Reviewerly/exHarmony huggingface dataset:
Necropolitics and Trans Identities: Language Use as Structural Violence

Despite the increasing visibility of transgender and gender diverse (TGD) people in U.S. society, current linguistic practices within forensic anthropology and death investigation in general are not TGD-inclusive. This lack of consideration for TGD decedents can cause unnecessary delays in the identification and disposition of their remains; moreover, failing to recognize their true identities is a form of forced post-mortem detransition. Using De León’s concept of necroviolence as a framework, we argue that language can also harm the dead and that the (mis)use of language within medicolegal death investigation reflects and reinforces structural violence against TGD people. Examples drawn from a qualitative review of public details for 87 cases are used to demonstrate how language and language-enforced bureaucratic structures can harm TGD decedents, their loved ones, the broader TGD community, and the process of medicolegal death resolution itself. We then suggest steps that anthropologists, death investigators, and their affiliated partners can take to reduce the systemic necropolitical violence faced by the TGD community. While TGD-inclusive methods will take time to implement at the institutional level, individual practitioners can enact significant change within the system by upholding core standards that recognize and respect the personhood and lived experiences of TGD decedents.
Universal backdoor attack on deep neural networks for malware detection

Backdoor attacks targeting the deep neural network are flourishing recently and are more stealthy than existing adversarial attacks. A deep understanding of the backdoor attacks targeting malware detection models is still missing. We design a highly transferable backdoor attack targeting three benchmark convolutional neural networks (CNNs) for malware detection. The designed backdoor attack involves two steps: trigger generation and trigger insertion. Firstly, based on the computation of the most significant byte sub-sequence from samples of a chosen target label, the trigger patterns are generated by training a class activation mapping-based deep neural network (CAM-DNN). Then, the byte sequence with the maximum class activation mapping score is chosen as the candidate trigger pattern. The computed trigger pattern is then inserted into an index-based place that satisfies the minimum distance between a predefined feature space to the target label. Through detailed experiments, the CAM-DNN-based backdoor considers many influential factors, including the number of backdoor triggers, the degree of perturbations applied on a single trigger pattern, the length of the inserted trigger, etc. The experiments demonstrate that the CAM-DNN-based backdoor attack achieves an 89.58% success rate on average at the cost of a 2.25% accuracy drop on clean inputs. More importantly, the poisoned malware ensures high integrity because the original malicious functions are preserved to a large extent.
A modified Q-learning path planning approach using distortion concept and optimization in dynamic environment for autonomous mobile robot

Autonomous mobile robot path planning in unknown and dynamic environment is a crucial task for successful mobile robot navigation. This study proposes an improved Q-learning (IQL) algorithm to address the challenges of path planning in such environments. To this end, three different modes are introduced into the IQL algorithm, namely the normal mode, the distortion mode, and the optimization mode. The normal mode operates according to the standard Q-learning procedures. The distortion mode distorts the Q-values of states around dynamic obstacles to facilitate avoidance, while the optimization mode is employed to overcome the local minimum problem. The efficacy of the IQL algorithm is assessed through a series of comparative studies involving fourteen navigation environments, each with distinct obstacle layouts and types. Comparative analyses are performed based on several metrics, including computational time, travelled distance, collision rate, and success rate. The proposed IQL algorithm exhibits a lower collision rate and a higher success rate when compared to dynamic window approach, influence zone and inflated A*.
Low R2 in ecology: Bitter, or B-side?

Low coefficient of determination (R2) is a basic source of the pains ubiquitous in ecology, since its emergences in assessing numerous ecological indicators often mean the bitter disasters. Our theoretical rethinking about it suggested that one of its key causes is the universal diversity of individual subjective adaptability, always combating with the ecological principle of pursuing generality. This inference inspired us to reversely reason that low R2 in ecology may mirror the B-side of this generality. With the diversity of individual subjective adaptability considered, we proposed two related B-sides – functional diversity and plant intelligence, and their case studies validated our reasoning. For seeking more B-sides, we devised exploratory analysis regression as the supportable methodological paradigm, for future study. Overall, when meeting low R2 in ecology, people should not just feel bitter but positively take advantage of it for expandingly making new cross-disciplinary discoveries. Our perspective, with philosophy conversion and paradigm innovation for converting low R2 from an assessor of ecological indicators to a novel ecological indicator, is of extensive implication on initiating new ecology-enlightened avenues for promoting many other fields.
Double Deep Reinforcement Learning

In many application areas, Deep Reinforcement Learning (DRL) has led to breakthroughs. In Curriculum Learning, the Machine Learning algorithm is not randomly presented with examples, but in a meaningful order of increasing difficulty. This has been used in many application areas to further improve the results of learning systems or to reduce their learning time. Such approaches range from learning plans created manually by domain experts to those created automatically. The automated creation of learning plans is one of the biggest challenges.In this work, we investigate an approach in which a trainer learns in parallel and analogously to the student to automatically create a learning plan for the student during this Double Deep Reinforcement Learning (DDRL). Three Reward functions, Friendly, Adversarial, and Dynamic based on the learner’s reward are compared. The domain for evaluation is kicking with variable distance, direction and relative ball position in the SimSpark simulated soccer environment.As a result, Statistic Curriculum Learning (SCL) performs better than a random curriculum with respect to training time and result quality. DDRL reaches a comparable quality as the baseline and outperforms it significantly in shorter trainings in the distance-direction subdomain reducing the number of required training cycles by almost 50%.
Machine Learning for Ancient Languages: A Survey

Abstract Ancient languages preserve the cultures and histories of the past. However, their study is fraught with difficulties, and experts must tackle a range of challenging text-based tasks, from deciphering lost languages to restoring damaged inscriptions, to determining the authorship of works of literature. Technological aids have long supported the study of ancient texts, but in recent years advances in artificial intelligence and machine learning have enabled analyses on a scale and in a detail that are reshaping the field of humanities, similarly to how microscopes and telescopes have contributed to the realm of science. This article aims to provide a comprehensive survey of published research using machine learning for the study of ancient texts written in any language, script, and medium, spanning over three and a half millennia of civilizations around the ancient world. To analyze the relevant literature, we introduce a taxonomy of tasks inspired by the steps involved in the study of ancient documents: digitization, restoration, attribution, linguistic analysis, textual criticism, translation, and decipherment. This work offers three major contributions: first, mapping the interdisciplinary field carved out by the synergy between the humanities and machine learning; second, highlighting how active collaboration between specialists from both fields is key to producing impactful and compelling scholarship; third, highlighting promising directions for future work in this field. Thus, this work promotes and supports the continued collaborative impetus between the humanities and machine learning.
Potential and limitations of quantum extreme learning machines

Quantum reservoir computers (QRC) and quantum extreme learning machines (QELM) aim to efficiently post-process the outcome of fixed -- generally uncalibrated -- quantum devices to solve tasks such as the estimation of the properties of quantum states. The characterisation of their potential and limitations, which is currently lacking, will enable the full deployment of such approaches to problems of system identification, device performance optimization, and state or process reconstruction. We present a framework to model QRCs and QELMs, showing that they can be concisely described via single effective measurements, and provide an explicit characterisation of the information exactly retrievable with such protocols. We furthermore find a close analogy between the training process of QELMs and that of reconstructing the effective measurement characterising the given device. Our analysis paves the way to a more thorough understanding of the capabilities and limitations of both QELMs and QRCs, and has the potential to become a powerful measurement paradigm for quantum state estimation that is more resilient to noise and imperfections.
CLEGAN: Toward Low-Light Image Enhancement for UAVs via Self-Similarity Exploitation

Low-light remote sensing image enhancement (LLIE) for unmanned aerial vehicles (UAVs) has significant scientific and practical value because unfavorable lighting conditions make capture more difficult, resulting in undesired images. As acquiring real-world low-light/normal-light image pairs in the field of remote sensing is almost infeasible, performing LLIE in an unpaired manner is practical and valuable. However, without the paired data as the supervision, learning an LLIE network is challenging. To address the challenges, the paper proposes a novel yet effective method to unpaired LLIE, which maximizes the mutual information between low-light and restored images through self-similarity contrastive learning (SSCL) in a fully unsupervised fashion within a single deep GAN framework, named CLEGAN. Instead of supervising the learning using ground truth data, we propose to regularize the unpaired training using the information extracted from the input itself. The non-local patch sampling strategy in SSCL naturally makes the negative samples differ from the positive samples for discriminative representation. Moreover, the single GAN embeds the dual illumination perception module (DIPM) to handle the internal recurrence of information and overall uneven illumination distribution in remote sensing images. DIPM mainly consists of two cooperative blocks: spatial adaptive light adjustment module (SALAM) and global adaptive light adjustment module (GALAM). Specifically, SALAM exploits the internal recurrence of information in remote sensing images to encode a wider range of contextual information into local features and make proper light estimation. Simultaneously, GALAM enhances the most valuable illumination-related channels in the feature map to achieve better light estimation. The experiments on several datasets including low-light remote sensing image dataset and public low-light image datasets show that CLEGAN performs favorably against the existing unpaired LLIE approaches, and even outperforms several fully-supervised methods.
A Comparative Analysis of Cross-Validation Techniques for a Smart and Lean Pick-and-Place Solution with Deep Learning

As one of the core applications of computer vision, object detection has become more important in scenarios requiring high accuracy but with limited computational resources such as robotics and autonomous vehicles. Object detection using machine learning running on embedded device such as Raspberry Pi provides the high possibility to detect any custom objects without the recalibration of camera. In this work, we developed a smart and lean object detection model for shipping containers by using the state-of-the-art deep learning TensorFlow model and deployed it to a Raspberry Pi. Using EfficientDet-Lite2, we explored the different cross-validation strategies (Hold-out and K-Fold). The experimental results show that compared with the baseline EfficientDet-Lite2 algorithm, our model improved the mean average precision (mAP) by 44.73% for the Hold-out dataset and 6.26% for K-Fold cross-validation. We achieved Average Precision (AP) of more than 80% and best detection scores of more than 93% for the Hold-out dataset. For the 5-Fold lean dataset, the results show the Average Precision across the three lightweight models are generally high as the models achieved more than 50% average precision, with YOLOv4 Tiny performing better than EfficientDet-Lite2 and Single Shot Detector (SSD) MobileNet V2 Feature Pyramid Network (FPN) 320 as a lightweight model.
Remote Sensing Small Object Detection Network Based on Attention Mechanism and Multi-Scale Feature Fusion

In remote sensing images, small objects have too few discriminative features, are easily confused with background information, and are difficult to locate, leading to a degradation in detection accuracy when using general object detection networks for aerial images. To solve the above problems, we propose a remote sensing small object detection network based on the attention mechanism and multi-scale feature fusion, and name it AMMFN. Firstly, a detection head enhancement module (DHEM) was designed to strengthen the characterization of small object features through a combination of multi-scale feature fusion and attention mechanisms. Secondly, an attention mechanism based channel cascade (AMCC) module was designed to reduce the redundant information in the feature layer and protect small objects from information loss during feature fusion. Then, the Normalized Wasserstein Distance (NWD) was introduced and combined with Generalized Intersection over Union (GIoU) as the location regression loss function to improve the optimization weight of the model for small objects and the accuracy of the regression boxes. Finally, an object detection layer was added to improve the object feature extraction ability at different scales. Experimental results from the Unmanned Aerial Vehicles (UAV) dataset VisDrone2021 and the homemade dataset show that the AMMFN improves the APs values by 2.4% and 3.2%, respectively, compared with YOLOv5s, which represents an effective improvement in the detection accuracy of small objects.
Simulation-based Test Case Generation for Unmanned Aerial Vehicles in the Neighborhood of Real Flights

Unmanned aerial vehicles (UAVs), also known as drones, are acquiring increasing autonomy. With their commercial adoption, the problem of testing their functional and non-functional, and in particular their safety requirements has become a critical concern. Simulation-based testing represents a fundamental practice, but the testing scenarios considered in software-in-the-loop testing may not be representative of the actual scenarios experienced in the field.In this paper, we propose SURREALIST (teSting UAVs in the neighboRhood of REAl flIghtS), a novel search-based approach that analyses the logs from real UAV flights and automatically generates simulation-based test cases in the neighborhood of such real flights, thereby improving the realism and representativeness of the simulation-based tests. This is done in two steps: first, SURREALIST faithfully replicates the given UAV flight in the simulation environment, generating a simulation-based test that mirrors a pre-logged real-world behavior. Then, it smoothly manipulates the replicated flight conditions to discover slightly modified test cases that are challenging or trigger misbehaviors of the UAV under test in simulation. In our experiments, we were able to replicate a real flight accurately in the simulation environment and to expose unstable and potentially unsafe behavior in the neighborhood of a replicated flight, which even led to crashes.
A Comparative Study of SMOTE, Borderline-SMOTE, and ADASYN Oversampling Techniques using Different Classifiers

With the advent of machine learning and its numerous techniques, many real-world problems have been solved like credit card fraud detection, cancer susceptibility and survival prediction, identification of spam, and customer segmentation, to name a few. Machine learning works on huge loads of data to give the correct prediction and maximum accuracy. Now, accuracy of any machine learning model depends on the dataset been fed into that model, in the first place. And from here comes the concept of oversampling and under-sampling. Under-sampling is the process of shortening the majority class or deleting samples from the majority class in order to balance the dataset, and over-sampling is the process of adding additional synthetic samples to the minority class. So, this study is based on the three methods namely, SMOTE, Borderline-SMOTE, and ADASYN. This study includes the collation of the above-mentioned oversampling techniques based on their accuracy, precision, recall, F1-measure and ROC curve.
PriFR: Privacy-preserving Large-scale File Retrieval System via Blockchain for Encrypted Cloud Data

As a fundamental and commonly used service, file retrieval has been extensively studied by information retrieval, cryptography, and big data communities. In this paper, we consider the problem of privacy-preserving file retrieval. A new framework named PriFR is proposed by integrating the blockchain and cloud computing infrastructures. The large-scale original files are encrypted and outsourced to the public cloud server. The encrypted retrieval indexes are stored on the full nodes in the blockchain to support traceable and unforgeable retrieval services. This design embraces the benefits brought by both cloud computing and blockchain. For the first time, PriFR decomposes the file retrieval problem into the numerical query and keyword search on the file metadata. In doing so, each file can be characterized more precisely than the traditional keyword search based schemes. In addition to functionality, PriFR only applies fast and lightweight symmetric cryptographical primitives to reach near plaintext retrieval efficiency. In specific, the numerical query is implemented atop order-preserving encryption (OPE) that is perfectly compatible with plaintext indexing techniques. The price for such high efficiency is its vulnerability to inference attacks. Given enough background knowledge, the plaintext can be recovered with high probability. To resist this attack, PriFR injects differential privacy noises into the raw data to offer guaranteed privacy-preserving strength with a negligible extra efficiency cost. The experimental results have demonstrated the effectiveness of PriFR.
Mobile-assisted language learning with Babbel and Duolingo: comparing L2 learning gains and user experience

AbstractAbstractDespite the influx of research examining various aspects of mobile-assisted language learning (MALL) applications (apps) over the past two decades, there have been no head-to-head studies that have investigated the comparative effectiveness of different mobile apps. The current study addresses this gap by directly comparing two of the most well-researched and popular MALL apps: Babbel and Duolingo. In this mixed methods study, adult learners (N = 59) engaged in studying Turkish as a foreign language using either Babbel (n = 27) or Duolingo (n = 32) for eight weeks. Participants then completed two exit assessments, including (1) a posttest gauging their development of various language skills (i.e. reading, writing, speaking, listening, vocabulary, and grammatical competence), and (2) a survey assessing their user experience (e.g. enjoyment, motivation, beliefs about effectiveness). The results of this study showed that although both the Babbel and Duolingo groups made progress, there were no statistically significant differences between their L2 learning gains. However, for the Babbel group, there was a stronger correlation between participants' study time and their posttest scores. Finally, Babbel users also felt the app was more effective for learning grammar, speaking/pronunciation skills, and for learning about the target language culture. This study discusses the implications of these findings for researchers and MALL users more broadly.Keywords: CALLgaminglearner beliefsmobile appsmotivation Disclosure statementNo potential conflict of interest was reported by the authors.Additional informationNotes on contributorsMatt KesslerMatt Kessler is an Assistant Professor at the University of South Florida in the Department of World Languages. His research focuses on issues surrounding L2 writing, genre-based teaching and learning, and computer-assisted language learning. E-mail: mattjkess@gmail.comShawn LoewenShawn Loewen is a Professor at Michigan State University in the Department of Linguistics, Languages, and Cultures. His research centers on second language acquisition and L2 classroom interaction. E-mail: loewens@msu.eduTalip GönülalTalip Gönülal is an Associate Professor at Erzincan Binali Yildirim University in the Department of English Language Teaching. His research centers on quantitative research methods, statistical literacy, and technology-enhanced language teaching and learning. E-mail: talip.gonulal@erzincan.edu.trNotes1 In the background-screening questionnaire, we inquired as to whether participants had previously used Babbel or Duolingo. Those who had preexisting, unfavorable views of either app were not included in the study. We also provided participants with the opportunity to select a preference for which app they used during the study. For all participants who made such a request, their request was granted.
Unsupervised industrial image ensemble anomaly detection based on object pseudo-anomaly generation and normal image feature combination enhancement

With the development of industrial video technology, the use of cameras rather than a variety of expensive sensors to obtain process or product data has gained more attention. One of the important applications is the use of image data for anomaly detection. It is difficult to collect anomaly data in actual engineering practice, which makes the anomaly detection of industrial products often need to be carried out under the condition of a single data type. How to achieve anomaly detection without anomaly data has become a new challenge. An unsupervised ensemble anomaly detection method based on image enhancement is proposed for image detection with normal data only. The proposed method first uses local pseudo-anomaly generation and object location to generate high-quality pseudo-anomaly images. Then, the pseudo-anomaly images and pseudo-labels are used to guide the training of a reconstruction model and a self-supervised model. In the detection phase, an unsupervised feature screening method is designed to extract sensitive filters, and the normal image features in the feature space output by these sensitive filters are combined and enhanced. Finally, ensemble detection is implemented using different anomaly scores. The experiments show that the proposed method can achieve performance improvements in 15 real datasets.