Reviewerly/exHarmony huggingface dataset:
Are Neural Ranking Models Robust?

Recently, we have witnessed the bloom of neural ranking models in the information retrieval (IR) field. So far, much effort has been devoted to developing effective neural ranking models that can generalize well on new data. There has been less attention paid to the robustness perspective. Unlike the effectiveness, which is about the average performance of a system under normal purpose, robustness cares more about the system performance in the worst case or under malicious operations instead. When a new technique enters into the real-world application, it is critical to know not only how it works in average, but also how would it behave in abnormal situations. So, we raise the question in this work: Are neural ranking models robust? To answer this question, first, we need to clarify what we refer to when we talk about the robustness of ranking models in IR. We show that robustness is actually a multi-dimensional concept and there are three ways to define it in IR: (1) the performance variance under the independent and identically distributed (I.I.D.) setting; (2) the out-of-distribution (OOD) generalizability ; and (3) the defensive ability against adversarial operations. The latter two definitions can be further specified into two different perspectives, respectively, leading to five robustness tasks in total. Based on this taxonomy, we build corresponding benchmark datasets, design empirical experiments, and systematically analyze the robustness of several representative neural ranking models against traditional probabilistic ranking models and learning-to-rank (LTR) models. The empirical results show that there is no simple answer to our question. While neural ranking models are less robust against other IR models in most cases, some of them can still win two out of five tasks. This is the first comprehensive study on the robustness of neural ranking models. We believe the way we study the robustness as well as our findings would be beneficial to the IR community. We will also release all the data and codes to facilitate the future research in this direction.
A new fast and accurate heuristic for the Automatic Scene Detection Problem

The Automatic Scene Detection Problem (ASDP) is a combinatorial optimization problem that arises in the context of video processing and that has a central role in the management, storing and content retrieval of videos. The problem consists of partitioning the shots of a given video into scenes by optimizing a measure related to the similarity between the given shots. In this article, we build up upon the results from the literature on the ASDP in order to design a new approximate solution algorithm able to outperform the current state-of-the-art both in terms of speed and quality of the solution.
An adaptive recognition method for take-off action images of back-style high jump based on feature extraction

In the process of human movement, the behavioral characteristics of human body are complex and diverse. Thus, it is difficult to use accurate shape to represent the uncertainty of human behavior, which brings difficulties for 3D modeling in the state of motion. To solve this problem, an adaptive image recognition method based on feature extraction is proposed in this paper. Firstly, the image noise threshold was obtained by wavelet coefficient conversion. Then the wavelet coefficient matrix was constructed on the basis of decomposed image multi-resolution, and the image noise was removed according to the threshold. Based on this, Gaussian function in Canny method is introduced to obtain the edge coordinates of moving targets by convolution operation. After that, the image rotation and translation matrices were resolved according to the Murkowski distance to obtain the motion feature vectors. Finally, the hidden Markov model was constructed to segment the background and the target according to the feature sequence and the target edge coordinates, so as to realize the adaptive recognition of the take-off action images of the back-style high jump. Experimental results showed that this method had the advantages of high denoising effect, accurate feature extraction, error free edge division and accurate recognition result.
OPERATIONAL CONTROL FOR EARTH-TO-AIR HEAT EXCHANGER BY REINFORCEMENT LEARNING (PART 1): APPLICABILITY VERIFICATION OF ALGORITHM CONSIDERING COUNTERFACTUAL REWARD

Earth-to-air heat exchangers (EAHE) utilize the heat capacity of soil to pre-cool or pre-heat outside air (OA), and then introduce OA into the air handling unit to reduce the heat load of OA. However, at the operational phase, the control of an EAHE is not optimized (e.g., inefficient energy saving or lower quality of introduced air). It is also difficult to establish the optimal control rule that considering the future effects by sequential operations since the control response takes extremely long time. In this study, we focused on the reinforcement learning (RL) control, which does not require the construction of control rules. In RL, an agent learns to maximize reward in the state s within an environment, and this algorithm achieves its purpose by passing action a that maximizes reward to the environment. One of the advantages of RL is maximizing the cumulative reward instead of maximizing the sequential reward. For this reason, RL is suitable for unsteady problems.
A Novel Privacy-Preserving Neural Network Computing Approach for E-Health Information System

Electronic health (e-health) information system relies on cloud computing technologies to provide massive medical data computing and storage services. Especially, the recently proposed Machine Learning as a Service (MLaaS) on these medical data can not only effectively improve the healthcare service quality, but also support the end users with limited computing resources. However, MLaaS on the massive medical data faces the challenge of privacy. Homomorphic encryption technology has been explored to assure the privacy of medical data owners in MLaaS but with the weaknesses of limited homomorphic operations and low efficiency. To alleviate these weaknesses, this paper proposes a novel privacy-preserving non-collusion dualcloud (NCDC) model-based e-health information system using neural network (NN) computing. The system can not only assure medical data privacy through adopting homomorphic encryption technology but also assure NN model privacy by adding fake neurons to the NN. In addition, the proposed e-health information system also has the following advantages: (i) Simple key generation. (ii) No constraint on the size of medical data to be encrypted. (iii) The less loss of prediction accuracy between encrypted and original medical data. (iv) Supporting more homomorphic operations and having better computing efficiency through experiment verification.
Security and Usability Considerations for an mHealth Application for Emergency Medical Services

Mobile applications for healthcare must be both secure and usable while operating within the boundaries of a healthcare organizational network to access critical information resources, such as the electronic health record (EHR). However, in an increasingly interorganizational mobile work environment, organizations are seeking provider-centered apps for continuity of care between and across organizations. Emergency medical services (EMS) provide one example where a single transport agency may direct patients to any number of separate health systems within a region. This research reports on the design and live field testing of an interorganizational mobile app for EMS. Participants included 20 ambulances and seven hospital emergency departments transmitting 1513 live patient records with multimedia data. Evaluation results were used to present a heuristic for achieving usability and security goals for practitioner-oriented interorganizational mobile health applications, especially for time- and information-critical contexts.
Random Dimension Low Sample Size Asymptotics

A first investigation of high-dimensional low-sample-size (HDLSS) asymptotics, Hall, Marron and Neeman (2005) discovered a surprisingly rigid geometric structure. A sample of size k taken from the standard m-dimensional normal distribution is for large m close to the vertices of the k-dimensional simplex in m-dimensional vector space. It follows from the analysis of three geometric statistics: the length of an observation, the distance between any two independent observations and the angle between these vectors. We generalize and refine the results constructing the second order Chebyshev-Edgeworth expansions under assumption that the data dimension is random and different scaling factors are chosen.
A RESTful Privacy-Aware and Mutable Decentralized Ledger

During the last decade, blockchain technology has gained massive attention due to its decentralized, transparent, and verifiable features. However, data stored on the blockchain is publicly available, immutable, and may link to the data owner, thus making privacy management and data modification major challenges. In this paper, we present a RESTful decentralized storage framework that provides data privacy and mutability. To do so, it combines blockchain with distributed hash table, role-based access control, ring signature, and multiple encryption mechanisms. We designed a protocol that exploits metadata and pointers stored on the blockchain, while corresponding encrypted data are stored off-chain, so that data owners are able to control their data. Each peer in our framework offers RESTful APIs to operate, thus ensuring interoperability over the Web. In this paper, we present the operation of our framework and its components that enable data protection at run-time. We also evaluate its performance with time measurements from our proof-of-concept implementation.
Combining electrocardiogram signal with Accelerometer signals for Human Activity Recognition using Convolution neural network

Abstract As the environment getting polluted, people are suffering with different medical problems also people are causes about their health as well. Considering this in the mind, Body sensor based human activity recognition attracting researcher towards this direction. A fusion of electrocardiogram signals and accelerometer signals processed through convolution neural network is proposed in this paper. Accelerometer placed at different location of the human body are fused with the electrocardiogram signals, generated through the ECG sensors placed at the chest of the human body. These fused signal are processed through convolution neural network to automatically detect the features and finally apply softmax for classification of the activities. We choose mHEALTH dataset for the experiment and achieve 98.91% validation accuracy.
Utilizing Quality Measures in Evaluating Image Encryption Methods

The metrics or measures used in evaluating image quality usually serve applications that aim at enhancing image appearance or at preventing the image quality from degrading after image processing operations. On the other hand, the main goal of cryptography applications is to produce unrecognizable encrypted images that can resist various kinds of attacks. Furthermore, cryptography must consider extra measures such as keyspace and key sensitivity. This paper discusses the most useful quality metrics used in image cryptography and explains the type of values that indicate good encryption according to each metric. These metrics include statistical analysis measures, sensitivity measures, and other metrics.
Multi-task learning modeling of attribute mutual association based on confidence and imputation of missing values

Incomplete datasets with missing values increase the difficulty of data analysis. In this paper, the incomplete data are modeled based on the Auto-Association Neural Network(AANN) and the missing values are imputation. In order to strengthen the correlation among the attributes of samples, we propose an Attribute Mutual Associative Multi-Task Learning Model Based on Confidence (AM-MTL). By optimizing the data transmission path of nodes in the output layer, we construct an MTL architecture in which the main imputation task is parallel to the secondary classification task, and use the missing rate of sample attributes as the initial sample confidence to weaken the influence of incomplete input samples on the network parameter adjustment. The missing values and confidence are used as variables for training, and they can be updated iteratively, thus continuously reduce the estimation error of missing values. Experiments demonstrate the effectiveness of the proposed method in missing value imputation.
Image super resolution model enabled by wavelet lifting with optimized deep convolutional neural network

Abstract This paper plans to develop an intelligent super resolution model with the linkage of Wavelet lifting scheme and Deep learning algorithm. Before initiating the resolution procedure, the entire HR images are converted into Low Resolution (LR) images using bicubic interpolation‐based downsampling and upsampling. Further, the Wavelet lifting scheme helps to generate the four subbands of each image like LR wavelet Sub‐Bands for LR images, and High Resolution (HR) wavelet Sub‐Bands for HR images. The residual image is generated by taking the difference between the LR wavelet Sub‐Bands and HR wavelet Sub‐Bands images. The proposed model involves two main phases: Training phase and Testing. The training phase trains the residual image of all images by Deep Convolutional Neural Network with LR wavelet Sub‐Bands as input and residual image as target. On the other hand, in testing phase, the LR wavelet Sub‐Bands query image is subjected to Deep Convolutional Neural Network, which outputs the concerned residual image. This generated residual image is summed with LR wavelet Sub‐Bands image, followed by inverse wavelet lifting scheme to obtain the final super resolution image. The main contribution of this paper is to improve the conventional Deep Convolutional Neural Network by optimizing the number of hidden layer, and hidden neurons using modified Whale Optimization Algorithm called Average Fitness Enabled Whale Optimization Algorithm by considering the objective of maximizing the Peak Signal‐to‐Noise Ratio. Finally, the proposed method achieves an improved quality of the results which is comparable the existing models.
Foreign money transfer using blockchain

This paper focuses on using blockchain technology to enhance efficiency and cost reduction in foreign transactions through the banking system. Blockchain provides crucial features such as immutability of records and decentralization, which is then used to carry out foreign transactions. This process could be a major change in the transactions carried out by removing middle banks during transactions thereby speeding up the process. This paper aims at designing an Ethereum decentralized Banking Application for foreign money transfer using blockchain technology. Smart Contract helps to eliminate middle banks in the process by acting on rules specified in the contract. Every participant needs to abide by these rules which make it trustworthy and maintain the authenticity of the process.
Evaluating Anytime Performance on NAS-Bench-101

Neural architecture search (NAS) is a field where computational effort poses a significant challenge, requiring large computing clusters and specialized hardware. Furthermore, the lack of common experimental guidelines often compromises NAS comparison or induces premature conclusions. In a recent work, NAS-Bench-101 was proposed to help mitigate those factors, providing both a common benchmark and experimental guidelines for its use. In this work, we discuss the design choices in NAS-Bench-101 and propose improvements that increase the potential of the benchmark. First, we bridge NAS and the research on anytime performance, showing how a bi-objective formulation of NAS can improve the insights provided by NAS-Bench-101. Then, we discuss choices made in the design of the benchmark, namely (i) the fixed-size encoding, (ii) the effects of the limited variability available, (iii) the assessment of algorithms only from a TPU time perspective, and; (iv) the number of repetitions proposed. We demonstrate our contributions assessing the best-performing algorithms originally benchmarked on NAS-Bench-101 and also irace, one of the best-performing algorithm configurators from the literature. Results indicate that (i) the anytime performance methodology enriches the insights obtained from the assessment on the original NAS-Bench-101; (ii) algorithm comparison is strongly affected by the design choices discussed, and; (iii) the performance of SMAC in this benchmark is significantly improved by our alternative setups.
Expanding smart assistant accessibility through dysarthria speech-trained transformer networks

Smart assistant usage has increased significantly with the AI boom and growth of IoT. Speech as an input modality brings a level of personalization to the various smart voice assistant products and applications; however, many smart assistants underperform when tasked with interpreting atypical speech input. Dysarthria, heavy accents, and deaf and hard-of-hearing speech characteristics prove difficult for smart assistants to interpret despite the large amounts of diverse data used to train automatic speech recognition models. In this study, we explore the Transformer architecture for use as an automatic speech recognition model for speech with medium to low intelligibility scores. We utilize the Transformer model pre-trained on the Librispeech dataset and fine-tuned on the Torgo dataset of atypical speech, as well as a subset of the University of Memphis Speech Perception Assessment Laboratory's (UMemphis SPAL) Deaf speech dataset. We also develop a methodology for performing automatic speech recognition using a Node.JS application running on a Raspberry Pi 4 to function as a pipeline between the user and a Google Home smart assistant device. The highest performing Transformer model shows a 20.2% character error rate with a corresponding 29.0% word error rate on a subset of medium intelligibility audio samples from the UMemphis SPAL dataset. This study highlights the importance for a large, transcribed dataset, fueling a large atypical-speech data gathering effort through a newly developed web application, My-Voice.
A New Automatic Conditioning Algorithm for SONAR Images

The acquisition of SONAR images is affected by two phenomena: the presence of a multiplicative noise called speckle and the loss of some pixels. This paper presents an automatic SONAR images conditioning system composed by a denoising subsystem and an inpainting sub-system. We have selected a denoising system based on the Hyperanalytic Wavelet Transform (HWT) associated with a Maximum a Posteriori (MAP) filter named bishrink. The denoising algorithm is followed by a robust exemplar based inpainting algorithm used for filling the gaps produced by the missing pixels. We justify why we have selected these algorithms and we present in detail their implementation. Some simulation results and comparisons made on synthesized and real SONAR images show the performance of the proposed conditioning algorithm.
The Earth Is Flat and the Sun Is Not a Star: The Susceptibility of GPT-2 to Universal Adversarial Triggers

This work considers universal adversarial triggers, a method of adversarially disrupting natural language models, and questions if it is possible to use such triggers to affect both the topic and stance of conditional text generation models. In considering four "controversial" topics, this work demonstrates success at identifying triggers that cause the GPT-2 model to produce text about targeted topics as well as influence the stance the text takes towards the topic. We show that, while the more fringe topics are more challenging to identify triggers for, they do appear to more effectively discriminate aspects like stance. We view this both as an indication of the dangerous potential for controllability and, perhaps, a reflection of the nature of the disconnect between conflicting views on these topics, something that future work could use to question the nature of filter bubbles and if they are reflected within models trained on internet content. In demonstrating the feasibility and ease of such an attack, this work seeks to raise the awareness that neural language models are susceptible to this influence--even if the model is already deployed and adversaries lack internal model access--and advocates the immediate safeguarding against this type of adversarial attack in order to prevent potential harm to human users.
Fairer Chess: A Reversal of Two Opening Moves in Chess Creates Balance Between White and Black

Unlike tic-tac-toe or checkers, in which optimal play leads to a draw, it is not known whether optimal play in chess ends in a win for White, a win for Black, or a draw. But after White moves first in chess, if Black has a double move followed by a double move of White and then alternating play, play is more balanced because White does not always tie or lead in moves. Symbolically, Balanced Alternation gives the following move sequence: After White's (W) initial move, first Black (B) and then White each have two moves in a row (BBWW), followed by the alternating sequence, beginning with W, which altogether can be written as WB/BW/WB/WB/WB… (the slashes separate alternating pairs of moves). Except for reversal of the 3 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">rd</sup> and 4th moves from WB to BW (underscored), this is the standard chess sequence. Because Balanced Alternation lies between the standard sequence, which favors White, and a comparable sequence that favors Black, it is highly likely to produce a draw with optimal play, rendering chess fairer. This conclusion is supported by a computer analysis of chess openings and how they would play out under Balanced Alternation.
Software Requirement-Related Information Extraction from Online News using Domain Specificity for Requirements Elicitation

Stakeholder involvement is vital in the requirements elicitation process. However, a lack of stakeholder involvement in the project often occurs. Various data sources, such as business processes and software documentation, will help determine what stakeholders need. The limitation of these documents is that they are technical and do not contain numerous stakeholder's needs. This paper aims to extract information related to software requirements from online news using domain specificity. The online news comprises elements that compose the user story, namely: the aspect of who (stakeholder), the aspect of what (functional), and the aspect of why (reason). User stories are standard practices used to capture and write functional software requirements in agile software development. This paper proposes to use domain specificity to extract aspects of what from online news to increase understanding of domain knowledge, especially software functionality. We use SRS documents and online news as data sources. Based on these datasets, domain specificity is calculated to produce software specific vocabulary. POS tags are being used to extract software requirements-related information from online news. When examining two datasets, our approach improved precision and recall with average values of 0.56 and 0.579.