Reviewerly/exHarmony huggingface dataset:
"I wouldn’t say offensive but...": Disability-Centered Perspectives on Large Language Models

Large language models (LLMs) trained on real-world data can inadvertently reflect harmful societal biases, particularly toward historically marginalized communities. While previous work has primarily focused on harms related to age and race, emerging research has shown that biases toward disabled communities exist. This study extends prior work exploring the existence of harms by identifying categories of LLM-perpetuated harms toward the disability community. We conducted 19 focus groups, during which 56 participants with disabilities probed a dialog model about disability and discussed and annotated its responses. Participants rarely characterized model outputs as blatantly offensive or toxic. Instead, participants used nuanced language to detail how the dialog model mirrored subtle yet harmful stereotypes they encountered in their lives and dominant media, e.g., inspiration porn and able-bodied saviors. Participants often implicated training data as a cause for these stereotypes and recommended training the model on diverse identities from disability-positive resources. Our discussion further explores representative data strategies to mitigate harm related to different communities through annotation co-design with ML researchers and developers.
DeepQ Residue Analysis of Brain-Computer Classification and Prediction using Deep CNN

Purpose: During this article, we are going to consistently explore the kinds of brain signals for Brain Computer Interface (BCI) and discover the related ideas of the in-depth learning of brain signal analysis. We talk review recent machine Associate in Nursing deep learning approaches within the detection of two brain unwellness just like Alzheimer' disease (AD), brain tumor. In addition, a quick outline of the varied marker extraction techniques that want to characterize brain diseases is provided. Project work, the automated tool for tumor classification supported by image resonance information. It is given by various convolutional neural network (CNN) samples with ResNet Squeeze. Objectives: This paper is to analyse brain diseases classification and prediction using deep learning concepts. Deep learning is a group of machine learning in computer science that has networks capable of unattended learning from data that's unstructured or unlabelled. conjointly called deep neural learning could be a operation of Al that mimics however, the human brain works in process data to be used in object detection, speech recognition, language translation, and call making. Methodology: To test the result by measuring the semantics in the input sentence, the creation of embedded vectors with the same value is achieved. In this case, a sentence with a different meaning is used. Since it is difficult to collect a large amount of labelled data, it simulates the signal in different sentences. As you progress, teach for extra complicated capabilities with layers from the shared output of preceding layers. We examine forms of deep getting to know methods: LSTM Model with RNN, CNN results. CNN is a multi-layer feed-ahead neural community. The gadget weight is up to date via way of means of the Backpropagation Error procedure. TF-IDF of time period t in record d. Unlike traditional precis models, the ahead engineering feature is predicated on understanding of the required records area. In addition, this framework is related to synthetic abbreviations, which might be then used to put off the impact of guide function improvement and records labelling. Results: We will follow this option of 257 factors as vector enter category algorithms. It is a aggregate of the subsequent forms with enter layer, convolution layer, linear unit (ReLU) layer, pooling layer, absolutely coupled layer. A recurrent neural community (RNN) is a form of a neural community that defines connections among loop units. This creates an inner community country that allows. Feature choice is a extensively used approach that improves the overall performance of classifiers. Here, we examine the consequences of conventional magnificence fires with correlation-primarily based totally man or woman choice. Originality: Analysis of Brain Diseases with the approach of Computer Classification and Prediction using Deep CNN with ResNet Squeeze. Type of Paper: Conceptual research paper.
Plan-and-Solve Prompting: Improving Zero-Shot Chain-of-Thought Reasoning by Large Language Models

Large language models (LLMs) have recently been shown to deliver impressive performance in various NLP tasks.To tackle multi-step reasoning tasks, few-shot chain-of-thought (CoT) prompting includes a few manually crafted step-by-step reasoning demonstrations which enable LLMs to explicitly generate reasoning steps and improve their reasoning task accuracy.To eliminate the manual effort, Zeroshot-CoT concatenates the target problem statement with "Let's think step by step" as an input prompt to LLMs.Despite the success of Zero-shot-CoT, it still suffers from three pitfalls: calculation errors, missing-step errors, and semantic misunderstanding errors.To address the missing-step errors, we propose Planand-Solve (PS) Prompting.It consists of two components: first, devising a plan to divide the entire task into smaller subtasks, and then carrying out the subtasks according to the plan.To address the calculation errors and improve the quality of generated reasoning steps, we extend PS prompting with more detailed instructions and derive PS+ prompting.We evaluate our proposed prompting strategy on ten datasets across three reasoning problems.The experimental results over GPT-3 show that our proposed zero-shot prompting consistently outperforms Zero-shot-CoT across all datasets by a large margin, is comparable to or exceeds Zero-shot-Program-of-Thought Prompting, and has comparable performance with 8-shot CoT prompting on the math reasoning problem.The code can be found at https://github.com/AGI-Edgerunners/Plan-and-Solve-Prompting.
Outlier detection using iterative adaptive mini-minimum spanning tree generation with applications on medical data

As an important technique for data pre-processing, outlier detection plays a crucial role in various real applications and has gained substantial attention, especially in medical fields. Despite the importance of outlier detection, many existing methods are vulnerable to the distribution of outliers and require prior knowledge, such as the outlier proportion. To address this problem to some extent, this article proposes an adaptive mini-minimum spanning tree-based outlier detection (MMOD) method, which utilizes a novel distance measure by scaling the Euclidean distance. For datasets containing different densities and taking on different shapes, our method can identify outliers without prior knowledge of outlier percentages. The results on both real-world medical data corpora and intuitive synthetic datasets demonstrate the effectiveness of the proposed method compared to state-of-the-art methods.
A Knowledge-Based Semisupervised Hierarchical Online Topic Detection Framework

Topic models have achieved big success in recent years. To detect topics in a text stream, various online topic models have been proposed in the literature. The limitations of these works include that: 1) most of them run with fixed topic numbers and 2) the overlaps between the topics may enlarge in the evolving process. Hierarchical topic model is a candidate solution to these problems since it can reveal many useful relationships between the topics. These relationships can help to find high quality topics and reduce topic overlaps. In this paper, a knowledge-based semisupervised hierarchical online topic detection framework is proposed. The proposed framework can detect topics in an online hierarchical way. In addition, it has been proven that introducing external knowledge can improve the performance of text mining. Therefore, the knowledge from external knowledge sources and human experts are also integrated in the proposed framework. Experiments are conducted to evaluate the proposed framework with different metrics. The results show that compared with the baseline methods, our framework can achieve better performance with competitive time efficiency.
Answering Science Exam Questions Using Query Reformulation with Background Knowledge

Open-domain question answering (QA) is an important problem in AI and NLP that is emerging as a bellwether for progress on the generalizability of AI methods and techniques. Much of the progress in open-domain QA systems has been realized through advances in information retrieval methods and corpus construction. In this paper, we focus on the recently introduced ARC Challenge dataset, which contains 2,590 multiple choice questions authored for grade-school science exams. These questions are selected to be the most challenging for current QA systems, and current state of the art performance is only slightly better than random chance. We present a system that reformulates a given question into queries that are used to retrieve supporting text from a large corpus of science-related text. Our rewriter is able to incorporate background knowledge from ConceptNet and -- in tandem with a generic textual entailment system trained on SciTail that identifies support in the retrieved results -- outperforms several strong baselines on the end-to-end QA task despite only being trained to identify essential terms in the original source question. We use a generalizable decision methodology over the retrieved evidence and answer candidates to select the best answer. By combining query reformulation, background knowledge, and textual entailment our system is able to outperform several strong baselines on the ARC dataset.
Hydra: a federated resource manager for data-center scale analytics

Microsoft’s internal data lake processes exabytes of data over millions of cores daily on behalf of thousands of tenants. Scheduling this workload requires 10x to 100x more decisions per second than existing, general-purpose resource management frameworks are known to handle. In 2013, we were faced with a growing demand for workload diversity and richer sharing policies that our legacy system could not meet. In this paper, we present Hydra, the resource management infrastructure we built to meet these requirements. Hydra leverages a federated architecture, in which a cluster is comprised of multiple, loosely coordinating subclusters. This allows us to scale by delegating placement of tasks on machines to each sub-cluster, while centrally coordinating only to ensure that tenants receive the right share of resources. To adapt to changing workload and cluster conditions promptly, Hydra’s design features a control plane that can push scheduling policies across tens of thousands of nodes within seconds. This feature combined with the federated design allows for great agility in developing, evaluating, and rolling out new system behaviors. We built Hydra by leveraging, extending, and contributing our code to Apache Hadoop YARN. Hydra is currently the primary big-data resource manager at Microsoft. Over the last few years, Hydra has scheduled nearly one trillion tasks that manipulated close to a Zettabyte of production data.
An embodiment paradigm in evaluation of human-in-the-loop control

This study introduces a novel approach for evaluating the quality of human-in-the-loop control using the psychological construct of embodiment in a haptic human-machine interaction task. Despite the fact that various forms of assistive control have been introduced, these methods mainly design semi-autonomous control to improve task-specific interaction performance. From a user perspective, however, the introduction of semi-autonomous control reduces controllability of the system, which could lead to negative user experience. Psychological research suggests sensory-motor factors dynamically modulate cognition of an external entity belonging to one’s own body, i.e., embodiment. In our paradigm, we apply methods for evaluating embodiment in a virtual reality (VR) environment where the human users perform a reaching task with semi-autonomous haptic assistance to measure the degree to which the embodiment is effected by the quality of semi-autonomy Our results with 8 participants show good persistence of subjective embodiment and subcomponents of presence within VR environment when a predictable assistive control is introduced while unpredictable assistance hindered the subjective embodiment. Results indicate embodiment can be exploited as a quantitative evaluation method of semi-autonomous controllers from a user-centric perspective.
Using a Vertical-Stream Variational Auto-Encoder to Generate Segment-Based Images and Its Biological Plausibility for Modelling the Visual Pathways

Human beings have a strong capability to identify objects in different viewpoints. Unlike computer vision that requires sufficient training samples in various scales and rotations, biological visual systems can efficiently recognize objects in diverse spatial states. To achieve this objective, images are processed into a segment-based representation and then a vertical stream variational auto-encoder (VSVAE) is utilized to generate images based on the preprocessed segments in this study. The novel structure of the two vertical streams can be also considered as a computational model for the interaction between the ventral pathway and the dorsal pathway in the visual cortex. The reconstructive capability of the VSVAE is testified by using a series of geometric information sets to enhance the segment-based representation. By visualizing the learnt features in the hidden layers of VSVAE, the biological plausibility of the model is discussed. In addition, the proposed methodology is able to facilitate the classification accuracy, especially when the images are severely transformed.
No-Reference Image Quality Assessment with Reinforcement Recursive List-Wise Ranking

Opinion-unaware no-reference image quality assessment (NR-IQA) methods have received many interests recently because they do not require images with subjective scores for training. Unfortunately, it is a challenging task, and thus far no opinion-unaware methods have shown consistently better performance than the opinion-aware ones. In this paper, we propose an effective opinion-unaware NR-IQA method based on reinforcement recursive list-wise ranking. We formulate the NR-IQA as a recursive list-wise ranking problem which aims to optimize the whole quality ordering directly. During training, the recursive ranking process can be modeled as a Markov decision process (MDP). The ranking list of images can be constructed by taking a sequence of actions, and each of them refers to selecting an image for a specific position of the ranking list. Reinforcement learning is adopted to train the model parameters, in which no ground-truth quality scores or ranking lists are necessary for learning. Experimental results demonstrate the superior performance of our approach compared with existing opinion-unaware NR-IQA methods. Furthermore, our approach can compete with the most effective opinion-aware methods. It improves the state-of-the-art by over 2% on the CSIQ benchmark and outperforms most compared opinion-aware models on TID2013.
Decentralized Energy Networks Based on Blockchain: Background, Overview and Concept Discussion

This paper provides a snapshot of the globally ongoing decentralization of (business) relations in the energy sector. This tendency can be observed in other domains as well and is accompanied by new digital technological developments. Blockchain technology is assigned disruptive potential when it comes to realize those decentralization ideas. This hype about Blockchain is mainly company-driven without a solid academic basis yet. The authors are currently involved in several research efforts for utilizing distributed energy resources like photovoltaic systems, batteries and electric cars for the setup of energy communities and marketplaces. The paper, therefore, presents detailed investigations of background and motivations for decentralization and the building of (local) energy communities and (peer-to-peer) marketplaces for sustainable utilization of renewable energies. An overview of recent related Blockchain-based works is presented, and the current state and feasibility for the realization of the envisioned decentralized solutions are discussed. In this way, the work aimed at contributing to a research-based decision foundation for upcoming Blockchain-based decentralization efforts.
Composing Entropic Policies using Divergence Correction

Composing previously mastered skills to solve novel tasks promises dramatic improvements in the data efficiency of reinforcement learning. Here, we analyze two recent works composing behaviors represented in the form of action-value functions and show that they perform poorly in some situations. As part of this analysis, we extend an important generalization of policy improvement to the maximum entropy framework and introduce an algorithm for the practical implementation of successor features in continuous action spaces. Then we propose a novel approach which addresses the failure cases of prior work and, in principle, recovers the optimal policy during transfer. This method works by explicitly learning the (discounted, future) divergence between base policies. We study this approach in the tabular case and on non-trivial continuous control problems with compositional structure and show that it outperforms or matches existing methods across all tasks considered.
Quantum photonic transistor controlled by an atom in a Floquet cavity-QED system

The photon transmission of a Floquet cavity quantum electrodynamic (QED) system containing three periodically modulated cavities interacting with a two-level atom is investigated. The input-output relations and the second-order correlation functions of the output fields are calculated. The system demonstrates the feature of a quantum photonic transistor, i.e., the photon transmission is controlled by the quantum states of the atom. This device can be used as a building block for various quantum information processing.
MinAtar: An Atari-inspired Testbed for More Efficient Reinforcement Learning Experiments.

The Arcade Learning Environment (ALE) is a popular platform for evaluatingreinforcement learning agents. Much of the appeal comes from the fact thatAtari games are varied, showcase aspects of competency we expect from anintelligent agent, and are not biased towards any particular solution approach.The challenge of the ALE includes 1) the representation learning problem ofextracting pertinent information from the raw pixels, and 2) the behaviourallearning problem of leveraging complex, delayed associations between actionsand rewards. Often, in reinforcement learning research, we care more about thelatter, but the representation learning problem adds significant computationalexpense. In response, we introduce MinAtar, short for miniature Atari, a newevaluation platform that captures the general mechanics of specific Atarigames, while simplifying certain aspects. In particular, we reduce therepresentational complexity to focus more on behavioural challenges. MinAtarconsists of analogues to five Atari games which play out on a 10x10 grid.MinAtar provides a 10x10xn state representation. The n channels correspond togame-specific objects, such as ball, paddle and brick in the game Breakout.While significantly simplified, these domains are still rich enough to allowfor interesting behaviours. To demonstrate the challenges posed by thesedomains, we evaluated a smaller version of the DQN architecture. We also triedvariants of DQN without experience replay, and without a target network, toassess the impact of those two prominent components in the MinAtarenvironments. In addition, we evaluated a simpler agent that used actor-criticwith eligibility traces, online updating, and no experience replay. We hopethat by introducing a set of simplified, Atari-like games we can allowresearchers to more efficiently investigate the unique behavioural challengesprovided by the ALE.
An effective texture descriptor for retrieval of biomedical and face images based on co-occurrence of similar center-symmetric local binary edges

In this paper, a new texture descriptor is proposed for face and biomedical image retrieval. The proposed texture descriptor extracts the co-occurrence of similar center-symmetric local binary edge...
A Quantum Algorithm for the Commutativity of Finite Dimensional Algebras

A quantum procedure for testing the commutativity of a finite dimensional algebra is introduced.This algorithm, based on Grover's quantum search, is shown to provide a quadratic speed-up (when the number of queries to the algebra multiplication constants is considered) over any classical algorithm (both deterministic and randomized) with equal success rate and shown to be optimal among the class of probabilistic quantum query algorithms.This algorithm can also be readily adapted to test commutativity and hermiticity of square matrices, again with quadratic speed-up.The results of the experiments carried out on a quantum computer simulator and on one of IBM's 5-qubit quantum computers are presented.
The Cornwall a-book: An Augmented Travel Guide Using Next Generation Paper

Electronic publishing usually presents readers with book or e-book options for reading on paper or screen.In this paper, we introduce a third method of reading on paper-and-screen through the use of an augmented book ('a-book') with printed hotlinks than can be viewed on a nearby smartphone or other device.Two experimental versions of an augmented guide to Cornwall are shown using either optically recognised pages or embedded electronics making the book sensitive to light and touch.We refer to these as second generation (2G) and third generation (3G) paper respectively.A common architectural framework, authoring workflow and interaction model is used for both technologies, enabling the creation of two future generations of augmented books with interactive features and content.In the travel domain we use these features creatively to illustrate the printed book with local multimedia and updatable web media, to point to the printed pages from the digital content, and to record personal and web media into the book.
Streamlined forensic reporting: Rhetoric and reality

Commentary detailing some concerns with Streamlined Forensic Reporting in the UK.
Signature maps for automatic identification of prostate cancer from colorimetric analysis of H&amp;E- and IHC-stained histopathological specimens

Abstract Prostate cancer (PCa) is a major cause of cancer death among men. The histopathological examination of post-surgical prostate specimens and manual annotation of PCa not only allow for detailed assessment of disease characteristics and extent, but also supply the ground truth for developing of computer-aided diagnosis (CAD) systems for PCa detection before definitive treatment. As manual cancer annotation is tedious and subjective, there have been a number of publications describing methods for automating the procedure via the analysis of digitized whole-slide images (WSIs). However, these studies have focused only on the analysis of WSIs stained with hematoxylin and eosin (H&amp;E), even though there is additional information that could be obtained from immunohistochemical (IHC) staining. In this work, we propose a framework for automating the annotation of PCa that is based on automated colorimetric analysis of both H&amp;E and IHC WSIs stained with a triple-antibody cocktail against high-molecular weight cytokeratin (HMWCK), p63, and α-methylacyl CoA racemase (AMACR). The analysis outputs were then used to train a regression model to estimate the distribution of cancerous epithelium within slides. The approach yielded an AUC of 0.951, sensitivity of 87.1%, and specificity of 90.7% as compared to slide-level annotations, and generalized well to cancers of all grades.