reasoning ability, and have therefore not achieved supremacy in domains requiring human understanding, such as science or common sense reasoning. Here we introduce deep distilling, a machine learning method that learns patterns from data using explainable deep learning and then condenses it into concise, executable computer code. The code, which can contain loops, nested logical statements, and useful intermediate variables, is equivalent to the neural network but is generally orders of magnitude more compact and human-comprehensible. On a diverse set of problems involving arithmetic, computer vision, and optimization, we show that deep distilling generates concise code that generalizes out-of-distribution to solve problems orders-of-magnitude larger and more complex than the training data. For problems with a known ground-truth rule set, deep distilling discovers the rule set exactly with scalable guarantees. For problems that are ambiguous or computationally intractable, the distilled rules are similar to existing human-derived algorithms and perform at par or better. Our approach demonstrates that unassisted machine intelligence can build generalizable and intuitive rules explaining patterns in large datasets that would otherwise overwhelm human reasoning.