Reviewerly/exHarmony huggingface dataset:
From Mild to Wild: Reimagining Friendships and Romance in the Time of Pandemic Using Design Fiction

With the forced reboot of our lives due to the COVID-19 pandemic, our interpersonal relationships are nowhere yet everywhere. However, opportunities for initiating or maintaining friendships and romance in the physical world have dwindled. Within the context of India where multiple realities exist, the question arises – what is the future of these relationships? In this paper, we present the outcomes of a workshop looking at the future of relationships using design fiction. Participants worked in small teams to create scenarios that critically consider the future of love, friendships, and romance within the Indian context. Through the lenses of criticality, empowerment, and value creation, we examine the design scenarios and the design process including criticality of the designs, empowering experiences of the participants, and the perceived value gained from participating in such a workshop. Our findings indicate the potential of design fiction to allow participants to step out of their comfort zone into a critical stance in discussing love and intimacy. Based upon our findings, we discuss implications for design research, practice, and education.
Detecting Text Reuse in Cryptocurrency Whitepapers

Thousands of new cryptocurrencies have been introduced in recent years. Most are introduced with a so-called "whitepaper" containing a mix of technical documentation, legal boilerplate and marketing material. Notably, many proposed currencies reuse text from previous established cryptocurrencies. We analyze the whitepapers from 1 260 actively traded cryptocurrencies and 2 039 ICOs. We develop two measures of similarity. Moderately similar papers reuse text in a portion of the paper, often the legal disclaimers. By contrast, some highly similar whitepapers appear to copy most of the text. 4% of coin and 19% of ICO whitepapers are highly similar to those of traded coins. The fraction rises to 64% for coins and 67% for ICOs when we consider moderate text reuse.
Understanding the Design Space of Mouth Microgestures

As wearable devices move toward the face (i.e. smart earbuds, glasses), there is an increasing need to facilitate intuitive interactions with these devices. Current sensing techniques can already detect many mouth-based gestures; however, users' preferences of these gestures are not fully understood. In this paper, we investigate the design space and usability of mouth-based microgestures. We first conducted brainstorming sessions (N=16) and compiled an extensive set of 86 user-defined gestures. Then, with an online survey (N=50), we assessed the physical and mental demand of our gesture set and identified a subset of 14 gestures that can be performed easily and naturally. Finally, we conducted a remote Wizard-of-Oz usability study (N=11) mapping gestures to various daily smartphone operations under a sitting and walking context. From these studies, we develop a taxonomy for mouth gestures, finalize a practical gesture set for common applications, and provide design guidelines for future mouth-based gesture interactions.
Building Reliable Explanations of Unreliable Neural Networks: Locally Smoothing Perspective of Model Interpretation

We present a novel method for reliably explaining the predictions of neural networks. We consider an explanation reliable if it identifies input features relevant to the model output by considering the input and the neighboring data points. Our method is built on top of the assumption of smooth landscape in a loss function of the model prediction: locally consistent loss and gradient profile. A theoretical analysis established in this study suggests that those locally smooth model explanations are learned using a batch of noisy copies of the input with the L1 regularization for a saliency map. Extensive experiments support the analysis results, revealing that the proposed saliency maps retrieve the original classes of adversarial examples crafted against both naturally and adversarially trained models, significantly outperforming previous methods. We further demonstrated that such good performance results from the learning capability of this method to identify input features that are truly relevant to the model output of the input and the neighboring data points, fulfilling the requirements of a reliable explanation.
The Value of Postdigital Humans as Objects, or Subjects, in McDonaldised Society

Postdigital human encounters could be said to take shape differently depending on how they are either subjectively valued or objectively evaluated. Digital technologies and humans are now intimately intertwined with shared and sometimes equal capabilities to perform human tasks. Yet still it may be argued that different disciplinary identities prevent computing and the humanities from being thought of as equivalent. Over many decades, humans and computers have been objectively evaluated in McDonaldised society, via rational language and measures where computing techniques are simply applied to improve productivity. Since the Covid-19 lockdown, people have described more personal and subjective digital encounters from their homes, with their virtual identities growing as their physical presence has diminished. This chapter speculates on whether new postdigital positionalities are emerging that might finally challenge more dominant, rational interpretations of what computing means in individual lives. If so, perhaps a more subjective analysis of these new forms of postdigital participation will bring the humanities into computing, instead of vice versa. This could help to reveal the unique positionality in each individual postdigital human encounter, where subjective self-description may now be seen to be more appropriate than objective rationality.
Affine invariance of meta-heuristic algorithms

An algorithm whose performance depends on the objective function being aligned with a privileged coordinate system is a poor choice in general because it is unlikely that the optimal orientation will be known in advance. In this paper, a property of meta-heuristic algorithms, named affine invariance, is introduced to verify whether the algorithm is depended on the privileged coordinate system or not. The concept of affine invariance is described in detail, and some classical algorithms, efficient in most test and actual problems, are proved to be affine invariant. While some recent algorithms in the literature are proved to be not affine invariant. As a conclusion, particle swarm optimization (PSO), differential evolution (DE) and optimal foraging algorithm (OFA) are affine invariant, while grey wolf optimizer (GWO), sine cosine algorithm (SCA) and butterfly optimization algorithm (BOA) are not affine invariant. Furthermore, comparison tests are designed to support the theoretical analysis results. In these tests, same random numbers and initial population are used to avoid the influence of randomness, thus, the conclusion is reliable.
An Annealing Mechanism for Adversarial Training Acceleration

Despite the empirical success in various domains, it has been revealed that deep neural networks are vulnerable to maliciously perturbed input data that can dramatically degrade their performance. These are known as adversarial attacks. To counter adversarial attacks, adversarial training formulated as a form of robust optimization has been demonstrated to be effective. However, conducting adversarial training brings much computational overhead compared with standard training. In order to reduce the computational cost, we propose an annealing mechanism, annealing mechanism for adversarial training acceleration (Amata), to reduce the overhead associated with adversarial training. The proposed Amata is provably convergent, well-motivated from the lens of optimal control theory, and can be combined with existing acceleration methods to further enhance performance. It is demonstrated that, on standard datasets, Amata can achieve similar or better robustness with around 1/3-1/2 the computational time compared with traditional methods. In addition, Amata can be incorporated into other adversarial training acceleration algorithms (e.g., YOPO, Free, Fast, and ATTA), which leads to a further reduction in computational time on large-scale problems.
A Countermeasure Method Using Poisonous Data Against Poisoning Attacks on IoT Machine Learning

In the modern world, several areas of our lives can be improved, in the form of diverse additional dimensions, in terms of quality, by machine learning. When building machine learning models, open data are often used. Although this trend is on the rise, the monetary losses since the attacks on machine learning models are also rising. Preparation is, thus, believed to be indispensable in terms of embarking upon machine learning. In this field of endeavor, machine learning models may be compromised in various ways, including poisoning attacks. Assaults of this nature involve the incorporation of injurious data into the training data rendering the models to be substantively less accurate. The circumstances of every individual case will determine the degree to which the impairment due to such intrusions can lead to extensive disruption. A modus operandi is proffered in this research as a safeguard for machine learning models in the face of the poisoning menace, envisaging a milieu in which machine learning models make use of data that emanate from numerous sources. The information in question will be presented as training data, and the diversity of sources will constitute a barrier to poisoning attacks in such circumstances. Every source is evaluated separately, with the weight of each data component assessed in terms of its ability to affect the precision of the machine learning model. An appraisal is also conducted on the basis of the theoretical effect of the use of corrupt data as from each source. The extent to which the subgroup of data in question can undermine overall accuracy depends on the estimated data removal rate associated with each of the sources described above. The exclusion of such isolated data based on this figure ensures that the standard data will not be tainted. To evaluate the efficacy of our suggested preventive measure, we evaluated it in comparison with the well-known standard techniques to assess the degree to which the model was providing accurate conclusions in the wake of the change. It was demonstrated during this test that when the innovative mode of appraisal was applied, in circumstances in which 17% of the training data are corrupt, the degree of precision offered by the model is 89%, in contrast to the figure of 83% acquired through the traditional technique. The corrective technique suggested by us thus boosted the resilience of the model against harmful intrusion.
Projected Distribution Loss for Image Enhancement

Features obtained from object recognition CNNs have been widely used for measuring perceptual similarities between images. Such differentiable metrics can be used as perceptual learning losses to train image enhancement models. However, the choice of the distance function between input and target features may have a consequential impact on the performance of the trained model. While using the norm of the difference between extracted features leads to limited hallucination of details, measuring the distance between distributions of features may generate more textures; yet also more unrealistic details and artifacts. In this paper, we demonstrate that aggregating 1D-Wasserstein distances between CNN activations is more reliable than the existing approaches, and it can significantly improve the perceptual performance of enhancement models. More explicitly, we show that in imaging applications such as denoising, super-resolution, demosaicing, deblurring and JPEG artifact removal, the proposed learning loss outperforms the current state-of-the-art on reference-based perceptual losses. This means that the proposed learning loss can be plugged into different imaging frameworks and produce perceptually realistic results.
Multi-objective optimal trajectory planning for manipulators in the presence of obstacles

Abstract This paper presents a trajectory planning method based on multi-objective optimization, including time optimal and jerk optimal for the manipulators in the presence of obstacles. The proposed method generates a trajectory configuration in the joint space with kinematic and obstacle constraints using quintic B-spline. Gilbert–Johnson–Keerthi detecting algorithm is utilized to detect whether there is a collision and obtain the minimum distance between the manipulator and obstacles. The degree of constraint violations is introduced to redefine the Pareto domination, and the constrained multi-objective particle swarm algorithm (CMOPSO) is adopted to solve the time-jerk optimization problem. Finally, the Z-type fuzzy membership function is proposed to select the best optimal solution in the Pareto front obtained by CMOPSO. Test results show the effectiveness of the proposed method.
Evolving transformer architecture for neural machine translation

The transformer models have achieved great success on neural machine translation tasks in recent years. However, the hyper-parameters of the transformer are often manually designed by expertise, where the layer is often regularly stacked together without exploring potentially promising ordering patterns. In this paper, we propose a transformer architecture design algorithm based on genetic algorithm, which can automatically find the proper layer ordering pattern and hyper-parameters for the tasks at hand. The experimental results show that the models designed by the proposed algorithm outperform the vanilla transformer on the widely used machine translation benchmark, which reveals that the performance of transformer architecture can be improved by adjusting layer ordering pattern and hyper-parameters by the proposed algorithm.
An End-to-End Pipeline for Early Diagnosis of Acute Promyelocytic Leukemia Based on a Compact CNN Model

Timely microscopy screening of peripheral blood smears is essential for the diagnosis of acute promyelocytic leukemia (APL) due to the occurrence of early death (ED) before or during the initial therapy. Screening manually is time-consuming and tedious, and may lead to missed diagnosis or misdiagnosis because of subjective bias. To address these problems, we develop a three-step pipeline to help in the early diagnosis of APL from peripheral blood smears. The entire pipeline consists of leukocytes focusing, cell classification and diagnostic opinions. As the key component of the pipeline, a compact classification model based on attention embedded convolutional neural network blocks is proposed to distinguish promyelocytes from normal leukocytes. The compact classification model is validated on both the combination of two public datasets, APL-Cytomorphology_LMU and APL-Cytomorphology_JHH, as well as the clinical dataset, to yield a precision of 96.53% and 99.20%, respectively. The results indicate that our model outperforms the other evaluated popular classification models owing to its better accuracy and smaller size. Furthermore, the entire pipeline is validated on realistic patient data. The proposed method promises to act as an assistant tool for APL diagnosis.
Depth-efficient proofs of quantumness

A proof of quantumness is a type of challenge-response protocol in which a classical verifier can efficiently certify the quantum advantage of an untrusted prover. That is, a quantum prover can correctly answer the verifier's challenges and be accepted, while any polynomial-time classical prover will be rejected with high probability, based on plausible computational assumptions. To answer the verifier's challenges, existing proofs of quantumness typically require the quantum prover to perform a combination of polynomial-size quantum circuits and measurements. In this paper, we give two proof of quantumness constructions in which the prover need only perform constant-depth quantum circuits (and measurements) together with log-depth classical computation. Our first construction is a generic compiler that allows us to translate all existing proofs of quantumness into constant quantum depth versions. Our second construction is based around the learning with rounding problem, and yields circuits with shorter depth and requiring fewer qubits than the generic construction. In addition, the second construction also has some robustness against noise.
Audio-based Toxic Language Classification using Self-attentive Convolutional Neural Network

The monumental increase in online social interaction activities such as social networking or online gaming is often riddled by hostile or aggressive behavior that can lead to unsolicited manifestations of cyberbullying or harassment. In this work, we develop an audio-based toxic language classifier using self-attentive Convolutional Neural Networks (CNNs). As definitions of hostility or toxicity can vary depending on the platform or application, in this work we take a more general approach for identifying toxic utterances, one that does not depend on individual lexicon terms, but rather considers the entire acoustical context of the short verse or utterance. In the proposed architecture, the self-attention mechanism captures the temporal dependency of the verbal content by summarizing all the relevant information from different regions of the utterance. The proposed audio-based self-attentive CNN model is evaluated on a public and an internal dataset and achieves 75% accuracy, 79% precision, and 80 % recall in identifying toxic speech recordings.
A Deep Pedestrian Tracking SSD-Based Model in the Sudden Emergency or Violent Environment

Public security monitoring is a hot issue that the government and citizens pay close attention to. Multiobject tracking plays an important role in solving many problems for public security. Under crowded scenarios and emergency places, it is a challenging problem to predict and warn owing to the complexity of crowd intersection. There are still many deficiencies in the research of multiobject trajectory prediction, which mostly employ object detection and data association. Compared with the tremendous progress in object detection, data association still relied on hand-crafted constraints such as group, motion, and spatial proximity. Emergencies usually have the characteristics of mutation, target diversification, low illumination, or resolution, which makes multitarget tracking more difficult. In this paper, we harness the advance of the deep learning framework for data association in object tracking by jointly modeling pedestrian features. The proposed deep pedestrian tracking SSD-based model can pair and link pedestrian features in any two frames. The model was trained with open dataset, and the results, accuracy, and speed of the model were compared between normal and emergency or violent environment. The experimental results show that the tracking accuracy of mAP is higher than 95% both in normal and abnormal data sets and higher than that of the traditional detection algorithm. The detection speed of the normal data set is slightly higher than that of the abnormal data set. In general, the model has good tracking results and credibility for multitarget tracking in emergency environment. The research provides technical support for safety assurance and behavior monitoring in emergency environment.
Robust depth map inpainting using superpixels and non-local Gauss–Markov random field prior

Depth maps contain 3D geometrical information of a scene which can be useful in many applications. In this work we address the task of inpainting a depth map afflicted by regions with missing data. Initially, we inpaint depth values missing at random locations as well as due to overlaid text. Subsequently, we propose an approach for filling large holes in the input depth map wherein superpixel division of the corresponding RGB image is also exploited. We use a non-local extension of the classical Gauss–Markov random field model for the completed depth map, so that missing information in the degraded observation can be estimated depending upon self-similarities between non-local patches inside a superpixel based search window. Several experiments performed with disparity maps and real world depth data exhibit the efficacy of the proposed algorithm.
Blockchains’ federation for integrating distributed health data using a patient-centered approach

Today's world is a globalized and connected one, where people are increasingly moving around and interacting with a greater number of services and devices of all kinds, including those that allow them to monitor their health. However, each company, institution or health system usually store its patients' data in an isolated way. Although this approach can have some benefits related with privacy, security, etc., it also implies that each one of them generates different, incomplete and possibly contradictory views of a patient's health data, losing part of the value that this information could bring to the patient. That is the reason why researchers from all over the world are determined to replace the current institution-centered health systems with new patient-centered ones. In these new systems, all the health information of a patient is integrated into a unique global vision. However, some questions are still unanswered. Specifically, who should store and maintain the information of a given patient and how should this information be made available for other systems. To address this situation, this work proposes a new solution towards making the Personal Health Trajectory of patients available for both, the patients themselves and health institutions. By using the concept of blockchains' federation and web services access to the global vision of a person health can be granted to existing and new solutions. To demonstrate the viability of the proposal, an implementation is provided alongside the obtained results in a potential scenario.
Information Flows of Diverse Autoencoders

The outstanding performance of deep learning in various fields has been a fundamental query, which can be potentially examined using information theory that interprets the learning process as the transmission and compression of information. Information plane analyses of the mutual information between the input-hidden-output layers demonstrated two distinct learning phases of fitting and compression. It is debatable if the compression phase is necessary to generalize the input-output relations extracted from training data. In this study, we investigated this through experiments with various species of autoencoders and evaluated their information processing phase with an accurate kernel-based estimator of mutual information. Given sufficient training data, vanilla autoencoders demonstrated the compression phase, which was amplified after imposing sparsity regularization for hidden activities. However, we found that the compression phase is not universally observed in different species of autoencoders, including variational autoencoders, that have special constraints on network weights or manifold of hidden space. These types of autoencoders exhibited perfect generalization ability for test data without requiring the compression phase. Thus, we conclude that the compression phase is not necessary for generalization in representation learning.