Reviewerly/exHarmony huggingface dataset:
GailBot: An automatic transcription system for Conversation Analysis

Researchers studying human interaction, such as conversation analysts, psychologists, and linguists, all rely on detailed transcriptions of language use. Ideally, these should include so-called paralinguistic features of talk, such as overlaps, prosody, and intonation, as they convey important information. However, creating conversational transcripts that include these features by hand requires substantial amounts of time by trained transcribers. There are currently no Speech to Text (STT) systems that are able to integrate these features in the generated transcript. To reduce the resources needed to create detailed conversation transcripts that include representation of paralinguistic features, we developed a program called GailBot. GailBot combines STT services with plugins to automatically generate first drafts of transcripts that largely follow the transcription standards common in the field of Conversation Analysis. It also enables researchers to add new plugins to transcribe additional features, or to improve the plugins it currently uses. We describe GailBot’s architecture and its use of computational heuristics and machine learning. We also evaluate its output in relation to transcripts produced by both human transcribers and comparable automated transcription systems. We argue that despite its limitations, GailBot represents a substantial improvement over existing dialogue transcription software.
Continual Federated Learning Based on Knowledge Distillation

Federated learning (FL) is a promising approach for learning a shared global model on decentralized data owned by multiple clients without exposing their privacy. In real-world scenarios, data accumulated at the client-side varies in distribution over time. As a consequence, the global model tends to forget the knowledge obtained from previous tasks while learning new tasks, showing signs of "catastrophic forgetting". Previous studies in centralized learning use techniques such as data replay and parameter regularization to mitigate catastrophic forgetting. Unfortunately, these techniques cannot adequately solve the non-trivial problem in FL. We propose Continual Federated Learning with Distillation (CFeD) to address catastrophic forgetting under FL. CFeD performs knowledge distillation on both the clients and the server, with each party independently having an unlabeled surrogate dataset, to mitigate forgetting. Moreover, CFeD assigns different learning objectives, namely learning the new task and reviewing old tasks, to different clients, aiming to improve the learning ability of the model. The results show that our method performs well in mitigating catastrophic forgetting and achieves a good trade-off between the two objectives.
Small Object Detection and Tracking in Satellite Videos With Motion Informed-CNN and GM-PHD Filter

Small object tracking in low-resolution remote sensing images presents numerous challenges. Targets are relatively small compared to the field of view, do not present distinct features, and are often lost in cluttered environments. In this paper, we propose a track-by-detection approach to detect and track small moving targets by using a convolutional neural network and a Bayesian tracker. Our object detection consists of a two-step process based on motion and a patch-based convolutional neural network (CNN). The first stage performs a lightweight motion detection operator to obtain rough target locations. The second stage uses this information combined with a CNN to refine the detection results. In addition, we adopt an online track-by-detection approach by using the Probability Hypothesis Density (PHD) filter to convert detections into tracks. The PHD filter offers a robust multi-object Bayesian data-association framework that performs well in cluttered environments, keeps track of missed detections, and presents remarkable computational advantages over different Bayesian filters. We test our method across various cases of a challenging dataset: a low-resolution satellite video comprising numerous small moving objects. We demonstrate the proposed method outperforms competing approaches across different scenarios with both object detection and object tracking metrics.
Complex natural gradient optimization for optical quantum circuit design

Few-mode optical quantum circuits can be simulated and optimized using gradient-descent methods, as optical gates can be parametrized by continuous parameters. However, the parameter space as seen by a nonholomorphic cost function is not Euclidean, which means that the Euclidean gradient does not generally point in the direction of steepest ascent. In order to retrieve the true steepest-ascent direction, one must take into account the local metric in what is known as the natural-gradient (NG) method. In this work we implement the NG for the optimization of optical quantum circuits. Specifically, we adapt the NG approach to a complex-valued parameter space, allowing us to apply the NG method directly within the Bargmann formalism, which we rely on for transforming between phase space and Fock space. We then compare the NG approach to vanilla gradient descent and to Adam over two standard state-preparation benchmarks: a single-photon source and a Gottesman-Kitaev-Preskill state source. We observe that the NG approach converges significantly faster (due in part to the possibility of using larger learning rates) and with a smoother decay of the cost function throughout the optimization. This result further improves our ability to design quantum circuits via classical optimization methods.
Implicit Feedback for Dense Passage Retrieval

In this paper we study how to effectively exploit implicit feedback in Dense Retrievers (DRs). We consider the specific case in which click data from a historic click log is available as implicit feedback. We then exploit such historic implicit interactions to improve the effectiveness of a DR. A key challenge that we study is the effect that biases in the click signal, such as position bias, have on the DRs. To overcome the problems associated with the presence of such bias, we propose the Counterfactual Rocchio (CoRocchio) algorithm for exploiting implicit feedback in Dense Retrievers. We demonstrate both theoretically and empirically that dense query representations learnt with CoRocchio are unbiased with respect to position bias and lead to higher retrieval effectiveness. We make available the implementations of the proposed methods and the experimental framework, along with all results at https://github.com/ielab/Counterfactual-DR.
Democracy &amp; Distrust in an Era of Artificial Intelligence

Abstract Our legal system has historically operated under the general view that courts should defer to the legislature. There is one significant exception to this view: cases in which it appears that the political process has failed to recognize the rights or interests of minorities. This basic approach provides much of the foundational justifications for the role of judicial review in protecting minorities from discrimination by the legislature. Today, the rise of AI decision-making poses a similar challenge to democracy's basic framework. As I argue in this essay, the rise of three trends-privatization, prediction, and automation in AI - have combined to pose similar risks to minorities. In this essay, I outline what a theory of judicial review would look like in an era of artificial intelligence, analyzing both the limitations and the possibilities of judicial review of AI. Here, I draw on cases in which AI decision-making has been challenged in courts, to show how concepts of due process and equal protection can be recuperated in a modern AI era, and even integrated into AI, to provide for better oversight and accountability.
Research on motion capture of dance training pose based on statistical analysis of mathematical similarity matching

Abstract In order to verify the effectiveness and feasibility of the combination of motion capture technology and teaching, based on dance teaching, this paper proposes a dance posture analysis method based on feature vector matching and applies it to dance teaching.. The main research work includes the following: (1) according to the characteristics of human motion poses-free editing, extracting human skeleton models, establishing a human motion model database, analysing the application of motion capture systems in dance training, and proposing a method of feature plane similarity matching to calculate model components and motion parameters. After verification, the method has high accuracy and robustness for the analysis of human posture, so that dancers can accurately compare the differences with standard dance movements, and provide theoretical support for scientific dance training. (2) Aiming at the complexity of learning dance, a dance teaching method based on motion capture technology is proposed. Using motion capture technology, a whole complex dance movement is decomposed into many small segments to make a teaching animation, which guides students to learn based on small dance movement. Imitation makes the abstract theory vivid, intuitive and easy to understand, which is conducive for the innovation of education and teaching methods.
When the Curious Abandon Honesty: Federated Learning Is Not Private

In federated learning (FL), data does not leave personal devices when they are jointly training a machine learning model. Instead, these devices share gradients, parameters, or other model updates, with a central party (e.g., a company) coordinating the training. Because data never "leaves" personal devices, FL is often presented as privacy-preserving. Yet, recently it was shown that this protection is but a thin facade, as even a passive, honest-but-curious attacker observing gradients can reconstruct data of individual users contributing to the protocol. In this work, we show a novel data reconstruction attack which allows an active and dishonest central party to efficiently extract user data from the received gradients. While prior work on data reconstruction in FL relies on solving computationally expensive optimization problems or on making easily detectable modifications to the shared model's architecture or parameters, in our attack the central party makes inconspicuous changes to the shared model's weights before sending them out to the users. We call the modified weights of our attack trap weights. Our active attacker is able to recover user data perfectly, i.e., with zero error, even when this data stems from the same class. Recovery comes with near-zero costs: the attack requires no complex optimization objectives. Instead, our attacker exploits inherent data leakage from model gradients and simply amplifies this effect by maliciously altering the weights of the shared model through the trap weights. These specificities enable our attack to scale to fully-connected and convolutional deep neural networks trained with large mini-batches of data. For example, for the high-dimensional vision dataset ImageNet, we perfectly reconstruct more than 50% of the training data points from mini-batches as large as 100 data points.
BLOCKCHAIN FOR ENVIRONMENTALLY SUSTAINABLE ECONOMIES: CASE STUDY ON 5IRECHAIN

Blockchain technology, therefore, creates opportunities to make things differently. The field of sustainability is one of the areas where efforts of the international community are scarce and deficient. For this reason, blockchain could be implemented to improve and advance the achievement of UN-SDGs. This paper focuses on the application of blockchain to address pressing environmental challenges such as climate change, biodiversity loss, and water scarcity along with a case study of 5ireChain. It looks at emerging applications including those that might be the biggest game-changers in managing our global environmental commons while assessing the potential challenges and developing recommendations to address them.
Open Set Wireless Standard Classification Using Convolutional Neural Networks

In congested electromagnetic environments, cognitive radios require knowledge about other emitters in order to optimize their dynamic spectrum access strategy. Deep learning classification algorithms have been used to recognize the wireless signal standards of emitters with high accuracy, but are limited to classifying signal classes that appear in their training set. This diminishes the performance of deep learning classifiers deployed in the field because they cannot accurately identify signals from classes outside of the training set. In this paper, a convolution neural network based open set classifier is proposed with the ability to detect signal that are not from known classes by thresholding the output sigmoid activation. The open set classifier was trained on 4G LTE, 5G NR, IEEE 802.11ax, Bluetooth Low Energy 5.0, and Narrowband Internet-of-Things signals impaired with Rayleigh or Rician fading, AWGN, frequency offsets, and in-phase/quadrature imbalances. Then, the classifier was tested on OFDM, SC-FDMA, SC, AM, and FM signals, which did not appear in the training set classes. The closed set classifier achieves an average accuracy of 94.5% for known signals with SNR's greater than 0 dB, but by design, the closed set classifier cannot detect signals from unknown classes. On the other hand, the open set classifier retains an 86% accuracy for known signal classes, but can detect 95.5% of signals from unknown classes with SNR's greater than 0 dB.
Template-Based Headline Generator for Multiple Documents

In this paper, we develop a neural multi-document summarization model, named MuD2H (refers to Multi-Document to Headline) to generate an attractive and customized headline from a set of product descriptions. To the best of our knowledge, no one has used a technique for multi-document summarization to generate headlines in the past. Therefore, multi-document headline generation can be considered new problem setting. Our model implements a two-stage architecture, including an extractive stage and an abstractive stage. The extractive stage is a graph-based model that identified salient sentences, whereas the abstractive stage uses existing summaries as soft templates to guild the seq2seq model. A series of experiments are conducted by using KKday dataset. Experimental results show that the proposed method outperforms the others in terms of quantitative and qualitative aspects.
Remote Memory-Deduplication Attacks

Memory utilization can be reduced by merging identical memory blocks into copy-on-write mappings.Previous work showed that this so-called memory deduplication can be exploited in local attacks to break ASLR, spy on other programs, and determine the presence of data, i.e., website images.All these attacks exploit memory deduplication across security domains, which in turn was disabled.However, within a security domain or on an isolated system with no untrusted local access, memory deduplication is still not considered a security risk and was recently re-enabled on Windows by default.In this paper, we present the first fully remote memorydeduplication attacks.Unlike previous attacks, our attacks require no local code execution.Consequently, we can disclose memory contents from a remote server merely by sending and timing HTTP/1 and HTTP/2 network requests.We demonstrate our attacks on deduplication both on Windows and Linux and attack widely used server software such as Memcached and InnoDB.Our side channel leaks up to 34.41 B/h over the internet, making it faster than comparable remote memory-disclosure channels.We showcase our remote memory-deduplication attack in three case studies: First, we show that an attacker can disclose the presence of data in memory on a server running Memcached.We show that this information disclosure channel can also be used for fingerprinting and detect the correct libc version over the internet in 166.51 s.Second, in combination with InnoDB, we present an information disclosure attack to leak MariaDB database records.Third, we demonstrate a fully remote KASLR break in less than 4 minutes allowing to derandomize the kernel image of a virtual machine over the Internet, i.e., 14 network hops away.We conclude that memory deduplication must also be considered a security risk if only applied within a single security domain.
Artificial Intelligence Trends and Ethics: Issues and Alternatives for Investors

Artificial intelligence (AI) based technology, machine learning, and cognitive systems have played a very active role in society's economic and technological transformation.For industrial value chains and international businesses, it means that a structural change is necessary since these machines can learn and apply new information in making forecasts, processing, and interacting with people.Artificial intelligence (AI) is a science that uses powerful enough techniques, strategies, and mathematical modelling to tackle complex actual problems.Because of its inevitable progress further into the future, there have been considerable safety and ethical concerns.Creating an environment that is AI friendly for the people and vice versa might be a solution for humans and machines to discover a common set of values.In this context, the goal of this study is to investigate the emerging trends of AI (the benefits that it brings to the society), the moral challenges that come from ethical algorithms, learned or pre-set ideals, as well as address the ethical issues and malpractices of AI and AI security.This paper will address the consequences of AI in relation to investors and financial services.The article will examine the challenges and possible alternatives for resolving the potential unethical issues in finance and will propose the necessity of new AI governance mechanisms to protect the efficiency of the capital markets as well as the role of financial authority in the regulation and monitoring of the huge expansion of AI in finance.
Deeply Explain CNN via Hierarchical Decomposition

In computer vision, some attribution methods for explaining CNNs attempt to study how the intermediate features affect the network prediction. However, they usually ignore the feature hierarchies among the intermediate features. This paper introduces a hierarchical decomposition framework to explain CNN's decision-making process in a top-down manner. Specifically, we propose a gradient-based activation propagation (gAP) module that can decompose any intermediate CNN decision to its lower layers and find the supporting features. Then we utilize the gAP module to iteratively decompose the network decision to the supporting evidence from different CNN layers. The proposed framework can generate a deep hierarchy of strongly associated supporting evidence for the network decision, which provides insight into the decision-making process. Moreover, gAP is effort-free for understanding CNN-based models without network architecture modification and extra training process. Experiments show the effectiveness of the proposed method. The code and interactive demo website will be made publicly available.
Cognitive Computing Driven Healthcare: A Precise Study

As medical and computer technology has developed rapidly, interest and investment in the medical field has grown tremendously. However, the majority of healthcare systems do not take into consideration patients’ emergencies or could not offer individualized resource assistance. The following discussion explores a smart-healthcare system using Cognitive Computing approach in order to solve this issue. Based on this cognitive computing view, (Electronic Medical Record) EMR systems can be more proactively used not just for bookkeeping but as data dictionary which can be used for analyzing and providing cures to not only patients with critical medical conditions but can also be used as a precautionary analyst which can track patients medical history and provide early diagnostic results. Cognitive computing is also an active entity which if combined with EMR can provide healthcare providers with access to vast amounts of information related to medical sciences, drug information, and medical ontologies. This discussion focuses on understanding various different methods in which cognitive approaches can be put together in healthcare. The later section of the discussion throws light on how different modern technologies can be merged with cognitive approaches to enhance the healthcare sector. An exploration of past and current research advances in the field of creating cognitive systems in medical practice is presented. The comparison analysis section gives an overview of different types of cognitive approaches in a generalized manner whereas the last section (i.e.) results and analysis examines some experimental samples to show the extent of cognitive agents.
IT5: Large-scale Text-to-text Pretraining for Italian Language Understanding and Generation

The T5 model and its unified text-to-text paradigm contributed in advancing the state-of-the-art for many natural language processing tasks. While some multilingual variants of the T5 model have recently been introduced, their performances were found to provide suboptimal performances for languages other than English if compared to monolingual variants. We are motivated by these findings to introduce IT5, the first family of encoder-decoder transformer models pretrained specifically on Italian. We perform a thorough cleaning of a web-crawled Italian corpus including more than 40 billion words and use it to pretrain three IT5 models of different sizes. The performance of IT5 models and their multilingual counterparts is then evaluated on a broad range of natural language understanding and generation benchmarks for Italian. We find the monolingual IT5 models to provide the best scale-to-performance ratio across tested models, consistently outperforming their multilingual counterparts and setting a new state-of-the-art for most Italian conditional language generation tasks.
Stimulative Coordination Models for Cooperative and Competitive Enterprise Alliances Based on Token Economy

The rapid development of blockchain technology offers new ways for businesses to establish an efficient management system with consensus-based governance. Token economy, however, is a newly born notion that may have great potential in stimulating coordination to be more valuable and efficient. This article classifies enterprise alliances into two categories based on their participation motivations and operating modes. Research has been conducted on ten industries facing the pressure of digitalization to analyze participation motivations and operating modes in enterprise alliances. This research has found that cooperative enterprise alliances tend to have relatively negative attitudes and pursue open and fair coordination. On the contrary, competitive enterprise alliances tend to have relatively positive attitudes and pursue secure and efficient coordination. Therefore, models for cooperative enterprise alliances utilize the blockchain and token economy to encourage open resource sharing and ensure fair benefits. In contrast, models for cooperative enterprise alliances utilize the blockchain and token economy to alleviate unfair advantages and encourage efficient resource sharing. This research has also recognized that the value of tokens lies in obtaining future resources, exchanging for monetary resources, and making rules in DAO. Moreover, the integration of token economy could improve corporate innovation capability and promote sustainable development of enterprise alliances. The main contribution of this article is the design of two stimulative coordination models for cooperative and competitive enterprise alliances based on their differences and the token economy. The significance of this paper is to provide a guideline for corporations to share resources and create value efficiently in enterprise alliances and help enterprise alliances operate and develop more sustainably using blockchain technology.